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19th International Conference on Mobility, Sensing and Networking (MSN)

作者： Sun, Yu Mu, Chen Sun, Jing He, Yue Changan Univ Sch Informat Engn Xian Peoples R China

ISBN: (纸本)9798350358261;9798350358278

Internet ride sharing allows multiple passengers to share a trip in the same vehicle, enabling cost sharing as well as reducing traffic congestion. However, existing technological limitations and uncertainties in the service (e.g., uncertainty in driver and passenger locations) make it difficult to achieve accurate and efficient real-time responses for ride-sharing matching. Balancing the optimal solutions of drivers, passengers, and platforms, dynamically matching passengers and drivers, and planning optimal paths are complex challenges. Therefore, this paper proposes a greedy algorithm based on the nearest match insertion operation to synthesize the interests of platforms, drivers and passengers. Compared with static one-time matching, this algorithm can effectively realize dynamic matching of drivers and passengers, meet real-time demand, provide drivers with optimal driving paths, and improve the scheduling efficiency of the platform. In this thesis, a dynamic carpooling optimization model is constructed and used to design comparison experiments with the traditional greedy algorithm. This study helps improve the efficiency of the ride-hailing system and enhance the passenger experience, providing valuable references for the promotion and application of dynamic carpooling models in smart cities.

关键词： dynamic carpooling route planning smart city optimization algorithm

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Two-stage fuzzy object grasping controller for a humanoid robot with proximal policy optimization

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第1期125卷

作者： Kuo, Ping-Huan Chen, Kuan-Lin Natl Chung Cheng Univ Dept Mech Engn Chiayi 62102 Taiwan Natl Chung Cheng Univ Adv Inst Mfg High Tech Innovat AIM HI Chiayi 62102 Taiwan Natl Pingtung Univ Dept Intelligent Robot Pingtung 900392 Taiwan

As science and technology have developed, an increasing amount of research on humanoid robots has been conducted. In this paper, a method based on deep reinforcement learning, optimization algorithms, and fuzzy logic for self-guided learning in humanoid robots is proposed. The method primarily relies on proximal policy optimization. The proposed model enables the humanoid robot to determine the optimal action on the basis of environmental feedback. A task was divided into two steps to train the optimal model for each step of the task;these models were then integrated. This division of the task was completed to prevent bias towards a single step. The performance of numerous optimization algorithms was evaluated, and the artificial bee colony algorithm was found to be the most successful algorithm for determining the optimal combination of parameters for the task. Deep reinforcement learning was demonstrated to be an effective method for enabling the humanoid robot to learn how to grasp objects and place them in target areas. The proposed learning method also combines optimization algorithms with fuzzy logic theory to further improve performance. The feasibility of the proposed method was validated through experiments.

关键词： Humanoid robot Intelligent control PPO Fuzzy logic optimization algorithm ABC

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A Method of Multiple Targets DOA Tracking with Vector Hydrophone Based on Unite Processing in Time and Frequency Domain 3

A Method of Multiple Targets DOA Tracking with Vector Hydrop...

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3rd International Conference on Information Communication and Software Engineering (ICICSE)

作者： Zhang, Wei Shang, Ling Tian, Hongxing Zhang, Yangbo Yichang Res Inst Testing Technol Div 105 Yichang Peoples R China Yichang Res Inst Testing Technol Metrol Dept Yichang Peoples R China

ISBN: (纸本)9798350313048

Acoustic pressure and particle velocity is narrowband filtered to obtain multi-band signal. High-order correlation equations composed of acoustic pressure and particle velocity are solved by optimization algorithm and DOA (Direction of arrive) distribution for different signal in multi-band is counted. Then the DOA of multiple targets can be distinguished and tracked. In order to improve the accuracy, the least square method is used to fit the measurement results and a prediction model is established, and then DOA trajectory is optimized by Kalman filter. The experimental results indicate that DOA of three targets can be distinguished and trajectory tracked, DOA of interference source is also observed by the pseudo color picture of DOA distribution, the accuracy of DOA estimation is less than 5 degrees.

关键词： vector hydrophone DOA estimation least square method Kalman filter optimization algorithm

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optimization of track and field training methods based on SSA-BP and its effect on athletes' explosive power

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HELIYON 2024年第3期10卷 e25465页

作者： Jing, Ruibin Wang, Zhengwei Suo, Peng DongShin Univ Sch Sports Naju Si 58245 Jeonranam Do South Korea Shandong Sport Univ Dept Phys Educ Jinan 250102 Peoples R China

Digitalization and informationization are important trends in the development of the sports industry. The study first introduced the sparrow search algorithm to improve the generalization ability of traditional neural networks, optimizing the assignment of initial weights and thresholds of neural networks;Secondly, the chicken swarm algorithm is introduced to optimize the training combination, period, and intensity of athletes based on the evaluation results, improving the subjective limitations of traditional training methods. The results of model performance analysis show that the sparrow search algorithm is better than other intelligent optimization algorithms in finding fitted parameters, and the solution error is less than 0.50. The evaluation model performs well in terms of accuracy, recall, average relative error, and R2 evaluation indicators. The model has high repeatability and is suitable for evaluating track and field training methods. The accuracy and computational speed of the chicken swarm algorithm are relatively good;Compared with other optimization models, the chicken swarm algorithm has better optimization ability and accuracy. Friedman test found significant differences in the chicken swarm algorithm, and the optimized training method has a significant positive impact on the explosive power of athletes, and the training period and intensity arrangement are reasonable and more helpful to the improvement of athletic performance. This study improves the scientific rationality of the development of track and field training methods, which is conducive to optimizing the training effect of track and field sports, and facilitates the risk management and personalized training of athletes. At the same time, it greatly promotes the integration and development of sports and computer disciplines.

关键词： BP neural network optimization algorithm Track and field Novel intelligent optimization algorithm Sparrow search algorithm Athletic explosiveness Chicken swarm optimization

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Determination of Tracking Errors with Respect to the Geometrical Errors based on optimization algorithm

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Energy Procedia 2014年 49卷 2211-2220页

作者： Feihu Sun Zhifeng Wang Minghuan Guo Fengwu Bai Zijian Xu Key Laboratory of Solar Thermal Energy and Photovoltaic System of Chinese Academy of Sciences Institute of Electrical Engineering.No.6 North ertiaoZhongguancun Haidian district of Beijing China100190

Accurate dual-axis sun tracking is the key feature of a heliostat and is critical for the performance of a solar tower power plant. The primary tracking errors with respect to the geometrical errors could be theoretically determined from the measurements of the BCS based on optimization algorithm. Tests are performed on two heliostats in DAHAN solar tower plant and analyses are performed to evaluate the comprehensive effect of the six angular geometrical errors on the heliostat tracking accuracy. The test results show that the altitude-azimuth tracking angle formulas for several fixed geometrical errors work well and have a effectiveness for a given period of time.

关键词： BCS tracking angle geometrical errors optimization algorithm

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A Hybrid Common Mode Choke optimization Method for Input Line EMI Filter Design

ADVANCED ELECTROMAGNETICS

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ADVANCED ELECTROMAGNETICS 2021年第2期10卷 56-66页

作者： Khelladi, Sofiane Saci, Khadidja Hadjadj, Abdelchafik Ales, Achour Amar Telidji Univ Laghouat Elect Engn Dept LACOSERE Lab Laghouat Algeria Polytech Mil Sch Bordj El Bahri Dept Electromagnet Compatibil Algiers Algeria

In this paper, an optimization method of toroidal core-based Hybrid common mode chokes (HCMCs) for the design of an Electromagnetic interferences (EMI) filter is proposed. A dedicated algorithm is developed using MATLAB to characterize compact HCMCs that exhibit effective Common Mode (CM) chokes with optimized leakage inductances by systematic variations in the winding patterns and geometric dimensions of their magnetic cores. It takes into consideration the physical limitations of these components as well as the constraints related to the design of EMI filters. The proposed algorithm allows through a small computational task to propose a variety of configurations for optimal HCMCs. Finite element method (FEM) simulations are conducted on the HCMCs to extract their CM and leakage inductances. The results are compared with those calculated analytically and yield a good match. The performances of optimized HCMCs are evaluated through their implementation in the designed filter. All the cases of HCMCs including the smallest one allow the EMI filter to easily qualify a power converter to an electromagnetic compatibility (EMC) standard.

关键词： Electromagnetic interferences filter (EMI) Hybrid common mode choke (HCMC) optimization algorithm

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Multi-wavelength method based on global optimization for particle size distribution

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MEASUREMENT 2023年第1期219卷

作者： Wang, Zhisong Liu, Qingming Yue, Lidan Wang, Dan Jing, Qi Liu, Changqi He, Zongling Yang, Zhou Beijing Inst Technol State Key Lab Explos Sci & Technol Beijing 100081 Peoples R China China Acad Safety Sci & Technol Beijing 100081 Peoples R China Beijing Inst Technol State Key Lab Explos Sci & Technol Room 1065 South Zhongguancun St Beijing 100081 Peoples R China

The particle size distribution (PSD) of particle medium plays an important role in the field of particle science, so the inversion of PSD is of great significance. To study spherical particle PSD, a multi-wavelength detection model based on a global optimization algorithm called OptQuest nonlinear programming (OQNLP) is established in this paper and an experiment to verify the reliability of the system is designed. The numerical results show that the selection of detection wavelength has great influence on the results of PSD inversion. The relative error of PSD parameters is minimized by choosing the wavelength at the peak of extinction coefficient curve of appropriate particle size. Both simulation and experimental results indicate that the five-wavelength method has the highest testing accuracy. When high accuracy is not required, choosing the four-wavelength method is the most suitable testing method. Furthermore, the universality of the model is also confirmed for the Rosin-Rammer (R-R) function, normal (N-N) function, and lognormal (L-N) function.

关键词： Multi-wavelength method optimization algorithm Particle size distribution Wavelength selection

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Learning-imitation strategy-assisted alpine skiing optimization for the boom of offshore drilling platform

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OCEAN ENGINEERING 2023年第1期278卷

作者： Yuan, Yongliang Yang, Qingkang Ren, Jianji Fan, Junkai Shen, Qianlong Wang, Xiaobang Zhao, Yong Henan Polytech Univ Sch Mech & Power Engn Jiaozuo 454003 Peoples R China Henan Polytech Univ Sch Software Jiaozuo 454003 Peoples R China Dalian Maritime Univ Naval Architecture & Ocean Engn Coll Dalian 116026 Peoples R China

The boom of a semi submersible platform has large moment of inertia and high operating cost. How to effectively achieve the optimal solution of the boom to minimize the design and operation cost is a dynamic problem. In this study, a learning-imitation strategy-assisted alpine skiing optimization (LISASO) is proposed to find the optimal solution of the semi submersible platform boom. Firstly, the optimization model of the boom of the semi submersible platform is established. Secondly, the learning-imitation strategy (LIS) is implemented to improve the performance of the alpine skiing optimization (ASO). In LIS, the learning ability of individuals and the imitation of competitions are introduced to strengthen the association between individuals and the first individual. The performance of the LISASO is verified by three truss examples. The statistical results demonstrate that the LISASO is more competitive compared with other state-of-the-art optimization algorithms. Finally, the LISASO is applied to solve the optimal structural parameters of the boom. Results show that the energy consumption is reduced by 18.32% compared with the initial design.

关键词： Semi submersible platform Alpine skiing optimization Learning -imitation strategy Truss optimization optimization algorithm

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Optimal Operation of the NACA0012-IK30 Airfoil

Optimal Operation of the NACA0012-IK30 Airfoil

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AIAA Aviation Forum

作者： Carnacho, Emanuel A. R. Silva, Andrd R. R. Marques, Flavio D. UBI Aerosp Sci Dept Covilha Portugal EESC USP Dept Mech Engn Sao Paulo SP Brazil

ISBN: (数字)9781624107047

ISBN: (纸本)9781624107047

The kinematics of oscillating airfoils are crucial to understanding subjects such as rotor dynamics and bio-inspired flows. Unsteady airfoils have been studied extensively, but there is an overall lack of knowledge regarding newer and more complex kinematics. The present paper builds upon previous studies of the NACA0012-IK30 airfoil by implementing a gradient-based method that searches for a leading-edge pitching amplitude that maximizes propulsive power. All of this is done numerically by solving the Reynolds-Averaged Navier-Stokes equations coupled with the Intermittency Transition model. Results indicate that for higher reduced frequencies, higher leading-edge pitching amplitudes are required to maximize the mean propulsive power. Additionally, propulsive power is achieved with near-optimal propulsive efficiency, which is a common limitation of traditional flapping airfoils.

关键词： 4 Digit Airfoils Flapping Frequency Propulsive Efficiency Reynolds Averaged Navier Stokes Rotor Dynamics Lift Coefficient optimization algorithm Power Coefficient Aerodynamic Characteristics ANSYS

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Optimized capacity allocation in a UAM vertiport network utilizing efficient ride matching

Optimized capacity allocation in a UAM vertiport network uti...

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AIAA Aviation Forum

作者： Swaid, Majed Pertz, Jan Niklass, Malte Linke, Florian German Aerosp Ctr DLR Inst Air Transport D-21079 Hamburg Germany

ISBN: (数字)9781624107047

ISBN: (纸本)9781624107047

In this study, we present an approach that allows to design efficient UAM fleets and corresponding vertiport networks for specific demand patterns. Therefore, we apply a trajectory-based simulation model that controls the circulation of vehicles in a UAM network. The model allocates vehicles from an unlimited fleet pool to the requested missions, such that the boundary conditions of the optimization are fulfilled. Applying a combination of graph-based optimization and solving integer linear programming problems, a ride matching algorithm is implemented that minimizes empty relocation flights in the network and reduces the fleet size. The results comprise the quantification of a fleet pool, defined by fleet size, fleet mix, and starting positions in the vertiport network. We analyze a set of 20 vertiports in the City of Hamburg, Germany, regarding the local peak loads of parking positions that are needed for battery charging and waiting periods of unoccupied vehicles. The results show that the reduction of battery charging time has a significant impact on fleet size, which affects the minimum ground infrastructure requirements as well. Finally, the fleet analysis shows that average load factors of 45% are feasible at fleet sizes with varying occupancy rates of up to 80%.

关键词： optimization algorithm Dijkstra's algorithm Ground Segment German Aerospace Center Energy Storage Energy Consumption Flight Operation Histograms Propulsion System Alternative Fuels

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