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1
Butner, Karen, and Grace Ho. "How the human-machine interchange will transform business operations." Strategy & Leadership 47, no. 2 (2019): 25–33. http://dx.doi.org/10.1108/sl-01-2019-0003.
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Purpose Machine learning is beginning to transform the way businesses organize their operations and benefit from technology investments. Design/methodology/approach To learn more about how far along organizations are in deploying intelligent automation and in developing plans and strategies for its adoption, the IBM Institute for Business Value, in collaboration with Oxford Economics surveyed and interviewed 550 technology and operations executives. Findings The primary purpose of intelligent automation is to augment employees’ skills, experience and expertise, extending the human mind in ways that allow for higher productivity, creative problem-solving and more engaging jobs for employees. Practical implications Automation is not a plug-and-play solution: companies cannot just buy the technology, flip the switch and watch robots run the business without any human intervention. Originality/value This recent survey of operations executive with specific knowledge of their companies plans provides insights into best practice. Executives believe that layering new technologies on top of old business processes is apt to be less productive ? and less cost-effective ? than rethinking processes to make the most of intelligent automation. Executives must optimize workflows for automation; this means envisioning the end result, enabling it through logical steps and prototyping the process ? then repairing as necessary before scaling.
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Zhang, Yiyun. "Large Language Model in SD-WAN Intelligent Operations and Maintenance." Research Briefs on Information and Communication Technology Evolution 9 (December 19, 2023): 178–88. http://dx.doi.org/10.56801/rebicte.v9i.177.
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The integration of LLM (Large Language Model) into the intelligent operations and maintenance of SD-WAN (Software-Defined Wide Area Networks) is comprehensively explored. As the scale of the network continues to expand and its complexity increases, traditional operation and maintenance methods can no longer meet efficient and accurate requirements. Therefore, LLM technology was introduced, and the intelligent operation and maintenance of the data center network were realized by leveraging its powerful natural language processing capabilities. The importance and shortcomings of SD-WAN and the application scenarios of LLM in network operation and maintenance are first introduced, followed by an elaboration on how LLM can be used for network configuration, network optimization, network maintenance, and network security risk management. Through specific analysis, the significant effect of LLM in enhancing the efficiency and quality of operation and maintenance has been demonstrated. Finally, the advantages and challenges of employing LLM for intelligent network operation and maintenance are summarized, and future research directions are anticipated.
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Lei, Nuoa, Zhu Cheng, Zhi Cao, and Eric Masanet. "An integrated decision-making framework for sustainable data center operation through intelligent load scheduling." Journal of Physics: Conference Series 2042, no. 1 (2021): 012091. http://dx.doi.org/10.1088/1742-6596/2042/1/012091.
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Abstract Intelligent load scheduling is an emerging approach that has the potential to facilitate extreme sustainable data center (DC) operation. However, scarcity of straightforward tools in the public domain challenges decision makers performing quantitative analysis of the DC load planning and its potential benefits. In this work, a novel integrated decision-making framework was developed to address this issue, which provides the basis for the multi-objective optimization of carbon-, water-, and economic-intelligent load scheduling. The proposed framework was demonstrated with a case study DC in California, which showed the usefulness of the proposed framework in informing sustainable DC operations.
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Brydia, Robert E., Shawn M. Turner, William L. Eisele, and Jyh C. Liu. "Development of Intelligent Transportation System Data Management." Transportation Research Record: Journal of the Transportation Research Board 1625, no. 1 (1998): 124–30. http://dx.doi.org/10.3141/1625-16.
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The intelligent transportation system (ITS) components deployed in U.S. urban areas produce vast amounts of data. These ITS data often are used for real-time operations and then are discarded. Few transportation management centers have any mechanism for sharing the data resources among other transportation groups or agencies within the same jurisdiction. Meanwhile, transportation analysts and researchers often struggle to obtain accurate, reliable data about existing transportation performance and patterns. The development of an ITS data management system (referred to as ITS DataLink) that is used to store, access, analyze, and present data from the TransGuide center in San Antonio, Texas, is presented. Data outputs are both tabular and graphical. No user costs are associated with the system except for an Internet connection.
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Tong, Xin Shun, and Li Hua Yang. "Research on Integrated Scheduling Model of Automated High-Rise Warehouse and Traditional Flat Storehouse for Cigarette Distribution Center." Applied Mechanics and Materials 65 (June 2011): 99–103. http://dx.doi.org/10.4028/www.scientific.net/amm.65.99.
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Along with the market demand increasing, cigarette distribution center urgently needs to improve their working efficiency by reasonable storage modeal. This paper proposed an integrated scheduling model of automated high-rise warehouse combined with traditional flat storehouse, by planning the storage capacity in traditional flat storehouse and optimizing the storage spaces in automated high-rise warehouse, ensuring intelligent management while achieving flexible storage operations. This model effectively shortened the operating time compared to other storage models. Finally take Zhengzhou Cigarette Distribution Center as an example to analyze.
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Tavana, Madjid. "Intelligent flight support system (IFSS): a real-time intelligent decision support system for future manned spaceflight operations at Mission Control Center." Advances in Engineering Software 35, no. 5 (2004): 301–13. http://dx.doi.org/10.1016/j.advengsoft.2004.04.002.
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Epicoco, I., S. Mocavero, F. Macchia, et al. "Performance and results of the high-resolution biogeochemical model PELAGOS025 within NEMO." Geoscientific Model Development Discussions 8, no. 12 (2015): 10585–625. http://dx.doi.org/10.5194/gmdd-8-10585-2015.
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Abstract. The present work aims at evaluating the scalability performance of a high-resolution global ocean biogeochemistry model (PELAGOS025) on massive parallel architectures and the benefits in terms of the time-to-solution reduction. PELAGOS025 is an on-line coupling between the physical ocean model NEMO and the BFM biogeochemical model. Both the models use a parallel domain decomposition along the horizontal dimension. The parallelisation is based on the message passing paradigm. The performance analysis has been done on two parallel architectures, an IBM BlueGene/Q at ALCF (Argonne Leadership Computing Facilities) and an IBM iDataPlex with Sandy Bridge processors at CMCC (Euro Mediterranean Center on Climate Change). The outcome of the analysis demonstrated that the lack of scalability is due to several factors such as the I/O operations, the memory contention, the load unbalancing due to the memory structure of the BFM component and, for the BlueGene/Q, the absence of a hybrid parallelisation approach.
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Epicoco, Italo, Silvia Mocavero, Francesca Macchia, et al. "Performance and results of the high-resolution biogeochemical model PELAGOS025 v1.0 within NEMO v3.4." Geoscientific Model Development 9, no. 6 (2016): 2115–28. http://dx.doi.org/10.5194/gmd-9-2115-2016.
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Abstract. The present work aims at evaluating the scalability performance of a high-resolution global ocean biogeochemistry model (PELAGOS025) on massive parallel architectures and the benefits in terms of the time-to-solution reduction. PELAGOS025 is an on-line coupling between the Nucleus for the European Modelling of the Ocean (NEMO) physical ocean model and the Biogeochemical Flux Model (BFM) biogeochemical model. Both the models use a parallel domain decomposition along the horizontal dimension. The parallelisation is based on the message passing paradigm. The performance analysis has been done on two parallel architectures, an IBM BlueGene/Q at ALCF (Argonne Leadership Computing Facilities) and an IBM iDataPlex with Sandy Bridge processors at the CMCC (Euro Mediterranean Center on Climate Change). The outcome of the analysis demonstrated that the lack of scalability is due to several factors such as the I/O operations, the memory contention, the load unbalancing due to the memory structure of the BFM component and, for the BlueGene/Q, the absence of a hybrid parallelisation approach.
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Donkoh, Elvis Kobina, Rebecca Davis, Emmanuel D. J. Owusu-Ansah, Emmanuel A. Antwi, and Michael Mensah. "Application of Combinatorial Techniques to the Ghanaian Board Game Zaminamina Draft." European Journal of Pure and Applied Mathematics 12, no. 1 (2019): 159–75. http://dx.doi.org/10.29020/nybg.ejpam.v12i1.3308.
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Games happen to be a part of our contemporary culture and way of life. Often mathematical models of conflict and cooperation between intelligent rational decision-makers are studied in these games. Example is the African board game ’Zaminamina draft’ which is often guided by combinatorial strategies and techniques for winning. In this paper we deduce an intelligent mathematical technique for playing a winning game. Two different starting strategies were formulated; center starting and edge or vertex starting. The results were distorted into a 3x3 matrix and elementary row operations were performed to establish all possible wins. MatLab was used to distort the matrix to determine the diagonal wins. A program was written using python in artificial intelligence (AI) to help in playing optimally
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Xiao-wei, Xiong. "Study on the intelligent system of sports culture centers by combining machine learning with big data." Personal and Ubiquitous Computing 24, no. 1 (2019): 151–63. http://dx.doi.org/10.1007/s00779-019-01307-z.
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AbstractWith the vigorous development of sports, people’s awareness of engaging in sports has gradually increased, and the requirements for a sports culture center have been higher. However, the service system of traditional sports cultures center is single, which cannot meet people’s growing experience needs. Therefore, it is urgent for the service system of sports culture centers to move towards intellectualization. Firstly, this paper discusses the service system of traditional sports culture centers and finds that there are some problems, such as slow transmission of information, poor sharing of resources, and weak flexibility of response, which seriously affect the consumer experience of users and restrict the development of sports culture centers. Then, with the help of computer network technology, the design of intelligent system architecture of sports culture centers is completed, which makes many intelligent subsystems interconnected and interoperable, integrates information, realizes the integration of data application network, and achieves the goal of resource sharing and function upgrading. Then, based on the intelligent system, the big data platform is built with the help of big data technology, and the support vector machine-back propagation (SVM-BP) neural network composite model is used to realize the prediction of the passenger flow in the cultural center, which provides guidance for adjusting the service plan in advance, effectively coping with the peak passenger flow and improving the user experience. Finally, through empirical analysis, we know that the design of an intelligent system greatly improves the service quality of cultural centers. The research results not only achieve a significant increase in passenger flow but also provide an effective way for the service of sports culture centers to move towards intellectualization.
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Yan, Honglin. "Intelligent Analysis of Logistics Information Based on Network Dynamic Data." Journal of Control Science and Engineering 2022 (July 30, 2022): 1–7. http://dx.doi.org/10.1155/2022/6016072.
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Based on the concept of market sharing, the author has developed Internet technology-based logistics distribution data exchange to control the cost of shipping to benefit the transfer for new types of management and operations. Investigate the needs of export shipments, such as external management responsibilities, requirements of management responsibilities, data recovery, and data recovery requirements, based on the current state of the shared platform model integration, and determine the business model. Based on this, three types of IoT cloud, such as single-center multiterminal, multicenter multiterminal, and data and application layer, can be completed and the Internet of Things can be defined by education. About the use of the cost of distribution, the transportation contract is to identify the needs of Internet technology-based logistics distributions and complete the construction of Internet technology-based logistics distribution information exchange platform. The results of the experiment showed that the results recorded in the test group did not differ from the actual results only twice, the level of difference was small, and the mean error was less than 1%. In terms of logistics distribution exposure, the average level of the assessment group is significantly higher than that of the management group, with a minimum collection rate of 90.07%, while the minimum value of the control group is as low as 76.32%. Compared with blockchain-based integration, Internet of Things technology-enabled sharing platforms can capture real-time data, transfer data, monitor shipping costs, and improve management and operations.
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Jin, Chengming, and Donghui Tong. "Wearable Device-Based Intelligent Patrol Inspection System Design and Implementation." International Journal of Distributed Systems and Technologies 14, no. 2 (2023): 1–10. http://dx.doi.org/10.4018/ijdst.317938.
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The traditional on-site operation of power stations includes inspection and maintenance. However, it heavily relies on experience for maintenance. Most on-site operation and maintenance data are text records. On the one hand, the data processing is tedious for experience to affect the safe on-site operation. On the other hand, we usually cannot give full consideration to the value of maintenance experience, so that the corresponding efficiency is very low. Therefore, this paper proposes a wearable device based remote and intelligent patrol inspection system that uses the cloud video transmission mode of both public and private clouds to realize the video connection between the power stations and the remote diagnosis center and uses the wearable devices for real experience. In this way, the authors can simulate real operation guidance and safety supervision, etc. so as to realize the remote management patrol operations, improve the fault detection efficiency, and improve equipment reliability.
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Kumar, Kartik. "An Educational Chatbot Using AI in Radiotherapy." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34122.
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The surge in demand for information in cancer centers and hospitals, particularly during the pandemic, overwhelmed the limited manpower available. To address this challenge, there arose a need to develop an educational chatbot tailored for diverse user groups in the field of radiotherapy, including patients and their families, the general public, and radiation staff. Objective: In response to the pressing clinical demands, the primary aim of this endeavor is to delve into the intricacies of designing an educational chatbot for radiotherapy using artificial intelligence.Methods: The chatbot is meticulously crafted using a dialogue tree and layered structure, seamlessly integrated with artificial intelligence functionalities, notably natural language processing (NLP). This adaptable chatbot can be deployed across various platforms, such as IBM Watson Assistant, and embedded in websites or diverse social media channels.Results: Employing a question-and-answer methodology, the chatbot adeptly engages users seeking information on radiotherapy, presenting an approachable and reassuring interface. Recognizing that users, often anxious, may struggle to articulate precise questions, the chatbot facilitates the interaction by offering a curated list of questions. The NLP system augments the chatbot's ability to discern user intent, ensuring the provision of accurate and targeted responses. Notably, the study reveals that functional features, including mathematical operations, are preferred in educational chatbots, necessitating routine updates to furnish fresh content and features.Conclusions: The study culminates in the affirmation that leveraging artificial intelligence facilitates the creation of an educational chatbot capable of disseminating information to users with diverse backgrounds in radiotherapy. Furthermore, the importance of rigorous testing and evaluation, informed by user feedback, is emphasized to iteratively enhance and refine the chatbot's performance. Keywords: AI, machine learning, NLP, chatbot, radiotherapy, IoT, healthcare.
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Sheu, Jinn Jong, and T. C. Fu. "Development of an Intelligent Virtual Reality System for High Speed Machining." Materials Science Forum 505-507 (January 2006): 625–30. http://dx.doi.org/10.4028/www.scientific.net/msf.505-507.625.
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An intelligent E-Manufacturing system was developed in this paper. The high speed machining center was rebuilt using 3D CAD system. The moving table (X axis), span column (Y axis), spindle (Z axis) and the tool changing system were modeled precisely. The high speed cutting (HSC) experiments of AL 6061 were carried to obtain the cutting forces and surface roughness for different cutting conditions. The backward propagation supervised artificial neural network (ANN) system was developed to predict the results of the high speed cutting. The intelligent virtual reality (VR) system of high speed cutting was developed integration the ANN and the VR environment. The users were able to learn the manual and the CNC operations of the HSC machine. The actions of users were recorded and evaluated to judge the learning results. The cutting results of forces and surface roughness of user’s NC program was predicted by the ANN system to assist the NC programmer to adopt the suitable cutting parameters. The developed VR system was deployed to the internet webpage to supply a good E-Learning and E-Manufacturing environment.
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Liu, Zhan Qiang, Jun Bo Zhang, and Zhi Tao Tang. "Intelligent Error Compensation in CNC Machining through Synergistic Interactions Among Modeling, Sensing and Learning." Materials Science Forum 471-472 (December 2004): 178–82. http://dx.doi.org/10.4028/www.scientific.net/msf.471-472.178.
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Industry is looking for practical means to improve the accuracy of the parts machined on CNC machines. Some artificial intelligence (AI) systems have been applied in modeling and compensating manufacturing process errors in CNC machining. However, these systems are not capable of predicting the results of a new operation if no sufficient data on a number of similar operations is available. A generalized AI approach named synergistic interactions amongst modeling, sensing and learning is proposed in this paper. Based on the AI approach, a new strategy of error compensation of workpiece dimension in CNC machining is developed and applied in a CNC turning center. Error compensation results are illustrated the effectiveness of the error compensation strategy. The learning curve shows that the error compensation confidence gradually progresses towards 100% confidence from zero along with the CNC machine operation time increasing.
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Gao, De Quan, Yi Ying Zhang, and Xiang Zhen Li. "The Internet of Things for Electric Vehicles: Wide Area Charging-Swap Information Perception, Transmission and Application." Advanced Materials Research 608-609 (December 2012): 1560–65. http://dx.doi.org/10.4028/www.scientific.net/amr.608-609.1560.
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Electric vehicle (EV) is an emerging type of mobile intelligent power consumption device and energy storage terminal in Smart Grid. In order to solve automation and intelligence support problem for wide area electric vehicles charging-swap services, we make full use of technology. we analyze practical demands of electric vehicle charging-swap networks operation service scenarios and define the Internet of Things (IoT) architecture for electric vehicles for wide area unified operations and monitoring management in this paper. Then, we design a perception integration model for battery packs real-time information collection in mobile scenes. We also present a multimode collaborative communications method to implement large scale interconnection and intercommunication among electric vehicles, charging-swap devices and operation center. Finally we develop operations and monitoring platform demo for cross-regional electric vehicle charging-swap networks management using Geographic Information Systems (GIS) and Service-Oriented Architecture (SOA).
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Carpenter, Chris. "Autonomous Directional Drilling Achieved With Industry 4.0 Platform." Journal of Petroleum Technology 75, no. 02 (2023): 62–64. http://dx.doi.org/10.2118/0223-0062-jpt.
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_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 204607, “Autonomous Directional-Drilling Planning and Execution Using an Industry 4.0 Platform,” by Samba Ba, SPE, Maja Ignova, and Kate Mantle, Schlumberger, et al. The paper has not been peer reviewed. _ In the complete paper, the authors present an autonomous-directional-drilling (ADD) framework using an Industry 4.0 platform built on intelligent planning and execution capabilities and supported by surface and downhole automation technologies to achieve consistent directional-drilling operations accessible for remote operations. This ADD framework minimizes operational risk and cost per foot drilled; maximizes performance and procedural adherence; and establishes consistent results across fields, rigs, and trajectories. Anatomy of ADD The authors write that they identified a need for technology to support all aspects of the drilling process, not just what happens at the wellsite or the bottomhole assembly (BHA). That meant focusing on intelligent planning and intelligent execution capabilities, along with surface automation that would complement the suite of downhole automation features of the steering tools. Fig. 1 highlights what the authors term the four pillars of ADD. To connect these pillars, an integrated data architecture is crucial. In the planning phase, all associated digital data are passed to the intelligent execution pillar to drive both surface and downhole automation. Once the execution phase is complete, the same data pipelines are leveraged to close a feedback loop that would drive further refinements of machine-learning (ML) models in the intelligent planning phase to start the cycle over again. Technology blocks within the pillars will evolve naturally as part of an agile development process. The intelligent execution is at the center of the system because it interacts with each component and manages information-sharing between them. Pillars of ADD Intelligent Planning. In the planning stage, an understanding of BHA steering performance is key. It is important to have a model that will predict BHA directional tendency accurately. Traditionally, physics models were used to quantify the response and qualify the trajectory plan. While physics models can provide very accurate tendency predictions with the help of historical operations data, the prediction accuracy can be improved. This is called a hybrid ML strategy, which combines historical data with the physics model, taking advantage of ML capabilities and synergies with domain knowledge. The Industry 4.0 platform enables the implementation of this hybrid model directly inside the new well-construction-planning portion of the platform. The engineering design will be validated automatically by comparing predicted tool-steering performance with the trajectory plan, with no human intervention required. With this model, the BHA, trajectory, and operating parameters can be optimized for application and delivered digitally for wellsite execution.
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Hu, W. "An improved flower pollination algorithm for optimization of intelligent logistics distribution center." Advances in Production Engineering & Management 14, no. 2 (2019): 177–88. http://dx.doi.org/10.14743/apem2019.2.320.
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Wu, C. H., G. T. S. Ho, Cathy H. Y. Lam, and W. H. Ip. "Franchising decision support system for formulating a center positioning strategy." Industrial Management & Data Systems 115, no. 5 (2015): 853–82. http://dx.doi.org/10.1108/imds-10-2014-0291.
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Purpose – The purpose of this paper is to propose a franchising decision support system (FDSS) for future development planning by center positioning strategy formulation under a franchising business model. Design/methodology/approach – The system makes use of data collected from the franchising business and external environment analysis for decision making in center positioning problems. The fuzzy logic approach is integrated into the system for analyzing the geographical market dynamics including profitability and competitiveness in the district concerned. To demonstrate the application of the proposed FDSS, a case study is conducted in a Hong Kong-based franchising private education center, i.e. Dr I-Kids Education Center. Findings – The tailor made FDSS helps to facilitate the business operations of the franchising education center and develops a district positioning model for the centers located in the 18 districts of Hong Kong. The findings provide a solid foundation for marketing strategy and expansion direction formulation. Originality/value – Due to the globalization of business, managing a growing franchising business model becomes challenging to the franchisor. In order to fully leverage the merits of the franchising system, an intelligent decision support model, focusing on making strategic development plans, is needed so as to expand the scale of business efficiently.
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Trappey, Amy J. C., Ging-Bin Lin, and Li-Ping Hung. "Intelligent Text Mining for Ontological Knowledge Graph Refinement and Patent Portfolio Analysis—Case Study of Net-Zero Data Center Innovation Management." Information 15, no. 7 (2024): 374. http://dx.doi.org/10.3390/info15070374.
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Ontological knowledge graph (OKG) is a well-formed visual representation that depicts knowledge organization in formal elements (e.g., entities and attributes) and their interrelationships. OKG is crucial for innovation management analysis as it provides a clear boundary to understand complex knowledge domain in detail. In the patent analysis field, it facilitates the definition of a well-defined patent portfolio, aiming for accurate and complete patent retrievals and subsequent analyses. In recent decade, the rapid growth of the information and communication technology (ICT) sector has rendered data centers (DCs) indispensable for data processing, storage, and cloud computing, while ensuring security and privacy during DC operations. However, their energy-intensive operations pose challenges to global efforts toward achieving net-zero emissions goals. In response, this research develops a formal OKG refinement process and uses DC net-zero technology OKG as case study for in-depth OKG refinement and application in patent portfolio analysis. The net-zero DC domain covers five sub-technologies. Utilizing the proposed OKG refinement and patent portfolio analysis framework, the 1801 most recent decade’s patents related to relevant “DC net-zero technologies” are retrieved and analyzed. Particularly in this case, DC colocation and server-as-a-service perspectives are the newly discovered sub-domains for OKG refinement. Furthermore, the research also adopts the technology function matrix and technology maturity to assess current and future technology development trends, providing crucial insights supporting strategic innovation management.
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Ahmed, Rana K. A., Ryham Ali Zubaid, Fay Fadhil, and Israa Habeeb Naser. "Text and Social Analytics with Fusion Techniques Enhance Hospital Health Management." Fusion: Practice and Applications 10, no. 2 (2023): 95–107. http://dx.doi.org/10.54216/fpa.100209.
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the impact of social analytics on hospital health management: a multilevel fusion approach for data-driven decision-making and brand improvement. The hospital health management center should use feature extraction techniques to learn more about customers' feelings towards their services and optimize their business strategies and promotions accordingly. The proposed multi-levelhybrid level fusion system architectures can effectively integrate dataimages from multiple sources, including social networks, to collect and process essential data for score level and rank level decision-making. This approach leverages intelligent techniques, such as deep learning models, fuzzy logic, and optimization algorithms, to improve fusion scores and achieve optimal fusion performance. The proposed framework can also be extended to various applications, including multimedia data fusion, e-systems data fusion, and spatial data fusion, to enable intelligent systems for information fusion and decision-making in diverse domains. Therefore, this paper proposes Improved Customer Relation and Business Operations (ICR-BO) to enhance customer relationships in business development using text and social analytics. A case study is carried out to explore the online debate of computer brands operated in hospital environments and Twitter suppliers. The authors used text-mining strategies and social analytics to analyze business operations. Social Media uses data sets to view important observations and trends to identify consumer awareness after collecting critical tweets using Twitter search. The experimental results show that ICR-BO achieves the highest customer relation compared to other existing methods.
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Peters, Joseph I., and King M. Roberts. "Human Factors and Advanced Traffic Management Systems." Proceedings of the Human Factors Society Annual Meeting 36, no. 15 (1992): 1068–72. http://dx.doi.org/10.1518/107118192786749739.
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Advanced Traffic Management Systems (ATMS) are those components of Intelligent Vehicle Highway Systems (IVHS) that integrate traffic detection, communication, and control functions to be responsive to dynamic traffic conditions and increase the efficiency of existing traffic networks. ATMS provide the management foundation that will enable and integrate other IVHS components such as Commercial Vehicle Operations, Advanced Traveler Information Systems, Advanced Vehicle Control Systems, and Advanced Public Transportation Systems. This paper defines Advanced Traffic Management Systems. It also describes the functions that may take place within an ATMS-class Traffic Management Center (TMC), a scenario that a future TMC operator may encounter, and some of the human factors issues that must be addressed in the design of an ATMS-class TMC.
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Zhou, Li, Zhaochan Li, Ning Shi, Shaohua Liu, and Ke Xiong. "Performance Analysis of Three Intelligent Algorithms on Route Selection of Fishbone Layout." Sustainability 11, no. 4 (2019): 1148. http://dx.doi.org/10.3390/su11041148.
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The Internet of Things (IoT) has become an important strategy in the current round of global economic growth and technological development and provides a new path for the intelligent development of the logistics industry. With the development of the economy, the demand for logistics benefits is becoming more important. The appropriate use of technologies related to IoT to improve logistics efficiency, such as cloud computing, mobile computing and data mining, has become a topic of considerable research interest. Picking operations are currently an extremely important and cumbersome aspect of logistics center tasks. To shorten the picking distance and improve work efficiency, this paper uses the genetic algorithm, ant colony algorithm and cuckoo algorithm to optimize the picking path in a fishbone-layout warehouse and establishes an optimized model of the warehouse picking path under the fishbone layout. Data-mining technology is used to simulate the model and obtain the simulation data under the condition of multiple orders. The results provide a theoretical basis for the study of the fishbone-layout picking path model and has certain practical significance for the efficient operation of logistics enterprises. Through optimization, it is conducive to the sustainable development of enterprises and to achieving long-term profitability.
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Korotchenya, V. M., G. I. Lichman, and I. G. Smirnov. "Digitalization of Technological Processes of Crop Production in Russia." Agricultural Machinery and Technologies 13, no. 1 (2019): 14–20. http://dx.doi.org/10.22314/2073-7599-2018-13-1-14-20.
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Currently, the influence of program documents on digital agriculture development is rather great in our country. Within the framework of the European Association of Agricultural Mechanical Engineering, a relevant definition of agriculture 4.0 has been elaborated and introduced.Research purpose: offering general recommendations on the digitalization of agriculture in RussiaMaterials and methods. The authors make use of the normative approach: the core of digital agriculture is compared with the current state of the agricultural sector in Russia.Results and discussion. The analysis has found that digital agriculture (agriculture 4.0 and 5.0) is based on developed mechanized technologies (agriculture 2.0), precision agriculture technologies (agriculture 3.0), the use of such digital technologies and technical means as the Internet of things, artificial intelligence, and robotics. The success of introducing digital agriculture depends on the success of all the three levels of the system. However, the problem of the lack of agricultural machinery indicates insufficient development of mechanized technologies; poor implementation of precision agriculture technologies means the lack of experience of using these technologies by the majority of farms in our country; an insufficient number of leading Russian IT companies (such as Amazon, Apple, Google, IBM, Intel, Microsoft etc.) weakens the country’s capacity in making a breakthrough in the development of the Internet of things, artificial intelligence, and robotics.Conclusions.The authors have identified the need to form scientific approaches to the digitization of technological operations used in the cultivation of agricultural crops and classified precision agriculture technologies. They have underlined that the digitization of agricultural production in Russia must be carried out along with intensified mechanization (energy saturation); also, to introduce technologies of precision agriculture and digital agriculture, it is necessary to organize state-funded centers for training farmers in the use of these technologies. Finally, it is necessary to take measures to strengthen the development of the IT sphere, as well as formulate an integral approach to the problem of digitalization.
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Piao, Xing Zhe, Yu Shan Xue, Cheng Gao, and Ying Shun Li. "Intelligent Heat Meter of Monitoring and Controlling System for Heating Supply Based on GPRS Network." Advanced Materials Research 614-615 (December 2012): 695–700. http://dx.doi.org/10.4028/www.scientific.net/amr.614-615.695.
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In order to realize long-range real-time online monitor and control of the measured data on the spot, and bring about control consumption of heat-energy according to need, a heat meter is developed for heating supply based on GPRS network technology. Low-power single-chip microcomputer MSP430FW427 is adopted as the measuring instruments in which SIEMENS MC55 module was used as a GPRS communication and LC oscillator coupling technology as flow sensor. In addition, the measurement principle of the heat quantity, the design methods of the software and hardware for heat meter are explained in detail, and the mathematical models of temperature and liquid sensor are established in the paper. Application of GPRS communication technology provides a transparent data transmission channel, thus realizing real-time data exchange between fared knots and monitoring center. On-the-spot operations prove that when the system is in real operation, data transmission rate is about 61.8kbps, thus having more advantages of security，accuracy and data transmission rate, effectively solving the problem of a large number of investment on traditional remote monitoring and control system.
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Huang, Su-Wen, Shao-Jen Weng, Shyue-Yow Chiou, et al. "A Study on Decision-Making for Improving Service Efficiency in Hospitals." Healthcare 12, no. 3 (2024): 405. http://dx.doi.org/10.3390/healthcare12030405.
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The provision of efficient healthcare services is essential, driven by the increasing demand for healthcare resources and the need to optimize hospital operations. In this context, the motivation to innovate and improve services while addressing urgent concerns is critical. Hospitals face challenges in managing internal dispatch services efficiently. Outsourcing such services can alleviate the burden on hospital staff, reduce costs, and introduce professional expertise. However, the pressing motivation lies in enhancing service quality, minimizing costs, and exploring innovative approaches. With the rising demand for healthcare services, there is an immediate need to streamline hospital operations. Delays in internal transportation services can have far-reaching implications for patient care, necessitating a prompt and effective solution. Drawing upon dispatch data from a healthcare center in Taiwan, this study constructed a decision-making model to optimize the allocation of hospital service resources. Employing simulation techniques, we closely examine how hospital services are currently organized and how they work. In our research, we utilized dispatch data gathered from a healthcare center in Taichung, Taiwan, spanning from January 2020 to December 2020. Our findings underscore the potential of an intelligent dispatch strategy combined with deployment restricted to the nearest available workers. Our study demonstrates that for cases requiring urgent attention, delay rates that previously ranged from 5% to 34% can be notably reduced to a much-improved 3% to 18%. However, it is important to recognize that the realm of worker dispatch remains subject to a multifaceted array of influencing factors. It becomes evident that a comprehensive dispatching mechanism must be established as part of a broader drive to enhance the efficiency of hospital service operations.
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Li, Qiang. "The Use of Artificial Intelligence Combined with Cloud Computing in the Design of Education Information Management Platform." International Journal of Emerging Technologies in Learning (iJET) 16, no. 05 (2021): 32. http://dx.doi.org/10.3991/ijet.v16i05.20309.
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Based on cloud computing theory and service-oriented architecture (SOA) design pattern, a smart education management platform is designed by using cloud computing and artificial intelligence technology. The platform is deployed in the server cluster environment, with Hadpoop managed storage cluster as the data storage center. For the education management, education portal and remote classroom, the corresponding services are launched, which has the advantages of intelligent and efficient, massive data access and intelligent collaborative management. The test results show that the platform can be used normally in the ports of computer, mobile phone and tablet computer, and can successfully complete the basic operations such as user registration and login, educational administration, and content storage. The results of database stress test show that the total time consumption of sequential read, sequential write, random read and random write is 752s, 312s, 968s and 211s, respectively when millions of simulated data are inserted into the database, which indicates that the database can support large-scale data access. The stress test results of content storage service show that when the number of clients is adjusted to 500, 1000 and 3000, the output quantity of each read-write interface can be maintained at about 3500 pages/min in 60s, indicating that the system can still run stably under the condition of high concurrency. The management platform discussed has practical significance to promote the development of intelligent and information-based education management.
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Ai, Yun Ping, Dong Yan Liu, Jia-Bao Liu, and Yun Liu. "Research on Optimization of Intelligent Logistics Agile Distribution Model in Supply Chain Networks." Mathematical Problems in Engineering 2022 (April 14, 2022): 1–8. http://dx.doi.org/10.1155/2022/4225202.
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In the supply chain environment, agile distribution refined the division of operations, responded quickly to the customer needs of the mobile terminal, and promoted the construction of an intelligent logistics distribution system. In order to integrate the distribution resources of the distribution system of the logistics industry and maximize the overall benefits of distribution, this paper designs an intelligent logistics agile distribution model and optimizes the distribution strategy. Firstly, this paper puts forward a rationalization evaluation system based on the established logistics agile distribution model. Make full use of the fuzzy subsets of each level to quantify the fuzzy indexes to evaluate, and then comprehensively clarify the index attributes through the fuzzy transformation criteria to obtain the evaluation results. Secondly, aiming at the networked agile distribution organization form, the scientific location mathematical model of agile distribution center and the path mathematical model of the agile distribution are established. Finally, the uncertain influence of many objective conditions on distribution agility has been analyzed, and the dynamic optimization model of an agile distribution path with the concept of time window under random demand is constructed. Through the experimental analysis, the rationalization evaluation strategy and index of verification test results show that the model has significant optimization advantages, which not only improve the rationality and economic benefits of distribution, but also meet the time requirements of all users.
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Wu, Xiaoyan, and Shu Wang. "Design and experiment of an intelligent surface mobile robot for the microbial slow-release control of pollution." Journal of Computational Methods in Sciences and Engineering 24, no. 1 (2024): 547–57. http://dx.doi.org/10.3233/jcm-226988.
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In this paper, to solve the current problems of water quality pollution of rivers, such as multiple types, high toxicity and difficult degradation, we design an intelligent surface mobile robot based on the slow release of microorganisms. In the robot, the skeleton has a ship-type structure welded with stainless steel equilateral angle steel; the function module comprises a power supply part, motor part, transmission part, and release part; the wireless monitoring module is based on GPRS and EtherCAT networking technologies and consists of four submodules, including a data acquisition submodule, transmission submodule, database server and monitoring center. The field test results show stable overall transmission performance and wireless data transmission performance of the robot. When the water depth of the rotating cylinder is 0.3 m, and the speed of the rotating cylinder is 15 r/min, the pollution control ability of the robot is outstanding. The experimental results also verify the feasibility of the robot in treating water pollution. The robot can greatly improve the mechanization, automation and efficiency of water pollution control and facilitate the centralized monitoring of joint operations, so it is worth popularizing.
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Ruiz, Maria, Juan José Rodriguez, Gorka Erlaiz, and Iratxe Olibares. "PHYRON: cognitive computing for the creation of an innovative Intelligence Experience Center." International Journal of Production Management and Engineering 9, no. 2 (2021): 103. http://dx.doi.org/10.4995/ijpme.2021.15300.
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<p>This research presents the results of a project called “PHYRON: Cognitive Computing for the creation of an innovative Intelligence Experience Center”, funded by the Basque Government (Economic Development, Sustainability and Environment Department). The project started in April 2019 and it will end in December 2021. Its main objective was to arrange an industrial research about cognitive computing. The main aim was the application of these systems for the development of an Intelligent Experience Center (IExC) to facilitate: i) enrichment of processes, products and services, in general client experiences, ii) automatic generation of technical predictions related to the product and the client behaviour through the exploitation of acquired knowledge, and iii) rationalization and automation of the processes that are involved in the after sale services both at technical and management level. The technological outcome presented in this paper is built using cognitive engines to enable learning from the client experience, and predictive models to anticipate client necessities.</p>
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Yu, Zhonghe, Zuochao Wang, Xuemei Ren, et al. "Practical exploration and research of Watson for oncology clinical decision support system in real-world and localized practice." Journal of Clinical Oncology 37, no. 15_suppl (2019): e18304-e18304. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e18304.
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e18304 Background: Watson for Oncology (WFO) is a cognitive computing system developed by the IBM and trained by Memorial Sloan Kettering Cancer Center to provide evidence-based treatment recommendation for cancer patients. This study is to explore the impact on clinical treatment decision and patient communication based on the standard workflow we established of applying WFO in real-world clinical practice. Methods: From May 2018 to January 2019, 57 patients with advanced malignant tumors, including lung, breast, colorectal, gastric, esophageal, liver, ovarian and endometrial cancer, treated at Oncology Department of Beijing Chaoyang Integrative Medicine Emergency Medical Center were included in the study, of which 28 males and 29 females aged 29-89 years old. Treatment plan of each patient generated by WFO was compared with NCCN guidelines and Chinese local CSCO guidelines, and then evaluated and determined by MDT for final decision. Proportion of WFO recommendation fully or partially followed by clinicians was analyzed. Moreover, survey by questionnaire was conducted to collect feedback from these patients on their satisfaction of treatment and communication process. Results: Of all 57 patients enrolled, WFO’s options were highly concordant with NCCN guidelines and CSCO guidelines, and the finally adopted treatment plans after deliberately evaluated and discussed by MDT were 100% in the WFO recommended or for consideration category. Patient satisfaction and the compliance with instruction was significantly improved compared with historical data. Localization was most required in gastric, liver and esophageal cancer due to local drugs and therapies not available in WFO. Conclusions: This study verified that the artificial intelligent clinical decision supporting system can be applied in Chinese clinical practice to achieve high patient satisfaction and good efficacy. WFO meets Chinese and international standards for treatment, which will promote the standardized development of cancer treatment in China. Optimization, iteration, upgrading and localization are required for better practicability of WFO.
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Nikolic, Jelena, Marija Marinkovic, Dragana Lekovic, and Isidora Djozic. "Evaluation of hand injury management at the emergency department - are we getting better?" Vojnosanitetski pregled, no. 00 (2021): 35. http://dx.doi.org/10.2298/vsp200308035n.
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Introduction. Hand injuries are one of the most common injuries seen in emergency departments. Inadequate treatment can lead to prolonged healing, complications, significant morbidity and serious disability. The aim of this study was to evaluate epidemiology, risk factors and treatment of hand injuries in one tertiary care level clinical center in order to be able to suggest targeted strategies for better management of injuries. Methods. This study was designed as a descriptive retrospective epidemiological study that involved all patients with hand injuries that were treated in Clinical center of Vojvodina in a seven year period. Authors collected sociodemographic and clinical data such as age, gender, mechanism of injury, type of injury, days of hospitalization, type of defect reconstruction, time of injury, timing of surgery and reasons for operative treatment delay. For every hospitalized patient modified hand injury severity score (MHISS) was calculated. All data was analyzed using SPSS IBM 21.0 software. Results. There were 34796 patients from 2012. to 2018. treated for hand injury at the Clinical Center of Vojvodina, with 554 (1,6%) being hospitalized. Mean age of patients was 43.2, majority (87.55%) were men and most of them were injured at home (47,2%). Most injuries occurred during knife handling. Average length of stay for hospitalized patients was 4 days. MHISS score for most patients was over 50 and was classified as severe. Waiting time for operation was observed to get shorter throughout selected years. Conclusion. Hand injuries present a complex problem that can be sometimes underestimated by patients. Requirement of highly specialized hand surgeons, sometimes special equipment (e.g. microscope), multiple operations, prolonged rehabilitation, possible invalidity and high cost of treatment calls for careful evaluation of problem and development of proper strategies in order to be able to lower the costs and obtain better medical care for all people with higher injury risk.
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Xue, Qingping. "The Improved Nonparametric Regression Model for the IoT Link Load Balancing Control Algorithm." Journal of Sensors 2022 (April 4, 2022): 1–12. http://dx.doi.org/10.1155/2022/2332835.
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In order to improve the load balance control effect of the Internet of Things link, this paper combines the nonparametric regression model to improve the load balancing algorithm of the Internet of Things link. Moreover, this paper proposes a load balancing research strategy based on data plane data flow, which is aimed at improving the load balancing problem of data flow in the link. The data center network uses a multilayer fat tree topology to store information in the flow table corresponding to the switch for data flow processing and forwarding operations. In addition, this paper constructs a load balancing model for intelligent Internet of Things link and verifies the model in this paper through experimental research. The research results show that the load balancing control algorithm for Internet of Things link based on the nonparametric regression model proposed in this paper can effectively improve the internal scheduling of the Internet of Things link system and promote the load balancing effect.
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Alimoradi, Reza, Fateme Amjadi, Seiied-Mohammad-Javad Razavian, and M. H. Noorallahzadeh. "A Modified Hierarchical Multiple Key Agreement Scheme for WSN." International Journal of Advanced Networking and Applications 14, no. 03 (2022): 5493–98. http://dx.doi.org/10.35444/ijana.2022.14312.
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Nowadays, sensor networks are one of the hottest scientific issues. A lot of research has been done to improve their efficiency. Wireless Sensor Networks (WSN) are applied as an important and efficient technology in many industries such as military operations, security systems, intelligent transportation systems, medics, agriculture, and many others. Key agreement is a challenging point in the security of these networks. Sensor nodes connect to each other using cryptography techniques, however, use of the classic key management techniques such as key distribution center is inefficient because of resource-constrained nature of the sensor nodes. This paper proposes a hierarchical multiple key agreement scheme. In the proposed scheme, two nodes can produce multiple session keys, just with only one run of the key agreement protocol by two nodes in the hierarchical system. As well as its efficiency, this new scheme is based on identity and non-interactive protocol. Being zero-knowledge proof is another advantage of the scheme.
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Kong, Jili, and Yi Yang. "Research on Multi-Objective Flexible Job Shop Scheduling Problem with Setup and Handling Based on an Improved Shuffled Frog Leaping Algorithm." Applied Sciences 14, no. 10 (2024): 4029. http://dx.doi.org/10.3390/app14104029.
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Flexible job shop scheduling problem (FJSP), widely prevalent in many intelligent manufacturing industries, is one of the most classic problems of production scheduling and combinatorial optimization. In actual manufacturing enterprises, the setup of machines and the handling of jobs have an important impact on the scheduling plan. Furthermore, there is a trend for a cluster of machines with similar functionalities to form a work center. Considering the above constraints, a new order-driven multi-equipment work center FJSP model with setup and handling including multiple objectives encompassing the minimization of the makespan, the number of machine shutdowns, and the number of handling batches is established. An improved shuffled frog leading algorithm is designed to solve it through the optimization of the initial solution population, the improvement of evolutionary operations, and the incorporation of Pareto sorting. The algorithm also combines the speed calculation method in the gravity search algorithm to enhance the stability of the solution search. Some standard FJSP data benchmarks have been selected to evaluate the effectiveness of the algorithm, and the experimental results confirm the satisfactory performance of the proposed algorithm. Finally, a problem example is designed to demonstrate the algorithm’s capability to generate an excellent scheduling plan.
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Wang, Xiaodong, and Feng Liu. "Triplet Loss Guided Adversarial Domain Adaptation for Bearing Fault Diagnosis." Sensors 20, no. 1 (2020): 320. http://dx.doi.org/10.3390/s20010320.
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Recently, deep learning methods are becomingincreasingly popular in the field of fault diagnosis and achieve great success. However, since the rotation speeds and load conditions of rotating machines are subject to change during operations, the distribution of labeled training dataset for intelligent fault diagnosis model is different from the distribution of unlabeled testing dataset, where domain shift occurs. The performance of the fault diagnosis may significantly degrade due to this domain shift problem. Unsupervised domain adaptation has been proposed to alleviate this problem by aligning the distribution between labeled source domain and unlabeled target domain. In this paper, we propose triplet loss guided adversarial domain adaptation method (TLADA) for bearing fault diagnosis by jointly aligning the data-level and class-level distribution. Data-level alignment is achieved using Wasserstein distance-based adversarial approach, and the discrepancy of distributions in feature space is further minimized at class level by the triplet loss. Unlike other center loss-based class-level alignment approaches, which hasto compute the class centers for each class and minimize the distance of same class center from different domain, the proposed TLADA method concatenates 2 mini-batches from source and target domain into a single mini-batch and imposes triplet loss to the whole mini-batch ignoring the domains. Therefore, the overhead of updating the class center is eliminated. The effectiveness of the proposed method is validated on CWRU dataset and Paderborn dataset through extensive transfer fault diagnosis experiments.
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Rudas, Imre J. "Intelligent Engineering Systems." Journal of Advanced Computational Intelligence and Intelligent Informatics 2, no. 3 (1998): 69–71. http://dx.doi.org/10.20965/jaciii.1998.p0069.
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Building intelligent systems has been one of the great challenges since the early days of human culture. From the second half of the 18th century, two revolutionary changes played the key role in technical development, hence in creating engineering and intelligent engineering systems. The industrial revolution was made possible through technical advances, and muscle power was replaced by machine power. The information revolution of our time, in turn, canbe characterized as the replacement of brain power by machine intelligence. The technique used to build engineering systems and replace muscle power can be termed "Hard Automation"1) and deals with industrial processes that are fixed and repetitive in nature. In hard automation, the system configuration and the operations are fixed and cannot be changed without considerable down-time and cost. It can be used, however, particularly in applications calling for fast, accurate operation, when manufacturing large batches of the same product. The "intelligent" area of automation is "Soft Automation," which involves the flexible, intelligent operation of an automated process. In flexible automation, the task is programmable and a work cell must be reconfigured quickly to accommodate a product change. It is particularly suitable for plant environments in which a variety of products is manufactured in small batches. Processes in flexible automation may have unexpected or previously unknown conditions, and would require a certain degree of "machine" intelligence to handle them.The term machine intelligence has been changing with time and is machinespecific, so intelligence in this context still remains more or less a mysterious phenomenon. Following Prof. Lotfi A. Zadeh,2) we consider a system intelligent if it has a high machine intelligence quotient (MIQ). As Prof. Zadeh stated, "MIQ is a measure of intelligence of man-made systems," and can be characterized by its well defined dimensions, such as planning, decision making, problem solving, learning reasoning, natural language understanding, speech recognition, handwriting recognition, pattern recognition, diagnostics, and execution of high level instructions.Engineering practice often involves complex systems having multiple variable and multiple parameter models, sometimes with nonlinear coupling. The conventional approaches for understanding and predicting the behavior of such systems based on analytical techniques can prove to be inadequate, even at the initial stages of setting up an appropriate mathematical model. The computational environment used in such an analytical approach is sometimes too categoric and inflexible in order to cope with the intricacy and complexity of real-world industrial systems. It turns out that, in dealing with such systems, one must face a high degree of uncertainty and tolerate great imprecision. Trying to increase precision can be very costly.In the face of the difficulties above, Prof. Zadeh proposes a different approach for Machine Intelligence. He separates Hard Computing techniques based Artificial Intelligence from Soft Computing techniques based Computational Intelligence.•Hard computing is oriented toward the analysis and design of physical processes and systems, and is characterized by precision, formality, and categorization. It is based on binary logic, crisp systems, numerical analysis, probability theory, differential equations, functional analysis, mathematical programming approximation theory, and crisp software.•Soft computing is oriented toward the analysis and design of intelligent systems. It is based on fuzzy logic, artificial neural networks, and probabilistic reasoning, including genetic algorithms, chaos theory, and parts of machine learning, and is characterized by approximation and dispositionality.In hard computing, imprecision and uncertainty are undesirable properties. In soft computing, the tolerance for imprecision and uncertainty is exploited to achieve an acceptable solution at low cost, tractability, and a high MIQ. Prof. Zadeh argues that soft rather than hard computing should be viewed as the foundation of real machine intelligence. A center has been established - the Berkeley Initiative for Soft Computing (BISC) - and he directs it at the University of California, Berkeley. BISC devotes its activities to this concept.3) Soft computing, as he explains2),•is a consortium of methodologies providing a foundation for the conception and design of intelligent systems,•is aimed at formalizing of the remarkable human ability to make rational decision in an uncertain, imprecise environment.The guiding principle of soft computing, given by Prof. Zadeh2) is: Exploit the tolerance for imprecision, uncertainty, and partial truth to achieve tractability, robustness, low solution cost, and better rapport with reality.Fuzzy logic is mainly concerned with imprecision and approximate reasoning, neurocomputing mainly with learning and curve fitting, genetic computation mainly with searching and optimization and probabilistic reasoning mainly with uncertainty and propagation of belief. The constituents of soft computing are complementary rather than competitive. Experience gained over the past decade indicates that it can be more effective to use them combined, rather than exclusively.Based on this approach, machine intelligence, including artificial intelligence and computational intelligence (soft computing techniques) is one pillar of Intelligent Engineering Systems. Hundreds of new results in this area are published in journals and international conference proceedings. One such conference, organized in Budapest, Hungary, on September 15-17, 1997, was titled'IEEE International Conference on Intelligent Engineering Systems 1997' (INES'97), sponsored by the IEEE Industrial Electronics Society, IEEE Hungary Section, Bá{a}nki Doná{a}t Polytechnic, Hungary, National Committee for Technological Development, Hungary, and in technical cooperation with the IEEE Robotics & Automation Society. It had around 100 participants from 29 countries. This special issue features papers selected from those papers presented during the conference. It should be pointed out that these papers are revised and expanded versions of those presented.The first paper discusses an intelligent control system of an automated guided vehicle used in container terminals. Container terminals, as the center of cargo transportation, play a key role in everyday cargo handling. Learning control has been applied to maintaining the vehicle's course and enabling it to stop at a designatedlocation. Speed control uses conventional control. System performance system was evaluated by simulation, and performance tests slated for a test vehicle.The second paper presents a real-time camera-based system designed for gaze tracking focused on human-computer communication. The objective was to equip computer systems with a tool that provides visual information about the user. The system detects the user's presence, then locates and tracks the face, nose and both eyes. Detection is enabled by combining image processing techniques and pattern recognition.The third paper discusses the application of soft computing techniques to solve modeling and control problems in system engineering. After the design of classical PID and fuzzy PID controllers for nonlinear systems with an approximately known dynamic model, the neural control of a SCARA robot is considered. Fuzzy control is discussed for a special class of MIMO nonlinear systems and the method of Wang generalized for such systems.The next paper describes fuzzy and neural network algorithms for word frequency prediction in document filtering. The two techniques presented are compared and an alternative neural network algoritm discussed.The fifth paper highlights the theory of common-sense knowledge in representation and reasoning. A connectionist model is proposed for common-sense knowledge representation and reasoning, and experimental results using this method presented.The next paper introduces an expert consulting system that employs software agents to manage distributed knowledge sources. These individual software agents solve users' problems either by themselves or thorough mutual cooperation.The last paper presents a methodology for creating and applying a generic manufacturing process model for mechanical parts. Based on the product model and other up-to-date approaches, the proposed model involves all possible manufacturing process variants for a cluster of manufacturing tasks. The application involves a four-level model structure and Petri net representation of manufacturing process entities. Creation and evaluation of model entities and representation of the knowledge built in the shape and manufacturing process models are emphasised. The proposed process model is applied in manufacturing process planning and production scheduling.References:1) C. W. De Silva, "Automation Intelligence," Engineering Application of Artificial Intelligence, 7-5, 471-477, (1994).2) L. A. Zadeh, "Fuzzy Logic, Neural Networks and Soft Computing," NATO Advanced Studies Institute on Soft Computing and Its Application, Antalya, Turkey, (1996).3) L. A. Zadeh, "Berkeley Initiative_in Soft Computing," IEEE Industrial Electronics Society Newsletter. 41-3, 8-10, (1994).
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Ling, Le, Xuejian Zhang, Xiaobing Hu, et al. "Research on Spraying Quality Prediction Algorithm for Automated Robot Spraying Based on KHPO-ELM Neural Network." Machines 12, no. 2 (2024): 100. http://dx.doi.org/10.3390/machines12020100.
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In the intelligent transformation of spraying operations, the investigation into the robotic spraying process holds significant importance. The spraying process, however, falls within the realm of experience-driven technology, characterized by high complexity, diverse parameters, and coupling effects. Moreover, the quality of manual spraying processes relies entirely on manual experience. Thus, the crux of the intelligent transformation of spraying robots lies in establishing a mapping model between the spraying process and the resultant spraying quality. To address the challenge of intelligently transforming empirical spraying processes and achieving the mapping from the spraying process to spraying quality, an algorithm employing an enhanced extreme learning machine-based neural network is proposed for predicting spraying process parameters with respect to the evaluation index of spraying quality. In this approach, an algorithmic model based on the Extreme Learning Machine (ELM) neural network is initially constructed utilizing five spraying process parameters: spraying speed, spraying height, spraying width pressure, atomization pressure, and oil spraying pressure. Two spraying quality evaluation indexes, namely average film thickness at the center point and surface roughness, are also incorporated. Subsequently, the prediction neural network is optimized using the K-means improved predator optimization algorithm (KHPO) to enhance the model’s prediction accuracy. This optimization step aims to improve the efficiency of the model in predicting spraying quality based on the specified process parameters. Finally, data collection and model validation for the spraying quality prediction algorithm are conducted using a designed robotic automated waterborne paint spraying experimental system. The experimental results demonstrate a significant reduction in the prediction error of the KHPO-ELM neural network model for the average film thickness center point, showcasing a decrease of 61.95% in comparison to the traditional ELM neural network and 50.81% in comparison to the BP neural network. Likewise, the improved neural network model yields a 2.31% decrease in surface roughness prediction error compared to the traditional ELM neural network and a substantial 54.0% reduction compared to the BP neural network. Consequently, the KHPO-ELM neural network, incorporating the prediction algorithm, effectively facilitates the prediction of multi-spraying process parameters for the center point of average film thickness and surface roughness in automated robot spraying. Notably, the prediction algorithm exhibits a commendable level of accuracy in these predictions.
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Bouleft, Yousra, and Ahmed Elhilali Alaoui. "Dynamic Multi-Compartment Vehicle Routing Problem for Smart Waste Collection." Applied System Innovation 6, no. 1 (2023): 30. http://dx.doi.org/10.3390/asi6010030.
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The rapid increase in urbanization results in an increase in the volume of municipal solid waste produced every day, causing overflow of the garbage cans and thus distorting the city’s appearance; for this and environmental reasons, smart cities involve the use of modern technologies for intelligent and efficient waste management. Smart bins in urban environments contain sensors that measure the status of containers in real-time and trigger wireless alarms if the container reaches a predetermined threshold, and then communicate the information to the operations center, which then sends vehicles to collect the waste from the selected stations in order to collect a significant waste amount and reduce transportation costs. In this article, we will address the issue of the Dynamic Multi-Compartmental Vehicle Routing Problem (DM-CVRP) for selective and intelligent waste collection. This problem is summarized as a linear mathematical programming model to define optimal dynamic routes to minimize the total cost, which are the transportation costs and the penalty costs caused by exceeding the bin capacity. The hybridized genetic algorithm (GA) is proposed to solve this problem, and the effectiveness of the proposed approach is verified by extensive numerical experiments on instances given by Valorsul, with some modifications to adapt these data to our problem. Then we were able to ensure the effectiveness of our approach based on the results in the static and dynamic cases, which are very encouraging.
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Yang, Wang, Junhui Xi, Zhihao Wang, et al. "Embedded Field Stalk Detection Algorithm for Digging–Pulling Cassava Harvester Intelligent Clamping and Pulling Device." Agriculture 13, no. 11 (2023): 2144. http://dx.doi.org/10.3390/agriculture13112144.
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Cassava (Manihot esculenta Crantz) is a major tuber crop worldwide, but its mechanized harvesting is inefficient. The digging–pulling cassava harvester is the primary development direction of the cassava harvester. However, the harvester clamping–pulling mechanism cannot automatically adjust its position relative to the stalks in forward movement, which results in clamping stalks with a large off-center distance difficulty, causing large harvest losses. Thus, solving the device’s clamping location problem is the key to loss reduction in the harvester. To this end, this paper proposes a real-time detection method for field stalks based on YOLOv4. First, K-means clustering is applied to improve the consistency of cassava stalk detection boxes. Next, the improved YOLOv4 network’s backbone is replaced with MobileNetV2 + CA, resulting in the KMC-YOLO network. Then, the proposed model’s validity is demonstrated using ablation studies and comparison tests. Finally, the improved network is embedded into the NVIDIA Jetson AGX Xavier, and the model is accelerated using TensorRT, before conducting field trials. The results indicate that the KMC-YOLO achieves average precision (AP) values of 98.2%, with detection speeds of 33.6 fps. The model size is reduced by 53.08% compared with the original YOLOv4 model. The detection speed after TensorRT acceleration is 39.3 fps, which is 83.64% faster than before acceleration. Field experiments show that the embedded model detects more than 95% of the time at all three harvest illumination levels. This research contributes significantly to the development of cassava harvesters with intelligent harvesting operations.
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Fang, Juemin, Xianling Guo, Zhongzheng Zhu, et al. "Quality control system of Watson for oncology: Artificial intelligence for supporting clinical decisions in oncology." Journal of Clinical Oncology 37, no. 15_suppl (2019): 6616. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.6616.
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6616 Background: Watson for Oncology (WFO) is an artificial intelligent clinical decision-support system (AI-CDSS) developed by IBM and trained by Memorial Sloan Kattering Cancer Center to assist in cancer care by providing evidence-based treatment options with priority. However, there are disagreements argue that WFO recommends “unsafe and incorrect” cancer treatments. Also, guidelines and drug availability in China are different from USA. Therefore, a quality control system of WFO is urgently needed to help oncologists better use WFO in China. Methods: Experts from medical oncology, surgical oncology, radiology, intervention, radiology and pathology etc. forming a Multiple Disciplinary Team (MDT) to score Watson recommendations in 6 aspects (shown in the table). Results: With this quality control system, the value of WFO was carefully evaluated by MDT. Recommendations with higher score(especially more than 80) were more standardized, reasonable and evidence-based thus more likely to be chosen. Localization and drug availability problem was solved by taking Chinese guidelines and drug approval into evaluation within this scoring system. Treatment options unsuitable or unavailable for patients by the system will be removed and replaced by the advices of MDT. Conclusions: Reliability and security are the top concerns of applying new technology in healthcare. With the MDT quality control system, AI-CDSS can be used safely and efficiently before it is fully mature. Also, the accuracy and advancement are assessed in this system to help oncologists better use WFO in China in the future. Indicators evaluating the WFO recommendations. [Table: see text]
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Musaiev, Zaur, and Svitlana Ponikarovska. "Study of the interaction of a short-base loader with a supporting surface during transport operations." Bulletin of Kharkov National Automobile and Highway University 1, no. 101 (2023): 112. http://dx.doi.org/10.30977/bul.2219-5548.2023.101.0.112.
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Problem. The relevance of the work is to create and improve loading and unloading machines. Goal. The article deals with the transport mode of the PMTS 1200 short-base loader, namely the process of moving over a single obstacle. Methodology. Due to the short base and the center of gravity, which is structurally located closer to the rear axle, the machine loses stability when overcoming unevenness in the working surface due to the rear wheels coming off the bearing surface, which reduces its efficiency and productivity. Results. Therefore, there is a need to increase the efficiency of short wheel loaders when overcoming unevenness on the work site by using an intelligent control system in the transport mode, which increases the stability of the machine.Originality.. On the other hand, machine control automation systems require consideration of the transport mode, mathematical modeling of the machine, and algorithmization of work processes, as these parameters are essential for the development of control systems. Thus, the creation of adaptive systems for automatic control of short-base loaders is an urgent scientific and technical problem, the solution of which is of great economic importance for the construction industry. Practical value. Using the developed mathematical models, the dependencies characterizing the interaction of the forklift wheels with the bearing surface were obtained, which are complex in nature but reflect the interaction of a short wheel loader with an obstacle and can be used to assess the stability of the machine and the traction capabilities of its individual wheels.
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Ishak, Sherif. "Deriving Traffic-Performance Measures and Levels of Service from Second-Order Statistical Features of Spatiotemporal Traffic Contour Maps." Transportation Research Record: Journal of the Transportation Research Board 1858, no. 1 (2003): 148–57. http://dx.doi.org/10.3141/1858-22.
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Little information has been successfully extracted from the wealth of data collected by intelligent transportation systems. Such information is needed for the efficiency of operations and management functions of traffic-management centers. A new set of second-order statistical measures derived from texture characterization techniques in the field of digital image analysis is presented. The main objective is to improve the data-analysis tools used in performance-monitoring systems and assessment of level of service. The new measures can extract properties such as smoothness, homogeneity, regularity, and randomness in traffic operations directly from constructed spatiotemporal traffic contour maps. To avoid information redundancy, a correlation matrix was examined for nearly 14,000 15-min speed contour maps generated for a 3.4-mi freeway section over a period of 5 weekdays. The result was a set of three second-order measures: angular second moment, contrast, and entropy. Each measure was analyzed to examine its sensitivity to various traffic conditions, expressed by the overall speed mean of each contour map. The study also presented a tentative approach, similar to the conventional one used in the Highway Capacity Manual, to evaluate the level of service for each contour map. The new set of level-of-service criteria can be applied in real time by using a stand-alone module that was developed in the study. The module can be readily implemented online and allows traffic-management center operators to tune a large set of related parameters.
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Ma, Clement, Madhumitha Sridharan, Hasan Al-Sayegh, et al. "Building a Harmonized Datamart by Integrating Cross-Institutional Systems of Clinical, Outcome, and Genomic Data: The Pediatric Patient Informatics Platform (PPIP)." JCO Clinical Cancer Informatics, no. 5 (February 2021): 202–15. http://dx.doi.org/10.1200/cci.20.00083.
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PURPOSE Siloed electronic medical data limits utility and accessibility. At the Dana-Farber/Boston Children's Cancer and Blood Disorders Center, cross-institutional data were inconsistent and difficult to access. To unify data for clinical operations, administration, and research, we developed the Pediatric Patient Informatics Platform ( PPIP), an integrated datamart harmonizing multiple source systems across two institutions into a common technology. PATIENTS AND METHODS Starting in 2009, user requirements were gathered and data sources were prioritized. Project teams, including biostatisticians, database developers, and an external contractor, were formed. Read-access to source systems was established. The 3-layer PPIP architecture was developed: STAGING, a near-exact copy of source data; INTEGRATION, where data were reorganized into domains; and, CONSUMPTION, where data were optimized for rapid retrieval. The diverse systems were integrated into a common IBM Netezza technology. Data filters were defined to accurately capture the Center's patients, and derived data items were created for harmonization across sources. An interactive online query tool, PPIP360, was developed using Microstrategy Analytics. RESULTS Driven by scientific objectives, the PPIP datamart was created, including 33,674 patients, 2,983 protocols, and 3.6 million patient visits from 14 source databases, 164 source tables, and 2,622 source data items. The PPIP360 has 605 data items and 33 metrics across 11 reports and dashboards. Dana-Farber and Boston Children's established a legal data-sharing agreement. The PPIP has supported hundreds of faculty, staff, and projects, including planning clinical trials and informing strategic planning. CONCLUSION The PPIP has successfully harmonized and integrated diagnostic, demographic, laboratory, treatment, clinical outcome, pathology, transplant, meta-protocol, and –omics data, for efficient, daily operational and research activities at Dana-Farber/Boston Children's Cancer and Blood Disorders Center, and future external sharing.
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Höyhtyä, Marko, and Jussi Martio. "Integrated Satellite–Terrestrial Connectivity for Autonomous Ships: Survey and Future Research Directions." Remote Sensing 12, no. 15 (2020): 2507. http://dx.doi.org/10.3390/rs12152507.
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An autonomous vessel uses multiple different radio technologies such as satellites, mobile networks and dedicated narrowband systems, to connect to other ships, services, and the remote operations center (ROC). In-ship communication is mainly implemented with wired technologies but also wireless links can be used. In this survey paper, we provide a short overview of autonomous and remote-controlled systems. This paper reviews 5G-related standardization in the maritime domain, covering main use cases and both the role of autonomous ships and that of people onboard. We discuss the concept of a connectivity manager, an intelligent entity that manages complex set of technologies, integrating satellite and terrestrial technologies together, ensuring robust in-ship connections and ship-to-outside connections in any environment. This survey paper describes the architecture and functionalities of connectivity management required for an autonomous ship to be able to operate globally. As a specific case example, we have implemented a research environment consisting of ship simulators with connectivity components. Our simulation results on the effects of delays to collision avoidance confirm the role of reliable connectivity for safety. Finally, we outline future research directions for autonomous ship connectivity research, providing ideas for further work.
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Kirwan, Barry. "The Impact of Artificial Intelligence on Future Aviation Safety Culture." Future Transportation 4, no. 2 (2024): 349–79. http://dx.doi.org/10.3390/futuretransp4020018.
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Artificial intelligence is developing at a rapid pace, with examples of machine learning already being used in aviation to improve efficiency. In the coming decade, it is likely that intelligent assistants (IAs) will be deployed to assist aviation personnel in the cockpit, the air traffic control center, and in airports. This will be a game-changer and may herald the way forward for single-pilot operations and AI-based air traffic management. Yet in aviation there is a core underlying tenet that ‘people create safety’ and keep the skies and passengers safe, based on a robust industry-wide safety culture. Introducing IAs into aviation might therefore undermine aviation’s hard-won track record in this area. Three experts in safety culture and human-AI teaming used a validated safety culture tool to explore the potential impacts of introducing IAs into aviation. The results suggest that there are indeed potential negative outcomes, but also possible safety affordances wherein AI could strengthen safety culture. Safeguards and mitigations are suggested for the key risk owners in aviation organizations, from CEOs to middle managers, to safety departments and frontline staff. Such safeguards will help ensure safety remains a priority across the industry.
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Belikova, Ksenia Michailovna. "Trends and prospects for the development and implementation of artificial intelligence in the military sphere in South Africa." Право и политика, no. 9 (September 2021): 1–23. http://dx.doi.org/10.7256/2454-0706.2021.9.36076.
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The subject of this research is the trends and prospects for the development and implementation of artificial intelligence in the military sphere of one of the BRICS member-states &ndash; South Africa in the context of national acts (for example, the Law of 2008 &ldquo;On the Right of Intellectual Property for State-Funded Research and Development&rdquo;), the potential and needs of this country, as well as achievements in design and manufacturing of unmanned aerial vehicles by the competitor companies (Seeker 400, MA 380, etc.). The relevance of this topic is substantiated by timely consideration of the legal perspective of the approaches of South Africa towards the implementation of artificial intelligence. The scientific novelty of this article is defined by the focus of research and the acquired results. It is determined that South Africa takes the path of institutional, legal and practical consolidation of the development of artificial intelligence in form of creation of designated infrastructure (on the premises of the universities, for example, Intelligent Systems Group at the University of Pretoria), as startups, scientific network structures (Center for Artificial Intelligence Research), etc. It is demonstrated that South Africa is the manufacturer and seller of the line of unmanned aerial vehicles that are controlled by the artificial intelligence and capable of performing various civil or military tasks &ndash;&nbsp; from moving cargo (including laser-guided bombs) to monitoring the territory (search and rescue or reconnaissance operations, damage assessment from natural disasters or combat operations, control conduct of fire at enemy positions, etc.).
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Wang, Tingting, Ruoyan Dong, Rui Zhang, and Dongchen Qin. "Research on Stability Design of Differential Drive Fork-Type AGV Based on PID Control." Electronics 9, no. 7 (2020): 1072. http://dx.doi.org/10.3390/electronics9071072.
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As one of the important components of intelligent warehousing logistics, Automated Guided Vehicles (AGVs) have greatly improved the efficiency of warehousing operations. AGVs are responsible for the delivery of goods in warehousing and logistics, and it is extremely important to maintain a stable running state. In this paper, an AGV in-situ steering dynamic model is established according to the actual size, and the center deviation phenomenon during AGV steering is theoretically analyzed to obtain the parameters that affect the AGV’s in-situ steering stability. Secondly, the dynamic simulation method is used to analyze the law of the stability of the AGV in-situ steering parameters to verify the correctness of the theoretical derivation equation. According to the analysis results, the motion parameters related to AGV in-situ steering are analyzed, and a reasonable design scheme is given. Based on the optimized fork-type AGV, the AGV in-situ steering control strategy is studied, and the adaptive fuzzy PID control algorithm is used to construct the fork-type AGV steering control system. Then the software and hardware design of the AGV steering control system is carried out. The optimized fork-type AGV has been turned to work stably after commissioning, meeting the actual work requirements.
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Sha, Junwei, Junpu Wang, Huanran Hu, Yongqiang Ye, and Guili Xu. "Development of an Accurate and Automated Quality Inspection System for Solder Joints on Aviation Plugs Using Fine-Tuned YOLOv5 Models." Applied Sciences 13, no. 9 (2023): 5290. http://dx.doi.org/10.3390/app13095290.
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The quality inspection of solder joints on aviation plugs is extremely important in modern manufacturing industries. However, this task is still mostly performed by skilled workers after welding operations, posing the problems of subjective judgment and low efficiency. To address these issues, an accurate and automated detection system using fine-tuned YOLOv5 models is developed in this paper. Firstly, we design an intelligent image acquisition system to obtain the high-resolution image of each solder joint automatically. Then, a two-phase approach is proposed for fast and accurate weld quality detection. In the first phase, a fine-tuned YOLOv5 model is applied to extract the region of interest (ROI), i.e., the row of solder joints to be inspected, within the whole image. With the sliding platform, the ROI is automatically moved to the center of the image to enhance its imaging clarity. Subsequently, another fine-tuned YOLOv5 model takes this adjusted ROI as input and realizes quality assessment. Finally, a concise and easy-to-use GUI has been designed and deployed in real production lines. Experimental results in the actual production line show that the proposed method can achieve a detection accuracy of more than 97.5% with a detection speed of about 0.1 s, which meets the needs of actual production
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Boakye-Boateng, Kwasi, Ali A. Ghorbani, and Arash Habibi Lashkari. "Implementation of a Trust-Based Framework for Substation Defense in the Smart Grid." Smart Cities 7, no. 1 (2023): 99–140. http://dx.doi.org/10.3390/smartcities7010005.
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The Smart Grid is a cyber-integrated power grid that manages electricity generation, transmission, and distribution to consumers and central to its functioning is the substation. However, integrating cyber-infrastructure into the substation has increased its attack surface. Notably, sophisticated attacks such as the PipeDream APT exploit multiple device protocols, such as Modbus, DNP3, and IEC61850. The substation’s constraints pose challenges for implementing security measures such as encryption and intrusion detection systems. To address this, we propose a comprehensive trust-based framework aimed at enhancing substation security. The framework comprises a trust model, a risk posture model, and a trust transferability model. The trust model detects protocol-based attacks on Intelligent Electronic Devices and SCADA HMI systems, while the risk posture model dynamically assesses the substation’s risk posture. The trust transferability model evaluates the feasibility of transferring and integrating a device and its trust capabilities into a different substation. The practical substation emulation involves a Docker-based testbed, employing a multi-agent architecture with a real-time Security Operations Center-influenced dashboard. Assessment involves testing against attacks guided by the MITRE ICS ATT&CK framework. Our framework displays resilience against diverse attacks, identifies malicious behavior, and rewards trustworthy devices.