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design of Pelton Turbine Test Stand in Plateau Environment 4

Design of Pelton Turbine Test Stand in Plateau Environment

引用

4th IAHR Asian Working Group Symposium on Hydraulic Machinery and systems

作者： Cai, Jinhan Zhou, Xuejun Luo, Hongying Wang, Zhengwei Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology China College of Mechanical Engineering Tianjin Sino-German University of Applied Science No. 2 Yashen Road Tianjin China College of Hydraulic and Civil Engineering Tibet Agricultural and Animal Husbandry University China Department of Energy and Power Engineering Tsinghua University China

In recent years, with the continuous development of China's hydropower projects, Pelton turbines are gradually being valued, and the focus of development has shifted from plain areas to plateau areas. Environmental impacts such as high altitude, water temperature, and high sediment content in the plateau area have caused serious interference to equipment that originally worked in a stable environment, for example, the failure of the cooperation between the parts of the turbine and the mutual seizure, the increased wear of the nozzle needle, the decrease of the flow rate and the water outlet speed of the nozzle, and the decrease of the air pressure in the plateau environment lead to the change of the flow state of the nozzle. In view of the above problems, this paper puts forward the design scheme of impulse turbine test-bed, using the method of changing specific speed to determine the basic parameters of impulse turbine unit, so as to eliminate the influence of plateau environment on the normal operation of turbine, obtain the efficiency condition of model experiment under low ambient pressure, and improve the operation efficiency and reliability of turbine unit. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Nozzles

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Energy-aware Trajectory Optimization for UAV-enabled wireless powered WSN

Energy-aware Trajectory Optimization for UAV-enabled wireles...

引用

Fully Actuated system Theory and Applications (CFASTA), Conference on

作者： Feihang Qiu Jing Guo Huineng Diao Xu Zhang School of Mechatronic Engineering and Automation Foshan University Foshan China School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350373691

ISBN: (纸本)9798350373707

Unmanned Aerial Vehicle enabled (UAV-enabled) wireless powered Wireless Sensor Networks (WSN) provide an effective way to deploy massive and passive sensors for environmental monitoring in harsh environments. This study focuses on maximizing the total energy received by all Sensor Nodes (SNs) through the optimization of the UAV trajectory. An energy-aware smoothing trajectory method is proposed by considering the dynamic energy requirements of SNs. First, the optimization problem (P1) is formulated according to the mathematical model. Second, the B-spline method is introduced to turn the smoothing problem into a solvable problem (P2). Finally, problem (P2) is solved under the constraints to obtain the trajectory solution. Simulation results indicate the proposed method can adjust the overall trajectory according to the SNs demands. Meanwhile, according to the wake-up distance and energy-aware, the trajectory of some nodes can be locally adjusted without affecting other trajectory segments.

关键词： Wireless communication Wireless sensor networks Smoothing methods Simulation Autonomous aerial vehicles Mathematical models Trajectory

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UWB Non-Line-of-Sight Recognition and Suppression Methodology

UWB Non-Line-of-Sight Recognition and Suppression Methodolog...

引用

International Conference on Communication Software and Networks, ICCSN

作者： Zhengshuai Ni Haibo Zhou Guozheng Zhang Wangbei Xu National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin China Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University Tianjin China

Ultra-Wide Band (UWB) positioning system has become the main research object in indoor positioning field due to its superior positioning performance. Accuracy at the scale of centimeters is achievable in an ideal positioning scenario. Nonetheless, the intricate and changeable indoor surroundings may cause UWB signals to propagate beyond the line-of-sight (LOS) and weaken in signal strength, resulting in a considerable reduction in the precision of positioning. In this paper, methods for identifying non-line-of-sight (NLOS) errors, alleviating NLOS errors, and integrating multi-sensor information are analyzed and studied to provide reference for improving the NLOS positioning accuracy of UWB.

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Logistics Demand Forecasting and Scheduling Optimization Using K-Means Clustering and Deep Q-Network

Logistics Demand Forecasting and Scheduling Optimization Usi...

引用

2025 IEEE International Conference on Electronics, Energy systems and Power Engineering, EESPE 2025

作者： Liang, Kun Zhang, Hang School of Management Tianjin University of Technology Tianjin China School of Mechanical Engineering Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin China

ISBN: (纸本)9798350389579

With the rapid development of e-commerce and supply chain management, logistics demand forecasting and scheduling optimization have become key links in improving logistics efficiency and service quality. To meet this challenge, this paper proposes a logistics demand forecasting and scheduling optimization method that combines K-Means clustering with deep Q network (DQN). Cluster analysis is used to reduce data complexity, and deep reinforcement learning is used to achieve intelligent scheduling. First, the background, challenges and research motivation of logistics demand forecasting and scheduling optimization are introduced. Secondly, the research results in related fields are reviewed, and the shortcomings of current research are pointed out. Then, the K-Means clustering algorithm, deep Q network theory and specific models of logistics demand prediction and scheduling optimization are elaborated. Then, the performance of the proposed method is designed and tested. The experimental results show that the proposed method can significantly improve the accuracy of logistics demand prediction, up to 0.93, optimize scheduling strategies, reduce logistics costs, and improve customer satisfaction. © 2025 IEEE.

关键词： K-means clustering

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Logistics Demand Forecasting and Scheduling Optimization Using K-Means Clustering and Deep Q-Network

Logistics Demand Forecasting and Scheduling Optimization Usi...

引用

Electronics, Energy systems and Power Engineering (EESPE), IEEE International Conference on

作者： Kun Liang Hang Zhang School of Management Tianjin University of Technology Tianjin China 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)9798350389579

ISBN: (纸本)9798350389586

With the rapid development of e-commerce and supply chain management, logistics demand forecasting and scheduling optimization have become key links in improving logistics efficiency and service quality. To meet this challenge, this paper proposes a logistics demand forecasting and scheduling optimization method that combines K-Means clustering with deep Q network (DQN). Cluster analysis is used to reduce data complexity, and deep reinforcement learning is used to achieve intelligent scheduling. First, the background, challenges and research motivation of logistics demand forecasting and scheduling optimization are introduced. Secondly, the research results in related fields are reviewed, and the shortcomings of current research are pointed out. Then, the K-Means clustering algorithm, deep $\mathbf{Q}$ network theory and specific models of logistics demand prediction and scheduling optimization are elaborated. Then, the performance of the proposed method is designed and tested. The experimental results show that the proposed method can significantly improve the accuracy of logistics demand prediction, up to 0.93, optimize scheduling strategies, reduce logistics costs, and improve customer satisfaction.

关键词： Training Accuracy Costs Computational modeling Customer satisfaction Demand forecasting Clustering algorithms Predictive models Scheduling Logistics

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Dimensional synthesis and static/dynamic analysis of a robot in automobile hubs polishing

Dimensional synthesis and static/dynamic analysis of a robot...

引用

IEEE International Conference on mechatronics and Automation

作者： Long Chen Manru Xi Yue Ma Bin Li Qi Liu Changrui Li 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

Focusing on the polishing operation of automobile hubs, this paper proposed a polishing robot by combining a 1T2R parallel mechanism (PM) and an X-Y worktable. The dimensional synthesis and static/dynamic performance analysis of the 1T2R PM are also presented with the key points as follows. Firstly, the inverse displacement model and velocity model of the PM are built to formulate the motion/force transmissibility indices on the basis of the generalized Jacobian matrix. Secondly, the dimensional synthesis problem is formulated to determine the optimized normalized dimensional parameters by maximizing the global force/motion transmissibility of the PM subject to geometric constraints without mechanical interference. Finally, the finite element (FE) model of the 1T2R PM is built with the aid of the SAMCEF, and the static stiffness and lower-order natural frequencies of the PM are evaluated. The research results of this paper provide dimensions and mechanical structures for developing the physical prototype of the automobile hub polishing robot.

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intelligent Diagnosis of Planetary Gearboxes Based on DAE-CNN

Intelligent Diagnosis of Planetary Gearboxes Based on DAE-CN...

引用

Prognostics and system Health Management Conference (PHM-Qingdao)

作者： Tianbo Kang Mian Zhang Ruitong Xie Jiwei Chen Hongbiao Xiang Yuhang Wang 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

Planetary gearboxes (PGs) are the core transmission components of complex rotating machinery. The diverse forms of internal gear faults often complicate diagnosis and maintenance work. To improve the fault diagnosis accuracy of PGs, this paper proposes a method for diagnosing PG faults based on deep autoencoders (DAE) and convolutional neural networks (CNN). The method fully utilizes the deep learning capability of DAE and combines it with the precise recognition ability of CNN for fault features. Through precise learning in the encoding layer, it achieves deep dimensionality reduction of fault features and further learns these features after dimensionality reduction by CNN. Compared to traditional CNN, the method reduces the computation parameters and resource consumption, resulting in higher precision intelligent fault diagnosis. Experimental results demonstrate that DAE-CNN exhibits stronger fault diagnosis ability than traditional networks.

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control Strategy of Permanent Magnet Synchronous Motor and Brushed DC Servo Motor Based on ADRC

Control Strategy of Permanent Magnet Synchronous Motor and B...

引用

IEEE International Conference on mechatronics and Automation

作者： Liang Liu Haikui Liu Guangyin Nie Yang Gao 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

Adaptive disturbance rejection control (ADRC) is a rapidly evolving and promising control method that has been widely applied in various industrial fields and may yield better results than traditional method. In order to promote the controller performance, ADRC technology was employed to construct a three-loop structure for the controllers of permanent magnet synchronous motor (PMSM) and brushed DC Servo Motor (BDSM). Firstly, the mathematical models of the both kinds of motors were presented and combined with the ADRC, respectively. Secondly, the simulation models based on ADRC were constructed in MATLAB/SIMULINK, respectively. Finally, comparative experiments were carried out by ADRC and Proportional-Integral (PI) methods. The simulation results showed that ADRC outperforms PI control in terms of response speed, overshoot reduction, and anti-interference capability, which verified the proposed method can exhibit superior dynamic properties in the induction motor control.

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An optimal design and fuzzy control method for a new flapping-wing robot

An optimal design and fuzzy control method for a new flappin...

引用

Electrical Engineering and control Science (IC2ECS), International Conference on

作者： Chunxiao Zhang Yansen Zhao Ting Zhang Qi Liu Bin Li Yue Ma Mengze Suo Changrui Li 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

Bionic flapping-wing robot has the advantages of good maneuverability, low noise and strong stealth performance. It is widely used in military intelligence collection, field rescue and investigation. In this paper, an optimal design method of bionic flapping-wing robot non-phase difference driving mechanism is proposed. By setting the constraints of flapping angle amplitude, fuselage height and slider stroke, the scale parameters of the mechanism are optimized by using the minimum transmission angle; on this basis, the effectiveness of fuzzy PD control in improving the tracking accuracy of the drive system is verified by means of ADAMS and MATLAB / Simulink electromechanical coupling simulation. This research has practical value for designing new flapping-wing robot and improving its control quality.

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A control Method of Robotic Arm Based on Improved Deep Deterministic Policy Gradient

A Control Method of Robotic Arm Based on Improved Deep Deter...

引用

IEEE International Conference on mechatronics and Automation

作者： Yanpeng Shao Haibo Zhou Shuaishuai Zhao Xiaoyan Fan Jiayi Jiang National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin China Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin China

With the continuous development of Machine Learning, Reinforcement Learning has achieved excellent results in many fields. An improved Deep Deterministic Policy Gradient (DDPG) algorithm is proposed in this paper to optimize Reinforcement Learning control performance, because of the issue of sparse rewards during training in continuous action spaces such as robotic arms, leading to slow convergence and low success rates. The algorithm merges the DDPG algorithm with the Hindsight Experience Relay (HER) algorithm, enabling the agent to learn from the failed experience when learning. Moreover, a double experience replay buffer is introduced, comprising a primary buffer and a secondary buffer containing high-reward experiences, which enhances the sampling probability of high-quality samples. At long last, a PyBullet simulation environment was utilized to execute a simulation experiment. The robotic arm using the improved DDPG algorithm achieved a success rate of nearly 100% in about 220 epochs of training, and the algorithm convergence speed was greatly improved. The trained robotic arm can reach any target point in the workspace. The efficacy of the improved DDPG algorithm is demonstrated by the outcomes, providing a valuable reference for further exploration into the intelligent control of robotic arms for object grasping.

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