Relevant bibliographies by topics / Optimized Network Engineering Tool

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Optimized Network Engineering Tool'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 February 2022

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Journal articles on the topic "Optimized Network Engineering Tool"

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Li, Xi, and Dan Feng Feng. "Bayesian Network Learn Method for Machine Tool Thermal Stability Modeling." Advanced Materials Research 284-286 (July 2011): 932–35. http://dx.doi.org/10.4028/www.scientific.net/amr.284-286.932.

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Maintain the thermal stability of the machine tool is a common problem to achieve intelligent and precise processing control, and its difficulty lies in modeling and real-time compensation. In this paper, considering the correlation of various factors, the correlation of those factors according to experiment data was analysis and optimized, and a dynamic model of thermal error compensation of CNC machine tool based on Bayesian Network theory was found. Moreover, because of the self-learning feature of Bayesian network, the model can be continuously optimized by updating dynamic coefficient, and reflect the changes of processing condition. Finally, the feasibility and validation of this model were proved through the experiment.
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Ekwem, Edith N., and Kashif Nisar. "An Experimental Study." International Journal of Advanced Pervasive and Ubiquitous Computing 6, no. 3 (July 2014): 35–53. http://dx.doi.org/10.4018/ijapuc.2014070103.

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A network whose interconnections between nodes are implemented without using wires is referred to as wireless network and is usually related to a telecommunication network. As related to wired local area network, wireless channels are error-prone. Performance study and optimization of Wireless Local Area Network (WLAN) becomes more essential as its gaining popularity. For performance modelling and evaluation of wireless networks, computer simulation has become one of most widespread tools. While numerous network simulators exist for building a variety of network models, selecting a good network simulator tool is vital in modelling and performance study of wireless networks. Optimized Network Engineering Tools (OPNET) Modeller available to academic institutions at no cost is becoming one of the most widespread network simulators. In this study, the authors used OPNET Modeller 14.5 simulator tool to develop and validate a model for campus based WLAN. The results are expected to display that OPNET Modeller offers credible simulation outcomes close to a genuine system. The effect of network parameters such as the processing time on the performance metrics such as delay and throughput of the various scenarios in the entire network was investigated. The analysis of the results from the simulations carried out can assist the management of computer centre that manages the network in identifying the bottleneck node on the network and for future network capacity building. However, this wireless network involves too many numbers of users which OPNET is not capable to simulate; the authors limited the network to only users in the computer building.
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Paluzo-Hidalgo, Eduardo, Rocio Gonzalez-Diaz, Miguel A. Gutiérrez-Naranjo, and Jónathan Heras. "Optimizing the Simplicial-Map Neural Network Architecture." Journal of Imaging 7, no. 9 (September 1, 2021): 173. http://dx.doi.org/10.3390/jimaging7090173.

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Simplicial-map neural networks are a recent neural network architecture induced by simplicial maps defined between simplicial complexes. It has been proved that simplicial-map neural networks are universal approximators and that they can be refined to be robust to adversarial attacks. In this paper, the refinement toward robustness is optimized by reducing the number of simplices (i.e., nodes) needed. We have shown experimentally that such a refined neural network is equivalent to the original network as a classification tool but requires much less storage.
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Li, Dian Xin, Hong Lin Zhao, Shi Min Zhang, Dai Geng, Xian Long Liu, and Shan Jun Zheng. "Structure Optimization of Slip by the Combination of Artificial Neural Network and Genetic Algorithm." Advanced Materials Research 199-200 (February 2011): 1223–29. http://dx.doi.org/10.4028/www.scientific.net/amr.199-200.1223.

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The bridge plug is a staple tool used in downhole operation and the performance of the slips has a directly influence on the oil well productivity and production safety. We raised an optimize method based on BP network and genetic algorithm to make sure the slips satisfy the high temperature and high pressure demands. Establishing the slips system and making finite element analysis by ANSYS, abtaining sixteen group datas to constitute the BP network training samples, establishing the BP simulation model reflecting curvature radius of the slip fluke, dip angle of the fluke, angle of the fluke and distance between flukes using nonlinearity mapping ability of the neural network, applying optimize design for the simulation model using global optimization ability of the genetic algorithm and abtaining the optimum structure parameters of the slip. The optimized results indicate the whole performance of the slips system has increased notably.
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Alshbatat, Abdel Ilah, and Liang Dong. "Performance Analysis of Mobile Ad Hoc Unmanned Aerial Vehicle Communication Networks with Directional Antennas." International Journal of Aerospace Engineering 2010 (2010): 1–14. http://dx.doi.org/10.1155/2010/874586.

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Unmanned aerial vehicles (UAVs) have the potential of creating an ad hoc communication network in the air. Most UAVs used in communication networks are equipped with wireless transceivers using omnidirectional antennas. In this paper, we consider a collection of UAVs that communicate through wireless links as a mobile ad-hoc network using directional antennas. The network design goal is to maximize the throughput and minimize the end-to-end delay. In this respect, we propose a new medium access control protocol for a network of UAVs with directional antennas. We analyze the communication channel between the UAVs and the effect of aircraft attitude on the network performance. Using the optimized network engineering tool (OPNET), we compare our protocol with the IEEE 802.11 protocol for omnidirectional antennas. The simulation results show performance improvement in end-to-end delay as well as throughput.
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Yan, Wang Xiao, Wang Pin, and Lang He. "Reliability Prediction of CNC Machine Tool Spindle Based on Optimized Cascade Feedforward Neural Network." IEEE Access 9 (2021): 60682–88. http://dx.doi.org/10.1109/access.2021.3074505.

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Jiang, Tian Ying. "Study on Assessment of Enterprise Intellectual Capital Based on the Genetic Neural Network." Advanced Materials Research 204-210 (February 2011): 237–40. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.237.

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Compared to the neural network BP algorithm, the optimized model of genetic neural network based on the genetic algorithm has a more close assessed result to the expected one and smaller relative mistakes. Practical applications show that the new assess way of enterprise intellectual capital is rational and accessible, and it provides as an important tool to enterprise for intellectual capital decision.
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Li, Bo, Xitian Tian, and Min Zhang. "Thermal error modeling of machine tool spindle based on the improved algorithm optimized BP neural network." International Journal of Advanced Manufacturing Technology 105, no. 1-4 (September 11, 2019): 1497–505. http://dx.doi.org/10.1007/s00170-019-04375-w.

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Hu, Ru Fu, and Xiao Ping Chen. "Research on Structural Dynamic Optimal Design of NC Internal Grinder." Advanced Materials Research 129-131 (August 2010): 814–18. http://dx.doi.org/10.4028/www.scientific.net/amr.129-131.814.

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Structural dynamics optimal design of key components is basis to reach optimal design of whole machine tool. The method of sensitivity analysis is applied to optimize the arrangement shapes and parameters of the strengthened bars of components. The BP neural networks model of the spindle system is established and corrected based on comparing with the experimental result, and the structure parameters of the spindle are optimized. These technologies will benefit to realize optimal design of whole NC internal grinder and guide dynamic optimal design of other machine tools.
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Yeh, Yu-En. "Prediction of Optimized Color Design for Sports Shoes Using an Artificial Neural Network and Genetic Algorithm." Applied Sciences 10, no. 5 (February 25, 2020): 1560. http://dx.doi.org/10.3390/app10051560.

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Product design is a complicated activity that is highly reliant on individual impressions, feelings and emotions. Back-propagated neural networks have already been applied in Kansei engineering to solve difficult design problems. However, artificial neural networks (ANNs) have a slow rate of convergence, and find it difficult to devise a suitable network structure and find the global optimal solution. This study developed an ANN-based predictive model enhanced with a genetic algorithm (GA) optimization technique to search for close-to-optimal sports shoe color schemes for a given product image. The design factors of the sports shoe were set as the network inputs, and the Kansei objective value was the output of the GA-based ANN model. The results show that a model built with three hidden layers (28 × 38 × 19) could predict the object value reliably. The R2 of the preference objective was equal to 0.834, suggesting that the developed model is a feasible and efficient tool for predicting the objective value of product images. This study also found that the prediction accuracy for shoes with two colors was higher than that for shoes with only one color. In addition, the prediction accuracy for shoes with a relatively familiar shape was also higher. However, the prediction of color preferences is relatively difficult, because the respondents had different individual color preferences. Exploring the sensitivity and importance of the visual factors (form, color, texture) for various image words is a worthy topic for future research in this field.
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Dissertations / Theses on the topic "Optimized Network Engineering Tool"

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Nawaz, Shah. "Performance Study of ZigBee-based Green House Monitoring System." Thesis, Mittuniversitetet, Avdelningen för informations- och kommunikationssystem, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-24963.

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Wireless Sensor Network (WSN) is an emerging multi-hop wireless network technology, and the greenhouse network monitoring system is one of the key applications of WSNs in which various parameters such as temperature, humidity, pressure and power can be monitored. Here, we aim to study the performance of a simulation-based greenhouse monitoring system. To design the greenhouse monitoring system based on WSN, we have used ZigBee-based devices (end devices, routers, coordinators, and actuators. Our proposed greenhouse monitoring network has been designed and simulated using the network simulator OPNET Modeller.The investigation is split into two; first, the aim is to find the optimal Transmit (Tx) power set out at sensor nodes and second, the focus is on studying how increasing the number of sensor nodes in the same greenhouse network will affect the overall network performance. ZigBee-based greenhouses corresponded to 4 network scenarios and are simulated using OPNET Modeller in which 22 different transmit (Tx) power (22 cases) in Scenario 1 is simulated, scenario 2, 3 and 4 estimated to 63, 126, 189 number of sensor nodes respectively. Investigating the performance of the greenhouse monitoring network performance metrics such as network load, throughput, packets sent/received and packets loss are considered to be evaluated under varied transmit (Tx) power and increasing number of sensor nodes. Out of the comprehensive studies concerning simulation results for 22 different transmit (Tx) power cases underlying the greenhouse monitoring network (Scenario1), it is found that packets sent/received and packets loss perform the best with the transmitted (Tx) power falling in a range of 0.9 mWatt to 1.0 mWatt while packet sent/received and packet loss are found to perform moderately with the transmitted (Tx) power values that lie in a range of 0.05 mWatt to 0.8 mWatt. Less than 0.05 mWatt and greater than 0.01 microWatt Tx power experience, the worst performance in terms of particularly packet dropped case. For instance, in the case of the packet dropped (not joined packet, i.e., generated at the application layer but not able to join the network due to lack of Tx power), with a Tx power of 0.01 mWatt, 384 packets dropped with a Tx power of 0.02 and 0.03 mWatt, 366 packets dropped, and with a Tx power of 0.04 and 0.05, 336 packet dropped.While increasing the number of sensor nodes, as in scenario 2, 3 and 4, dealing with sensor nodes 63, 126 and 189 correspondingly, the MAC load, MAC throughput, packet sent/received in scenario 2 are found to perform better than that of scenario 3 and scenario 4, while packet loss in scenarios 2, 3 and 4 appeared to be 15%, 12% and 83% correspondingly.
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Radhakrishna, Deekonda, and Jannu Keerthipramukh. "OPNET simulation of voice over MPLS With Considering Traffic Engineering." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3434.

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Multiprotocol Label Switching (MPLS) is an emerging technology which ensures the reliable delivery of the Internet services with high transmission speed and lower delays. The key feature of MPLS is its Traffic Engineering (TE), which is used for effectively managing the networks for efficient utilization of network resources. Due to lower network delay, efficient forwarding mechanism, scalability and predictable performance of the services provided by MPLS technology makes it more suitable for implementing real-time applications such as voice and video. In this thesis performance of Voice over Internet Protocol (VoIP) application is compared between MPLS network and conventional Internet Protocol (IP) network. OPNET modeler 14.5 is used to simulate the both networks and the comparison is made based on some performance metrics such as voice jitter, voice packet end-to-end delay, voice delay variation, voice packet sent and received. The simulation results are analyzed and it shows that MPLS based solution provides better performance in implementing the VoIP application. In this thesis, by using voice packet end-to-end delay performance metric an approach is made to estimate the minimum number of VoIP calls that can be maintained, in MPLS and conventional IP networks with acceptable quality. This approach can help the network operators or designers to determine the number of VoIP calls that can be maintained for a given network by imitating the real network on the OPNET simulator.
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Sjöholm, Oskar, and Yngve Filip Törnqvist. "RAPTOR (Rapid tool for optimized real time concept evaluation) : A development tool for early investigations of concepts." Thesis, Linköping University, Linköping University, Department of Management and Engineering, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-16998.

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Early concept generation is the base for the final products appearance. Since most of the products character and cost is set at an early stage increased knowledge at an early stage is vital. This master thesis aims at creating a method called RAPTOR. The purpose of RAPTOR is to reduce the cost and increase the knowledge by combining CAD and optimization with an easy excel interface.

For illustrative purpose an electric motor example has been used. The master thesis has been demonstrated at GM power train, Trollhättan Sweden, and a considerable interest was shown.

The inspiration comes from the GlobaLiTH project, a project at the machine design department at Linköping University, Sweden, where a sustainable light utility vehicle was designed.

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Nestoras, Konstantinos Nav E. Massachusetts Institute of Technology. "A tool to create hydrodynamically optimized hull-forms with geometrical constraints from internal arrangements." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/81587.

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Thesis (Nav. E.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and, (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 145-146).
Internal arrangements and bulky equipment like machinery have been treated for many years as a secondary aspect of the ship design. Traditionally, in the design process, the centerpiece of the effort is the hull and its hydrodynamic performance. Once the hull of a ship has been selected, all the other systems, like propulsion and electric plants, are selected and fitted in the ship. Due to the fact that the hull is considered as the most important system of the ship, any compromises and systems trade-offs that need to be done in the design process are focused mainly on all the systems apart from the hull-form. This inherent prioritization in the traditional design process, might lead to the selection of suboptimal solutions for the other systems like the propulsion and electric plants, which in turns might lead to a global suboptimal solution for the whole ship design. Unfortunately, these decisions bound the designed ship for lifetime and, down the road, might lead to excess operational costs. The tool developed in this thesis treats the internal arrangements and the hull-form of the ship as two systems that need to be optimized together and not on a decoupled manner. Thus, the selection of the propulsion and electric plants or even large weapon systems like VLCs becomes as important as the hull during the design process. Propulsion and electric systems can be preselected in the early stage design, based on their efficiency and then a hull can be wrapped around them. The optimization of the hull can be done either with the use of the Holtrop method or a potential flow panel method, which provides higher fidelity. The designer has the ability to utilize this tool in order to easily conduct trade-off studies between the internal arrangements and the hull-form or save time from their integration and allocate it in other important problems of the design. This could aid the decision-making process in the early stage of the design, where information is scarce, decisions are crucial and uncertainty is high.
by Konstantinos Nestoras.
S.M.
Nav.E.
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Rojas, Ivan Yair. "Optimized Photogrammetric Network Design with Flight Path Planner for UAV-based Terrain Surveillance." BYU ScholarsArchive, 2014. https://scholarsarchive.byu.edu/etd/5573.

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This work demonstrates the use of genetic algorithms as a stochastic optimization technique for developing a camera network design and the flight path for photogrammetricapplications using Small Unmanned Aerial Vehicles. This study develops a Virtual Optimizer for Aerial Routes (VOAR) as a new photogrammetric mapping tool for acquisition of images to be used in 3D reconstruction. 3D point cloud models provide detailed information on infrastructure from places where human access may be difficult. This algorithm allows optimized flight paths to monitor infrastructure using GPS coordinates and optimized camera poses ensuring that the set of images captured is improved for 3D point cloud development. Combining optimization techniques, autonomous aircraft and computer vision methods is a new contribution that this work provides.This optimization framework is demonstrated in a real example that includes retrieving the coordinates of the analyzed area and generating autopilot coordinates to operate in fully autonomous mode. These results and their implications are discussed for future work and directions in making optical techniques competitive with aerial or ground based LiDAR systems.
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Williams, Leevar (Leevar Christoff). "GARNET : a Graphical Attack graph and Reachability Network Evaluation Tool." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/46367.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 101-102).
Attack graphs are valuable tools in the assessment of network security, revealing potential attack paths an adversary could use to gain control of network assets. Creating an effective visualization for attack graphs is essential to their utility, but many previous efforts produce complex displays that are difficult to relate to the underlying networks. This thesis presents GARNET (Graphical Attack graph and Reachability Network Evaluation Tool), an interactive visualization tool intended to facilitate the task of attack graph analysis. The tool provides a simplified view of critical steps that can be taken by an attacker and of host-to-host network reachability that enables these exploits. It allows users to perform "what-if" experiments including adding new zero-day attacks, following recommendations to patch software vulnerabilities, and changing the attacker starting location to analyze external and internal attackers. Users are able to view a set of attack graph metrics that summarize different aspects of overall network security for a specific set of attacker models. An initial user evaluation of GARNET identified problematic areas of the interface that assisted in the development of a more functional design.
by Leevar Williams.
M.Eng.
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Aliwarga, Ivan Sutopo. "A tool for comparing costs of conveyor network layouts." Ohio : Ohio University, 2001. http://www.ohiolink.edu/etd/view.cgi?ohiou1173461043.

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Selvadhurai, Arunprasaath. "NETWORK MEASUREMENT TOOL COMPONENTS FOR ENABLING PERFORMANCE INTELLIGENCE WITHIN CLOUD-BASED APPLICATIONS." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1367446588.

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Reddy, Rohini Mopu. "Easy Net Admin: Inventory tool for network administrators." CSUSB ScholarWorks, 2004. https://scholarworks.lib.csusb.edu/etd-project/2582.

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The purpose of this project was to develop a tool / utility for network administrators to maintain information about all the systems operated by the College of Natural Sciences at California State University, San Bernardino.
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Drake, Bryan (Bryan G. ). "Enabling strategic fulfillment : a decision support tool for fulfillment network optimization." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/73440.

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Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science; in conjunction with the Leaders for Global Operations Program at MIT, 2012.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 48).
Dell's Third-Party (3P) Product network uses several different order fulfillment methods, though the determination of which products are fulfilled under which method is not clearly delineated. We have developed a tool to assist in the decision making process for Dell's 3P distribution network. This tool transparently presents the results of cost modeling and forecast variance simulation while maintaining usability to achieve broad adoption and exert influence on product fulfillment method decisions. The cost model created takes into account product, overhead, logistics, and capital costs and has the capability to deal with volume uncertainties through simulation. This tool solidifies the discussion around choosing the correct fulfillment method decision process and is the first step towards quantifying the fulfillment method decision.
by Bryan Drake.
S.M.
M.B.A.
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Books on the topic "Optimized Network Engineering Tool"

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Varlamov, Oleg. Fundamentals of creating MIVAR expert systems. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1513119.

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Methodological and applied issues of the basics of creating knowledge bases and expert systems of logical artificial intelligence are considered. The software package "MIV Expert Systems Designer" (KESMI) Wi!Mi RAZUMATOR" (version 2.1), which is a convenient tool for the development of intelligent information systems. Examples of creating mivar expert systems and several laboratory works are given. The reader, having studied this tutorial, will be able to independently create expert systems based on KESMI. The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems. Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.
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Technisches Englisch-Deutsch Wörterbuch Automobiltechnik Kraftfahrzeugtechnik Luftfahrt kfz-Mechatronik: Technische Fachbegriffe werden mit einer autocomplete-suche / auto-vervollständige suche gefunden. 63500 Seligenstadt, Germany: Verlag Lehrmittel Wagner, 2010.

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Book chapters on the topic "Optimized Network Engineering Tool"

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Czachórski, Tadeusz, and Ferhan Pekergin. "Diffusion Approximation as a Modelling Tool." In Network Performance Engineering, 447–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-02742-0_20.

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Schwerdel, Dennis, David Hock, Daniel Günther, Bernd Reuther, Paul Müller, and Phuoc Tran-Gia. "ToMaTo - A Network Experimentation Tool." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 1–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29273-6_1.

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Nawi, Nazri Mohd, Abdullah Khan, and Muhammad Zubair Rehman. "A New Optimized Cuckoo Search Recurrent Neural Network (CSRNN) Algorithm." In Lecture Notes in Electrical Engineering, 335–41. Singapore: Springer Singapore, 2014. http://dx.doi.org/10.1007/978-981-4585-42-2_39.

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Celiński, Ireneusz. "Transport Network Parameterisation Using the GTAlg Tool." In Contemporary Challenges of Transport Systems and Traffic Engineering, 111–23. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43985-3_10.

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Nawi, Nazri Mohd, Abdullah Khan, Nurul Syazwani Muhamadan, and M. Z. Rehman. "HAPSOENN: Hybrid Accelerated Particle Swarm Optimized Elman Neural Network." In Proceedings of the International Conference on Data Engineering 2015 (DaEng-2015), 315–22. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-1799-6_33.

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Sethi, Anita, and Sandip Vijay. "Secure Intelligent Optimized Link Heuristic in Cross-Network Handover for IoT." In Micro-Electronics and Telecommunication Engineering, 31–40. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2329-8_4.

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Wang, Peng, and Shurong Li. "Resistivity Inversion Solving Based on a GA Optimized Convolutional Neural Network." In Lecture Notes in Electrical Engineering, 634–45. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8450-3_67.

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Kotenko, Igor, and Evgenia Novikova. "VisSecAnalyzer: A Visual Analytics Tool for Network Security Assessment." In Security Engineering and Intelligence Informatics, 345–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40588-4_24.

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Li, Yuanjing, and Jie Dong. "Application of BP Neural Network Optimized by Stein Loss in Plant Identification." In Lecture Notes in Electrical Engineering, 1217–21. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5959-4_149.

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Wang, Xinli. "Electric Power Client Credit Assessment Based on GA Optimized BP Neural Network." In Lecture Notes in Electrical Engineering, 85–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35470-0_11.

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Conference papers on the topic "Optimized Network Engineering Tool"

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Chahadi, Youssef, and Herbert Birkhofer. "Semantic Product Requirement Network as Approaches for a Requirement Terminology and Tool for Linguistic Analysis of Requirements in Continuous Text." In ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/detc2008-49652.

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The Collaborative Research Center 666 (CRC666) is concentrated on the research of methods and procedures for the optimized illustration of branched structures in integral sheet metal construction by regarding their functions and strains [1]. The virtual development of such branched structures emphasizes the found methods and procedures with regard to make them based on algorithms. This paper describes several approaches, which help to identify customer requirements based on algorithms. A standardized structure for the requirements is developed that builds the basis for a system of requirement terms. Product requirements can have relations among themselves. To make these relations directly and automatically available for the stakeholders during the identification process a semantic product requirement network was developed. This network helps the stakeholders to clearly identify and handle the requirements. This semantic product requirement network is based on the graph theory. A mathematical model for the access to the graph nodes (requirement) is introduced in the system for requirement terms that helps with the identification of requirements by using algorithms. Today requirements for a product are also collected in the form of a continuous text. For this case an approach is shows how requirements can be extracted from the continuous text by using linguistic procedures within the semantic product requirement network.
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Hatamizadeh, Ali, Yuanping Song, and Jonathan B. Hopkins. "Geometry Optimization of Flexure System Topologies Using the Boundary Learning Optimization Tool (BLOT)." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-67465.

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In this paper, we introduce a new computational tool called the Boundary Learning Optimization Tool (BLOT) that rapidly identifies the boundary of the performance capabilities achieved by a general flexure topology if its geometric parameters are allowed to vary from their smallest allowable feature sizes to the largest geometrically compatible feature sizes for a given constituent material. The boundaries generated by the BLOT fully define a flexure topology’s design space and allow designers to visually identify which geometric versions of their synthesized topology best achieve a desired combination of performance capabilities. The BLOT was created as a complementary tool to the Freedom And Constraint Topologies (FACT) synthesis approach in that the BLOT is intended to optimize the geometry of the flexure topologies synthesized using the FACT approach. The BLOT trains artificial neural networks to create sufficiently accurate models of parameterized flexure topologies using the fewest number of design instantiations and their corresponding numerically generated performance solutions. These models are then used by an efficient algorithm to plot the desired topology’s performance boundary. A FACT-synthesized flexure topology is optimized using the BLOT as a case study.
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Chen, Eason, Jeng Yuan Lai, Ming Zong Wang, Yu-Po Wang, and C. S. Hsiao. "Optimized Thermal Design for USB-WLAN Module." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-82247.

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With the increasing demands of wireless network, WLAN modules are developed with various kinds of product patterns; USB type among all is popular for portability. In the market, most USB-WLAN products are made in small form factors that advantage to carry and use as daily portable electronic products. However, the compact size of USB-WLAN elevates the junction temperatures of every package in the module that potentially downgrade the reliability of product. This is a severe challenge for package and module designers that drive a necessity to characterize the thermal behavior from package level to module level to ensure the success of product development. A thermal study was therefore considered for an USB-WLAN module having Baseband, RF and PA packages mounted on a PCB. The characterizations for a commercial USB-WLAN module were conducted with a computational fluid dynamics (CFD) tool to cover the following factors: package design of Base-band and PA, power input level, PCB size and PCB design. The selections of Base-band and PA packages with better Theta_JB performance resulted in better thermal dissipation into PCB. Several power density levels were performed to observe the heat transfer interactions among packages. Different designs of PCB Cu layer, thermal via number and distribution were then investigated in module level simulations for package thermal enhancement. Finally a sample of USB-WLAN module was also built to examine the accuracy of modeling with good agreement.
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Fysikopoulos, Apostolos, Theocharis Alexopoulos, George Pastras, Panos Stavropoulos, and Georgios Chryssolouris. "On the Design of a Sustainable Production Line: The MetaCAM Tool." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-52960.

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Nowadays, manufacturing enterprises face enormous environmental challenges, due to complex and diverse economic trends, including shorter product life cycles, rapid advances in science and technology, increased diversity in customer demands and globalization of production activities. Consequently, the cost is highly affected by environmentally related factors. Energy efficiency is one of the main factors, which together with waste management, affect manufacturing decisions. The complexity and diversity of the factors that determine energy efficiency require intelligent systems for their optimization at each “manufacturing level”. Manufacturing decisions should be taken as fast as possible and with the highest possible accuracy. Artificial intelligence/machine learning tools have made significant progress during the last decade and are suitable for such applications. The main objective of the current study is that an architecture for the development of a networked, online, decision support tool, be provided towards achieving sustainable value chain management. The main idea behind the proposed design is that stakeholders be assisted in taking decisions towards improving the energy and eco-efficiency of the entire value chain or parts of it. This is suggested within the context of a multi-objective optimization procedure, taking into account other important decision making attributes, such as flexibility, quality and time for the final reduction in the overall cost. This architecture incorporates real time information modules that interact with online monitoring systems, using any available information within the value chain and the existing IT tools. A partial realization of the proposed idea is implemented in the form of a user friendly software tool (the MetaCAM tool). This based, decision support tool aiming to optimize a current production line or to propose alternatives for the manufacturing of a product. The tool performs optimization based on a set of predefined criteria, namely energy, waste, cost and time. For each of these criteria, the end-user selects the desired weight factor in order to drive the optimization procedure accordingly. The tool presents the characteristics of the setup of the proposed optimized line and maintains all used data and calculations in order to be reused when necessary. For the tool’s validation, three real case studies from different industrial sectors have been used. The first case study comes from the domestic appliances sector (refrigerator door panel), the second one from the automotive sector (a two seat bench for light commercial vehicles) and finally, the third case study derives from the aeronautics sector and deals with the production of the loading ramp hinge of a military aircraft.
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M. Ali, Ahmed, and Dirk Söffker. "Realtime Power Management of a Multi-Source HEV Using Adaptive Dynamic Programing and Probabilistic Drive State Model." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-67568.

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Power management in multi-source hybrid electric vehicles (HEVs) is a nontrivial problem dealing with different forms of energy. Optimal-based approaches are not facile to apply in realtime due to their high computational requirements. Rule-based (RB) algorithms are suitable for realtime control; however, the solution provided is non-optimal. Development of applicable optimal-based solution in realtime control can ensure higher efficiency of HEVs. This paper presents a new method for realtime optimal control of multisource HEVs using adaptive dynamic programming (ADP). The developed concept is based on drive state recognition in terms of physics-based parameters. Vehicle operating conditions are offline optimized for each state using NSGA-II optimization tool. The optimized solution can be applied state-wise in realtime using adaptive RB method. To apply ADP, probabilistic drive state model is developed to provide a lookahead window and generate state transition network for the specified horizon. The algorithm is customized in terms of prediction stepsize/length to solve the shortest path problem in realtime. Experimental application is conducted using emulation test-rig to validate the results. Both simulation and experimental results show reduction of total cost function in terms of fuel consumption and on-board charge sustaining.
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Patterson, William R., and Matthew I. Campbell. "PipeSynth: An Algorithm for Automated Topological and Parametric Design and Optimization of Pipe Networks." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-47906.

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This paper describes a design automation approach that combines various optimization research and artificial intelligence methods for synthesizing fluid networks. Unlike traditional software tools available today, this approach does not rely on having any predefined network topology to design and optimize its networks. PipeSynth generates its designs by using only desired port locations, and the desired fluid properties at each of those ports. An ideal network is found by optimizing the number and connectivity of pipes and pipe fittings, the size and length of each pipe, and the size and orientation of each fitting. A Uniform-Cost-Search is used for topology optimization along with a combination of non-gradient based optimization methods for parametric optimization. PipeSynth demonstrates how advances in automated design can enable engineers to manage much more complex fluid network problems. PipeSynth uses a unique representation of fluid networks that synthesizes and optimizes networks one pipe at a time, in three-dimensional space. PipeSynth has successfully solved several problems containing multiple interlaced networks concurrently with multiple inputs and outputs. PipeSynth shows the power of automated design and optimization in producing solutions more effectively and efficiently than traditional design approaches.
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Chen, Tongjian, Yonghong Peng, Weiqiang Xie, and Hongming Deng. "GA-Based Fuzzy Neural Network for Adaptive Fuzzy Modeling and Control." In ASME 1998 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1998. http://dx.doi.org/10.1115/detc98/cie-5556.

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Abstract Fuzzy logic theory has provided a model-free tool to develop intelligent control system for complex industrial processes by means of simulating the fuzzy reasoning process of human being. However, the performance of such a control system depends on the knowledge base (control rules and membership functions of fuzzy sets). For the control of complex industrial process in which the dynamic parameters of process is time-varying and non-linear, it is necessary to modify and optimize the knowledge base on-line. Adaptive fuzzy control provides a efficient approach for this objective. In this paper, a new fuzzy neural network (FNN) and an adaptive learning mechanism based on genetic algorithm has been proposed for modeling the fuzzy reasoning process and constructing an efficient adaptive fuzzy control systems. Experiment results show that the FNN is capable of modeling complex functions and simulating fuzzy reasoning process of human being.
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Cao, M., K. W. Wang, L. DeVries, Y. Fujii, W. E. Tobler, G. M. Pietron, T. Tibbles, and J. McCallum. "Automotive Hydraulic Valve Fluid Field Estimator Based on Non-Dimensional Artificial Neural Network (NDANN)." In ASME 2003 International Mechanical Engineering Congress and Exposition. ASMEDC, 2003. http://dx.doi.org/10.1115/imece2003-42523.

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A conventional automatic transmission (AT) hydraulic control system includes many spool-type valves that have highly asymmetric flow geometry. An accurate analysis of their flow fields typically requires a time-consuming computational fluid dynamics (CFD) technique. A simplified flow field model that is based on a lumped geometry is computationally efficient. However, it often fails to account for asymmetric flow characteristics, leading to an inaccurate analysis. In this work, a new hydraulic valve fluid field model is developed based on a non-dimensional neural network (NDANN) to provide an accurate and numerically efficient tool in AT control system design applications. A “grow-and-trim” procedure is proposed to identify critical non-dimensional inputs and optimize the network architecture. A hydraulic valve testing bench is designed and built to provide data for neural network model development. NDANN-based fluid force and flow rate estimator are established based on the experimental data. The NDANN models provide more accurate predictions of flow force and flow rates under broad operating conditions compared with conventional lumped flow field models. The NDANN fluid field estimator also exhibits input-output scalability. This capability allows the NDANN model to estimate the fluid force and flow rate even when the design geometry parameters are outside the range of the training data.
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McCorkle, Douglas S., and Kenneth M. Bryden. "New Evolutionary Techniques for Optimization of Energy Systems Utilizing Computational Fluid Dynamics." In ASME 2003 International Mechanical Engineering Congress and Exposition. ASMEDC, 2003. http://dx.doi.org/10.1115/imece2003-41284.

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Optimization techniques that search a solution space without designer intervention are becoming important tools in the engineering design of many thermal fluid systems. Evolutionary algorithms are among the most robust of these optimization methods because the ability to optimize many designs simultaneously makes evolutionary algorithms less susceptible to premature convergence. However application of evolutionary algorithms to thermal and fluid systems described by high fidelity models (e.g. computational fluid dynamics) has been limited due to the high computational cost of the fitness evaluation. This paper presents a novel technique that combines two technologies used in the optimization of thermal fluids systems. The first is graph based evolutionary algorithms that are implemented to help increase the diversity of the evolving population of designs. The second is an algorithm utilizing a feed forward neural network that develops a stopping criterion for computational fluid dynamics solutions. This reduces the time required for each future evaluation in the evolutionary process and allows for more complex thermal fluids systems to be optimized. In the system examined here the overall reduction in computational time is approximately 8 times.
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Long, Yanli, Limin Xu, and Jinglei Yu. "Optimization Design for a New Cascade of Helium Compressor With Enhanced Pressure Ratio." In 18th International Conference on Nuclear Engineering. ASMEDC, 2010. http://dx.doi.org/10.1115/icone18-30023.

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The High Temperature Gas-cooled Reactor (HTGR) is provided with good safety, high quality of thermal source and low cost of power generation in full life cycle. Furthermore, when the helium turbine is used for heat-work conversion, the efficiency of the HTGR is high and up to a magnitude of 50%. One of the key technologies of helium turbine is the helium compressor design. According to the conventional design rule of the air-compressor, the stage number of the helium compressor was too much excessive. Therefore, this thesis has analyzed and optimized a new cascade of helium compressor with enhanced pressure ratio in order to increase the pressure ratio and decrease the stage number. The Artificial Neural Network is used to build the approximate function which is based on database sample space. The Genetic Algorithm is used to search a new design, and the Artificial Neural Network is reused to predict the aerodynamic performance of the new design. The mean camber line and thickness distribution are optimized respectively, and the optimization results show that the total pressure loss coefficient can be reduced by 14.48% than that of the primary.
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