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检索条件"任意字段=13th International Conference on Knowledge-Based Intelligent Information and Engineering Systems"

共 10176 条记录，以下是131-140 订阅

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Location Selection of Transfer Station based on Binary Semantic and Fuzzy Evaluation Tendency Model 13

Location Selection of Transfer Station Based on Binary Seman...

引用

13th international conference on Logistics and systems engineering

作者： Junhao, Chen Xiu, Wu Zubin, Gao Yue, Cai Meisheng, Xiao Guangdong Tobacco Shaoguan Limited Company GuangDong512000 China Information Engineering College Shaoguan University 512000 China

ISBN: (纸本)9781713896708

this paper focuses on the location selection of tobacco logistics transfer station based on the situation of scattered cigarette retailers, rough roads and mountainous terrain. By establishing a quantitative and qualitative index system, and using triangular fuzzy numbers, the qualitative indicators are quantified by using binary semantic model, combined with other quantitative indicators. the paper com- pares and selects 11 alternative locations for tobacco logistics transfer station in the city, then picks out the best 4 locations. the reliability of the algorithm model and the accuracy of the calculation results are also verified. While effectively integrating local delivery resources, it provides a certain reference for the location selection of transfer station for tobacco commercial enterprises in other regions. © 2024 Proceedings of the 13th international conference on Logistics and systems engineering. All rights reserved.

关键词： Semantics

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A Novel LSTM Optimized by Neurodynamic Network for Heat Prediction of Power Equipment 13

A Novel LSTM Optimized by Neurodynamic Network for Heat Pred...

引用

13th international conference on intelligent Control and information Processing, ICICIP 2025

作者： Wei, Huang Zhu, Minghao Zheng, Shiyuan Qiu, Lingfeng Zhang, Yansheng Zhang, Zhijun Meizhou Power Supply Bureau Guangdong Power Grid Corporation Meizhou China School of Automation Science and Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798331516147

With the rapid development of smart grid and substation equipment, the heating problem of power equipment has become an important factor affecting the stability and safety of equipment. the traditional equipment monitoring method has lag, and it is difficult to predict the heating trend of the equipment in time. therefore, a long short-term memory based on neurodynamic network(NN-LSTM) is proposed to improve the accuracy of predicting the heating trend of power equipment. In this paper, the predictive ability of the LSTM network is improved by combining the neurodynamic optimization strategy. And it is applied to power load forecasting and transformer oil temperature forecasting. Experimental results show that NN-LSTM is superior to the traditional LSTM model on the two datasets, the mean square error is effectively reduced, and the prediction accuracy is higher. © 2025 IEEE.

关键词： Oil filled transformers

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Teaching Feedback System based on ViT Expression Recognition in Distance Education 13

Teaching Feedback System Based on ViT Expression Recognition...

引用

13th international conference on Educational and information Technology, ICEIT 2024

作者： Wang, Kexin Cheng, Meng The College of Information Mechanical and Electrical Engineering The Shanghai Engineering Research Center of Intelligent Education and Bigdata Shanghai Normal University Shanghai200234 China

ISBN: (纸本)9798350372663

this paper proposes an online emotional feedback system for distance education based on the Vision Transformer (ViT). the objective is to provide teachers with realtime information on students' emotional states, such that the teaching strategies can be properly adapted to improve the quality of education. In the proposed system, the OpenCV is used to call the camera to collect the video in class, and the facial information is then detected by the Dlib. After that, the acquired facial images are processed with the ViT model augmented with two attentive pooling ViT (APViT) modules, to calculate students' emotions during class. then, the emotional states of the students will be analyzed and repeatedly fed back to the teachers. It has been found from the simulations that the APViT model can achieve a training accuracy of 94.68% with the OL-SFED dataset, exhibiting the advantage of applying the ViT technique to the field of distance education. © 2024 IEEE.

关键词： Students

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Research on Teaching Mode and Platform Construction of intelligent Manufacturing Practical Training based on Digital Twins Under the Background of New engineering 13

Research on Teaching Mode and Platform Construction of Intel...

引用

13th international conference on Educational and information Technology, ICEIT 2024

作者： Cao, Jianhua Jiang, Zhigang Hu, Wei Xia, Xuhui Liu, Xiang Liu, Xiaoqin Bao, Xiong Wuhan University of Science and Technology Engineering Training Center Wuhan China Wuhan University of Science and Technology Key Laboratory of Metallurgical Equipment and Control Technology Wuhan China

ISBN: (纸本)9798350372663

Aiming at the issues of high risks, high costs, and low efficiency in the training and teaching process of intelligent manufacturing, which seriously affect the quality of talent cultivation in intelligent manufacturing. By adopting digital twin technology, the intelligent manufacturing production line is mapped to virtual space, and a two-stage practical training teaching model that integrates virtual space simulation operations and physical space actual operations is constructed. And an intelligent manufacturing digital twin teaching platform was established to support the teaching model. the safe, efficient and economical operation of intelligent manufacturing practical training and teaching has been achieved. © 2024 IEEE.

关键词： E-learning

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A Bias in Training Data Degrades the Quality of Variable Importance Scores of Random Forests 13

A Bias in Training Data Degrades the Quality of Variable Imp...

引用

Joint 13th international conference on Soft Computing and intelligent systems and 25th international Symposium on Advanced intelligent systems, SCIS and ISIS 2024

作者： Kimura, Shuhei Yuasa, Kenta Tokuhisa, Masato Tottori University Faculty of Engineering Tottori Japan Graduate School of Sustainability Sciences Tottori University Tottori Japan

ISBN: (纸本)9798350373332

the random forest, a machine learning technique, offers several preferable features that have drawn the interest and focus of researchers. In practically applying the random forest, we found that the training data containing many examples similar to each other degraded the quality of the importance scores. To overcome this harmful effect, we have proposed a method that assigns weight values to the training data. Our experimental results showed that the quality decline of the importance scores could be resolved by considering the weight values determined by the weighting method in the training of the random forest. It turned out, however, that this harmful effect and the effectiveness of the weighting method in overcoming it could only be confirmed on a specified task. Although the main purpose of machine learning techniques is to obtain a good approximation of the target function, the importance scores are often used to extract information from the given data. From what we can see, the degradation in the quality of the importance scores is not yet widely known. In this study we attempted to reveal the conditions under which this harmful effect occurs. We confirmed that the weighting approach works well even in other problems. © 2024 IEEE.

关键词： Adversarial machine learning

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Abnormal Condition Monitoring based on Stacked Sparse Autoencoder 1

引用

13th international conference on Computer engineering and Networks (CENet)

作者： Qiao, Lin Qu, Ruiting Song, Qing Liu, Biqi Liu, Xiaoqiang Liu, Yubo State Grid Liaoning Elect Power Co Ltd Informat & Commun Branch Shenyang Peoples R China

ISBN: (数字)9789819992430

ISBN: (纸本)9789819992454;9789819992430;9789819992423

In recent years, abnormal condition monitoring has provoked vast amount o attention and research from multiple disciplines. Especially in the microservice architecture based on the cloud platform, abnormal condition monitoring has emerged as a powerful instrument for improving the stability of service. However, along with the development of geometric growth in the number of microservice, due to the lack of systematic methods, it is difficult to effectively analyze and extract valuable information from the large amount of data collected, the abnormal monitoring in cloud platform O&M is facing new difficulties and challenges. Traditional operation and maintenance work mainly relies on human experience to analyze a large number of indicators to determine whether there is a failure, which is very inefficient and relies heavily on expert opinions. To address these problems, in this paper, we propose a abnormal condition monitoring method based on stacked sparse autoencoder for cloud platform. Our method aims to model the operating state based on the historical experience with deep autoencoder, then the constructed model is used to analyze the current status of the service and predict the possible abnormal condition. Experimental results on real-world datasets demonstrate the effectiveness of our method compared to the traditional methods.

关键词： Abnormal condition monitoring Stacked sparse autoencoder intelligent operation and maintenance

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Research on visualization of minority music knowledge graph based on Neo4j 24

Research on visualization of minority music knowledge graph ...

引用

7th international conference on information Science and systems, ICISS 2024

作者： Mao, ChunXia Wen, ShuangBing Liu, Hong Hu, Tao Li, Jun College of Intelligent Systems Science and Engineering Hubei Minzu University China Hubei Engineering Research Center of Selenium Food Nutrition and Health Intelligent Technology Hubei Minzu University China School of Music and Dance Hubei Minzu University China The Key Laboratory of Performing Art Equipment and System Technology Ministry of Culture and Tourism Hubei Minzu University China

ISBN: (纸本)9798400717567

the digital age has created various channels for the sustainable development of traditional culture. A Neo4j-based knowledge graph for Tujia Intangible cultural heritage music was constructed to address the potential loss of Tujia Intangible cultural heritage music. the project involved integrating and consolidating Tujia Intangible cultural heritage music resources, organizing and cleaning the collected data, storing it in a Neo4j graph database, designing a knowledge base ontology model, and completing the construction of the Tujia Intangible cultural heritage music knowledge graph. the knowledge graph makes the knowledge of music and cultural heritage explicit from vast information resources, effectively expanding its dissemination breadth and promoting the protection and inheritance of intangible cultural heritage music in ethnic areas. © 2024 Copyright held by the owner/author(s).

关键词： knowledge graph

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A Method for Integrating of knowledge Model and Functional Component and Application in intelligent Problem Solver 37th

A Method for Integrating of Knowledge Model and Functional C...

引用

37th international conference on Industrial, engineering and Other Applications of Applied intelligent systems (IEA-AIE)

作者： Tran, Nha P. Nguyen, Hien D. Nguyen, Diem Tran, Dung A. Huynh, Anh T. Le, Tu T. Univ Transport & Commun Ho Chi Minh City Vietnam Univ Informat Technol Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Vietnam Nguyen Tat Thanh Univ Ho Chi Minh City Vietnam

ISBN: (纸本)9789819746767;9789819746774

knowledge base is an important component in intelligent problems solver. based on knowledge base, the inference engine of this system can be designed to solve problems in the knowledge domain. Ontology emerges as a potent methodology for the formulation of the knowledge base in intelligent systems. In this paper, a method for integrating of ontology and functional knowledge is proposed. this ontology, which represents relational knowledge, plays a foundation to connect with other intellectual components. the integrating model, called Rela-Funcs model, is useful to represent knowledge domains of functions. the study delves deeper into the functional intellectual component, exploring robust knowledge representation methods and automated inference algorithms tailored to this crucial element. based on this model, an intelligent problems solver in high-school 2D-Analytic geometry is proposed. this application delivers clear, step-by-step explanations, facilitating student learning and research through its pedagogical design and alignment with student reasoning patterns.

关键词： Ontology Functional knowledge knowledge-based systems Automated reasoning intelligent problem solvers

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Decentralized Federated Learning with Auto-switchable Local Optimizers 13

Decentralized Federated Learning with Auto-switchable Local ...

引用

13th international conference on intelligent Control and information Processing, ICICIP 2025

作者： Li, Ting Chang, Zhongyi Pan, Zhiyao Yang, Shaofu Xu, Wenying Chen, Zhongying School of Computer Science and Engineering Southeast University Nanjing211189 China

ISBN: (纸本)9798331516147

In this paper, we propose a novel decentralized learning algorithm over networks, termed as DLAGD, which combines the consensus mechanism with an auto-switchable local optimizer. Specifically, each node updates its local model parameter based on either gradient descent algorithm or Adam-like algorithm, which depends on a local switching function. Such switchable local optimizer can better capture the structure information of local loss function than existing algorithms relying on a fixed local optimizer, then has the potential to accelerate the convergence rate and improve the generalization performance. We theoretically prove that DLAGD can converge to the neighborhood of a stationary point at a sublinear rate for non-convex problems. Finally, numerical experiments are presented to show the superior performance of DLAGD in terms of the training rate and test accuracy. © 2025 IEEE.

关键词： Federated learning

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Proactive Highway Collision Avoidance using YOLOv8 and Ultrasonic Sensors 13

Proactive Highway Collision Avoidance using YOLOv8 and Ultra...

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13th international conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Kavitha, M. Akshaya, A. Jotheeswaran, K. Kavin, M. Kuppulakshmi, K. Information Technology Velalar College of Engineering and Technology Tamil Nadu Erode India Velalar College of Engineering and Technology Department of Information Technology Tamil Nadu Erode India

ISBN: (纸本)9798350380583

the Internet of things (IoT) has become a key component of vehicle-to-vehicle communication with collision avoidance systems. However, certain vehicle models lack the required interaction capabilities, enabling other design techniques. this paper provides an innovative method for a real-time identification system with a vehicle position prediction based on YOLOv8. the machine learning framework enhances stability and prediction accuracy by integrating vehicle dynamics. this method makes use of YOLOv8's capabilities to identify vehicles in complex environments and dynamically alter the prediction based on current patterns and courses of action. the performance of these algorithms has been validated by multiple studies, which demonstrate that they provide better prediction accuracy than current techniques. this development helps to create intelligent modes of transport that can function well in various types of traffic scenarios and car capacities, which in turn improves road safety by providing a strong foundation for active, realtime accident avoidance. © 2024 IEEE.

关键词： Highway accidents

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