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第32届中国控制与决策会议

作者： Shunli Feng Yindong Shen Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of science and technology

The order picking and distribution are two necessary activities in logistics,the cost of which accounts for a considerable portion of the logistics *** there are dependencies between these two activities,a lot of work has been carried out on them independently;hence,the optimization may be *** paper proposes an integrated order batching and distribution *** objectives are to minimize the makespan（including picking time and delivery time） and to minimize the number of delivery *** solve the integrated model,an EDA approach is devised,in which an elaborate encoding/decoding method is employed and an incremental learning probability model is *** show that the proposed integrated model and its solution approach can lead to a significant reduction in the total cost（weighted sum of the makespan and the number of delivery trips）.

关键词： Order Batching Order picking Logistics Distribution EDA approach

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Grey Wolf Optimization Based Self-Organizing Fuzzy Multi-Objective Evolution Algorithm

Research Square

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Research Square 2021年

作者： Xie, Jialiang Zhang, Shanli Wang, Honghui Wu, Dongrui School of Science Jimei University Xiamen361021 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control China

Two goals of multi-objective evolutionary algorithms are effectively improving its convergence and diversity, and making the Pareto set evenly distributed and close to the real Pareto Front. This paper proposes a grey wolf optimization based self-organizing fuzzy multi-objective evolutionary algorithm. Grey wolf optimization algorithm is used to optimize the initial weights of the self-organizing map network. New neighborhood relationships for individuals are built by self-organizing map, which can maintain the invariance of feature distribution and map the structural information of the current population into Pareto Sets. Based on this neighborhood relationship, this paper uses the fuzzy differential evolution operator, which constructs a fuzzy inference system to dynamically adjust the weighting parameter in the differential operator, to generate a new initial solution, and the polynomial mutation operator to refine them. Boundary processing is then conducted. Experiments on fifteen test problems were conducted to verify its effectiveness. Results show that the convergence and diversity of the proposed algorithm are better than several state-of-the-art multi-objective evolutionary algorithms. © 2021, CC BY.

关键词： Self organizing maps

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Vision-based Autonomous Landing of Unmanned Aerial Vehicle on a Motional Unmanned Surface Vessel

Vision-based Autonomous Landing of Unmanned Aerial Vehicle o...

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第三十九届中国控制会议

作者： Zhe-Cheng Xu Bin-Bin Hu Bin Liu XD Wang Hai-Tao Zhang School of Artificial Intelligence and Automation the Key Laboratory of Image Processing and Intelligent ControlHuazhong University of Science and Technology

This paper has developed a vision-based control approach for autonomous tracking and landing on a motional Unmanned surface vessel（USV） with an unmanned aerial vehicle（UAV）. First, a novel three-stage visual detection method is proposed to estimate the relative 3 D position between UAV and USV, where the USV stays in a suitable view of the camera of the UAV. Then a PID controller is designed to regulate the attitude of the quadrotor and the movement of the vessel, which enables the successful landing procedure of the UAV on a motional USV. Finally, lake experiments are conducted to verify the effectiveness of the proposed algorithm.

关键词： Autonomous landing Unmanned aerial vehicle Unmanned surface vessel Computer vision

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Uncertainty-guided cross teaching semi-supervised framework for histopathology image segmentation with curriculum self-training

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Applied Soft Computing 2025年 180卷

作者： Rui Xu Nan Zhou Siyang Feng Zhenbing Liu Huahu Deng Yajun An Jian Li Chu Han Zaiyi Liu Rushi Lan Cheng Lu Xipeng Pan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin Guangxi 541004 China Department of Radiology Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Guangzhou Guangdong 510080 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou Guangdong 510080 China International Joint Research Laboratory of Spatio-temporal Information and Intelligent Location Services Guilin University of Electronic Technology Guilin Guangxi 541004 China

Histopathology image segmentation is crucial in disease diagnosis, therapeutic response evaluation, and prognosis. However, manually annotating pixel-level labels for histopathology images is both time-consuming and labor-demanding task. In this study, we propose a novel semi-supervised semantic segmentation framework called UTCS ( U ncertainty-guided cross T eaching and C urriculum S elf-training) to address the challenges of limited labeled data. UTCS effectively harnesses the benefits of consistency regularization and self-training in semi-supervised learning. Our approach introduces a mutual consistency network, where one network’s prediction is used as a pseudo mask to supervise the other network and vice versa. Addressing the issue of unreliable pseudo labels, we propose a dynamically re-weighted loss function that leverages uncertainty to perform pixel-level selection during the mutual teaching process, referred to as uncertainty-guided cross teaching. Furthermore, inspiring from curriculum learning, we incorporate an self-training strategy, focusing on image-level selection, that prioritizes reliable images during the re-training stage and aims to generate high-quality pseudo-labels for less reliable images. Extensive experiments on two publicly available histopathology datasets, BCSS and LUAD-HistoSeg, demonstrate the superior performance of our method compared to state-of-the-art semi-supervised semantic segmentation methods.

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FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classification

arXiv

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arXiv 2023年

作者： Deng, Tianpeng Huang, Yanqi Han, Guoqiang Shi, Zhenwei Lin, Jiatai Dou, Qi Liu, Zaiyi Guo, Xiao-Jing Chen, C.L. Philip Han, Chu The School of Computer Science and Engineering South China University of Technology Guangdong Guangzhou510006 China Southern Medical University Guangzhou510080 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou510080 China The Department of Computer Science and Engineering The Chinese University of Hong Kong 999077 Hong Kong The Department of Breast Pathology and Lab Tianjin Medical University Cancer Institute and Hospital National Clinical Research Center of Cancer Key Laboratory of Cancer Prevention and Therapy Tianjin Tianjin’s Clinical Research Center for Cancer Key Laboratory of Breast Cancer Prevention and Therapy Tianjin Medical University Ministry of Education Tianjin300060 China

Histopathological tissue classification is a fundamental task in computational pathology. Deep learning-based models have achieved superior performance but centralized training with data centralization suffers from the privacy leakage problem. Federated learning (FL) can safeguard privacy by keeping training samples locally, but existing FL-based frameworks require a large number of well-annotated training samples and numerous rounds of communication which hinder their practicability in the real-world clinical scenario. In this paper, we propose a universal and lightweight federated learning framework, named Federated Deep-Broad Learning (FedDBL), to achieve superior classification performance with limited training samples and only one-round communication. By simply associating a pre-trained deep learning feature extractor, a fast and lightweight broad learning inference system and a classical federated aggregation approach, FedDBL can dramatically reduce data dependency and improve communication efficiency. Five-fold cross-validation demonstrates that FedDBL greatly outperforms the competitors with only one-round communication and limited training samples, while it even achieves comparable performance with the ones under multiple-round communications. Furthermore, due to the lightweight design and one-round communication, FedDBL reduces the communication burden from 4.6GB to only 276.5KB per client using the ResNet-50 backbone at 50-round training. Since no data or deep model sharing across different clients, the privacy issue is well-solved and the model security is guaranteed with no model inversion attack risk. Code is available at https://***/tianpeng-deng/FedDBL. © 2023, CC BY.

关键词： Tissue

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A Pushing-Grasping Collaborative Method Based on Deep Q-Network Algorithm in Dual Viewpoints

Research Square

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Research Square 2021年

作者： Peng, Gang Liao, Jinhu Guan, Shangbin Yang, Jin Li, Xinde School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Ministry of Education China School of Automation South East University Nanjing China

In the field of intelligent manufacturing, robot grasping and sorting is an important content. However, in the traditional 2D camera-based robotic arm grasping method, the grasping efficiency is low and the grasping accuracy is low when facing the scene of stacking and occlusion. Insufficiency and other issues, a dual perspective-based deep reinforcement learning promotion and capture method is proposed. In this case, a novel method of pushing-grasping collaborative based on the deep Q-network in dual viewpoints is proposed in this paper. This method adopts an improved deep Q-network algorithm, with an RGB-D camera to obtain the information of objects’ RGB images and point clouds from two viewpoints, and combines the pushing and grasping actions, so that the trained manipulator can make the scenes better for grasping, so that it can perform well in more complicated grasping scenes. What’s more, we improved the reward function of the deep Q-network and propose the piecewise reward function to speed up the convergence of the deep Q-network. We trained different models and tried different methods in the V-REP simulation environment, and it drew a conclusion that the method proposed in this paper converges quickly and the success rate of grasping objects in unstructured scenes raises up to 83.5%. Besides, it shows the generalization ability and well performance when novel objects appear in the scenes that the manipulator has never grasped before. © 2021, CC BY.

关键词： Manipulators

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Down-Level Control Based on Level Prediction of Landslide Evolutionary State

Down-Level Control Based on Level Prediction of Landslide Ev...

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International Conference on Information Science and Technology (ICIST)

作者： Shu Sun Cheng Lian Xiaoping Wang School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Automation Wuhan University of Technology Wuhan China School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China

Accurate prediction and effective control are the key to reducing the impact of landslide disasters. In practice, most of recent studies focus on landslide displacement prediction and control. In this paper, we propose a new control method based on the level prediction of landslide evolution state, namely down-level control. The core components are level predictor and interval predictor in this method. Specially, we first transform the traditional displacement regression prediction problem into a level classification prediction problem by labeling discrete category information for landslide sample points. Then the level predictor based on Multi-task learning-Stacked long-short time memory network (MTL-SLSTM) is established to predict the state of the landslide and judge whether the system needs to be controlled. Finally, we design a safe rainfall interval predictor based on bootstrap method to obtain the safe value of control variation. The effectiveness of the proposed control method is verified on Baishuihe and Shiliushubao landslides. The results show the proposed down-level control method is valid and more intuitive.

关键词： Information science Geology Transforms Predictive models Terrain factors Complexity theory Labeling

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Fs-Woa-Stacking: A Novel Ensemble Model for Early Diagnosis of Breast Cancer

SSRN

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SSRN 2024年

作者： Xiao, Tianyun Kong, Shanshan Zhang, Zichen Liu, Fengchun Yang, Aimin Hua, Dianbo Hebei Key Laboratory of Data Science and Application North China University of Science and Technology Hebei Tangshan063210 China The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and Technology Hebei Tangshan063210 China College of Science North China University of Science and Technology Hebei Tangshan063210 China Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes North China University of Science and Technology Hebei Tangshan China Tangshan Intelligent Industry and Image Processing Technology Innovation Center North China University of Science and Technology Hebei Tangshan China Beijing Sitairui Cancer Data Analysis Joint Laboratory Beijing101149 China

In this paper, a new integrated scheme is proposed to accurately predict breast cancer, help doctors make early diagnosis and treatment plans, and improve the prognosis of patients. We selects five mainstream machine learning models: support vector machine (SVM), artificial neural network (ANN), random forest (RF), extreme gradient boosting (XGBoost) and adaptive boosting (AdaBoost). The Wisconsin Breast Cancer Database (WBCD) and Wisconsin Diagnostic Breast Cancer (WDBC) are used as datasets to investigate the predictive performance of these single and ensemble models. Then, we use multiple linear regression method for feature selection (FS), the experimental results show that the change of feature subset will significantly affect the performance of the model. After that, we apply whale optimization algorithm (WOA) to optimize the hyperparameters of the model to improve their prediction performance. In addition, we ensemble these models by stacking, investigate the performance changes of the ensemble model when different models are used as meta learners. Finally, the FS-WOA-Stacking model achieves 99.56% accuracy on WBCD and 99.65% accuracy on WDBC. Compared with the existing breast cancer prediction models, the performance of the proposed model is at an excellent level. © 2024, The Authors. All rights reserved.

关键词： Adaptive boosting

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Convergence analysis of nonconvex distributed stochastic zeroth-order coordinate method

arXiv

引用

arXiv 2021年

作者： Zhang, Shengjun Dong, Yunlong Xie, Dong Yao, Lisha Bailey, Colleen P. Fu, Shengli OSCAR Laboratory Department of Electrical Engineering University of North Texas DentonTX76207 United States School of Artificial Intelligence and Automation The MOE Key Laboratory of Image Processing and Intelligent Control The State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China

This paper investigates the stochastic distributed nonconvex optimization problem of minimizing a global cost function formed by the summation of n local cost functions. We solve such a problem by involving zeroth-order (ZO) information exchange. In this paper, we propose a ZO distributed primal–dual coordinate method (ZODIAC) to solve the stochastic optimization problem. Agents approximate their own local stochastic ZO oracle along with coordinates with an adaptive smoothing parameter. We show that the proposed algorithm achieves the convergence rate of O(√p/√T) for general nonconvex cost functions. We demonstrate the efficiency of proposed algorithms through a numerical example in comparison with the existing state-of-the-art centralized and distributed ZO algorithms. Copyright © 2021, The Authors. All rights reserved.

关键词： Stochastic systems

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Personalized Heart Disease Detection via ECG Digital Twin Generation

arXiv

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arXiv 2024年

作者： Hu, Yaojun Chen, Jintai Hu, Lianting Li, Dantong Yan, Jiahuan Ying, Haochao Liang, Huiying Wu, Jian College of Computer Science and Technology Zhejiang University China Computer Science Department University of Illinois at Urbana-Champaign United States Medical Big Data Center Guangdong Provincial People’s Hospital Southern Medical University China Guangdong Cardiovascular Institute Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences China School of Public Health Zhejiang University China State Key Laboratory of Transvascular Implantation Devices of The Second Affiliated Hospital Zhejiang University School of Medicine China

Heart diseases rank among the leading causes of global mortality, demonstrating a crucial need for early diagnosis and intervention. Most traditional electrocardiogram (ECG) based automated diagnosis methods are trained at population level, neglecting the customization of personalized ECGs to enhance individual healthcare management. A potential solution to address this limitation is to employ digital twins to simulate symptoms of diseases in real patients. In this paper, we present an innovative prospective learning approach for personalized heart disease detection, which generates digital twins of healthy individuals’ anomalous ECGs and enhances the model sensitivity to the personalized symptoms. In our approach, a vector quantized feature separator is proposed to locate and isolate the disease symptom and normal segments in ECG signals with ECG report guidance. Thus, the ECG digital twins can simulate specific heart diseases used to train a personalized heart disease detection model. Experiments demonstrate that our approach not only excels in generating high-fidelity ECG signals but also improves personalized heart disease detection. Moreover, our approach ensures robust privacy protection, safeguarding patient data in model development. The code can be found at https://***/huyjj/LAVQ-Editor. Copyright © 2024, The Authors. All rights reserved.

关键词： Electrocardiograms

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