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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681-690 订阅

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Compound Disturbance Rejection control for Nanopositioning Using a Phase-Locking Loop Observer 9

Compound Disturbance Rejection Control for Nanopositioning U...

引用

9th ieee data driven control and learning systems conference (ddcls)

作者： Wei, Wei Xia, Pengfei Liu, Zaiwen Zuo, Min Beijing Technol & Business Univ Sch Comp & Informat Engn Beijing 100048 Peoples R China

ISBN: (纸本)9781728159225

In nano-positioning, accuracy and speed are important issues to guarantee the system performance. Integral resonant control (IRC) is able to improve the bandwidth, and phase-locking loop observer (PLLO) based active disturbance rejection control (ADRC) is capable of achieving better closed-loop accuracy. By combining the advantages of PLLO based ADRC and IRC, a compound control technique is proposed. the compound control can deal with hysteresis and vibration, which are main factors affecting the accuracy and speed of a nano-positioning stage driven by a piezoelectric actuator. An identified model of a nano-positioning stage is utilized, and simulations have been performed. Presented numerical results confirm the proposed compound control technology.

关键词： Active Disturbance Rejection control Phase-locking Loop Observer Integral Resonant control Piezoelectric Actuator

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

Machine Learning-Driven Blockchain for Enhanced Transparency...

引用

International conference on Communication systems and Network Technologies (CSNT)

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

ISBN: (数字)9798350305463

ISBN: (纸本)9798350305470

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.

关键词： Machine learning algorithms Government Machine learning Real-time systems Natural language processing Blockchains Safety

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GOSMART: A data-driven Web Platform for Personalized Study Recommendations and Academic Performance Improvement

GOSMART: A Data-Driven Web Platform for Personalized Study R...

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ieee conference on systems, Process & control (ICSPC)

作者： Nasrul Arif Bin Abu Kasim Suhaila Binti Khalip Ahgalya Subbiah Faculty of Information Science and Engineering Management and Science University Section 13 Shah Alam Selangor

ISBN: (数字)9798350391398

ISBN: (纸本)9798350391404

Education has evolved significantly over the years, transitioning from traditional, one-size-fits-all models to more dynamic, personalized learning approaches. Today’s students face unique challenges such as diverse learning styles, time management issues, and a need for high-quality resources. these factors impact academic performance and attrition rates, especially in rapidly growing undergraduate populations in Malaysia. this paper presents GOSmart, an innovative webbased system designed to enhance undergraduate students’ study skills through personalized learning recommendations at Management and Science University. the primary objectives are to develop a system that provides personalized study recommendations, improves time management skills, and enhances overall academic performance. GOSmart integrates adaptive learning algorithms, progress tracking, and data visualization to provide tailored study recommendations. Developed using agile methodologies, the system includes a userfriendly front-end (HTML, CSS, JavaScript) and a robust back end (PHP, MySQL). A mixed-methods approach, involving structured questionnaires administered to $\mathbf{5 0 0}$ undergraduate students at Management and Science University, informed the design and feature set of the application. data analysis revealed that $\mathbf{7 5 \%}$ of students experienced improved time management and study strategies, while overall academic performance increased by $\mathbf{2 5 \%}$. GOSmart not only enhances student learning experiences but also serves as a prototype for future research in personalized education technologies.

关键词： learning systems Adaptive learning data analysis Prototypes data visualization Faces

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the Potential of Industrial Smart Grids in Optimizing Energy Consumption

The Potential of Industrial Smart Grids in Optimizing Energy...

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International conference on Communication systems and Network Technologies (CSNT)

作者： H S Shreenidhi Satish Upadhyay Amandeep Gill Department of Computer Science Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of uGDX ATLAS SkillTech University Mumbai Maharashtra India Department of R&D Vivekananda Global University Jaipur India

ISBN: (数字)9798350305463

ISBN: (纸本)9798350305470

In order to maximize the benefits of industrial smart grids in terms of energy optimization, this research presents the Dynamic Energy Optimization System for Industrial Smart Grids (DEOS-ISG). DEOS-ISG is an adaptive and efficient energy management system that combines three important algorithms: Predictive Modeling for Energy Demand (PMED), Adaptive Demand Response and Load Shifting (ADRLS), and Integration of Distributed Energy Resources (IDER). PMED is centered on forecasting energy needs, which enables forethought in strategy and improvement. With ADRLS, load shifting and demand response may be adjusted dynamically in response to changes in energy costs and grid circumstances. In order to lessen our dependency on the grid and boost sustainability, IDER maximizes DERs. data-driven decision making is facilitated by the mathematical underpinnings of these techniques, which include regression analysis, time series forecasting, reinforcement learning, and mixed-integer linear programming.

关键词： Costs Adaptive systems Time series analysis Watersheds Reinforcement learning Demand response Smart grids

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Blockchain and Machine learning for Intelligent Traffic Management systems in Urban Planning

Blockchain and Machine Learning for Intelligent Traffic Mana...

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International conference on Communication systems and Network Technologies (CSNT)

作者： Vijilius Helena Raj Y Manohar Reddy Pushpendra Singh Danghi H Pal thethi Muntather Muhsen Praveen Department of Applied Sciences New Horizon College of Engineering Bangalore Institute of Aeronautical Engineering Dundigal Hyderabad Department of Computer Science & Engineering IES College of Technology Bhopal M. P. India Lovely Professional University Phagwara The Islamic university Najaf Iraq Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh

ISBN: (数字)9798350305463

ISBN: (纸本)9798350305470

Managing traffic and urban development in today's densely populated cities is becoming more difficult. Fortunately, there is hope in the form of a novel approach to problem solving: the combination of blockchain and machine learning. this article delves at the potential of combining blockchain technology with machine learning for use in Smart City Intelligent Traffic Management systems (ITMS). With blockchain technology, data can be managed safely and openly, allowing for authenticated, near-real-time monitoring of traffic data. thus, the accuracy of traffic management data is improved, and the possibility of manipulation is reduced. Algorithms in machine learning enable data analysis and predictive analytics. Large-scale traffic data analysis can optimize traffic patterns to improve urban mobility. this article examines how machine learning and blockchain technology may complement each other. this study examines the benefits of the two technologies and how their combination strengthens IT management systems. the research also examines possible issues and benefits of a holistic approach. this innovative combination of technologies can improve urban traffic management and quality of life by improving data security, traffic forecast accuracy, and urban planning efficiency.

关键词： Productivity data analysis Smart cities Urban planning Transportation Machine learning Traffic control

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Improving Zero-shot Adversarial Robustness via Integrating Image Features of Foundation Models

Improving Zero-shot Adversarial Robustness via Integrating I...

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ieee Global conference on Consumer Electronics (GCCE)

作者： Koshiro Toishi Keisuke Maeda Ren Togo Takahiro Ogawa Miki Haseyama School of Engineering Hokkaido University Hokkaido Japan Data-Driven Interdisciplinary Research Emergence Department Hokkaido University Hokkaido Japan Faculty of Information Science and Technology Hokkaido University Hokkaido Japan

ISBN: (数字)9798350355079

ISBN: (纸本)9798350355086

the Contrastive Language-Image Pre-training (CLIP) model is one of the most successful multi-modal foundation models and is indispensable in the field of computer vision. While the CLIP model performs zero-shot image recognition tasks with high accuracy, it is vulnerable to adversarial examples created by white-box attacks against the CLIP model. To improve the zero-shot adversarial robustness of the CLIP model, almost all methods rely on adversarial training, and other approaches have not been sufficiently explored. In this paper, we propose a novel defense strategy that integrates the CLIP model with another foundation model, ImageBind, which has a different structure and learning mechanism from the CLIP model. Experimental results confirm the effectiveness of the proposed method, showing a significant improvement in the classification accuracy for adversarial examples targeting the CLIP model.

关键词： Training learning systems Computer vision Accuracy Image recognition Computational modeling Robustness Consumer electronics Glass box

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A Comparative Study of the First Order Linear ADRC and PI controller in the Speed control System of Permanent Magnet Synchronous Motor 9

A Comparative Study of the First Order Linear ADRC and PI Co...

引用

9th ieee data driven control and learning systems conference, ddcls 2020

作者： Lei, Yang Xu, Jing Shuang, MenYi Hao, Qiang Yue, Sun China North Vehicle Research Institute Beijing100072 China

ISBN: (纸本)9781728159225

By analyzing the speed control performance of PMSM and establishing the mathematical model of PMSM, the velocity PI controller structure and parameters based on the model are designed in the frequency domain. By analyzing the frequency domain characteristics of the PI controller, a control parameter setting algorithm of ADRC controller is proposed. there are approximately the same frequency domain characteristics between the adjusted ADRC controller and PI controller. According to the characteristics of frequency domain, the fair comparison environment of two controllers is constructed. Finally, a speed control environment based on PMSM is built through simulation. through the comparison of stability, rapidity and overshoot, the stability margin of ADRC and PI controller is approximately the same. However, ADRC controller is superior to PI controller in rapidity and overshoot performance. © 2020 ieee.

关键词： Speed control

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IoT Network Security with PCA and Deep learning for Unmasking Anomalies

IoT Network Security with PCA and Deep Learning for Unmaskin...

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International conference on Communication systems and Network Technologies (CSNT)

作者： E Geo Francis S. Sheeja E.F. Antony John Jismy Joseph Dept. of Computer Science Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Dept. of Mathematics St. Thomas College (Autonomous) Thrissur Kerala India Dept. of Mathematics Carmel College (Autonomous) Mala Kerala India

ISBN: (数字)9798350305463

ISBN: (纸本)9798350305470

the rapid proliferation of Internet of things (IoT) devices has revolutionized various domains, introducing unprecedented convenience and efficiency. However, this expansion has concurrently intensified the obstacles associated with network security. Detecting and mitigating anomalies in IoT networks are of utmost importance to protect critical systems and sensitive data. this paper presents an innovative approach to IoT network security that combines Principal Component Analysis (PCA) with advanced deep learning approaches, encompassing Generative Adversarial Networks (GANs) and Transformers. In this research, we investigate the potential of PCA for dimensionality reduction and feature selection within IoT network data. By simplifying data complexity, PCA aids in uncovering critical features and patterns contributing to network anomalies. Advanced deep learning models, such as GANs for generating synthetic data and Transformers for sequence modeling, are harnessed to construct anomaly detection systems capable of identifying irregularities in real-time IoT data streams. Our approach is rigorously evaluated on a comprehensive dataset collected from diverse IoT devices and networks. the results underscore the efficacy of the PCA and deep learning-based anomaly detection model, leveraging GANs and Transformers, in accurately and efficiently unmasking anomalies in IoT network traffic. through extensive experiments and evaluations, we underscore substantial advancements over traditional security methods. this research significantly enhances IoT network security by presenting a sophisticated, data-driven anomaly detection system adaptable to the dynamic and evolving nature of IoT environments. As the IoT landscape continues to expand, our approach offers a crucial solution to safeguard against the ever-evolving threat of network anomalies.

关键词： Deep learning Dimensionality reduction Adaptation models Telecommunication traffic Network security Transformers data models

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Comparison of Stochastic Steepest Gradient Descent and Extended Kalman Filter as ARMA-FNN learning Algorithms for data-driven System Identification of Batch Distillation Column

Comparison of Stochastic Steepest Gradient Descent and Exten...

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ieee International conference on System Engineering and Technology

作者： Muhammad Alifsyah Putra Nasution Dimitri Mahayana Pranoto Hidaya Rusmin Hasan Zidni School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

A process that is almost always present in the chemical industry is distillation. the plant used in this study is a batch-type distillation column system located in the ITB Honeywell control systems Laboratory, capable of separating binary mixtures of ethanol and water. To apply the control scheme effectively, the plant’s dynamics model is required. However, the challenge lies in the fact that the distillation column plant is a highly nonlinear and multivariable system based on its physical laws, with a limited number of sensors available to collect all states information, where only 1 state, which is also an output, can be measured. As a result, this research performs data-driven system identification through black-box modeling based on experimental input-output data using a neural network to generate a linear model. this research employs a discrete-time ARMA-FNN linear model for online identification of the distillation column system, with a focus on comparing learning algorithms for weight updating in the FNN. Specifically, a comparison is made between the stochastic Steepest Gradient Descent (SGD) algorithm and the Extended Kalman Filter (EKF) algorithm. Both algorithms are suitable for comparison because they both operate in an instance-by-instance mode and can be employed for online system identification. Based on the results presented in TABLE II, it can be observed that the best Mean Squared Error (MSE) is achieved by entry number 2, utilizing the EKF algorithm, with a value of 5,5418e-05. Furthermore, as shown in TABLE II, entry number 3 demonstrates that the EKF algorithm can converge the fastest, approximately by the $6^{\mathrm{t}\mathrm{h}}$ instance. this achievement comes with a trade-off, as the resulting MSE slightly deteriorates.

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Employing Deep learning Algorithms for Real-Time Analysis and Prediction of Financial Markets and Investment Strategies

Employing Deep Learning Algorithms for Real-Time Analysis an...

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International conference on Communication systems and Network Technologies (CSNT)

作者： Naveen Kumar Rajendran Sweta Kumari Vijay Kumar Pandey Department of Aerospace Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of ISME ATLAS SkillTech University Mumbai Maharashtra India Department of Mechanical Engineering Vivekananda Global University Jaipur India

ISBN: (数字)9798350305463

ISBN: (纸本)9798350305470

A new age of data-driven decision-making has begun with the incorporation of deep learning algorithms into the domain of financial markets and investment strategies. A summary of our thorough investigation of this groundbreaking convergence is given in this abstract, which covers all the important points from the introduction to the techniques used, the outcomes attained, and the final observations. the introduction lays the groundwork for the paradigm change in finance, which has enabled real-time analysis and prediction via the use of deep learning models. the complexities of financial markets, characterized by high-dimensional time series data and non-linearities, have traditionally presented difficulties for conventional statistical techniques. With the use of deep learning techniques, particularly Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs), it is now possible to anticipate stock prices, exchange rates, and other financial variables with greater accuracy. In comparison tests, the Moving Average Convergence Divergence (MACD) approach yielded a Sharpe ratio of 1.8, whereas LSTM showed a ratio of 2.5. In a similar vein, the Transformer model outperformed the Moving Average (MA) technique, with a Sharpe ratio of 3.0 against 1.5. the main conclusions from this investigation are summarized in the conclusion. Although there are still issues with computing needs and model interpretability, deep learning in banking has the potential to revolutionize the industry. Deep learning algorithms are redefining investing techniques and enabling financial professionals to better traverse the complexity of the financial world.

关键词： Deep learning Analytical models Biological system modeling Computational modeling Predictive models Prediction algorithms data models

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