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IEEE Transactions on Signal and Power Integrity 2023年 2卷 32-42页

作者： He, Jiayi Zhang, Ling Kiguradze, Zurab Chada, Arun Klivans, Adam Mutnury, Bhyrav Li, Er-Ping Fan, Jun Missouri University of Science and Technology Electromagnetic Compatibility Laboratory RollaMO65409 United States Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Enterprise Product Group in Dell Round RockTX78682 United States University of Texas at Austin Department of Computer Science AustinTX78712-0803 United States College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China

This article presents a flexible and efficient methodology to optimize stack-up for multilayer printed circuit boards (PCBs) with enormous search space and various design constraints. PCB stack-up optimization is crucial in high-speed system design to achieve the desired electrical performance while reducing system costs. The stack-up optimization process is labor-intensive and time-consuming for a large number of layers. Moreover, after the optimization process, the electrical performance of a real design, such as the impedance and loss, may deviate from the target design due to manufacturing variations. Estimating the worst cases due to the manufacturing variations, referred to as 'corner cases' in this article, is essential for a confident PCB design but challenging since the number of related parameters is large. In this article, PCB stack-up optimization and corner-case searching are addressed and greatly accelerated using the integer programming technique. All constraints are converted to mathematical equalities and inequalities that can be solved rapidly by an integer programming solver to obtain feasible stack-up solutions. After the cross sections of the transmission lines are optimized based on the stack-up design to achieve a target electrical performance, the upper and lower bound of impedance and loss are acquired using integer programming when the design parameters vary in a particular range. The proposed method is verified using multilayer PCB designs with practical constraints and demonstrates its effectiveness and high efficiency. © 2022 IEEE.

关键词： Printed circuit boards

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Computation of an Emptiable Minimal Siphon in a Subclass of Petri Nets Using Mixed-Integer Programming

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IEEE/CAA Journal of Automatica Sinica 2021年第1期8卷 219-226页

作者： Shouguang Wang Wenli Duo Xin Guo Xiaoning Jiang Dan You Kamel Barkaoui MengChu Zhou the School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China the Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China the Department of Computer Science Conservatoire National des Arts et MétiersParis 75141France the Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and Systems Macao University of Science and TechnologyMacao 999078China

Deadlock resolution strategies based on siphon control are widely *** computational efficiency largely depends on siphon ***-integer programming(MIP)can be utilized for the computation of an emptiable siphon in a Petri net(PN).Based on it,deadlock resolution strategies can be designed without requiring complete siphon enumeration that has exponential *** to this reason,various MIP methods are proposed for various subclasses of *** work proposes an innovative MIP method to compute an emptiable minimal siphon(EMS)for a subclass of PNs named S^(4)*** particular,many particular structural characteristics of EMS in S4 PR are formalized as constraints,which greatly reduces the solution *** results show that the proposed MIP method has higher computational ***,the proposed method allows one to determine the liveness of an ordinary S^(4)PR.

关键词： Automated manufacturing systems deadlocks discrete event system mixed-integer programming(MIP) Petri nets(PN) siphons

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Reinforcement Learning based MIMO Controller for Virtual Inertia Control in Isolated Microgrids

Reinforcement Learning based MIMO Controller for Virtual Ine...

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2022 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2022

作者： Skiparev, Vjatseslav Belikov, Juri Petlenkov, Eduard Levron, Yoash Tallinn University of Technology Department of Software Science Tallinn Estonia Tallinn University of Technology Department of Computer Systems Tallinn Estonia Technion Faculty of Electrical Engineering Haifa Israel

ISBN: (数字)9781665480321

ISBN: (纸本)9781665480321

In this paper, we propose a multi-input multi-output controller for optimal control of nonlinear energy storage, using deep reinforcement learning (DRL) algorithm. This controller provides the frequency support in an isolated microgrid with high penetration of variable renewable energy sources and varying system inertia. To achieve an optimal control we redesigned neural network of actor and critic, simplified deep deterministic policy gradient (DDPG) rules, and reorganized the reward/punishment system. Simulation results show the efficiency of the proposed virtual inertia control architecture in several scenarios. © 2022 IEEE.

关键词： Reinforcement learning

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VisioSLR: A Vision Data-Driven Framework for Sign Language Video Recognition and Performance Evaluation on Fine-Tuned YOLO Models

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Procedia computer science 2025年 257卷 85-92页

作者： Leila Ismail Nada Shahin Henok Tesfaye Alain Hennebelle Intelligent Distributed Computing and Systems (INDUCE) Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates Independent Researcher

With the emergence of AI for good, there has been an increasing interest in building computer vision data-driven deep learning inclusive AI solutions. Sign language Recognition (SLR) has gained attention recently. It is an essential component of a sign-to-text translation system to support the deaf and hard-of-hearing population. This paper presents a computer VISIOn data-driven deep learning framework for Sign Language video Recognition (VisoSLR). VisioSLR provides a precise measurement of translating signs for developing an end-to-end computational translation system. Considering the scarcity of sign language datasets, which hinders the development of an accurate recognition model, we evaluate the performance of our framework by fine-tuning the very well-known YOLO models, which are built from a signs-unrelated collection of images and videos, using a small-sized sign language dataset. Gathering a sign language dataset for signs training would involve an enormous amount of time to collect and annotate videos in different environmental setups and multiple signers, in addition to the training time of a model. Numerical evaluations of VisioSLR show that our framework recognizes signs with a mean average precision of 97.4%, 97.1%, and 95.5% and 11, 12, and 12 milliseconds of recognition time on YOLOv8m, YOLOv9m, and YOLOv11m, respectively.

关键词： Artificial intelligence computer vision Deep learning Fine-tuning Machine translation Neural machine translation Sign language Sign language recognition Transfer learning YOLO

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Towards Integrating Emerging AI Applications in SE Education

arXiv

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arXiv 2024年

作者： Vierhauser, Michael Groher, Iris Antensteiner, Tobias Sauerwein, Clemens University of Innsbruck Department of Computer Science Austria Johannes Kepler University Linz Institute of Business Informatics – Software Engineering Austria

Artificial Intelligence (AI) approaches have been incorporated into modern learning environments and software engineering (SE) courses and curricula for several years. However, with the significant rise in popularity of large language models (LLMs) in general, and OpenAI’s LLM-powered chatbot ChatGPT in particular in the last year, educators are faced with rapidly changing classroom environments and disrupted teaching principles. Examples range from programming assignment solutions that are fully generated via ChatGPT, to various forms of cheating during exams. However, despite these negative aspects and emerging challenges, AI tools in general, and LLM applications in particular, can also provide significant opportunities in a wide variety of SE courses, supporting both students and educators in meaningful ways. In this early research paper, we present preliminary results of a systematic analysis of current trends in the area of AI, and how they can be integrated into university-level SE curricula, guidelines, and approaches to support both instructors and learners. We collected both teaching and research papers and analyzed their potential usage in SE education, using the ACM computer science Curriculum Guidelines CS2023. As an initial outcome, we discuss a series of opportunities for AI applications and further research areas. © 2024, CC BY-NC-ND.

关键词： Adversarial machine learning

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A Case Study on Blockchain-based Anonymous Reviewer Incentive Token (BARIT)

A Case Study on Blockchain-based Anonymous Reviewer Incentiv...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Pratiksha Shrestha Suman Bhunia Arthur Carvalho Chad Anderson Gabe Lee Department of Computer Science and Software Engineering Miami University Oxford Ohio Farmer School of Business Miami University Oxford Ohio

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Peer review is an integral part of academic publication and is necessary to maintain high standards and novelty of published research. Despite its importance, peer reviewers are rarely provided incentives, leading to journals having difficulty finding reviewers inclined to accept invitations and submit reviews on time. This paper proposes a Blockchain-based Anonymous Reviewer Incentive Token (BARIT) to incentivize peer reviewers. BARIT introduces flexible incentive schemes to provide both recognition and tangible benefits for the reviewers' contribution while preserving the anonymity of reviewers. Using blockchain technology to record reward tokens ensures their permanence and acceptance across different publishers. Incentive models are designed to encourage the involvement of researchers as reviewers, reduce reviewer refusal rates, and prompt the timely submission of review reports.

关键词： Measurement Reviews Prototypes Database systems Blockchains Standards Secure storage Cybernetics Cost accounting Incentive schemes

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Enhanced Machine Learning for Real-Time Plant Replication in Embedded Systems

Enhanced Machine Learning for Real-Time Plant Replication in...

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Annual Conference of Industrial Electronics Society

作者： Abhisek Chowdhury Jane Jung Matthew M. Y. Kuo Roopak Sinha Computer Science and Software Engineering Department Auckland University of Technology Auckland New Zealand School of IT Deakin University Melbourne Australia

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

This paper examines the use of supervised machine learning to construct a digital twin model replicating a physical plant. An inverted pendulum simulation has been used as a case study.A comparative study was conducted on single-step and multistep Dense models, Convolutional Neural Network (CNN), Recurrent Neural Net (RNN), and a Residual Neural Net (RNN2) models to investigate the most appropriate model for replicating the plant *** study found that single-step models were consistently more accurate than multi-step models due to single-step model’s iterative nature. Whereas, multi-step models were better at revealing prediction patterns and identifying causes for large deviations. The best-performing model was the RNN2 model, however, signs of overfitting were observed. In general, all models were able to take into account minor random actuation, however, large changes such as the pendulum falling over caused the models to behave sporadically.

关键词： Industrial electronics Recurrent neural networks Embedded systems Machine learning Predictive models Real-time systems Digital twins Convolutional neural networks Iterative methods Overfitting

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EDUZONE - A Educational Video Summarizer and Digital Human Assistant for Effective Learning 7

EDUZONE - A Educational Video Summarizer and Digital Human A...

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7th International Conference on Information Technology Research, ICITR 2022

作者： Wangchen, Tandin Tharindi, P. Navodya Chaveena De Silva, K.C.C. Thushan Sandeepa, W.D. Kodagoda, Nuwan Suriyawansa, Kushnara Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Sri Lanka

ISBN: (数字)9798350332032

ISBN: (纸本)9798350332032

The availability of technology and the expansive nature of the internet have created a surge in the demand for online learning. Despite so many advantages, there are some existing drawbacks related to online learning. The lengthy recorded video lectures of different subjects and modules in a static manner, are extremely tedious for the learner to understand the contents available. and lack of assistance for academic-related problems of students is also stated as a major issue that comes with online education. EDUZONE provides a reliable solution to mitigate and overcome these challenges. This tool is educational assistance that generates a summarized version of the video lectures which depicts the overall idea of the whole video with the capability of a lecture notes generator along with a digital human which helps to clarify students' problems and build an efficient conversational flow. The summarized video content can be used by the learners for revisions and as a quick reference before any examinations. In addition to generating short and precise content, EDUZONE also indexes any specific topics to make it easier to find content and generate class notes, highlighting all the important content. Overall EDUZONE can be considered a time-efficient educational assistant which helps students with their studies. © 2022 IEEE.

关键词： Students

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Symmetrical Supervision with Transformer for Few-shot Medical Image Segmentation

Symmetrical Supervision with Transformer for Few-shot Medica...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Niu, Yao Luo, Zhiming Lian, Sheng Li, Lei Li, Shaozi Song, Haixin Xiamen University Department of Artificial Intelligence Fujian China Fuzhou University College of Computer and Data Science Fujian China Xiamen University Department of Software Engineering Fujian China

ISBN: (纸本)9781665468190

Few-shot learning can potentially learn the target knowledge in extremely few data regimes. Existing few-shot medical image segmentation methods fail to consider the global anatomy correlation between the support and query sets. They generally adopt a weak one-way information transmission that can not fully explore the knowledge to segment query data. To address this problem, we propose a novel Symmetrical Supervision network based on traditional two-branch methods. We raise two main contributions: (1) The Symmetrical Supervision Mechanism is leveraged to strengthen the supervision of network training;(2) A transformer-based Global Feature Alignment module is introduced to increase the global consistency between the two branches. Experimental results on two challenging datasets (abdominal segmentation dataset CHAOS and cardiac segmentation dataset MS-CMRSeg) show a remarkable performance compared to other comparing methods. © 2022 IEEE.

关键词： Image segmentation

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Data Migration from Relational to NoSQL Database: Review and Comparative Study

Data Migration from Relational to NoSQL Database: Review a...

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International Conference on Advanced Intelligent Systems for Sustainable Development, AI2SD 2022

作者： Saadouni, Chaimae El Bouchti, Karim Reda, Oussama Mohamed Ziti, Soumia Department of Computer Science Intelligent Processing Systems and Security Team Mohammed V University Rabat Morocco Algorithms Networks Intelligent Systems and Software Engineering Research Team Faculty of Sciences Mohammed V University Rabat Morocco

ISBN: (纸本)9783031263835

New storage requirements, analysis, and visualization of Big Data, which includes structured, semi-structured, and unstructured data, have caused the developers in the past decade to begin preferring Big Data databases, such as NoSQL, that comes to overcome the boundaries of relational Databases such as unlimited scalability, high performance, data modeling, data distribution, and continuous availability. Due to those benefits ensured by this new technology, quite recently, considerable attention has been paid to data migration from relational Database to NoSQL Database. So, in this context, the purpose of this paper is to provide a comparative study of some recent approaches proposed by various researchers to migrate data from relational to NoSQL databases, as well as discussing and analyzing the results of those proposed techniques. This research allowed us to gain a basic understanding of the contributions and approaches that have already been made, as well as the benefits and limits of each proposal. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Big data

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