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检索条件"任意字段=International Multi-Conference in Computer Science and Computer Engineering"

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Transient Stability Improvement of multi-Machine System by using STATCOM with Power System Stabilizer

Transient Stability Improvement of Multi-Machine System by u...

引用

2024 IEEE international Students' conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Kumar, Amit Chandel, Ashwani Kumar Tiwari, Sukriti National Institute of Technology Department of Electrical Engineering Himachal Pradesh Hamirpur India National Institute of Technology Department of Electrical Engineering Kerala Calicut India

ISBN: (纸本)9798350348460

In recent years, there has been a significant surge in power demand, while the progress in power generation and transmission has been hampered by environmental and resource limitations. Consequently, a few transmission lines have become overloaded, posing a challenge to the stability of the power system for effective power transfer. Flexible AC transmission systems (FACTS) regulators have been widely applied to address various steady-state control issues in power systems. However, recent research reveals that FACTS controllers can not only fulfill their primary role of power flow control but also contribute to enhancing power system stability. This paper presents the analysis of PSS with STATCOM to stabilize the power system. © 2024 IEEE.

关键词： FACTS Power System Stability STATCOM Transient Stability

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Healthcare Analytics: Conceptualizing a Research Agenda

Healthcare Analytics: Conceptualizing a Research Agenda

引用

2023 international conference on ENTERprise Information Systems, CENTERIS 2023 - international conference on Project MANagement, ProjMAN 2023 - international conference on Health and Social Care Information Systems and Technologies, HCist 2023

作者： Elragal, Rawan Elragal, Ahmed Habibipour, Abdolrasoul Department of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden

This research recognizes the pressing need for innovative research in healthcare, enabling the transition towards analytics, by explaining how previous studies utilized big data, AI, and machine learning to identify, address, or solve healthcare problems. Healthcare science methods are combined with contemporary data science techniques to understand the literature, identify research gaps, and posit research questions for researchers, academic institutions, and governmental healthcare organizations. We intend to explain how contemporary analytics have been used to address healthcare concerns as well as to posit several research questions for future studies based on gaps which we have identified. The study has multi-folds contribution areas: first, it provides a state-of-the-art review to healthcare analytics, second, it posits a research agenda to advance the knowledge in this area further. © 2024 The Author(s). Published by Elsevier B.V.

关键词： Adversarial machine learning

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SMGC-SBERT: A multi-Feature Fusion Chinese Short Text Similarity Computation Model Based on Optimised SBERT

SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Simila...

引用

2024 IEEE international conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhao, Shuo Gu, Qiliang Zhang, Jianqiang Song, Gongpeng Lu, Qin Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9781665410205

Chinese short text similarity computation stands as a pivotal task within natural language processing, garnering significant attention. However, existing models grapple with limitations in handling intricate semantic relationships, such as the challenge of discerning subtle semantic nuances in text, inadequacies in effectively integrating diverse levels of semantic information, and the struggle to capture polysemous meanings accurately. In addressing these issues, this paper introduces an innovative Chinese short text similarity computation model, SMGC-SBERT. This model addresses shortcomings of the existing models by employing a multi-module fusion strategy, thereby enabling a more precise measurement of semantic similarity between texts. Primarily, the model incorporates SAT embedding to acquire phrase-level semantic information and leverages the MS-BERT model to encode text, improving the model's comprehension of textual polysemy and obtaining richer semantic representation. Then, the fusion of module features, including multi-branch convolutional networks and mix pooling, enables the extraction of textual features from varied levels, bolstering the model's representational capacity. Additionally, to further reduce overfitting risks while improving accuracy and other performance, a multi-layer feature adjustment network is utilized for short text similarity calculation. The final resultant experimental findings showcase the superiority of the SMGC-SBERT model over other neural network models, demonstrating significant advancements across both Chinese-SNLI and CCKS2018_Task3 Chinese short text datasets. © 2024 IEEE.

关键词： Semantics

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An Integrated Approach Based on Fuzzy Logic and Machine Learning Techniques for Reliable Wine Quality Prediction 4

An Integrated Approach Based on Fuzzy Logic and Machine Lear...

引用

4th international conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Kaur, Narinder Kaur, Gaganjot Aruchamy, Prasanth Chaudhary, Neelu Department of Computer Science & Engineering Chandigarh University Mohali Gharuan India Department of Computer Science & Engineering Raj Kumar Goel Institute of Technology UP Ghaziabad India Department of Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science & Engineering Manav Rachna University Haryana Faridabad India

In recent decades, wine quality has been one of the predominant problems in many wine industries. However, the analysis of wine quality is inherently complex owing to its multivariate characteristics and the instigation of several sensory features. Most of the existing prediction methods lagged in providing higher detection accuracy for multi-dimensional datasets. To overcome this, a novel Adaptive Wine Quality Prediction (AWQP) approach has been proposed to assess the quality of the wine in an accurate manner. The proposed AWQP methodology entails the development of a Hybrid detection model that encompasses the fuzzy logic principles with the predictive influence of machine learning techniques. Primarily, the sensory features like aroma, taste, and color are delineated by exemplifying the linguistic variables. The fuzzy rules are then determined to collare the qualitative relationships among these different variables. Subsequently, the finest machine learning algorithm can be carried out to train and test the prediction model. The proposed AWQP methodology ameliorates the comprehensibility of the decision-making process through the hybridization of fuzzy logic and the finest machine learning. This proper hybridization facilitates the proposed method to achieve a superior detection accuracy of 98.75% when compared to the existing prediction methods. © 2025 The Authors. Published by Elsevier B.V.

关键词： Adversarial machine learning

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Reliability evaluation of a communication network with Transmission Distance Constraint 4

Reliability evaluation of a communication network with Trans...

引用

4th international conference on Electronic Information engineering and computer Communication, EIECC 2024

作者： Feng, Weiming Chongqing University of Posts and Telecommunications College of Science Chongqing China

ISBN: (纸本)9798331534622

Considering transmission distance constraint, a reliability evaluation method is proposed to evaluate the data transmission capability of a communication network in an uncertain environment. Network reliability is defined as the probability that the amount of data transmitted by the network can meet the requirements of devices when each edge has random transmission capacity and there is a limit on the transmission distance. By utilizing the concept of the minimal path, a reliability model based on minimal capacity vector is established, and an effective calculation method is designed. Finally, the results of numerical experiments show that the data transmission distance has a significant impact on the reliability of a communication network, and this impact is even greater under high demand levels. © 2024 IEEE.

关键词： Communication network Minimum path multi-state network reliability Transmission Distance

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Applications and Analysis of Factory Production Using computer Vision 23

Applications and Analysis of Factory Production Using Comput...

引用

23rd international conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Shao, Zhong-Ping Tang, Cheng-Yuan Wu, Yi-Leh Huafan University Department of Mechatronic Engineering New Taipei Taiwan Huafan University Department of Technology for Smart Living New Taipei Taiwan National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798331528041

The integration of computer vision technology into factory production offers significant improvements in efficiency, quality, and safety. This study explores the applications and benefits of multi-camera systems and AI models in monitoring and analyzing production processes. Key applications include quality control, process optimization, worker safety, and preventive maintenance. By utilizing real-time data, these technologies enhance product quality, optimize operations, and prevent accidents. Despite challenges such as occlusion, varying illumination, and the need for robust AI models, advancements in 3D point clouds and AI frameworks like YOLOS and Google Mediapipe show preliminary and promising results. This research demonstrates the transformative potential of computer vision in modernizing traditional manufacturing, leading to more efficient, safer, and higher-quality production environments. Future work will focus on refining these technologies and developing prototype systems for widespread industrial application. © 2024 IEEE.

关键词： Preventive maintenance

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A denoising model for MC rendered images based on fusion kernel prediction and generation of adversarial networks 4

A denoising model for MC rendered images based on fusion ker...

引用

4th international conference on computer Vision and Data Mining, ICCVDM 2023

作者： Yang, Yuqi School of Computer Science and Technology Changchun University of Science and Technology 7186 Weixing Road Changchun130022 China

ISBN: (纸本)9781510674448

This research offers a denoising model that organically blends Generative Adversarial Network (GAN) and Kernel Prediction Network (KPN) to address the issue of probable loss of fine details in denoised Monte Carlo pictures. To preserve image details during the denoising process and overcome the denoising network's inadequacy in image feature extraction, the model introduces a multi-Scale Feature Module to expand the receptive field of feature extraction, allowing the model to extract image features at different scales. And employs Haar Wavelet Transform to suppress high-frequency noise in images, hence increasing the model's denoising performance. The suggested network model efficiently lowers noise in diverse image scenes, improving quality metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). © 2024 SPIE.

关键词： Generative adversarial networks

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MFENet: multi-scale and Local Frequency Enhancement Network for Skin Lesion Classification 1

引用

41st computer Graphics international conference, CGI 2024

作者： Jin, Yuyu Xiao, Zhiyong Yuan, Jiaqi He, Mingwei Liu, Li Yu, Feng Jiang, Minghua School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Engineering Research Center of Hubei Province for Clothing Information Wuhan China

ISBN: (数字)9783031820243

ISBN: (纸本)9783031820236

Dermatoscopy is a common diagnostic tool used by dermatologists to examine skin lesions. However, existing classification methods often rely on spatial domain feature extraction. Dermatoscopic images are frequently affected by various sources of noise, such as hair and pigmented areas, resulting in non-robust feature extraction. Therefore, relying solely on spatial domain feature extraction is limited and cannot fully differentiate the subtle features of skin lesions. This paper proposes a multi-scale and local frequency enhancement network (MFENet) for skin lesion, which significantly improves the diagnostic accuracy and interpretability of dermatoscopic images. Specifically, we introduce the multi-scale and local of frequency (MLF) module to reduce noise interference and enhance edge, shape, and texture features. The module selects low-frequency information and suppresses high-frequency noise at different scales. It further enhances details and texture information in the image by processing local frequency blocks. This makes important morphological features, such as the shape and edge, more prominent in diagnosis. Additionally, a lesion channel attention (LCA) module is proposed to further optimize the extraction and amplification of key features by learning dependencies between channels. The results show that the algorithm achieves optimal performance on the ISIC 2017 and HAM10000 datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Design and construction of computer laboratory security monitoring system based on STM32 microcontroller 4

Design and construction of computer laboratory security moni...

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4th international conference on Telecommunications, Optics, and computer science, TOCS 2023

作者： Shen, Yanmei Office of Academic Affairs Nanjing University of Finance & Economics Nanjing210023 China

ISBN: (数字)9781510679849

ISBN: (纸本)9781510679832

A multi parameter monitoring system based on STM32 microcontroller was designed to address the security risks in computer laboratories, achieving dynamic monitoring and real-time alarm of the laboratory environment to improve the safety and management level of the laboratory. Parameters such as humidity and gas concentration were used to monitor the laboratory environment. The STM32 microcontroller serves as the control core of the system, controlling and collecting data from various sensors through analog input and output ports. At the same time, the system adopts network communication technology to transmit data to the upper computer for processing, achieving abnormal alarm function. The experiment shows that the system can accurately monitor the environmental parameters of the laboratory and transmit data to the host for processing through network communication technology, achieving abnormal alarm function. During the real-time monitoring process, the system can timely detect and alert laboratory safety hazards, effectively improving the safety and management level of the laboratory. The system has achieved real-time monitoring and abnormal alarm of the laboratory environment, improving the safety management level of the laboratory. This system is practical and feasible, and can provide reference for laboratory safety management. © 2024 SPIE.

关键词： Microcontrollers

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Credit scoring model based on SyMProD-ENN and logistic regression 24

Credit scoring model based on SyMProD-ENN and logistic regre...

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8th international conference on Electronic Information Technology and computer engineering, EITCE 2024

作者： Feng, Jiangang Jiang, Yuwen Bo, Chen Jiang, Chengxuan School of Information Engineering Hainan Vocational University of Science and Technology Hainan Haikou China School of Computer Science and Engineering Guangzhou Institute of Science and Technology GuangDong Guangzhou China School of Mathematics and Statistics Shenzhen University Guangdong Shenzhen China International Course Center of GuangZhou No.6 Middle School Guangdong Guangzhou China

ISBN: (纸本)9798400710094

Unbalanced data distribution is a common problem in the development process of credit scoring cards. This paper proposes a new method of mixed sampling, SyMProD-ENN, to improve the problem of developing effective credit scoring cards on imbalanced datasets. The experimental results show that the SyMProD-ENN method can effectively improve the classification performance of the credit scoring model constructed based on the logistic regression methods, especially suitable for credit data with imbalance category, high-dimensional characteristics, non-normal distribution and outliers. © 2024 Copyright held by the owner/author(s).

关键词： Logistic regression

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