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2023 International Conference on Optics and Machine Vision, ICOMV 2023

作者： Qiu, Zixia Ma, Junchao Zhang, Haitang Sibahee, Mustafa A.Al. Abduljabbar, Zaid Ameen Nyangaresi, Vincent Omollo College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China College of Applied Technology Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah61001 Iraq Technical Computer Engineering Department Al-Kunooze University College Basrah61001 Iraq Huazhong University of Science and Technology Shenzhen Institute Shenzhen518063 China Faculty of Biological & Physical Sciences Tom Mboya University Homabay40300 Kenya

ISBN: (纸本)9781510664777

Compared with rasterization rendering, ray tracing rendering can improve the image’s visual effect and make the image look more realistic. Real-time ray tracing requires very high computing power of graphics processing unit (GPU). When the number of GPUs is limited and the performance of a single GPU cannot be fully utilized, high rendering latency will occur. In this paper, we propose Multi-Queue Concurrent Pipeline Rendering (MQCPR), a novel ray-tracing parallel rendering scheme based on GPU multi-queue. This scheme divides the image area into multiple parts and uses multi-queue of GPU to enable the computation and transmission tasks in the rendering process to be executed simultaneously, which can maximize the performance of a single GPU and improve the graphics rendering speed. MQCPR may keep the GPU busy to make full use of the GPU resources. Experiments illustrate that in the case of a single GPU, compared with the single queue serial rendering scheme, the number of frames per second (FPS) is increased by 1.5 times after using MQCPR. © 2023 SPIE. All rights reserved.

关键词： Graphics processing unit

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Optimization-Based Comparative system Evaluation of Single and Dual Traction Inverters with Focus on Partial Load Efficiency and Chip Area

Optimization-Based Comparative System Evaluation of Single a...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Christoph Sachs Fabian Stamer Jan Allgeier Duleepa Thrimawithana Martin Neuburger Faculty of Mobility and Technology Esslingen University Germany Institute for System Dynamics University of Stuttgart Germany Robert Bosch GmbH Germany Dept. of Electrical Computer and Software Engineering The University of Auckland New Zealand

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

The transition to electric transportation demands efficient and cost-effective powertrains. Optimizing energy use is crucial for extending range and reducing expenses. However, comparing inverter and motor efficiency based on inverter topologies is challenging due to biased methodologies that favor certain designs over others. This document introduces a novel optimization-based approach for enhancing partial load efficiency and minimizing chip area of single and dual traction inverters, indicating potential energy savings and cost reduction. Recent publications of both industry [1], [2] and academia [3] underscore the importance of these design goals achieved by either novel inverter topologies or enhanced control methods. Two promising topologies with the inherent capability of partial load optimization are evaluated regarding chip area and system efficiency to find the most suitable concept for future electric vehicle power trains.

关键词： MOSFET Costs Silicon carbide Modulation Mechanical power transmission Motors Electric vehicles Inverters Topology Optimization

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Imperceptible and Sparse Adversarial Attacks via a Dual-Population-Based Constrained Evolutionary Algorithm

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第2期4卷 268-281页

作者： Tian, Ye Pan, Jingwen Yang, Shangshang Zhang, Xingyi He, Shuping Jin, Yaochu Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230088 China Anhui University School of Computer Science and Technology Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Hefei230601 China Anhui University Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Hefei230601 China Bielefeld University Faculty of Technology Bielefeld33619 Germany

The sparse adversarial attack has attracted increasing attention due to the merit of a low attack cost via changing a small number of pixels. However, the generated adversarial examples are easily detected in vision since the perturbation to each pixel is relatively large. To achieve imperceptible and sparse adversarial attacks, this article formulates a bi-objective constrained optimization problem, simultaneously minimizing the 0 and 2 distances to the original image, and proposes a dual-population-based constrained evolutionary algorithm to solve it. The proposed method solves the optimization problem by evolving two populations, where one population is responsible for finding feasible solutions (i.e., successful attacks) and the other one is to minimize both the 0 and 2 distances. Moreover, a population initialization strategy and two genetic operators are customized to accelerate the convergence speed. Experimental results indicate that the proposed method can achieve high success rates with low attack costs, and strikes a better balance between the 0 and 2 distances than state-of-the-art methods. © 2020 IEEE.

关键词： Evolutionary algorithms

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Performance Analysis of Energy and Secure Efficient Routing for Wireless Sensor Networks

Performance Analysis of Energy and Secure Efficient Routing ...

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Decision Aid Sciences and Application (DASA), International Conference on

作者： Amjed Abbas Ahmed Zainab Mohammed Hussein Ali Hamza Najim Khattab M. Ali Alheeti Nurhizam Safie Mohd Satar Aisha Hassan Abdallah Hashim Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Department of Computer Techniques Engineering Imam AlKadhum College (IKC) Baghdad Iraq Department of Computer Science Information System University of Technology Imam AIKadhum College (IKC) Baghdad Iraq Department of Computer Technical Engineering Imam Al-Kadhum College (IKC) Al-Diwaniyah Iraq Computer Networking Systems Department College of Computer and Information Technology University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia (UKM) Bangi Malaysia Department of Electrical and Computer Engineering Faculty of Engineering International Islamic University Malaysia Selangor Malaysia

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

Wireless Sensor Networks (WSNs) are essential for various applications, but their architecture makes them vulnerable to attacks. While traditional security methods like authentication and encryption offer some protection, they often fall short against specific node attacks. This paper introduces the Secure Energy Aware Routing (SEAR) strategy, which uses trust-based assessments to exclude malicious nodes while optimizing energy consumption. SEAR factors in node trust, hop time, and residual energy to ensure reliable data transmission through trustworthy nodes. MATLAB simulations on networks of varying sizes (100–500) compare SEAR with existing methods (EAD and EASER), demonstrating SEAR's superior performance. Results show significant improvements in packet delivery ratio (13-16%), network throughput (19–24 %), energy consumption (32-36 % ), packet loss (16-190/0), and end-to-end latency (21-26 % ), proving SEAR's effectiveness in enhancing WSN security, energy efficiency, and performance.

关键词： Wireless communication Wireless sensor networks Energy consumption Packet loss Routing Throughput Routing protocols Energy efficiency Encryption Security

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Deep Learning Approaches for Battery Capacity and State of Charge Estimation with the NASA B0005 Dataset

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computers, Materials and Continua 2025年第3期83卷 4795-4813页

作者： Zeyang Zhou Zachary James Ryan Utkarsh Sharma Tran Tien Anh Shashi Mehrotra Angelo Greco Jason West Mukesh Prasad School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Ultimo NSW 2007 Australia School of Data Science and Engineering Indian Institute of Science Education and Research Bhopal 462066 India Research Institute of Marine Systems Engineering Seoul National University Seoul 08826 Republic of Korea Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Delhi NCR Campus Ghaziabad 201204 India Battery System Modelling Department CLARIOS VARTA Hannover GmbH Hanover 30419 Germany Science and Innovation Group Bureau of Meteorology Brisbane 4000 Australia

Accurate capacity and State of Charge (SOC) estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric vehicles. This study examines ten machine learning architectures, Including Deep Belief Network (DBN), Bidirectional Recurrent Neural Network (BiDirRNN), Gated Recurrent Unit (GRU), and others using the NASA B0005 dataset of 591,458 instances. Results indicate that DBN excels in capacity estimation, achieving orders-of-magnitude lower error values and explaining over 99.97% of the predicted variable’s variance. When computational efficiency is paramount, the Deep Neural Network (DNN) offers a strong alternative, delivering near-competitive accuracy with significantly reduced prediction times. The GRU achieves the best overall performance for SOC estimation, attaining an R 2 of 0.9999, while the BiDirRNN provides a marginally lower error at a slightly higher computational speed. In contrast, Convolutional Neural Networks (CNN) and Radial Basis Function Networks (RBFN) exhibit relatively high error rates, making them less viable for real-world battery management. Analyses of error distributions reveal that the top-performing models cluster most predictions within tight bounds, limiting the risk of overcharging or deep discharging. These findings highlight the trade-off between accuracy and computational overhead, offering valuable guidance for battery management system (BMS) designers seeking optimal performance under constrained resources. Future work may further explore advanced data augmentation and domain adaptation techniques to enhance these models’ robustness in diverse operating conditions.

关键词： Battery capacity estimation state of charge deep learning prediction efficiency energy storage systems

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Brain Tumor Classification system Using Decision Tree Algorithm

Brain Tumor Classification System Using Decision Tree Algori...

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Cybernetics and Innovations (ICCI), International Conference on

作者： Sattarpoom Thaiparnit Thanakrit Janchidfah Adil Farooq Dep. Of Information System and Computer Business Faculty of Business Administration Rajamangala University of Technology Suvarnabhumi Suphan Buri Thailand dep. Of Digital Media Technology Muban Chombueng Rajabhat University Ratchaburi Thailand Department of Electronic Engineering Maynooth University Ireland

ISBN: (数字)9798331533267

ISBN: (纸本)9798331533274

This research aims to study, design, and develop a brain tumor classification system using artificial intelligence, specifically decision tree algorithms. The system's primary objective is to assist medical personnel in making decisions, as distinguishing between two types of brain tumors-benign and malignant (cancerous)-can be challenging, especially for less experienced medical staff. Due to the similarities between these two types of tumors, it may be difficult for medical practitioners with limited expertise to differentiate between them in a timely manner. The proposed brain tumor classification system is designed to address this challenge. The research team conducted the study over approximately seven months, starting from the second semester of the 2023 academic year through the first semester of the 2024 academic year. Experimental results revealed that among the tree-based algorithms, Random Forest achieved the highest accuracy, followed by M5P. The best average accuracy from the experiments was obtained using a 70% training and 30% testing split, yielding an accuracy of 96.83%. Therefore, the research team selected the 70% training and 30% testing split for model development, as it provided the highest accuracy compared to the 60% training and 40% testing split, and the 80% training and 20% testing split, which showed lower accuracy. The results demonstrate that the 70% training and 30% testing approach is optimal for the brain tumor classification system, ensuring high accuracy in classifying brain tumors.

关键词： Training Technological innovation Accuracy Machine learning algorithms Brain tumors Brain modeling Classification algorithms Decision trees Testing Cancer

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Mobile Apps for Healthy Life Education for Young People 2

Mobile Apps for Healthy Life Education for Young People

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2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Suwandi, Benedictus Evan Warnars, Diana Teresia Spits Warnars, Harco Leslie Hendric Spits Utomo, Wiranto Herry Ramadhan, Arief Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Information Systems Information System Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Computing President University Department of Magister Science of Information Technology Bekasi17550 Indonesia

ISBN: (纸本)9781665400527

Health education is crucial for the community, especially the younger generation, because adolescent behavior/lifestyle will carry over into adulthood, and it is difficult to change it. This paper proposes designing and implementing a mobile application for young people for healthy lifestyle education. This mobile application implemented with the android studio is designed using use case diagrams to describe the business processes of this mobile application. In addition, activity diagrams describe the process details of each activity use case, and class diagrams describe the design of the database model, which is implemented using a MySQL database. © 2022 IEEE.

关键词： Mobile computing

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DEPAS: DE-NOVO PATHOLOGY SEMANTIC MASKS USING A GENERATIVE MODEL

arXiv

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arXiv 2023年

作者： Larey, Ariel Daniel, Nati Aknin, Eliel Fisher, Yael Savir, Yonatan Department of Physiology Biophysics and System Biology Faculty of Medicine Technion Israel Institute of Technology Haifa Israel Faculty of Computer Science Technion Israel Institute of Technology Haifa Israel Faculty of Industrial Engineering Technion Israel Institute of Technology Haifa Israel Division of Pathology Rambam Health Care Campus Haifa Israel

The integration of artificial intelligence into digital pathology has the potential to automate and improve various tasks, such as image analysis and diagnostic decision-making. Yet, the inherent variability of tissues, together with the need for image labeling, lead to biased datasets that limit the generalizability of algorithms trained on them. One of the emerging solutions for this challenge is synthetic histological images. However, debiasing real datasets require not only generating photorealistic images but also the ability to control the features within them. A common approach is to use generative methods that perform image translation between semantic masks that reflect prior knowledge of the tissue and a histological image. However, unlike other image domains, the complex structure of the tissue prevents a simple creation of histology semantic masks that are required as input to the image translation model, while semantic masks extracted from real images reduce the process's scalability. In this work, we introduce a scalable generative model, coined as DEPAS, that captures tissue structure and generates high-resolution semantic masks with state-of-the-art quality. We demonstrate the ability of DEPAS to generate realistic semantic maps of tissue for three types of organs: skin, prostate, and lung. Moreover, we show that these masks can be processed using a generative image translation model to produce photorealistic histology images of two types of cancer with two different types of staining techniques. Finally, we harness DEPAS to generate multi-label semantic masks that capture different cell types distributions and use them to produce histological images with on-demand cellular features. Overall, our work provides a state-of-the-art solution for the challenging task of generating synthetic histological images while controlling their semantic information in a scalable way. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Visualizing the Battery Degradation through Non-destructive Methods on Predicting the State of Health (SoH) of Second Life Batteries (SLB) 1

Visualizing the Battery Degradation through Non-destructive ...

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1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Vignesh, S. Selvaraj, Jeyraj A-L Che, Hang Seng Tey, Kok Soon Universiti Malaya HiCoE UMPEDAC Level 4 WISMA R&D Faculty of Institute for Advanced Studies Kuala Lumpur59990 Malaysia Universiti Malaya HiCoE UMPEDAC Level 4 WISMA R&D Kuala Lumpur59990 Malaysia Universiti Malaya Department of Computer System & Technology Kuala Lumpur50603 Malaysia

ISBN: (纸本)9798350342796

Ever since the vehicle electrification era had started, lithium-ion batteries become the sole fore runner in powering the Electric Vehicles (EV). Vehicle electrification contributed vastly in public transportation which further promoted the surge in usage of EV in recent days. The EVs are deployed in different climatic environment, and user defined handling which lead the battery to undergo non-uniform degradation as it undergoes a series of charge and discharge cycles. State of Health (SoH) is an indicator to quantify the battery health. When the SoH of lithium-ion batteries deployed in EV application reaches 80% of its nominal capacity, then the battery is said to be reached its End of Life (EoL) in EV application. Such batteries, when translated in to second life applications, its SoH estimation at second life is vital information on defining the operating points of the Second Life Battery (SLB). The SoH estimation of SLB is expected to be non-destructive in nature. This article deals with the SoH estimation of pouch cells that underwent lab level testing for 100 charge and discharge cycles. The battery degradation from cycle 1 to 100 is visualized through Incremental Capacity Differential Voltage (ICDV) method. Based on the degradation modes observed in the battery, the Health Indicators (HI) were summarized and the insights on dominant degradation phase can be visualized through bifurcating the operating voltage of the cell in to a particular zones of interest. This voltage band in particular, caters preliminary insights regarding the battery degradation and the same information can be translated to second life thereby reducing the cycle life testing time and efforts. © 2023 IEEE.

关键词： Battery degradation Incremental Capacity Differential Voltage Non-destructive test methods Second Life Batteries State of Health

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A Secure Multiparty Quantum Homomorphic Encryption Scheme

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computers, Materials & Continua 2022年第11期73卷 2835-2848页

作者： Jing-Wen Zhang Xiu-Bo Chen Gang Xu Heng-Ji Li Ya-Lan Wang Li-Hua Miao Yi-Xian Yang Information Security Center State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing100876China School of Information Science and Technology North China University of TechnologyBeijing100144China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Sichuan610025China Quantum Technology Lab&Applied Mechanics Group University of MilanMilan20133Italy Department of Computer Science Faculty of Engineering and Physical SciencesUniversity of SurreyGuildfordSurreyGU27XHUnited Kingdom Huawei Technologies Co.Ltd Shenzhen518129China

The significant advantage of the quantum homomorphic encryption scheme is to ensure the perfect security of quantum private *** this paper,a novel secure multiparty quantum homomorphic encryption scheme is proposed,which can complete arbitrary quantum computation on the private data of multiple clients without decryption by an almost dishonest ***,each client obtains a secure encryption key through the measurement device independent quantum key distribution protocol and encrypts the private data by using the encryption operator and ***,with the help of the almost dishonest server,the non-maximally entangled states are preshared between the client and the server to correct errors in the homomorphic evaluation of T gates,so as to realize universal quantum circuit evaluation on encrypted ***,from the perspective of the application scenario of secure multi-party computation,this work is based on the probabilistic quantum homomorphic encryption scheme,allowing multiple parties to delegate the server to perform the secure homomorphic *** operation and the permission to access the data performed by the client and the server are clearly pointed ***,a concrete security analysis shows that the proposed multiparty quantum homomorphic encryption scheme can securely resist outside and inside attacks.

关键词： Quantum homomorphic encryption secure multiparty computation almost dishonest server security

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