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检索条件"任意字段=International Conference on Advances in Science, Engineering and Robotics Technology"

共 47866 条记录，以下是1541-1550 订阅

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PCEM-SQL: Enhancing Text-to-SQL Capabilities by Large Language Models and Prompt engineering with Self-Consistency and Self-Evaluation Mechanism 5

PCEM-SQL: Enhancing Text-to-SQL Capabilities by Large Langua...

引用

5th international conference on Control, robotics, and Intelligent System, CCRIS 2024

作者： Zhu, Xiuling Tang, Lianggui Lai, Xuan Xiao, Liyong Yang, Jiajun Chen, Zhuo The Artificial Intelligence College Chongqing Technology and Business University Chongqing China Chongqing Key Laboratory of IntelliSense and Blockchain Technology Chongqing China

ISBN: (数字)9781510685871

ISBN: (纸本)9781510685864

In recent years, natural language processing has experienced rapid development, especially in utilizing natural language statements to quickly query target data in databases. Transforming user’s natural language into computer executable SQL statements has become an important research direction for improving user database interaction. However, with the rise of large language models, enhancing their performance in Text-to-SQL tasks through prompt engineering has become a feasible research direction. This article proposes the PCEM-SQL method, which enables a large language model to generate high-quality SQL statements through clear prompt template and carefully constructed prompt words. By introducing self-consistency and self-evaluation Mechanism of the large language model, the accuracy of Text-to-SQL is significantly improved on the open-source large language model Qwen. To verify the effectiveness of the method, we construct a small dataset (Spider-SM) and conducted experiments on GPT3.5 and Qwen, once again proving that our method has a significant improvement (13%) in Qwen’s Text-to-SQL performance, and can achieve similar results as GPT3.5. © 2024 SPIE.

关键词： Structured Query Language

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Managing Cloud Networks: A Classification of SDN-Enabled Clouds

Managing Cloud Networks: A Classification of SDN-Enabled Clo...

引用

2023 international conference on advances in Computation, Communication and Information technology, ICAICCIT 2023

作者： Singh, Vaishali Kehkeshan Jallal, S. Das, Sunanda Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India

ISBN: (纸本)9798350344387

The idea of separating the management plane from the data-forwarding gear was first introduced by Software-Defined Networking (SDN), which has brought about a revolutionary new age in networking. This innovation makes networks programmable, flexible, and dynamically reconfigurable. These characteristics have great potential for the field of computer science in the cloud, where dynamic adjustments and reconfiguration are essential owing to on-demand consumption patterns. The many simulation and empirical assessment techniques that have been created expressly for SDN-enabled cloud settings are also highlighted by us. Finally, study conducted perform a critical review of the state of the research, highlighting gaps and suggesting possibilities for further study. © 2023 IEEE.

关键词： application framework cloud computing hardware resources programmable controller resource provisioning simulation and energy modeling Software-Defined Networking (SDN)

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international conference on advances in Communication technology and Computer engineering, ICACTCE 2023

International Conference on Advances in Communication Techno...

引用

international conference on advances in Communication technology and Computer engineering, ICACTCE 2023

ISBN: (纸本)9783031371639

The proceedings contain 54 papers. The special focus in this conference is on international conference on advances in Communication technology and Computer engineering. The topics include: Integration of a UHF Fractal Antenna into a 1U CubeSat for Low-Earth Orbit Mission;gesture Recognition Based Virtual Mouse and Keyboard;QCNN—A Conceptual Framework for Duplicate Removal in Big Data Using Quality Assured Crowd Sourcing Coupled with Neural Networks;Evaluation of Text Classification Using Support Vector Machine Compare with Naive Bayes, Random Forest Decision Tree and K-NN;a Review on Blockchain Smart Contract Applications;Factors Impacting a UK Based Software Business on international Expansion;predicting Fish Habitat in the Persian Gulf Using Artificial Intelligence;junk Mail Content Detection Using Logistic Regression Algorithm;research Protocol for a Preliminary Evaluation of a Gamified Tool for Adult Wellbeing;initial Evaluation of a Mobile Therapeutic Game for Adolescent Anxiety Disorders;help over Harm: Practical and Ethical Considerations for the Evaluation and Deployment of Therapeutic Games;comparative Analysis on the Use of Teleconsultation Using Support: Vector Regression and Decision Tree Regression to Predict Patient Satisfaction;bibliometric Analysis of Scientific Literature on Mental Health Research in Africa;new Technologies and Interventions to Improve the Mental Health of People with Diabetes;robotics Cyber Security Issues;Improving Data Centre Power Efficiency by Applying the VM Consolidation Approach;development and Evaluation of "Perspective: Counselling Simulator": A Gamified Tool for Developing Risk-Assessment Skills in Trainee Counsellors;Online Buying Behaviour of UK University Students;multi-modal Sentiment Analysis Using Text and Audio for Customer Support Centers;preface.

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Based on Static And Dynamic:Fuzzy Logic-Based Clustering Optimization for Wireless Sensor Networks

Based on Static And Dynamic:Fuzzy Logic-Based Clustering Opt...

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2024 international conference on advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Ali, Adnan Hussein Kareem, Marwa Mahdi Sabbar, Bayan Mahdi Monir, Hind Q. Mohammad Ibrahim, Raed Khalid Mnati, Mohannad Jabbar Institute Of Technology Middle Technical University Electrical Techniques Department Baghdad Iraq Middle Technical University Technical Management Institute Baghdad Iraq College Of Engineering And Engineering Technologies AlMustaqbal University Iraq Al-Farahidi University Department Of Medical Instruments Engineering Techniques Baghdad Iraq

ISBN: (纸本)9798350371567

Fuzzy C-Means (FCM) clustering, a widely employed and sophisticated data analysis method, offers a robust structure for handling intricate and uncertain data patterns. Its versatility extends to numerous domains, such as data mining, pattern recognition, and image segmentation. Unlike conventional hard clustering approaches, FCM is particularly suitable for scenarios involving ambiguous and overlapping data by assigning data points to distinct clusters with varying degrees of membership. This synopsis provides a summary of FCM, with particular emphasis on its core concepts, iterative optimization process, and capability to handle complex data patterns. It also describes the practical benefits and applications of FCM, emphasizing how it can be utilized to generate flexible and nuanced cluster assignments. Despite the presence of ambiguity and insufficient memberships, FCM continues to be a valuable tool for practitioners and researchers seeking efficient methods to cluster data. © 2024 IEEE.

关键词： Pattern recognition

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Fault Estimation for T-S Fuzzy Systems via an L∞ Switching Observer Scheme 14

Fault Estimation for T-S Fuzzy Systems via an L∞ Switching ...

引用

14th international conference on Information science and technology, ICIST 2024

作者： Wu, Yue Zhang, Kai Wang, Yang Sun, Xiaojie Zhang, Shanfeng School of Electrical Engineering Southwest Jiaotong University Chendu China School of Aeronautics and Astronautics Sichuan University Chengdu China Navigation College Dalian Maritime University Dalian China College of Information Engineering Henan University of Science and Technology Luoyang China

ISBN: (纸本)9798350353334

Ahstract- This paper addresses the fault estimation (FE) problem for T-S fuzzy systems based on the switching observer scheme. Firstly, a fuzzy FE observer with switching rules is designed, where the product of time derivative of the membership functions and Lyapunov matrix can be guaranteed to be negative definite. Furthermore, to increase the robustness of the observer against the persistence disturbance, an L∞ performance index is considered for synthesizing the FE observer. Then, based on the Lyapunov stability analysis method and the average dwell-time technique, the synthesis conditions of the L∞ FE switching observer are given in terms of linear matrix inequalities (LMIs). The merit of the proposed method is that by using the switching rules, the constraint in the conventional methods, where the state should be limited into a local area, can be removed. Finally, an example is given to show the efficacy of the presented FE scheme. © 2024 IEEE.

关键词： Membership functions

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High Precision Positioning through Advanced Robotic technology Development

High Precision Positioning through Advanced Robotic Technolo...

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2024 international conference on Advanced robotics and Intelligent Systems, ARIS 2024

作者： Hsieh, Hsin-Che Lin, Chih-Kuang Ho, Jeng-Rong Tung, Pi-Cheng National Central University Mechanical Engineering Department Taoyuan Taiwan

ISBN: (纸本)9798350362572

This research focuses on enhancing robotic technology, particularly for achieving high-precision positioning in manual wiring production lines for electronic connectors. This system addresses the needs of customized, small-batch production by incorporating advanced image recognition technologies to boost production accuracy. Experimental results demonstrate that the robotic arm can achieve a positioning accuracy of up to ±10 μm. Considering the plastic component holes are about 600 μm long and 150 μ m wide, a dual-lens system is employed for precise identification and positioning. A high-resolution 10-megapixel camera is mounted on the robotic arm, utilizing an image positioning technique that adjusts minor movements based on pixel errors between the center and positioning holes. This process ensures that the robotic arm's positioning accuracy is not affected by the repeatability of collaborative robots, achieving a final precision of ±0.01 mm. This technological advancement, facilitated by image processing, allows the robotic arm to align with the plastic component holes precisely, significantly simplifying the terminal insertion process with exceptional accuracy. © 2024 IEEE.

关键词： Robotic arms

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A New Paradigm of Human-robot Dialogue Facilitating Human and Robot Teaming in Sample Exploration

A New Paradigm of Human-robot Dialogue Facilitating Human an...

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2023 IEEE international conference on Real-Time Computing and robotics, RCAR 2023

作者： Fu, Yan Wang, Hao Xue, Shuqi Zhang, Bojun Cheng, Jianmei Gao, Zhengke Huazhong University of Science and Technology School of Mechanical Engineering and Science Wuhan Hubei430074 China Astronaut Research and Training Center of China National Key Laboratory of Human Factors Engineering Beijing100094 China Xi'an Jiaotong University School of Mechanical Engineering Shaanxi Xi'an710049 China

ISBN: (纸本)9798350327182

When human-robot collaboration is used as an optimal for conducting sample collection for lunar missions, the heterogeneity between human and robot spatial information processing modes pose a challenge to the human-robot collaboration. To address the communication obstacles, a conceptual framework, named Co-SRP is constructed, integrating perception of current scenarios, comprehension of elements observed in complete task context, and the prediction of the system and task's status in the near term to achieve spatial co-awareness. The proposed framework's effectiveness was validated on a simulation platform for lunar surface sampling missions. The results indicate that the framework improved the lunar surface sampling robot's ability to understand astronaut guidance commands and increased efficiency in human-robot collaboration. © 2023 IEEE.

关键词： Simulation platform

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A Fast Walsh-Hadamard transform (FWHT) For Detection of Freezing of Gait in Parkinson's disease using Machine learning techniques. 4

A Fast Walsh-Hadamard transform (FWHT) For Detection of Free...

引用

4th international conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Nancy, R. Thomas George, S. Prasanna, J. Subathra, M.S.P. Karunya Institute of Technology and Science Department of Robotics Engineering Coimbatore India Karunya Institute of Technology and Science Department of Biomedical Engineering Coimbatore India

ISBN: (纸本)9798350349856

Freezing of gait stands out as the most severe detrimental motor signs of advanced PD patients with an increased prevalence in the elderly, and frequently leads to falls and injuries among individuals associated with PD. This research proposes an efficient detection method for freezing of gait based on the Fast Walsh -Hadamard Transform (FWHT). The approach involves decomposing the gait signals, and key features are derived by analyzing the statistical parameters and entropy characteristics of the decomposed coefficients. The Artificial Neural Network (ANN) and K-Nearest Neighbor classifier (KNN) are employed to classify the obtained statistical and entropy measures. The tests utilize the benchmark dataset DAPHNET for performance assessment. Remarkably, the K-nearest neighbor (KNN) algorithm demonstrated noteworthy outcomes, achieving 98.42%, 98.24%, 98.57%, 98.38%, and 98.45% accuracy, specificity, sensitivity, positive predictive value, and negative predictive value, respectively. © 2024 IEEE.

关键词： Neural networks

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Community Detection in Signed Networks Based on Normalized Laplacian Matrix and Convex Non-negative Matrix Factorization 2

Community Detection in Signed Networks Based on Normalized L...

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2nd international conference on Automation, robotics and Computer engineering, ICARCE 2023

作者： Zhang, Yibo Nan, Tian Zhang, Zhaozhi School of Information Science and Engineering Lanzhou University Lanzhou China State Grid Gansu Electric Power Company Material Company Lanzhou China

ISBN: (纸本)9798350308341

Signed networks can intuitively and flexibly describe the positive and negative interactions among entities in a complex network. Community structure detection is a basic research topic of signed network analysis, which is of theoretical significance and practical value to reveal the organizational structure, functional structure, and individual behavior of complex systems. However, it is still a challenging task to accurately detect the community structure in a signed network. In this paper, we design a community detection algorithm based on normalized Laplacian matrix and convex non-negative matrix factorization for signed networks. The proposed algorithm applies convex non-negative matrix decomposition to a signed network, and decomposes the adjacency matrix of the network into a community membership matrix and a node weight matrix. Meanwhile, the algorithm introduces normalized Laplacian graph regularization terms to reduce noise generated during the decomposition process. Experimental results verify that the proposed algorithm is effective in community detection. © 2023 IEEE.

关键词： Non-negative matrix factorization

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Enhanced Diabetes Prediction Using Deep Autoencoder Framework and Electronic Health Records 2

Enhanced Diabetes Prediction Using Deep Autoencoder Framewor...

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2nd IEEE international conference on advances in Information technology, ICAIT 2024

作者： Sivakumar, T.B. Malakar, Antara Lekshmi, S. Shailaja, G. Kalaivani, E. Babu, Dhananjaya K. Department of CSE School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamilnadu Chennai India Department of Computer Science & Engineering The Assam Royal Global University Guwahati India Basic Science and Humanities Department Mangalam College of Engineering Kottayam Ettumanoor India Department IT CVR College of Engineering Hyderabad India Department of Computer Science and Engineering Bannari Amman Institute of Technology sathyamangalam India Rajampet India

ISBN: (纸本)9798350383867

Type II diabetes, which nowadays has become one of the most common chronic metabolic illnesses, needs cloud-based information systems with great predictive models to ensure timely intervention and proper management. Given the fact that EHRs and deep learning are quite advanced serves as a solution to the given problem. The study is about the application of the improved diabetes prediction model that makes use of prevailing deep learning architecture and EHR data. The framework constitutes a three-step process: It encapsulates a preprocessing step, a feature extraction stage, and a training procedure for a deep autoencoders that learns levels of representation. Demonstration of the system' s effectiveness becomes evident when it shows its improved ability of prediction compared to baseline models which in turn outperform in basic indicators such as accuracy, precision, recall, F1 score, and AUC-ROC. The result of the sensitivity check demonstrates that the model is able to perform well even under different hyperparameter arrangements. Latent feature analysis shows some unequivocal ways of hidings that reflect diabetes risk factors thus making it easy to understand. The clinical validation in a healthcare setting presents high accuracy, the specificity and the predictive values that are known in indicating those who are at the risk of diabetes onset. This research aims at deepening the understanding for the diagnosis of diabetes through the implementation of deep learning methods and generating real-world medical data. It attempts to provide better prognosis, lower healthcare expenditures and alleviate diabetes burden on patients and society. © 2024 IEEE.

关键词： Electronic health record

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