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检索条件"任意字段=13th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2024"

共 716 条记录，以下是291-300 订阅

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Emotion-driven Intelligent Product Design Based on Deep learning 13

Emotion-Driven Intelligent Product Design Based on Deep Lear...

引用

13th ieee International conference on Communication systems and Network Technologies, CSNT 2024

作者： Zhang, Mengmeng Polus International College Chengdu China

ISBN: (纸本)9798350305463

this research investigates the integrated application of deep learning technologies in the field of emotion-driven interior soft decoration product design. through comprehensive online surveys conducted with over 1000 participants and coupled with biometric feedback from 300 individuals, this study offers a multi-faceted analysis of user emotions during interactions with interior soft decoration products. the employment of a finely-tuned Convolutional Neural Network (CNN) model facilitated the extraction of key emotional design elements, indicating that around 520 of the surveyed participants were satisfied with certain interior soft decoration designs, while a minority of 80 expressed dissatisfaction. Moreover, the biometric data revealed a significant correlation between increased heart rates (exceeding 85 beats per minute) and intense emotional responses. the integration of biometric and questionnaire textual data suggested that users indeed experienced strong positive emotions in interactions with specific soft decoration products. the study also addresses potential challenges, including possible biases in data collection and the inherent limitations of deep learning models. the findings underscore the significant potential of deep learning in capturing the complex patterns of user emotions, providing valuable insights for the future design of emotion-centric interior soft decoration products. through this interdisciplinary approach, the study charts a promising direction for the advancement of artistic disciplines such as interior design and soft decoration product design, emphasizing the importance of understanding and incorporating user emotion in the design process. © 2024 ieee.

关键词： Deep learning

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Practical Research on Intelligent Evaluation of Generative learning Resource Quality 13

Practical Research on Intelligent Evaluation of Generative L...

引用

13th International conference on Educational and Information Technology, ICEIT 2024

作者： Li, Ruikun Li, Kangkang School of Smart Education Jiangsu Normal University Jiangsu Xuzhou China

ISBN: (纸本)9798350372663

this study is based on the intelligent evaluation method of large language model, aiming at the evaluation problem of generative learning resources. An empirical study is conducted on the effect of intelligent evaluation on improving the quality of generative learning resources among 31 postgraduate subjects. Specifically, this study set up a control group evaluated by experts and an experimental group involved in intelligent evaluation, and obtained data of generative learning resources created within eight weeks for analysis and comparison. the results showed that intelligent evaluation can achieve a comparable level with expert evaluation in evaluating resources and continuously improving resource quality. this study suggests that by using intelligent evaluation systems, we can quickly and accurately evaluate a large number of learning resources and provide evidence for their improvement. © 2024 ieee.

关键词： Quality control

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Blockchain and Machine learning for data-driven Insights in Consumer Behavior Analytics 13

Blockchain and Machine Learning for Data-Driven Insights in ...

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13th ieee International conference on Communication systems and Network Technologies, CSNT 2024

作者： Rawat, Bhupender Singh thapliyal, Nitin Pal, Sunil Kumar Mishra, Nikhil Verma, Rohan College of Smart Computing Coer University Roorkee India Graphic Era Hill University Department of Computer Science & Engineering Dehradun India Axis Colleges Department of Computer Science & Application Kanpur India Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350305463

Comparing with neural network method to more known methods, this article assesses their simplicity and effectiveness. Our approach uses new network pruning, interpretability-based regularization, and gradient-based sensitivity analysis. Numerous dataset trials show that the suggested method is more accurate, faster, and more durable than others. Also easier to understand. It turns out that small neural networks may simplify models without compromising accuracy. this comparative study shows how neural networks can handle complex data and make good predictions. these networks will be increasingly accessible in daily life. © 2024 ieee.

关键词： Sensitivity analysis

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Voltage Quality Pattern Recognition in Microgrids Using a data-driven Machine learning-Based Methodology 13

Voltage Quality Pattern Recognition in Microgrids Using a Da...

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13th ieee International conference and Expositions on Electric and Power Engineering, EPEi 2024

作者： Galbau, Stefania Grigoras, Gheorghe Neagu, Bogdan-Constantin 'Gheorghe Asachi' Technical University of Iasi Faculty of Electrical Engineering Energetics and Applied Informatics Iasi Romania

ISBN: (纸本)9798350356199

the paper proposes a methodology for voltage quality evaluation in low-voltage microgrids using a data-driven machine-learning process. the data inputs of this process are the voltage quality indicators on each phase (average voltage deviation, the variance of voltage deviation, and mean squared value of voltage deviation) at the level of each node from the microgrid. the indicators are determined based on the voltage profiles from the power flow calculations, considering the data from the smart meters installed at each end-user. All nodes are classified into one of the following four voltage quality patterns ('Unsatisfactory', 'Satisfactory, 'Good', and 'Very Good') using the K-means clustering algorithm based on a global quality coefficient calculated as the weighted sum of the voltage indicators. A real low-voltage microgrid with 36 nodes and 53 end-users (consumers and prosumers) without voltage control devices integrated has been used to test the proposed methodology. Finally, two voltage quality patterns, 'Satisfactory' and 'Good', associated with the first two phases, 'Good' and 'Very Good' for the third, have been recognized. However, the distribution network operator should take technical measures to improve the voltage quality such that the ranking of each node is inside the 'Good' and 'Very Good' patterns. © 2024 ieee.

关键词： data quality

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A data-driven Approach for Predictive Maintenance of Impellers in Flexible Impeller Pumps Using Prophet 13

A Data-Driven Approach for Predictive Maintenance of Impelle...

引用

13th International conference on data Science, Technology and Applications, data 2024

作者： Demir, Efe Can Sultanoğlu, Sencer Eliar Electronics Corp. Istanbul Turkey

ISBN: (纸本)9789897587078

this article presents a data-driven approach aimed at improving the efficiency of fabric dyeing operations in the textile industry. It specifically focuses on the predictive maintenance of flexible impeller pumps (FIP) and the application of the Prophet algorithm. the study extensively explores the potential of machine learning and data analytics to increase operational efficiency and enable early failure detection. By using the Facebook Prophet model and time series data for early detection of wear and tear, it offers an approach to maintain pump efficiency without installing new hardware, relying solely on data. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Predictive maintenance

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Design of data-driven Sliding Mode PID controller for Unknown Nonlinear System

Design of Data-driven Sliding Mode PID Controller for Unknow...

引用

data driven control and learning systems (ddcls)

作者： Shuang Cao Shuangshuang Xiong School of Automation Beijing Information Science and Technology University Beijing P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In this article, a new data-driven adaptive sliding-mode PID control (DASPIDC) algorithm is proposed for a class of unknown nonlinear discrete systems based on full form dynamic linearization model. the designed algorithm leads to a better stability of the system. theoretical analyses demonstrate the BIBO stability of the system, simulation results verify the effectiveness and superiority of the DASPIDC algorithm.

关键词： learning systems Adaptation models PI control Adaptive systems Uncertainty Heuristic algorithms Stability analysis

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Machine learning-driven Blockchain for Enhanced Transparency in Government Public Records 13

Machine Learning-Driven Blockchain for Enhanced Transparency...

引用

13th ieee International conference on Communication systems and Network Technologies, CSNT 2024

作者： Lakhanpal, Sorabh Reddy, Ch Vijendar Reddy, Uma Varkale, Akshay Kumar, Ashwani Albawi, Ali Lovely Professional University Phagwara Pakistan Institute of Aeronautical Engineering Dundigal Hyderabad India New Horizon College of Engineering Department of Artificial Intelligence and Machine Learning Bangalore India Ies Insititute of Technology and Management Ies University Department of Computer Science & Engineering M.P. Bhopal India Lloyd Institute of Engineering & Technology Uttar Pradesh Greater Noida201306 India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq

ISBN: (纸本)9798350305463

With more things done online, citizens have never wanted government data to be more available and dependable. this paper proposes a solution using blockchain and powerful algorithms. the recommended approach uses blockchain's immutability and safety, machine learning to discover outliers, and NLP to organize data. this coordinated strategy automates and opens government records to increase data quality, accessibility, and speed. Several tests and performance assessments compared the recommended solution to the traditional record-keeping method. Finding anomalies is faster using machine learning anomaly detection and natural language processing record sorting. this ensures data accuracy and clarity in real time. Spreading blockchain keeps documents secure and unchangeable. this makes changing difficult for those who shouldn't. the study found that the proposed strategy might improve public record management. It improves transparency, a crucial trait for responsible leadership, in a powerful, safe, and automated way using blockchain technology and cutting-edge algorithms. © 2024 ieee.

关键词： Transparency

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Preassigned Time Prescribed Performance Tracking control for High-Order Nonlinear systems with Time-Varying Powers

Preassigned Time Prescribed Performance Tracking Control for...

引用

data driven control and learning systems (ddcls)

作者： Wei Tian Yan Jiang School of Electrical Engineering Guangxi University Nanning China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this paper studies prescribed performance control of high-order nonlinear systems with unknown time-varying pow-ers. A state feedback control is designed to guarantee the prescribed tracking performance as the power of the systems are unknown time-varying functions and the nonlinearities are unknown. the tracking error between output and reference signal can converge to a predefined region in settling time. the effectiveness of the proposed scheme is verified by an example.

关键词： learning systems State feedback control systems Nonlinear systems Time-varying systems Convergence

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Finite-Time Quantized Iterative learning control for Discrete Nonlinear systems

Finite-Time Quantized Iterative Learning Control for Discret...

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data driven control and learning systems (ddcls)

作者： Jinyao Cheng Yongpeng Weng Hao Chen Yongchun Ma Xin Li College of Marine Electrical Engineering Dalian Maritime University Dalian P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In this paper, the finite-time iterative learning control problem for a class of non-affine nonlinear systems with data quantization is studied in the data-driven framework. Based on the quantized error signals, a finite time quantized iterative learning control (FQILC) scheme is proposed. the convergence of tracking error and the finiteness of the convergent iteration of FQILC scheme are analyzed. the findings demonstrate that employing the finite-time technique can decrease the required number of iterations for convergence and guarantee the boundedness of tracking error. the viability of the approach is confirmed by means of a simulation example.

关键词： learning systems Quantization (signal) control systems Nonlinear systems Iterative learning control Convergence

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Frequency-Domain-Based Forgetting-Factor data-driven Iterative learning control Algorithm

Frequency-Domain-Based Forgetting-Factor Data-Driven Iterati...

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data driven control and learning systems (ddcls)

作者： Wen-Yuan Fu Zhuoyun Nie College of Information Science and Engineering Huaqiao University Xiamen P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this paper introduces a data-driven iterative learning control (ILC) algorithm based on the forgetting factor within the frequency domain. Different from the conventional time-domain design of the forgetting factor algorithm, this approach employs an adaptive decomposition technique to construct the data model, utilizing frequency-domain sampling data of the system's input and output. Building upon this foundation, the tracking errors undergo orthogonalization to extract maximum error energy, thereby enhancing the convergence speed of the ILC.

关键词： learning systems Discrete-time systems Adaptive systems Frequency-domain analysis Buildings data models Time-domain analysis

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