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24th International Conference on control Systems and computer Science, CSCS 2023

作者： Boicu, Mihaela-Gabriela Stamatescu, Grigore Fudulu, Cosmin-Florin Dobrea, Marius-Alexandru Vasluianu, Mihaela Neculoiu, Giorgian University Politehnica of Bucharest Faculty of Automatic Control and Computer Science Romania Technical University of Civil Engineering of Bucharest Faculty of Hidrotechnics Romania

ISBN: (纸本)9798350313390

This paper presents the development of a system that uses fuzzy logic in order to evaluate the thermal comfort conditions expressed by means of a score by the occupants of an office room The inputs of the system are represented by three datasets relevant to the study: the air temperature, the positioning of the occupant and the level of activity carried out by the occupant. Based on the comfort score obtained, the HVAC system located at the room level will maintain a comfortable environment. The proposed solution also contributes to the minimization of energy consumption in buildings. In order to collect the data sets necessary for the study, we used the *** module as a wearable device. © 2023 IEEE.

关键词： Energy efficiency

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Energy Efficient IoT Air Quality Monitoring System 24

Energy Efficient IoT Air Quality Monitoring System

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24th International Conference on control Systems and computer Science, CSCS 2023

作者： Petrica, Silvian-Marian Fagarasan, Ioana Arghira, Nicoleta Stamatescu, Iulia Neculoiu, Giorgian Flangea, Oana University Politehnica of Bucharest Faculty of Automatic Control and Computer Science Romania Technical University of Civil Engineering of Bucharest Faculty of Hidrotechnics Romania

ISBN: (纸本)9798350313390

Air pollution is one of the major environmental issues discussed lately due to its influence on human health. Particular attention is paid to air quality monitoring today and most developed societies have implemented weather and air quality monitoring stations in various locations inside or outside cities to monitor and reduce the effects of poor air quality. Modern standards and sensors have started to be used in recent years to monitor air quality. Conventional air quality monitoring systems, on the other hand, are not able to provide real-time air quality data with acceptable spatial and temporal resolutions at a low cost, so the challenge is to achieve low-cost and energy-independent solutions. Using modern technologies, e.g. Internet of-things (IoT), an original approach is proposed for building an air quality monitoring system considering low cost equipment and efficient energy autonomous approach. Smart cities have successfully implemented air quality monitoring systems that combine both hardware and software. The results presented in the paper show the viability of the proposed solution, exposing the designed solution and different ways of presenting air quality monitoring. © 2023 IEEE.

关键词： Energy efficiency

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Quality Assessment of Electrocardiogram Signals Using Contrastive Learning 13

Quality Assessment of Electrocardiogram Signals Using Contra...

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13th International Conference on Information Technology in Medicine and Education, ITME 2023

作者： Zhong, Meiping Li, Zuoyong Wang, ChuanSheng Huang, Jiayan Zhao, WanQi Fan, Haoyi College of Computer and Big Data Fuzhou University Fuzhou350108 China College of Computer and Control Engineering Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou350121 China Polytechnic University of Catalonia Department of Automatic Control Technical Barcelona08034 Spain New Engineering Industry College Putian University Putian351100 China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China

ISBN: (纸本)9798350319156

Quality assessment of electrocardiogram (ECG) signals holds significant importance in reducing the high-frequency false alarms in automated arrhythmia diagnosis. Existing quality assessment methods primarily rely on empirical threshold setting or the calculation of relevant statistical values to formulate decision rules. However, the thresholds set in this manner are typically based on experience and knowledge, resulting in decision rules that lack flexibility and struggle to adapt to changes in the dataset. To address this issue, many researchers have turned to deep learning algorithms for ECG signal quality assessment. Nevertheless, most existing deep learning methods usually require large-scale and labeled ECG data for training, whose acquiring is typically challenging. Therefore, this paper proposes an efficient method for ECG signal quality assessment. First, we use a contrast learning framework to construct sample pairs through multilevel data augmentation. Second, a densely connected convolutional network (DenseNet) is used to improve the model's ability of extracting local features in ECG signals, and a Squeeze-and-Excitation attention module is introduced to make the model pay more attention to critical features. Finally, the feature representation is learned by minimizing the contextual contrast loss to bring similar samples closer and push dissimilar samples farther away. In this way, we can utilize the contrastive learning framework to extract temporal representations from unlabeled data and transfer them to downstream ECG signal quality assessment tasks. Extensive experiments on the Physionet/CinC Challenge 2011 dataset demonstrate that our approach outperforms most existing quality assessment methods. © 2023 IEEE.

关键词： Electrocardiograms

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Virtual Reality-based Emergency Robot Rescue Training Simulation System 7

Virtual Reality-based Emergency Robot Rescue Training Simula...

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2022 7th International Conference on Electronic Technology and Information Science, ICETIS 2022

作者： Yang, Guotian Su, Tian Wang, Xiaowei Xie, Ruibiao Zhi, Jingwen Yan, Xingcheng School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (纸本)9783800757794

Power is the indispensable main energy and power in modern society and the power distribution room is the key place. If the power danger situation cannot be found in time and take counter measures, it will cause irreparable losses. With the development of robot and artificial intelligence technology, the emergency robot is widely used in field of rescue, and the operation training is particularly important. Virtual reality exercise is an efficient, flexible, and economical method. Through three-dimensional scene reconstruction of power distribution room, danger situation simulation, rescue path intelligent planning guidance, combining training and assessment, this paper discusses the method and application of constructing a rescue training simulation system, which provides theoretical support for emergency rescue strategy formulation and rescue personnel training and assessment. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Personnel training

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Fault Grading Techniques for Evaluating Software-Based Self-Test with Respect to Small Delay Defects

Fault Grading Techniques for Evaluating Software-Based Self-...

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IEEE Symposium on On-Line Testing (IOLTS)

作者： Michelangelo Bartolomucci Nikolaos I. Deligiannis Riccardo Cantoro Matteo Sonza Reorda Department of Control and Computer Engineering Politecnico di Torino Italy

ISBN: (数字)9798350370553

ISBN: (纸本)9798350370560

A widely adopted practice for in-field testing of electronic devices uses Software-Based Self-Test (SBST) in the form of Software Test Libraries (STLs). Typically, STLs target the stuck-at and Transition Delay Fault (TDF) models. However, to face the new defects introduced by the most recent semiconductor technologies, new fault models must be adopted. Small Delay Defects (SDDs) play an increasingly important role in this scenario. Unlike TDFs, SDDs slightly increase the paths’ timing, whose size is not in the same order of magnitude of the clock period. These defects can cause failures during the operational phase if they affect the critical paths. Remarkably, in scan testing the propagation time of a fault is limited, as a fault effect has to reach the scan flip-flops to be detected. However, in functional testing, the fault effect may require several clock cycles before reaching an observable point. Thus, the delay due to the fault cannot be indefinitely *** there will be the need to move to delay faults when developing STLs, it is important to use the timing information correctly in functional fault simulations. SDDs are the typical choice. In this paper, we implemented a fault grading process for STLs to show how the fault coverage they can achieve changes when the delay defect increases (from SDDs to the extreme case of TDFs). The work uses static timing analysis; although this is known to yield pessimistic results in some cases, it gives a very good indication of the trend in fault coverage as the SDDs approximate TDFs. Differences in fault coverages with respect to the TDF model are highlighted, while an assessment of the effects of multi-cycle delays is also provided.

关键词： Industries Fault detection Built-in self-test Market research Software Libraries Delays

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Latency-aware Scheduling in the Cloud-Edge Continuum

Latency-aware Scheduling in the Cloud-Edge Continuum

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IEEE Symposium on Network Operations and Management

作者： Cristopher Chiaro Doriana Monaco Alessio Sacco Claudio Casetti Guido Marchetto Department of Control and Computer Engineering Politecnico di Torino Italy

ISBN: (数字)9798350327939

ISBN: (纸本)9798350327946

In recent years, containerized deployment models have gained favor across many domain of applications. Kubernetes, the de-facto standard for containers orchestration, can efficiently manage heterogeneous devices, but fails to adapt to possibly stringent requirements, as it only considers computing metrics for scheduling decisions. In addition, the rising prominence of distributed cloud environments, which enable the development of highly available, performant solutions, requires modifications to the default Kubernetes scheduler. To address these challenges, we introduce LAIS, a multi-cluster Kubernetes scheduler optimized for end-to-end latency measurements to enhance user Quality of Experience (QoE). Unlike existing approaches, we define a geographically distributed environment and deploy a solution that satisfies user-specified intents in terms of latency. Depending on user needs, LAIS can either meet a specific latency constraint or schedule pods in the cluster with the lowest latency. After implementing LAIS in a multi-cluster environment, we found it highly effective in accommodating a range of user intents, outperforming the default Kubernetes scheduler in this regard.

关键词： Performance evaluation Schedules Processor scheduling Computational modeling Containers Real-time systems Quality of experience

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Analyzing the Impact of Scheduling Policies on the Reliability of GPUs Running CNN Operations

Analyzing the Impact of Scheduling Policies on the Reliabili...

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VLSI Test Symposium

作者： Robert Limas Sierra Juan-David Guerrero-Balaguera Francesco Pessia Josie E. Rodriguez Condia Matteo Sonza Reorda Department of Control and Computer Engineering (DAUIN) Politecnico di Torino

ISBN: (数字)9798350363784

ISBN: (纸本)9798350363791

The programming flexibility and parallelism of Graphics Processing Units (GPUs) contribute to their effective adoption in complex and data-intensive fields like Machine Learning, especially in the deployment of Convolutional Neural Networks (CNNs). CNNs are also used in some safety-critical applications with severe reliability constraints, such as autonomous driving and robotics. Modern GPUs efficiently combine hardware schedulers controllers and in-chip accelerators (e.g., Tensor Core Units, or TCUs) to enhance CNN’s performance. Interestingly, fine-grain reliability analyses combining the operation of task scheduling policies in GPUs and TCUs have remained unexplored. This work analyses the reliability impact of scheduling policies on GPUs when permanent faults affect TCUs, during the execution of CNN operations. We developed a configurable architectural GPU model (in terms of clusters and parallel cores) that implements five selectable scheduling policies and supports the instruction-accurate execution of TCUs. Our results indicate that the GPU’s architecture and the scheduling policy play a crucial role in the application’s corruption from faulty TCUs. From the experiments, we found that some policies can reduce the corruption effects by up to 22% for large GPUs. In addition, we evaluated the dynamic variability of the scheduling policies and their complexity on identifying deterministic effects on the application’s outputs.

关键词： Tensors Graphics processing units computer architecture Very large scale integration Programming Dynamic scheduling Software

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Correction To: Photovoltaic system fault detection techniques: a review

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Neural Computing and Applications 2024年第29期36卷 18547-18548页

作者： El-Banby, Ghada M. Moawad, Nada M. Abouzalm, Belal A. Abouzaid, Wessam F. Ramadan, E. A. Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt Department of Electrical Engineering of Computer and Control Systems Faculty of Engineering Kafrelsheikh University Kafrelsheikh Egypt

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Research on music evolution characteristics analysis based on factor analysis and Euclidean distance

Research on music evolution characteristics analysis based o...

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2021 International Conference on computer Vision and Pattern Analysis, ICCPA 2021

作者： Zhang, Yutong Li, Su Wang, Haorui School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (数字)9781510651937

ISBN: (纸本)9781510651920

Music is an indispensable part of human society since ancient times. Its evolution is affected by the development of human history and has a wide impact on humans. This paper establishes a musical impact model based on factor analysis and a music similarity model based on the European distance model to quantify and analyze music evolution. First, we use Graph Theory, connect all artists, establish a network and draw an image. In order to measure the influence of each musicians, we selected three indicators, using TOPSIS to establish a factor-based music influence model. Finally, a subnet connected to the network is generated with five artists for the parent node. Next, we have retained 14 music characteristics in the complete music data related to the similarity of artist. We use factor analysis to reduce these 14 music features to obtain five representative main components. Finally, the SPSS software has established a music similarity model based on the Euclidean distance model, and the similarity of artists within the genre and the artists is calculated, 0.7842 and 0.7804, respectively. It can be seen that the artists of the same flow are more similar © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Factor analysis

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Personalized Privacy Protection Incentive Mechanism for Mobile Crowdsourcing Based on Homomorphic Encryption and Edge Computing

Personalized Privacy Protection Incentive Mechanism for Mobi...

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IEEE International Conference on Web Services (ICWS)

作者： Yingxin Li Weilong Wang Yingjie Wang Tong Xiangrong Peiyong Duan Zhipeng Cai Department of Computer and Control Engineering Yantai University Department of Computer Science and Engineering Southeast University Department of Electrical and Control Qilu University of Technology Department of Computer Science Georgia State University

ISBN: (数字)9798350368550

ISBN: (纸本)9798350368567

With the rapid development of crowd sensing computing, Mobile crowdsourcing (MCS) has become an indispensable part of today’s society. While MCS brings convenience to people, it also exposes them to the risk of privacy leakage. In addition, the demand for data is increasing, and the personalized privacy requirements of crowd workers may affect the service quality. In order to address these problems, this paper proposes a personalized privacy protection incentive mechanism (PPPIM) for MCS based on homomorphic encryption and edge computing. Firstly, this paper designs a personalized privacy metric, using social attributes and private attributes of crowd workers to calculate the privacy level required by crowd workers. Then, based on homomorphic encryption and edge computing, a personalized residual federated security learning scheme (PRFSL) is proposed to ensure the security, timeliness, integrity of task data and the privacy of crowd workers’ needs to improve encryption efficiency. Finally, based on the evolutionary game, a personalized privacy incentive mechanism is proposed to improve the overall service utility. Experimental comparisons based on real datasets show that the proposed scheme can not only ensure the security, timeliness, and integrity of task data more effectively. It can also effectively reduce data processing time, improve the probability of crowd workers actively completing tasks and the overall service quality utility.

关键词： Crowdsourcing Measurement Privacy Data privacy Web services Sensors Homomorphic encryption Protection Mobile computing Edge computing

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