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46th International Spring Seminar on Electronics Technology, ISSE 2023

作者： Choi, Halim Skwarek, Agata Hurtony, Tamás Byun, Jaeduk Illés, Balázs Budapest University of Technology and Economics Faculty of Electrical Engineering and Computer Science Department of Electronics Technology Budapest Hungary Lukasiewicz Research Network - Institute of Microelectronics and Photonics Department of Microelectronics Kraków Poland Dankook University College of Science & Technology Department of Physics Cheonan Korea Republic of

ISBN: (纸本)9798350334845

In the current study, tin (Sn) whisker growth was observed on the surface of the SAC0307-SiC composite solder joint. A commercial SAC0307 solder alloys were reinforced with SiC nanophases to produce composite solder joints. SiC nanoparticles and nanowires were mixed with SAC0307 solder paste using the ball milling method. The solder paste was deposited by stencil printing on the test PCBs, and 0805 chip resistors were soldered by IR reflow method. The solder joints facilitated the growth of the Sn whisker through accelerated lifetime tests. The samples were observed by scanning electron microscopy (SEM) every 1000 hours of the test. On the SiC composite solder joints, Sn whiskers and large corrosion spots were observed after 1000 hours of the THB test. In the case of SAC0307 reference samples, only minor corrosion signs were found after 1000 hours of the THB test. The main finding was that the addition of SiC nanophases promoted Sn whisker growth and decreased the corrosion resistance of the composite solder. © 2023 IEEE.

关键词： Silicon carbide

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Demand Forecasting of a Microgrid-Powered Electric Vehicle Charging Station Enabled by Emerging Technologies and Deep Recurrent Neural networks

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computer Modeling in Engineering & sciences 2025年第5期143卷 2237-2259页

作者： Sahbi Boubaker Adel Mellit Nejib Ghazouani Walid Meskine Mohamed Benghanem Habib Kraiem Department of Computer and Network Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia The International Centre for Theoretical Physics Trieste UniversityTrieste34151Italy Faculty of Science and Technology University of JjelJijel18000Algeria Department of Civil Engineering College of EngineeringNorthern Border University Arar1321Saudi Arabia SAMATWAIQ for Drones Company Prince Sultan Roadeddah23621Saudi Arabia Physics Department Faculty of ScienceIslamic University of MadinahMadinah4235Saudi Arabia Department of Electrical Engineering College of EngineeringNorthern Border UniversityArar91431Saudi Arabia Center for Scientifc Research and Entrepreneurship Northern Border UniversityArar73213Saudi Arabia

Electric vehicles(EVs)are gradually being deployed in the transportation *** they have a high impact on reducing greenhouse gas emissions,their penetration is challenged by their random energy demand and difficult scheduling of their optimal *** cope with these problems,this paper presents a novel approach for photovoltaic grid-connected microgrid EV charging station energy demand *** present study is part of a comprehensive framework involving emerging technologies such as drones and artificial intelligence designed to support the EVs’charging scheduling *** using predictive algorithms for solar generation and load demand estimation,this approach aimed at ensuring dynamic and efficient energy flow between the solar energy source,the grid and the electric *** main contribution of this paper lies in developing an intelligent approach based on deep recurrent neural networks to forecast the energy demand using only its previous ***,various forecasters based on Long Short-term Memory,Gated Recurrent Unit,and their bi-directional and stacked variants were investigated using a real dataset collected from an EV charging station located at Trieste University(Italy).The developed forecasters have been evaluated and compared according to different metrics,including R,RMSE,MAE,and *** found that the obtained R values for both PV power generation and energy demand ranged between 97%and 98%.These study findings can be used for reliable and efficient decision-making on the management side of the optimal scheduling of the charging operations.

关键词： Microgrid electric vehicles charging station forecasting deep recurrent neural networks energy management system

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Real-Time data-Driven Maintenance Logistics: A Public-Private Collaboration

Real-Time Data-Driven Maintenance Logistics: A Public-Privat...

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2024 Commit2data, C2D 2024

作者： van Jaarsveld, Willem Bliek, Laurens de Weerdt, Mathijs Kapodistria, Stella Pronk, Verus Verleijsdonk, Peter Voorberg, Simon Akçay, Alp da Costa, Paulo Eshuis, Rik Kaymak, Uzay van Houtum, Geert-Jan Verwer, Sicco Zhang, Yingqian Department of Industrial Engineering and Innovation Sciences Eindhoven University of Technology Netherlands Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Netherlands Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands Philips Research Laboratories Eindhoven Netherlands Department of Information Systems Supply Chain Management and Decision Analysis NEOMA Business School Rouen France Etsy Inc. Dublin Ireland Jheronimus Academy of Data Science Eindhoven University of Technology Netherlands

ISBN: (纸本)9783959773515

The project "Real-time data-driven maintenance logistics" was initiated with the purpose of bringing innovations in data-driven decision making to maintenance logistics, by bringing problem owners in the form of three innovative companies together with researchers at two leading knowledge institutions. This paper reviews innovations in three related areas: How the innovations were inspired by practice, how they materialized, and how the results impact practice. © Willem van Jaarsveld, Alp Akçay, Laurens Bliek, Paulo da Costa, Mathijs de Weerdt, Rik Eshuis, Stella Kapodistria, Uzay Kaymak, Verus Pronk, Geert-Jan van Houtum, Peter Verleijsdonk, Sicco Verwer, Simon Voorberg, and Yingqian Zhang.

关键词： Scheduled maintenance

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ML_Recognition: A Robust Model for Handwritten Digit Recognition Using Machine Learning Classifiers 3

ML_Recognition: A Robust Model for Handwritten Digit Recogni...

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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

作者： Abdelminaam, Diaa Salama Madbouly, Magda M. Zeid, Magdy Abd-Elghany Abualigah, Laith Misr International University Faculty of Computer Science Cairo Egypt Benha University Faculty of Computers and Artificial Intelligence Benha Egypt Alexandria University Faculty of Computer and Data Science Alexandria Egypt Alobour High Institute for Computer and Informatics Ibrahim Abd Elatif Gomaa Computer Science Department Cairo Egypt Lebanese American University Department of Electrical and Computer Engineering Byblos13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Middle East University Meu Research Unit Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia

ISBN: (纸本)9798350306231

Handwritten digit recognition is a branch of machine learning in which a computer is taught to recognize hand-written numbers. Classification and regression are applied using deep learning and machine learning algorithms. In this paper, we discussed the efficiency of different algorithms: Random Forest, KNN, Naive Bayes, SVM, CNN, and Decision Tree Algorithm. Those algorithms were applied in the handwritten digit recognition process using the MINIST data set. By comparing each algorithm by training every one of them on the data set, then by testing, we got the best algorithm to get the optimal results with the highest accuracy. After comparing the results of each algorithm, it has shown that the neural network has the best results with 97.3% classification accuracy followed by K- the nearest neighbor with 97.2%. © 2023 IEEE.

关键词： Support vector machines

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A Fast Optimistic Method for Monotone Variational Inequalities

arXiv

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arXiv 2023年

作者： Sedlmayer, Michael Nguyen, Dang-Khoa Bot, Radu Ioan Research Network Data Science University of Vienna Vienna Austria Faculty of Mathematics University of Vienna Vienna Austria

We study monotone variational inequalities that can arise as optimality conditions for constrained convex optimisation or convex-concave minimax problems and propose a novel algorithm that uses only one gradient/operator evaluation and one projection onto the constraint set per iteration. The algorithm, which we call fOGDA-VI, achieves a o(1/k) rate of convergence in terms of the restricted gap function as well as the natural residual for the last iterate. Moreover, we provide a convergence guarantee for the sequence of iterates to a solution of the variational inequality. These are the best theoretical convergence results for numerical methods for (only) monotone variational inequalities reported in the literature. To empirically validate our algorithm we investigate a two-player matrix game with mixed strategies of the two players. Concluding, we show promising results regarding the application of fOGDA-VI to the training of generative adversarial nets. Copyright © 2023, The Authors. All rights reserved.

关键词： Generative adversarial networks

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CFPChain: An Optimization of research Funding Selection System Employing A Consortium Blockchain Based Approach 10

CFPChain: An Optimization of Research Funding Selection Syst...

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10th International Conference on computer, Control, Informatics and its Applications, IC3INA 2023

作者： Ramdhani, Taufik Iqbal Basuki, Akbari Indra Susanto, Heru Umbara, Raditya Panji Arifin, Samsul Faiza, Ninon Nurul Research Center for Artificial Intelligence and Cyber Security National Research and Innovation Agency Bandung Indonesia Research Center for Geological Disaster National Research and Innovation Agency Tangerang Selatan Indonesia Statistics Department School of Computer Science Bina Nusantara University Jakarta Indonesia Data and Information Center National Research and Innovation Agency Jakarta Indonesia Faculty Computer Science Institut Teknologi Tangerang Selatan Indonesia Department of Informatics Faculty of Science Nad Technology Universitas Al Azhar Indonesia Jakarta Indonesia Department of Informatics Universitas Bunda Mulia Tangerang Selatan Indonesia

ISBN: (纸本)9798350394870

This paper addresses the challenges in the current centralized research funding selection system and proposes a solution using permissioned blockchain technology. The existing system suffers from drawbacks such as concealed data modification and lack of interoperability. The Blockchain approach, based on the principles of Bitcoin technology, offers high security, auditability, traceability, and integrity. By utilizing interconnected blocks and cryptography, the proposed system ensures the authenticity and consistency of data over time. The chosen blockchain platform, Hyperledger Fabric, provides crash-fault-tolerance consensus and private communication options for enhanced security and efficiency. The objective of this study is to develop a consortium blockchain-based research funding platform that enables collaboration among researchers from public agencies, universities, and companies, with funding provided by financial organizations. The proposed architecture model ensures tamper-proof data, accountability, and interoperability, benefiting researchers, reviewers, auditors, funders, and companies. © 2023 IEEE.

关键词： Smart contract

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Rapid Maize Seedling Detection Based on Receptive-Field and Cross-Dimensional Information Interaction

Rapid Maize Seedling Detection Based on Receptive-Field and ...

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

作者： Hao, Fengqi Zhu, Shulei Ma, Dexin Dong, Xiangjun Kong, Hoiio Liu, Xia Mu, Chunhua Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan250014 China City University of Macau Faculty of Data Science 999078 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China Communication College Qingdao Agricultural University Qingdao266109 China Maize Research Institute Shandong Academy of Agricultural Sciences National Engineering Laboratory of Wheat and Maize Key Laboratory of Biology and Genetic Improvement of Maize in Northern Yellow-huai River Plain Ministry of Agriculture Jinan250100 China

ISBN: (纸本)9781665410205

The seedling stage is important in the growth and development of maize, and it is also a critical period that affects maize yield and quality. Accurately recognizing this stage of maize is challenging, as current maize seedling detection methods struggle with small sizes and complex environments. This paper proposes a rapid maize seedling detection method that emphasizes spatial feature extraction in the receptive field and cross-dimensional interaction - receptive field triplet attention YOLO (RFT-YOLO). First, we design a convolutional module for cross-dimensional information interaction within the receptive field. Second, we introduce an advanced selective fusion network, which boosts the multi-scale fusion capability of small object features while reducing the GFLOPs of the model. Additionally, we employ Inner-IoU to diminish sensitivity to positional biases of small objects and expedite the convergence of the loss function. Finally, we create a dataset of maize seedlings to conduct our experiments. Experimental results demonstrate that the RFT-YOLO significantly enhances performance, reducing the number of parameters by 35.3% and GFLOPs by 14.24%, compared to the baseline model. Moreover, the mean average precision (mAP) improves from 89.6% to 91.5%. These improvements confirm the model's effectiveness in detecting maize seedlings. © 2024 IEEE.

关键词： Deep learning Maize seedling detection Object detection UAV

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Lightweight Deep Learning-Based network Traffic Classification Using Explainable Artificial Intelligence

Lightweight Deep Learning-Based Network Traffic Classificati...

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Computing (ICOCO), IEEE International Conference on

作者： Afif Haziq Haris Azizi Ariffin Faiz Zaki Nor Badrul Anuar Centre of Research for Cyber Security and Network (CSNET) Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia

ISBN: (数字)9798331530303

ISBN: (纸本)9798331530310

To address the growing demand for internet usage and connected devices, it is essential to implement robust network Traffic Classification (NTC) methods that guarantee network security and optimised resource utilisation. Deep learning approaches are continuously gaining traction due to their capabilities in learning complex network traffic patterns and effective classification. However, the performance of deep learning (DL) approaches comes with the cost of huge computational resources that struggle to perform under a resource-constrained environment. Therefore, this study proposes enhancing DL-based NTC by integrating an Explainable Artificial Intelligence (XAI) technique, the Shapley Additive exPlanations (SHAP), to identify and select the most important features. This study leverages the explainability of XAI to enhance model performance and optimise resource utilisation through feature selection. To evaluate the selected features, this paper compares three algorithms, which are Convolutional Neural networks (CNN), Multi-Layer Perceptron (MLP), and Gated Recurrent Units (GRU), using the ISCXVPN2016 dataset on an NVIDIA Tesla T4. The result shows a $\mathbf{1 2 . 4 2 \%}, \mathbf{1 0 . 7 2 \%}$, and $\mathbf{1 0 . 2 3 \%}$ decrease in RAM usage for MLP, CNN, and GRU, respectively, while reducing the GPU memory usage by $6 \%$ and an improvement up to $4.99 \%$ F1-score compared to the state-of-the-art.

关键词： Deep learning Performance evaluation Explainable AI Random access memory Graphics processing units Telecommunication traffic Traffic control network security Convolutional neural networks Resource management

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HPC-in-Containers: A Containerized Parallel Environment for Parallel Programming Learning Using Docker

HPC-in-Containers: A Containerized Parallel Environment for ...

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International Conference on Information Technology Systems and Innovation (ICITSI)

作者： Andria Arisal Heru Suhartanto Research Center for Data and Information Sciences National Research and Innovation Agency Bandung Indonesia Faculty of Computer Science University of Indonesia Depok Indonesia

ISBN: (数字)9798331511470

ISBN: (纸本)9798331511487

This paper presents HPC-in-Containers, a novel containerized parallel computing environment using Docker. It is designed to facilitate learning parallel programming concepts, where users do not have to deploy a multicomputer infrastructure. Instead, they can use their desktop and laptop to easily have a multicomputer environment by utilizing containerization on the multi-core CPU. The environment offers a comprehensive platform for exploring various parallel programming paradigms, including distributed memory parallelism, shared memory parallelism, and hybrid approaches. We include source code samples for each paradigm to demonstrate its capabilities: parallel multicomputer implementations using MPI, parallel thread implementations for single computers using OpenMP, and hybrid parallelization techniques combining both MPI and OpenMP. Furthermore, to enhance user interaction and ease of use, the Docker image incorporates a Jupyter Notebook interface with a C interpreter, allowing users to write, execute, and visualize parallel code through a web browser. This feature makes HPC-in-Containers particularly suitable for educational settings, enabling students to experiment with parallel programming concepts in a controlled, reproducible environment. Our work contributes to the field of parallel computing education by providing a flexible, scalable, and easily deployable platform that bridges the gap between theoretical concepts and practical implementation. It offers a valuable resource for educators, students, and researchers seeking to explore and master the intricacies of parallel programming in a containerized setting.

关键词： Visualization Technological innovation Portable computers Parallel programming Source coding Instruction sets Education Parallel processing User experience Monitoring

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DRLO: Optimizing edge server placement in dynamic MEC scenarios using deep reinforcement learning

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computer networks 2025年 268卷

作者： Guo, Yingya Chen, Cen College of Computer and Data Science Fuzhou University China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University China Engineering Research Center of Big Data Intelligence Ministry of Education Fujian China

As an emerging computing paradigm, Mobile Edge Computing (MEC) significantly enhances user experience and alleviates network congestion by strategically deploying edge servers in close proximity to mobile users. However, the effectiveness of MEC hinges on the precise placement of these edge servers, a critical factor in determining the Quality of Experience (QoE) for mobile users. While existing studies predominantly focus on optimizing edge server placement in static scenarios, they often fall short when faced with user mobility, resulting in a degradation of QoE. To address this challenge, we propose an adaptive edge server placement approach that leverages Deep Reinforcement Learning (DRL) to select the base stations for placing edge servers in a dynamic MEC environment. Our objective is to minimize access delay by optimizing edge server placement for adapting to dynamic environment. To tackle the vast action space associated with edge server placement, we introduce a novel activation function in the actor neural network for efficient exploration. Furthermore, to enhance the adaptability of the derived edge server placement strategy, we meticulously design a new reward function, which takes into account the minimization of total access delay within dynamic MEC scenarios. Finally, to validate the effectiveness of our proposed method, extensive experiments were conducted using the Shanghai Telecom dataset. The results demonstrate that our approach outperforms baseline methods in minimizing access delay for users in dynamic MEC scenarios. © 2025 Elsevier B.V.

关键词： Reinforcement learning

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