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A Novel Method of Evaluating the Harmonic Power Analyzer's Performance for PC-Based Testing and Measurement Applications Related to Electromagnetic Compatibility 2

A Novel Method of Evaluating the Harmonic Power Analyzer's P...

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2nd International Conference on computing and Data Analytics, ICCDA 2024

作者： Adeline Sneha, J. Wilfred Blessing, N.R. Palarimath, Suresh Poorana Senthilkumar, S. Manigandansekar Sutherlin Subitha, G. School of Computing Asia Pacific University of Technology & Innovation Kuala Lumpur Malaysia College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman College of Computing and Information Sciences-Salalah University of Technology and Applied Sciences Oman Dr. N. G. P Arts and Science College Department of Computer Applications Tamil Nadu Coimbatore India Newyork University Abudhabi United Arab Emirates Stella Mary's College of Engineering Department of Computer Science and Engineering Tamil Nadu Kanyakumari India

ISBN: (纸本)9798331542573

This paper discusses the methods for performance analysis of a PC-based harmonic power analyzer used in EMC testing and measuring applications. According to IEC 61000-3-2, this is an easy-to-use, reasonably priced way to assess how well a harmonics emission measurement system is performing in real time. There is no mention of any alternative approach or calibration method to verify the performance analysis in the IEC 61000-3-2 standard. Different vendor equipment is used to create a single harmonic emission monitoring setup. Thus, getting calibrating services is a highly difficult task that also requires a significant financial investment. Using this straightforward, low-cost analysis technique, it is possible to monitor the operation of the measurement setup locally and address all of these problems. Overall, the paper provides a comprehensive overview of the new approach to performance analysis of a PC-based harmonics power analyzer for EMC test and measurement applications, emphasizing the importance of a simple, cost-effective, and reliable method for maintaining measurement system performance. © 2024 IEEE.

关键词： Cost effectiveness

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Oracle Character Recognition Based on Attention Enhancement and Multi-level Feature Fusion 27th

Oracle Character Recognition Based on Attention Enhancement...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Han, Zhiwang Yadikar, Nurbiya Xu, Xuebin Aysa, Alimjan Ubul, Kurban School of Computer Science and Technology Xinjiang University Urumqi830046 China Xinjiang Multilingual Information Technology Key Laboratory Xinjiang University Urumqi830046 China Joint International Research Laboratory of Silk Road Multilingual Cognitive Computing Xinjiang University Urumqi830046 China

ISBN: (纸本)9783031781186

Oracle bone characters represent the earliest inscriptions in China. Recognizing and deciphering these characters is significant. Despite some progress made by recent methods, their recognition accuracy remains limited by two major issues: 1) how to focus on characters features within complex background noise images, and 2) how to effectively fuse shallow detail information with deep semantic information. To address these issues, we propose a novel deep learning model called Character Feature Enhancement Network (CFE-Net). The model consists of two key components: Character Feature Enhancement (CFE) and Adaptive Multi-level Classifier Fusion (AMCF). Specifically, CFE utilizes the Spatial Focus Attention Module (SFAM) to focus on extracting foreground character features and suppressing background noise, thereby significantly enhancing high-level semantic representation capabilities. AMCF, on the other hand, achieves multi-level feature fusion by adaptively fusing the outputs of different classifiers, effectively avoiding information loss or interference that simple fusion strategies might cause. We evaluated the CFE-Net on two rubbing oracle bone characters benchmark datasets, OBC306 and Oracle-MNIST. The experimental results demonstrate that CFE-Net significantly outperforms several existing methods in terms of Top-1 accuracy, establishing it as the new state-of-the-art. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantics

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Analysis of Labanotation generation based on motion capture data 5

Analysis of Labanotation generation based on motion capture ...

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5th International Conference on Artificial Intelligence and Computer science, AICS 2023

作者： Chen, Boran Wang, Jiaji Miao, Zhenjiang Li, Jianpo Yang, Shengchun Jilin Electric Power Big Data Intelligent Processing Engineering Technology Research Center School of Computing Northeast Electric Power University Jilin Jilin132012 China Institute of Information Science School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9781510668621

Labanotation is an approach to record human movement with different distributions in space that have different representations of symbols. Motion capture is a technique that records the trajectory of an object in the time domain by tracking the motion of key points, observed using different sensors, and recorded in files. This paper is to manually mark the Labanotation based on 3D human motion capture data, to divide the 3D human motion into gaits and poses, and to record and represent different human movements and positions in space in the notation. This is an exploration of motion capture data to generate Labanotation, while also contributing to the preservation of folk-art forms. © 2023 SPIE.

关键词： Time domain analysis

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Dynamic Task Scheduling for Coordinated Truck-Drone Parcel Delivery: A Hybrid Genetic Tabu Algorithm Approach 22

Dynamic Task Scheduling for Coordinated Truck-Drone Parcel D...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Li, Ruitai Yuan, Quan Liao, Tongxin Luo, Lijun Hu, Menglan Lai, Pan Zhang, Xiao South-Central Minzu University College of Computer Science China State Ethnic Affairs Commission Key Laboratory of Cyber-Physical Fusion Intelligent Computing China Huazhong University of Science and Technology School of Electronic Information and Communications China Hubei Key Laboratory of Smart Internet Technology China

ISBN: (纸本)9798331509712

With the rapid growth of the on-demand economy, logistics companies and merchants increasingly struggle to meet customer demands in dynamic and uncertain conditions. This paper studies the coordinated delivery of parcels by trucks and drones under such demands, proposing a Dynamic Task Scheduling Algorithm based on Hybrid Genetic Tabu algorithm (DTSAGT) for route optimization. Simulating dynamic customer demands with a Poisson distribution and statistical methods, the algorithm addresses timeliness issues due to variations in customer needs. It optimizes drone path planning and task allocation considering drone endurance and payload limits to minimize total delivery time. The algorithm includes three steps: initial solution construction, iterative optimization, and dynamic operations. Experimental results show that DTSAGT reduces the total service time by 15.05%, 34.13%, and 35.71% on average compared to the baseline algorithms. This paper's contribution is the combination of hybrid genetic and tabu search algorithms applied to dynamic task scheduling in truck-drone delivery, enhancing logistics efficiency. © 2024 IEEE.

关键词： Poisson distribution

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An Ensemble Framework for Dropout Prediction in Online Learning

An Ensemble Framework for Dropout Prediction in Online Learn...

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2022 IEEE International Conference on Knowledge Engineering and Communication Systems, ICKES 2022

作者： Srinivasan, Sruthi Dewan, M. Ali Akber School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computing and Information Systems Athabasca University Faculty of Sceinece and Technology AB Canada

ISBN: (纸本)9781665456371

Online learning has gained traction over recent years, especially since online education has become more widespread. However, it comes with its own set of challenges of which high dropout is still a major one. Identifying at-risk learners at an early stage is pivotal to offering personalized attention that can potentially prevent them from dropping out from the online courses. This work proposes two methods to analyze students' progress in an online course and subsequently identify dropout prone students. The first method performs fusion of course activity features by concatenating previous weeks' features before training. The second method extracts course activity features from the start date of the courses to a current week instead of concatenating as the first method does. A set of machine learning models and an ensemble framework were trained and tested on these two types of feature sets. On evaluating the models, the benchmark dataset KDDCup15 has been used, where the first method yielded an F1-score 91% while the second method yielded a score 92%. It was observed that both feature fusion methods produce comparable results although we expected that the concatenation of the features over time would produce better results. We also found that using features over a longer duration of time can help in achieving better performance. Further, ensemble model consistently outperformed the base classifiers. © 2022 IEEE.

关键词： Students

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RPF-ELD: Regional Prior Fusion using Early and Late Distillation for Breast Cancer Recognition in Ultrasound Images

RPF-ELD: Regional Prior Fusion using Early and Late Distilla...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, Haosen Zhang, Gengyuan Zhao, Yingnan Lai, Fang Cui, Wenwei Xue, Jiexiao Wang, Qihang Zhang, Hao Lin, Yi Beijing Jiaotong University School of Computer Science and Information Technology Beijing China Sun Yat-Sen University School of Computer Science and Engineering Guangzhou China Harbin Engineering University School of Computer Science and Technology Harbin China University of Denver Department of Computer Science Denver United States University of York Wentworth College York United Kingdom Institute of Heilongjiang The Second Surveying and Mapping Harbin China Southeast University School of Information Science and Engineering Nanjing China Harbin Institute of Technology Faculty of Computing Harbin China Harbin Medical University School of Interdisciplinary Medicine and Engineering Harbin China

ISBN: (纸本)9798350386226

Breast cancer is one of the main factors responsible for the deaths of women worldwide. Ultrasound imaging is a key method for early detection of breast cancer, which can help patients gain valuable treatment time and improve their chances of survival. The computer-aided system of breast cancer recognition has started to receive attention due to the lack of experienced sonographers. Presently, most breast cancer recognition methods typically suffer from uncertain locations and proportions of tumor regions in ultrasound images. In this paper, we propose a novel Regional Prior Fusion framework using Early and Late Distillation (RPF-ELD), inspired by the knowledge distillation of the teacher-student framework, for breast cancer recognition in ultrasound images. Firstly, to enhance the concentration of the tumor regions, a high-performing prior-fused model is trained as the teacher model using ultrasound images with the corresponding regional prior information. Next, a diagnostic model is trained as the student model under the prior-fused model distillation using early and late features to implicitly obtain the regional prior knowledge. Finally, the diagnostic model recognizes the categories of breast cancer from only ultrasound images using the experience from distilled prior knowledge. Two publicly released datasets are used to evaluate the proposed RPF-ELD framework. Experimental results demonstrate that the proposed RPF-ELD surpasses current state-of-the-art methods. © 2024 IEEE.

关键词： Ultrasonic imaging

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Fast Image matting based on matting pyramids and surrogate models 2

Fast Image matting based on matting pyramids and surrogate m...

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2nd International Conference on Robotics, Artificial Intelligence and Intelligent Control, RAIIC 2023

作者： Liang, Yihui Gou, Hongshan Liu, Guisong Huang, Sibo University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China University of Electronic Science and Technology of China Zhongshan Institute School of Computer Zhongshan China Guiyang Institute of Humanities and Technology College of Big Data and Information Engineering Guiyang China Southwestern University of Finance and Economics School of Computing and Artificial Intelligence Chengdu China Huizhou University Network and Information Centre Huizhou China

ISBN: (纸本)9798350328004

Image matting is a foundation task in the field of computer vision. Pixel-pair-optimization-based image matting methods have attracted lots of attention due to their distinct advantages on parallelization and handling spatially disconnected foregrounds. However, these methods struggle when the computing time is limited. To address this issue, this study presents the image matting method based on matting pyramid and surrogate model (IMMPSM). IMMPSM speeds up the process of pixel pair optimization by replaces the evolutionary algorithm in the pyramid matting framework, which consumes a lot of computing time, with the efficient surrogate-model-based method. The constructed surrogate model is reused by the presented surrogate model share strategy to save computing time. Specifically, the constructed surrogate model is shared with the pixel pair optimization subproblem adjacent to current subproblem avoiding repeated computation on the surrogate model construction. Extensive experimental results show that IMMPSM can provide acceptable alpha mattes even under limited computing time. © 2023 IEEE.

关键词： Pixels

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Unlocking the Capabilities of Thought: A Reasoning Boundary Framework to Quantify and Optimize Chain-of-Thought 38

Unlocking the Capabilities of Thought: A Reasoning Boundary ...

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38th Conference on Neural information Processing Systems, NeurIPS 2024

作者： Chen, Qiguang Qin, Libo Wang, Jiaqi Zhou, Jinxuan Che, Wanxiang Research Center for Social Computing and Information Retrieval China Harbin Institute of Technology China School of Computer Science and Engineering Central South University China The Chinese University of Hong Kong Hong Kong

Chain-of-Thought (CoT) reasoning has emerged as a promising approach for enhancing the performance of large language models (LLMs) on complex reasoning tasks. Recently, a series of studies attempt to explain the mechanisms underlying CoT, aiming to deepen the understanding of its efficacy. Nevertheless, the existing research faces two major challenges: (1) a lack of quantitative metrics to assess CoT capabilities and (2) a dearth of guidance on optimizing CoT performance. Motivated by this, in this work, we introduce a novel reasoning boundary framework (RBF) to address these challenges. To solve the lack of quantification, we first define a reasoning boundary (RB) to quantify the upper-bound of CoT and establish a combination law for RB, enabling a practical quantitative approach applicable to various real-world CoT tasks. To address the lack of optimization, we propose three categories of RBs. We further optimize these categories with combination laws focused on RB promotion and reasoning path optimization for CoT improvement. Through extensive experiments on 27 models and 5 tasks, the study validates the existence and rationality of the proposed framework. Furthermore, it explains the effectiveness of 10 CoT strategies and guides optimization from two perspectives. We hope this work can provide a comprehensive understanding of the boundaries and optimization strategies for reasoning in LLMs. Our code and data are available at https://***/LightChen233/reasoning-boundary. © 2024 Neural information processing systems foundation. All rights reserved.

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Efficiency Management of Electronic Communication information Storage in Multithreaded Networks 3

Efficiency Management of Electronic Communication Informatio...

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3rd International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy, SPIoT2023

作者： Yang, Xinsheng Qi, Yongjun Zhang, Shukun School of Electrical and Information Engineering Guangdong Baiyun University Guangdong Guangzhou510450 China Faculty of Megadata and Computing Guangdong Baiyun University Guangdong Guangzhou510450 China School of Information and Communication Technology Mongolian University of Science and Technology Bayanzurkh district Ulaanbaatar13341 Mongolia

The emergence of the Internet has brought revolutionary changes to modern information technology. The development of electronic communication information storage technology facilitates people to access information anytime and anywhere, effectively achieving resource sharing. However, traditional methods scatter communication information to all nodes, but neglect the extraction of electronic communication information association features, resulting in low information storage efficiency. This article analyzes the efficiency management of electronic communication information storage in multi-threaded networks and sets up a control experiment. Comparing the method proposed in this article with traditional methods, this study focuses on five aspects: information redundancy storage, information space utilization, productivity, data accuracy, and data transmission efficiency. It is concluded that the average information redundancy storage capacity of traditional electronic information storage methods is 8.35 x 107 T which is 6 x 107 T higher than using multi wire network electronic communication information storage methods. It indicates that traditional redundant information occupies a large amount of storage space, and its storage value exceeds the upper limit of electronic communication information storage. This means that the stored information may be overwritten by redundant information, resulting in the loss of stored information. The experimental results show that using the method proposed in this paper for electronic communication information storage results in a high distribution density of storage space and a significant improvement in information storage efficiency. © 2023 The Authors. Published by Elsevier B.V.

关键词： Storage efficiency

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CurvDrop: A Ricci Curvature Based Approach to Prevent Graph Neural Networks from Over-Smoothing and Over-Squashing 23

CurvDrop: A Ricci Curvature Based Approach to Prevent Graph ...

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32nd ACM World Wide Web Conference, WWW 2023

作者： Liu, Yang Zhou, Chuan Pan, Shirui Wu, Jia Li, Zhao Chen, Hongyang Zhang, Peng Academy of Mathematics and Systems Science Chinese Academy of Sciences China Amss Chinese Academy of Science School of Cyber Security University of Chinese Academy of Science China School of Information and Communication Technology Griffith University Australia School of Computing Macquarie University Australia Hangzhou link2do Technology China Research Center for Graph Computing Zhejiang Lab China Cyberspace Institute of Advanced Technology Guangzhou University China

ISBN: (纸本)9781450394161

Graph neural networks (GNNs) are powerful models to handle graph data and can achieve state-of-the-art in many critical tasks including node classification and link prediction. However, existing graph neural networks still face both challenges of over-smoothing and over-squashing based on previous literature. To this end, we propose a new Curvature-based topology-aware Dropout sampling technique named CurvDrop, in which we integrate the Discrete Ricci Curvature into graph neural networks to enable more expressive graph models. Also, this work can improve graph neural networks by quantifying connections in graphs and using structural information such as community structures in graphs. As a result, our method can tackle the both challenges of over-smoothing and over-squashing with theoretical justification. Also, numerous experiments on public datasets show the effectiveness and robustness of our proposed method. The code and data are released in https://***/liu-yang-maker/Curvature-based-Dropout. © 2023 ACM.

关键词： Graph neural networks

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