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2023 International Conference on Sustainable Emerging Innovations in engineering and Technology, ICSEIET 2023

作者： Patil, Trushant Nandan, Durgesh Sharma, Kanhaiya Parashar, Deepak Department of Robotics and Automation Pune India Department of Electronics and Telecommunication Engg. Pune India Department of Computer Science & Engineering Pune India Faculty of Engineering Pune India

ISBN: (纸本)9798350329186

Drone-based predictive structure analysis is a rapidly growing field that has the potential to revolutionize infrastructure and building inspection. In this review of the research, we give a general overview of how drones could increase the effectiveness and precision of structural analysis, particularly for keeping an eye on difficult-to-reach locations. The usage of drones for 3D mapping, inspection, and monitoring of diverse constructions, such as concrete buildings, bridges, tunnels, and dams. The investigations show that drone-based inspection can produce 3D models and high-resolution photos, allowing for remote monitoring and the early identification of possible problems. The findings show that drones may greatly boost infrastructure management's safety and effectiveness, making them an important tool for predictive structural research. © 2023 IEEE.

关键词： Drones

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Control of Microgrid Cluster Using Raspberry Pi

Control of Microgrid Cluster Using Raspberry Pi

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2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

作者： Carrasco-González, David Sarrias-Mena, Raúl Horrillo-Quintero, Pablo Llorens-Iborra, Francisco Fernández-Ramírez, Luis M. Suret Research Group Department of Electrical Engineering Algeciras Spain Uca Suret Reseach Group Dept. of Engineering in Automation Electronics and Computer Architecture and Networks Algeciras Spain

ISBN: (纸本)9781665471640

Currently, microgrid clusters (MGC) are being increasingly used owing to their benefits and human needs. MGs can be composed of power generation, consumption, and storage elements. They permit the combination of DC and AC technologies, even for a single microgrid (MG). In this paper, a new control system of a MGC consisting of two MGs connected to the main grid is developed. One of them is a DC MG that is composed of a wind turbine (WT), electrolyzer (EZ), ultracapacitor (UC), fuel cell (FC) and DC loads. The other MG is an AC MG, which is made up of a PV generator, a battery bank and AC loads. The MGC is tested under different working conditions in an experimental setup composed of a Raspberry Pi microcontroller, an OPAL-RT 4510 unit and a PC host. The experimental setup allows the system to run in real time and validate the results obtained. Finally, the control system implemented in the MGC exhibits a correct behaviour under different conditions, according to the results obtained. © 2023 IEEE.

关键词： energy management system Microgrid cluster Raspberry Pi real-time simulation

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User-Oriented Interest Representation on Knowledge Graph for Long-Tail Recommendation 9th

User-Oriented Interest Representation on Knowledge Graph fo...

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Zhang, Zhipeng Zhang, Yuhang Wang, Anqi Zhou, Pinglei Zhang, Yao Ren, Yonggong School of Computer Science and Artificial Intelligence Liaoning Normal University Dalian China Mechanical Engineering and Automation Dalian Polytechnic University Dalian China

ISBN: (纸本)9783031466731

Graph neural networks have demonstrated impressive performance in the field of recommender systems. However, existing graph neural network recommendation approaches are proficient in capturing users’ mainstream interests and recommending popular items but fall short in effectively identifying users’ niche interests, thus failing to meet users’ personalized needs. To this end, this paper proposes the User-oriented Interest Representation on Knowledge Graph (UIR-KG) approach, which leverages the rich semantic information on the knowledge graph to learn users’ long-tail interest representation. UIR-KG maximizes the recommendation of long-tail items while satisfying users’ mainstream interests as much as possible. Firstly, a popular constraint-based long-tail neighbor selector is proposed, which obtains the target user’s long-tail neighbors by conducting high-order random walks on the collaborative knowledge graph and constraining item popularity. Secondly, a knowledge-enhanced hybrid attention aggregator is proposed, which conducts high-order aggregation of users’ long-tail interest representations on the collaborative knowledge graph by comprehensively combining relationship-aware attention and self-attention mechanisms. Finally, UIR-KG predicts the ratings of uninteracted items and provides Top-N recommendation results for the target user. Experimental results on real datasets show that compared with existing relevant approaches, UIR-KG can effectively improve recommendation diversity while maintaining recommendation accuracy, especially in long-tail recommendations. Code is available at https://***/ZZP-RS/UIR-KG © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Knowledge graph

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Reinforcement Learning for Online Dispatching Policy in Real-Time Train Timetable Rescheduling

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IEEE Transactions on Intelligent Transportation Systems 2024年第1期25卷 478-490页

作者： Yue, Peng Jin, Yaochu Dai, Xuewu Feng, Zhenhua Cui, Dongliang Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China University of Bielefeld Nature Inspired Computing and Engineering Group Bielefeld33619 Germany University of Surrey Department of Computer Science GuildfordGU2 7XH United Kingdom University of Surrey School of Computer Science and Electronic Engineering GuildfordGU2 7XH United Kingdom

Train Timetable Rescheduling (TTR) is a crucial task in the daily operation of high-speed railways to maintain punctuality and efficiency in the presence of unexpected disturbances. However, it is challenging to promptly create a rescheduled timetable in real time. In this study, we propose a reinforcement-learning-based method for real-time rescheduling of high-speed trains. The key innovation of the proposed method is to learn a well-generalized dispatching policy from a large amount of samples, which can be applied to the TTR task directly. At first, the problem is transformed into a multi-stage decision process, and the decision agent is designed to predict dispatching rules. To enhance the training efficiency, we generate a small yet good-quality action set to reduce invalid explorations. Besides, we propose an action sampling strategy for action selection, which implements forward planning with consideration of evaluation uncertainty, thus improving search efficiency. Extensive experimental results demonstrate the effectiveness and competitiveness of the proposed method. It has been proven that the local policies trained by the proposed method can be applied to numerous problem instances directly, rendering it unnecessary to use human-designed rules. © 2000-2011 IEEE.

关键词： Reinforcement learning

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Collaborative Control for Multi Vehicle Platoons Using Longitudinal and Lateral Decoupling Method

Collaborative Control for Multi Vehicle Platoons Using Longi...

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2023 IEEE International Conference on Unmanned Systems, ICUS 2023

作者： Ding, Mengtao Huang, Longwang Zhu, Yongxin Li, Yongfu School of Automation Chongqing University of Posts and Telecommunications Chongqing China College of Computer Science and Engineering Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9798350316308

This paper studies the formation control of autonomous vehicle with vehicle to vehicle (V2V) communication connection. Firstly, this paper introduces the dynamic model of a vehicle and the cooperation mechanism. Then, a leader-follower driving pattern based on coordinated control protocol is designed. Particularly, a longitudinal and lateral controller is designed for the vehicle platoon, such that cooperative control for multi-string vehicle platoons can be achieved. The proposed control protocol not only ensure that the vehicle can follow the leader vehicle but also change the lanes flexibly. Eventually, the analysis of stability and the simulation study are presented to illustrate the effectiveness of the proposed scheme. © 2023 IEEE.

关键词： Military applications

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High-quality Trajectory Generation for Autonomous Driving: A Lightweight Federated Learning-based Diffusion Model

High-quality Trajectory Generation for Autonomous Driving: A...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Gao, Runquan Kang, Jiawen Lai, Bingkun Xu, Minrui Sun, Geng Zhang, Tao Zhang, Weiting Yang, Dong Guangdong University of Technology School of Automation Guangzhou China Nanyang Technological University School of Computer Science and Engineering Singapore Jilin University College of Computer Science and Technology Changchun China Beijing Jiaotong University School of Software Engineering Beijing China Beijing Jiaotong University School of Electronic and Information Engineering Beijing China

ISBN: (纸本)9798350351255

Vehicle trajectory data plays a pivotal role in simulation testing for autonomous driving. Hence, there exist well-established trajectory generation methods employing deep generative models to generate trajectories mapping the distribution of the original dataset, thereby augmenting existing trajectory datasets. However, these methods typically rely on large datasets gathered by governmental or organizational entities for central training, which may pose data privacy, security, and accessibility issues. Therefore, it is challenging to generate high-quality traffic trajectory data while preserving privacy which involves a delicate balance between these two objectives. To deal with this challenge, we introduce Federated Learning into the diffusion model and propose a Federated Learning-based diffusion model (FedDifftraj) to generate traffic trajectory data. Unlike existing central training methods, FedDifftraj aggregates model parameters uploaded by different vehicles and then updates a global model. Additionally, there is a substantial communication overhead incurred during the training of the federated diffusion model. Therefore, we quantize the local diffusion model before uploading it to the parameter server. Through extensive simulations on real-world datasets, FedDifftraj can generate high-quality traffic trajectory data that is consistent with the results of the central training while preserving privacy and reducing communication overhead by 93.74% when utilizing 8-bit quantization. © 2024 IEEE.

关键词： Differential privacy

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Resilient Observer-based Security Control for Cyber-Physical Systems under Multiple Attacks 13

Resilient Observer-based Security Control for Cyber-Physical...

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13th International Conference on Information Science and Technology, ICIST 2023

作者： Yang, Ximing Li, Tieshan Yang, Hanqing Gao, Xiaoyang Liu, Ran Long, Yue Chen, C.L. Philip School of Automation Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Computer Science and Engineering South China University of Technology Guangzhou510641 China

ISBN: (纸本)9798350313925

The issue of security control for cyber-physical systems (CPSs) is now receiving a lot of attention. Among them, the network layer is more seriously affected by cyber-attacks than the physical layer. In this paper, we investigate the denial-of-service (DoS) attacks and false data injection (FDI) attack on data transmission. Firstly, the FDI attack that exists in the actuator is estimated by proposing the observer. Then, due to the presence of DoS attacks in the sensor channels, the estimation effect of the former observer is affected. To solve this problem, a new resilient observer is proposed by introducing the switching mechanism. The proposed resilient observer can effectively reduce the adverse effects of DoS attacks on data transmission. Next, a false data estimation-based security controller is proposed, which can ensure the stability of the closed-loop system. Finally, a simulation is given to verify the effectiveness of the proposed method. © 2023 IEEE.

关键词： Cyber Physical System

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Texture Analysis of Breast US Images Using Morphological Transforms, Hausdorff Dimension and Bagging Ensemble Method 32

Texture Analysis of Breast US Images Using Morphological Tra...

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32nd Mediterranean Conference on Control and automation, MED 2024

作者： Tabacaru, Gigi Moldovanu, Simona Barbu, Marian 'Dunarea de Jos' University of Galati Faculty of Automation Computers Electrical Engineering and Electronics Department of Automatic Control and Electrical Engineering Romania 'Dunarea de Jos' University of Galati Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics Romania Dunarea de Jos University of Galati The Modelling & Simulation Laboratory Galati800008 Romania

ISBN: (纸本)9798350395440

In this work, an attempt is made for the first time to use the measurement pattern generated by morphological transformation quantified by Hausdorff fractal dimension (HFD) and classified with ensemble learning based on bagging. The proposed work uses three morphological transformations for image preprocessing: hit-and-miss transform (HMT), white (WHT), and black top-hat (BHT). The pattern texture of US breast images is described by extracting the HFD from the regions of interest (ROI) after the ultrasound (US) images have been preprocessed. The main objective of this study was achieved by comparatively analyzing the classification performance of features using the Random Forest (RF), Extra Trees (ET) classifier, and bagging ensemble method based on XGBoot classifier. In presented study, the XGBoost classifier and BHT image processing method give an accuracy of 89.8% in a binary classification, benign versus malignant breast cancer. © 2024 IEEE.

关键词： Fractal dimension

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Flower Image Identification and Feature Extracting Method Based on Transfer Learning 26

Flower Image Identification and Feature Extracting Method Ba...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Xingt, Xiaofei Wang, Zimeng Chen, Xiaoli Zeng, Yunxuan He, Siqi Guangzhou University School of Computer Science and Cyber Engineering Guangzhou510006 China Hangzhou Dianzi University School of Automation Hangzhou310018 China

ISBN: (纸本)9798350331684

Artificial intelligence (AI) technology is booming in information society, and its role has become one of the hot research topics in the field of image identification. Flowers play essential role in daily life, but the identification and know well of some flower species is not clear. Therefore, the system is of great significance to help people obtain the detailed information of plant flowers. The proposed method is to use machine learning algorithm to process and identify flower images, mainly using transfer learning model, based on the trained model parameters, and taking 25 reptiles as the training base. After the optimal training and parameters adjustment, the extracted features were transferred to the flower model. The experimental results show that the effective recognition accuracy of different flowers is up to 98.20%. The proposed algorithm can be adopted to the small-scale training task, and can reduces the amount of machine operation. In addition, the algorithm also can obtain satisfactory recognition accuracy within a short time. The flower information is presented in the form of dynamic web pages, which improved readability of idenfication results. © 2023 IEEE.

关键词： Websites

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A Vision-Based Autonomous AI Tic-Tac-Toe Manipulation System 12

A Vision-Based Autonomous AI Tic-Tac-Toe Manipulation System

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12th International Electrical engineering Congress, iEECON 2024

作者： Rakprayoon, Panjawee Ounsiri, Akechai Chongyoa, Ratchanon Bangkok University Faculty Of Engineering Dept. Ai Engineering And Data Science Pathum Thani Thailand Dna Robotics And Automation System Company Limited Pathum Thani Thailand Bangkok University Faculty Of Engineering Dept. Computer And Robotics Engineering Pathum Thani Thailand

ISBN: (纸本)9798350383591

For an AI tic-tac-toe manipulator application, a real-time vision-based approach is proposed. The technique employs the RealSense camera to capture color and depth images. Combining object detection and image processing techniques to identify X's and O's positions on the game board. However, we recognize that errors can occur during object movement when using only 2D images. To solve this problem, a calibration method between RealSense and the manipulator based on the point-to-plane ICP algorithm is presented. This method provides the 3D object position in the robot coordinate, resulting in increasing the accuracy from 86.25% to 97.25%. Our experiment shows that this technique is highly effective and can accurately identify position objects. © 2024 IEEE.

关键词： Object detection

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