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共 2957 条记录，以下是571-580 订阅

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Parallel Classification Method of Library Borrowing data Based on Bayes 3

Parallel Classification Method of Library Borrowing Data Bas...

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2023 IEEE 3rd international conference on data Science and Computer Application, ICDSCA 2023

作者： Yi, Cheng Liu, Jieling Library of Wuhan University of Technology Wuhan430200 China

ISBN: (纸本)9798350341546

Based on the parallel classification of library borrowing data, the dynamic management and service ability of library borrowing information are improved, and a parallel classification method of library borrowing data based on Bayesian is proposed. According to the borrowing reports of user groups and individuals, a sampling model of library borrowing data is constructed, and a comprehensive borrowing index analysis model for users' personal construction system is established. Bayesian classification model is used to realize the feature mining and classification of borrowing data, and the correlation between different types of literature resources is mined according to the results of spectral feature extraction. Combined with the borrowing intensity between users with different attributes and different book classifications, Naive Bayesian classification algorithm is used to fully explore the internal relationship between fields. Based on the causal relationship between influencing factors, a dynamic model of parallel classification of library borrowing data is constructed, and the library borrowing data is matched according to certain rules. After weighted calculation, the reading behavior is quantified in the form of comprehensive index to realize parallel classification of library borrowing data. The test results show that the parallel classification of library borrowing data by this method improves the mining efficiency and accuracy of borrowing data, and the parallel processing efficiency is high. © 2023 IEEE.

关键词： Bayesian Borrowing data dig Library Parallel classification

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Development of High-Performance Single Inductor Quadratic Multilevel DC-DC Step-Up Converter with MPPT Controller 3

Development of High-Performance Single Inductor Quadratic Mu...

引用

3rd international conference on Robotics, Electrical and signal processing Techniques, ICREST 2023

作者： Akter, Khadiza Motakabber, S.M.A. Alam, A.H.M. Zahirul Yusoff, Siti Hajar Binti International Islamic University Malaysia Department of Electrical and Computer Engineering 53100 KL Malaysia

ISBN: (纸本)9798350346435

Here a new model of single inductor employed quadratic structured multilayer boost converter circuit is developed to be implemented in high-gain renewable energy systems. The study and functioning of a photovoltaic system utilizing a hybrid dc-dc configuration as a high step-up converter along with the incorporation of the Maximum Power Point Tracking (MPPT) technique are elucidated here. The characteristics of the quadratic multi-layered converter circuit have been tuned to provide high voltage gain and efficacy (98%) with minimal ripple contamination in input current. To ensure the adaptability of the proposed circuit, a distinct performance comparison of various topologies has been executed. The proposed circuitry showed improvement across all evaluated aspects compared to other existing topologies. The performance analysis of the recommended structure has been substantiated by PSIM and MATLAB Simulink software. © 2023 IEEE.

关键词： Boost converter

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Analysis of Spatio-temporal Behavior of Research Travelers Based on Spark Big data Computing Model 3

Analysis of Spatio-temporal Behavior of Research Travelers B...

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3rd international conference on Machine Learning and Big data Analytics for IoT Security and Privacy, SPIoT2023

作者： Gou, Zhiguo Zeng, Yizhou Rattanakosin International College of Creative Entrepreneurship Rajamangala University of Technology Rattanakosin Bangkok Thailand

There are still many defects in the process of data processing, storage and analysis, which cannot adapt to the needs of big data processing and analysis. In order to more fully reflect the value of big data processing, it is necessary to realize efficient data processing. Therefore, people invented the Spark operating system and integrated the idea of SIMBA, built a big data analysis system, and searched the information through the database query method. In Spark operating system can import search mechanism, in order to improve the search effect of information, through the establishment of segment tree storage method can improve the retrieval effect, in big data analysis can use partition strategy method for big data partition, and adopted the global filtering and local search method, make in information retrieval can achieve high throughput and low delay performance. © 2023 The Authors. Published by Elsevier B.V.

关键词： Big data

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Cooperative Prediction Approach for the Small-signal Stability Margin of Renewable Power System Based on Vertical Federated Learning 3

Cooperative Prediction Approach for the Small-Signal Stabili...

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3rd international conference on Power Systems and Electrical Technology, PSET 2024

作者： Lai, Xiang Chen, Zhihao Liu, Yun Dai, Tianchen Cai, Haiqing Gu, Haohan Chen, Wei South China University of Technology School of Electric Power Engineering Guangzhou China CSG Electric Power Research Institute State Key Laboratory of HVDC Guangzhou China Laboratory of Intelligent Operation and Control for New Energy Power System Guangdong Provincial Key Guangzhou China

ISBN: (纸本)9798350353204

Since sub-synchronous oscillations have been frequently witnessed in large-scale wind farms under weak grid conditions, it is significant to predict the critical stability situations in short term so that timely preventative actions can be taken to avoid potential small-signal stability (SS) risks. To this end, this paper proposes a SS prediction approach based on the grid strength metric of generalized short-circuit ratio (gSCR) under the mechanism of vertical federated learning. Specifically, the improved ridge regression prediction model is employed and its corresponding distributed training and the distributed predicting are derived by utilizing the separable characteristic of gradient vector calculation in the context of federated learning. On this basis, the original largescale predicting problem is decomposed into a large number of small-scale subproblems, and each subproblem is calculated locally by the corresponding wind farm. The feature data information of adjacent wind farms can be effectively used to improve the accuracy of the prediction of the stability margin while ensuring the data privacy and security of all participants. Finally, simulation results illustrate the effectiveness and efficiency of the proposed approach under various scenarios. © 2024 IEEE.

关键词： Federated learning

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Integrating Reconfigurable Foot Design, Multi-modal Contact Sensing, and Terrain Classification for Bipedal Locomotion 3

Integrating Reconfigurable Foot Design, Multi-modal Contact ...

引用

3rd Modeling, Estimation and Control conference (MECC)

作者： Tyler, Ted Malhotra, Vaibhav Montague, Adam Zhao, Zhigen Hammond, Frank L., III Zhao, Ye Georgia Inst Technol George W Woodruff Sch Mech Engn Atlanta GA 30309 USA

ISBN: (纸本)9781713872344

The ability of bipedal robots to adapt to diverse and unstructured terrain conditions is crucial for their deployment in real-world environments. To this end, we present a novel, bio-inspired robot foot design with stabilizing tarsal segments and a multifarious sensor suite involving acoustic, capacitive, tactile, temperature, and acceleration sensors. A real-time signal processing and terrain classification system is developed and evaluated. The sensed terrain information is used to control actuated segments of the foot, leading to improved ground contact and stability. The proposed framework highlights the potential of the sensor-integrated adaptive foot for intelligent and adaptive locomotion.

关键词： Mechatronic Systems Robotics Modeling Identification and signal processing

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An Overview of Edge Computing for Big data Analytics 3rd

An Overview of Edge Computing for Big Data Analytics

引用

3rd international conference on Smart Computing and Cyber Security—Strategic Foresight, Security Challenges and Innovation, SMARTCYBER 2023

作者： Sachdev, Siddharth Dey, Debraj Mohanty, Suneeta Pattnaik, Prasant Kumar School of Computer Engineering KIIT Deemed to be University Odisha Bhubaneswar India

ISBN: (纸本)9789819705726

Edge computing has revolutionized the way how Big data is stored, processed, and analyzed by organizations. Since the volume, velocity, and variety of data are increasing, traditional computing systems have become inadequate for big data analytics. Edge computing facilitates the processing and analysis of data in proximity to its origin and allows organizations to store and process large volumes of data while reducing latency and improving data security. In this paper, we provide an overview of how edge computing technologies can be used for big data and its analysis and discuss their advantages and challenges, and presented useful cases of edge computing in big data analytics, highlighting their potential for improving business performance, reducing costs, and enhancing user experience. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Big data

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Diagnosing Faults of Reciprocating Air Compressor (RAC) Setup Using signal processing Technique and Machine Learning Approach 3rd

Diagnosing Faults of Reciprocating Air Compressor (RAC) Setu...

引用

3rd international conference on Mechanical and Energy Technologies, ICMET 2023

作者： Dhakar, Atul Singh, Bhagat Gupta, Pankaj Jaypee University of Engineering and Technology Madhya Pradesh Guna India

ISBN: (纸本)9789819749461

This paper presents a method for identifying RAC faults using acoustic signals obtained from both healthy and unhealthy conditions. The entire procedure is carried out with microphones. Accumulated one healthy and seven unhealthy signals of RAC setup processed using unconventional method of signal processing called "local mean decomposition" (LMD). Additionally, the ‘6’ statistical properties (SP) have been evaluated in order to extract features: mean (US), variance (σs2), root square of mean (Mrms), root amplitude of mean (Mrma), absolute amplitude of mean (Mama), and kurtosis index (Ki). Extracted fault features are classified using lazy learning-based (LLB) classifiers. It has been observed that from different types of LLB classifiers, K-star classifier is quite accurate and has higher accuracy (93.05%) as compared to the other two classifiers accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reciprocating compressors

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Assessing the Ability of AI-Driven Natural Language processing to Accurately Analyze Unstructured Text data 3

Assessing the Ability of AI-Driven Natural Language Processi...

引用

3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Vigenesh, M. RaviKumar, V. Patil, Vivek D. Kumar, Sharma Sonu Geyi, Nyarik Chattopadhyay, Subir Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Mangalayatan University Department of Computer Science Aligarh India Himalayan University Department of Bussiness & Management Itanagar India Usha Martin University Faculty of Business Mangagement & Commerce Ranchi India

ISBN: (纸本)9798350319125

This technical abstract examines the ability of herbal language processing (NLP)-driven synthetic Intelligence (AI) to analyze unstructured textual content facts. Unstructured textual content records include information from assets with webpages, social media, emails, and consumer comments. They are widely used in sentiment evaluation, hazard detection, and entity extraction programs. NLP-pushed AI answers along with natural language knowledge (NLU), herbal language knowledge (NLG), and natural language understanding (NLP) are used to manner and examine unstructured textual content information. The analysis technique starts evolving with tokenizing the facts, breaking the textual content into smaller factors, including phrases and phrases. After tokenizing the statistics, the AI device discovers styles inside the records. Those styles can then discover the emotions and entities inside the data. NLU and NLG models are used to expand the facts' expertise by assigning sentiment and issue-level scores. The accuracy of NLP-driven AI is similarly progressed through more excellent complex fashions consisting of deep studying and reinforcement. © 2023 IEEE.

关键词： extraction phrases statistics tokenized webpages

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Action Localization and Recognition through Unsupervised I3D and TSN 3

Action Localization and Recognition through Unsupervised I3D...

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3rd international conference on Computing and Information Technology, ICCIT 2023

作者： Umran, Munzir Muchtar, Kahlil Abidin, Taufik Fuadi Arnia, Fitri Universitas Syiah Kuala Dept. of Electrical and Computer Engineering Banda Aceh Indonesia Dept. of Electrical and Computer Engineering Banda Aceh Indonesia Universitas Syiah Kuala Faculty of Mathematics and Natural Sciences Department of Informatics Banda Aceh Indonesia

ISBN: (纸本)9798350321487

Since CCTV technology was widely introduced as a video surveillance system, it has become very popular due to increased security concerns. However, detecting suspicious behavior is still done manually by humans, which is inefficient in terms of accuracy and manpower (prone to distraction and fatigue). Many studies use the entire frame of a video to recognize human action, whereas in this study first localize the area of object movement using the unsupervised inflated 3D (I3D) network which produces several frames that have been determined as the region of interest (ROI) from the total frames in the video. Then the collection of ROI frames is generated into a video which will then be classified to recognize human action through the Temporal Segment Networks (TSN). For efficiency purposes, the proposed pipeline can be run separately in the edge (for localization), and cloud environment (for recognition), respectively. The results show that there are 54 classes of ROI video data sets that have an average human action recognition prediction similarity rate above 84.6% which is the average of the prediction similarity rate of the entire ROI video data. In terms of prediction accuracy, 37 classes of data (36.63%) are better than the original class, including 25 classes of data (24.75%) above the threshold, and the processing speed of classification with ROI video data is on average 5 times faster than full-frame video data. © 2023 IEEE.

关键词： Forecasting

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Evaluation of Machine Learning Algorithms for Text Classification Tasks 3

Evaluation of Machine Learning Algorithms for Text Classific...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Aravindan, M.K. Raajini, X. Mercilin Ingle, Yashwant S. Jangid, Pradeep Kumar Katyal, Ashu Naval, Preeti Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350319125

Assessing machine-learning algorithms for texttype responsibilities is critical to advancing the world of herbal language processing (NLP). As using applications that use NLP grows, so does the want to pick the maximum accurate algorithms out of a pool of to-be-had solutions. This paper reviews the current United States of America research concerning assessing system reading algorithms for textual content-type obligations. Numerous evaluation metrics, consisting of accuracy, precision, consider, and f-degree, are discussed, in addition to the strategies used to examine algorithms and determine the foremost solutions. The diverse challenges confronted in this venture are highlighted, giving readers an outline of the challenges related to assessing the tool and getting to know algorithms for text-kind responsibilities. © 2023 IEEE.

关键词： Natural language processing systems

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