Pub Date : 2018-12-01DOI: 10.1109/ICSEE.2018.8646091
R. Machlev, Y. Levron
Maximum power point tracking (MPPT) techniques are being used to improve the efficiency of photovoltaic (PV) systems. In this paper, a Nearest Neighbor(NN)-based MPPT with Cross-Entropy (CE) Method optimization algorithm is proposed. The proposed method is system-independent, accurate and easy to implement. the performance of the algorithm is validate in simulation using profile of different irradiances.
{"title":"Nearest Neighbor MPPT with Cross-Entropy Method optimization","authors":"R. Machlev, Y. Levron","doi":"10.1109/ICSEE.2018.8646091","DOIUrl":"https://doi.org/10.1109/ICSEE.2018.8646091","url":null,"abstract":"Maximum power point tracking (MPPT) techniques are being used to improve the efficiency of photovoltaic (PV) systems. In this paper, a Nearest Neighbor(NN)-based MPPT with Cross-Entropy (CE) Method optimization algorithm is proposed. The proposed method is system-independent, accurate and easy to implement. the performance of the algorithm is validate in simulation using profile of different irradiances.","PeriodicalId":254455,"journal":{"name":"2018 IEEE International Conference on the Science of Electrical Engineering in Israel (ICSEE)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133265499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-12-01DOI: 10.1109/ICSEE.2018.8646066
Netanel Katzburg, Amit Golander
Persistent memory (PM) is an emerging technology that has substantial advantages compared to current flash and block-based storage devices. With PM, system performance may be substantially improved, storage bottlenecks are reduced, and application design can be made simpler. Yet, new software is required to allow application to efficiently leverage PM. In this paper we survey the entire stack bottom up. We begin with partitioning PM hardware types and describe the three main software approaches to leverage such PM. We then zoom in on PM-based file systems and provide architectural insight into the only feature-rich industrial PM-based file system (to date). Finally, we broaden the scope again and classify the 12 academic and industrial PM-related file systems proposed to date.
{"title":"Persistent Memory Based and Feature Rich File System Design","authors":"Netanel Katzburg, Amit Golander","doi":"10.1109/ICSEE.2018.8646066","DOIUrl":"https://doi.org/10.1109/ICSEE.2018.8646066","url":null,"abstract":"Persistent memory (PM) is an emerging technology that has substantial advantages compared to current flash and block-based storage devices. With PM, system performance may be substantially improved, storage bottlenecks are reduced, and application design can be made simpler. Yet, new software is required to allow application to efficiently leverage PM. In this paper we survey the entire stack bottom up. We begin with partitioning PM hardware types and describe the three main software approaches to leverage such PM. We then zoom in on PM-based file systems and provide architectural insight into the only feature-rich industrial PM-based file system (to date). Finally, we broaden the scope again and classify the 12 academic and industrial PM-related file systems proposed to date.","PeriodicalId":254455,"journal":{"name":"2018 IEEE International Conference on the Science of Electrical Engineering in Israel (ICSEE)","volume":"518 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133824964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-12-01DOI: 10.1109/ICSEE.2018.8646020
Netanel Katzburg, Amit Golander, S. Weiss
The architecture of Database Management Systems (DBMS) is closely related to the characteristics of the storage hierarchy, because durability and response time are highly dependent on the physical properties of the target storage. Main memory volatility requires a DBMS to provide durability by software means as data continuously moves between volatile memory buffers and input/output persistent media. In traditional storage systems applications use complex concurrency control schemes to reduce latency and increase throughput and in order to utilize multicore hardware and shared system resources. New persistent memory (PM) devices emerging in the last decade, such as PCM, RRAM and MRAM, exhibit near-DRAM speed and characteristics, provide data persistence, and could be game changing for storage bound applications. In this paper we focus on benefits of persistent memory and their impact on database management systems. We consider methods for application speedup that are applicable to DBMSs that use PM. These optimization methods depend on the characteristics of PM storage. We consider concurrency and mutual resource contention, explore and rethink major application components, and finally combine static code optimization. Running the on-line transaction processing (OLTP) workload, the DBMSs explored here show performance gains relative to traditional storage systems by a factor of 3.17 and 1.79 for PostgreSQL and SQLite respectively.
{"title":"NVDIMM-N Persistent Memory and its Impact on Two Relational Databases","authors":"Netanel Katzburg, Amit Golander, S. Weiss","doi":"10.1109/ICSEE.2018.8646020","DOIUrl":"https://doi.org/10.1109/ICSEE.2018.8646020","url":null,"abstract":"The architecture of Database Management Systems (DBMS) is closely related to the characteristics of the storage hierarchy, because durability and response time are highly dependent on the physical properties of the target storage. Main memory volatility requires a DBMS to provide durability by software means as data continuously moves between volatile memory buffers and input/output persistent media. In traditional storage systems applications use complex concurrency control schemes to reduce latency and increase throughput and in order to utilize multicore hardware and shared system resources. New persistent memory (PM) devices emerging in the last decade, such as PCM, RRAM and MRAM, exhibit near-DRAM speed and characteristics, provide data persistence, and could be game changing for storage bound applications. In this paper we focus on benefits of persistent memory and their impact on database management systems. We consider methods for application speedup that are applicable to DBMSs that use PM. These optimization methods depend on the characteristics of PM storage. We consider concurrency and mutual resource contention, explore and rethink major application components, and finally combine static code optimization. Running the on-line transaction processing (OLTP) workload, the DBMSs explored here show performance gains relative to traditional storage systems by a factor of 3.17 and 1.79 for PostgreSQL and SQLite respectively.","PeriodicalId":254455,"journal":{"name":"2018 IEEE International Conference on the Science of Electrical Engineering in Israel (ICSEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125806906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-12-01DOI: 10.1109/icsee.2018.8645999
G. Leshem, Esther David, M. Domb
The Internet of Things (IoT) is composed of a vast number of connected devices, interacting among them in real-time and high messaging volume. Such setting is in high probability to be targeted by malicious attackers. Therefore, robust security measures are required. Encryption is one of the ways to prevent the exposure of the transmitted messages and authenticate it. The main challenge of implementing encryption, is the need to frequently and securely change the encryption keys, which require constant key construction and key distribution. IoT devices have poor memory, storage, and processing bandwidth. Most of the existing security solutions cannot be implemented on them, and so leading to lack of adequate security. Allowing safe interaction between any two IoT-devices, means having a unique encryption key per conversation. This requires frequent changes of the encryption keys. To increase the availability of keys at each IoT-device, we propose an ongoing key construction process that loads the network with a common key-pool. The protocol is scalable to ensure long term security sustainability and encryption availability. The proposed protocol is based on a probability analysis that ensures the existence of a common key between any pair of IoT devices in a predefine probability which is set by the system designer. The implementation proves the feasibility of our proposed security protocol for IoT networks.
{"title":"Probability Based Keys Sharing for IOT Security","authors":"G. Leshem, Esther David, M. Domb","doi":"10.1109/icsee.2018.8645999","DOIUrl":"https://doi.org/10.1109/icsee.2018.8645999","url":null,"abstract":"The Internet of Things (IoT) is composed of a vast number of connected devices, interacting among them in real-time and high messaging volume. Such setting is in high probability to be targeted by malicious attackers. Therefore, robust security measures are required. Encryption is one of the ways to prevent the exposure of the transmitted messages and authenticate it. The main challenge of implementing encryption, is the need to frequently and securely change the encryption keys, which require constant key construction and key distribution. IoT devices have poor memory, storage, and processing bandwidth. Most of the existing security solutions cannot be implemented on them, and so leading to lack of adequate security. Allowing safe interaction between any two IoT-devices, means having a unique encryption key per conversation. This requires frequent changes of the encryption keys. To increase the availability of keys at each IoT-device, we propose an ongoing key construction process that loads the network with a common key-pool. The protocol is scalable to ensure long term security sustainability and encryption availability. The proposed protocol is based on a probability analysis that ensures the existence of a common key between any pair of IoT devices in a predefine probability which is set by the system designer. The implementation proves the feasibility of our proposed security protocol for IoT networks.","PeriodicalId":254455,"journal":{"name":"2018 IEEE International Conference on the Science of Electrical Engineering in Israel (ICSEE)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126195488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-12-01DOI: 10.1109/ICSEE.2018.8646165
Merav Allouch, A. Azaria, Rina Azoulay, Ester Ben-Izchak, M. Zwilling, D. Zachor
An overall goal of our work is to use machine-learning based solutions to assist children with communication difficulties in their communication task. In this paper, we concentrate on the problem of recognizing insulting sentences the child says, or insulting sentences that are told to him. An automated agent that is able to recognize such sentences can alert the child in real time situations, and can suggest how to respond to the resulting social situation. We composed a dataset of 1241 non-insulting and 1255 insulting sentences. We trained different machine learning methods on 90% randomly chosen sentences from the dataset and tested it on the remaining. We used the following machine learning methods: Multi-Layer Neural Network, SVM, Naive Bayes, Decision Tree, and Tree Bagger for the task. We found that the best predictors of the insulting sentences, were the SVM method, with 80% recall and over 75%precision, and the Multi-Layer Neural Network and the Tree Bagger, with precision and recall exceeding 75%, We also found that adding additional data to the learning process, such as 9500 labeled sentences from twitter, or adding the word “positive” and the word “negative” to sentences including positive or negative words, respectively, slightly improves the results in most of the cases. Our results provide the cornerstones for an automated system that would enable on-line assistance and consultation for children with communication disabilities, and also for other persons with communication problems, in a way that will enable them to function better in society through this assistance.
{"title":"Automatic Detection of Insulting Sentences in Conversation","authors":"Merav Allouch, A. Azaria, Rina Azoulay, Ester Ben-Izchak, M. Zwilling, D. Zachor","doi":"10.1109/ICSEE.2018.8646165","DOIUrl":"https://doi.org/10.1109/ICSEE.2018.8646165","url":null,"abstract":"An overall goal of our work is to use machine-learning based solutions to assist children with communication difficulties in their communication task. In this paper, we concentrate on the problem of recognizing insulting sentences the child says, or insulting sentences that are told to him. An automated agent that is able to recognize such sentences can alert the child in real time situations, and can suggest how to respond to the resulting social situation. We composed a dataset of 1241 non-insulting and 1255 insulting sentences. We trained different machine learning methods on 90% randomly chosen sentences from the dataset and tested it on the remaining. We used the following machine learning methods: Multi-Layer Neural Network, SVM, Naive Bayes, Decision Tree, and Tree Bagger for the task. We found that the best predictors of the insulting sentences, were the SVM method, with 80% recall and over 75%precision, and the Multi-Layer Neural Network and the Tree Bagger, with precision and recall exceeding 75%, We also found that adding additional data to the learning process, such as 9500 labeled sentences from twitter, or adding the word “positive” and the word “negative” to sentences including positive or negative words, respectively, slightly improves the results in most of the cases. Our results provide the cornerstones for an automated system that would enable on-line assistance and consultation for children with communication disabilities, and also for other persons with communication problems, in a way that will enable them to function better in society through this assistance.","PeriodicalId":254455,"journal":{"name":"2018 IEEE International Conference on the Science of Electrical Engineering in Israel (ICSEE)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116750544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-12-01DOI: 10.1109/ICSEE.2018.8646076
Ifat Abramovich, Tomer Ben-Yehuda, R. Cohen
Deep learning has led to great successes in computer vision tasks such as image classification. This is mostly attributed to the availability of large image datasets such as ImageNet. However, the progress in video classification has been slower, especially due to the small size of available video datasets and larger computational and memory demands. To promote innovation and advancement in this field, Google announced the YouTube-8M dataset in 2016, which is a public video dataset containing about 8-million tagged videos. In this paper, we train several deep neural networks for video classification on a subset of YouTube-8M. Our approach is based on extracting frame-level features using the Inception-v3 network, which are later used by recurrent neural networks with LSTM/BiLSTM units for video classification. We focus on network architectures with low computational requirements and present a detailed performance comparison. We show that for 5 categories, more than 96% of the videos are labeled correctly, where for 10 categories more than 89% of the videos are labeled correctly. We demonstrate that transfer learning leads to substantial saving in training time, while offering good results.
{"title":"Low-Complexity Video Classification using Recurrent Neural Networks","authors":"Ifat Abramovich, Tomer Ben-Yehuda, R. Cohen","doi":"10.1109/ICSEE.2018.8646076","DOIUrl":"https://doi.org/10.1109/ICSEE.2018.8646076","url":null,"abstract":"Deep learning has led to great successes in computer vision tasks such as image classification. This is mostly attributed to the availability of large image datasets such as ImageNet. However, the progress in video classification has been slower, especially due to the small size of available video datasets and larger computational and memory demands. To promote innovation and advancement in this field, Google announced the YouTube-8M dataset in 2016, which is a public video dataset containing about 8-million tagged videos. In this paper, we train several deep neural networks for video classification on a subset of YouTube-8M. Our approach is based on extracting frame-level features using the Inception-v3 network, which are later used by recurrent neural networks with LSTM/BiLSTM units for video classification. We focus on network architectures with low computational requirements and present a detailed performance comparison. We show that for 5 categories, more than 96% of the videos are labeled correctly, where for 10 categories more than 89% of the videos are labeled correctly. We demonstrate that transfer learning leads to substantial saving in training time, while offering good results.","PeriodicalId":254455,"journal":{"name":"2018 IEEE International Conference on the Science of Electrical Engineering in Israel (ICSEE)","volume":"153 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115547168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-12-01DOI: 10.1109/ICSEE.2018.8646009
T. G. Dvorkind, A. Cohen
The goal of covert communication is to establish a communication link between two parties (Alice and Bob) while assuring that a warden (Willie) will not be able to detect the very presence of communication. In this work, we address the covert communication problem over a packet insertion channel. Assuming a Poisson channel model, we first consider an all-aware warden, who wishes to minimize the sum of his miss detection and false alarm probabilities. We show that the covertness extent versus Alice’s transmission rate can be computed exactly. We also derive simplified bounds which are tighter than previously known results. Such a warden, however, has to be aware of Alice’s transmission rate. A more practical scenario, is when the warden does not have this information, hence wishes to operate at a certain false alarm rate. For this case, we discuss Alice’s strategy to ensure the warden’s miss detection probability is lower bounded. Finally, we study covertness in the case where Willie has uncertainty regarding the nominal channel rate as well, and show that in this case, Alice’s covert transmission rate can be even higher.
{"title":"Rate vs. Covertness for the Packet Insertion Problem","authors":"T. G. Dvorkind, A. Cohen","doi":"10.1109/ICSEE.2018.8646009","DOIUrl":"https://doi.org/10.1109/ICSEE.2018.8646009","url":null,"abstract":"The goal of covert communication is to establish a communication link between two parties (Alice and Bob) while assuring that a warden (Willie) will not be able to detect the very presence of communication. In this work, we address the covert communication problem over a packet insertion channel. Assuming a Poisson channel model, we first consider an all-aware warden, who wishes to minimize the sum of his miss detection and false alarm probabilities. We show that the covertness extent versus Alice’s transmission rate can be computed exactly. We also derive simplified bounds which are tighter than previously known results. Such a warden, however, has to be aware of Alice’s transmission rate. A more practical scenario, is when the warden does not have this information, hence wishes to operate at a certain false alarm rate. For this case, we discuss Alice’s strategy to ensure the warden’s miss detection probability is lower bounded. Finally, we study covertness in the case where Willie has uncertainty regarding the nominal channel rate as well, and show that in this case, Alice’s covert transmission rate can be even higher.","PeriodicalId":254455,"journal":{"name":"2018 IEEE International Conference on the Science of Electrical Engineering in Israel (ICSEE)","volume":"155 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116427352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-12-01DOI: 10.1109/ICSEE.2018.8646292
Mor Goren, R. Zamir
Diversity “multiple description” (MD) source coding promises graceful degradation in the presence of an unknown number of erasures in the channel. A simple solution in the case of two descriptions consists of oversampling the source by a factor of two and delta-sigma quantization. This approach was applied successfully to JPEG-based image coding over a lossy packet network, where the interpolation into two packets is done in the discrete cosine transform (DCT) domain. The extension to a larger number of descriptions, however, suffers from noise amplification whenever the received descriptions do not form a uniform sampling pattern. In this work we show how noise amplification can be reduced by optimizing the interpolation filter. We propose two interpolation methods which, for a given total coding rate, minimize the average distortion over all (K out of N) patterns of received packets. We provide simulation results comparing low pass and irregular interpolation filters, and discuss the advantage of each method.
{"title":"Combating Packet Loss in Image Coding using Oversampling and Irregular Interpolation","authors":"Mor Goren, R. Zamir","doi":"10.1109/ICSEE.2018.8646292","DOIUrl":"https://doi.org/10.1109/ICSEE.2018.8646292","url":null,"abstract":"Diversity “multiple description” (MD) source coding promises graceful degradation in the presence of an unknown number of erasures in the channel. A simple solution in the case of two descriptions consists of oversampling the source by a factor of two and delta-sigma quantization. This approach was applied successfully to JPEG-based image coding over a lossy packet network, where the interpolation into two packets is done in the discrete cosine transform (DCT) domain. The extension to a larger number of descriptions, however, suffers from noise amplification whenever the received descriptions do not form a uniform sampling pattern. In this work we show how noise amplification can be reduced by optimizing the interpolation filter. We propose two interpolation methods which, for a given total coding rate, minimize the average distortion over all (K out of N) patterns of received packets. We provide simulation results comparing low pass and irregular interpolation filters, and discuss the advantage of each method.","PeriodicalId":254455,"journal":{"name":"2018 IEEE International Conference on the Science of Electrical Engineering in Israel (ICSEE)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116633214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-12-01DOI: 10.1109/ICSEE.2018.8646164
Yishai Cohen, I. Lapidot
This paper focuses on estimating the quality of a clustering process. In our case - the task is to cluster short speech segments that belong to different speakers. Moreover, speaker clustering quality may be well estimated on several clustering approaches if they all based on the same features. This is very important, as it allows us to use the same quality estimation system without retraining, and achieve reasonable results even when the clustering method is changed. We predict the system’s quality by applying a logistic regression estimator on a several statistical parameters of the clustering. In this paper, mean-shift clustering with either cosine or probabilistic linear discriminant analysis (PLDA) score as similarity measure, and stochastic vector quantization (VQ) with cosine distance were applied in order to cluster the short speaker segments represented by i-vectors. The quality of the clustering is measured using the average cluster purity (ACP), average speaker purity (ASP) and K. We show that these measures can be estimated fairly well by applying logistic regression based on various clustering statistics that calculated once clustering is over. These statistical parameters are used as a feature vector representing the clustering.
{"title":"Robust speaker clustering quality estimation","authors":"Yishai Cohen, I. Lapidot","doi":"10.1109/ICSEE.2018.8646164","DOIUrl":"https://doi.org/10.1109/ICSEE.2018.8646164","url":null,"abstract":"This paper focuses on estimating the quality of a clustering process. In our case - the task is to cluster short speech segments that belong to different speakers. Moreover, speaker clustering quality may be well estimated on several clustering approaches if they all based on the same features. This is very important, as it allows us to use the same quality estimation system without retraining, and achieve reasonable results even when the clustering method is changed. We predict the system’s quality by applying a logistic regression estimator on a several statistical parameters of the clustering. In this paper, mean-shift clustering with either cosine or probabilistic linear discriminant analysis (PLDA) score as similarity measure, and stochastic vector quantization (VQ) with cosine distance were applied in order to cluster the short speaker segments represented by i-vectors. The quality of the clustering is measured using the average cluster purity (ACP), average speaker purity (ASP) and K. We show that these measures can be estimated fairly well by applying logistic regression based on various clustering statistics that calculated once clustering is over. These statistical parameters are used as a feature vector representing the clustering.","PeriodicalId":254455,"journal":{"name":"2018 IEEE International Conference on the Science of Electrical Engineering in Israel (ICSEE)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123986813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-12-01DOI: 10.1109/ICSEE.2018.8646087
Yiftah Kowal, A. Selivanov, E. Fridman
We consider a large-scale LTI system with multiple local communication networks connecting sensors, controllers, and actuators. The local networks operate asynchronously and independently of one another. The main novelty is that the decentralized controllers are subject to saturation. Our objective is to achieve a regional exponential stability providing a decentralized bound on the domain of attraction for each plant. We introduce a sampled-data event-triggering mechanism from sensors to controllers to reduce the amount of transmitted signals. Using the time-delay approach to networked control systems and appropriate Lyapunov-Krasovskii functionals, we derive linear matrix inequalities that allow to find the decentralized bounds on the domains of attraction for each plant. Numerical example of coupled cart-pendulums illustrates the efficiency of the method.
{"title":"Decentralized event-triggered control of large-scale systems with saturated actuators","authors":"Yiftah Kowal, A. Selivanov, E. Fridman","doi":"10.1109/ICSEE.2018.8646087","DOIUrl":"https://doi.org/10.1109/ICSEE.2018.8646087","url":null,"abstract":"We consider a large-scale LTI system with multiple local communication networks connecting sensors, controllers, and actuators. The local networks operate asynchronously and independently of one another. The main novelty is that the decentralized controllers are subject to saturation. Our objective is to achieve a regional exponential stability providing a decentralized bound on the domain of attraction for each plant. We introduce a sampled-data event-triggering mechanism from sensors to controllers to reduce the amount of transmitted signals. Using the time-delay approach to networked control systems and appropriate Lyapunov-Krasovskii functionals, we derive linear matrix inequalities that allow to find the decentralized bounds on the domains of attraction for each plant. Numerical example of coupled cart-pendulums illustrates the efficiency of the method.","PeriodicalId":254455,"journal":{"name":"2018 IEEE International Conference on the Science of Electrical Engineering in Israel (ICSEE)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124038428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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