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arXiv 2024年

作者： Li, Siyang Wang, Ziwei Luo, Hanbin Ding, Lieyun Wu, Dongrui Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China Henan Key Laboratory of Brain Science and Brain Computer Interface Technology School of Electrical and Information Engineering Zhengzhou University China School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan430074 China

Objective: An electroencephalogram (EEG)-based brain-computer interface (BCI) enables direct communication between the human brain and a computer. Due to individual differences and non-stationarity of EEG signals, such BCIs usually require a subject-specific calibration session before each use, which is time-consuming and user-unfriendly. Transfer learning (TL) has been proposed to shorten or eliminate this calibration, but existing TL approaches mainly consider offline settings, where all unlabeled EEG trials from the new user are available. Methods: This paper proposes Test-Time Information Maximization Ensemble (T-TIME) to accommodate the most challenging online TL scenario, where unlabeled EEG data from the new user arrive in a stream, and immediate classification is performed. T-TIME initializes multiple classifiers from the aligned source data. When an unlabeled test EEG trial arrives, T-TIME first predicts its labels using ensemble learning, and then updates each classifier by conditional entropy minimization and adaptive marginal distribution regularization. Our code is publicized. Results: Extensive experiments on three public motor imagery based BCI datasets demonstrated that T-TIME outperformed about 20 classical and state-of-the-art TL approaches. Significance: To our knowledge, this is the first work on test time adaptation for calibration-free EEG-based BCIs, making plug-and-play BCIs possible. © 2024, CC BY.

关键词： Calibration

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Missile-Target Situation Assessment Model Based on Reinorcement Learning

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Journal of Shanghai Jiaotong university(Science) 2020年第5期25卷 561-568页

作者： ZHANG Yun LU Runyan CAI Yunze Department of Automation Key Laboratory of System Control and Information Processing of Ministry of EducationKey Laboratory of Marine Intelligent Equipment and System of Ministry of EducationShanghai Jiao Tong UniversityShanghai 200240China

In situation assessment(SA)of missile versus target fighter,the traditional SA models generally have the characteristics of strong subjectivity and poor dynamic *** paper considers SA as an expectation of future returns and establishes a missile-target simulation battle *** actor-critic(AC)algorithm in reinforcement learning(RL)is used to train the evaluation network,and a missile-target SA model is established in simulation battle *** and comparative experiments show that the model can effectively estimate the expected effect of missile attack under the current situation,and it provides an effective basis for missile attack decision.

关键词： situation assessment(SA) battle model reinforcement learning(RL) actor-critic(AC)algorithm

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Object relationship graph reasoning for object detection of remote sensing images 6

Object relationship graph reasoning for object detection of ...

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6th International Conference on image, Vision and Computing, ICIVC 2021

作者： Li, Zeng Liu, Yifan Liu, Jingdong Yuan, Ye Raza, Asif Huo, Hong Fang, Tao Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (纸本)9781665443685

In recent years, deep learning has been successfully applied in object detection of remote sensing images, due to its powerful feature extraction and representation capabilities. However, it usually ignores the relationships among different objects, which greatly hinders the further improvement of the detection precision. In this paper, a novel object relationship graph reasoning deep learning method is proposed for object detection of remote sensing images. The reasoning is carried out on a graph constructed by region proposals. It mainly contains three modules for relationship generation in consideration of different factors that related to object detection: 1) Location-based relationship generation module (LRGM) aims to find spatial relationships among objects according to their positions;2) Feature-based relationship generation module (FRGM) focuses on mining semantic relationships among objects according to their appearances that implied in feature maps and 3) Class-dependent relationship generation module (CRGM) introduces class dependencies into the relationship generation process. Class dependencies are the prior knowledge that is obtained by computing class co-occurrence. The experimental results on a public twenty-class remote sensing object detection dataset have shown that the proposed method has better detection performance compared to other object detection deep networks with no considering object relationships. © 2021 IEEE.

关键词： Object detection

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Fall Detection Based on Person Detection and Multi-target Tracking 11

Fall Detection Based on Person Detection and Multi-target Tr...

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11th International Conference on Information Technology in Medicine and Education, ITME 2021

作者： Xu, Teng Chen, Jian Li, Zuoyong Cai, Yuanzheng School of Electronic Electrical Engineering and Physics Fujian University of Technology Fuzhou350118 China College of Computer and Control Engineering Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou350121 China

ISBN: (纸本)9781665406796

Recently, official statistics reported that the Chinese population aged 60 and above has been 26.402 million, which accounts for 18.70% of total population. It is urgent to develop fall detection technologies for alleviating the risk causing by falling of elder person. In this paper, we propose a real-time, high-precision, and deep learning-based fall detection method with automatic person detection and tracking. Specifically, the proposed method first improves the YOLOv3 network to more efficiently detect person and extract feature maps of the object. Then, it inputs the extracted feature maps from the YOLOv3 into a multi-target tracking network for cascade matching and IOU matching in a Deep SORT algorithm, respectively. Next, it improves YOLOv5 network to detect posture anomalies. Finally, it refines the detected posture anomalies for obtaining the final fall detection result. Experimental results show that the proposed method simultaneously improves accuracy and efficiency of the fall detection. © 2021 IEEE.

关键词： Fall detection

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Generative Adversarial Network-Based Electromagnetic Signal Classification: A Semi- Supervised Learning Framework

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China Communications 2020年第10期17卷 157-169页

作者： Huaji Zhou Licheng Jiao Shilian Zheng Lifeng Yang Weiguo Shen Xiaoniu Yang Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial IntelligenceXidian UniversityXi'an 710071China Science and Technology on Communication Information Security Control Laboratory Jiaxing 314033China

Generative adversarial network(GAN)has achieved great success in many fields such as computer vision,speech processing,and natural language processing,because of its powerful capabilities for generating realistic *** this paper,we introduce GAN into the field of electromagnetic signal classification(ESC).ESC plays an important role in both military and civilian ***,in many specific scenarios,we can’t obtain enough labeled data,which cause failure of deep learning methods because they are easy to fall into ***,semi-supervised learning(SSL)can leverage the large amount of unlabeled data to enhance the classification performance of classifiers,especially in scenarios with limited amount of labeled *** present an SSL framework by incorporating GAN,which can directly process the raw in-phase and quadrature(IQ)signal *** to the characteristics of the electromagnetic signal,we propose a weighted loss function,leading to an effective classifier to realize the end-to-end classification of the electromagnetic *** validate the proposed method on both public RML2016.04c dataset and real-world Aircraft Communications Addressing and Reporting System(ACARS)signal *** experimental results show that the proposed framework obtains a significant increase in classification accuracy compared with the state-of-the-art studies.

关键词： generative adversarial network semi-supervised learning electromagnetic signal classification end-to-end classification weighted loss function

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Computationally Expensive High-Dimensional Multiobjective Optimization via Surrogate-Assisted Reformulation and Decomposition

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Pan, Linqiang Lin, Jianqing Wang, Handing He, Cheng Tan, Kay Chen Jin, Yaochu School of Artificial Intelligence and Automation Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China Huazhong University of Science and Technology Wuhan China School of Artificial Intelligence Xidian University Xi’an China School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan China Department of Computing The Hong Kong Polytechnic University Hong Kong Hong Kong School of Engineering Trustworth and General Artificial Intelligence Laboratory Westlake University Hangzhou China

In recent decades, various surrogate-assisted evolutionary algorithms (SAEAs) have been proposed to solve computationally expensive multiobjective optimization problems (EMOPs). Nevertheless, designing an SAEA to handle high-dimensional EMOPs and balance convergence, diversity, and computational complexity remains challenging. Here, we propose a two-phase SAEA (TP-SAEA), which follows the idea of convergence first and diversity second, for solving high-dimensional EMOPs. In Phase I, a surrogate-assisted problem reformulation method is proposed to fast-track the Pareto optimal set in association with some reference solutions. Specifically, the high-dimensional EMOP is reformulated into an expensive single-objective one with low-dimensional decision space. Then, the surrogate-assisted optimization is utilized to obtain well-converged solutions. In Phase II, the high-dimensional EMOP is decomposed into two subproblems to explore subregions of the decision space that can effectively promote the diversity of the solutions. The two subproblems are optimized independently via surrogate-assisted optimization, aiming to push the population towards different regions of the Pareto optimal front. Experiments are conducted on EMOPs with 100 to 500 decision variables compared with four state-of-the-art SAEAs. The proposed TP-SAEA obtains well-converged and diverse solutions with only 509 real function evaluations. Moreover, its superiority is examined in six real-world instances with up to 12,000 decision variables. IEEE

关键词： Iron

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An intelligent Evaluation Method of Root Canal Therapy Quality Based on Deep Learning

An Intelligent Evaluation Method of Root Canal Therapy Quali...

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Chinese Automation Congress (CAC)

作者： Jie Liu Gang Peng Shiqian Yan School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan China Hubei Eya Medical Investment Management Co. Ltd Wuhan China

Dentists judge the quality of root canal therapy for each patient very time-consuming, and inefficient, lack of quantitative evaluation criteria, easy to cause judgment errors. At the same time, the traditional method of extracting the root canal image features based on experience is difficult to accurately extract the filling area of the root canal and the tooth area where it is located, resulting in low accuracy of root canal target segmentation, and thus affecting the accuracy of root canal treatment quality evaluation. In this paper, for the real root canal treatment images of patients, the CenterNet network was used to detect the root canal target of root canal images. Then, according to the test results, the U-Net full convolution neural network was used to segment the root canal filling region and its tooth region. Finally, the segmented images were quantitatively evaluated according to the professional evaluation index of doctors. The experimental results show that the coincidence rate between the evaluation results of this method and the evaluation results of professional doctors is 83.3 %, which can better complete the quality evaluation of root canal therapy for patients, effectively improve the work efficiency of doctors, and has reference significance for the application of artificial intelligence in medical field.

关键词： Irrigation image segmentation Convolution Neural networks Teeth Feature extraction Filling

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Multi-target tracking control system of phased array antenna with mechanical scanning and beam scanning method

Multi-target tracking control system of phased array antenna...

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Fully Actuated System Theory and Applications (CFASTA), Conference on

作者： Jijun Ma Dewei Li Lianghai Li Shibei Xue Yuning Wang Jun Jia Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China Beijing Research Institute of Telemetry Beijing China

In the field of telemetry, track, and command system, this study presents a phased array antenna control system for achieving multi-target tracking tasks through a set of systems. Firstly, electric beam scanning of a planar array phased array antenna is used in this paper to implement multi-target tracking. In addition, the phased array antenna is mounted on the antenna pedestal, which can operate on the azimuth and elevation mechanical axes. Thirdly, the mechanical scanning method of the azimuth-elevation antenna pedestal and the beam scanning method of the phased array antenna are combined to realize multi-target tracking tasks. Finally, the experiments of flight tracking are completed by employing the technique of UAV tagging, and the results validate the viability of the system.

关键词：

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Retinal Vessels Segmentation Based on Multi-scale Hybrid Convolutional Network 4th

Retinal Vessels Segmentation Based on Multi-scale Hybrid Con...

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4th International Conference on Smart Vehicular Technology, Transportation, Communication and Applications, VTCA 2021

作者： Li, Rui Li, Zuoyong Cao, Xinrong Teng, Shenghua College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China Fuzhou Digital Healthcare Industry Technology Innovation Center Minjiang University Fuzhou350121 China

ISBN: (纸本)9789811640384

Retinal fundus image can reveal the information on the early symptoms of diabetes, hypertension, hyperlipidemia and other diseases. Accurate segmentation of retinal vessels can assist the detection and diagnosis of the related diseases. Due to the intricate characteristic information of retinal vessels images, traditional segmentation methods lead to inaccurate segmentation for small vessels and pathological segmentation errors. In this paper, we propose a new multi-scale hybrid convolution U-Net. Firstly, we take the hybrid convolution module by combining dilated convolution and standard convolution as the core structure for feature extraction to obtain more abundant semantic feature information, while expanding the receptive field. Then, we add a multi-scale fusion module to the network encoding and decoding connection part, which fuses the feature information of different layers to reduce the loss of information and enhance the representation ability of the network. We evaluate the performance of the proposed method on two public retinal datasets (DRIVE and CHASE_DB1). The results of quantitative and qualitative experiments show that the proposed model can improve good accuracy in retinal vessels segmentation. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolution

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Sample Weighted Gradient Regularization for Source-Free Domain Adaptation

SSRN

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SSRN 2024年

作者： Long, Yunchong Xu, Bingrong Zhang, Qingyong Yin, Jianhua Zeng, Zhigang School of Automation Wuhan University of Technology Wuhan430070 China Intelligent Transportation Systems Research Center Wuhan University of Technology Wuhan430063 China The Engineering Research Center of Transportation Information and Safety Ministry of Education Wuhan430063 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The Key Laboratory of Image Processing and Intelligent Control Education Ministry of China Wuhan430074 China

Conventional Domain Adaptation(DA) algorithms typically rely on access to the source data,which may not always be practical due to privacy concerns or limited storage *** address these challenges,Source-Free Domain Adaptation(SFDA) has emerged as an alternative that relies solely on a pre-trained source model rather than the actual source *** SFDA,pseudo-label supervised learning is commonly employed and initially plays a significant role in enhancing model performance during the early stages of target domain ***,as adaptation progresses,the performance can stagnate or even deteriorate due to the noise inherent in pseudo-labels,a phenomenon known as Confirmation *** noise present in target domain pseudo-labels introduces additional uncertainty and overfitting risks to the *** this study,a novel framework named Sample Weighted Gradient Regularization (SWGR) was introduced to tackle this *** first propose the Label Uncertainty Estimation Metric (LUEM),a reweighting strategy that assesses the reliability of *** metric evaluates label uncertainty from both the source domain pre-trained model and spatial distribution of target domain samples,allowing the model to achieve improved cross-domain transfer performance with high-confidence samples in the target domain. Additionally,we impose a constraint on the gradient descent process of the total loss function in the form of an inequality to prevent entropy from *** Gradient Regularization eliminates overfitting while also guiding the model in lowering prediction uncertainty *** approach reduces the uncertainty in the target domain and enhances the model's generalization *** results demonstrate that our method surpasses competing SFDA approaches and even outperforms many traditional DA methods which require extensive amounts of source domain data. © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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