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2022 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2022

作者： Butola, Rajat Li, Yiming Kola, Sekhar Reddy National Yang-Ming Chiao Tung University Parallel and Scientific Computing Laboratory Hsinchu300 Taiwan National Yang-Ming Chiao Tung University Electrical Engineering and Computer Science International Graduate Program Hsinchu300 Taiwan National Yang-Ming Chiao Tung University Institute of Communications Engineering Hsinchu300 Taiwan National Yang-Ming Chiao Tung University Department of Electrical Engineering and Computer Engineering Hsinchu300 Taiwan National Yang-Ming Chiao Tung University Institute of Biomedical Engineering Hsinchu300 Taiwan Center for mmWave Smart Radar System and Technologies National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300 Taiwan

ISBN: (纸本)9781665409230

In this paper, for the first time, deep learning (DL) based artificial neural network (ANN) is applied to model the effects of various random variations: work function fluctuation, random dopant fluctuation, and interface trap fluctuation, on gate-all-around silicon nanosheet MOSFETs. The number of fluctuations for each source variation is used as input features and their effects on devices of interest are studied qualitatively and quantitatively. The key figures of merit (FoM) are also extracted accurately from the transfer characteristics, which shows the competency of the ANN model in the domain of device modeling. © 2022 IEEE.

关键词： Nanosheets

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Assisting Visually Impaired People using Autonomous Navigation System and computer Vision for Grocery Shopping

Assisting Visually Impaired People using Autonomous Navigati...

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2022 IEEE International Conference on Electro Information Technology, eIT 2022

作者： Miralles, Patricia Sanchez Gonzalez, Laura Fernandez Yu, Xinrui Saniie, Jafar Illinois Institute of Technology Embedded Computing and Signal Processing Department of Electrical and Computer Engineering Research Laboratory ChicagoIL60616 United States Universidad Politécnica de Madrid E.T.S.I. Industriales Madrid Spain Universidad Politécnica de Madrid E.T.S.I. Telecommunications Madrid Spain

ISBN: (纸本)9781665480093

Visually impaired people face lots of challenges when doing daily tasks like grocery shopping. Hence, a grocery shopping navigation assistant that covers the shopping preparation, the route creation, the navigation through the store and the product identification has been designed. In this paper we present the design flow of a novel system using the Internet of Things (IoT) and computer Vision (CV) to assist people with visual impairment (PVI) for grocery shopping. The system starts with assisting the user creating his shopping list using the personal smart phone and Speech-to-Text (STT). Then, the system accesses the database of grocery store to optimizes the route for locating the items in the shopping list with minimal time and effort. Once the route is created and the user is in the store, the smart phone physical navigation system starts using landmarks-QR codes and using Text-to-Speech (TTS) for guidance. When the user is correctly placed, the product recognition starts and TTS is used to fetch the product. © 2022 IEEE.

关键词： computer vision

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Intelligent evaluation system for new energy vehicles based on sentiment analysis: An MG-PL-3WD method

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engineering Applications of Artificial Intelligence 2024年第PartE期133卷

作者： Zhang, Chao Wen, Qifei Li, Deyu Sangaiah, Arun Kumar Lin, Mingwei School of Computer and Information Technology Shanxi University Taiyuan030006 China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Taiyuan030006 China International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Yunlin64002 Taiwan Department of Electrical and Computer Engineering Lebanese American University Byblos1102-2801 Lebanon College of Computer and Cyber Security Fujian Normal University Fuzhou350117 China

As a critical strategic tool within the framework of the digital economy (DE), artificial intelligence (AI) plays a crucial role in realizing an AI-assisted economy. This novel paradigm has gained widespread acceptance from governments globally, positioning itself as a cornerstone in the practical implementation of the DE. The increasing reliance on digital means for economic transformation, the treatment of data as a critical resource, and the integration of information technologies highlight the synergy between AI and the DE. Against this backdrop, online reviews and SA play a pivotal role, offering rich data streams continually emerging on various platforms. Therefore, this article utilizes a probabilistic linguistic term set (PLTS) to establish a multi-granularity probabilistic linguistic (MG-PL) information system. This approach, through sentiment analysis (SA), enables quantitative analysis of user emotional expressions. The integration of these technological aspects holds the promise of enhancing decision-making processes, facilitating the intellectualization of economic activities, and addressing existing challenges in the application of AI within the DE domain. Initially, SA is conducted on text-based online comment data, forming a probabilistic linguistic incomplete information system. The maximum similarity method is employed to establish a probabilistic linguistic complete information system. Subsequently, an adjustable MG-PL decision-theoretic rough set (DTRS) is developed, followed by the establishment of a three-way group decision model using three-way decisions (3WD) and a directed graph. The model's applicability, stability, and feasibility are demonstrated via a case study centered on the acquisition of new energy vehicles (NEVs). © 2024 Elsevier Ltd

关键词： Sentiment analysis

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Federated Multimedia Recommendation Systems with Privacy Protection

Federated Multimedia Recommendation Systems with Privacy Pro...

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IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Shih Yu Chang Hsiao-Chun Wu Kun Yan Scott Chih-Hao Huang Yiyan Wu Department of Applied Data Science San Jose State University San Jose CA USA School of Electrical Engineering and Computer Science Louisiana State University Baton Rouge LA USA Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin University of Electronic Technology Guilin Guangxi China Department of Information and Telecommunication Guilin University of Electronic Technology Guilin Guangxi China Department of Electrical Engineering National Tsinghua University Hsinchu Taiwan

ISBN: (数字)9798350364262

ISBN: (纸本)9798350364279

In this big data era, distributed machine learning and global data pooling have become quite critical in practical implementation of any multimedia data-science technique. The emerging federated multimedia recommendation systems (FeMRSs) offer a promising approach to address the need of personalized recommendations subject to both users’ privacy and data security. However, there exists no quantitative method for assessing the discrepancy between the large original rating matrix stored on the server and the dimensionality-reduced rating matrix produced on each client’s equipment. Meanwhile, there lacks a systematic means to evaluate the differential privacy (DP), which is a critical security measure in many distributed systems, particularly when sensitive data are processed. In this work, we first introduce a novel personalized dimensionalityreduction algorithm utilizing the matrix sketching technique. This new algorithm effectively control the difference between the original rating matrix on the server and the dimensionality-reduced rating matrix on each client’s equipment. Moreover, a randomized DP matrix factorization algorithm is designed to be executed at each client’s equipment. The theoretical proof is also carried out to show how much DP can be attained by use of the aforementioned randomized DP matrix factorization algorithm. Finally, extensive numerical studies are presented to evaluate the effectiveness of our proposed novel algorithms using both simulated and real datasets for building the FeMRSs.

关键词： Dimensionality reduction Digital multimedia broadcasting Privacy Systematics Distributed databases Filtering algorithms Servers

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Abstraction-Based Verification of Approximate Pre-Opacity for Control Systems

arXiv

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arXiv 2022年

作者： Hou, Junyao Liu, Siyuan Yin, Xiang Zamani, Majid Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University China Department of Electrical and Computer Engineering Technical University of Munich Germany Department of Computer Science LMU Munich Germany Department of Computer Science University of Colorado Boulder United States

In this paper, we consider the problem of verifying pre-opacity for discrete-time control systems. Preopacity is an important information-flow security property that secures the intention of a system to execute some secret behaviors in the future. Existing works on pre-opacity only consider non-metric discrete systems, where it is assumed that intruders can distinguish different output behaviors precisely. However, for continuous-space control systems whose output sets are equipped with metrics (which is the case for most real-world applications), it is too restrictive to assume precise measurements from outside observers. In this paper, we first introduce a concept of approximate pre-opacity by capturing the security level of control systems with respect to the measurement precision of the intruder. Based on this new notion of pre-opacity, we propose a verification approach for continuous-space control systems by leveraging abstraction-based techniques. In particular, a new concept of approximate pre-opacity preserving simulation relation is introduced to characterize the distance between two systems in terms of preserving pre-opacity. This new system relation allows us to verify pre-opacity of complex continuous-space control systems using their finite abstractions. We also present a method to construct pre-opacity preserving finite abstractions for a class of discrete-time control systems under certain stability assumptions. Copyright © 2022, The Authors. All rights reserved.

关键词： Opacity

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Recent Advances in Fibrous Materials for Hydroelectricity Generation

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Nano-Micro Letters 2025年第2期17卷 109-133页

作者： Can Ge Duo Xu Xiao Feng Xing Yang Zheheng Song Yuhang Song Jingyu Chen Yingcun Liu Chong Gao Yong Du Zhe Sun Weilin Xu Jian Fang College of Textile and Clothing Engineering Soochow UniversitySuzhou 215123People’s Republic of China National Engineering Laboratory for Modern Silk Soochow UniversitySuzhou 215123People’s Republic of China State Key Laboratory of New Textile Materials and Advanced Processing Technologies Wuhan Textile UniversityWuhan 430200People’s Republic of China Institute of Functional Nano&Soft Materials(FUNSOM) Soochow UniversitySuzhou 215123People’s Republic of China Department of Electrical and Computer Engineering University of MassachusettsAmherstMA 01003USA Department of Materials Imperial College LondonSouth Kensington CampusLondon SW72AZUK College of Textile Science and Engineering Zhejiang Sci-Tech UniversityHangzhou 310018People’s Republic of China School of Materials Science and Engineering Shanghai Institute of Technology100 Haiquan RoadShanghai 201418People’s Republic of China

Depleting fossil energy sources and conventional polluting power generation pose a threat to sustainable *** generation from ubiquitous and spontaneous phase transitions between liquid and gaseous water has been considered a promising strategy for mitigating the energy *** materials with unique flexibility,processability,multifunctionality,and practicability have been widely applied for fibrous materials-based hydroelectricity generation(FHG).In this review,the power generation mechanisms,design principles,and electricity enhancement factors of FHG are first ***,the fabrication strategies and characteristics of varied constructions including 1D fiber,1D yarn,2D fabric,2D membrane,3D fibrous framework,and 3D fibrous gel are ***,the advanced functions of FHG during water harvesting,proton dissociation,ion separation,and charge accumulation processes are analyzed in ***,the potential applications including power supply,energy storage,electrical sensor,and information expression are also ***,some existing challenges are considered and prospects for future development are sincerely proposed.

关键词： Hydroelectricity Fibrous material Streaming potential Ion diffusion

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Detection of White Matter Hyperintensities in Magnetic Resonance Imaging by Hyperspectral Subpixel Detection

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IEEE Access 2024年 12卷 111992-112007页

作者： Yung-Chieh Chang Chein-I Chang Yen-Chieh Ouyang Jyh-Wen Chai Wen-Hsien Chen Kuan-Jung Pan Hsin-Che Wang Clayton Chi-Chang Chen Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Department of Medical Imaging and Radiological Sciences Central Taiwan University of Science and Technology Taichung Taiwan Department of Computer Science and Electrical Engineering Remote Sensing Signal and Image Processing Laboratory University of Maryland Baltimore County Baltimore MD USA Department of Electrical Engineering National Cheng Kung University Tainan Taiwan Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung Taiwan College of Medicine China Medical University Taichung Taiwan Department of Industrial Engineering and Enterprise Information Tunghai University Taichung Taiwan Department of Medical Research Center for Quantitative Imaging in Medicine (CQUIM) Taichung Veterans General Hospital Taichung Taiwan Department of Medical Imaging China Medical University Hospital Taichung Taiwan

White matter hyperintensities (WMHs) are lesion in brain magnetic resonance images generally associated with Alzheimer’s disease (AD) and cognitive decline. Finding WMHs of AD poses a great challenge for diagnosis. This paper interprets a brain MR image as a hyperspectral image so that a well stablished hyperspectral subpixel detection algorithm, constrained energy minimization (CEM), is applicable to solving the WMHs detection problem at mixed pixel and subpixel level. To resolve nonlinear mixing in detecting WMHs nearby boundaries, a nonlinear CEM, called kernel CEM (KCEM) is also developed. Since CEM is a hyperspectral technique without taking spatial correlation into account, CEM was also extended to iterative CEM (ICEM) by including spatial filters to capture spatial information for WMHs detection. This paper combines ICEM and KCEM to derive a new WHMs detection algorithm, iterative KCEM (IKCEM) to improve ICEM and KCEM on WMHs detection. To evaluate the WMHs detection performance, two criteria, Dice similarity index (DSI) and 3D ROC analysis are used as evaluation tools. In order to show the superiority of IKCEM, two commonly used software packages, statistical parametric mapping (SPM)-based algorithms, SPM-lesion growth algorithm (SPM-LGA) and SPM-lesion prediction algorithm (SPM-LPA) are implemented for validation and comparison.

关键词： Hyperspectral imaging Magnetic resonance imaging Iterative methods Medical diagnostic imaging White matter Manuals Energy management Neuroimaging Brain modeling Alzheimer's disease Energy measurement

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Fast Particle Swarm optimization for Balanced Clustering 11

Fast Particle Swarm optimization for Balanced Clustering

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11th International Conference on Intelligent Control and Information processing, ICICIP 2021

作者： Zhang, Meng Xiao, Yao Song, Xiaoling Dai, Xiangguang Zhang, Nian Chongqing Three Gorges University Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education Chongqing40044 China University of the District of Columbia Department of Electrical and Computer Engineering WashingtonDC20008 United States

ISBN: (纸本)9781665425155

There are balanced priorities in various engineering fields (e.g. medicine, statistics, artificial intelligence, and economics, etc.). Some clustering algorithms cannot maintain the natural balanced structure of data. This paper proposes a soft-balanced clustering framework, which can achieve a balanced clustering for each cluster. The model can be formulated d as a mixed-integer optimization problem. We transform the problem into several subproblems and utilize PSO to search the global solution. Experiments show that the proposed algorithm can achieve satisfactory clustering results than other clustering algorithms. © 2021 IEEE.

关键词： Clustering algorithms

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WaterTS: Integrating Enhanced Transformer, Sliding Block, and Channel Independence for Long-term Water Quality Prediction

WaterTS: Integrating Enhanced Transformer, Sliding Block, an...

引用

IEEE International Conference on Automation Science and engineering (CASE)

作者： Jing Bi Lifeng Xu Ziqi Wang Haitao Yuan Shichao Chen Mu Gu MengChu Zhou Faculty of Information Technology Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and Beijing Huairou Academy of Parallel Sensing Beijing China Beijing Aerospace Smart Manufacturing Technology Development Co. Ltd Beijing China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Nowadays, the deterioration of water resources leads to negative ecological impacts. To effectively inhibit the deterioration of water resources, a water quality prediction model based on enhanced transformer, sliding block, and channel independence (WaterTS) is proposed by comprehensively analyzing the indicators of water resources and making long-term predictions of the dissolved oxygen index. WaterTS adopts a sliding block method to extract the short-term temporal features of the water quality series and combine them with channel independence to make independent predictions of multi-featured data. Moreover, it upgrades the internal encoder structure of the transformer and improves the attention mechanism to Probsparse-attention and Auto-Correlation to speed up the prediction speed. Furthermore, Post LayerNormal is adjusted to Pre LayerNormal, which makes the training gradient more stable and enhances the accuracy of predictions. Experiments are conducted using real-world water environment data, and comparison results with state-of-the-art prediction models show that the WaterTS achieves accurate predictions on both short-term and long-term water quality data.

关键词： Training Accuracy Biological system modeling Time series analysis Water quality Predictive models Transformers Feature extraction Real-time systems Water resources

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An Efficient Dynamic Resource Allocation Framework for Evolutionary Bilevel Optimization

arXiv

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arXiv 2024年

作者： Xu, Dejun Ye, Kai Zheng, Zimo Zhou, Tao Yen, Gary G. Jiang, Min Department of Artificial Intelligence School of Informatics Xiamen University Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen361005 China School of Electrical and Computer Engineering Oklahoma State University StillwaterOK74075 United States

Bilevel optimization problems are characterized by an interactive hierarchical structure, where the upper level seeks to optimize its strategy while simultaneously considering the response of the lower level. Evolutionary algorithms are commonly used to solve complex bilevel problems in practical scenarios, but they face significant resource consumption challenges due to the nested structure imposed by the implicit lower-level optimality condition. This challenge becomes even more pronounced as problem dimensions increase. Although recent methods have enhanced bilevel convergence through task-level knowledge sharing, further efficiency improvements are still hindered by redundant lower-level iterations that consume excessive resources while generating unpromising solutions. To overcome this challenge, this paper proposes an efficient dynamic resource allocation framework for evolutionary bilevel optimization, named DRCBLEA. Compared to existing approaches, DRC-BLEA introduces a novel competitive quasi-parallel paradigm, in which multiple lower-level optimization tasks, derived from different upperlevel individuals, compete for resources. A continuously updated selection probability is used to prioritize execution opportunities to promising tasks. Additionally, a cooperation mechanism is integrated within the competitive framework to further enhance efficiency and prevent premature convergence. Experimental results compared with chosen state-of-the-art algorithms demonstrate the effectiveness of the proposed method. Specifically, DRCBLEA achieves competitive accuracy across diverse problem sets and real-world scenarios, while significantly reducing the number of function evaluations and overall running time. © 2024, CC BY-NC-ND.

关键词： Structural optimization

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