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Electronic Systems and Intelligent Computing (ICESIC), International Conference on

作者： Koushik Sundar Bhavani M Jaeyalakshmi M Vijayakumar R Citibank NA Jersey City NJ USA Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India Department of Computer Science and Design Rajalakshmi Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798331522988

ISBN: (纸本)9798331522995

A new era of massive and varied data repositories has been brought about by the digitization of documents, necessitating the creation of reliable systems for interpreting and classifying scanned documents. Using deep learning capabilities, this study investigates state-of-the-art methods for document analysis and classification. Optical character recognition (OCR) systems have been revolutionized by deep learning models, especially CNN and transformer-based architectures. These algorithms are remarkably accurate at extracting text from scanned documents, independent of language, font size, or style. Transformer models perform well in comprehending textual material, whereas CNNs are skilled at extracting discriminative features from document images. Utilizing transfer learning approaches to maximize model performance while minimizing the requirement for large amounts of labelled data has been the subject of recent study. Deep learning has also resulted in notable advancements in Named Entity Recognition within scanned documents. Document organization and comprehension have been made easier by the remarkable accuracy with which names, dates, and locations can be extracted by bi-directional LSTMs, BERT-based models, and their variations. Improved document processing, organization, and accessibility are made possible by the approaches covered here, which have a wide range of applications in the financial, medical, and legal services sectors.

关键词： Deep learning Training Accuracy Text analysis Computational modeling Optical character recognition Organizations Feature extraction Transformers Convolutional neural networks

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design Intervention for Sole Cutting Operation in Hand-Sewn Kolhapuri Footwear Manufacturing in India

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Journal of The Institution of Engineers (India): Series C 2022年第4期103卷 967-983页

作者： Jadhav, Ganesh S. Arunachalam, M. Salve, Urmi R. Devadula, Suman Shete, Hemant K. Thorat, Sandeep A. School of Design Dr. Vishwanath Karad MIT World Peace University Maharashtra Pune India School of Industrial Design Karnavati University Gujarat Gandhinagar382422 India Department of Design Indian Institute of Technology Guwahati India Department of Mechanical Engineering Dr. Daulatrao Aher College of Engineering Karad India Computer Science Department Rajarambapu Institute of Technology Rajaramnagar India

Handcrafted footwear is a vital segment of leather industries worldwide. Among all handcrafted footwear, Kolhapuri handcrafted footwear is notable for its craft and design in India. This article presents design intervention in hand-sewn footwear of Kolhapur, India, to improve the well-being (in terms of better posture, and force exertion) of artisans and productivity (in terms of reduced time of task/work). Observations on field indicate that the sole (base and heel) cutting operation requires considerable manual effort and time. This motivated this study on the need for a design intervention to reduce/eliminate manual effort required in Kolhapuri footwear manufacturing without affecting craftsmanship. Evaluation of manual sole-cutting activity was performed using production and ergonomics techniques (i.e. task-time, two-handed process chart, posture and force exertion). Based on this identification of most felt needs, a novel design for Sole-cutting machine is conceptualized, developed and evaluated using production and ergonomics techniques. The evaluation of design intervention indicated its effectiveness in sole-cutting operations. This novel footwear Sole-cutting machine mounted as a workstations satisfy the ergonomics characteristics and end-user requirements of artisans, thereby improving their well-being. © 2022, The Institution of Engineers (India).

关键词： Ergonomics

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Brief Industry Paper: Towards Efficient Task Scheduling for AUTOSAR using Parallel Pruning 44

Brief Industry Paper: Towards Efficient Task Scheduling for ...

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44th IEEE Real-Time Systems Symposium, RTSS 2023

作者： Yang, Yanxing Zhang, Nan Yan, Dengke Wei, Xian Zhou, Junlong Liu, Hong Chen, Mingsong MoE Engineering Research Center of SW/HW Co-Design Technology and Application East China Normal University China School of Computer Science and Engineering Nanjing University of Science and Technology China Shanghai Uni-Sentry Intelligent Technology Co. Ltd. China

ISBN: (纸本)9798350328578

As a standardized software framework and open E/E system architecture, the AUTomotive Open System ARchitecture (AUTOSAR) has been widely applied to autonomous driving systems to enable real-time control. However, due to the increasing design complexity and the lack of efficient algorithms and design automation tools, it is difficult to quickly figure out an optimal task scheduling scheme for an AUTOSAR-based system. To address this problem, we introduce a novel task scheduling method that can parallelly search for an optimal solution with the help of our proposed pruning strategy. Experimental results on a real-world AUTOSAR-based autonomous driving system demonstrate that our approach can achieve much better task scheduling solutions than the ones obtained manually and significantly reduce the overall task scheduling time. © 2023 IEEE.

关键词： Real time control

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Low Complexity Wireless Interference Identification in Antagonistic Environments Based on Multi-Task Learning

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IEEE Transactions on Vehicular Technology 2025年

作者： Zhao, Ruosi Jiang, Weiheng Li, Donggen Niyato, Dusit Xiong, Zehui Fu, Shu Lu, Jie Chongqing University School of Microelectronics and Communication Engineering Chongqing400044 China Nanyang Technological University School of Computer Science and Engineering Singapore Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore Singapore

As the fundamental premise of anti-interference communication, wireless interference identification (WII) has garnered extensive research and yielded substantial results, especially for deep learning (DL)-enabled WII. However, existing studies are conducted typically under the closed-set assumption, whereby the type of interference is known. This aspect poses a challenge in identifying the unknown interference signals under the d-set assumption. To tackle this issue, this paper proposes a multi-task learning-enabled WII (MTL-WII) algorithm in antagonistic environments. Firstly, we generate the semantic feature space of known classes, forming the mapping relationship from the original signal data to the semantic feature space while calculating the semantic center vectors of each known class through multi-task learning. Secondly, we obtain the semantic feature space of the test set incorporating interference signals of known and unknown classes through the established mapping relationships. Subsequently, the similarity between the semantic feature vectors of the interference signals under test and the semantic center vectors of the interference signals within each class gets calculated using the clustering method. Finally, the attributed class is determined by comparing the similarity against a threshold. Nevertheless, the computational complexity of the proposed method limits its applicability across various scenarios. To this end, this paper further proposes a binarized MTL-WII (BMTL-WII) algorithm. By binarizing the semantic spatial generative network in MTL-WII, both the weights and activation of the semantic spatial generative network replace the 32-bit floating-point numbers with 1-bit fixed-point numbers, which can save memory and improve the speed of the operations. Experimental results show that the MTL-WII model achieves an average identification accuracy of 95.4%, with an 89.3% accuracy for unknown interference signals. Compared to the MTL-WII algorithm, th

关键词： Multi-task learning

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Canvil: designerly Adaptation for LLM-Powered User Experiences 25

Canvil: Designerly Adaptation for LLM-Powered User Experienc...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Feng, K. J. Kevin Liao, Q. Vera Xiao, Ziang Wortman Vaughan, Jennifer Zhang, Amy X. McDonald, David W. Human Centered Design & Engineering University of Washington SeattleWA United States Microsoft Research MontrealQC Canada Computer Science Johns Hopkins University BaltimoreMD United States CSE University of Washington SeattleWA United States

ISBN: (纸本)9798400713941

Advancements in large language models (LLMs) are sparking a proliferation of LLM-powered user experiences (UX). In product teams, designers often craft UX to meet user needs, but it is unclear how they engage with LLMs as a novel design material. Through a formative study with 12 designers, we find that designers seek a translational process that enables design requirements to shape and be shaped by LLM behavior, motivating a need for designerly adaptation to facilitate this translation. We then built Canvil, a Figma widget that operationalizes designerly adaptation. We used Canvil as a probe to study designerly adaptation in a group-based design study (6 groups, N = 17), finding that designers constructively iterated on both adaptation approaches and interface designs to enhance end-user interaction with LLMs. Furthermore, designers identified promising collaborative workflows for designerly adaptation. Our work opens new avenues for processes and tools that foreground designers' human-centered expertise when developing LLM-powered applications. © 2025 Copyright held by the owner/author(s).

关键词： User centered design

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Comprehensive Analysis of Machine Learning Techniques for Crack Detection

Comprehensive Analysis of Machine Learning Techniques for Cr...

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International Conference on Technological Innovations in Multidisciplinary engineering and sciences, TIMES 2024

作者： Arpitha, L.M. Kumar, R. Anil Fathima, Zoya Yeshaswini, R. Dhanyashree, G. Roshini, D. M S Ramaiah Institute of Technology Karnataka Bengaluru India Department of Civil Engineering M S Ramaiah Institute of Technology Karnataka Bengaluru India TCSiON Tata Consultancy Services Karnataka Bengaluru India Department of Computer Science and Design BGS College of Engineering and Technology Karnataka Bengaluru India

ISBN: (纸本)9783031837494

Crack detection is critical to the safety and performance of engineering structures including highways, bridges, and buildings. This paper reviews the different types of image processing techniques for crack detection which includes traditional, advanced and machine learning based methods. One of the sixty works chosen and written from 1999–2023. The results indicate that machine learning is preferred simply because it can be more precise, given its automated nature. The paper reviews different methodologies applied, their performances based on the datasets employed as well as limitations governing one approach over another to realize real-time crack detection. It also highlights the need for standardized methods of categorizing and measuring cracks, as well as efficient ways to proceed in many different situations. Specifically, the study addresses potential benefits and limitations of using machine learning in infrastructure management with respect to data-driven approaches linked to big data sets as well as computing costs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Remaining Useful Life Estimation of Lenses for an Ion Beam Etching Tool in Semiconductor Manufacturing Using Deep Convolutional Neural Networks 13

Remaining Useful Life Estimation of Lenses for an Ion Beam E...

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13th International Conference on Electronics, Communications and Networks, CECNet 2023

作者： Wan, Jian McLoone, Seán Department of Mechanical Biomedical and Design Engineering School of Engineering and Technology College of Engineering and Physical Sciences Aston University BirminghamB4 7ET United Kingdom School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast BelfastBT9 5BN United Kingdom

ISBN: (纸本)9781643684802

Maintenance plays a significant role in semiconductor manufacturing as plant yield, factory downtime and operation cost are all closely related to maintenance efficiency. Accordingly, maintenance strategies in semiconductor manufacturing industries are increasingly shifting from traditional preventive maintenance (PM) to more efficient predictive maintenance (PdM). PdM uses manufacturing process data to develop predictive models for remaining useful life (RUL) estimation of key equipment components. Traditional approaches to building predictive models for RUL estimation involve manual selection of features from manufacturing process data. This paper proposes to use deep convolutional neural networks (CNN) for the task of estimating RUL of lenses for an ion beam etch tool in semiconductor manufacturing. The proposed approach has the advantage of automatic feature extraction through the use of convolution and pool filters along the temporal dimension of the optical emission spectroscopy (OES) data from the endpoint detection system. Simulation studies demonstrate the feasibility and the effectiveness of the proposed approach. © 2024 The authors and IOS Press.

关键词： Optical emission spectroscopy

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Ownership Tokenization and Incentive design for Learning-based User-Generated Content

Ownership Tokenization and Incentive Design for Learning-bas...

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2023 IEEE International Conference on Metaverse Computing, Networking and Applications, MetaCom 2023

作者： Zhang, Qinnan Xiong, Zehui Zhu, Jianming Gao, Sheng Yang, Wanting Niyato, Dusit Central University of Finance and Economics School of Information Beijing China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore Jilin University School of Telecommunication Changchun China Nanyang Technological University School of Computer Science and Engineering Singapore

ISBN: (纸本)9798350333336

The Metaverse is established by twining a practical world in a virtual form, where users could become creators of learning-based user-generated content (UGC). However, the digital assets are not owned by the creators, which deviates from the decentralized Metaverse based on blockchain in Web 3.0. The contribution incentive design for UGC still faces the challenges of ownership verification and privacy protection. To this end, we propose minting the learning model into the non-fungible token (NFT) with federated learning (FL) assistance (referred to as FL-NFT), such that users as stakeholders can control the ownership and share the economic value of UGC. Specifically, the users are encouraged to establish a decentralized autonomous organization (DAO) to aggregate local models and mint FL-NFT. We formulate an auction interaction of FL-NFT as imperfect information Stackelberg game (IISG) to optimize the bidding strategies to realize individual rationality. Finally, we conduct simulations to show the effectiveness of the proposed scheme. © 2023 IEEE.

关键词： Blockchain

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Hyperspectral Tensor Completion Using Low-Rank Modeling and Convex Functional Analysis

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IEEE Transactions on Neural Networks and Learning Systems 2024年第8期35卷 10736-10750页

作者： Lin, Chia-Hsiang Liu, Yangrui Chi, Chong-Yung Hsu, Chih-Chung Ren, Hsuan Quek, Tony Q. S. National Cheng Kung University Department of Electrical Engineering Tainan70101 Taiwan National Tsing Hua University Department of Electrical Engineering Hsinchu300044 Taiwan National Cheng Kung University Department of Statistics Tainan70101 Taiwan National Central University Center for Space and Remote Sensing Research Taoyuan320317 Taiwan Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore487372 Singapore National Cheng Kung University Department of Computer Science and Information Engineering Tainan70101 Taiwan

Hyperspectral tensor completion (HTC) for remote sensing, critical for advancing space exploration and other satellite imaging technologies, has drawn considerable attention from recent machine learning community. Hyperspectral image (HSI) contains a wide range of narrowly spaced spectral bands hence forming unique electrical magnetic signatures for distinct materials, and thus plays an irreplaceable role in remote material identification. Nevertheless, remotely acquired HSIs are of low data purity and quite often incompletely observed or corrupted during transmission. Therefore, completing the 3-D hyperspectral tensor, involving two spatial dimensions and one spectral dimension, is a crucial signal processing task for facilitating the subsequent applications. Benchmark HTC methods rely on either supervised learning or nonconvex optimization. As reported in recent machine learning literature, John ellipsoid (JE) in functional analysis is a fundamental topology for effective hyperspectral analysis. We therefore attempt to adopt this key topology in this work, but this induces a dilemma that the computation of JE requires the complete information of the entire HSI tensor that is, however, unavailable under the HTC problem setting. We resolve the dilemma, decouple HTC into convex subproblems ensuring computational efficiency, and show state-of-the-art HTC performances of our algorithm. We also demonstrate that our method has improved the subsequent land cover classification accuracy on the recovered hyperspectral tensor. © 2012 IEEE.

关键词： Convex optimization

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Vision-Based Object Sorting in Dynamic Environments using YOLO for RoboCup ARM Challenge 2023 7

Vision-Based Object Sorting in Dynamic Environments using YO...

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7th IEEE Conference on Information and Communication Technology, CICT 2023

作者： Sahu, Yash Kumar Mittal, Radhika Patel, Deep Maiti, Chayan Sreekumar, M. Indian Institute of Information Technology Design and Manufacturing Centre for AI IoT and Robotics Department of Mechanical Engineering Kancheepuram Chennai India Indian Institute of Information Technology Design and Manufacturing Centre for AI IoT and Robotics Department of Computer Science Kancheepuram Chennai India

ISBN: (纸本)9798350305173

In the intersection of Artificial Intelligence (AI) and Robotics, autonomous object manipulation has emerged as a pivotal research domain, finding applications in sectors as diverse as manufacturing and healthcare. While significant strides have been made in object identification, trajectory planning, and manipulation tasks, these advancements often fall short in real-world, dynamic settings, revealing a critical research gap in algorithmic adaptability and computational efficiency. This paper addresses the gap by applying YOLOv4, a state-of-the-art object detection AI algorithm, within the context of the RoboCup Autonomous Robot Manipulation (ARM) Challenge - a competitive platform designed to simulate real-world complexities in autonomous systems. The research objectives are twofold: firstly, the performance of YOLOv4 in object detection and classification under dynamically changing conditions is rigorously evaluated, employing varied training strategies in both simulated and real-world scenarios. Secondly, the robustness and adaptability of the algorithm in object manipulation tasks are scrutinized and facilitated by the real-world dynamics inherent to the competition. Empirical results demonstrate the algorithm's high accuracy, efficiency, and robustness in dynamic environments, substantiating its broader applicability in the development of autonomous systems. © 2023 IEEE.

关键词： Object detection

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