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Fabric4show:real-time vision system for fabric defect detection and post-processing

Visual Intelligence 2024年第1期2卷 146-158页

作者： Huaizhou Lin Dan Cai Zengmin Xu Jinsong Wu Lixian Sun Haibin Jia School of Materials Science and Engineering Guangxi Key Laboratory of InformationGuilin University of Electronic TechnologyGuilinChina School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and ComputationGuilin University of Electronic TechnologyCenter for Applied Mathematics of Guangxi(GUET)GuilinChina Anview.ai GuilinChina School of Artificial Intelligence Guilin University of Electronic TechnologyGuilinChina Department of Electrical Engineering University of ChileSantiagoChile

The exploration of computer vision applications for fabric defect detection has immense potential ***,current relevant research in this area has primarily focused on detection models that aim for high detection accuracy and algorithmic efficiency,while neglecting the practical industrial production ***,we propose a fabric defect detection and post-processing system that integrates an optimized region with convolutional neural network(CNN)features(i.e.,Faster R-CNN)for defect detection,defect localization and detection model *** addition,the proposed intelligent system incorporates novel approaches,such as a rearranged fabric dataset,anomaly detection,recommended clipping region division,and a replenishment *** study illustrates an example of artificial intelligence(AI)-driven automated technology in fabric *** accuracy and detection speed of different detection models under identical hardware conditions are evaluated and compared with related *** results demonstrate that the proposed approach achieves comparable performance to other models,while significantly reducing computational resource *** potential efficiency of using two-stage networks on hardware systems for fabric defect detection tasks is highlighted,which is likely to have relevant implications for the textile industry.

关键词： Computer vision Fabric manufacturing Automation Defect detection Faster R-CNN

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MathReader : Text-to-Speech for Mathematical Documents

MathReader : Text-to-Speech for Mathematical Documents

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Sieun Hyeon Kyudan Jung Nam-Joon Kim Hyun Gon Ryu Jaeyoung Do Department of Electrical and Computer Engineering Seoul National University Department of Mathematics Chung-Ang University NVIDIA Interdisciplinary Program in Artificial Intelligence Seoul National University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

TTS (Text-to-Speech) document reader from Microsoft, Adobe, Apple, and OpenAI have been serviced worldwide. They provide relatively good TTS results for general plain text, but sometimes skip contents or provide unsatisfactory results for mathematical expressions. This is because most modern academic papers are written in LaTeX, and when LaTeX formulas are compiled, they are rendered as distinctive text forms within the document. However, traditional TTS document readers output only the text as it is recognized, without considering the mathematical meaning of the formulas. To address this issue, we propose MathReader, which effectively integrates OCR, a fine-tuned T5 model, and TTS. MathReader demonstrated a lower Word Error Rate (WER) than existing TTS document readers, such as Microsoft Edge and Adobe Acrobat, when processing documents containing mathematical formulas. MathReader reduced the WER from 0.510 to 0.281 compared to Microsoft Edge, and from 0.617 to 0.281 compared to Adobe Acrobat. This will significantly contribute to alleviating the inconvenience faced by users who want to listen to documents, especially those who are visually impaired. The code is available at https://***/hyeonsieun/MathReader.

关键词： Text recognition Error analysis Pipelines Optical character recognition Graphics processing units Signal processing Real-time systems Mathematical models Text to speech Speech processing

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HiMOSS: A Novel High-dimensional Multi-objective Optimization Method via Adaptive Gradient-Based Subspace Sampling for Analog Circuit Sizing 24

HiMOSS: A Novel High-dimensional Multi-objective Optimizatio...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Gu, Tianchen Lyu, Ruiyu Bi, Zhaori Yan, Changhao Yang, Fan Zhou, Dian Cui, Tao Liu, Xin Zhang, Zaikun Zeng, Xuan State Key Laboratory of Integrated Chips and Systems School of Microelectronics Fudan University Shanghai China Academy of Mathematics and Systems Science Chinese Academy of Sciences The University of Chinese Academy of Sciences Beijing China Department of Applied Mathematics The Hong Kong Polytechnic University Hong Kong Department of Electrical Engineering The University of Texas at Dallas RichardsonTX United States University of Texas Dallas United States

ISBN: (纸本)9798400706011

This study presents a novel high-dimensional multi-objective optimization method via adaptive gradient-based subspace sampling for analog circuit sizing. To handle constrained multi-objective optimization, we exploit promising regions from a non-crowded Pareto front, with lightweight Bayesian optimization (BO) based on a novel approximate constrained expected hypervolume improvement. This lightweight BO is computational efficient with constant complexity concerning simulation numbers. To tackle high-dimensional challenges, we reduce the effective dimensionality around promising regions by sampling candidates in an adaptive subspace. The subspace is constructed with gradients and previous success steps with their significance decaying over iterations. The gradients are approximated by sparse regression without additional simulations. The experiments on synthetic benchmarks and analog circuits illustrate advantages of the proposed method over Bayesian and evolutionary baselines. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Constrained optimization

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A Low Complexity ML-Based Methods for Malware Classification

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Computers, Materials & Continua 2024年第9期80卷 4833-4857页

作者： Mahmoud E.Farfoura Ahmad Alkhatib Deema Mohammed Alsekait Mohammad Alshinwan Sahar A.El-Rahman Didi Rosiyadi Diaa Salama Abd Elminaam Cybersecurity Department Al-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science and Information Technology Applied CollegePrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan Computer Systems Program-Electrical Engineering Department Faculty of Engineering-ShoubraBenha UniversityCairo11629Egypt Research Center for Artificial Intelligence and Cybersecurity Electronics and Informatics OrganizationNational Research and Innovation Agency(BRIN)KST Samaun SamadikunBandung40135Republic of Indonesia Jadara Research Center Jadara UniversityIrbid21110Jordan MEU Research Unit Middle East UniversityAmman11831Jordan

The article describes a new method for malware classification,based on a Machine Learning(ML)model architecture specifically designed for malware detection,enabling real-time and accurate malware *** an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique(IFDRT),the authors have significantly reduced the feature space while retaining critical information necessary for malware *** technique optimizes the model’s performance and reduces computational *** proposed method is demonstrated by applying it to the BODMAS malware dataset,which contains 57,293 malware samples and 77,142 benign samples,each with a 2381-feature *** the IFDRT method,the dataset is transformed,reducing the number of features while maintaining essential data for accurate *** evaluation results show outstanding performance,with an F1 score of 0.984 and a high accuracy of 98.5%using only two reduced *** demonstrates the method’s ability to classify malware samples accurately while minimizing processing *** method allows for improving computational efficiency by reducing the feature space,which decreases the memory and time requirements for training and *** new method’s effectiveness is confirmed by the calculations,which indicate significant improvements in malware classification accuracy and *** research results enhance existing malware detection techniques and can be applied in various cybersecurity applications,including real-timemalware detection on resource-constrained *** and scientific contribution lie in the development of the IFDRT method,which provides a robust and efficient solution for feature reduction in ML-based malware classification,paving the way for more effective and scalable cybersecurity measures.

关键词： Malware detection ML-based models dimensionality reduction feature engineering

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A deep neural network based surrogate model for damage identification in full-scale structures with incomplete noisy measurements

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Frontiers of Structural and Civil engineering 2024年第3期18卷 393-410页

作者： Tram BUI-NGOC Duy-Khuong LY Tam T TRUONG Chanachai THONGCHOM T.NGUYEN-THOI Laboratory for Computational Mechanics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 70000Vietnam Faculty of Mechanical-Electrical and Computer Engineering School of TechnologyVan Lang UniversityHo Chi Minh City 70000Vietnam Faculty of Civil Engineering School of TechnologyVan Lang UniversityHo Chi Minh City 70000Vietnam Department of Computer Science Aarhus UniversityAarhus 8000Denmark Thammasat University research unit in structural and foundation engineering Department of Civil EngineeringThammasat UniversityPathumthani 12120Thailand Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 70000Vietnam Thammasat School of Engineering Thammasat UniversityPathumthani 12120Thailand

The paper introduces a novel approach for detecting structural damage in full-scale structures using surrogate models generated from incomplete modal data and deep neural networks(DNNs).A significant challenge in this field is the limited availability of measurement data for full-scale structures,which is addressed in this paper by generating data sets using a reduced finite element(FE)model constructed by SAP2000 software and the MATLAB programming *** surrogate models are trained using response data obtained from the monitored structure through a limited number of measurement *** proposed approach involves training a single surrogate model that can quickly predict the location and severity of damage for all potential *** achieve the most generalized surrogate model,the study explores different types of layers and hyperparameters of the training algorithm and employs state-of-the-art techniques to avoid overfitting and to accelerate the training *** approach’s effectiveness,efficiency,and applicability are demonstrated by two numerical *** study also verifies the robustness of the proposed approach on data sets with sparse and noisy measured ***,the proposed approach is a promising alternative to traditional approaches that rely on FE model updating and optimization algorithms,which can be computationally *** approach also shows potential for broader applications in structural damage detection.

关键词： vibration-based damage detection deep neural network full-scale structures finite element model updating noisy incomplete modal data

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Improved-GWO for Shunt Capacitors Placements and Dimensions in Distribution Networks 1

Improved-GWO for Shunt Capacitors Placements and Dimensions ...

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1st International Conference on engineering and Advanced Technology, ICEEAT 2023

作者： Mustafa, Mosbah Abdelouahab, Khattara Rabie, Zine Djamel, Mohammedi Ridha Maawiya, Ould Sidi Yousef, Soufi Amar Telidji University Laghouat University of Ghardaia LACoSERE-Laboratory Department of Electrical-Engineering Algeria Université de Ghardaia Mestel Laboratory Algeria Al Akhawayn University School of Science and Engineering Ifrane Morocco Ziane Achour University Applied-Automation-and-Industrial Diagnostics Laboratory Djelfa Algeria Jouf University Sakaka College of Science Mathematics-Department Saudi Arabia Larbi-Tebessi University Electrical Engineering Department Tebessa Algeria

ISBN: (纸本)9798350383485

Distribution system operators continually grapple with the challenge of minimizing Total Line Losses (TLLs). In response, our research integrates Shunt Capacitors (ShCs) and deploys the Improved Grey Wolf Optimizer (I-GWO) algorithm to determine the optimal sizing and placement of these capacitors. The analysis is conducted considering multiple constraints, such as line limitations, load point voltage levels, and prescribed capacitor thresholds. The approach is evaluated on IEEE distribution systems with 10, 15, 85, and 118 buses, ensuring a realistic assessment. Our results underscore the superior performance of the proposed algorithm when compared to existing literature, offering an effective solution for reducing losses in electric networks. © 2023 IEEE.

关键词： Electric loads

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Evaluation of Improved MobileNetV3-YOLO Models for IoT Devices by Fuzzy Multi-Criteria Decision-Making

Evaluation of Improved MobileNetV3-YOLO Models for IoT Devic...

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International Conference on Computational Intelligence and Applications (ICCIA)

作者： Shiyan Du Jiacheng Li Masato Noto Field of Electrical Electronics and Information Engineering Graduate School of Engineering Kanagawa University Yokohama Japan Department of Applied Systems and Mathematics Kanagawa University Yokohama Japan

ISBN: (数字)9798350352214

ISBN: (纸本)9798350352221

Fire detection is crucial in natural disaster prevention, but the limited computational resources of Internet of Things (IoT) devices impose strict requirements for lightweight and efficient models. This study aims to evaluate and compare the performance of various object detection models for fire detection to optimize model selection in resource-constrained environments. We selected the YOLOv8 series, YOLOv10 series, and our improved MobileNetV3-YOLOv8n and MobileNetV3-YOLOv10n models for evaluation. The results demonstrate that the MobileNetV3-YOLOv8n model achieves the best overall performance, followed by the YOLOv8n and MobileNetV3-YOLOv10n models. Sensitivity analysis confirms the stability of these results under minor variations in metric weights, particularly within the top three rankings. On the basis of these findings, we provide specific model selection recommendations for IoT devices with varying computational capabilities.

关键词： Performance evaluation Analytical models Uncertainty Sensitivity analysis Computational modeling Object detection Analytic hierarchy process Hardware Computational efficiency Internet of Things

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Child Tracking and Prediction of Violence on Children In Social Media Using Natural Language Processing and Machine Learning 1

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22nd International Conference on Artificial Intelligence and Soft Computing, ICAISC 2023

作者： Nallakaruppan, M.K. Srivastava, Gautam Gadekallu, Thippa Reddy Reddy, Praveen Kumar Krishnan, Sivarama Polap, Dawid School of Information Technology and Engineering Vellore Institute of Technology Vellore India Department of Mathematics and Computer Science Brandon University Brandon Canada School of Information Technology and Engineering Vellore Institute of Technology Department of Electrical and Computer Engineering Lebanese American University Lebanon Zhongda Group Zhejiang Jiaxing314312 China Faculty of Applied Mathematics Silesian University of Technology Kaszubska 23 Gliwice44-100 Poland

ISBN: (数字)9783031425059

ISBN: (纸本)9783031425042

Crimes against children are a direct threat to the world of tomorrow. The future of the world is in the hands of children who are the real wealth of mankind. The challenge for society is to address and handle challenges against children such as sexual abuse, trafficking, child labour, and harassment. Even though measures are taken by several governments globally, violence against children is an evident day-to-day process. This becomes the most challenging issue in the post-Internet era, where organized crime against children takes place around the world through a targeted group of people through virtual private networks and the dark web. What comes next is the emergence of social media, which provides an opportunity for these criminals to harm children globally through powerful social networks [15]. The existing methods focus on classifiers that do not work across multiple keywords [6] to look for sentiment analysis regarding a single classifier. There are only a few works that address the issue and these methods do not employ the multi-classification model [2]. In this paper, we propose a model which includes the Twitter API, the APIFY web scraper framework, as well as the VADER semantic analyzer in Python which possesses Decision tree analysis of sentiment prediction. The proposed work provides 99.6904% accuracy and predicts the classification of sentiment analysis. Our work has novelty in attribute selection, child trade, and violence analysis in social media and prediction of classification accuracy using the Decision Tree algorithm. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Forward Invariance-Based Hybrid Control Using Uncertified Controllers

Forward Invariance-Based Hybrid Control Using Uncertified Co...

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IEEE Conference on Decision and Control

作者： Paul K. Wintz Ricardo G. Sanfelice Department of Applied Mathematics University of California Santa Cruz Department of Electrical and Computer Engineering University of California Santa Cruz

For a constrained nonlinear control system, an automated supervisor is proposed that determines switching between a barrier function-certified controller and an uncertified controller. The switching strategy allows for properties of the uncertified controller to be exploited while preserving the forward invariance that is guaranteed by the barrier function for the certified controller. Tunable threshold functions determine regions of the state space where the supervisor switches between controllers. Conditions are given to prevent chattering by establishing a positive minimum time between switches. An example illustrates achieving forward invariance despite using an uncertified MPC controller with delayed computations.

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Unified Variational Approach Description of Ground-State Phases of the Two-Dimensional Electron Gas

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Physical Review Letters 2024年第26期133卷 266504-266504页

作者： Conor Smith Yixiao Chen (陈一潇) Ryan Levy Yubo Yang (杨煜波) Miguel A. Morales Shiwei Zhang Center for Computational Quantum Physics Flatiron Institute New York New York 10010 USA Department of Electrical and Computer Engineering University of New Mexico Albuquerque New Mexico 87131 USA Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA

The two-dimensional electron gas (2DEG) is a fundamental model, which is drawing increasing interest because of recent advances in experimental and theoretical studies of 2D materials. Current understanding of the ground state of the 2DEG relies on quantum Monte Carlo calculations, based on variational comparisons of different Ansätze for different phases. We use a single variational ansatz, a general backflow-type wave function using a message-passing neural quantum state architecture, for a unified description across the entire density range. The variational optimization consistently leads to lower ground-state energies than previous best results. Transition into a Wigner crystal (WC) phase occurs automatically at rs=37±1, a density lower than currently believed. Between the liquid and WC phases, the same ansatz and variational search strongly suggest the existence of intermediate states in a broad range of densities, with enhanced short-range nematic spin correlations.

关键词： Wave functions

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