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Dynamic Color Transform Networks for Wheat Head Detection

Plant Phenomics 2022年第1期4卷 395-408页

作者： Chengxin Liu Kewei Wang Hao Lu Zhiguo Cao Key Laboratory of Image Processing and Intelligent Control Ministry of EducationSchool of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhanChina

Wheat head detection can measure wheat traits such as head density and head *** wheat breeding largely relies on manual observation to detect wheat heads,yielding a tedious and inefficient *** emergence of affordable camera platforms provides opportunities for deploying computer vision(CV)algorithms in wheat head detection,enabling automated measurements of wheat *** wheat head detection,however,is challenging due to the variability of observation circumstances and the uncertainty of wheat head *** this work,we propose a simple but effective idea—dynamic color transform(DCT)—for accurate wheat head detection.

关键词： Wheat breeding wheat

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Kalman Filter Adaptive Speed Tracking control for High-speed Trains with Noise Interference 7

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

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7th International Conference on intelligent Robotics and control Engineering, IRCE 2024

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

ISBN: (纸本)9798350352399

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. © 2024 IEEE.

关键词： Kalman filters

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A Survey on Negative Transfer

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 305-329页

作者： Wen Zhang Lingfei Deng Lei Zhang Dongrui Wu the Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China IEEE the School of Microelectronics and Communication Engineering Chongqing UniversityChongqing 400044China

Transfer learning(TL)utilizes data or knowledge from one or more source domains to facilitate learning in a target *** is particularly useful when the target domain has very few or no labeled data,due to annotation expense,privacy concerns,***,the effectiveness of TL is not always *** transfer(NT),i.e.,leveraging source domain data/knowledge undesirably reduces learning performance in the target domain,and has been a long-standing and challenging problem in *** approaches have been proposed in the literature to address this ***,there does not exist a systematic *** paper fills this gap,by first introducing the definition of NT and its causes,and reviewing over fifty representative approaches for overcoming NT,which fall into three categories:domain similarity estimation,safe transfer,and NT *** areas,including computer vision,bioinformatics,natural language processing,recommender systems,and robotics,that use NT mitigation strategies to facilitate positive transfers,are also ***,we give guidelines on NT task construction and baseline algorithms,benchmark existing TL and NT mitigation approaches on three NT-specific datasets,and point out challenges and future research *** ensure reproducibility,our code is publicized at https://***/chamwen/NT-Benchmark.

关键词： Domain adaptation domain similarity negative transfer positive transfer transfer learning

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Attentive Neighborhood Feature Augmentation for Semi-supervised Learning

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intelligent Automation & Soft Computing 2023年第8期37卷 1753-1771页

作者： Qi Liu Jing Li Xianmin Wang Wenpeng Zhao School of Computer Science and Cyber Engineering Guangzhou UniversityGuangzhou510002China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang UniversityFuzhou350121China

Recent state-of-the-art semi-supervised learning(SSL)methods usually use data augmentations as core *** methods,however,are limited to simple transformations such as the augmentations under the instance’s naive representations or the augmentations under the instance’s semantic *** tackle this problem,we offer a unique insight into data augmentations and propose a novel data-augmentation-based semi-supervised learning method,called Attentive Neighborhood Feature Aug-mentation(ANFA).The motivation of our method lies in the observation that the relationship between the given feature and its neighborhood may contribute to constructing more reliable transformations for the data,and further facilitating the classifier to distinguish the ambiguous features from the low-dense ***,we first project the labeled and unlabeled data points into an embedding space and then construct a neighbor graph that serves as a similarity measure based on the similar representations in the embedding ***,we employ an attention mechanism to transform the target features into augmented ones based on the neighbor ***,we formulate a novel semi-supervised loss by encouraging the predictions of the interpolations of augmented features to be consistent with the corresponding interpolations of the predictions of the target *** carried out exper-iments on SVHN and CIFAR-10 benchmark datasets and the experimental results demonstrate that our method outperforms the state-of-the-art methods when the number of labeled examples is limited.

关键词： Semi-supervised learning attention mechanism feature augmentation consistency regularization

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Transmission-Constrained Consensus of Multiagent Networks

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IEEE Transactions on control of Network Systems 2023年第3期10卷 1484-1495页

作者： Wang, Xiaotian Su, Housheng Huazhong University of Science and Technology School of Artificial Intelligence and Automation Hubei430074 China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan430074 China

This article studies the consensus problem for multiagent systems with transmission constraints. A novel model of multiagent systems is proposed where the information transmissions between agents are disturbed by irregular distortions or interferences (named transmission constraint functions), and this model is universal which can be applied in many cases, such as interval consensus and discarded consensus. In the transmission-constrained consensus problem, we obtain the necessary and sufficient condition that agents can converge to state consensus. Furthermore, a more general case is studied in which the system reaches an equilibrium. Based on some techniques of stability theory, the existence, uniqueness, and stability of the system equilibrium point can be proven, which means the system can reach an asymptotically stable equilibrium. Moreover, the state values of the equilibrium are only decided by the network structure and transmission constraint functions but not the agents' initial states. Finally, numerical simulations are presented to illustrate the proposed theorems and corollaries. © 2014 IEEE.

关键词： Multi agent systems

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Grain condition inspection: one improved grey wolf algorithm for nodes deployment optimization combining with path planning of multi-inspection robots

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Soft Computing 2024年第20期28卷 11971-11986页

作者： Zhu, Chunhua Zhang, Jilong Yang, Jing Key Laboratory of Grain Information Processing and Control Ministry of Education Henan University of Technology Henan Zhengzhou450001 China Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Henan Zhengzhou450001 China Henan Engineering Laboratory of Grain Condition Intelligent Detection and Application Henan University of Technology Henan Zhengzhou450001 China

Grain inspection robots can detect abnormal information such as grain insects and impurities on the grain surface by deploying detection nodes, it has a significant for the safety of grain storage. Generally, the grey wolf optimization (GWO) algorithm has significant advantages such as fewer control parameters and easy implementation, is one of the commonly employed approaches aiming to solve the problem in many fields. However, the traditional GWO has the shortcomings such as poor population initialization diversity, slow convergence speed and easily fall into local optimal values, it is difficult to solve deployment of inspection nodes problem in the grain surface. In this study, an improved Latin Hypercube grey wolf optimization (LH-GWO) is presented to overcome the disadvantages of the traditional GWO algorithm in solving inspection node deployment problem. The LH-GWO algorithm contains two novel mechanisms which are the Latin Hypercube Sampling population initialization and an improved nonlinear convergence factor. Moreover, a grain surface boundary inspection nodes deployment strategy is proposed to optimize the location of nodes at the boundary. For verifying the effectiveness of the proposed LH-GWO, comparing with six commonly used algorithms based on benchmark functions experiment and coverage rate experiment. The experimental results demonstrate the merits of LH-GWO in terms of convergence speed, optimization accuracy and area coverage. Finally, the optimal inspection nodes are divided into three inspection regional areas by k-means algorithm clustering, and in each inspection regional area, the optimal inspection path of each robot is determined by solving the travel salesman problem (TSP). © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Motion planning

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Algorithm for Solving Traveling Salesman Problem Based on Self-Organizing Mapping Network

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Journal of Shanghai Jiaotong university(Science) 2024年第3期29卷 463-470页

作者：朱江辉叶航航姚莉秀蔡云泽 Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of System Control and Information Processing Ministry of EducationShanghai 200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China Key Laboratory of Marine Intelligent Equipment and System Ministry of EducationShanghai 200240China Huawei Technologies Co. Ltd.Shanghai 201206China

Traveling salesman problem(TSP)is a classic non-deterministic polynomial-hard optimization *** on the characteristics of self-organizing mapping(SOM)network,this paper proposes an improved SOM network from the perspectives of network update strategy,initialization method,and parameter *** paper compares the performance of the proposed algorithms with the performance of existing SOM network algorithms on the TSP and compares them with several heuristic *** show that compared with existing SOM networks,the improved SOM network proposed in this paper improves the convergence rate and algorithm *** with iterated local search and heuristic algorithms,the improved SOM net-work algorithms proposed in this paper have the advantage of fast calculation speed on medium-scale TSP.

关键词： traveling salesman problem(TSP) self-organizing mapping(SOM) combinatorial optimization neu-ral network

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Improved beluga whale optimization algorithm based cluster routing in wireless sensor networks

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Mathematical Biosciences and Engineering 2024年第3期21卷 4587-4625页

作者： Yuan, Hao Chen, Qiang Li, Hongbing Zeng, Die Wu, Tianwen Wang, Yuning Zhang, Wei Chongqing Key Laboratory of Geological Environmental Monitoring and Disaster Early Warning in the Three Gorges Reservoir Area Chongqing Three Gorges University Chongqing404120 China Internet of Things and Intelligent Control Technology Chongqing Engineering Research Center Chongqing Three Gorges University Chongqing404120 China Chongqing Municipal Key Laboratory of Intelligent Information Processing and Control Chongqing Three Gorges University Chongqing404120 China

Cluster routing is a critical routing approach in wireless sensor networks (WSNs). However, the uneven distribution of selected cluster head nodes and impractical data transmission paths can result in uneven depletion of network energy. For this purpose, we introduce a new routing strategy for clustered wireless sensor networks that utilizes an improved beluga whale optimization algorithm, called tCBWO-DPR. In the selection process of cluster heads, we introduce a new excitation function to evaluate and select more suitable candidate cluster heads by establishing the correlation between the energy of node and the positional relationship of nodes. In addition, the beluga whale optimization (BWO) algorithm has been improved by incorporating the cosine factor and t-distribution to enhance its local and global search capabilities, as well as to improve its convergence speed and ability. For the data transmission path, we use Prim’s algorithm to construct a spanning tree and introduce DPR for determining the optimal route between cluster heads based on the correlation distances of cluster heads. This effectively shortens the data transmission path and enhances network stability. Simulation results show that the improved beluga whale optimization based algorithm can effectively improve the survival cycle and reduce the average energy consumption of the network. © 2024 the Author(s).

关键词： Heuristic algorithms

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Research and application of composite stochastic resonance in enhancement detection

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Chinese Physics B 2024年第1期33卷 264-273页

作者：高蕊焦尚彬薛琼婕 School of Automation and Information Engineering Xi'an University of TechnologyXi'an 710048China School of Electronic and Electrical Engineering Baoji University of Arts and SciencesBaoji 721016China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of TechnologyXi'an 710048China

Aiming at the problem that the intermediate potential part of the traditional bistable stochastic resonance model cannot be adjusted independently, a new composite stochastic resonance(NCSR) model is proposed by combining the Woods–Saxon(WS) model and the improved piecewise bistable model. The model retains the characteristics of the independent parameters of WS model and the improved piecewise model has no output saturation, all the parameters in the new model have no coupling characteristics. Under α stable noise environment, the new model is used to detect periodic signal and aperiodic signal, the detection results indicate that the new model has higher noise utilization and better detection ***, the new model is applied to image denoising, the results showed that under the same conditions, the output peak signal-to-noise ratio(PSNR) and the correlation number of NCSR method is higher than that of other commonly used linear denoising methods and improved piecewise SR methods, the effectiveness of the new model is verified.

关键词： Woods–Saxon improved piecewise model composite stochastic resonance(SR) image denoising

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An Optimal control-Based Distributed Reinforcement Learning Framework for A Class of Non-Convex Objective Functionals of the Multi-Agent Network

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IEEE/CAA Journal of Automatica Sinica 2023年第11期10卷 2081-2093页

作者： Zhe Chen Ning Li Department of Automation Shanghai Jiao Tong UniversityShanghai 200240 Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai 200240 Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China Department of Automation Tsinghua UniversityBeijing 100084China IEEE

This paper studies a novel distributed optimization problem that aims to minimize the sum of the non-convex objective functionals of the multi-agent network under privacy protection, which means that the local objective of each agent is unknown to others. The above problem involves complexity simultaneously in the time and space aspects. Yet existing works about distributed optimization mainly consider privacy protection in the space aspect where the decision variable is a vector with finite dimensions. In contrast, when the time aspect is considered in this paper, the decision variable is a continuous function concerning time. Hence, the minimization of the overall functional belongs to the calculus of variations. Traditional works usually aim to seek the optimal decision function. Due to privacy protection and non-convexity, the Euler-Lagrange equation of the proposed problem is a complicated partial differential ***, we seek the optimal decision derivative function rather than the decision function. This manner can be regarded as seeking the control input for an optimal control problem, for which we propose a centralized reinforcement learning(RL) framework. In the space aspect, we further present a distributed reinforcement learning framework to deal with the impact of privacy protection. Finally, rigorous theoretical analysis and simulation validate the effectiveness of our framework.

关键词： Distributed optimization multi-agent optimal control reinforcement learning(RL)

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