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第36届中国控制会议

作者： Ri Zhang Yong He Zhen-Tao Liu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

ISBN: (纸本)9781538629185

Communication atmosphere in Human-Robot Interaction（HRI） is estimated by integrating emotional states of humans and robots based on the concept of Fuzzy Atmosfleld（FA）,where human emotion is estimated from bimodal communication cues（i.e.,speech and facial expression） and robot emotion is generated by emotional expression *** Analytical Hierarchy Process（FAHP） is used for dynamic weight calculation of emotional states of humans and robots in the FA model,in which a three-level hierarchy is adopted for the analysis of communication atmosphere and four acoustic cues（i.e.,volume,rate,pitch,and duration） are utilized to calculate the dynamic *** is conducted in a multi-modal emotional communication based humans-robots interaction（MEC-HRI） system,by which experimental results demonstrate the validity of proposed FA *** the communication atmosphere can help to facilitate smooth communication between humans and robots by adjusting robot behavior for emotion *** proposal is being planned for an atmosphere representation system for realizing causal communication in HRI and background music is being considered for the FA model.

关键词： Communication Atmosphere Human-Robot Interaction Fuzzy Analytical Hierarchy Process Dynamic Weight

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Application of neural network in modeling of activated sludge wastewater treatment process 36

Application of neural network in modeling of activated sludg...

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第36届中国控制会议

作者： Li Jun Zhang Neng Li Jing Jing Zhang Xi Yong Tian School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

ISBN: (纸本)9781538629185

This paper present a prediction model for three different objection(the airflow rate,the carbonaceous biochemical oxygen demand(CBOD) of the effluent,and the total suspend solids(TSS) of the *** model is built by the MLP neural *** accurancy of the prediction result of MLP neural network is compared with the accurancy of the result of trational stational autoregressive model(AR).The conclution is that the percentage error prediction model of the MLP neural network(PE),the fractional deviation(FB),normalized mean square error(NMSE),the mean absolute error(MAE) and mean square error(MSE) prediction model of evaluation index is better than the AR *** other words,the prediction model based on MLP neural network provides a reliable basis for reducing the energy consumption in the activated sludge process of industrial waste water treatment and further improving its effect on the treatment of industrial waste water.

关键词： Industrial waste water treatment Activated sludge process MLP neural network Data mining

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Experimental Study on Decentralized Concurrent Learning for Multi-Agent system with complex Dynamics 36

Experimental Study on Decentralized Concurrent Learning for ...

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第36届中国控制会议

作者： Ting Fei Xin Chen Min Wu Chi Wang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

ISBN: (纸本)9781538629185

A cooperative multi-agent system entitles some independent agents to complete complex tasks through coordination and *** the dynamics of physical agents are so complex that the environment of learning is indeed stochastic,the paper introduces the decentralized multi-agent reinforcement learning（MARL） algorithm,named as Decentralized Concurrent Learning with Cooperative Policy Exploration（DCL-CPE）,in order to solve cooperative learning within stochastic *** investigate its feasibility in practical multi-agent systems,the box-pushing test with DCL-CPE is designed with a group of two-wheel driven robots acting as learning *** to physical properties,such as nonholonomic dynamics,rolling and sliding frictions,unreliable sense,rigid body collision,etc.,the cooperative learning is a high stochastic learning *** simulation test in Webots shows that DCL-CPE is good at exploring best cooperative policy in a decentralized way,even as state transition and rewards are all stochastic.

关键词： Multi-agent system Decentralized cooperative learning Stochastic environment Webots

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Global Rice Multi-Class Segmentation Dataset (RiceSEG): A Comprehensive and Diverse High-Resolution RGB-Annotated Images for the Development and Benchmarking of Rice Segmentation Algorithms

arXiv

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arXiv 2025年

作者： Zhou, Junchi Wang, Haozhou Kato, Yoichiro Nampally, Tejasri Rajalakshmi, P. Balram, M. Katsura, Keisuke Lu, Hao Mu, Yue Yang, Wanneng Gao, Yangmingrui Xiao, Feng Chen, Hongtao Chen, Yuhao Li, Wenjuan Wang, Jingwen Yu, Fenghua Zhou, Jian Wang, Wensheng Hu, Xiaochun Yang, Yuanzhu Ding, Yanfeng Guo, Wei Liu, Shouyang Engineering Research Center of Plant Phenotyping Ministry of Education Jiangsu Collaborative Innovation Center for Modern Crop Production Academy for Advanced Interdisciplinary Studies Sanya Institute of Nanjing Agricultural University Nanjing Agricultural University Nanjing China Graduate School of Agricultural and Life Sciences The University of Tokyo Tokyo Japan Department of Artificial Intelligence Indian Institute of Technology Hyderabad India Department of Electrical Engineering Indian Institute of Technology Hyderabad India Institute of Biotechnology Professor Jayashankar Telangana Agricultural State University Hyderabad India Graduate School of Agriculture Kyoto University Kyoto Japan Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China National Key Laboratory of Crop Genetic Improvement National Center of Plant Gene Research Hubei Key Laboratory of Agricultural Bioinformatics Huazhong Agricultural University Wuhan China State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China the Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences Beijing China Center for Geospatial Information Shenzhen Institutes of Advanced Technology Chinese Academy of Science Shenzhen China School of Information and Electrical Engineering Shenyang Agricultural University Shenyang China Rice Research Institute Jilin Academy of Agricultural Sciences Changchun China Institute of Crop Sciences National Key Facility for Crop Gene Resources and Genetic Improvement Chinese Academy of Agricultural Sciences Beijing China Yuan Long Ping High-Tech Agriculture Co. Ltd. Changsha China

Developing computer vision–based rice phenotyping techniques is crucial for precision field management and accelerating breeding, thereby continuously advancing rice production. Among phenotyping tasks, distinguishing image components is a key prerequisite for characterizing plant growth and development at the organ scale, enabling deeper insights into eco-physiological processes. However, due to the fine structure of rice organs and complex illumination within the canopy, this task remains highly challenging, underscoring the need for a high-quality training dataset. Such datasets are scarce, both due to a lack of large, representative collections of rice field images and the time-intensive nature of annotation. To address this gap, we established the first comprehensive multi-class rice semantic segmentation dataset, RiceSEG. We gathered nearly 50,000 high-resolution, ground-based images from five major rice-growing countries (china, Japan, India, the Philippines, and Tanzania), encompassing over 6,000 genotypes across all growth stages. From these original images, 3,078 representative samples were selected and annotated with six classes (background, green vegetation, senescent vegetation, panicle, weeds, and duckweed) to form the RiceSEG dataset. Notably, the sub-dataset from china spans all major genotypes and rice-growing environments from the northeast to the south. Both state-of-the-art convolutional neural networks and transformer-based semantic segmentation models were used as baselines. While these models perform reasonably well in segmenting background and green vegetation, they face difficulties during the reproductive stage, when canopy structures are more complex and multiple classes are involved. These findings highlight the importance of our dataset for developing specialized segmentation models for rice and other crops. The RiceSEG dataset is publicly available at ***. © 2025, CC BY.

关键词： Semantic Segmentation

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Image Jacobian Matrix Estimation based on Fuzzy Adaptive Robust Kalman Filter

Image Jacobian Matrix Estimation based on Fuzzy Adaptive Rob...

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Chinese control Conference

作者： Zhenzhu Liu Xinmei Wang Zhenjiang Feng Zhongzhao Xie Shuai Ke Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan P. R. China

Aiming at the problem of image Jacobian matrix estimation, this paper proposes a method to get the motion state estimation of the object feature point at the current time by using the combination of robust Kalman filter and fuzzy adaptive method from the image feature space, and the estimation of the image Jacobian matrix can be obtained. Firstly, an adaptive robust decorrelation Kalman filler algorithm with colored measurement noise is proposed by reconstructing process equation and measurement equation and combining the mathematical characteristics of the standard Kalman filter noise. Secondly, by monitoring if the ratio between theoretical residual and actual residual is near 1, the fuzzy inference system constantly adjust the weighted measurement noise covariance and recursively correct the measurement noise covariance of the adaptive Kalman filter, and thus be able to estimate the position and velocity of the object feature point at the current time in the image space more accurately, then the estimation of image Jacobian matrix can be achieved accurately under unknown dynamic environment. The feasibility and superiority of the proposed method can be verified by the simulation and experimental results.

关键词： Jacobian matrices Noise measurement Kalman filters Mathematical model Estimation Current measurement Robots

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Unscented Kalman Filter for Nonlinear systems with One-step Randomly Delayed Measurements and Colored Measurement Noises

Unscented Kalman Filter for Nonlinear Systems with One-step ...

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Chinese control Conference (CCC)

作者： Xinmei Wang Zhenzhu Liu Leimin Wang Feng Liu Wei Liu Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan P. R. China

In this paper, a new unscented Kalman filter (Unscented Kalman filter, UKF) for nonlinear system with both one-step randomly delayed measurements and colored measurement noises is proposed. Firstly, the first-order Markov sequence model is used to whiten colored noise, at the same time, an independent and identically distributed Bernoulli variable is used to model the delay of measurement data transmission, then the model of nonlinear one-step randomly time delay system with colored noise whitening is established. Secondly, filter recursion formula of UKF under the above model is proposed through unscented transformation (Unscented transformation, UT) to calculate the posterior mean and covariance of the nonlinear state based on the Bayesian filter framework. The proposed new UKF method can effectively deal with the issue that traditional UKF is failure under the condition of one-step randomly delayed measurements and colored measurement noises. The efficiency and superiority of the proposed method are illustrated in a numerical example for a target tracking problem.

关键词： Noise measurement Colored noise State estimation Delays Nonlinear systems Target tracking

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Multi-Convolution Neural Networks-Based Deep Learning Model for Emotion Understanding

Multi-Convolution Neural Networks-Based Deep Learning Model ...

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第37届中国控制会议

作者： Luefeng Chen Min Wu Wanjuan Su Kaoru Hirota School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Tokyo Institute of Technology

Multi-convolution neural networks-based deep learning model in combination with multimodal data for emotion understanding is proposed, in which facial expression and body gesture are used to achieve emotional states recognition for emotion understanding. It aims to understand coexistence multimodal information in human-robot interaction by using multi-convolution neural networks, where multilayer convolutions are connected in series and multiple networks are executed in parallel. Moreover,when optimizing the weights of deep neural network by traditional method, it is easy to fall into poor local optimal. To address this problem, a hybrid genetic algorithm with stochastic gradient descent is developed, which has the capacity of inherent implicit parallelism and better global optimization of genetic algorithm so that it can adaptively find the better weights of the *** in order to speed up the convergence of the proposal, the weights optimized by stochastic gradient descent will be taken as a chromosome of genetic algorithms initial population, and it also can be used as a priori knowledge. To verify the effectiveness of the proposal, experiments on benchmark database of spontaneous emotion expressions are developed, and experimental results show that the proposal outperforms the state-of-the-art methods. Meanwhile, the preliminary application experiments are also carried out and the results indicate that the proposal can be extended to human-robot interaction.

关键词： Deep learning emotion understanding convolution neural network facial expression body gesture

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Convolutional Neural Networks for Facial Expression Recognition with Few Training Samples

Convolutional Neural Networks for Facial Expression Recognit...

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Chinese control Conference

作者： Zhongzhao Xie Yongbo Li Xinmei Wang Wendi Cai Jing Rao Zhenzhu Liu Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan P. R. China

Facial expression recognition (FER) plays an important role in human-machine interaction. An assistant robot having a close interaction with human being should be able to recognize human facial expression. FER is a non-trivial problem because each individual has his own way to reveal his emotion and the facial expressions of two different persons may not be totally identical. Hence, facial expression recognition is still a challenging problem in computer vision. In this work, we propose a simple solution for facial expression recognition that uses a combination of Convolutional Neural Network and specific image pre-processing steps. The experiments employed to evaluate our technique were carried out using two largely used public databases(CK+, JAFFE). A study of the impact of each image pre-processing operation in the accuracy rate is presented. The proposed method: achieves competitive results when compared with other facial expression recognition methods-97.85% of accuracy in the CK+ database-it is fast to train, and it allows for real time facial expression recognition with standard computers.

关键词： Training Face recognition Convolutional neural networks Neurons Convolution Feature extraction

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Synchronous Detector for GMI Magnetic Sensor Based on Lock-in Amplifier

Synchronous Detector for GMI Magnetic Sensor Based on Lock-i...

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Chinese control Conference (CCC)

作者： Jinchao Wang Fang Jin Lei Zhu Zhi Zhao Hengchang Rao Junlei Song Kaifeng Dong M Wenqin Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan P. R. China

A new concept of a synchronous detector for Giant Magneto-Impedance (GMI) sensors is presented. This concept combines a lock-in amplifier, with outstanding capabilities, high speed and a feedback approach that ensures the amplitude detection with easily adjustable gain. The synchronous detector is capable of measuring high-frequency and very low amplitude signals without the use of diode-based active rectifiers or analog switches. In comparison with most of the commercially available diode-based peak detectors, the linearity of the synchronous detector is generally better, especially for low level amplitudes. The synchronous detector has been used for the amplitude measurement of single frequency sine signal and for the demodulation of amplitude-modulated signal. It has also been successfully integrated in a GMI sensor prototype. Magnetic field measurements in open- and closed-loop of this sensor have been conducted. The measured sensitivity was about 1.02 V/Oe in open-loop while it was 0.12V/Oe in close-loop. The above research provides technical accumulation for the design of sensor nodes based on GMI sensor wireless sensor networks.

关键词： Detectors Magnetic sensors Sensitivity Magnetic cores Voltage measurement Linearity

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A threshold segmentation method for non-uniform illumination image based on brightness equalization

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IOP Conference Series: Materials Science and Engineering 2019年第5期569卷

作者： Hui Guo Peng Chen Shun Huang Cong Hu Lijun Zhang School of Automation China University of Geosciences Wuhan 430074 China Hubei key laboratory of Advanced Control and Intelligent Automation for Complex System Wuhan 430074 China

In the process of image acquisition, non-uniform illumination images are common due to poor lighting, surface reflection, or a combination of these two factors. In order to improve the quality of image segmentation, an image threshold segmentation method is proposed for non-uniform illumination images. First, the brightness of different regions in the image is compensated to make the brightness background of the whole image consistent. Second, the gradation histogram of the processed image is obtained and simulated into Gaussian distribution curve, then the inflection points of the curve are calculated. Finally, the two inflection points are set as thresholds to segment the image after brightness equalization. This method eliminates the influence of non-uniform illumination to a certain extent and gets a better segmentation effect. Experiments were carried out on images containing several different types of non-uniform illumination. The results demonstrate that the proposed method outperforms the compared enhancement algorithms in threshold segmentation.

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