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2024 IEEE International Conference on Imaging Systems and Techniques, IST 2024

作者： Furusawa, Sota Premachandra, Chinthaka Graduate School of Engineering and Science Shibaura Institute of Technology Dept. of Electrical Engineering and Computer Science Tokyo Japan

ISBN: (纸本)9798350378214

With the recent development of information technology, the importance of protecting personal information has increased. Because of the vulnerability in passwords, biometric authentication is now being used as a method of personal information protection. However, biometric authentication has the possibility of malicious authentication due to the emergence of technology that can generate biometric data that is difficult to duplicate. Therefore, this study aimed to solve the security problems posed by Generative Adversarial Networks (GANs) in biometric authentication by using Hyperspectral Images (HSI). We searched for solutions to the problems identified in previous studies, such as the inability to correctly identify individuals who have not been previously learned and the length of time required for identification. In this paper, we used three channels of data randomly selected from hyperspectral face images for learning to identify individuals by binary classification into 'learned subject (class)' and 'unlearned subjects (classes)'. In this case, discriminators based on the least-squares generative adversarial network (LSGAN) model was developed for each class using three randomly selected channels of data from the HSI for two class classification. The proposed method significantly reduced the identification time and achieved very high discrimination accuracy in all classes. In addition, the results showed high accuracy throughout multiple face identification experiments. © 2024 IEEE.

关键词： Generative adversarial networks

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Exploiting Machine Learning and LSTM for Human Activity Recognition: Using Physiological and Biological Sensor Data from Actigraph 25

Exploiting Machine Learning and LSTM for Human Activity Reco...

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Oyeleye, Matthew Chen, Tianhua Su, Pan Antoniou, Grigoris School Of Computing And Engineering University Of Huddersfield Department Of Computer Science United Kingdom School Of Control And Computer Engineering North China Electric Power University China

ISBN: (纸本)9798350340266

Human activity recognition involves identifying the daily living activities of an individual through the utilization of sensor attributes and intelligent learning algorithms. The identification of intricate human activities proves to be a labo-rious task, given the inherent difficulty of capturing long-term dependencies and extracting efficient features from unprocessed sensor data. For this purpose, this study aims at recognizing and classifying human activities using physiological and biological sensor data generated by Actigraph, as they can accurately measure moderate-to-vigorous intensity physical which is mostly affected by body composition and also better suited for self-monitoring. We examined the effectiveness of these features by applying prevalent machine learning classifiers and long short-term memory (LSTM) networks on recently publicly available data, which includes accelerometer and heart rate recordings. The results from our experiments showed that LSTM models performed better than conventional ML classifiers with the best result achieving an accuracy of 86.5%. The findings also confirms the significance of the heart rate in accurately classifying and identification of human activity more. © 2024 IEEE.

关键词： Long short-term memory

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A critical review on techno-economic analysis of hybrid renewable energy resources-based microgrids

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Journal of engineering and Applied science 2023年第1期70卷 148页

作者： Manas, Munish Sharma, Shivi Reddy, K. Shashidhar Srivastava, Abhinav Department of Electrical Engineering School of Engineering and Technology Central University of Haryana Mahendragarh India Department of Computer Science and Engineering Jain University Bangalore India Department of Electrical and Electronics Engineering CVR College of Engineering Hyderabad India

Now that the population is growing, the expenditure on basic needs of life is also increasing due to a lack of or less availability of resources. The economy consumed electricity is reaching peaks as its main fuel, coal, is decreasing day by day. Due to this, 90% of the population who are in the middle class, lower middle class, or rural areas are economically poor and are unable to bear the prices. To overcome the financial problems, many researchers have prepared various types of microgrids that generate electricity from various types of flow resources, like hydro, solar, biogas, and air current power stations, whose system is called a compound flow power system. This paper gives a combined review of various research papers that discuss some case studies and some research on various models designed on software like HOMER Pro, how microgrids become economic barriers, optimal power supply solutions with CFPS, distributed and centralized microgrid components, the technical and economic feasibility of EV charging stations, and the analysis of various combinations of power systems at various locations like Bangladesh, Canada, the Republic of Djibouti, China, Indonesia, Sierra Leone, some rural sites in India, and some developing countries. This overview provides a glimpse into the various aspects of CFPS, including fusion approaches, techno-economic analysis, simulation platforms, storage technologies, design specifications, unit sizing methodologies, and control techniques. Further research and analysis in these areas are needed to explore their applications and advancements in CFPS development. The main reason for the study is to analyze and bring various ideas and models of various researchers together on a common platform and make a combined conceptual framework for further proceedings. © 2023, The Author(s).

关键词： Renewable energy resources

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Finite-time synchronization control for a class of delayed neural networks:an improved two-step control method

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science China(Information sciences) 2025年

作者： Yue CHEN Song ZHU Mouquan SHEN Xiaoyang LIU Shiping WEN School of Mathematics China University of Mining and Technology College of Electrical Engineering and Control Science Nanjing Technology University School of Computer Science and Technology Jiangsu Normal University Faculty of Engineering and Information Technology University of Technology Sydney

In this paper,a two-step control method is proposed,leveraging the generalized Halanay inequality and existing finite-time stability theorems,to achieve finite-time synchronization for a class of neural networks with bounded time-varying *** the first step,the system state is attenuated from V （t0） to γV （t0） using the generalized Halanay inequality,where0<γ 1 is a free *** the second step,by applying existing finite-time stability theorems,the system state further decays from γV （t0） to *** on the above ideas,two novel finite-time stability lemmas for the error system are presented,and the convergence rate as well as the settling time is ***,the value of γ that results in the shortest settling time for the error system is also *** the help of the derived lemmas,several sufficient algebraic criteria are established to achieve finite-time synchronization between the considered delayed neural *** results of this paper not only improve the existing two-step control method but also overcome the limitations of certain one-step finite-time control ***,the validity and practical applicability of the obtained theoretical results are demonstrated through two numerical examples and an image protection experiment.

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Data Quality-Driven Top-k Rule Discovery 3rd

Data Quality-Driven Top-k Rule Discovery

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3rd International Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Han, Ziyan Chen, Wanjia Han, Yunpeng School of Computer Science and Engineering Beihang University Beijing China School of Mathematical Sciences Shenzhen University Shenzhen China School of Electrical Engineering and Computer Science Louisiana State University Baton Rouge United States

ISBN: (纸本)9789819616978

Existing works on rule discovery often yield an overwhelming number of rules with a high computational cost. Moreover, few studies consider the impact of data quality on the reliability of rules produced. This paper studies the top-k rule discovery problem that returns a set of top-k reliable rules. We introduce a novel scoring function that incorporates data quality, providing a more comprehensive and fine-grained assessment of rules. Based on this scoring function, we develop an efficient algorithm for discovering top-k high-quality rules in relational data. To enhance the performance of our algorithm, we propose effective pruning and optimization strategies that significantly reduce the search space and improve computational efficiency;additionally, we parallelize it for further acceleration. Through extensive experiments on real-life datasets, we verify the effectiveness and efficiency of the proposed method, confirming its practical utility in data quality-driven top-k rule discovery. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data reliability

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ML Based Light-Weight Security Protocol for IoFT Device 6

ML Based Light-Weight Security Protocol for IoFT Device

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6th International Conference on Robotics and Automation in Industry, ICRAI 2024

作者： Khan, Awais Aziz, Ayesha Tariq, Muhamad Kamal, Mohsin National Univeristy of Computer and Emerging Sciences Department of Electrical Engineering Islamabad Pakistan School of Computer Science National Univeristy of Computer and Emerging Sciences Islamabad Pakistan School of Electrical Engineering and Computer Science National Univeristy Science and Technology Islamabad Pakistan

ISBN: (纸本)9798350355703

Forest plays a vital role in environmental change and the protection of forests is the utmost important thing. The study attempts to suggest a solution for a smart forest (Internet of Forest Things). As nowadays, the Internet of Things is emerging very vastly and a large number of researchers are working towards the field of Internet of Forest Things (loFT). The protection of smart forests is of utmost importance because the infrastructure is physically unprotected and they generate huge data about the forest. Due to scalability, smart forest controllers are small-sized and low-cost devices with less computational capabilities. Keeping it in mind, the designed algorithms should be lightweight so that they can meet the requirements. To address this issue, this paper proposes a machine learning approach to develop a lightweight security solution to address data altering and man-in-the-middle attacks. The algorithm generates a link fingerprint of the received signal strength indicator (RSSI) value of the communicating IoFT node. The packet is sent to the server unit and it is decoded and matched with the table to identify the path. A trained machine learning model at the server gets the RSSI value from the packet and successfully classify about the presence of an attack. © 2024 IEEE.

关键词： k-nearest neighbors

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Inhibiting liquid permeation into silicone rubber by superhydrophobic coating for prolonged service life

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High Voltage 2023年第5期8卷 986-996页

作者： Shuming Liu Ying Zhou Zhou Zuo Shuqi Liu Qian Wang Zhong Chen Xidong Liang State Key Laboratory of Control and Simulation of Power System and Generation Equipment Department of Electrical EngineeringTsinghua UniversityBeijingChina School of Material Science and Engineering Nanyang Technological UniversitySingaporeSingapore

Liquid permeation into silicone rubber(SR)may lead to the degradation or complete failure of the composite *** to inhibit liquid permeation,two kinds of superhydrophobic coatings are applied to the SR *** liquid permeation properties of SR with different types of liquids are investigated,and the Langmuir diffusion model is applied to analyse the experimental *** results show that the composite superhydrophobic coating can effectively inhibit liquid permeation by reducing the contact area between the surface and liquid,and the stable structure of the composite coating can maintain the performance after the liquid permeation and ***,the nano‐particle coating without the polymer binder experiences severe performance degradation during the immersion test,and it cannot restrain the liquid permeation anymore after it loses the *** a validation of the effective protection,the insulation resistivity change was *** resistivity decreases sharply for the pristine SR after water permeation;however,the decrease is much smaller for the coated *** current study not only provides new insight into the liquid permeation properties of SR with different surface wettability but also suggests an effective measure to prevent liquid permeation into SR.

关键词： coating permeation liquid

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Charging Infrastructure for Electric Vehicles Forecasting: A Study Based on ARIMA Algorithm and OLS Regression Model

Charging Infrastructure for Electric Vehicles Forecasting: A...

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2024 International Conference on Telecommunications and Power Electronics, TELEPE 2024

作者： Ma, Haoxin Wang, Jiaqi Zhu, Mingkai School of Computer and Control Engineering Qiqihar University Qiqihar China School of Computer Science China University of Geosciences Wuhan China

ISBN: (纸本)9798350369212

This study comprehensively analyzes the future production, sales and charging infrastructure expansion of new energy electric vehicles in China over the next decade, including production, sales and charging infrastructure expansion. The study uses the ARIMA algorithm, combined with industry data analysis, to forecast the future development of the industry. In addition, the impact of new energy vehicles on the traditional automotive industry is analyzed through OLS regression analysis to gain insight into the challenges and opportunities brought about by the rise of new energy vehicles. This study provides valuable experience for the development of new energy vehicles and global sustainable development strategies. © 2024 IEEE.

关键词： ARIMA algorithm New energy electric vehicles OLS regression analysis

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Generating Adversarial Patterns in Facial Recognition with Visual Camouflage

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Journal of Shanghai Jiaotong University (science) 2024年 1-12页

作者： Bao, Qirui Mei, Haiyang Wei, Huilin Lü, Zheng Wang, Yuxin Yang, Xin Key Laboratory of Social Computing and Cognitive Intelligence of Ministry of Education School of Control Science and EngineeringSchool of Computer Science and Technology Dalian University of Technology Liaoning Dalian116024 China Systems Engineering Institute Academy of Military Sciences Beijing100141 China

Deep neural networks, especially face recognition models, have been shown to be vulnerable to adversarial examples. However, existing attack methods for face recognition systems either cannot attack black-box models, are not universal, have cumbersome deployment processes, or lack camouflage and are easily detected by the human eye. In this paper, we propose an adversarial pattern generation method for face recognition and achieve universal black-box attacks by pasting the pattern on the frame of goggles. To achieve visual camouflage, we use a generative adversarial network (GAN). The scale of the generative network of GAN is increased to balance the performance conflict between concealment and adversarial behavior, the perceptual loss function based on VGG19 is used to constrain the color style and enhance GAN’s learning ability, and the fine-grained meta-learning adversarial attack strategy is used to carry out black-box attacks. Sufficient visualization results demonstrate that compared with existing methods, the proposed method can generate samples with camouflage and adversarial characteristics. Meanwhile, extensive quantitative experiments show that the generated samples have a high attack success rate against black-box models. © 2023, Shanghai Jiao Tong University.

关键词： Face recognition

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EADR: Entropy Adjusted Dynamic Routing Capsule Networks 59

EADR: Entropy Adjusted Dynamic Routing Capsule Networks

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59th Annual Conference on Information sciences and Systems, CISS 2025

作者： Babaei, Reza Pham, Huong Cheng, Samuel Zhao, Shangqing The University of Oklahoma School of Electrical and Computer Engineering Norman United States The University of Oklahoma School of Computer Science Norman United States

ISBN: (纸本)9798331513269

Capsule networks offer a useful approach for modeling part-whole hierarchies in visual data, yet they remain limited in effectively handling uncertainty in part-object relationships. This work introduces an entropy-adjusted dynamic routing (EADR) algorithm that leverages information-theoretic principles to enhance both the performance and interpretability of capsule networks. By incorporating an entropy-based regularization term into the final iteration of the dynamic routing process, our approach refines routing decisions, reducing reliance on uncertain capsule connections. Experimental evaluations on the CIFAR-10 dataset demonstrate that our method achieves a mean accuracy of 85.73%, surpassing the baseline accuracy of 84.22% achieved by standard dynamic routing. Comparative analysis with recent entropy-based routing methods highlights our approach's balance of computational efficiency and routing flexibility, achieved without much additional model complexity. These results suggest that EADR routing can be a useful tool for enhancing both the interpretability and efficacy of capsule networks in uncertain environments. © 2025 IEEE.

关键词： Dynamic routing algorithms

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