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8th International Symposium on Electrical and Electronics engineering, ISEEE 2023

作者： Moldovanu, Simona Pavel, Sorin Aiordachioaie, Dorel Dunarea de Jos University of Galati Faculty of Automation Computers Electrical Engineering and Electronics Department of Computer Science and Information Technology Galati Romania Dunarea de Jos University of Galati Faculty of Automation Computers Electrical Engineering and Electronics Department of Electronics and Telecommunications Galati Romania

ISBN: (纸本)9798350301670

Due to the common limitation of the human visual system, internal features of thermal images cannot be fully discovered. To overcome these drawbacks, a lot of studies analyzed the facial expressions corroborating the features extracted from thermal images with machine learning tools. This paper proposed the first-order features extracted from the image histogram and a binary classification performed by the k-nearest neighbor algorithm (K-NN). The proposed method tests separately the performance of different input data, firstly, the features were extracted from raw images and the robustness of the K-NN algorithm led to an accuracy of 92.6%. Secondly, the features were extracted sequentially from corrupted images with noise. The novelty of the proposed method consists of verifying the quality classification when the images have bad quality, in this case, the accuracy decreased to 70.7%, as a result, the classification of facial expressions is sensitive to the quality of images. © 2023 IEEE.

关键词： Emotion Recognition

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Diabetic Retinopathy Image Classification Using Machine Learning and Local Binary Patterns Features 8

Diabetic Retinopathy Image Classification Using Machine Lear...

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8th International Symposium on Electrical and Electronics engineering, ISEEE 2023

作者： Miron, Mihaela Culea-Florescu, Anisia Moldovanu, Simona Dunarea de Jos University of Galati Faculty of Automation Computers Electrical Engineering and Electronics Department of Computer Science and Information Technology Galati Romania Dunarea de Jos University of Galati Faculty of Automation Computers Electrical Engineering and Electronics Department of Electronics and Telecommunications Galati Romania

ISBN: (纸本)9798350301670

Diabetic Retinopathy (DR) is a condition caused by diabetes that affects the blood vessels in the retina. Detecting the disease early and providing appropriate treatment are crucial in slowing its progression. Therefore, there is great potential in utilizing Machine Learning (ML) to improve the identification and monitoring of DR development in patients. Our study aims to explore the performance of six ML algorithms, namely Random Forest (RF), Adaptive Boosting (AB), K-Nearest Neighbor (K-NN), Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), and Quadratic Discriminant Analysis (QDA), in two binary classifications involving three classes: non-diabetic retinopathy (NoDR), moderate retinopathy (MR), and severe retinopathy (SV). These ML algorithms were applied to ten features extracted using local binary patterns (LBP). The first classification task involved distinguishing between NoDR and MR, while the second task involved differentiating between NoDR and SV. The RF technique achieved the highest classification accuracy, with 0.912 for the first task and 0.94 for the second task. © 2023 IEEE.

关键词： Local binary pattern

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Energy-Efficient and Latency-Optimized Computation Offloading with Improved MOEA for Industrial Internet of Things

Energy-Efficient and Latency-Optimized Computation Offloadin...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhai, Jiahui Bi, Jing Yuan, Haitao Yang, Jinhong Zhang, Jia Zhou, MengChu College of Computer Beijing University of Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Cssc Systems Engineering Research Institute Beijing China Southern Methodist University Dept. of Computer Science DallasTX75275 United States New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (纸本)9781665410205

The unprecedented prosperity of the industrial Internet of Things has thoroughly facilitated the transition from traditional manufacturing towards intelligent manufacturing. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm for lower latency and energy consumption for IEs. However, computational offloading and coordinating of multiple IEs with diverse task types and multiple edge nodes in industrial environments poses challenges. To address this challenge, we propose a multi-task approach encompassing scientific and concurrent workflow tasks to achieve energy-efficient and latency-optimized computation offloading. Furthermore, this work designs an improved Quantum Multi-objective Grey wolf optimizer with Manta ray foraging and Associative learning (QMGMA) to optimize multi-task computation offloading. Comprehensive experiments demonstrate the superior efficiency and stability of QMAGA compared to state-of-the-art algorithms in balancing latency and energy consumption. QMAGA improves average inverse generation distance and average spacing by 37% and 31% on average than multi-objective grey wolf optimizer, non-dominated sorting genetic algorithm II, and multi-objective multi-verse optimization, proving the convergence and diversity of its non-dominated solutions. © 2024 IEEE.

关键词： Smart manufacturing

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Data-driven online learning recommendation system

Data-driven online learning recommendation system

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2023 International Conference on computer Application and Information Security, ICCAIS 2023

作者： Liu, Yuxiang Wang, Han Liu, Minghao School of Computer and Information Security Guilin University of Electronic Technology Guangxi Guilin541004 China School of Electronic Engineering and Automation Guilin University of Electronic Technology Guangxi Guilin541004 China

ISBN: (纸本)9781510675032

The purpose of this study is to improve the performance and efficiency of online learning recommendation systems. The performance and efficiency of recommendation systems depend on multiple factors, including distributed computing, data storage, collaborative filtering algorithms, and optimization of recommendation accuracy. To address these issues, this study built a distributed computing system and HDFS data storage on the Spark platform, and implemented multiple recommendation algorithms to improve the performance and efficiency of the recommendation system. Through analysis of the Mooper dataset, this study conducted feature modeling and proposed a learning resource recommendation system based on Java, Scala, and web technologies, which can provide users with a better learning resource recommendation experience. In addition, this study also developed an anomaly learning behavior detection algorithm, which can detect anomalies in user behavior. Experimental results show that the accuracy and recall rate of the recommendation system have been improved, and the anomaly detection algorithm has high accuracy and robustness, thereby improving the reliability and stability of the recommendation system. In summary, the innovation of this study lies in proposing a recommendation system based on distributed computing and collaborative filtering algorithm, and making new progress in anomaly learning behavior detection. This study provides a new method for the field of recommendation systems, which can provide better solutions for the performance and efficiency of recommendation systems. © 2024 SPIE.

关键词： Anomaly detection

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A Note on the Frequency Characteristics of Discrete Systems in the Complex Plane

A Note on the Frequency Characteristics of Discrete Systems ...

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2022 International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2022

作者： Matousek, Radomil Svarc, Ivan Faculty of Mechanical Engineering Institute of Automation and Computer Science Brno University of Technology Brno Czech Republic

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

Frequency characteristics of discrete-time systems in the complex plane and in linear coordinates are used to design controllers (correction terms) by frequency methods, to determine stability by frequency stability criteria, to assess the dynamic properties and behavior of the discrete system with respect to the harmonic components of the input signal, and to design various filters. Therefore, their determination is of considerable importance and their mapping and compilation into comprehensive summaries from which their shape and waveform can be determined is not yet done for discrete systems. This part is the first aim of the paper. Furthermore, a comparison of the construction of frequency characteristics of continuous and discrete linear time-invariant (LTI) systems is made and their differences are highlighted. Also, a comparison is made between the shape and waveform of the frequency characteristics of continuous and discrete systems with formally similar transfer functions. This comparison showed a relatively high similarity, which could be used to determine the dynamical properties of these systems. From the comparison of the constructions can also be seen how difficult it is to construct discrete versus continuous characteristics. © 2022 IEEE.

关键词： Stability criteria

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Multimodal Representation Learning:Cross-modality and Shared Representation

Multimodal Representation Learning:Cross-modality and Shared...

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2022 International Conference on Industry 4.0 Technology, I4Tech 2022

作者： Mohan, Paul Brilley, Batley C Kumar A. A, Nippun Amrita School of Engineering Dept. of Computer Science & Engineering Bengaluru India Ai and Automation Litmus 7 Kochi India

ISBN: (数字)9781665471961

ISBN: (纸本)9781665471961

Various kinds of Information retrieval or processing task can be difficult basis on how the information is viewed or represented. Representation learning is a technique that allows a system to discover the representation of the data which used for detecting features and classifying things. As compared with learning from one representation, learning from different representations make the task easier. For example, learning something from images and their texts or learning from video and their audio produces good results than learning from one source (either image or text)or (video or audio). Learning from multiple modalities helps to achieve the target correctly within short time. This paper tries to design a Multimodal Autoencoder with minimum loss that can produce shared representation of images and its texts © 2022 IEEE.

关键词： Learning systems

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Microcomputer Prototyping of IEC61850-9-2 with Performance Analysis 14

Microcomputer Prototyping of IEC61850-9-2 with Performance A...

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14th IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2023

作者： Galkin, Nikolai Yang, Chen-Wei Etherden, Nicholas Vyatkin, Valeriy Luleå Tekniska Universitet Department of Computer Science Computer and Space Engineering Sweden Vattenfall R&d SolnaSE-169 79 Sweden Luleå Tekniska Universitet Department of Engineering Sciences and Mathematics Sweden Aalto University Department of Electrical Engineering and Automation Espoo Finland

ISBN: (纸本)9781665455541

The reliability of an electrical system depends on layered Protection, automation, and Control (PAC) functions in which differential relays are considered a fundamental method to isolate the problem. By having a PAC architecture with functionality independent of hardware we hypothesize that the life cycle of PAC devices can be extended by using modern, powerful, and highly reconfigurable IoT devices. This can be achieved by applying modularity to hardware architecture, where an IoT device can be considered a mezzanine module. The Raspberry Pi and BeagleBone manufacturers are keeping the hardware the same size while constantly increasing its power. However, it is not clear how powerful the IoT device should be to meet some of the IEC 61850 requirements (which are usually included in PAC).In this article, we provide a step-by-step process for designing a system that mimics a single-phase transformer differential protection system. First, we provide a requirements analysis (Sampled Values rate, traffic synchronization, reaction time, etc.). Then we analyze the BeagleBone Black device (BBB). We give an example of an electrical system and finally analyze, in terms of IEC 61850 requirements, the performance of two software applications developed for the BBB (based on the operating system and on bare metal). © 2023 IEEE.

关键词： Application programs

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Co-Opetition Network-Based Group Decision-Making Under Incomplete Information

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3464-3479页

作者： Ma, Xiwen Hu, Zhihuan Duan, Kairong Ai, Xiaolin Xie, Wei Yang, Jingsong Zhang, Weidong Shanghai Jiao Tong University School of Oceanography Shanghai200030 China Second Institute of Oceanography Ministry of Natural Resources State Key Laboratory of Satellite Ocean Environment Dynamics Hangzhou310012 China Shanghai Jiao Tong University Department of Automation Shanghai200030 China Hainan University School of Information and Communication Engineering Hainan Haikou570228 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Institute of Automation Chinese Academy of Sciences Beijing100190 China Zhuhai519082 China

The integration of cooperation and competition strategies in game theory emphasizes the systematic nature of strategy spaces and group interactions, forming the basis for achieving win-win scenarios. This is particularly crucial under coalition constraints and incomplete information. Addressing these challenges, this article introduces a comprehensive mathematical method for policy formation using co-opetition topological networks. This method enables autonomous decision-making and game equilibrium in group decision scenarios, considering individual preferences amidst constraints like incomplete information and alliance limitations. Leveraging the complementary entropy theorem on superiority, inferiority, and fuzzy measures, we propose a cognitive model for information interaction and attribute fusion. Utilizing the ordered weighted averaging operator and average tree solutions aids in identifying optimal alliance structures. We subsequently discuss evaluating missing information to complete the topological network. Updating the cognitive model and value function, we develop a Gaussian oscillation heuristic algorithm to explore alliance and component strategy spaces. Simulation results are provided and analyzed to illustrate the performance and effectiveness of our approach. © 2013 IEEE.

关键词： Network topology

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Covid-19 detection using chest X-rays: is lung segmentation important for generalization?

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Research on Biomedical engineering 2022年第4期38卷 1121-1139页

作者： Bassi, Pedro R. A. S. Attux, Romis Department of Computer Engineering and Industrial Automation School of Electrical and Computer Engineering University of Campinas - UNICAMP SP Campinas13083-970 Brazil Alma Mater Studiorum - University of Bologna BO Bologna40126 Italy Istituto Italiano Di Tecnologia GE Genoa16163 Italy

Purpose: We evaluated the generalization capability of deep neural networks (DNNs) in the task of classifying chest X-rays as Covid-19, normal or pneumonia, when trained in a relatively small and mixed datasets. Methods: We proposed a DNN to perform lung segmentation and classification, stacking a segmentation module (U-Net), an original intermediate module and a classification module (DenseNet201). To evaluate generalization capability, we tested the network with an external dataset (from distinct localities) and used Bayesian inference to estimate the probability distributions of performance metrics. Furthermore, we introduce a novel evaluation technique, which uses layer-wise relevance propagation (LRP) and Brixia scores to compare the DNN grounds for decision with radiologists. Results: The proposed DNN achieved 0.917 AUC (area under the ROC curve) on the external test dataset, surpassing a DenseNet without segmentation, which showed 0.906 AUC. Bayesian inference indicated mean accuracy of 76.1% and [0.695, 0.826] 95% HDI (high-density interval, which concentrates 95% of the metric’s probability mass) with segmentation and, without segmentation, 71.7% and [0.646, 0.786]. Conclusion: Employing an analysis based on LRP and Brixia scores, we discovered that areas where radiologists found strong Covid-19 symptoms are the most important for the stacked DNN classification. External validation showed smaller accuracies than internal, indicating difficulty in generalization, which is positively affected by lung segmentation. Finally, the performance on the external dataset and the analysis with LRP suggest that DNNs can successfully detect Covid-19 even when trained on small and mixed datasets. © 2022, The Author(s).

关键词： Deep neural networks

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Detection of Perfect Stealthy Attacks on Cyber-Physical Systems Subject to Measurement Quantizations: A Watermark-Based Strategy

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 114-125页

作者： Yu-Ang Wang Zidong Wang Lei Zou Bo Shen Hongli Dong the College of Information Science and Technology Donghua University the Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of Education IEEE the College of Electrical Engineering and Automation Shandong University of Science and Technology the Department of Computer Science Brunel University London Uxbridge the Sanya Offshore Oil and Gas Research Institute Northeast Petroleum University the Artificial Intelligence Energy Research Institute Northeast Petroleum University Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum University

In this paper, the attack detection problem is investigated for a class of closed-loop systems subjected to unknownbutbounded noises in the presence of stealthy attacks. The measurement outputs from the sensors are quantized before transmission.A specific type of perfect stealthy attack, which meets certain rather stringent conditions, is taken into account. Such attacks could be injected by adversaries into both the sensor-toestimator and controller-to-actuator channels, with the aim of disrupting the normal data flow. For the purpose of defending against these perfect stealthy attacks, a novel scheme based on watermarks is developed. This scheme includes the injection of watermarks(applied to data prior to quantization) and the recovery of data(implemented before the data reaches the estimator).The watermark-based scheme is designed to be both timevarying and hidden from adversaries through incorporating a time-varying and bounded watermark signal. Subsequently, a watermark-based attack detection strategy is proposed which thoroughly considers the characteristics of perfect stealthy attacks,thereby ensuring that an alarm is activated upon the occurrence of such attacks. An example is provided to demonstrate the efficacy of the proposed mechanism for detecting attacks.

关键词： Attack detection cyber-physical systems (CPSs) perfect stealthy attacks watermark-based strategy

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