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第三十九届中国控制会议

作者： Feng Yin Jianqi An Xiaoling Shen Takao Terano School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Chiba University of Commerce

Gas utilization rate（GUR） is an important indicator that reflects the state of a blast furnace（BF）. However, most researches only predict the point values of GUR, it is difficult for the operator to make corresponding operations. This paper presents an interval prediction model based on multi-time-scale to predict the GUR. First, this paper analyzes the impact of the burden distribution and the hot-blast supply on multi-time-scale of the blast furnace. Then, we build a multi-time-scale point prediction model based on support vector regression（SVR）. Next, an interval prediction model of multi-objective optimization based on interval prediction indicators and the point prediction model was proposed. Finally, some experiment results base on actual run data shows that the method predicts the GUR more effectively than the point prediction model based on single time scale.

关键词： Blast furnace Gas utilization rate Interval prediction Multi-time-scale Multi-objective optimization

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A comparative study on evolutionary algorithms for the agent routing problem in multi-point dynamic task

A comparative study on evolutionary algorithms for the agent...

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作者： Lu, Sai Xin, Bin Dou, Lihua Wang, Ling School of Automation Beijing Institute of Technology Beijing China Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing China Department of Automation Tsinghua University Beijing China

The agent routing problem in multi-point dynamic task (ARP-MPDT) proposed recently is a novel permutation optimisation problem. In ARP-MPDT, a number of task points are located at different places and their states change over time. The agent must go to the task points in turn to execute the tasks, and the execution time of each task depends on the task state. The optimisation objective is to minimise the time for the agent to complete all the tasks. In this paper, five evolutionary algorithms are redesigned and tried to solve this problem, including a permutation genetic algorithm (GA), a variant of the particle swarm optimisation (PSO) and three variants of the estimation of distribution algorithm (EDA). In particular, a dual-model EDA (DM-EDA) employing two probability models was proposed. Finally, comparative tests confirm that the DM-EDA has a stronger adaptability than the other algorithms though GA performs better for the large-scale instances. Copyright © 2020 Inderscience Enterprises Ltd.

关键词： Genetic algorithms

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A State Feedback Voltage controller for Inverter-based Islanded Microgrid

A State Feedback Voltage Controller for Inverter-based Islan...

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第三十九届中国控制会议

作者： Yuqiao Jiao Min Ding Danyun Li Qingyi Wang Zhijian Fang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System

Since there is no external power supply in islanded microgrid, the waveform of output power is extremely vulnerable to negative effects of voltage fluctuation. In this paper, a state feedback voltage controller is proposed to track the reference voltage of the point of common coupling（PCC） for distributed generation unit in an islanded microgrid. It employs the internal model principle and optimal control approach to deal with the changes in the reference voltage. The state feedback gains of the controller are tuned by using the multi-objective genetic algorithm NSGA-II to improve the rapidity and accuracy of voltage tracking performance. Simulations in Matlab/Simulink show the effectiveness of voltage tracking performance with zero steadystate error in changing scenarios.

关键词： Islanded Microgrid Inverter State Feedback controller Optimal control

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A threshold segmentation method for non-uniform illumination image based on brightness equalization 2

A threshold segmentation method for non-uniform illumination...

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2019 2nd International Conference on advanced Materials, intelligent Manufacturing and automation, AMIMA 2019

作者： Guo, Hui Chen, Peng Huang, Shun Hu, Cong Zhang, Lijun School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System Wuhan430074 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. © Published under licence by IOP Publishing Ltd.

关键词： Luminance

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Image retrieval based on convolutional neural network and linear discriminate analysis 2

Image retrieval based on convolutional neural network and li...

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2019 2nd International Conference on advanced Materials, intelligent Manufacturing and automation, AMIMA 2019

作者： Xia, Wei Hu, Cong Cheng, Xianghui Chen, Peng Jin, Xing School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System Wuhan430074 China

Image retrieval is a hot research topic in the field of computer vision image processing, and the user queries the image database for similar images and produces a list of recommendations. The paper firstly sets forth the research status of image retrieval, then the convolution neural network is briefly introduced. Due to the traditional image retrieval and recommendation system use manual extraction of image features is relatively cumbersome, and the retrieval accuracy is not high research status, the paper proposes an image retrieval method based on the improved convolutional neural network and linear discriminate analysis. Caltech256 and CIFAR-10 datasets were trained using the model in this paper, experimental, results show that the proposed method can effectively improve the performance of retrieval. © Published under licence by IOP Publishing Ltd.

关键词： Search engines

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Extended Dissipative control for Drill-String systems with Input Time-Varying Delays 38

Extended Dissipative Control for Drill-String Systems with I...

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第三十八届中国控制会议

作者： Chengda Lu Min Wu Luefeng Chen Aoxue Yang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Software and Electrical Engineering Swinburne University of Technology

ISBN: (纸本)9781728123295;9789881563972

The objective of this paper is to design an extended dissipative controller for drill-string systems by taking into account input time-delays. A multi-degree-of-freedom lumped parameter model is established for the drill-string and a state space model in perturbation coordinates is derived correspondingly. By using the Lyapunov-Krasovskii functional method, two sufficient conditions are provided to make extended dissipativity analysis of the drill-string and design a suitable extended dissipative controller for the system. The effectiveness of our results is verified through a numerical example.

关键词： Drill-string extended dissipative control time-delay

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Design of audio to image cross-modal learning and generation based on single-layer CoPt spin-orbit torque devices

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Journal of Magnetism and Magnetic Materials 2025年 629卷

作者： Qian, Likun Yang, Liu Zuo, Chao Tao, Ying Li, Wendi Jin, Fang Li, Huihui Dong, Kaifeng School of Future Technology China University of Geosciences Wuhan430074 China School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Wuhan Second Ship Design and Research Institute Wuhan430064 China Beijing Superstring Academy of Memory Technology Beijing100176 China

Despite remarkable achievements in neural network applications using spin–orbit torque (SOT) devices, current research predominantly focuses on unimodal classification tasks, while cross-modal learning tasks often suffer from suboptimal generation quality. To address this limitation, we employ generative adversarial networks (GANs) based on CoPt-SOT to achieve cross-modal learning and generation from speech to handwritten digit images. Initially, we explore the field-free switching characteristics of single-layer CoPt devices, developing spin-based implementations of Scaled Rectified Linear Unit (SReLu), Sigmoid, and Tanh neuronal functions. Simultaneously, we leverage the nonvolatile multistate characteristics of CoPt devices to realize excitatory-inhibitory synaptic plasticity. Subsequently, for the first time, we construct half-spin and full-spin GAN networks using these spin-based neurons and synapses, enabling cross-modal learning and generation between speech and image domains. Finally, we evaluate the performance of these networks through handwritten digit recognition tasks, achieving impressive recognition accuracies of 93.78 % and 88.61 % for half-spin and full-spin implementations, respectively. Notably, this work overcomes the existing bottleneck in SOT device applications, which have been largely confined to unimodal classification tasks, and demonstrates significant potential for expanding the scope of SOT-based technologies. © 2025 Elsevier B.V.

关键词： Generative adversarial networks

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Stacked Sparse Autoencoder based Automatic Detection of Ripples and Fast Ripples in Epilepsy

Stacked Sparse Autoencoder based Automatic Detection of Ripp...

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第三十九届中国控制会议

作者： Hongzhen Qin Min Wu Xiongbo Wan Yuxiao Du School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Automation Guangdong University of Technology

High frequency oscillations(HFOs) have been acknowledged as a putative biomarker of epileptic seizure onset zones(SOZs). Accurate detection of HFOs is significant for the preoperative localization of epileptic SOZs. In this paper, a new method is proposed to automatically detect ripples(Rs) and fast ripples(FRs) from intracranial electroencephalography(iEEG)in epilepsy. A moving-window technique is utilized to segment the filtered signals which are obtained by filtering the raw iEEG signals using two Chebyshev band-pass filters. Two stacked sparse autoencoder(SSAE) models are proposed to automatically detect Rs and FRs, respectively. By optimizing the parameters of the two SSAE models, our method yields higher sensitivity(88.9±2.4% for Rs and 83.2±2.5% for FRs) and higher specificity(92.3±2.8% for Rs and 86.1±2.8% for FRs) than other three methods do.

关键词： Epilepsy Intracranial electroencephalography Ripples Fast ripples Stacked sparse autoencoder

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Fusionplanner: A Multi-Task Motion Planner for Mining Trucks Using Multisensor Fusion Method

SSRN

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SSRN 2023年

作者： Teng, Siyu Chen, Long Li, Luxi Li, Yuchen Hu, Xuein Li, Lingxi Ai, Yunfeng Department of Computer Science Hong Kong Baptist University Hong Kong The School of Artificial Intelligence Hubei University Wuhan China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China Waytous Inc. Haidian Distinct Beijing China Indianapolis United States School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

In recent years, significant achievements have been made in motion planning for intelligent vehicles. However, as a typical unstructured environment, open-pit mining attracts limited attention due to its complex operational conditions and adverse environmental factors. A comprehensive paradigm for unmanned transportation in open-pit mines is proposed in this research, including a simulation platform, a testing benchmark, and a trustworthy and robust motion planner. Firstly, we introduce the Parallel Mining Simulator (PMS), a high-fidelity simulator specifically designed for open-pit mining scenarios. PMS enables to management and control of open-pit mine transportation from both the single-truck control and multi-truck scheduling perspectives. Then, we develop a novel benchmark called MiningNav, which offers three validation approaches suitable for transportation roads. The purpose of MiningNav is to evaluate the trustworthiness and robustness of algorithms in open-pit mines. Finally, we propose the first multi-task motion planning algorithm, called FusionPlanner, for the mining truck by the multisensor fusion method to adapt both lateral and longitudinal control tasks for unmanned transportation. We validate the performance of our planner using MiningNav in PMS, and empirical results demonstrate a significant reduction in the number of collisions and takeovers of FusionPlanner, thereby improving overall trustworthiness and robustness. © 2023, The Authors. All rights reserved.

关键词： Motion planning

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Graph Convolutional Extreme Learning Machine

Graph Convolutional Extreme Learning Machine

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International Joint Conference on Neural Networks (IJCNN)

作者： Zijia Zhang Yaoming Cai Wenyin Gong Xiaobo Liu Zhihua Cai School of Computer Science China University of Geosciences Wuhan China School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Extreme Learning Machine (ELM) has gained lots of research interest due to its universal approximation capability and fast learning speed. However, traditional ELMs are devised for regular Euclidean data, such as 2D grid and 1D sequence, and thus don't apply to non-Euclidean data, e.g., graph-structured data. To overcome this shortcoming, this paper presents a Graph Convolutional Extreme Learning Machine (termed as GCELM) for semi-supervised classification. Technically, a random graph convolutional layer is introduced to replace the random projection of original ELM, which endues ELM with the capability of dealing with graph-structured data directly. To generate a robust graph from the raw dataset, a self-representation model is adopted to construct a weighted graph. Extensive experiments on 27 UCI datasets demonstrate that GCELM outperforms many popular semi-supervised methods, and with faster learning speed. To the best of our knowledge, this is the first work that combines graph convolution with ELM.

关键词： Convolution Robustness Closed-form solutions Neural networks Training Convolutional codes Optimization

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