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第30届中国过程控制会议（CPCC 2019）

作者： Honggang Yu Zhentian Jiao Youmin Zhang Jing Xin Lingxia Mu Shangbin Jiao Han Liu Ding Liu Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Department of Mechanical Industrial and Aerospace Engineering Concordia University

Forest fire detection has great practical significance in actual production and ***, many current solutions are still not reliable in practice and usually affected by many *** paper presents a forest fire detection method based on deep learning approach using aerial images taken by Unmanned Aerial Vehicles(UAVs).We train a full image fire classifier by exploiting YOLOv3, a latest fast and accurate real-time object detection algorithm using deep learning neural ***, the feature map of a certain size is obtained by feature extraction network, such as 13×*** the input image is divided into 13×13 grid ***, if the central coordinate of an object in the ground truth falls in a grid cell, the grid cell will predict the object, because each grid cell will predict a fixed number of bounding *** the largest bounding box with ground truth IOU(Intersection Over Union) is used to predict *** fire patch detector developed in this paper obtains 91% mAP(mean Average Precision) on the data sets used and the FPS(Frames Per Second) average can reach 80.

关键词： Forest fire detection YOLOv3 deep learning Unmanned Aerial Vehicle(UAV)

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Adaptive Generalized Projective Synchronization of a New Fractional Chaotic system with uncertain parameters

Adaptive Generalized Projective Synchronization of a New Fra...

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第30届中国控制与决策会议

作者： Jiaoru Huang Kun Zhong Fucai Qian The International Joint Research Center of Autonomous Systems and Intelligent Control Xi'an Technological University Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology

This paper investigates the generalized projective synchronization scheme of a new fractional chaotic system, which can display a four-wing chaotic attractor when the order of system is in the range of 0.82 and ***, considering the situation with unknown parameters, an adaptive nonlinear controller is obtained for synchronization and parameters identification based on fractional-order Lyapunov stability theorem. The fractional-order adaptation law, which can update parameters estimation errors online, is derived to ensure the stability of synchronization error system. Numerical simulations are used to verify the results.

关键词： Four-wing chaotic attractors uncertain parameter generalized projective synchronization parameter identification

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EMOST: A dual-branch hybrid network for medical image fusion via efficient model module and sparse transformer

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Computers in Biology and Medicine 2024年 179卷 108771-108771页

作者： Wang, Wenqing He, Ji Liu, Han School of Automation and Information Engineering Xi'an University of Technology Xi'an710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an710048 China

Multimodal medical image fusion fuses images with different modalities and provides more comprehensive and integrated diagnostic information. However, current multimodal image fusion methods cannot effectively model non-local contextual feature relationships, and due to direct aggregation of the extracted features, they introduce unnecessary implicit noise into the fused images. To solve the above problems, this paper proposes a novel dual-branch hybrid fusion network called EMOST for medical image fusion that combines a convolutional neural network (CNN) and a transformer. First, to extract more comprehensive feature information, an effective feature extraction module is proposed, which consists of an efficient dense block (EDB), an attention module (AM), a multiscale convolution block (MCB), and three sparse transformer blocks (STB). Meanwhile, a lightweight efficient model (EMO) is used in the feature extraction module to exploit the efficiency of the CNN with the dynamic modeling capability of the transformer. Additionally, the STB is incorporated to adaptively maintain the most useful self-attention values and remove as much redundant noise as possible by developing the top-k selection operator. Moreover, a novel feature fusion rule is designed to efficiently integrate the features. Experiments are conducted on four types of multimodal medical images. The proposed method shows higher performance than the art-of-the-state methods in terms of quantitative and qualitative evaluations. The code of the proposed method is available at https://***/XUTauto/EMOST. © 2024 Elsevier Ltd

关键词： Feature extraction

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Sensorless Speed control of PMSMs based on an Improved Particle Filter

Sensorless Speed Control of PMSMs based on an Improved Parti...

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International Conference on Electrical, control and Instrumentation Engineering (ICECIE)

作者： Hai-Peng Ren Zi-Yuan Nan Jie Li Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China Xi'an University of Technology Xi'an China

ISBN: (纸本)9781728139401

Position sensors in permanent magnet synchronous motor (PMSM) are restrained in some applications because of the space restriction and the reliability of the system. A marginalized particle filter (MPF) can used to staminate the speed of the motor by combining a Kalman filter (KF) with a particle filter (PF). In the MPF algorithm, the rotor position of the permanent magnet synchronous motor (PMSM) is represented by a set of particles, and the rotor speed associated with each particle is estimated by using the KF. The PF here is used to handle the non-Gaussianity and nonlinearity of the system. In this paper, the uniform distribution of particles is proposed to replace the traditional Gaussian distribution of particles in the PF to get better performance at the low-speed range. The motor drive system prototype is built using a TMS320F28335 digital signal processor as a controller core. The proposed improved PF is used in the sensorless speed vector control PMSM system. The experimental results show that the proposed PF with a uniform distribution of initial particles enables more accurate speed estimation in the low-speed range compared to the conventional Gaussian distribution, while increasing number of particles also helps to improve the accuracy.

关键词： particle filter sensorless motor control initial distribution Particle filters Permanent magnet synchronous machines Sensors Gaussian distribution PARTICLE DISTRIBUTION Uniform distribution position sensor particles Speed Estimation

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Discussion on application of FLIC-Fluent coupled simulation technology on medium-scale and large-scale municipal solid waste incinerators 6

Discussion on application of FLIC-Fluent coupled simulation ...

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6th International Conference on intelligent Computing, Communication, and Devices, ICCD 2023

作者： Zhang, Hongze Guangxi Key Laboratory of Machine Vision and Intelligent Control Guangxi Wuzhou China Guangxi Colleges and Universities Key Lab. of Image Processing and Intelligent Information System Guangxi Wuzhou China School of Electronics and Information Engineering Wuzhou University Guangxi Wuzhou China

ISBN: (数字)9781510666269

ISBN: (纸本)9781510666252

Today, the world economy is in the stage of rapid development, followed by the rising quantity, the decreasing moisture content, and the increasing heat value of Municipal Solid Waste (MSW). Since waste incineration has the advantages of excellent volume reduction effect, high resource utilization efficiency, and less secondary pollution to the environment, waste incineration disposal technology has received extensive attention worldwide. With the continuous increase of the urban population, the amount of sewage sludge is growing year by year. The process of the sludge incineration system is highly complicated, and the cost of construction and operation is high. The waste incinerator can process not only MSW but also sewage sludge. Therefore, the co-incineration of sewage sludge with MSW in the existing waste incinerator is a safe and effective way to dispose of sewage sludge. In this paper, the simulation results of Computational Fluid Dynamics (CFD) are analyzed for medium-scale and large-scale waste incinerators, characterized by high moisture content, high ash content, low heat value of sewage sludge, and incomplete combustion and high pollutant emissions in waste incinerators. With FLIC-Fluent coupled simulation method, the critical information such as temperature field, velocity field, concentration field of principal components, and the pollutant emissions can be predicted. Based on the CFD simulation results, the performance and structure of waste incinerators can be optimized, and the new products can be designed and developed. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Waste incineration

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Inverse Problem Estimation of Heat Conduction using Parameters Adaptive PID Algorithm

Inverse Problem Estimation of Heat Conduction using Paramete...

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第34届中国控制与决策会议

作者： Huang Weichao Li Jiahao Liu Ding Faculty of automation and Information Engineering Xian University of Technology Shannxi Key Laboratory of Complex System Control and Intelligent Information Processing

A boundary inverse problem estimation method for heat conduction system based on a parameters adaptive PID algorithm is proposed in the *** method can solve the boundary heat flux estimation problem of onedimensional heat conduction model based on the inverse heat conduction *** reduce the error between the temperatures of the measurement points and the calculation values constantly by using feedback control of PID *** parameters of PID algorithm is optimized by Whale optimization *** is enhances the rapidity and stability of the system and solves the problem that PID parameters are difficult to *** experimental results show that,the method that proposed in the paper can realize the inverse estimation of thermal boundary conditions accurately and quickly while ensuring the stability and convergence of the system.

关键词： Inverse heat conduction problem PID algorithm Finite difference method Whale optimization algorithm

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Robust adaptive neural network consensus tracking control of multi-robot systems

Robust adaptive neural network consensus tracking control of...

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第33届中国控制与决策会议

作者： Huijun Guo Jintao Liang School of Automation and Information Engineering Xi'an University of Technology Key Laboratory of Shaanxi Province for Complex System Control and Intelligent Information Processing Guilin University of Electronic Technology

This paper studies the problem of distributed consensus tracking control for multi-robot systems with different friction coefficients and external disturbances based on the multi-agent *** the case where the communication topology is a weighted directed graph and leader node is the neighbor of a small portion of the follower *** follower nodes are modeled as two second-order nonlinear dynamical system with coupling parts.A distributed robust adaptive control law based on the neural networks(NN) is designed for each follower node,where the controller requires only relative state information from its adjacent *** such control scheme,the states of all follower nodes ultimately synchronize to the leader node with bounded residual *** results are provided to validate the effectiveness of the algorithm.

关键词： Consensus control Multi-robot systems Agent systems Neural networks Sliding mode control

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Optimization Deployment of Temperature Measurement Points for CZ Silicon Single Crystal Growth Equipment Based on Improved Fuzzy C-Means Clustering

Optimization Deployment of Temperature Measurement Points fo...

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第43届中国控制会议

作者： Weichao Huang Siyuan Cao Ding Lui School of Automation and Information Engineering Xi'an University of Technology Shannxi Key Laboratory of Complex System Control and Intelligent Information Processing

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The inverse heat transfer method is an effective inversion algorithm for solving the heat transfer at non-measurable places of the study object. However, the current inverse heat transfer inversion method has the problems of low accuracy and difficult selection of temperature measurement point locations. To solve this problem, this paper proposes an optimization algorithm for measurement point locations based on improved fuzzy c-means clustering(IFCM). The method first generates the initial clustering center through a specific density function and introduces the Mahalanobis distance to solve the problem that the traditional fuzzy c-means clustering results depend on the initial clustering center and the results are unstable. Then, an optimized deployment algorithm based on IFCM temperature measurement points is proposed and compared with other mean value clustering algorithms. Finally, the optimal temperature measurement position on the crystal surface is selected by the IFCM temperature measurement point optimization deployment algorithm on the CZ(Czochraski)silicon single crystal growth equipment, and the inversion results of the randomly selected temperature measurement point scheme and the optimized temperature measurement point scheme are compared by the real-time inversion algorithm based on the dynamic matrix control multi-boundary heat flux, and the results of the optimized temperature measurement point scheme are obtained to be better than that of the randomly selected temperature measurement point scheme.

关键词： Fuzzy c-means clustering IFCM Temperature measurement point optimization Inverse heat transfer

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Scene Classification Using Deep Networks Combined with Visual Attention

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Journal of Sensors 2022年第0期2022卷

作者： Shi, Jing Zhu, Hong Li, Yuxing Li, Yanghui Du, Sen School of Automation and Information Engineering Xi'An University of Technology Xi'an710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'An University of Technology Xi'an710048 China

In view of the scene's complexity and diversity in scene classification, this paper makes full use of the contextual semantic relationships between the objects to describe the visual attention regions of the scenes and combines with the deep convolution neural networks, so that a scene classification model using visual attention and deep networks is constructed. Firstly, the visual attention regions in the scene image are marked by using the context-based saliency detection algorithm. Then, the original image and the visual attention region detection image are superimposed to obtain a visual attention region enhancement image. Furthermore, the deep convolution features of the original image, the visual attention region detection image, and the visual attention region enhancement image are extracted by using the deep convolution neural networks pretrained on the large-scale scene image dataset Places. Finally, the deep visual attention features are constructed by using the multilayer deep convolution features of the deep convolution networks, and a classification model is constructed. In order to verify the effectiveness of the proposed model, the experiments are carried out on four standard scene datasets LabelMe, UIUC-Sports, Scene-15, and MIT67. The results show that the proposed model improves the performance of the classification well and has good adaptability. © 2022 Jing Shi et al.

关键词： Convolution

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Adaptive Back-stepping Based control for Active Suspension systems with Prescribed Performance

Adaptive Back-stepping Based Control for Active Suspension S...

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第40届中国控制会议

This paper proposes an adaptive back-stepping tracking control with prescribed performance function for vehicle suspension systems with uncertain parameters. The prescribed performance function can lead the tracking error from a large set to an arbitrarily small residual set. Firstly, the constrained tracking error system incorporating the prescribed performance functions is transformed into unconstrained form. Then an adaptive updating scheme based on the projection algorithm is designed to estimate the uncertain parameter. Following that an adaptive nonlinear controller is designed using the back-stepping technique, and the stability of whole controlled systems is proved by the Lyapunov theory. Meanwhile, the state convergence of the internal dynamical system is also guaranteed. Finally, simulation results are provided to verify the proposed approach.

关键词： Active Suspension system Adaptive control Back-stepping technique prescribed performance

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