To see the other types of publications on this topic, follow the link: Systems „man-machine environment” reliability.
Journal articles on the topic 'Systems „man-machine environment” reliability'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Systems „man-machine environment” reliability.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Feng, Qiang, Wei Jie Gao, Cai Xia Wang, and Xiao Jie Guo. "Application of MMESE in Engine Room Safety Management." Advanced Materials Research 487 (March 2012): 247–52. http://dx.doi.org/10.4028/www.scientific.net/amr.487.247.
Full textAbstract:
The reliability of the engine room is an important guarantee for the safety of ship. This article uses the theory of Man-machine environment Systems Engineering, combined with the engine room safety management practice, comprehensively analyze the characteristics of man, machine and environment and their impact on engine room safety, Finally, it gives some engine room safety management measures which are based on the theory of Man-machine environment Systems Engineering.
2
Rytov, Mikhail, and Valeriy Spasennikov. "Theoretical and applied issues of selection and training of operators of human-machine complexes in domestic ergonomics." Ergodesign 2020, no. 4 (2020): 203–23. http://dx.doi.org/10.30987/2658-4026-2020-4-203-223.
Full textAbstract:
The main criteria, stages and directions of professional selection of operators of the "man-machine-environment" systems are revealed. Mathematical models of professional aptitude assessment based on the use of a sequential probability ratio and the theory of pattern recognition are presented. Given the approach to assessment of efficiency of professional selection, taking into account the accuracy, reliability and validity of the forecast of professional suitability of operators and the possibility of assessing the professional suitability of operators and the quality of the workplace for temporary tochnostyu and probabilistic indicators. Modern training technologies used in military and civil man-machine complexes are considered.
3
Rytov, Mikhail, and Valeriy Spasennikov. "Theoretical and applied issues of selection and training of operators of human-machine complexes in domestic ergonomics." Ergodesign 2020, no. 4 (2020): 203–24. http://dx.doi.org/10.30987/2658-4026-2020-4-203-224.
Full textAbstract:
The main criteria, stages and directions of professional selection of operators of the "man-machine-environment" systems are revealed. Mathematical models of professional aptitude assessment based on the use of a sequential probability ratio and the theory of pattern recognition are presented. Given the approach to assessment of efficiency of professional selection, taking into account the accuracy, reliability and validity of the forecast of professional suitability of operators and the possibility of assessing the professional suitability of operators and the quality of the workplace for temporary tochnostyu and probabilistic indicators. Modern training technologies used in military and civil man-machine complexes are considered.
4
Shvetsova, V. I. "Optimization of “human-machine-environment” system: human operator reliability assessment." Monitoring systems of environment, no. 1 (March 25, 2021): 147–53. http://dx.doi.org/10.33075/2220-5861-2021-1-147-153.
Full textAbstract:
This article highlights the need to take into account the human component when solving optimization problems in the “man-machine-environment” system. However, currently there are no methods for quantifying the human operator reliability in such systems. An approach for assessing the human operator reliability is proposed. The mathematical apparatus for describing the human operator reliability in the ergatic system based on the Markov theory of random state transitions described by a polynomial system of differential equations for each human operation in the system in a theoretical form is presented. The implementation of this mathematical apparatus assumes the availability of statistical and experimental data on the failure rates and restoration of its productivity. The possibility of the designer to assess the final probability of the human operator of the ergatic environmental control system for one of the most critical operations (to achieve the final goal) in the absence of statistical (experimental) data by analogy with the “weak” link in the technical system is justified. The methodological solution for assessing the human operator reliability in an ergatic control system based on experimental data using the example of a manual trigger of a fire alarm system, based on the expression of a standard pointer in the form of a readiness factor of technical products is shown. The experiment was conducted on a stand assembled from commercially available products, simulating a fire alarm device. In the article the experimental research results by four age groups and their statistical processing are presented.
5
Kontogiannis, Tom. "User strategies in recovering from errors in man–machine systems." Safety Science 32, no. 1 (1999): 49–68. http://dx.doi.org/10.1016/s0925-7535(99)00010-7.
Full text
6
Loganathan, M. K., and O. P. Gandhi. "Reliability evaluation and analysis of CNC cam shaft grinding machine." Journal of Engineering, Design and Technology 13, no. 1 (2015): 37–73. http://dx.doi.org/10.1108/jedt-10-2012-0042.
Full textAbstract:
Purpose – Reliability assessment does require an effective structural modelling approach for systems, in general and manufacturing systems are no exception. This paper aims to develop it for large manufacturing systems using graph models, a systems approach. Design/methodology/approach – Structural graph models for reliability at various hierarchical levels are developed by considering a CNC cam shaft grinding machine. The system reliability expression is obtained by converting the reliability graphs into equivalent matrices, which helps to evaluate and analyse system. Findings – Using the obtained reliability expressions at various hierarchical levels of the system, it is possible not only to evaluate its reliability from structure point of view but also to identify weak structural elements from reliability point of view. Research limitations/implications – The approach can be extended to include the influence of other parameters, such as human, component and environment, etc., on the system reliability. Practical implications – The approach helps to design and develop manufacturing systems from reliability consideration by assessing their possible alternatives among these. Originality/value – The suggested methodology is useful for reliability evaluation of large and complex manufacturing systems.
7
Woropay, Maciej, Piotr Bojar, and Mirosław Szubartowski. "Assessment of the Level of Undesirable Actions of People Involved in Road Transport Systems and their Environments." Journal of Konbin 8, no. 1 (2008): 241–48. http://dx.doi.org/10.2478/v10040-008-0117-4.
Full textAbstract:
Assessment of the Level of Undesirable Actions of People Involved in Road Transport Systems and their Environments. Road transport systems are socio-technical systems of the type Human - Technical object- Environment <H-TO-E>. In these systems hazards can be caused by: the man C, the technical object OT, and influence of the environment on the technical object and people who are present in the system and its environment. The authors of the paper make an attempt to evaluate the level of threats resulting from intended or not intended actions of people situated in this environment.
8
Berger, Simon, Patrick Biehle, Miguel Villarroel, Nicolas Brückmann, Christoph Richter, and Gunther Reinhart. "Identification of Influence Parameters and Dependencies on the Illumination of Machine Vision Systems for Robots." Advanced Materials Research 1140 (August 2016): 408–15. http://dx.doi.org/10.4028/www.scientific.net/amr.1140.408.
Full textAbstract:
Machine vision systems need to cope with harsh environmental conditions, when used for guiding robots. To increase the reliability of these systems, they are either shielded from their environment or highly advanced, using sophisticated mathematical algorithms. In this paper the effects of external illumination on machine vision systems are investigated. For this the pose and the brightness of the external illumination and the internal illumination of the camera and its exposure time are varied. In a next step the correlation between the parameters of the external illumination and reliability of the machine vision system is analysed. Finally requirements are derived for further development of machine vision systems.
9
Ristov, Pančo, Pavao Komadina, and Vinko Tomas. "Reliability and Availability of the Vessel Traffic Management and Information Systems." Transactions on Maritime Science 2, no. 2 (2013): 101–8. http://dx.doi.org/10.7225/toms.v02.n02.002.
Full textAbstract:
This paper presents an approach to the reliability and availability of Vessel Traffic Management and Information Systems through the analysis of hardware, software and human reliability. The paper analyzes the critical subsystems and modules on the basis of the reliability theory, in order to achieve and ensure the availability of systems, i.e. to increase the safety of maritime traffic and the protection of the sea and marine environment. The paper discusses some of the techniques and mechanisms of hardware and software redundancy, as well as the activities that result in increasing the reliability of man.
10
Zhou, Guanghui, Fengtian Chang, Changle Tian, and Qi Lu. "A reliability optimization allocation method for the hydraulic system of CNC honing machine considering the operating condition information." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 232, no. 12 (2017): 2244–64. http://dx.doi.org/10.1177/0954406217719475.
Full textAbstract:
In recent years, reliability optimization allocation has drawn a broad spectrum of attentions, especially for complex systems consisting of a large number of subsystems and components. Hydraulic system, deemed as a complex system, plays a great role in reliability increment of the CNC honing machine. In order to realize the CNC honing machine tool’s balanced operating performance, the crucial hydraulic system is designed to obtain the optimal and well-balanced allocation solution under the constrains of cost and reliability. Meanwhile, the quantified operating factor that influences the allocation solution is rarely considered adequately when operating samples are insufficient. So, if hydraulic systems are used in a new required longer operating mission duration or a new severer operating environment, the dissatisfied reliability could not be better reallocated to adapt to this environment with appropriate methods. Aimed at overcoming the abovementioned shortcomings and optimizing the dissatisfied reliability for existing hydraulic systems, this paper proposes a reliability optimization allocation method considering operating the condition information. From the standpoint of the complex system, this method firstly builds a three-level reliability optimization allocation model based on the analytic target cascading for the specific hydraulic system of CNC honing machine 2MK2263 × 200. It transforms the complex reliability allocation problem to a set of smaller and coordinated optimization sub-problems. Secondly, through studying the operating reliability problems of small samples in the new operating scene, the reliability prediction results based on the operating condition information are applied to build the initial reliability targets, which are regarded as the initial constrains for the optimization. With the help of analytic target cascading model and initial target reliability, the optimal reliability for the complex hydraulic system would be obtained. It is also shown to provide an innovative route for the reliability redesign of the related complex systems for this new required operating scene.
11
Yang, Zhaojun, Xiaoxu Li, Chuanhai Chen, et al. "Reliability assessment of the spindle systems with a competing risk model." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 233, no. 2 (2018): 226–34. http://dx.doi.org/10.1177/1748006x18770343.
Full textAbstract:
Traditional reliability assessment of spindle systems of machine tools suffers from long testing time and high cost. Accelerated life testing is an alternative that overcomes the shortcomings of traditional reliability testing. In a life testing, identification of critical factors of service life and an accurate model are important. Based on the characteristic analysis and engineering experience, four reliability factors, which are the average power of spindle systems, the number of tool changing, the number of spindles restarting and environment temperature, are selected as accelerating environment variables. An accelerated failure time model is used to describe the inverse relationship between the variables and reliability for the catastrophic failure mode and the degradation failure mode separately. Then a competing risk model is built by considering competing risks of two modes. Parametric reliability models are proposed to capture the statistical independency and dependency separately, in which the Gumbel–Hougaard copula function is used to establish the joint cumulative distribution for dependency. Thereby the hypothesis testing is developed to determine the failure modes dependency. The reliability sensitivity of each environment variable is analyzed. Finally, the proposed model is illustrated with a real field case study.
12
KLEPOV, V. I. "СOMPLEX WATER-RESOURCE SYSTEMS. FEATURES OF OPERATION, RELIABILITY AND ENVIRONMENTAL SAFETY". Prirodoobustrojstvo, № 1 (2021): 100–106. http://dx.doi.org/10.26897/1997-6011-2021-1-100-106.
Full textAbstract:
Water-resource systems have become as much a reality as electric power, transport, communication systems and other large man-made systems. The concept of water resource systems (WRS) refers primarily to a hydraulically connected set of sources and means of managing and delivering water resources to water consumers and water users. The world experience shows that the largest WRS use river runoff as a source of water resources; however, as our research has shown, the future belongs to WRS which are based on the joint management of surface and underground water resources. Despite the significant similarity in the processes of creating and managing large man-made systems, WRS have a number of features that require solving specific problems. First of all, the strategy for the development and management of WRS should take into account the variability of river flow, as well as the fact that these systems are one of the most important elements of the environment. It is these factors that underlie the solution of the problem of the WRS guaranteed output and the reliability of the functioning of these systems on the basis of the calculated availability of their guaranteed to the socio-economic sphere and guaranteed satisfaction of the environmental requirements which is one of the principles of sustainable economic development.
13
Wang, Yu, Yunxiu Sai, and Dongliang Yuan. "Key technologies for energy saving and emission reduction of heating furnaces in petrochemical plants." Thermal Science 25, no. 4 Part B (2021): 3159–68. http://dx.doi.org/10.2298/tsci2104159w.
Full textAbstract:
The safety instrumented system is a system that is independent of the petrochemical production process control system and can independently complete the safety interlock protection function. It can avoid significant hazards to production equipment caused by process production failures, field instrument failures, system power supply failures, etc., and ensure safe operation of production equipment. In petrochemical plants, it is imperative to configure a safe, reliable, and timely safety instrumented system. Combined with applying the burner management system of the heating furnace in a refinery, the paper focuses on the technical realization of the hardware structure, system configuration, logic programming, man-machine interface configuration system debugging of the TRICON system. Practical application shows that TRICON?s safety instrumented system is a set of safety instrumented systems with comprehensive functions, safety and reliability, and easy to learn.
14
WALD, RANDALL, TAGHI M. KHOSHGOFTAAR, and BASSEM ALHALABI. "BASELINE-DIFFERENCING: A NOVEL APPROACH FOR BUILDING GENERALIZABLE OCEAN TURBINE RELIABILITY MODELS." International Journal of Reliability, Quality and Safety Engineering 19, no. 06 (2012): 1240005. http://dx.doi.org/10.1142/s0218539312400050.
Full textAbstract:
Ocean turbines are a promising new source of clean energy, but their remote and inhospitable environment (the open ocean) poses reliability challenges. Machine condition monitoring/prognostic health monitoring (MCM/PHM) systems assure the reliability of these turbines by detecting and predicting machine state. These MCM/PHM systems use sensor data (such as vibration information) to determine whether or not the machine is operating properly. However, not all sensor data corresponds to the machine state: some portions of the sensor signal are influenced by certain environmental conditions which do not directly relate to machine health. Therefore, models must be built which can detect system state regardless of these environmental operating conditions. The proposed baseline-differencing approach permits this by creating a baseline for different conditions (such that each baseline represents what the normal, healthy machine state looks like while in that operating condition) and using the difference of the observed data and this baseline to train and evaluate models. We present two case studies, both conducted on data from a dynamometer representing an ocean turbine, to demonstrate the improved predictive capabilities of models which incorporate baseline-differencing, compared to the models which use the nonbaselined data. The results show that significantly more high-quality models can be built with baseline-differencing.
15
GHODRATI, BEHZAD, ALIREZA AHMADI, and DIEGO GALAR. "SPARE PARTS ESTIMATION FOR MACHINE AVAILABILITY IMPROVEMENT ADDRESSING ITS RELIABILITY AND OPERATING ENVIRONMENT — CASE STUDY." International Journal of Reliability, Quality and Safety Engineering 20, no. 03 (2013): 1340005. http://dx.doi.org/10.1142/s0218539313400056.
Full textAbstract:
Industrial operation cost analysis shows that, in general, maintenance represents a significant proportion of the overall operating costs. Therefore, the improvement of maintenance follows the final goal of any company, namely, to maximize profit. This paper studies spare parts availability, an issue of the maintenance process, which is an important way to improve production through increased availability of functional machinery and subsequent minimization of the total production cost. Spare parts estimation based on machine reliability characteristics and operating environment is performed. The study uses an improved statistical-reliability (S-R) approach which incorporates the system/machine operating environment information in systems reliability analysis. For this purpose, two methods of Poisson process and renewal process are introduced and discussed. The renewal process model uses a multiple regression type of analysis based on Cox's proportional hazards modeling (PHM). The parametric approaches with baseline Weibull hazard functions and time-independent covariates are considered, and the influence of operating environment factors on this model is analyzed. The outputs represent a significant difference in the required spare parts estimation when considering or ignoring the influence of the relevant system operating environment. The difference is significant in the sense of spare parts forecasting and inventory management which can enhance the parts and consequently machine availability, leading to economical operation and savings.
16
Srikanth, Dodla, and M. S. Kulkarni. "Quality, Reliability and Maintenance (Q, R & M) Issues in Reconfigurable Manufacturing Systems (RMS)." Applied Mechanics and Materials 110-116 (October 2011): 1442–46. http://dx.doi.org/10.4028/www.scientific.net/amm.110-116.1442.
Full textAbstract:
Reconfigurable Manufacturing Systems (RMS) have the potential to emerge as a cost effective solution that will help manufacturing organizations to stay competitive in an environment where product mix changes frequently and product life cycles are getting shorter. Reconfigurable Manufacturing Systems can achieve this as they are designed for quick changes in its configuration, as well as software and hardware components. This not only helps in accommodating production capacity but also production of new variety of products and introduction of new product within part family. However, the configurations have a significant impact on the productivity and reliability of the machines and the manufacturing system. In the present paper, the main objective is to present a framework consisting of Maintenance plan to be followed for the reconfigured machine, Reliability of the reconfigured machine, Quality of the product obtained. This framework can form as the basic idea and a link between maintenance, reliability and quality issues.
17
Bojar, Piotr, Maciej Woropay, Mirosław Szubartowski, and Michał Bacior. "The Method of Assessment of Transport Safety / Metoda Oceny Bezpieczeństwa Procesu Transportowego." Journal of KONBiN 21, no. 1 (2012): 99–112. http://dx.doi.org/10.2478/jok-2013-0009.
Full textAbstract:
Abstract This article introduces classification of transport systems into land transport systems (road and rail) as well as land and water transport systems (inland and sea), depending on the type of environment in which these systems carry out their tasks. Such systems fall under the class of social engineering systems of the Man - Technological Object - Environment (M - TO - E) type. Such systems are influenced by forcing factors, leading to changes in their states. Such factors may be divided into operational, outside and anthropotechnical and they influence the system on various levels, including degradation of the degree of its safety. The article attempts to evaluate the safety of the operation of transport systems on the basis of the evaluation of the safety of the transport process carried out over a defined time interval Δt.
18
Komagata, Mitsuo, Tianyi Ko, Ko Yamamoto, and Yoshihiko Nakamura. "Experimental Study on Critical Design of Electro-Hydrostatic Actuators Small in Size and Light in Weight." Journal of Robotics and Mechatronics 32, no. 5 (2020): 911–22. http://dx.doi.org/10.20965/jrm.2020.p0911.
Full textAbstract:
Actuation systems for robots and other machines used in critical applications is an area that requires further research. In such applications, a machine works in a human environment and physically interacts with humans. Reliability and backdrivability are still insufficient in current systems. An electro-hydrostatic actuator has the potential advantage of high reliability by nature and high backdrivability in mechanical simplexity when it is designed to be small and light. This study provides a theoretical investigation of the methods for evaluating internal leaks and other mechanical losses, such as Coulomb and viscous friction, and experimentally evaluates two types (trochoid and involute gear) of prototyped hydraulic pumps.
19
Ferraro, James, Logan Clark, Naomi Christy, and Mustapha Mouloua. "Effects of Automation Reliability and Trust on System Monitoring Performance in Simulated Flight Tasks." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (2018): 1232–36. http://dx.doi.org/10.1177/1541931218621283.
Full textAbstract:
The current study was designed to empirically examine the effects of trust and automation reliability on multi-tasking performance in a simulated cockpit setting using the Multi-Attribute Task Battery II (MATB-II). The MATB-II simulates tasks often performed by pilots in-flight, tasking the operator with attending to automated systems and correcting errors when they inevitably occur. Over the course of three 30-minute trials, two levels of automation reliability were presented in the current study (R50% and R90%). It was hypothesized that automation reliability and trust would affect both workload and performance in this multi-tasking environment. Results indicated that reliability significantly affected monitoring performance on the MATB-II. More reliable automation resulted in poorer monitoring performance, while trust appeared to have little impact. These results provide further evidence for how operators trust and interact with automation, a topic that is relevant to the implementation of automated systems in a variety of human-machine systems such as aviation.
20
Lavrov, Evgeniy, Olga Siryk, Aleksandr Volosiuk, Aleksandr Zolkin, and Nelly Sedova. "Sustainability and reliability ensurance models for automated technological systems in chemical industry: systemic ergonomic approach." E3S Web of Conferences 280 (2021): 02005. http://dx.doi.org/10.1051/e3sconf/202128002005.
Full textAbstract:
The paper considers accidents and potential hazards of the world chemical enterprises, and provides statistics of accidents and human casualties. The authors investigate harmful factors affecting the production process and a human-operator, showing the central role of a human in the technological process (both as a source of errors and as an active element that eliminates errors, failures and cyber attacks’ consequences). We essentially consider automated technological complex as a Human-Machine-Environment system, thus a human-system approach should be applied. The authors developed a complex of systemic components and morphological models, which describe the human-machine system in the sections required for analysis, to ensure sustainable and reliable design with initial data. The authors also propose a method and information technology for interfaces’ ergonomic assessment; the principles for adaptive interfaces design; and mathematical models and information technology to assess safety and timeliness indicators of the chemical production operators’ activities. The models are based on the principles of the functional-structural theory by Anatoly Gubinsky, Vladimir Evgrafov, Akiva Asherov, Pavel Chabaneko and others, and on the mathematical apparatus of functional networks. Further, the authors develop an optimization model for decision supporting organizing the human-machine control technology, using the criterion of minimizing losses from unreliability and unsustainability. Both the models and the information technology have undergone extensive testing, including solving the tasks of: choosing the automation level for the control process; distributing functions between operators; control algorithms design; user interface design, design of agent-managers to support the operators’ activities. The results can be used as the basis for a decision support system to ensure sustainability and reliability of automated technological systems in chemical industry.
21
Sridevi, Gutta, and Midhunchakkravarthy. "A Hybrid Multi-level Statistical Load Balancer-Based Parameters Estimation Model in Realtime Cloud Computing Environment." Ingénierie des systèmes d information 25, no. 6 (2020): 771–82. http://dx.doi.org/10.18280/isi.250607.
Full textAbstract:
As the size of the cloud-based applications and its tasks are increasing exponentially, it is necessary to estimate the load balancing metrics in the real-time cloud computing environments. Hybrid load balancing framework play a vital role in the cloud-based applications and tasks monitoring and resource allocation. Most of the conventional load balancing metrics are dependent on limited number of cloud metrics and type of virtual machines. Also, these models require high computational memory and time on large number of tasks. In this paper, an advanced multi-level statistical load balancer-based parameters estimation model is designed and implemented on the real-time cloud computing environment. In this model, a novel statistical load balancing data collector is used to find the best metrics for the load balance computation. In this model, different types of tasks are simulated under different virtual machine types such as small, medium and large instances. Experimental results show that the proposed multi-level based statistical load balancing collector has better efficiency than the conventional models in terms of memory utilization, CPU utilization, runtime and reliability are concerned.
22
Li, Jie, Yang Pan, Shijian Ni, and Feng Wang. "A Hybrid Reliable Routing Algorithm Based on LQI and PRR in Industrial Wireless Networks." Wireless Communications and Mobile Computing 2021 (September 4, 2021): 1–16. http://dx.doi.org/10.1155/2021/6039900.
Full textAbstract:
In Industrial Wireless Networks (IWNs), the communication through Machine-to-Machine (M2M) is often affected by the noise in the industrial environment, which leads to the decline of communication reliability. In this paper, we investigate how to improve route stability through M2M in an industrial environment. We first compare different link quality estimations, such as Signal-Noise Ratio (SNR), Received Signal Strength Indicator (RSSI), Link Quality Indicator (LQI), Packet Reception Ratio (PRR), and Expected Transmission Count (ETX). We then propose a link quality estimation combining LQI and PRR. Finally, we propose a Hybrid Link Quality Estimation-Based Reliable Routing (HLQEBRR) algorithm for IWNs, with the object of maximizing link stability. In addition, HLQEBRR provides a recovery mechanism to detect node failure, which improves the speed and accuracy of node recovery. OMNeT++-based simulation results demonstrate that our HLQEBRR algorithm significantly outperforms the Collection Tree Protocol (CTP) algorithm in terms of end-to-end transmission delay and packet loss ratio, and the HLQEBRR algorithm achieves higher reliability at a small additional cost.
23
Suo, Na, and Lina Guo. "Research on thermal design control and optimization of relay protection and automation equipment." Thermal Science 24, no. 5 Part B (2020): 3119–28. http://dx.doi.org/10.2298/tsci191106086s.
Full textAbstract:
Relay protection devices and power automation systems are an important product in the power equipment manufacturing industry. They are customarily divided into secondary equipment for the transmission and distribution industry, and are responsible for protecting and controlling the primary equipment of the power grid and measuring the load of the power grid system. Thermal design is a major research topic for the reliability study of relay protection devices. The paper introduces the thermal design process of the relay protection device processing equipment, from the single-chip, module level, etc. to construct and isolate the airway facilities, and uses the FLOTHERM software to simulate the relay protection device model. Thermal simulation can guide the structural design, optimize the structure, make the single-machine structure more reasonable, and the heat dissipation more effective, improving the reliability of the single machine.
24
Chen, Liang, Prathik Anandhan, and Balamurugan S. "Analysis of performance-based issues in green transportation management systems in smart cities." Electronic Library 38, no. 5/6 (2020): 963–77. http://dx.doi.org/10.1108/el-07-2020-0205.
Full textAbstract:
Purpose In this paper, an intelligent information assisted communication transportation framework (II-CTF) has been introduced to reduce congestion, data reliability in transportation and the environmental effects. Design/methodology/approach The main concern of II-CTF is to mitigate public congestion using current transport services, which helps to improve data reliability under hazardous circumstances and to avoid accidents when the driver cannot respond reasonably. The program uses machine learning assistance to predict optimal routes based on movement patterns and categorization of vehicles, which helps to minimize congestion of traffic. Findings In II-CTF, scheduling traffic optimization helps to reduce the energy and many challenges faced by traffic managers in terms of optimization of the route, average waiting time and congestion of traffic, travel, and environmental impact due to heavy traffic collision. Originality/value The II-CTF definition is supposed to attempt to overcome some of the problems of the transportation environment that pose difficulties and make the carriage simpler, safer, more efficient and green for all.
25
Iacopetti, Fabrizio, Sergio Saponara, Luca Fanucci, and Bruno Neri. "Wireless Sensing Based on RFID and Capacitive Technologies for Safety in Marble Industry Process Control." Journal of Computer Networks and Communications 2013 (2013): 1–19. http://dx.doi.org/10.1155/2013/392056.
Full textAbstract:
This paper presents wireless sensing systems to increase safety and robustness in industrial process control, particularly in industrial machines for marble slab working. The process is performed by abrasive or cutting heads activated independently by the machine controller when the slab, transported on a conveyer belt, is under them. Current slab detection systems are based on electromechanical or optical devices at the machine entrance stage, suffering from deterioration and from the harsh environment. Slab displacement or break inside the machine due to the working stress may result in safety issues and damages to the conveyer belt due to incorrect driving of the working tools. The experimented contactless sensing techniques are based on four RFID and two capacitive sensing technologies and on customized hardware/software. The proposed solutions aim at overcoming some limitations of current state-of-the-art detection systems, allowing for reliable slab detection, outside and/or inside the machine, while maintaining low complexity and at the same time robustness to industrial harsh conditions. The proposed sensing devices may implement a wireless or wired sensor network feeding detection data to the machine controller. Data integrity check and process control algorithms have to be implemented for the safety and reliability of the overall industrial process.
26
Singh, Sushil Kumar, Mikail Mohammed Salim, Jeonghun Cha, Yi Pan, and Jong Hyuk Park. "Machine Learning-Based Network Sub-Slicing Framework in a Sustainable 5G Environment." Sustainability 12, no. 15 (2020): 6250. http://dx.doi.org/10.3390/su12156250.
Full textAbstract:
Nowadays, 5G network infrastructures are being developed for various industrial IoT (Internet of Things) applications worldwide, emerging with the IoT. As such, it is possible to deploy power-optimized technology in a way that promotes the long-term sustainability of networks. Network slicing is a fundamental technology that is implemented to handle load balancing issues within a multi-tenant network system. Separate network slices are formed to process applications having different requirements, such as low latency, high reliability, and high spectral efficiency. Modern IoT applications have dynamic needs, and various systems prioritize assorted types of network resources accordingly. In this paper, we present a new framework for the optimum performance of device applications with optimized network slice resources. Specifically, we propose a Machine Learning-based Network Sub-slicing Framework in a Sustainable 5G Environment in order to optimize network load balancing problems, where each logical slice is divided into a virtualized sub-slice of resources. Each sub-slice provides the application system with different prioritized resources as necessary. One sub-slice focuses on spectral efficiency, whereas the other focuses on providing low latency with reduced power consumption. We identify different connected device application requirements through feature selection using the Support Vector Machine (SVM) algorithm. The K-means algorithm is used to create clusters of sub-slices for the similar grouping of types of application services such as application-based, platform-based, and infrastructure-based services. Latency, load balancing, heterogeneity, and power efficiency are the four primary key considerations for the proposed framework. We evaluate and present a comparative analysis of the proposed framework, which outperforms existing studies based on experimental evaluation.
27
Goulter, I. C., and D. Forrest. "Use of Geographic Information Systems (GIS) in River Basin Management." Water Science and Technology 19, no. 9 (1987): 81–86. http://dx.doi.org/10.2166/wst.1987.0069.
Full textAbstract:
Geographic Information Systems (GIS) are shown to provide a number of capabilities which are of particular use to river basin operation and planning. GIS systems have the ability to display and graphically summarize both the input data for the analytical models and the results of application of management models using that data. The graphical display of input data can assist interpretation of conditions within the basin through depiction of spatial and temporal patterns in that data. GIS can also reduce the time required to enter data and improve the reliability of that data by reducing the errors in the data. The ability to display the results graphically improves the man-machine interaction which is generally accepted as being an integral part of multi-objective water resources analysis. Another important role for GIS is its use as an efficient interface between the data base and the operational computer models. It is asserted, however, that GIS should not be considered a means of providing final answers to complex water resources planning issues. It should be seen, rather, as an important component of Decision Support Systems by which information on the basin issues is transferred to the decision maker for his consideration. Specific aspects of the application of GIS are discussed in relation to a hypothetical river basin system used primarily for hydroelectric energy generation.
28
Umesh, I. M., and G. N. Srinivasan. "Optimum Software Aging Prediction and Rejuvenation Model for Virtualized Environment." Indonesian Journal of Electrical Engineering and Computer Science 3, no. 3 (2016): 572. http://dx.doi.org/10.11591/ijeecs.v3.i3.pp572-578.
Full textAbstract:
<p><em>Advancement in electronics and hardware has resulted in multiple softwares running on the same hardware. The result is multiuser, multitasking and virtualized environments. However, reliability of such high performance computing systems depends both on hardware and software. For hardware, aging can be dealt with replacement. But, software aging needs to be dealt with software only. For aging detection, a new approach using machine learning framework has been proposed in this paper. For rejuvenation, Adaptive Genetic Algorithm (A-GA) has been developed to perform live migration to avoid downtime and SLA violation. The proposed A-GA based rejuvenation controller (A-GARC) has outperformed other heuristic techniques such as Ant Colony Optimization (ACO) and best fit decreasing (BFD) for migration. Results reveal that the proposed aging forecasting method and A-GA based rejuvenation outperforms other approaches to ensure optimal system availability, minimum task migration, performance degradation and SLA violation.</em></p>
29
Shahbazi, Zeinab, and Yung-Cheol Byun. "Integration of Blockchain, IoT and Machine Learning for Multistage Quality Control and Enhancing Security in Smart Manufacturing." Sensors 21, no. 4 (2021): 1467. http://dx.doi.org/10.3390/s21041467.
Full textAbstract:
Smart manufacturing systems are growing based on the various requests for predicting the reliability and quality of equipment. Many machine learning techniques are being examined to that end. Another issue which considers an important part of industry is data security and management. To overcome the problems mentioned above, we applied the integrated methods of blockchain and machine learning to secure system transactions and handle a dataset to overcome the fake dataset. To manage and analyze the collected dataset, big data techniques were used. The blockchain system was implemented in the private Hyperledger Fabric platform. Similarly, the fault diagnosis prediction aspect was evaluated based on the hybrid prediction technique. The system’s quality control was evaluated based on non-linear machine learning techniques, which modeled that complex environment and found the true positive rate of the system’s quality control approach.
30
Jia, Yu Jing, Guang Zhen Cheng, and Ying Jun Dai. "Impellent Tramcar Machine Hydraulic and Electrical Control System." Advanced Materials Research 339 (September 2011): 223–26. http://dx.doi.org/10.4028/www.scientific.net/amr.339.223.
Full textAbstract:
This essay will introduce a impellent tramcar machine, used for mining enterprises’ vertical shaft hoisting transportation system, which is used to put tub into the cage, or pull it from the cage. The mechanical impellent tramcar machine are widely used at present, it rely on the direct-drive motor, there are two types of traction, one is towed by the sprocket and chain anther is towed by the reel and wire, whatever we use which type, both the traction drive components are exposed, the environment dust will lead to poor lubrication, running noise, shock and vibration significantly, poor stability. To overcome the mechanical impellent tramcar machine of these shortcomings, developed the hydraulic impellent tramcar machine, the hydraulic cylinder as the executing agency, the programmable logic controller to achieve electrical control, designed hydraulic systems and PLC control program, analyzed the impellent tramcar machine Structure and working principle, point out that the use of programmable logic controller, programming is easy, flexible, programmable logic controllers cater to the industrial production sites, with great anti-interference ability, hydraulic drive compact, smooth and high reliability.
31
Hendra, Anizar Indriani, Hernadewita, and Y. Rizal. "Assembly Programmable Logic Control (PLC) in the Rotary Dryer Machine for Processing Waste Liquid System." Applied Mechanics and Materials 842 (June 2016): 319–23. http://dx.doi.org/10.4028/www.scientific.net/amm.842.319.
Full textAbstract:
Programmable Logic Controller (PLC) is widely used to control systems on the manufacturing industry, automotive industry and etc. PLC is used to regulate the production process and machine systems to work simultaneously and continue. PLC has system to regulate the movement of machine components, displacement system of product, system settings motion or displacement of other components. PLC is a control system with some advantages such as simple in design, easy to use and circuit modification, low cost, easy maintenance, high reliability and safe. In this paper, we will focus on the PLC that used in the control of equipment movement on the rotary dryer system for waste liquid processing. PLC will be control the movement of the motor for drum of dryer, heating system, water circulation in the wet scrubber system, vacuum cleaner and smoke systems. All these systems are connected by PLC and working by arranged the setting time of system. The first process, PLC will activate the motion system of motor to drive the dryer drum rotating to left or right side. Then the burner is active for heating the drum of dryer. Effect in this process made smoke and dust happen in the rotary dryer system. The pump automatically will be activating to spray water on a wet scrubber system to catch the dust and smoke. Dust and smoke will be inhaled by the blower and flow back into the rotary dryer machine. Therefore there is no smoke and dust flowing into the environment. The results show that the PLC can be control the motion system and processes machine rotary dryer to drying the waste liquid in short time. Such as 20 minutes to 5 liters of liquid waste volume and 30 minutes to 15 liters and made the waste liquid be friendly environment.
32
Zheng, Hao, Xiaoshe Dong, Endong Wang, Baoke Chen, Zhengdong Zhu, and Chengzhe Liu. "Using Shadow Page Cache to Improve Isolated Drivers Performance." Scientific World Journal 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/896519.
Full textAbstract:
With the advantage of the reusability property of the virtualization technology, users can reuse various types and versions of existing operating systems and drivers in a virtual machine, so as to customize their application environment. In order to prevent users’ virtualization environments being impacted by driver faults in virtual machine, Chariot examines the correctness of driver’s write operations by the method of combining a driver’s write operation capture and a driver’s private access control table. However, this method needs to keep the write permission of shadow page table as read-only, so as to capture isolated driver’s write operations through page faults, which adversely affect the performance of the driver. Based on delaying setting frequently used shadow pages’ write permissions to read-only, this paper proposes an algorithm using shadow page cache to improve the performance of isolated drivers and carefully study the relationship between the performance of drivers and the size of shadow page cache. Experimental results show that, through the shadow page cache, the performance of isolated drivers can be greatly improved without impacting Chariot’s reliability too much.
33
Laqtib, Safaa, Khalid El Yassini, and Moulay Lahcen Hasnaoui. "A technical review and comparative analysis of machine learning techniques for intrusion detection systems in MANET." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 3 (2020): 2701. http://dx.doi.org/10.11591/ijece.v10i3.pp2701-2709.
Full textAbstract:
Machine learning techniques are being widely used to develop an intrusion detection system (IDS) for detecting and classifying cyber attacks at the network-level and the host-level in a timely and automatic manner. However, Traditional Intrusion Detection Systems (IDS), based on traditional machine learning methods, lacks reliability and accuracy. Instead of the traditional machine learning used in previous researches, we think deep learning has the potential to perform better in extracting features of massive data considering the massive cyber traffic in real life. Generally Mobile Ad Hoc Networks have given the low physical security for mobile devices, because of the properties such as node mobility, lack of centralized management and limited bandwidth. To tackle these security issues, traditional cryptography schemes can-not completely safeguard MANETs in terms of novel threats and vulnerabilities, thus by applying Deep learning methods techniques in IDS are capable of adapting the dynamic environments of MANETs and enables the system to make decisions on intrusion while continuing to learn about their mobile environment. An IDS in MANET is a sensoring mechanism that monitors nodes and network activities in order to detect malicious actions and malicious attempt performed by Intruders. Recently, multiple deep learning approaches have been proposed to enhance the performance of intrusion detection system. In this paper, we made a systematic comparison of three models, Inceprtion architecture convolutional neural network Inception-CNN, Bidirectional long short-term memory (BLSTM) and deep belief network (DBN) on the deep learning-based intrusion detection systems, using the NSL-KDD dataset containing information about intrusion and regular network connections, the goal is to provide basic guidance on the choice of deep learning methods in MANET.
34
Paprocka, Iwona, and Sonia Cyba. "Assessment of Production Capacity and Ability of Rapid Response to Changing Customer Expectations." Applied Mechanics and Materials 809-810 (November 2015): 1378–83. http://dx.doi.org/10.4028/www.scientific.net/amm.809-810.1378.
Full textAbstract:
Companies must respond quickly to customer needs and ensure the desired quality and low price in order to remain competitive in a market. It becomes necessary to create new concepts of production systems that meet all requirements imposed by consumers. The increase of reliability of machines and equipment, staff competence and forecasting a size and subject of demand increase the ability to react quickly to changes in the business environment. Therefore, the objective of this paper is to estimate the agility characteristics of a company (size of demand, interarrival time of orders and reliability of machines) and to verify its production capacity and rapid response capabilities. The characteristics are estimated for three variants of the production system: self-operating company, companies operating in cooperation, company buying additional machine.
35
Duran-Rodas, David, Emmanouil Chaniotakis, and Constantinos Antoniou. "Built Environment Factors Affecting Bike Sharing Ridership: Data-Driven Approach for Multiple Cities." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 12 (2019): 55–68. http://dx.doi.org/10.1177/0361198119849908.
Full textAbstract:
Identification of factors influencing ridership is necessary for policy-making, as well as, when examining transferability and aspects of performance and reliability. In this work, a data-driven method is formulated to correlate arrivals and departures of station-based bike sharing systems with built environment factors in multiple cities. Ridership data from stations of multiple cities are pooled in one data set regardless of their geographic boundaries. The method bundles the collection, analysis, and processing of data, as well as, the model’s estimation using statistical and machine learning techniques. The method was applied on a national level in six cities in Germany, and also on an international level in three cities in Europe and North America. The results suggest that the model’s performance did not depend on clustering cities by size but by the relative daily distribution of the rentals. Selected statistically significant factors were identified to vary temporally (e.g., nightclubs were significant during the night). The most influencing variables were related to the city population, distance to city center, leisure-related establishments, and transport-related infrastructure. This data-driven method can help as a support decision-making tool to implement or expand bike sharing systems.
36
Lu, Zhengxiong, Wei Guo, Shuanfeng Zhao, Chuanwei Zhang, Yuan Wang, and Yanping Jia. "A Cross-Platform Web3D Monitoring System of the Three-Machine Equipment in a Fully Mechanized Coalface Based on the Skeleton Model and Sensor Data." Journal of Sensors 2020 (November 30, 2020): 1–14. http://dx.doi.org/10.1155/2020/3147352.
Full textAbstract:
A fully mechanized coalface is a rugged environment that has poor visibility. The traditional video monitoring system has problems such as a lack of realism, a blurry monitoring effect, and poor reliability. It is an important task to monitor the operations of the three-machine equipment (we will refer to the shearer, hydraulic support, and scraper conveyor as the three-machine equipment) intuitively, accurately, and timely and ensure that it is operating safely. This study proposed a cross-platform Web3D monitoring system for the three-machine equipment. First, the virtual mesh model and skeleton model that was embedded in the mesh model were established according to three-machine ontology and basic motion units. Second, the kinematic model of the three-machine skeleton was established via the inverse kinematic modeling of the hydraulic support and the coordinate calculation of the vertices on the three-machine skeleton. Third, the motion data, which were captured by sensors, were applied to drive the movement of the three-machine skeleton and mesh model. Finally, WebGL, which is the latest Internet graphics standard, was used to render the three-machine models, and the performance of this monitoring system is tested on different equipment in the laboratory. The results of the test show that the three-machine cross-platform monitoring system has splendid performance, and it realizes cross-platform 3D monitoring effectively in the laboratory. In the future, this system will be used as a supervisory tool and be integrated with the traditional monitoring system to monitor the three-machine equipment with the field staff.
37
Polak, Ladislav, Stanislav Rozum, Martin Slanina, Tomas Bravenec, Tomas Fryza, and Aggelos Pikrakis. "Received Signal Strength Fingerprinting-Based Indoor Location Estimation Employing Machine Learning." Sensors 21, no. 13 (2021): 4605. http://dx.doi.org/10.3390/s21134605.
Full textAbstract:
The fingerprinting technique is a popular approach to reveal location of persons, instruments or devices in an indoor environment. Typically based on signal strength measurement, a power level map is created first in the learning phase to align with measured values in the inference. Second, the location is determined by taking the point for which the recorded received power level is closest to the power level actually measured. The biggest limit of this technique is the reliability of power measurements, which may lack accuracy in many wireless systems. To this end, this work extends the power level measurement by using multiple anchors and multiple radio channels and, consequently, considers different approaches to aligning the actual measurements with the recorded values. The dataset is available online. This article focuses on the very popular radio technology Bluetooth Low Energy to explore the possible improvement of the system accuracy through different machine learning approaches. It shows how the accuracy–complexity trade-off influences the possible candidate algorithms on an example of three-channel Bluetooth received signal strength based fingerprinting in a one dimensional environment with four static anchors and in a two dimensional environment with the same set of anchors. We provide a literature survey to identify the machine learning algorithms applied in the literature to show that the studies available can not be compared directly. Then, we implement and analyze the performance of four most popular supervised learning techniques, namely k Nearest Neighbors, Support Vector Machines, Random Forest, and Artificial Neural Network. In our scenario, the most promising machine learning technique being the Random Forest with classification accuracy over 99%.
38
Lin, Shih-Lin. "Application of Machine Learning to a Medium Gaussian Support Vector Machine in the Diagnosis of Motor Bearing Faults." Electronics 10, no. 18 (2021): 2266. http://dx.doi.org/10.3390/electronics10182266.
Full textAbstract:
In recent years, artificial intelligence technology has been widely used in fault prediction and health management (PHM). The machine learning algorithm is widely used in the condition monitoring of rotating machines, and normal and fault data can be obtained through the data acquisition and monitoring system. After analyzing the data and establishing a model, the system can automatically learn the features from the input data to predict the failure of the maintenance and diagnosis equipment, which is important for motor maintenance. This research proposes a medium Gaussian support vector machine (SVM) method for the application of machine learning and constructs a feature space by extracting the characteristics of the vibration signal collected on the spot based on experience. Different methods were used to cluster and classify features to classify motor health. The influence of different Gaussian kernel functions, such as fine, medium, and coarse, on the performance of the SVM algorithm was analyzed. The experimental data verify the performance of various models through the data set released by the Case Western Reserve University Motor Bearing Data Center. As the motor often has noise interference in the actual application environment, a simulated Gaussian white noise was added to the original vibration data in order to verify the performance of the research method in a noisy environment. The results summarize the classification results of related motor data sets derived recently from the use of motor fault detection and diagnosis using different machine learning algorithms. The results show that the medium Gaussian SVM method improves the reliability and accuracy of motor bearing fault estimation, detection, and identification under variable crack-size and load conditions. This paper also provides a detailed discussion of the predictive analytical capabilities of machine learning algorithms, which can be used as a reference for the future motor predictive maintenance analysis of electric vehicles.
39
Ahmad, Zaid, Shaiful Jahari Hashim, Fakhrul Zaman Rokhani, Syed Abul Rahman Al-Haddad, Aduwati Sali, and Ken Takei. "Quaternion Model of Higher-Order Rotating Polarization Wave Modulation for High Data Rate M2M LPWAN Communication." Sensors 21, no. 2 (2021): 383. http://dx.doi.org/10.3390/s21020383.
Full textAbstract:
With growing interest in Industry 4.0, machine-to-machine communication (M2M) will become the key enabler for low-power wide area networks (LPWANs) in connecting machines and sensor nodes distributed across a distance in the industrial environment. The choice of modulation and diversity techniques, and the selection of spectrum (licensed/unlicensed) will impact and influence the requirements of wireless M2M systems. Link reliability is one of the most important requirements for LPWAN deployment in industrial scenarios. Rotating Polarization Wave (RPW) system has been recently proposed as an LPWAN solution for reliable M2M communication in high clutter environment and it deploys BPSK modulation with polarization diversity (PD). This paper proposes a new multi-level Rotating Polarization Phase-Shift Keying (RP-MPSK) modulation to provide high data rate and energy efficiency. A novel quaternion model for RPW system (Q-RPW) is also proposed to reduce the complexity in modeling, simulation, and implementation. Results using Q-RPW model show that RP-MPSK modulation offers a high diversity gain over BPSK with second-order diversity. Bit error rate (BER) performance of RP-MPSK modulation compared against other LPWAN modulation like MPSK, FSK and QAM has shown high reliability and substantial improvement in SNR. To overcome the degradation in error performance caused by the proposed higher-order modulation, sampling rates are recommended based on BER performance. BER performance of RP-MPSK under multipath and interference conditions is also investigated.
40
Ahmad, Zaid, Shaiful Jahari Hashim, Fakhrul Zaman Rokhani, Syed Abul Rahman Al-Haddad, Aduwati Sali, and Ken Takei. "Quaternion Model of Higher-Order Rotating Polarization Wave Modulation for High Data Rate M2M LPWAN Communication." Sensors 21, no. 2 (2021): 383. http://dx.doi.org/10.3390/s21020383.
Full textAbstract:
With growing interest in Industry 4.0, machine-to-machine communication (M2M) will become the key enabler for low-power wide area networks (LPWANs) in connecting machines and sensor nodes distributed across a distance in the industrial environment. The choice of modulation and diversity techniques, and the selection of spectrum (licensed/unlicensed) will impact and influence the requirements of wireless M2M systems. Link reliability is one of the most important requirements for LPWAN deployment in industrial scenarios. Rotating Polarization Wave (RPW) system has been recently proposed as an LPWAN solution for reliable M2M communication in high clutter environment and it deploys BPSK modulation with polarization diversity (PD). This paper proposes a new multi-level Rotating Polarization Phase-Shift Keying (RP-MPSK) modulation to provide high data rate and energy efficiency. A novel quaternion model for RPW system (Q-RPW) is also proposed to reduce the complexity in modeling, simulation, and implementation. Results using Q-RPW model show that RP-MPSK modulation offers a high diversity gain over BPSK with second-order diversity. Bit error rate (BER) performance of RP-MPSK modulation compared against other LPWAN modulation like MPSK, FSK and QAM has shown high reliability and substantial improvement in SNR. To overcome the degradation in error performance caused by the proposed higher-order modulation, sampling rates are recommended based on BER performance. BER performance of RP-MPSK under multipath and interference conditions is also investigated.
41
Cong, Haoting, and Jinyi Che. "Research on Management Strategy of Cost-based Intelligent Manufacturing System." E3S Web of Conferences 218 (2020): 01029. http://dx.doi.org/10.1051/e3sconf/202021801029.
Full textAbstract:
The reliability, efficiency and accuracy of CNC machines as work cells of intelligent manufacturing systems (IMS) are criteria to measure the processing level of the latter. In order to improve the reliability of the IMS and reduce the maintenance cost, very sound preventive maintenance and management strategies concerning the CNC machines should be defined. We realized a parameter estimation of our reliability model for CNC machine units in an IMS environment, carried out a linear correlation test and a distribution fitting test for the model and obtained the failure distribution function and failure rate function. We then built a post-failure maintainability model and realized a maintainability evaluation. Following the above analyses, we built a cost-based preventive maintenance cycle model and obtained its optimal value by using the particle swarm optimization (PSO) algorithm. This research and its result can on the one hand guide the setting-up of preventive maintenance planning and management schemes and on the other hand reduce the production cost and enhance enterprise efficiency.
42
Elsisi, Mahmoud, Karar Mahmoud, Matti Lehtonen, and Mohamed M. F. Darwish. "Reliable Industry 4.0 Based on Machine Learning and IoT for Analyzing, Monitoring, and Securing Smart Meters." Sensors 21, no. 2 (2021): 487. http://dx.doi.org/10.3390/s21020487.
Full textAbstract:
The modern control infrastructure that manages and monitors the communication between the smart machines represents the most effective way to increase the efficiency of the industrial environment, such as smart grids. The cyber-physical systems utilize the embedded software and internet to connect and control the smart machines that are addressed by the internet of things (IoT). These cyber-physical systems are the basis of the fourth industrial revolution which is indexed by industry 4.0. In particular, industry 4.0 relies heavily on the IoT and smart sensors such as smart energy meters. The reliability and security represent the main challenges that face the industry 4.0 implementation. This paper introduces a new infrastructure based on machine learning to analyze and monitor the output data of the smart meters to investigate if this data is real data or fake. The fake data are due to the hacking and the inefficient meters. The industrial environment affects the efficiency of the meters by temperature, humidity, and noise signals. Furthermore, the proposed infrastructure validates the amount of data loss via communication channels and the internet connection. The decision tree is utilized as an effective machine learning algorithm to carry out both regression and classification for the meters’ data. The data monitoring is carried based on the industrial digital twins’ platform. The proposed infrastructure results provide a reliable and effective industrial decision that enhances the investments in industry 4.0.
43
Shah, Hurmat Ali, and Insoo Koo. "Reliable Machine Learning Based Spectrum Sensing in Cognitive Radio Networks." Wireless Communications and Mobile Computing 2018 (September 12, 2018): 1–17. http://dx.doi.org/10.1155/2018/5906097.
Full textAbstract:
Spectrum sensing is of crucial importance in cognitive radio (CR) networks. In this paper, a reliable spectrum sensing scheme is proposed, which uses K-nearest neighbor, a machine learning algorithm. In the training phase, each CR user produces a sensing report under varying conditions and, based on a global decision, either transmits or stays silent. In the training phase the local decisions of CR users are combined through a majority voting at the fusion center and a global decision is returned to each CR user. A CR user transmits or stays silent according to the global decision and at each CR user the global decision is compared to the actual primary user activity, which is ascertained through an acknowledgment signal. In the training phase enough information about the surrounding environment, i.e., the activity of PU and the behavior of each CR to that activity, is gathered and sensing classes formed. In the classification phase, each CR user compares its current sensing report to existing sensing classes and distance vectors are calculated. Based on quantitative variables, the posterior probability of each sensing class is calculated and the sensing report is classified into either representing presence or absence of PU. The quantitative variables used for calculating the posterior probability are calculated through K-nearest neighbor algorithm. These local decisions are then combined at the fusion center using a novel decision combination scheme, which takes into account the reliability of each CR user. The CR users then transmit or stay silent according to the global decision. Simulation results show that our proposed scheme outperforms conventional spectrum sensing schemes, both in fading and in nonfading environments, where performance is evaluated using metrics such as the probability of detection, total probability of error, and the ability to exploit data transmission opportunities.
44
Vávra, Jan, Martin Hromada, Luděk Lukáš, and Jacek Dworzecki. "Adaptive anomaly detection system based on machine learning algorithms in an industrial control environment." International Journal of Critical Infrastructure Protection 34 (September 2021): 100446. http://dx.doi.org/10.1016/j.ijcip.2021.100446.
Full text
45
Bignami, Daniele Fabrizio. "Testing solutions of a multi-disaster building’s certification functional to the built environment sustainability and resilience." International Journal of Disaster Resilience in the Built Environment 8, no. 1 (2017): 77–97. http://dx.doi.org/10.1108/ijdrbe-04-2016-0015.
Full textAbstract:
Purpose This paper aims to present phenomenological extensions and technical improvements of a recent research path on a potential operational scheme aiming at promoting an effective disasters risk reduction of built urban areas, namely, the proposal of a Certification of the Predisposition of Resistance of Edifices to Disasters. Design/methodology/approach The authors illustrate new steps of a multidisciplinary research conducted by merging different fields of study, such as disaster risk analysis, sustainable land use and urban planning, environmental assessment methods, different buildings safety topics and certification and auditing management systems tools. Findings This work is based on research developed in Italy by carrying out new tests in the coastal regions of Liguria and Tuscany to validate improvements and to confirm in different territories the assessment process of most significant natural and man-made hazards already included in the certification scheme previously developed on the basis of the continental regions of Lombardy and Piedmont. Originality/value Investigations and results give firmer foundations and exploitability to a certification such as the one presented here.
46
Liu, Chunting, and Guozhu Jia. "Industrial Big Data and Computational Sustainability: Multi-Method Comparison Driven by High-Dimensional Data for Improving Reliability and Sustainability of Complex Systems." Sustainability 11, no. 17 (2019): 4557. http://dx.doi.org/10.3390/su11174557.
Full textAbstract:
Sustainable development is of great significance. The emerging research on data-driven computational sustainability has become an effective way to solve this problem. This paper presents a fault diagnosis and prediction framework for complex systems based on multi-dimensional data and multi-method comparison, aimed at improving the reliability and sustainability of the system by selecting methods with relatively superior performance. This study took the avionics system in the industrial field as an example. Based on the literature research on typical fault modes and fault diagnosis requirements of avionics systems, three popular high-dimensional data-driven fault diagnosis methods—support vector machine, convolutional neural network, and long- and short-term memory neural network—were comprehensively analyzed and compared. Finally, the actual bearing failure data were used for programming in order to verify and compare various methods and the process of selecting the superior method driven by high-dimensional data was fully demonstrated. We attempt to provide a sustainable development idea that continuously explores multi-method integration and comparison, aimed at improving the calculation efficiency and accuracy of reliability assessments, optimizing system performance, and ultimately achieving the goal of long-term improvement of system reliability and sustainability.
47
Bogachov, Mykhailo. "Artificial intelligence as an anthropotechnology." Filosofska dumka (Philosophical Thought) -, no. 3 (2021): 180–200. http://dx.doi.org/10.15407/fd2021.03.180.
Full textAbstract:
Artificial intelligence is a computer system that thinks or acts like humans. Features of AI systems embody implicit beliefs concerning the human nature that AI developers have. “Strong” AI, which has the general cognitive abilities of an adult, has not yet been created, while “weak” AI is already part of the planetary computation infrastructure. Neural network AI mimics specific types of human behavior, generalizing data about the everyday lives of its users. This AI approach corresponds to the philosophical mainstream of the 20th century, when everyday life was seen as a source of the linguistic and the social pre-given that yields mutual understanding. This approach is also based on the traditional human-machine dichotomy and the corresponding idea that human nature is stable and independent of the technological condition. However, in the post-metaphysical age, when human interaction with technology is communicative rather than instrumental, data on everyday life cannot be an independent paragon of the human nature. AI systems do not only codify the descriptive features of human nature, but also discipline their users, as the digital environment in which everyday data can be collected is already organized by AI. Accordingly, in the digital environment, people are forced to reproduce new norms of behavior, codified by AI, which became one of the forms of human self-mastery, or anthropotechnology. The impact of AI is rarely noted, as the digital environment in which people interact with AI is not organized in a way that is clearly understandable. The anthropotechnological nature of AI is a side effect of the development of platforms, so AI developers rarely take responsibility for the norms embodied in the systems they create.
48
Han, Meifeng, Fuli Zhang, Ning Ning, Junwei Zhou, A. Shanthini, and G. N. Vivekananda. "FPLP3D: Security robot for face recognition in the workplace environment using face pose detection assisted controlled FACE++ tool position: A three-dimensional robot." Work 68, no. 3 (2021): 881–90. http://dx.doi.org/10.3233/wor-203422.
Full textAbstract:
BACKGROUND: In recent years, several tracker systems have been developed to monitor a 3-dimensional skull position for facial action whereas, various tracker systems simultaneously analyze the single sequence of video, which can be provided with low-quality cameras and less security. Initially, implementing a 2-D face detector and an unrepentance system has been suggested; furthermore, it has been improved using an integrated 3-D face initialized scheme for the real-time tracker in the present face recognition systems. OBJECTIVES: To overcome the present setbacks of the conventional systems, Face Pose Detection assisted controlled FACE++ tool position of Three-Dimensional Robot (FPLF3D) has been proposed in this article. Furthermore, the suggested proposed configuration has a high-end monitoring approach, which is used to improve the reliability of the robot’s human-machine contact in the workplace environment for security assistance. Additionally, the robot’s direction can be controlled by the operator’s head position assessment of the camera (or any active viewing system) using a three-dimensional robot. RESULTS: Besides, the applications that are imitated by headers like telepresence, computer-generated reality, and video competitions will directly take advantage of the strategies introduced in this paper. CONCLUSION: Finally, real video tests at the lab-scale level show the accuracy and usefulness of the approaches proposed in this research outperform the existing methods used for tracking.
49
Brovarets, O. O., and Yu V. Chovnyuk. "Improvement of the managing methodology of the development of complex agrotechnical systems of special purpose in modern conditions." Mathematical machines and systems 2 (2020): 79–88. http://dx.doi.org/10.34121/1028-9763-2020-2-79-88.
Full textAbstract:
Proper management of the agrobiological state of the soil environment is impossible without predicting the dynamics of changes in its condition. At the present stage, traditional monitoring systems based on laboratory analysis are most widely used. Such methods are quite accurate, but also cost-effective. Moreover, these systems have a rather large drawback – the speed of determining the agrobiological pa-rameters, in particular the nutrient content in the soil. All this ultimately affects the reliability and ef-fectiveness of decision making. However, even such information cannot provide the proper quality of technological operations in accordance with the agrobiological state of the soil environment. In this re-gard, there is an urgent need for the development and study of a methodology for managing the devel-opment of complex agrotechnical systems for special purposes in modern crop production technologies, the use of data that make it possible to ensure a given quality when performing a technological opera-tion. Based on the systematization of knowledge about managing the development of complex special-purpose systems, the features of their functioning in modern conditions, the directions of improving the management methodology, the principles of constructing a complex of methodological support for man-agement are determined, which implement closed cycles of developing control decisions and the organic relationship of long-term and current planning. As examples of complex special-purpose agrotechnical systems in this work, we use: agronomic (aerospace) monitoring systems for agricultural soils, precision farming systems using modern space navigation systems, and electrical conductivity monitoring sys-tems for agricultural soils.
50
Kahl, Matthias, Veronika Krause, Rudolph Hackenberg, et al. "Measurement system and dataset for in-depth analysis of appliance energy consumption in industrial environment." tm - Technisches Messen 86, no. 1 (2019): 1–13. http://dx.doi.org/10.1515/teme-2018-0038.
Full textAbstract:
AbstractTo support a rational and efficient use of electrical energy in residential and industrial environments, Non-Intrusive Load Monitoring (NILM) provides several techniques to identify state and power consumption profiles of connected appliances. Design requirements for such systems include a low hardware and installations costs for residential, reliability and high-availability for industrial purposes, while keeping invasive interventions into the electrical infrastructure to a minimum. This work introduces a reference hardware setup that allows an in depth analysis of electrical energy consumption in industrial environments. To identify appliances and their consumption profile, appropriate identification algorithms are developed by the NILM community. To enable an evaluation of these algorithms on industrial appliances, we introduce the Laboratory-measured IndustriaL Appliance Characteristics (LILAC) dataset: 1302 measurements from one, two, and three concurrently running appliances of 15 appliance types, measured with the introduced testbed. To allow in-depth appliance consumption analysis, measurements were carried out with a sampling rate of 50 kHz and 16-bit amplitude resolution for voltage and current signals. We show in experiments that signal signatures, contained in the measurement data, allows one to distinguish the single measured electrical appliances with a baseline machine learning approach of nearly 100 % accuracy.