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IEEE Texas Power and Energy Conference (TPEC)

作者： Muhammad Furqan Waqar Uddin Kamran Zeb Muhammad Ashar Ayaz Irfan Khan Muhammad Khalid School of Electrical Engineering and Computer Science (SEECS) National University of Science and Technology (NUST) Islamabad Pakistan Interdisciplinary Research Center for Sustainable Energy Systems (IRC-SES) King Fahd University of Petroleum and Minerals (KFUPM) Dhahran Saudi Arabia IRC-SES KFUPM Dhahran Saudi Arabia Dept. of Electrical and Computer Engneering Texas A and M University College Station TX USA Dept. of Elec. and Comp. Engineering Texas A and M University College Station TX USA Electrical Engineering Department KFUPM and IRC-SES KFUPM Dhahran Saudi Arabia

ISBN: (数字)9798331541125

ISBN: (纸本)9798331541132

This work presents a high-performance AC-DC quasi single-stage current-fed resonant converter based electric vehicles (EVs) charger. A non-linear control technique is applied which has two main objectives i.e; Power factor correction (PFC) and regulation of DC output voltage. For this purpose, super twisting sliding mode control (ST-SMC) is suggested in this study for 400V batteries of EVs. To reduce switching losses and improve efficiency, the secondary side switch shows turn OFF with zero voltage switching (ZVS) while the primary ones exhibit ZVS at turn ON instant. Hardware-in-loop (HIL) experimental validation based on the Delfino F28369D dual-core microcontroller is used to verify the performance of the controller and converter simulated on PSIM and MATLAB/Simulink.

关键词： Microcontrollers Switching loss Switches Resonant converters Zero voltage switching Regulation Batteries Safety Voltage control Sliding mode control

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Lightweight AI and Blockchain Optimization for Enhancing Consumer Electronics Decision-Making

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IEEE Transactions on Consumer Electronics 2025年

作者： Byeon, Haewon Alsaadi, Mahmood Keshta, Ismail Ahanger, Tariq Ahamed Safarova, Nodira Aldawsari, Hamad Cascone, Lucia Shabaz, Mohammad Korea University of Technology and Education Convergence Department Cheonan Korea Republic of University of Al Maarif College of Sciences Department of Computer Sciences Al Anbar31001 Iraq AlMaarefa University College of Applied Sciences Computer Science and Information Systems Department Riyadh Saudi Arabia CoBA Prince Sattam Bin Abdulaziz University Department of Management Information Systems Saudi Arabia Tashkent State University of Economics Department of Finance and Financial technologies Tashkent Uzbekistan University of Tabuk Department of Computer Science Haql University College Saudi Arabia University of Salerno Dept. of Computer Science Fisciano Italy Model Institute of Engineering and Technology J and K Jammu India

Under the "dual carbon" background, consumerelectronics consumption has become deeply ingrained in people'sminds. However, consumers often distrust the sustainabilityclaims of consumer electronics products. Artificial intelligence(AI) and blockchain technology can address this trust deficitthrough transparency and traceability mechanisms. This studyintegrates blockchain technology into traditional consumerelectronics supply chains, considering consumers' preferences andtrust in these products. An AI-based game model is proposed toanalyze the interactions among supply chain members before andafter implementing blockchain technology, under varying EdgeComputing-based power structures. This model quantitativelyevaluates emission reduction and pricing strategies, aiming tooptimize consumer surplus and total social welfare. By leveragingLightweight AI and blockchain, smart wholesale and cost-sharingcontracts are designed to establish reasonable ranges for wholesaleprices and optimal cost-sharing ratios, enhancing enterpriseoperational efficiency and achieving supply chain *** demonstrate that when consumers exhibit a strongerpreference for consumer electronics products, the adoption ofLightweight AI and blockchain delivers greater benefits across thesupply chain. Furthermore, as consumer willingness to purchasethese products increases, the advantages become morepronounced. Numerical analysis highlights that smart contractscan better coordinate the supply chain, particularly in retailer-dominated scenarios. Finally, empirical cases validate theeffectiveness of the proposed strategies and models. © 1975-2011 IEEE.

关键词： Supply chain management

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Crack Catcher AI — Enabling Smart Fracture Mechanics Approach for Damage Control in Thin Silicon Wafers for 3D Semiconductor Integrated Devices/Packages

Crack Catcher AI — Enabling Smart Fracture Mechanics Approa...

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Annual International Symposium on Reliability Physics

作者： Gabriel Preston Greer Gonzolez Andrea S. Karnyoto Fitya S. Mozar Matthew M. Henry Mahmud Isnan Kuncahyo S. Nugroho Henry Candra Muhamad Doris Dianing N. N. Putri Tyas K Sari Bens Pardamean Endang Djuana Derrric Speaks Arief S. Budiman Oregon Renewable Energy Center (OREC) Oregon Institute of Technology (Oregon Tech) Klamath Falls OR USA Dept. of Manufacturing & Mechanical Eng. & Tech. (MMET) Oregon Institute of Technology Klamath Falls OR USA Bioinformatics & Data Science Research Center (BDSRC) Bina Nusantara University Jakarta Indonesia Computer Sci. Dept. BINUS Grad. Program - Master of Computer Sci. Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Center for Artificial Intelligence and Advanced Technology (CAPTIVATE) Universitas TRISAKTI Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Universitas TRISAKTI Jakarta Indonesia Industrial Eng. Dept. BINUS Grad. Program – Master of Industrial Eng. Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331504779

ISBN: (纸本)9798331504786

The importance of silicon cannot be undermined - in photovoltaics (PV) as well as in semiconductor industries. However, silicon is very brittle. Silicon cells/wafers crack easily during manufacturing assembly and/or during device operations. Crack Catcher AI uses novel smart fracture mechanics approach with Artificial Intelligence (AI) methodologies to predict and control crack/damage evolution in thin silicon cells/wafers. This is critical as semiconductor 3D integration technology calls for wafer-to-wafer bonding with utmost alignment accuracy and yield/reliability.

关键词： Semiconductor device measurement Three-dimensional displays Accuracy Semiconductor device reliability Silicon Artificial intelligence Bonding Stress Three-dimensional integrated circuits Stress measurement

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CLIP-IT: CLIP-based Pairing for Histology Images Classification

arXiv

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arXiv 2025年

作者： Karimian, Banafsheh Avanzato, Giulia Belharbi, Soufian McCaffrey, Luke Shateri, Mohammadhadi Granger, Eric LIVIA ILLS Dept. of Systems Engineering ETS Montreal Canada Dept. of Computer Engineering University of Cagliari Italy Goodman Cancer Research Centre Dept. of Oncology McGill University Canada

Multimodal learning has shown significant promise for improving medical image analysis by integrating information from complementary data sources. This is widely employed for training vision-language models (VLMs) for cancer detection based on histology images and text reports. However, one of the main limitations in training these VLMs is the requirement for large paired datasets, raising concerns over privacy, and data collection, annotation, and maintenance costs. To address this challenge, we introduce CLIP-IT method to train a vision backbone model to classify histology images by pairing them with privileged textual information from an external source. At first, the modality pairing step relies on a CLIP-based model to match histology images with semantically relevant textual report data from external sources, creating an augmented multimodal dataset without the need for manually paired samples. Then, we propose a multimodal training procedure that distills the knowledge from the paired text modality to the unimodal image classifier for enhanced performance without the need for the textual data during inference. A parameter-efficient fine-tuning method is used to efficiently address the misalignment between the main (image) and paired (text) modalities. During inference, the improved unimodal histology classifier is used, with only minimal additional computational complexity. Our experiments4 on challenging PCAM, CRC, and BACH histology image datasets show that CLIP-IT can provide a cost-effective approach to leverage privileged textual information and outperform unimodal classifiers for histology. © 2025, CC BY.

关键词： Taxonomies

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Software tool for automatically verified operations on intervals and probability distributions

引用

Reliable Computing 1998年第1期4卷 71-82页

作者： Berleant, Daniel Cheng, Hang Dept. of Computer Systems Engineering University of Arkansas Fayetteville USA

We describe a software tool for performing automatically verified arithmetic operations on independent operands when the operands are intervals, or probability distribution functions, or one operand is an interval and the other is a distribution. Intervals and distributions are expressed using the same technique, so the algorithms do not need to distinguish between intervals and distributions in their operation. The tool can calculate common arithmetic operations with guaranteed results (as well as confidence limits on a distribution if the distribution is empirically estimated from samples). A previous paper [1] discusses the concepts, algorithms, and related work. Here we emphasize a software tool that implements the algorithms, interacts with the user via a graphical user interface, and saves, retrieves, and prints the results of its calculations.

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Lightweight Task-Oriented Semantic Communication Empowered by Large-Scale AI Models

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IEEE Transactions on Vehicular Technology 2025年

作者： Liu, Chuanhong Guo, Caili Yang, Yang Chen, Mingzhe Quek, Tony Q. S. Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL United States Singapore University of Technology and Design Dept. of Information Systems Technology and Design 487372 Singapore

Recent studies have focused on leveraging large-scale artificial intelligence (LAI) models to improve semantic representation and compression capabilities. However, the substantial computational demands of LAI models pose significant challenges for real-time communication scenarios. To address this, this paper proposes utilizing knowledge distillation (KD) techniques to extract and condense knowledge from LAI models, effectively reducing model complexity and computation latency. Nevertheless, the inherent complexity of LAI models leads to prolonged inference times during distillation, while their lack of channel awareness compromises the distillation performance. These limitations make standard KD methods unsuitable for task-oriented semantic communication scenarios. To address these issues, we propose a fast distillation method featuring a pre-stored compression mechanism that eliminates the need for repetitive inference, significantly improving efficiency. Furthermore, a channel adaptive module is incorporated to dynamically adjust the transmitted semantic information based on varying channel conditions, enhancing communication reliability and adaptability. In addition, an information bottleneck-based loss function is derived to guide the fast distillation process. Simulation results verify that the proposed scheme outperform baselines in term of task accuracy, model size, computation latency, and training data requirements. © 1967-2012 IEEE.

关键词： NP-hard

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A methodological approach to EMI resistant analog integrated circuit design

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IEEE Electromagnetic Compatibility Magazine 2015年第2期4卷 92-100页

作者： Redoute, Jean-Michel Richelli, Anna Dept. of Electrical and Computer Systems Engineering Monash University Australia Dept. of Information Engineering University of Brescia Italy

This paper describes the effect of EMI (electromagnetic interference) on analog integrated circuits, and introduces four design guidelines targeted at improving the susceptibility of analog integrated circuits to EMI. These guidelines are then applied to the practical design of operational amplifier topologies with a high degree of immunity to conductive EMI. © 2012 IEEE.

关键词： Timing circuits

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Bridging the Data Gap in AI Reliability Research and Establishing DR-AIR, a Comprehensive Data Repository for AI Reliability

arXiv

引用

arXiv 2025年

作者： Zheng, Simin Clark, Jared M. Salboukh, Fatemeh Silva, Priscila da Mata, Karen Pan, Fenglian Min, Jie Lian, Jiayi King, Caleb B. Fiondella, Lance Liu, Jian Deng, Xinwei Hong, Yili Department of Statistics Virginia Tech BlacksburgVA24061 United States Dept. of Engineering & Applied Science Univ. of Massachusetts DartmouthMA02747 United States Dept. of Electrical & Computer Engineering Univ. of Massachusetts DartmouthMA02747 United States Department of Systems & Industrial Engineering University of Arizona TucsonAZ85721 United States Department of Mathematics & Statistics University of South Florida TampaFL33620 United States Quantitative Analytics Wells Fargo CharlotteNC28262 United States JMP Division SAS CaryNC27513 United States

Artificial intelligence (AI) technology and systems have been advancing rapidly. However, ensuring the reliability of these systems is crucial for fostering public confidence in their use. This necessitates the modeling and analysis of reliability data specific to AI systems. A major challenge in AI reliability research, particularly for those in academia, is the lack of readily available AI reliability data. To address this gap, this paper focuses on conducting a comprehensive review of available AI reliability data and establishing DR-AIR: a data repository for AI reliability. Specifically, we introduce key measurements and data types for assessing AI reliability, along with the methodologies used to collect these data. We also provide a detailed description of the currently available datasets with illustrative examples. Furthermore, we outline the setup of the DR-AIR repository and demonstrate its practical applications. This repository provides easy access to datasets specifically curated for AI reliability research. We believe these efforts will significantly benefit the AI research community by facilitating access to valuable reliability data and promoting collaboration across various academic domains within AI. We conclude our paper with a call to action, encouraging the research community to contribute and share AI reliability data to further advance this critical field of study. Copyright © 2025, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Perturbed Gradient Descent via Convex Quadratic Approximation for Nonconvex Bilevel Optimization

arXiv

引用

arXiv 2025年

作者： Abolfazli, Nazanin Sharifi, Sina Fazlyab, Mahyar Hamedani, Erfan Yazdandoost Dept. of Systems & Industrial Engineering University of Arizona TucsonAZ85721 United States Dept. of Electrical & Computer Engineering Johns Hopkins University BaltimoreMD21218 United States

Bilevel optimization is a fundamental tool in hierarchical decision-making and has been widely applied to machine learning tasks such as hyperparameter tuning, meta-learning, and continual learning. While significant progress has been made in bilevel optimization, existing methods predominantly focus on the nonconvex-strongly convex, or the nonconvex-PL settings, leaving the more general nonconvex-nonconvex framework underexplored. In this paper, we address this gap by developing an efficient gradient-based method inspired by the recently proposed Relaxed Gradient Flow (RXGF) framework with a continuous-time dynamic [1]. In particular, we introduce a discretized variant of RXGF and formulate convex quadratic program subproblems with closed-form solutions. We provide a rigorous convergence analysis, demonstrating that under the existence of a KKT point and a regularity assumption (lower-level gradient PL assumption), our method achieves an iteration complexity of O(1/ϵ1.5) in terms of the squared norm of the KKT residual for the reformulated problem. Moreover, even in the absence of the regularity assumption, we establish an iteration complexity of O(1/ϵ3) for the same metric. Through extensive numerical experiments on convex and nonconvex synthetic benchmarks and a hyper-data cleaning task, we illustrate the efficiency and scalability of our approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Gradient methods

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Certain Investigation on IoT Based Cotton Dust Pollution Monitoring in Textile Plants

Certain Investigation on IoT Based Cotton Dust Pollution Mon...

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International Conference on Inventive Computation Technologies (ICICT)

作者： P Sivanesan Anand J V Trilok Sai Vaddanam Shaik Mohammad Irfan Department of Information Communication Technology School of Computing SASTRA University Thanjavur Tamilnadu India Sri Ramachandra Faculty of Engineering and Technology Sri Ramachandra Institute of Higher Education and Research Chennai Tamil Nadu India Dept. of Computer science and Business Systems SASTRA University Thanjavur Tamilnadu India Dept.of ECE Siddhartha Academy of Higher Education Vijayawada Andhra Pradesh India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

The objective of the work is to track the level of cotton dust in the textile sector. The IoT-enabled cotton dust monitoring system deploys three sensors: Sharp GP2Y1010AU0F; Shinyei PPD42NS; and Honeywell HPMA115S0-XXX measuring the dust levels. The Optical dust sensor GP2Y1010AU0F has a threshold of 1 mg/mS, which is placed inside the mask and when that threshold is crossed, buzzer alerts the worker with the air contaminants. It measures inhaled cotton dust and sends data to IoT, real-time concentration height of dust into the mobile devices. Experimental results show that Sharp GP2Y1010AU0F sensor has achieved award for acquiring the highest mean dust concentration of 95.167 mg/mS with a standard deviation of 3.014 among the three sensors, while Honeywell HPMA115S0-XXX and Shinyei PPD42NS recorded a mean of 87.833 mg/mS and 77.500 mg/mS, respectively. The observed response times of the sensors as follows for GP2Y1010AU0F-8s, HPMA115S0-XXX-6s, and PPD42NS- 5s, recording the fastest response by Honeywell HPMA115S0-XXX. A comparative study in accuracy, precision and sensitivity, with a response time, shows the merits and demerits for each sensor. Results emphasize the effectiveness of the system proposed for monitoring in terms of airborne cotton dust exposure in improving safety in textile workplaces. A meta-analysis is performed with Bayesian framework using JASP software.

关键词： Sensor phenomena and characterization Software Real-time systems Sensors Safety Bayes methods Pollution measurement Cotton Time factors Monitoring

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