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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Zhou, Zhengjun Mu, Lingxia Ning, Kaikai Zhang, Youmin Xue, Xianghong Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an710048 China Department of Mechanical Industrial and Aerospace Engineering Concordia University MontrealQCH3G 1M8 Canada

ISBN: (纸本)9789819733392

An inspection allocation algorithm based on different starting points and endurance capabilities of unmanned aerial vehicles (UAVs) is proposed for the multi-UAVs forest inspection process. Firstly, the area map to be inspected is rasterized, and the distances between the starting points of each UAV and individual grid cells are computed, which will be used for area decomposition. Subsequently, an evaluation function is employed to allocate individual grid cells reasonably to each UAV. Then, the boundary lines of the allocated sub-regions are generated, by which the raster map is converted into an inspection map with continuous boundaries. This ultimately yields the sub-regions for inspection by each UAV. Simulation results demonstrate that this method effectively allocates the forest inspection area to the various UAVs in a rational manner. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Inspection

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Vision-based Autonomous Landing of UAV on Dynamic Apron

Vision-based Autonomous Landing of UAV on Dynamic Apron

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2023 IEEE International Conference on Unmanned systems, ICUS 2023

作者： Yue, Pengyu Xin, Jing Mao, Zhijie Lu, Yongchang Zhang, Youmin Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an710048 China Industrial & Aerospace Engineering Concordia University Department of Mechanical MontrealQCH3G 1M8 Canada

ISBN: (纸本)9798350316308

Aiming at the landing problem of Unmanned Aerial Vehicle (UAV) without landmarks and global coordinates, this paper proposes an efficient vision-based UAV autonomous landing solution. Firstly, image information is obtained from the bottom visual sensor of the UAV, and the YOLOV5 object detection method is employed to determine the initial position of the dynamic apron. Subsequently, an improved dynamic apron tracking method based on Spatio-Temporal Transformer is designed. This method updates an initial template through target detection algorithm and utilizes multi-source perceptual data for confidence evaluation and dynamic template updates. It outputs the distribution estimation of the predicted bounding box corner coordinates, and achieves the high reliability continuous positional smoothing estimation and dynamic apron loss re-detection based on UAV vision. Finally, the UAV calculates the horizontal control quantity based on the horizontal relative position between the UAV and the apron obtained. The vertical control quantity is calculated based on the vertical relative position between the UAV and the apron. The vertical relative position is estimated using laser ranging and visual odometry techniques. Based on the results of horizontal tracking control and vertical regulation, velocity commands for the UAV in all three directions are generated, enabling the UAV to achieve autonomous landing on the dynamic apron. This paper achieves precise landing for dynamic apron in real-world environments. The experimental results demonstrate that this method avoids the limitations of labeled data and global information on autonomous landing of UAV. Even under GPS-denied conditions, the UAV can generate control commands based on visual data and achieve autonomous landing for randomly moving apron. © 2023 IEEE.

关键词： Antennas

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Data-Based Health State Analysis for the Axle of High Speed Train

Data-Based Health State Analysis for the Axle of High Speed ...

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International Conference on Computational Intelligence and Security

作者： Guo Xie Minying Ye Xinhong Hei Jinwei Zhao Fucai Qian Xi'an University of Technology Xi'an China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China

Taking into account that the present popular methods, such as the judgement of the axle failure based on temperature threshold, and the early warning of axle based on real-time temperature analysis, cannot analyze the changing of performance trends, a health state analysis method for the axle of high-speed train based on long-term temperature monitoring data is proposed in this paper, which including the following main steps: (1) Preprocessing of the original data to correct the singular zero value and complement the missing values, (2) Smoothing of the processed data in order to automatically extract the beginning and end points of every temperature rising stage of axles, (3) Establishment of the calculation method of temperature rising rate, and evaluating the health sate of axles based on the temperature rising rate. Finally, the proposed method is validated based on the data from a test line, the results demonstrate the effectiveness and practicability of the method.

关键词： Axles Temperature distribution Aging Safety Data mining Fault diagnosis Monitoring

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Kalman Filter Adaptive Speed Tracking control for High-speed Trains with Noise Interference

Kalman Filter Adaptive Speed Tracking Control for High-speed...

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IEEE International Conference of intelligent Robotic and control Engineering (IRCE)

作者： Chunli Zhang Yuwei Xie Lei Yan Fucai Qian College of Automation and Information Engineering Xi’an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an China

ISBN: (数字)9798350352399

ISBN: (纸本)9798350352405

In this paper, high-speed train is regarded as a single prime point, a new tracking control method combining adaptive control and Kalman filtering is proposed. This paper solves the noise interference problem in the tracking control process of high-speed train. The method first linearises the original system, then uses Kalman filtering to do model estimation, and the controller is designed for the predictive model through adaptive control. Finally, MATLAB is used to simulate and verify the effectiveness of this method. This proposed method successfully solves the problems of nonlinearity, noise interference and unknown parameters in system. The error caused by resistance in the high-speed train tracking control process can be dealt with effectively.

关键词： Resistance Filtering Noise Process control Interference Predictive models Mathematical models Kalman filters Adaptive control MATLAB

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Optimizing application layer multicast routing via artificial fish swarm algorithm

Optimizing application layer multicast routing via artificia...

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International Conference on Fuzzy systems and Knowledge Discovery (FSKD)

作者： Xuan Ma Rongjun Tang Jingyan Kang Qing Liu Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China Faculty of Automation and Information Engineering

ISBN: (纸本)9781509040940

In this paper, we propose an approach based on the use of artificial fish swarm algorithm (AFSA) for solving the problem of multicast routing on application layer. Taking delay, stretch, and degree as three optimization objectives, we design the behaviors of artificial fish individual (AF), i.e. moving randomly, preying, following, and use Pareto ranking to evaluate the fitness of AF. The simulation results show that the proposed algorithm is an appropriate method to explore the search space of the complex problem and leads to good solutions in a reasonable amount of time.

关键词： Delays Routing Optimization Fish Network topology Algorithm design and analysis Visualization

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Adaptive RBF Neural Network control Method for Pneumatic Position Servo system ⁎

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IFAC-PapersOnLine 2020年第2期53卷 8826-8831页

作者： Hai-Peng Ren Shan-Shan Jiao Xuan Wang Jie Li Shaanxi Key Laboratory Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an China School of Electrical Engineering Xi’an University of Technology Xi’an China

With the development of control theory and the pneumatic element, the application of pneumatic systems has attracted more attention because of the performance to price ratio improvement. Despite of these, there are still challenge to deal with the nonlinearity of the system, the uncertainty of the parameters, the input saturation and the unknown control direction in the tracking control of pneumatic system. In this paper, the nonlinearity and model uncertainty are treated with adaptive radial basis function neural network (RBFNN), meanwhile, the unknown control direction and input saturation are dealt with the Nussbaum function and Gauss error function, respectively. The stability of the designed controller is proved by Lyapunov theory. Finally, the experimental and comparison results show the effectiveness and superiority of the proposed method.

关键词： Pneumatic system RBFNN unknown model parameters unknown control direction input saturation

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A Low-complexity Decision Variables Classification Brainstorm Optimization Algorithm

A Low-complexity Decision Variables Classification Brainstor...

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2024 International Conference on Applied Mathematics, Modelling and Statistics Application, AMMSA 2024

作者： Li, Qiyue Qiu, Lipeng Zhou, Aoran Wu, Yali Wang, Ziren Department of Information and Control Engineering Xi'An University of Technolog Xi'an710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an710048 China State Key Laboratory of Metal Extrusion and Forging Equipment Technology China National Heavy Machinery Research Institute Co. Ltd. Xi'an710016 China

In the complex optimization scenarios of real life, we often need to not only weigh the conflicts between multi-objectives but also face the challenges brought by large-scale decision variables. When the scale of decision variables increases sharply, the search space will expand exponentially, which makes it difficult for the algorithm to traverse the whole search space under limited resources, so that the number of local optimal solutions increases sharply. The so-called 'dimension disaster' appears. To effectively deal with this problem, this paper proposes a large-scale multi-objective brainstorm optimization algorithm (LMaOBSO) based on low complexity decision variable classification method and improved penalty boundary intersection strategy. The algorithm uses a low-complexity tree sorting variable classification method to divide the decision variables into converges and diversity variables quickly. At the same time, the convergent variables are optimized by the improved penalty boundary crossover strategy to solve the problems existing in large-scale optimization problems. © Published under licence by IOP Publishing Ltd.

关键词： Disasters

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Prediction of piRNA-mRNA interactions based on an interactive inference network

Prediction of piRNA-mRNA interactions based on an interactiv...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Liu, Yajun Li, Ru Li, Aimin Fei, Rong Guo, Xie Wu, Fang-Xiang Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China University of Saskatchewan Division of Biomedical Engineering Department of Computer Science Department of Mechanical Engineering Saskatoon Canada

ISBN: (纸本)9798350337488

As the largest class of small non-coding RNAs, piRNAs primarily present in the reproductive cells of mammals, which influence post-transcriptional processes of mRNAs in multiple ways. Effective methods for predicting piRNA and mRNA target relationships can help identify piRNA functions, investigate the possibility of piRNAs as biomarkers and therapeutic targets. In this study, we propose a computational approach for classifying the relationships of piRNA-mRNA pairs based on an interactive inference network (IIN). First, we gather piRNA-mRNA target data, collect sequence data by position alignment, and construct a benchmark dataset. Furthermore, a reliable negative set is constructed by positive-unlabeled learning. Finally, we view a piRNA and a mRNA sequence as a premise and hypothesis sentence, respectively, and IIN model is used to predict the relationship between them. The experiments demonstrate that our method effectively characterizes piRNA-mRNA interaction and could be beneficial for researchers to investigate piRNA functions. © 2023 IEEE.

关键词： interactive inference network noncoding RNA piRNA-mRNA interactions positive-unlabeled learning

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The research of a novel single-phase three-level inverter circuit with its modulation strategy

The research of a novel single-phase three-level inverter ci...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Jiangjiang Li Qi Zhang Xiangdong Sun School of Automation and Information Engineering Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Processing Xi'an China

This paper proposes a new single-phase three-level inverter topology and describes the control method for the proposed inverter. The inverter contains a H-bridge inverter circuit and an auxiliary circuit. The auxiliary circuit enables H-bridge circuit to be operated in the mode of five kinds of voltage level with unipolar pulse width modulation (PWM) strategy. The operation principle of proposed inverter topology is analyzed in detail. The proposed inverter eliminates the problems of DC capacitors voltage unbalance without additional control. The control of inverter is simple and easy to implement. And it is suitable for small or medium power applications. In this paper, the proportional-resonant (PR) controller is used in the proposed inverter, the feasibility and the analysis of principle of the proposed inverter is verified by simulation. The results show that the proposed inverter can output currents of low total harmonic distortion and has higher efficiency than the H-bridge inverter.

关键词： Inverters Capacitors Integrated circuits Mathematical model Topology Switches Modulation

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Detection of Weak Signal Amplitude of Winding Deformation Based on Duffing Oscillator

Detection of Weak Signal Amplitude of Winding Deformation Ba...

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Chinese control Conference (CCC)

作者： Sheng-Min Li Yuan-Yuan He Yue-Yue Che Xu-Xia Sun Academy of Automation & Information Engineering Xi’an University of Technology Xi’an Key Laboratory of Shaanxi Province for Complex System Control and Intelligent Information Processing Xi’an

In order to solve the problem that it is difficult to detect the weak sinusoidal response signal when the frequency response method is used to detect the transformer winding deformation fault online, this paper proposes a method to detect the amplitude of the response signal by using Duffing oscillator. Secondly, in order to solve the influence of the target signal phase shift on the amplitude detection in the actual detection process, a method of first detecting the signal phase using a vibrator array composed of Duffing vibrators and then detecting the amplitude is proposed. Finally, using this method to process the two sets of measured response signals of online detection, the detection results show that the maximum error between the detected signal amplitude and the standard signal is 5. 69%, which meets the requirements of the power industry standard, which proves that the method used in this article is It is feasible in winding deformation detection.

关键词： Phased arrays Simulation Windings Frequency response Power industry Oscillators Standards

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