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Memristive Computing and Applications (ICMCA), IEEE International Conference on

作者： Ximei Zhao Hanyu Zhang Lijun Shao Lili Huang Jiao Li School of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Jinan China Collaborative Innovation Center of Memristive Computing Application(CICCA) Qilu Institute of Technology Jinan China College of Computer and Communication Engineering Qilu Institute of Technology Jinan China

In this article, we solve the fast finite-time stabilization as well as adaptive neural control design issues for a class uncertain stochastic nonlinear systems. By employing the mean value theorem, the pure-feedback form can be transformed into strict-feedback case. Firstly, a new fast finite-time control scheme is proposed in virtue of generalizing the fast finite-time adaptive control strategy for deterministic systems to the stochastic case. Next, a novel adaptive neural control strategy is developed to simultaneously deal with the stochastic nonlinear systems with completely unknown nonlinearities as well as the disturbances term. Then, stability analysis have been given based on a Jensen's inequality.

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Path Planning and controlling of Omni-Directional Robot Using Cartesian Odometry and PID Algorithm

Path Planning and Controlling of Omni-Directional Robot Usin...

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2019 International Conference on Computing, Power and Communication Technologies, GUCON 2019

作者： Chaudhari, Tejas Jha, Mayank Bangal, Rohit Chincholkar, Ganesh Govt. College of Engineering and Research Department of Instrumentation and Control Engineering Pune India Govt. College of Engineering and Research Department of Computer Engineering Pune India

ISBN: (纸本)9789353510985

Omni direction is the concept of existence of motion in every direction. Here, we have controlled the resulting vectors of individual wheels of a three wheel omni robot that are ultimately responsible for a stabilized motion of the robot in any direction. This phenomenon is termed as 'Holonomic Drive' and it provides higher efficiency and controllability to non-holonomic drives, when used with odometry and PID algorithms. Odometry basically is method of acquiring the real time positional data of a system in a world frame and using it for path planning. PID algorithm when applied on any system reduces the transient response and helps to attend steady state conditions with less settling time. Here, we have successfully designed and implemented the algorithms for path planning with the help of quadrature encoders and heading control of three wheel omni robot using PID, implemented on IMU(Inertial Measurement Unit). © 2019 Galgotias University.

关键词： Motion planning

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Lightweight Trust Model with Machine Learning scheme for secure privacy in VANET 18

Lightweight Trust Model with Machine Learning scheme for sec...

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18th International Learning and Technology Conference, L and T 2021

作者： Junejo, Muhammad Haleem Ab Rahman, Ab Al-Hadi Shaikh, Riaz Ahmed Yusof, Kamaludin Mohamad Kumar, Dileep Memon, Imran Faculty of Electrical Engineering Universiti Teknologi Malaysia Johor Skudai81310 Malaysia Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Department of Computer Science Bahria University Karachi campus sindh Pakistan

A vehicular ad hoc network (VANETs) is transforming public transport into a safer wireless network, increasing its safety and efficiency. The VANET consists of several nodes which include RSU (Roadside Units), vehicles, traffic signals, and other wireless communication devices that are communicating sensitive information in a network. Nevertheless, security threats are increasing day by day because of dependency on network infrastructure, dynamic nature, and control technologies used in VANET. The security threats could be addressed widely by using machine learning and artificial intelligence on the road transport nodes. In this paper, a comparison of trust and cryptography was presented based on applications and security requirements of VANET. © 2021 The Authors. Published by Elsevier B.V.

关键词： Machine learning

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Enhancing online industrial quality index prediction with a general deep temporal feature extraction and incremental ensemble modeling framework

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engineering Applications of Artificial Intelligence 2025年 156卷

作者： Zhao, Yuchen Yang, Rui Ying, Jie Wang, Song Zeng, Zhenyu College of Control Science and Engineering Zhejiang University Zheda Road 38 Hangzhou310027 China School of Automotive and Transportation Engineering Hefei University of Technology Tunxi Road 193 Hefei230000 China School of Computer Science and Engineering Nanyang Technological University Nanyang Avenue 50 Singapore639798 Singapore Alibaba Cloud Computing Co. Ltd. Alibaba Group Dengcai Road 1008 Hangzhou310030 China

Data-driven modeling methods for industrial quality index prediction often face the challenge of limited data representation. Using process variable snapshots from a single time step is insufficient for building high-performance soft sensors. Moreover, during online prediction, the performance of soft sensors is affected by diverse operating conditions and concept drift in industrial data streams, leading to performance degradation. To address these challenges, this paper proposes a Temporal Feature Extraction and Incremental Variational Bayesian Regression Ensemble (TFE-IVBRE) framework, which provides a general solution for various online industrial quality index prediction tasks. The TFE-IVBRE framework combines the temporal feature extraction capabilities of deep neural networks, the ability of ensemble learning to handle diverse operating conditions, and the online learning features of variational Bayesian models. An incremental update strategy is also incorporated to maintain consistent prediction performance. Experiments in the Debutanizer Column and Sulfur Recovery Unit scenarios show that the prediction performance of TFE-IVBRE significantly outperforms other static and online comparison models, with the effectiveness of its components validated through ablation studies. Finally, the overall model also demonstrates good robustness. These results offer valuable insights for the advancement of industrial soft sensor development. © 2025 Elsevier Ltd

关键词： Deep neural networks

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Edge Data Based Trailer Inception Probabilistic Matrix Factorization for Context-Aware Movie Recommendation

arXiv

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arXiv 2022年

作者： Chen, Honglong Li, Zhe Wang, Zhu Ni, Zhichen Li, Junjian Xu, Ge Aziz, Abdul Xia, Feng College of Control Science and Engineering China University of Petroleum Qingdao266580 China College of Computer and Control Engineering Minjiang University Fuzhou350108 China School of Software Dalian University of Technology Dalian116620 China School of Engineering IT and Physical Sciences Federation University Australia BallaratVIC3353 Australia

The rapid growth of edge data generated by mobile devices and applications deployed at the edge of the network has exacerbated the problem of information overload. As an effective way to alleviate information overload, recommender system can improve the quality of various services by adding application data generated by users on edge devices, such as visual and textual information, on the basis of sparse rating data. The visual information in the movie trailer is a significant part of the movie recommender system. However, due to the complexity of visual information extraction, data sparsity cannot be remarkably alleviated by merely using the rough visual features to improve the rating prediction accuracy. Fortunately, the convolutional neural network can be used to extract the visual features precisely. Therefore, the end-to-end neural image caption (NIC) model can be utilized to obtain the textual information describing the visual features of movie trailers. This paper proposes a trailer inception probabilistic matrix factorization model called Ti-PMF, which combines NIC, recurrent convolutional neural network, and probabilistic matrix factorization models as the rating prediction model. We implement the proposed Ti-PMF model with extensive experiments on three real-world datasets to validate its effectiveness. The experimental results illustrate that the proposed Ti-PMF outperforms the existing ones. Copyright © 2022, The Authors. All rights reserved.

关键词： Light trailers

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Design and Analysis of Weather Parameters Using MEMS Technology with Suitable Characteristics for Integration into a Single Chip for Environment Monitoring

Design and Analysis of Weather Parameters Using MEMS Technol...

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Smart Technologies and Systems for Next Generation Computing (ICSTSN), International Conference on

作者： Abdurrashid S. Hassan Vimala A. Juliet G. Pius Agbulu G. Joselin Retna Kumar Raghavendra Reddy N V S Gunasekar Department of Electronics and Control/Instrumentation Engineering SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Mechatronic Engineering School of Engineering Sciences & Technology Chinhoyi University of Technology Chinhoyi Zimbabwe Department of Electronics and Instrumentation Engineering SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Mechanical Engineering RV Institute of Technology and Management Karnataka India Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India

ISBN: (数字)9798350391565

ISBN: (纸本)9798350391572

This study designs and simulates weather parameters using MEMS technology with suitable characteristics for integration into a single chip for environment monitoring. The sensing and heating filament of the temperature sensor was designed using a thin film platinum resistor, examining its ability to experience stability, increase in resistivity, accuracy, and free-contamination. The temperature sensing and heating element are based on platinum RTD (Resistance Temperature Detector) with meander shape and aluminum as the bonding pad connecting the sensor to the external circuit. A capacitive humidity sensor is implemented in consolidation with a piezoresistive pressure sensor with a square membrane. The capacitive humidity sensor with hole pattern at the upper plate and a polyimide serves as water absorbent sandwiched between the electrodes. The simulation analysis was conducted using finite element analysis in COMSOL Multiphysics version 6.0 . It was observed that the temperature increases from -100oC to - 20oC, and 10oC to 50oC respectively as a result of deviation in relative humidity in the troposphere, and atmosphere. The average changes in capacitances were found at 0.0713/%RH, while the pressure was examined to vary from 1050hPa to 5hPa in a step of 200hPa with a sensitivity of 11.50mv/bar.

关键词： Temperature sensors Temperature Platinum Terrestrial atmosphere Humidity Stability analysis Finite element analysis

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Risk-Sensitive Reinforcement Learning with Exponential Criteria

arXiv

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arXiv 2022年

作者： Noorani, Erfaun Mavridis, Christos N. Baras, John S. Department of Electrical and Computer Engineering the Institute for Systems Research University of Maryland College Park United States Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

While reinforcement learning has shown experimental success in a number of applications, it is known to be sensitive to noise and perturbations in the parameters of the system, leading to high variability in the total reward amongst different episodes on slightly different environments. To introduce robustness, as well as sample efficiency, risk-sensitive reinforcement learning methods are being thoroughly studied. In this work, we provide a definition of robust reinforcement learning policies and formulate a risk-sensitive reinforcement learning problem to approximate them, by solving an optimization problem with respect to a modified objective based on exponential criteria. In particular, we study a model-free risk-sensitive variation of the widely-used Monte Carlo Policy Gradient algorithm, and introduce a novel risk-sensitive online Actor-Critic algorithm based on solving a multiplicative Bellman equation using stochastic approximation updates. Analytical results suggest that the use of exponential criteria generalizes commonly used ad-hoc regularization approaches, improves sample efficiency, and introduces robustness with respect to perturbations in the model parameters and the environment. The implementation, performance, and robustness properties of the proposed methods are evaluated in simulated experiments. © 2022, CC BY.

关键词： Contrastive Learning

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Two-Degree-of-Freedom PID controller Design of Unmaned Aerial Vehicles

Two-Degree-of-Freedom PID Controller Design of Unmaned Aeria...

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2022 International Conference on control, Automation and Diagnosis, ICCAD 2022

作者： Taher Azar, Ahmad Serrano, Fernando E. Ahmad Kamal, Nashwa Al Mhdawi, Ammar K. Khamis, Alaa M. Kasim Ibraheem, Ibraheem Humaidi, Amjad J. Ben Njima, Chakib Prince Sultan University College of Computer & Information Sciences Riyadh Saudi Arabia Benha University Faculty of Computers and Artificial Intelligence Benha Egypt Instituto de Investigación en Energía Tegucigalpa11101 Honduras Cairo University Faculty of Engineering Giza Egypt Edge Hill University United Kingdom General Motors Canada OshawaL1J 6J2 Canada Dijlah University College Department of Computer Techniques Engineering Baghdad10001 Iraq University of Technology Control and Systems Engineering Department Baghdad Iraq University of Sousse Tunisia

ISBN: (数字)9781665497947

ISBN: (纸本)9781665497947

This paper shows an analysis and controller design of independent two degrees of freedom (2DOF) PID controllers for unmanned aerial vehicles. The transfer functions of system dynamics are separated into two sections: lateral and longitudinal. A transient examination of the temporal response for the lateral and longitudinal control blocks is also included in this paper when a reference input is utilized as a pilot instruction. This research also provides a robustness analysis as well as an integral square error analysis to evaluate the performance of the suggested control technique. The fundamental contribution of this research is that independent control for unmanned aerial vehicles is limited, as described in the literature. A major contribution is made because disturbances are handled. The results show that in all simulations, a suitable settling time and overshoot are reached. © 2022 IEEE.

关键词： Longitudinal control

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An Adaptive Attitude controller for A Quadrotor Transporting the Payload With Unknown Mass

An Adaptive Attitude Controller for A Quadrotor Transporting...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Shihao Qiu Longlong Chen Shuang Li Zongyang Lv Huayi Tang School of Computer Science and Technology Dalian University of Technology Dalian China School of Control Science and Engineering Dalian University of Technology Dalian China College of Information Science and Engineering Henan University of Technology Zhengzhou China School of Mechanical Engineering Dalian University of Technology Dalian China

An adaptive control approach is presented in this article to deal with the impacts of carrying a payload with unknown mass by a quadrotor. This approach can effectively control the quadrotor’s attitude stability. The paper commences by presenting the dynamic model of the quadrotor, followed by the development of a controller to manage its attitude control. Through Lyapunov analysis, the stability of the controller is validated. Ultimately, the proposed controller and a backstepping controller are used to controlling the attitude of the quadrotor, respectively, and experimental outcomes substantiate the efficiency and feasibility of the introduced controller.

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Classification System Based on Data Mining Technique for Flat-Colored Textiles

SSRN

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SSRN 2025年

作者： Zhou, Min Lin, Yukai Hu, Wei Dai, Chai Tang, Guangfa He, Fei School of Information and Intelligent Engineering Guangzhou xinhua University Dongguan China School of Intelligent Control Hunan Railway Professional Technology College Zhuzhou China College of Electrical and Electrical Engineering Hunan University Changsha China Dept. of Pingdingshan Branch of China Mobile Communication Group Henan co. LTD Henan Pingdingshan China Dept.of Physics Electrical and Intelligent Manufacturing Huaihua University Changsha China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Manual visual inspection for defect detection in manufacturing can lead to high false positive rates. This paper compares data mining techniques for the binary classification of defects in flat products in a relatively open scenario. The background provides context on research into data mining-based detection. The methodology is divided into five sections: data collection, selection, preprocessing and transformation, classification, and model evaluation. Each section details the techniques applied, tailored to the application context, with specifying examples. Experiments apply real-world data sets self-collected from production lines. Statistics like prediction accuracy are generated by established data mining evaluation algorithms. Results indicate the efficacy of data mining for automated defect detection over manual approaches. Finally, conclusions summarize findings and discuss the limitations of the current research and planned future work to address the drawbacks. Overall, the formalized methodology and supportive experimental results demonstrate both the viability and potential areas of improvement for data mining in manufacturing quality control. © 2025, The Authors. All rights reserved.

关键词： Data accuracy

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