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IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)

作者： Luke Bassett Maria Apostle Federica Aveta Dept. of Electrical and Computer Engineering Program in Computer Engineering Wentworth Institute of Technology Boston MA USA Dept. of Electrical and Computer Engineering Program in Electrical Engineering Wentworth Institute of Technology Boston MA USA

ISBN: (数字)9798331540906

ISBN: (纸本)9798331540913

Semiconductors play a critical role in almost every sector of electronics. In the United States, semiconductor manufacturing is limited, having very few large-scale automation tools compared to those in Taiwan. Instead, these smaller fabs rely heavily on human interaction with wafer handling. This can add significant points of failure such as scratching, particle defects, and stress on the wafer. The goal of this project is to develop a low-cost, automated wafer handling robotic arm with the ability to automatically align the notch of a wafer, effectively removing an entire movement step in the fabrication process. The robotic arm is able to lift a wafer from a wafer cassette and move it freely in the x-y plane while simultaneously rotating the notch to the correct alignment. The robot design is based on a dual-arm SCARA robot and is fully custom designed and manufactured. The main components of the robot include parallel dual-joint arms, a base mount for the arm motors, an end effector with a pulley belt system for wafer rotation, and a wafer notch detection and alignment system using a computer vision algorithm. Future directions for the project include manufacturing the z-axis base for vertical movement and the rotation base for rotational motion, whose preliminary designs have been already completed.

关键词： Fabrication computer vision Pulleys Semiconductor device manufacture Motors Mobile communication Silicon End effectors Robots Stress

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A Monitoring and Warning System for Hazards in Coal Mines using CNN and Sensor Fusion

A Monitoring and Warning System for Hazards in Coal Mines us...

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International Conference on Advancements in Computational Sciences (ICACS)

作者： Abeera Farooq Alam Maham Tabassum Mubaraka Shabbir Muhammad Ali Raza Farhan Khan Computer Engineering Program Habib University Karachi Pakistan Electrical Engineering Program Habib University Karachi Pakistan

In the realm of coal mining, safety and risk management stand as paramount challenges, underscored by the inherent perils like structural failures and the presence of hazardous gases such as methane. Recognizing the urgent need for enhanced safety measures, this paper introduces a novel system seamlessly integrating structural integrity assessment with gas concentration analysis to ascertain the warning level in coal mines. The core objective revolves around fortifying safety protocols by implementing effective monitoring and early warning systems. This multifaceted approach encompasses addressing potential hazards, preventing accidents, reducing environmental impact, and, above all, safeguarding the well-being of mine workers. The system, intertwining structural monitoring and gas concentration measurement, dynamically generates real-time risk assessments and alerts based on the integrated data. Against the backdrop of these intricate challenges, the research strives to propel substantial advancements in mining safety practices. By harnessing the capabilities of camera imagery, machine learning, and gas sensors, it envisions a future marked by elevated safety standards and a noteworthy reduction in incidents within coal mines.

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Surface Defects Detection of Cylindrical High-Precision Industrial Parts Based on Deep Learning Algorithms: A Review

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Operations Research Forum 2024年第3期5卷 58页

作者： Wei, Li Solihin, Mahmud Iwan Saruchi, Sarah ‘Atifah Astuti, Winda Hong, Lim Wei Kit, Ang Chun School of Computer Science and Technology (School of Software) Guangxi University of Science and Technology Guangxi 545006 China Faculty of Engineering Technology and Built Environment UCSI University Kuala Lumpur 56000 Malaysia Faculty of Manufacturing and Mechatronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah Pahang Pekan 26600 Malaysia Computer Engineering Department Automotive and Robotics Engineering Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia

High-precision cylindrical parts are critical components across various industries including aerospace, automotive, and manufacturing. Since these parts play a pivotal role in the performance and safety of the systems they are integrated into, they are often subject to stringent quality control measures. Defects on the interior and exterior wall surfaces of these cylindrical parts can severely undermine their function, leading to degraded performance, increased wear, and even catastrophic failures in extreme cases. This article aims to comprehensively summarize the task definition, challenges, mainstream methods, public datasets, evaluation metrics, and other aspects of surface defect detection for high-precision cylindrical parts, in order to help researchers quickly grasp this field. Specifically, the background and characteristics of industrial defect detection are first introduced. Owing to the unique geometric features of cylindrical part surfaces, algorithms and equipment for image data acquisition used in surface defect detection are elaborated in detail. This article presents an extensive overview of state-of-the-art surface defect detection techniques designed for high-precision cylindrical components, all rooted in deep learning. The methods are systematically classified into three main categories: fully supervised, unsupervised, and alternative approaches, based on their data labeling strategies. Additionally, the paper conducts a comprehensive analysis within each category, shedding light on their unique strengths, limitations, and practical use cases. Concluding the discussion, the paper provides insights into future development trends and potential research directions in this field that will lead to manufacturing innovation. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Anomaly detection computer vision Deep learning Defect detection High-precision cylindrical parts Image processing Optical illumination

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High impedance fault classification in microgrids using a transformer-based model with time series harmonic synchrophasors under data quality issues

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Neural Computing and Applications 2024年第23期36卷 14017-14034页

作者： Cieslak, Dionatan A. G. Moreto, Miguel Lazzaretti, André E. Macedo-Júnior, José R. PPGEEL - Graduate Program in Electrical Engineering Federal University of Santa Catarina UFSC R. Eng. Agronômico Andrei Cristian Ferreira s/n Santa Catarina Florianópolis88040-900 Brazil CPGEI - Graduate Program in Electrical and Computer Engineering Federal University of Technology - Paraná UTFPR Av. Sete de Setembro 3165 Paraná Curitiba80230-901 Brazil PPGEELT - Graduate Program in Eletrical Engineering Federal University of Uberlândia UFU Av. João Naves de Ávila 2121 Minas Gerais Uberlândia38408-100 Brazil

Recent advances in distribution networks, driven by the integration of renewable energy sources, have spurred the emergence of microgrids, elevating concerns regarded reliability and stability. In this context, precise monitoring of events, particularly those elusive to detection like high-impedance faults (HIFs), becomes imperative. The development of phasor measurement units (PMUs) with their harmonic synchronized measurements has enhanced the monitoring task and fostering the application of synchrophasors even on microgrids. This work introduces a novel method for event classification in microgrids, utilizing combined low-rate PMU data and harmonic synchrophasor time series. Central to our approach is the usage of a state-of-the-art transformer neural network, based on the attention mechanism, to effectively discern HIFs from other faulty and non-fault events. Notably, this methodology accounts for prevalent PMU data quality issues, including noise, missing data, and synchronism errors. Results from real-world HIF data demonstrate a robust performance, with an accuracy rate of approximately 98% in event classification. This harmonic synchrophasor-based strategy showcases promise as an original approach for handling commercial PMU data, offering sufficient robustness for deployment in real-world applications. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Phasor measurement units

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A Multi-trace Surface Integral Equation Solver to Analyze Electromagnetic Field Interactions on Graphene-based Devices

A Multi-trace Surface Integral Equation Solver to Analyze El...

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2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022

作者： Zhao, Ran Chen, Liang Hu, Jun Bagci, Hakan Electrical and Computer Engineering Program Computer Electrical and Mathematical Science and Engineering Division Thuwal23955 Saudi Arabia School of Electronic Science and Engineering Chengdu610056 China

ISBN: (数字)9781665496582

ISBN: (纸本)9781665496582

A multi-trace surface integral equation (MT-SIE) solver is proposed to analyze electromagnetic field interactions on graphene-based devices. This solver decomposes the computation domain into an exterior subdomain (the background medium where the device resides in) and an interior subdomain that represents the device's dielectric substrate. The infinitesimally thin graphene sheet partially covers the interface between these two subdomains. On the graphene surface and the rest of this interface, resistive Robin transmission conditions (formulated for the first time in this work) and the traditional Robin transmission conditions are used to "connect"these two subdomains, respectively. In each subdomain, the electric and magnetic field equations are used as the governing equations. Various numerical examples are presented to demonstrate the accuracy and the applicability of the proposed MT-SIE solver. © 2022 IEEE.

关键词： Graphene

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Survey of Online Exam Proctoring Model to Detect Cheating Behavior based on Face Recognition 8

Survey of Online Exam Proctoring Model to Detect Cheating Be...

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8th International Conference on Systems and Informatics, ICSAI 2022

作者： Rabiha, Sucianna Ghadati Kartowisastro, Iman Herwidiana Setiawan, Reina Budiharto, Widodo Bina Nusantara University Binus Graduate Program - Computer Science. Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Computer Science Department Binus Graduate Program - Computer Science Computer Engineering Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665474481

Exams are an important component of any educational program, including online education. In any test, there is a possibility of cheating, so its detection and prevention is important. This study aims to conduct an in-depth study of the online exam monitoring model approach based on facial recognition used to detect cheating. Based on the inclusion and exclusion criteria designed, 13 selected studies were obtained. From these studies, we conducted further analysis regarding the Face Detection Method, Face Recognition Method, Initial Feature, Behavior Analysis and Evaluation Metrics used in each study so as to provide answers to research questions. the most frequently used Face detection method was Viola-Jones with a presentation of 20%, then CNN and MTCNN with a total presentation of 21%. The most widely used face recognition method in selected studies is CNN and metrics Accuracy is one of the most frequently used evaluations with a percentage of 33%. While the features that are usually used to detect cheating during online exams include facial motion and head pose which occupies the first position. The second is eye movement, then multiple faces gaze estimation and facial expression is in third place. Other features that also play a role in analyzing cheating behavior are mouth detection, facial vector, landmark location, gesture and posture. © 2022 IEEE.

关键词： Face recognition

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Effect of electrolyte anions on the cycle life of a polymer electrode in aqueous batteries

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eScience 2022年第1期2卷 110-115页

作者： Ye Zhang Lihong Zhao Yanliang Liang Xiaojun Wang Yan Yao Department of Electrical and Computer Engineering Materials Science and Engineering ProgramUniversity of HoustonHoustonTX77204USA Texas Center for Superconductivity at the University of Houston HoustonTX77204USA

Redox polymers are a class of high-capacity, low-cost electrode materials for electrochemical energy storage, butthe mechanisms governing their cycling stability are not well understood. Here we investigate the effect of anionson the longevity of a p-dopable polymer through comparing two aqueous zinc-based electrolytes. Galvanostaticcycling studies reveal the polymer has better capacity retention in the presence of triflate anions than that withsulfate anions. Based on electrode microstructural analysis and evolution profiles of the cell stacking pressure, theorigin of capacity decay is ascribed to mechanical fractures induced by volume change of the polymer activematerials during repeated cycling. The volume change of the polymer with the triflate anion is 61% less than thatwith the sulfate anion, resulting in fewer cracks in the electrodes. The difference is related to the different anionsolvation structures—the triflate anion has fewer solvated water molecules compared with the sulfate anion,leading to smaller volume expansion. This work highlights that anions with low solvation degree are preferablefor long-term cycling.

关键词： Aqueous batteries Polymer electrode Anions Volume chang

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From Pictures to Prompts: Analyzing and Reconstructing AI-Generated Images with BLIP2 and CLIP 24

From Pictures to Prompts: Analyzing and Reconstructing AI-Ge...

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8th International Conference on Natural Language Processing and Information Retrieval, NLPIR 2024

作者： Kuo, Yi-Hsuan Chang, Hsien-Tsung Department of Computer Science and Information Engineering Chang Gung University Taoyuan China Bachelor Program in Artificial Intelligence Chang Gung University Taoyuan China

ISBN: (纸本)9798400717383

This study presents a novel approach to analyzing and reconstructing AI-generated images using BLIP2 and CLIP models, focusing on a dataset of 268,000 Midjourney-generated images and prompts. We introduce a multi-level feature classification system encompassing 11 categories and 1,100 features, and employ BLIP2 for descriptive text generation and CLIP for feature similarity analysis. Our methodology involves image pre-processing, custom feature extraction, and image reconstruction under various conditions. The effectiveness of our approach is demonstrated through similarity scores between original and reconstructed images, ranging from 68.15 to 71.25 across different feature extraction conditions. This research contributes to the fields of natural language processing and information retrieval by offering insights into AI-generated image analysis, proposing new methods for feature extraction and reconstruction, and suggesting avenues for improving image generation models and multi-modal understanding. © 2024 Copyright held by the owner/author(s).

关键词： Sentiment analysis

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Spectral Recursive Approach to the Time-Domain Inverse Kernel

Spectral Recursive Approach to the Time-Domain Inverse Kerne...

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2023 United States National Committee of URSI National Radio Science Meeting, USNC-URSI NRSM 2023

作者： Liebau, Thomas J. Manteghi, Majid Virginia Tech Department of Electrical and Computer Engineering BlacksburgVA24060 United States Virginia Tech Engineering Mechanics Program BlacksburgVA24060 United States

ISBN: (纸本)9781946815187

We study the response of the infinite strip to an H-polarized impulsive line source. The infinite-plane solution, valid for times such that the influence of the source has yet to propagate to the boundary, is used to express the inverse kernel explicitly in terms of the effects of truncation. By extracting the immediate response of each boundary to the source, we obtain a form of the inverse kernel that separates the early-time response due to specular reflections, and the late-time response associated with resonances. © 2023 USNC-URSI.

关键词： Strips Reflection Kernel Time-domain analysis

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A Real-time Affordance-based Object Pose Estimation Approach for Robotic Grasp Pose Estimation

A Real-time Affordance-based Object Pose Estimation Approach...

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2023 International Conference on System Science and engineering, ICSSE 2023

作者： Wong, Shang-Wen Chiu, Yu-Chen Tsai, Chi-Yi Tamkang University Doctoral Program in Robotics College of Engineering New Taipei City Taiwan Tamkang University Department of Electrical and Computer Engineering New Taipei City Taiwan

ISBN: (纸本)9798350322941

This paper proposes a pose estimation system for robot grasping based on a novel Object Affordance Detection and Segmentation (OADS) network. The proposed system consists of four modules: (1) OADS network;(2) point cloud extraction;(3) object pose estimation;(4) grasp pose estimation. Based on the OADS network, the proposed system achieves affordance-based object pose estimation results. The proposed grasp pose estimation system is evaluated on a laboratory-made dual-arm robot. Experimental results show that the proposed system achieves high detection rate and high accuracy in affordance detection and segmentation tasks, leading to a high success rate in object grasping tasks with lab-made dual-arm robot. © 2023 IEEE.

关键词： Object detection

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