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检索条件"任意字段=2023 International Conference on Computer Science and Automation Technology, CSAT 2023"

共 68823 条记录，以下是1111-1120 订阅

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An Assist-as-Needed Controller with Fault-tolerant Region for Safe and Effective Training on End-effector Upper Limb Rehabilitation Robot 4

An Assist-as-Needed Controller with Fault-tolerant Region fo...

引用

4th international conference on Industrial automation, Robotics and Control Engineering, IARCE 2024

作者： Ding, Yangzuobin Liu, Xiaodong Li, Rong Chen, Peng Sichuan Institute of Computer Sciences Chengdu China School of Mechanical Engineering Southwest Jiaotong University Chengdu China West China Pitec Co. Ltd. Chengdu China

ISBN: (纸本)9798350380323

This paper proposes an assist-as-needed position-based impedance controller for rehabilitation training of upper limb motor function impairments in patients. The controller establishes a fault-tolerant region around the movement training trajectory, allowing the upper limb to move freely in this space without assistance from the robot. For the movement out of the fault-tolerant region, the robot provides compliant assistance to guide the upper limb back toward the movement training trajectory. This approach minimizes assistance from rehabilitation robots and maximizes the patient's active participation, which could promote rehabilitation efficacy. This study not only simulates the robot arm but also established an equivalent model of the upper limb coupled with a robot model and implemented a human-robot interaction system for control simulation that enhanced the reliability and practicality of the rehabilitation training. The simulation results possess enhanced reliability and practicality, and to some extent, could validate the performance for safe and effective rehabilitation training. © 2024 IEEE.

关键词： Human robot interaction

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Intelligent Food Identification System Using Improved YOLOv8 13

Intelligent Food Identification System Using Improved YOLOv8

引用

13th international Symposium on computer science and Educational technology, ISCSET 2024

作者： Nitiavintokana, Francia Rouf, Md Abdur Yu, Xiaoyu Wu, Haibin Wang, Aili Iwahori, Yuji Harbin University of Science and Technology Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application Harbin China College of Electron and Information University of Electronic Science and Technology of China Zhongshan Institute Zhongshan China Chubu University Department of Computer Science Aichi Japan

ISBN: (纸本)9798350390803

The field of computer vision has witnessed significant advances in object detection with real-world applications ranging from autonomous driving to surveillance systems. Due to its importance in a number of areas, such as dietary evaluation, food industry automation, and health monitoring, food identification has attracted a lot of interest in this context. The suggested approach makes use of the capabilities of two cutting-edge object recognition models, YOLOv8. Because of its speed and accuracy, YOLOv8 is a great option for real-Time food detection in a variety of settings. Our most recent YOLOv8 model is called BiTrains-YOLOv8. Supervision techniques are applied to improve the combined model's performance even more. This increases overall detection accuracy and helps the algorithm to more effectively generalize to novel food items. Based on the detection results, 96.7% of an average of detection accuracy, 96.4% for precision, 95.7% for recall, and 96.04 % for F1-Score were obtained. The results show that the combination of YOLOv8 and BiTrains-YOLOv8 improved model techniques, significantly improved the performance of food detection, outperforming the previous methods in terms of accuracy, speed and robustness. Furthermore, the paper analyzes potential real-world applications of the system, such as calorie counting, dietary assessment and smart kitchen automation. Implications of advanced intelligent food detection in healthcare, the culinary industry and personalized nutrition are also discussed. In conclusion, the results provide valuable insights for pushing the boundaries of food recognition technology, with promising applications in various fields that promote healthy lifestyles and improve efficiency in food-related processes. © 2024 IEEE.

关键词： Food industry

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Heart Disease Risk Prediction using K-Nearest Neighbor: A Study of Euclidean and Cosine Distance Metrics 3

Heart Disease Risk Prediction using K-Nearest Neighbor: A St...

引用

3rd international conference on Smart Cities, automation and Intelligent Computing Systems, ICON-SONICS 2023

作者： Prasetyo, Simeon Yuda Kurniawan, Afdhal Sihotang, Erna Fransisca Angela Puspita, Rani Setiawan, Karli Eka Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Statistics Department Jakarta11480 Indonesia

ISBN: (纸本)9781509062805

Cardiovascular disease, with an annual fatality rate of 17.9 million individuals, poses a significant global health crisis. Swift and precise diagnosis is paramount, particularly in regions with limited access to advanced medical technology. This paper employs machine learning techniques to predict heart disease, emphasizing hyperparameter optimization for heightened accuracy. Utilizing a diverse dataset to address issues like duplicate data, we conduct a comparative analysis of Euclidean and Cosine distances within the K-Nearest Neighbor (KNN) algorithm, filling a research gap and providing a comprehensive evaluation of these distance metrics. Label Encoding is applied for categorical variables, and KNN is implemented with varying neighbor counts (1,3,5,7,9) using both Euclidean and Cosine distances. Evaluation metrics, including accuracy, precision, recall, and F1 score, gauge model performance. Our experiments reveal the highest accuracy of 90.761% when KNN is configured with 7 neighbors and the Cosine distance metric, surpassing the Euclidean distance. This underscores the criticality of careful distance metric selection in heart disease prediction models. The study offers insights into predictive modeling in healthcare, emphasizing the significance of distance metric choice. To streamline the abstract, omitting details related to future research and maintaining brevity is recommended. © 2023 IEEE.

关键词： Forecasting

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Comparison of Linear Quadratic Gaussian and Recent Methods for 4-Wheel Independent Steering State Space Model 23

Comparison of Linear Quadratic Gaussian and Recent Methods f...

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23rd international conference on Control, automation and Systems, ICCAS 2023

作者： Yoo, Heonjong Choi, Seonggon Chungbuk National University Department of Electrical and Computer Engineering Chungcheongbuk-do Cheongju28644 Korea Republic of

ISBN: (纸本)9788993215274

In this study, we designed a disturbance observer (DOB) and linear quadratic Gaussian (LQG) filter for 4-wheel independent steering systems considering a state space model. First, the state space structure of a 4-wheel independent steering system was defined. Second, a DOB and Kalman filter problem was designed by estimating the sideslip angle of the 4-wheel state space model. The two design processes were compared based on the estimation results;the LQG method outperformed the DOB method by changing the Kalman filter gain via the observer and controller eigenvalue separation principle. © 2023 ICROS.

关键词： Kalman filters

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Cyclone Chronicles: A Comprehensive Survey of Advancements in Cyclone Prediction Techniques 2

Cyclone Chronicles: A Comprehensive Survey of Advancements i...

引用

2nd international conference on automation, Computing and Renewable Systems, ICACRS 2023

作者： Raj, Abhineet Tiwari, Tanmay Madurwar, Ayush Sandosh, S. Integrated Mtech in CSE specialization In Business Analytics Vellore Institute of Technology Chennai India School Of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350340235

Tropical cyclones represent formidable natural phenomena, demanding constant progress in prediction methodologies. The survey paper amalgamates diverse insights from recent research, offering an extensive overview of the state of tropical cyclone prediction. This study analyzes the stat-of-the-art approaches, such as Support Vector Machine and polynomial regression models to achieve pre cise cyclone tracking. Additionally, this study examines the critical role of satellite imagery in automation, shedding light on the automated techniques employed. Furthermore, this study explores the emerging applications of Generative Adversarial Networks (GANs) for learning physical characteristics within cyclone data distribution, enhancing predictive capabilities. It also addresses challenges like rapid intensification, presenting prototype visualization tools and bias correction techniques as innovative solutions. The survey underscores the significance of machine learning models and ensemble forecasting in elevating the precision of cyclone predictions. © 2023 IEEE.

关键词： Bias correction Convolutional Neural Networks Deep learning Ensemble forecasting Generative Adversarial Networks Machine learning Satellite imagery Tropical cyclone prediction Weather forecasting

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An Architecture Modeling Framework for Distributed automation Systems Using SysML and Semantic Web Technologies 6

An Architecture Modeling Framework for Distributed Automatio...

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6th international conference on Mechatronics, Robotics and automation (ICMRA)

作者： Wang, Dan Li, Xiaofeng Gu, Bin Cao, Yue Liu, Yusheng Acad Aerosp Prop Technol Innovat Ctr Liquid Rocket Engine Digital Res & De CNSA Xian Peoples R China Beijing Inst Control Engn Beijing Peoples R China Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou Peoples R China Zhejiang Univ State Key Lab CAD & CG Hangzhou Peoples R China

ISBN: (纸本)9798350307542;9798350307535

The rising interdisciplinarity and complexity of the Distributed automation Systems (DASs) require the systems to be modeled in an unambiguous and high-level abstract way for cross-discipline/stage communication and interoperability in the Model-Driven Development (MDD) lifecycle. The concept of the System Architecture Model in systems engineering has been adopted for this challenge. To support the creation and analysis of this model, a modeling framework with a modeling methodology, modeling language, knowledge base, and related toolkit is established based on SysML and Semantic Web Technologies. A modeling methodology which is the core of the framework for modeling the architecture of DASs is formally defined with domain-specificity, comprehensiveness, discipline-neutrality, and platformin-dependency. Based on it, the SysML-DAS modeling language is extended from SysML, and the System Architecture Ontology is built with the help of the Knowledge Extraction Tool. This ontology works as the knowledge base not only to provide a unified and unambiguous view of the system but also to support the automated accomplishment of tasks in the MDD process. As a typical task, the semantic correctness and integrity of the system architecture model can be assessed by the Knowledge Analysis Tool in this framework.

关键词： distributed automation system SysML Semantic Web technologies System Architecture Model

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Exploring The Significance of E-Learning Quality, Usefulness, and Effectiveness on Student Intention

Exploring The Significance of E-Learning Quality, Usefulness...

引用

2023 international conference on IoT, Communication and automation technology, ICICAT 2023

作者： Utomo, Satrio Matin Alamsyah, Doni Purnama Rohaeni, Heni Siswanto, Boby Binus Business School Undergraduate Program Bina Nusantara University Management Program Jakarta11480 Indonesia Binus Business School Undergraduate Program Bina Nusantara University Entrepreneurship Department Jakarta11480 Indonesia Universitas Bhayangkara Jakarta Raya Management Program Faculty of Economy and Management Jakarta17121 Indonesia School of Computer Science Bina Nusantara University Computer Science Study Program Jakarta11480 Indonesia

ISBN: (纸本)9798350302820

This study's purpose is to look at techniques and models that can increase students at universities' willingness to continue with e-learning while considering the most recent technological advances and data analysis. The study assesses the variables that can facilitate the continuance intention, focusing on the measurable aspects related to the quality, usefulness, and effectiveness of online learning. A quantitative survey was conducted to acquire relevant data, and questionnaires were completed by an intake of 491 individuals studying in several colleges in Bandung (Indonesia). The evaluation of the data demonstrates that online learning quality, usefulness, and effectiveness have a beneficial association with the intention to use online learning. This can reveal that participants who believe online learning is of good quality, useful, and effective are more likely to indicate a desire to use it in the decades to come. The study also highlights the significance of the three factors of user behavior in interactions with online learning in influencing the strength of learner intention. Using the most recent technological breakthroughs and data analysis, the research model produced in this study reveals the antecedent elements that enable the development of user intention to maintain online studies. The study's findings provide valuable insights for universities to develop effective strategies and indicators that can enhance the sustainable utilization of online learning in the future. It is essential to modify technology for e-learning to better support the dissemination of knowledge at universities, and this study can help guide universities in this endeavor. © 2023 IEEE.

关键词： E-learning

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AC-PBFT:An Authority and Credibility based PBFT Consensus Algorithm 4

AC-PBFT:An Authority and Credibility based PBFT Consensus Al...

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4th international conference on Blockchain technology and Information Security, ICBCTIS 2024

作者： Zhao, Xueqing Wang, Sanyuan Shi, Xin Wang, Yun Zhang, Guigang School of Computer Science Xi'an Polytechnic University Shaanxi Key Laboratory of Clothing Intelligence Shaanxi Xi'an710048 China Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9798331528324

For the consensus mechanism in the blockchain, an authority and trust-based PBFT consensus algorithm for blockchain is proposed in this paper, short for AC-PBFT. Firstly, a novel credit scoring model is constructed, employing a proportional probability block generation model to enhance the fairness of the permissioned chain, which is used to incentivize the permissioned chain in the credit scoring model. Secondly, a biased credit scoring mechanism is designed to selectively filter out credit nodes with bias. Meanwhile, a proportional probability block generation mechanism is added to give nodes with fewer credits a chance to generate blocks. Finally, simulation tests are conducted on nodes with different identities and behaviors. Experimental results demonstrate that the proposed AC-PBFT has certain fairness and decentralized block generation performance in this paper. © 2024 IEEE.

关键词： Consensus algorithm

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Message from the conference Chairs 24

Message from the Conference Chairs

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24th international conference on Control Systems and computer science, CSCS 2023

作者： Dumitrache, Ioan Florea, Adina Magda Moisescu, Mihnea-Alexandru Pop, Florin Dumitrașcu, Alexandru University Politehnica of Bucharest Romania Bucharest Romania

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A Hybrid Approach of Logistic Regression with Grid Search Optimization in Credit Scoring Modeling for Financial Institutions

A Hybrid Approach of Logistic Regression with Grid Search Op...

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2023 IEEE conference on computer Applications, ICCA 2023

作者： Moe, Sun Thurain Nwe, Than Than University of Computer Studies Yangon Myanmar University of Information Technology Yangon Myanmar

ISBN: (纸本)9781665435994

With the rapid development of data science and technology, the credit scoring system has changed with many improvements. Building and perfecting a credit scoring system is critical to economic development and economic system reform. On the contrary, the gap of the brand-new personal system facilitates financial institutions like microfinances and banks to perform additional credit functions for various groups of individuals. The purpose of this study was to develop and test an applied credit scoring model that would assist financial institutions in evaluating credit claims. Given the growing importance of credit to our society and concerns about risking it in the wake of the recent financial turmoil, a robust credit rating model will strengthen confidence in the assessments of financial institutions. There is already a stream of literature associated with credit rating. However, this paper emphasizes the need to demonstrate the effectiveness of discriminating between good and bad customers by applying data mining techniques. © 2023 IEEE.

关键词： Risk assessment

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