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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221-230 订阅

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Adaptive Hybrid Neural Network Sliding Mode control of Ultrasonic Motors

Adaptive Hybrid Neural Network Sliding Mode Control of Ultra...

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

作者： Xinqiang Wang Chenyang Ding Yunlang Xu Xinyi Su Academy for Engineering and Technology Fudan University Shanghai China School of Microelectronics Fudan University Shanghai China

Nonlinear disturbances such as viscous friction, static friction and external disturbances exist in ultrasonic motors, which seriously affect their high-performance motion control process. To realize the high precision scanning motion of ultrasonic motor, an adaptive hybrid neural network sliding mode control (AHNN-SMC) method is designed. the neural network basis function part adopts a combination of Gaussian basis and partial friction model, combined with an adaptive algorithm to achieve online nonlinear compensation of the ultrasonic motor. In order to solve the problem of overestimation of switching control gain caused by the difficulty of accurate estimation of uncertain upper bound in the control process, adaptive switching gain is further adopted in this paper to cut down the influence of chatter in the control process. In order to verify the performance of AHNN-SMC in ultrasonic motor scanning motion control, position tracking simulation experiments are performed for sinusoidal signals with a maximum displacement of 20mm and compared with several algorithms. the results show that the maximum tracking error (MAE) of AHNN-SMC is 3.15 μ m and the mean error (RMSE) is 1.25 μ m, and the results indicate that the method has good position tracking performance.

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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...

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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

关键词： Quality control

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Bidirectional Scanning Based Medium Access control Algorithm in Directional Aviation Relay Network with Multiple Air Nodes 6th

Bidirectional Scanning Based Medium Access Control Algorithm...

引用

6th international conference on Smart Grid and Internet of things, SGIoT 2022

作者： Ma, Gaoxiang Yan, Zhongjiang Yang, Mao Li, Bo School of Electronics and Information Northwestern Polytechnical University Xi’an China

ISBN: (纸本)9783031312748

Directional aviation relay networks are widely used to improve the performance of ad hoc networks. Aiming at the disadvantage of large data packet delay of directional aviation relay network with single air node, this paper proposes a medium access control algorithm based on multiple air nodes and bidirectional scanning, which can effectively reduce the total delay of data packets. First, the algorithm divides multiple air nodes into two groups, using clockwise and counterclockwise scanning modes respectively, to provide data relay and forwarding services for ground nodes. According to the number of air nodes and their scanning mode, as well as the sector in which the destination node of the to-be-sent traffic is located, the ground nodes determine whether the to-be-sent traffic uses a single-aircraft relay two-hop transmission or a double-aircraft relay four-hop transmission link in a distributed manner. Secondly, the theoretical model of unidirectional scanning and bidirectional scanning algorithm is built, and the theoretical packet delay distribution law of the two algorithms as well as the optimal ratio of bidirectional scanning algorithm in reducing packet delay compared with unidirectional scanning algorithm is obtained. Finally, the performance of the proposed algorithm is verified by simulation. the simulation results are consistent with the theoretical analysis, which show that in the case of 4 to 8 air nodes, compared with unidirectional scanning algorithm, the total delay of packets in bidirectional scanning algorithm is reduced by 23.10 % –28.81 %. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Aircraft

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Current control of EAST fast control power supply based on single neuron adaptive predictive PI

Current control of EAST fast control power supply based on s...

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China international Electrical and Energy conference (CIEEC)

作者： Haihong Huang Zhao Chen School of Electrical Engineering and Automation Hefei University of Technology Hefei China

the Experimental advanced Superconducting Tokamak (EAST) is an important device developed by China to realize controllable nuclear fusion. the vertical displacement of plasma is controlled by the current output from EAST fast control power supply. Traditional PI control has shortcomings in current dynamic response. A single neuron adaptive predictive PI (SNAD-PPI) control algorithm is proposed to ensure fast and accurate current output. the adaptive structure is used combined with the single neuron structure to improve the problem of insufficient dynamic response under the control of fixed PI parameters. Advance tracking reference current is used as the adaptive objective function of tutor-supervised learning for setting PI parameters in a single neuron. the proportional adjustment coefficient and the proportional learning rate of single neuron adaptive control are adjusted in real time to realize fast convergence of single neuron. Outstanding current dynamic response performance compare to traditional PI control is verified by experiments.

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Prediction of Silicon Content in Molten Iron of Blast Furnace Based on Multi-dimensional Sequential Characteristic Reconstruction

Prediction of Silicon Content in Molten Iron of Blast Furnac...

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Industrial Cyber-Physical Systems (ICPS)

作者： Zhuofu Zhang Weihua Cao Jie Hu Qifu Chen 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 Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

Molten iron is the primary output of blast furnace production. the content of silicon in molten iron clearly correlates with blast furnace temperature. However, due to the intricate conditions of blast furnace production, the silicon content in molten iron is nonlinear and unstable. therefore, this paper adopts variational mode decomposition (VMD) to decompose and extract the feature information of the real silicon content data of LY Steel in March 2022, then uses Grey Wolf optimization (GWO) algorithm to optimize the parameters of the support vector regression (SVR) prediction model, and takes the decomposed data as model input for experimental verification. By comparing the predicted results with the real historical data of blast furnace production, it is found that the degree of fit is about 94.2%, which offers a new idea for the prediction of silicon content.

关键词： Support vector machines Production Predictive models Blast furnaces Prediction algorithms Feature extraction Silicon

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Backstepping control of a Double-Stage Photovoltaic System Connected to the Tree-Phase Grid Under Partial Shading Conditions

Backstepping Control of a Double-Stage Photovoltaic System C...

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international conference on advanced Communication Technologies and Networking (CommNet)

作者： Khadija Sabri Ouadia El Maguiri Abdelmajid Farchi Engineering Industrial Management and Innovation Laboratory Faculty of Sciences and Technics Hassan 1st University Settat Morocco

this paper solves the problem of controlling maximum power in photovoltaic systems connected to the grid in order to track changes and meet energy demand. Our work focuses on two crucial objectives: i. extracting the maximum available power (MPPT) in case of partial shading, ii. unifying the power factor (FPU). To accomplish this, we integrated a non-linear backstepping controller to extract the Maximum Power Point (MPP) using the Particle Swarming Algorithm (PSO), influencing the duty cycle of the DC/DC converter. Additionally, we applied this approach to the three-phase voltage-source inverter for achieving a unified power factor (FPU). We validated the efficacy of the introduced system through modeling and simulation in MATLAB/Simulink. the controlled system’s asymptotic stability is demonstrated mathematically, and simulation results show that the controller has successfully accomplished all of its goals with excellent dynamic performance even in the partial shading conditions. the suggested controller also exhibits excellent robustness against system disturbances, which is its primary advantage over alternative control systems.

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DV-Hop Localization Strategy Based on Beacon Node Confidence Analysis

DV-Hop Localization Strategy Based on Beacon Node Confidence...

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

作者： Han Shen 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

To address the problem that the DV-Hop localization model for wireless sensor networks can no longer meet the current localization accuracy requirements, this paper proposes the concept of beacon node trustworthiness to provide ideas and basis for improving the optimization model. Based on the principle of least squares in the localization model, weighted least squares is introduced to present the trustworthiness for beacon nodes with different characteristics. To analyze the characteristics of the trustworthiness of beacon nodes, 18 functions are designed in terms of function instrumentality and decrement able, hierarchical faultiness, etc. Finally, the conclusions obtained from the credibility test are tested with weighted matrix and average hop distance weighting, and the localization error is reduced from 32.68% to 31.78%, with a role rate of 100%. the credibility characteristics of beacon nodes discussed in this paper provide ideas for subsequent optimization of the localization model.

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Real Time Surgical Instrument Object Detection Using YOLOv7 1

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6th EAI international conference on Robotics and Networks, EAI ROSENET 2022

作者： Zheng, Laiwang Liu, Zhenzhong Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin China

ISBN: (数字)9783031338267

ISBN: (纸本)9783031338250

With the rapid development of minimally invasive surgery (MIS) in the medical field, surgical robot technology has developed rapidly. Human-machine combined assisted surgical technology has begun to bring good news to the majority of doctors and patients who need surgery. It has gradually become a common medical treatment. Minimally invasive surgery can overcome the shortcomings of traditional surgery. MIS has the advantages of less trauma, less impact on patients, faster recovery, less intraoperative blood loss, and so on. For surgical robots, surgical instruments are important execution components that have been widely concerned and studied in recent years. One of the core technologies of surgical robots is the detection of surgical equipment based on deep learning, which can effectively assist doctors to complete the surgery. Current surgical instrument detection methods need to be improved in real-time performance and accuracy. therefore, this paper proposes a new real-time detection algorithm, which is based on the deep learning object detection system YOLOv7. the real dataset, which contained information on seven surgical instruments, was selected for training and validation. Experiments show that the method has good precision, recall rate, and mAP, which can be used to improve the ability of robot-assisted doctors to identify instruments during surgery. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object recognition

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Enhanced DDoS Detection using Machine Learning

Enhanced DDoS Detection using Machine Learning

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international conference on Information Systems and Computer Networks (ISCON)

作者： Rashmikiran Pandey Mrinal Pandey Alexey Nazarov Department of Radio Engineering and Cybernetics Moscow Institute of Physics and Technology Moscow Russia Federal Research Center Computer Science and Control Russian Academy of Sciences Moscow Russia

the rapid growth of internet population poses a serious challenge to the security of internet resources. the security is directly affected by the hits of Denial of Services (DoS) attack which is rampant nowadays. With this evolving threat, designing a cutting-edge method is difficult from a cyber-security perspective. In this study, we propose a deep learning-based system for detecting Distributed Denial of Service (DDoS) attacks, which utilizes Logistic Regression, K- Nearest Neighbor, and Random Forest algorithms. We assess proposed models using a recently updated NSL KDD dataset. Our research’s findings also demonstrate that proposed model is highly accurate in detecting Distributed Denial of Service (DDoS) attacks. Our results show that our proposed model significantly improves upon current state-of-the-art attack detection methods

关键词： Machine learning algorithms Computational modeling Sociology Forestry Denial-of-service attack Security Computer crime

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Federated Kalman Filter Based on KMEWMA control Chart for Integrated Navigation

Federated Kalman Filter Based on KMEWMA Control Chart for In...

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IEEE international conference on Electronic Information and Communication Technology (ICEICT)

作者： Zhiyuan Jiao Xiyuan Chen Ning Gao Yulu Zhong Junwei Wang Cundeng Wang School of Instrument Science and Engineering Southeast university Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology Ministry of Education Nanjing China

Integrated navigation systems are prone to faults in complex environments, necessitating the isolation or mitigation of their effects on the system. this paper introduces a federated Kalman filter (FKF) structure based on the kernel multivariate exponentially weighted moving-average (KMEWMA) control chart, which incorporates statistical process control techniques to detect and mitigate faults effectively in integrated navigation systems. the proposed method demonstrates outstanding fault detection capability, successfully addressing both gradual and sudden-varying faults. Compared with chi-square test, the KMEWMA control chart could detect faults more precisely. Furthermore, instead of solely isolating faults, the system adaptively adjusts them to enhance positioning accuracy and stability. the simulation results validate the effectiveness and superiority of the proposed method.

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