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检索条件"任意字段=2023 6th International Conference on Advanced Algorithms and Control Engineering, ICAACE 2023"

共 292 条记录，以下是191-200 订阅

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A Lightweight Sementic Segmentation Model for Metro Tunnel Scene Based on Vehicle Front Camera 1

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6th international conference on Electrical engineering and Information Technologies for Rail Transportation, EITRT 2023

作者： Bai, Jie Qin, Yong Cao, Zhiwei Lian, Lirong Gao, Yang Ge, Xuanyu Yu, Hang State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing100044 China School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China Key Laboratory of Railway Industry of Proactive Safety and Risk Control Beijing100044 China

ISBN: (数字)9789819993192

ISBN: (纸本)9789819993185

Metro intrusion seriously affects the safety of metro operation. Semantic segmentation is the main content of the research on metro intrusion. Tunnels account for a large proportion of metro lines, by processing the images captured by the vehicle front camera, the state of the elements in the tunnel can be obtained, among which the perception and understanding of the whole scene is an important part of the image processing. For on-board cameras, we propose a lightweight semantic segmentation algorithm for metro to satisfy the real-time requirements. Finally, for a 2048 × 1024 input, the algorithm achieves 75.21% MIoU on the metro tunnel data set with speed of 54FPS (Frames Per Second) on NVIDIA 3090 Ti card. © 2024, Beijing Paike Culture Commu. Co., Ltd.

关键词： Semantic Segmentation

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Text Classification Method for Urban Rail Transit Fire Accident Cases Based on Word2Vec + LSTM 6th

Text Classification Method for Urban Rail Transit Fire Accid...

引用

6th international conference on Electrical engineering and Information Technologies for Rail Transportation, EITRT 2023

作者： Yan, Han Ma, Xiaoping Chen, Fei Zhao, Ruhao Wang, Ruoxuan State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing100044 China School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China Key Laboratory of Railway Industry of Proactive Safety and Risk Control Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9789819993147

Fire accidents are the type of accidents in urban rail transit that occur frequently, are extremely dangerous, and cause huge losses, which and bring many difficulties to the safe operation and management of urban rail transit. Aiming at the data processing of urban rail transit fire accident cases, this paper first collects urban rail transit fire accident cases through multiple channels and divides them into five categories according to the fire causes: electrical reasons, mechanical reasons, artificial arson, improper operation, and accidental fire, and then establishes A text classification model based on Word2Vec feature processing and LSTM classification prediction is implemented to automatically classify fire texts with high accuracy, thereby solving the problems of time-consuming, low-quality, and strong subjectivity in manual text labeling. Furthermore, it provides high-quality data support for realizing refined risk prevention and control and accident management. Finally, this paper uses model comparison experiments to further verify the superiority of LSTM as the model backbone network. © 2024, Beijing Paike Culture Commu. Co., Ltd.

关键词： Fires

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Events-Based Model Predictive control for Polytopic Uncertain Systems Under False Data Injection Attacks

Events-Based Model Predictive Control for Polytopic Uncertai...

引用

Industrial Cyber-Physical Systems (ICPS)

作者： Fan Wei Chaoling Zhangabc Xiongbo Wan School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

In this paper, the events-based model predictive control (MPC) problem is studied for systems under false data injection (FDI) attacks. A time-varying event-triggered mechanism (ETM) is proposed to manage measurement data packet releases and a static ETM is used to reduce the influence of the FDI attacks on the controller. By using the properties of the defined robust positive invariant set, a solvable auxiliary optimization problem (OP) is proposed to design the controller. the recursive feasibility of the auxiliary OP and the input-to-state stability of the closed-loop system are guaranteed. the validity of the developed ETMs-based anti-attack MPC algorithm is shown by an example.

关键词： Uncertain systems Predictive models Prediction algorithms Stability analysis Data models Numerical models Closed loop systems

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Fault Detection for Positive Switched Systems with All Modes Unstable

Fault Detection for Positive Switched Systems with All Modes...

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IEEE international conference of Intelligent Robotic and control engineering (IRCE)

作者： Qingyu Su Zhongxin Fan Jian Li School of Automation Engineering Northeast Electric Power University Jilin P. R. China Jilin Provincial Key Laboratory of Advanced Control Technology of Smart Energy Jilin P. R. China School of Automation Southeast University Nanjing P. R. China

Exponential stability, $L_th$ -gain performance and fault detection are investigated in this paper for continuous positive switched system (PSS) when there exists no stable subsystems. To eliminate the instability brought by the unstable modes ensure the exponential stbility, a time-driven switching strategy is proposed by means of the average dwell time approach. then, the $L_th$ -gain performance for the PSS is guaranteed. Additionally, the stable filter design is converted into the $L_th$ -gain analysis. Sufficient conditions are deduced to guarantee the $L_th$ -gain performance with exponential stability in terms of linear matrices inequalities (LMIs). Finally, an example is provided to test and verify the effectiveness and practicality.

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Design of Switched Reluctance Motor(SRM) Servo control for Robot Joints

Design of Switched Reluctance Motor(SRM) Servo Control for R...

引用

international conference on Electronics Technology (ICET)

作者： Yongsheng Wang Yuehong Dai Junyao Wang School of Aeronautics and Astronautics University of Electronic Science and Technology of China UESTC Chengdu China

To explore the applicability of the Switched Reluctance Motor(SRM) used in robot joints as a servo motor, a SRM servo control system was designed based on MATLAB. In this paper, MotorSolve was used to obtain the simulation data of the discrete operation of the motor, and the cerebellar model articulation controller(CMAC) was designed to learn the inductance-position angle-current and torque-position angle-current nonlinear models, the accuracy of the model is higher than that of ordinary look-up tables. the position control of the SRM servo system is realized by using cascade PID. the result shows that the servo system has a fast response time of 0. 35s under constant load, an overshoot of 5%, and an output torque fluctuation of 5.4%., which basically meets the requirements of robot joint servo control. Simultaneously, the system provides a simulation experiment platform for the follow-up research on SRM servo control for robot joints, and more advanced servo control algorithms can be studied on this basis.

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Reinforcement Learning Scheduling Research for Edge Servers

Reinforcement Learning Scheduling Research for Edge Servers

引用

international conference on Computer Network, Electronic and Automation (ICCNEA)

作者： Yonggui Han Zhongsheng Wang School of Computer Science and Engineering Xi’an Technological University Xi’an China State and Provincial Joint Engineering Lab. of Advanced Network Monitoring and Control Xi'an Technological University Xi'an China

the rapid development of the Internet of things and big data has brought about massive amounts of data and the need for real-time responses, which traditional computing models can no longer meet. Edge computing comes into being, which pushes computing resources and services closer to the source of data generation so that data can be processed and analyzed closer to the user. the scheduling of siting strategies for edge servers is crucial for improving the performance of edge computing networks. Traditional scheduling methods for siting strategies are often based on rules or optimization algorithms, which lack adaptive and intelligent management of the network *** paper proposes a reinforcement learning DQN-based edge server siting policy scheduling method, which aims to achieve intelligent management and resource optimization of edge computing networks by learning the optimal policy. Experimental results show that the method has significant performance advantages in terms of delay, load balancing and energy consumption. the experiment adopts DQN algorithm, which is based on Q-learning and guides the agent to make decisions by optimizing Q-value function. In this paper, the reinforcement learning scheduling algorithm of edge server is studied and compared with other three algorithms. According to the experimental results, the proposed reinforcement learning scheduling algorithm reduces the delay by 7%, the energy consumption by 3%, and the load balancing by 20% compared with the other three algorithms. this shows that the algorithm has significant advantages in improving system performance.

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Obstacle Detection of Unmanned Surface Vessel based on Faster RCNN

Obstacle Detection of Unmanned Surface Vessel based on Faste...

引用

Industrial Cyber-Physical Systems (ICPS)

作者： Jiahe Cai Sheng Du Chengda Lu Bo Xiao Min Wu School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China Guangzhou Marine Geological Survey Guangzhou China

With the development of unmanned surface vessel in the world today, obstacle avoidance using environmental information is the basis to ensure its high maneuvering performance and safety. However, directly using standard algorithms will lead to missing and wrong identification severely for characteristics of marine obstacles. this paper adds a multi-scale feature extraction layer of dilation convolution and group convolution to Faster Region based Convolutional Neural Network (Faster-RCNN), the baseline model, and changes the classification algorithm to improve its robustness and accuracy. Soft-Non Maximum Suppression (Soft-NMS) is used to enhance the prediction effects further. After improvements, the mean average precision value increases by 3.35%, and the final loss value decreases by 0.20. Given the phenomenon of missing and misidentification in the prediction by the baseline model, the results of our new model show outstanding performance.

关键词： Convolution Predictive models Feature extraction Prediction algorithms Robustness Classification algorithms Safety

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A Robust control Technique for Pitch control of an Aeropendulum

A Robust Control Technique for Pitch Control of an Aeropendu...

引用

Robotics, control and Automation engineering (RCAE), international conference on

作者： M. Hassan Tanveer Charles Koduru Rupam Gupta Roy Girish V. Lakhekar Cary Chun Department of Robotics and Mechatronics Engineering Kennesaw State University Marietta GA United States Department of Electronics and Instrumentation Engineering National Institute of Technology Agartala Barjala Jirania Agartala Tripura India Department of Instrumentation Engineering College of Engineering Pune Maharashtra India

Pitch control of an aviation system presents a plethora of control complications, such as nonlinear effects, parameter uncertainties, disturbances, etc. In the face of parametric uncertainties and disturbances, as well as communication limits in an auditory medium, it is difficult to create efficient control algorithms that can accomplish precise path-following tasks and effective resilient control actions. this paper presents the Second Order Sliding Mode control method for regulating the pitch angle in an aero-pendulum mechatronic system. the fundamental purpose of this study is to develop a reliable controller capable of minimising the system error between the regulated signal and the planned signal that corresponds to the pitch angle of the vehicle. By employing the proposed approach, both the prevalence of chattering and the control efficiency can be greatly improved. A comparison is given between the proposed model and the classical technique now in use, such as the classical Sliding Mode controller.

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Bipartite Consensus control for Fixed and Switching Multi-Agent Systems via State Feedback, Observer and Output Feedback

Bipartite Consensus Control for Fixed and Switching Multi-Ag...

引用

international conference on Intelligent Autonomous Systems (ICoIAS)

作者： Yao Yao Jie Zhang Jian-An Wang Shanxi Key Laboratory of Advanced Control and Equipment Intelligence College of Electronic Information Engineering Taiyuan University of Science and Technology Taiyuan China

this paper studies the bipartite consensus problem of high-order linear leader-following multi-agent systems (MASs) under fixed and switching topologies. All followers are divided into two competitive subsets, where the followers in the same subset are cooperative. In different topological environment, we respectively design three different distributed controllers via state feedback, observer and output feedback to achieve the so-called bipartite consensus with the structural balanced condition. Finally, numerical examples are given to demonstrate the analytic results.

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Analysis and countermeasures of drilling challenges in complex ultra-deep exploration wells in thrust structural belt with multiple salt layers 6

Analysis and countermeasures of drilling challenges in compl...

引用

2023 6th international Workshop on Environment and Geoscience, IWEG 2023

作者： Zhang, Wei Zhou, Tuo Jin, Yulai Xiao, Yue Li, Hangyuan Baletabieke, Bahedaer Gu, Yixin CNPC Engineering Technology R&D Company Limited Beijing China College of Petroleum Engineering Beijing China

the deep and ultra-deep zones of Bokhtar oilfield in Tajikistan are the critical blocks for future exploration and development, which are imbedded between two salt layers, accompany with four reservoir and cap assemblages of middle and upper Jurassic, Lower Cretaceous, Upper Cretaceous and Paleogene. the reservoir and cap assemblages of pre-salt carbonate and salt-gypsum cap have great potential. the complicated geological structure, uncertain formation and non-developed pre-salt formations made the critical challenges for drilling operation, including: (1) the difficult casing program design caused by peripheral compression and thrust of the basin, the complicated geological structure, the large geological difference above and below the salt layer, the unfaithful prediction of multiple sets of salt layer, great formation pressure uncertainty, and many sealing points;(2) the upper salt formations are featured by developed thrust belt and large dip angle, the contradiction between deviation prevention and drilling acceleration in high steep structure is prominent, and the wellbore quality is difficult to control;(3) Due to the mud loss in upper salt layer, loss coexisted with blow out in the salt layer and inaccurate prediction of salt bottom, the drilling complexes such as mud loss, overflow and well collapse are occurred frequently;(4) Ultra-deep well depth (over 7000m), HthP (180°C,140MPa), multiple salt layers and long open hole section made high requirements on high temperature resistance of drilling fluid, cementing slurry and downhole tools, as well as high drilling risk and cost. It is necessary to carry out the drilling difficulties investigation of ultra-deep exploration well under the consideration of pertinence, safety, applicability and economy, then formed the suitable drilling engineering plan for Bokhtar Block through the optimization of domestic and foreign advanced and mature deep and ultra-deep well technologies, which is used to provide the techni

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