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15th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2020

作者： Yan, Xiaoli Zhang, Zhongsi Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789811613531

Automated detection of cervical cancer cells has the potential to reduce error and increase productivity in cervical cancer screening. However, the existing object detection methods to detect the cervical cancer cells make inadequate use of negative samples and fail to balance the number of hard samples and easy samples. In this work, we propose a image-level sampling method, called pair sampling, which can extract the representative negative samples and generate the sample pair image. Then, based on the sample pair image, we propose the hybrid sampling, a proposal-level sampling method, to balance the number of hard samples and easy samples. Combining the proposed sampling methods, we design a representative sampling based cervical cell detector, which can detect the cervical cancer cells effectively. In order to comprehensively evaluate our method, we use a dataset which is consisted of 16000 annotated cervical cell images with size of 1024 × 1024. The experimental result shows that our method achieves 57.1% mean Average Precision (mAP), and it is higher than cascade R-CNN and Faster R-CNN by 4.7% and 5.8%, respectively. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Object detection

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Efficient Fabric Defect Detection Algorithm Based on Improved YOLOv5

Efficient Fabric Defect Detection Algorithm Based on Improve...

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2023 China Automation Congress, CAC 2023

作者： Tian, LiHua Wang, AoZe Sun, ZhiGang Xiao, Li Huazhong University of Science and Technology Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education of China School of Artificial Intelligence and Automation Wuhan China

ISBN: (纸本)9798350303759

In order to address the issues of real-time performance and the low dependency between feature channels in fabric defect detection networks, this paper proposes the ESE-YOLOv5 network based on YOLOv5. Firstly, to address the relative redundancy of the neck detection network feature channels, a relatively lightweight and efficient convolution module is adopted to ensure accuracy while reducing computation and parameter volume. Furthermore, the Efficient Squeeze-Excitation (ESE) module is introduced into the backbone to optimize the dependency of feature channels, which enhances the model's feature extraction capacity and improves detection accuracy. Experimental results show that compared to YOLOv5, the proposed ESE-YOLOv5 model reduces computation and parameter volume while improving accuracy, meeting the needs of fabric defect detection for recognizing fabric defects that have similar characteristics to the background while maintaining real-time performance. © 2023 IEEE.

关键词： Feature extraction

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Analysis of a third-order circuit based on a memristor

Analysis of a third-order circuit based on a memristor

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2011 International Conference on Information Science and Technology, ICIST 2011

作者： Xiao, Jian Shen, Yi Zeng, Zhigang Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan 430074 China

ISBN: (纸本)9781424494408

In this paper, a third-order canonical circuit with a memristor is investigated. Unlike the conventional circuit systems, it has an equilibrium set, whose stability is affected by the initial state of the memristor. The corresponding dynamical behaviors of the memristor are illustrated both theoretically and numerically. © 2011 IEEE.

关键词： Memristors

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Spectrum Sensing Based on Deep Learning Classification for Cognitive Radios

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China Communications 2020年第2期17卷 138-148页

作者： Shilian Zheng Shichuan Chen Peihan Qi Huaji Zhou Xiaoniu Yang Science and Technology on Communication Information Security Control Laboratory Jiaxing 314033China State Key Laboratory of Integrated Service Networks Xidian UniversityXi’an 710071China Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education of ChinaXidian UniversityXi’an 710071China

Spectrum sensing is a key technology for cognitive *** present spectrum sensing as a classification problem and propose a sensing method based on deep learning *** normalize the received signal power to overcome the effects of noise power *** train the model with as many types of signals as possible as well as noise data to enable the trained network model to adapt to untrained new *** also use transfer learning strategies to improve the performance for real-world *** experiments are conducted to evaluate the performance of this *** simulation results show that the proposed method performs better than two traditional spectrum sensing methods,i.e.,maximum-minimum eigenvalue ratio-based method and frequency domain entropy-based *** addition,the experimental results of the new untrained signal types show that our method can adapt to the detection of these new ***,the real-world signal detection experiment results show that the detection performance can be further improved by transfer ***,experiments under colored noise show that our proposed method has superior detection performance under colored noise,while the traditional methods have a significant performance degradation,which further validate the superiority of our method.

关键词： spectrum sensing deep learning convolutional neural network cognitive radio spectrum management

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Multi-agent containment control with input saturation on switching topologies

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IET control Theory and Applications 2015年第3期9卷 399-409页

作者： Su, Housheng Chen, Michael A.Q. School of Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Luoyu Road 1037 Wuhan430074 China Department of Mechanical Engineering University of Hong Kong Hong Kong Hong Kong

This study aims to investigate containment control of linear multi-agent systems with input saturation on switching topologies. For such a multi-agent system, both state feedback and output feedback containment control protocols are proposed via a novel low-gain feedback approach. Consequently, the authors show that, under the assumption that the switching networks are jointly connected, semi-global state feedback and output feedback containment control of the multi-agent system can be attained. The effectiveness of the proposed algorithms is illustrated via numerical examples. © 2015 The Institution of Engineering and Technology.

关键词： Multi agent systems

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Model reference decoupling controller design via LMI optimization

Model reference decoupling controller design via LMI optimiz...

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2012 International Conference on Modelling, Identification and control, ICMIC 2012

作者： Wang, Zhishen Zheng, Zongzhun Liu, Lei Wang, Yongji Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan China Beijing Aerospace Automatic Control Institute Beijing China

ISBN: (纸本)9780956715715

This paper proposed a H∞ based controller design method for MIMO system. We use the bound real lemma to design the controller when the reference model is given. The BMI (Bi-linear Matrix Inequality) problem is turned into a LMI (Linear Matrix Inequality) problem by congruence transformation and variables substitution. Given a decoupled reference model, the decoupling controller can be obtained by computer. We apply the method to a linearized vehicle model and the simulation results show the effectiveness of the approach. © 2012 Huazhong Univ of Sci & Tec.

关键词： MIMO systems

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Semi-global containment control of multi-agent systems with input saturation

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IET control Theory and Applications 2014年第18期8卷 2229-2237页

作者： Su, Housheng Jia, Gui Chen, Michael Z.Q. School of Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Luoyu Road 1037 Wuhan430074 China Department of Mechanical Engineering University of Hong Kong Hong Kong Hong Kong

This study investigates containment control of multi-agent systems with input saturation and multiple leaders on directed networks. Both state feedback and output feedback containment control protocols are designed via low-gain feedback approach. Under the assumption that the system is asymptotically null controllable with bounded controls and there exists at least one leader that has a directed path to each follower, semi-global state feedback and output feedback containment control of the multi-agent system can be attained. Numerical examples are provided to illustrate the effectiveness of the theoretical results. © The Institution of Engineering and Technology 2014.

关键词： Multi agent systems

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High performance word level sequential and parallel coding methods and architectures for bit plane coding

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Science in China(Series F) 2008年第4期51卷 337-351页

作者： XIONG ChengYi TIAN JinWen LIU Jian College of Electronic Information Engineering South-Central University for Nationalities Wuhan 430074 China Institute of Pattern Recognition & Artificial Intelligence Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science & Technology Wuhan 430074 China

This paper introduced a novel high performance algorithm and VLSI architectures for achieving bit plane coding （BPC） in word level sequential and parallel mode. The proposed BPC algorithm adopts the techniques of coding pass prediction and parallel ＆ pipeline to reduce the number of accessing memory and to increase the ability of concurrently processing of the system, where all the coefficient bits of a code block could be coded by only one scan. A new parallel bit plane architecture （PA） was proposed to achieve word-level sequential coding. Moreover, an efficient high-speed architecture （HA） was presented to achieve multi-word parallel coding. Compared to the state of the art, the proposed PA could reduce the hardware cost more efficiently, though the throughput retains one coefficient coded per clock. While the proposed HA could perform coding for 4 coefficients belonging to a stripe column at one intra-clock cycle, so that coding for an NxN code-block could be completed in approximate N2/4 intra-clock cycles. Theoretical analysis and experimental results demonstrate that the proposed designs have high throughput rate with good performance in terms of speedup to cost, which can be good alternatives for low power applications.

关键词： bit plane coding high performance word-level sequential multi-word parallel

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Cross-user Gesture Recognition Based on Deep Domain Adaptation Networks 43

Cross-user Gesture Recognition Based on Deep Domain Adaptati...

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43rd Chinese control Conference, CCC 2024

作者： Fu, Zhongzheng He, Xinrun Wang, Haoyuan Chen, Xingjian Li, Yixuan Huang, Jian Huazhong University of Science and Technology Ministry of Education Key Laboratory of Image Processing and Intelligent Control Hubei Key Laboratory of Brain-inspired Intelligent Systems School of Artificial Intelligence and Automation Wuhan430074 China

ISBN: (纸本)9789887581581

This study introduces an innovative approach for gesture recognition in smart wearable devices using a deep domain adaptation model, focusing on the challenges posed by heterogeneous user environments and the need for precise, adaptable, and personalized gesture recognition systems. Traditional methods based on machine learning and deep learning techniques, while effective, struggle with the variance in data distribution among different users, leading to accuracy challenges. To address this, the study proposes the Domain Kernel-Alignment Adaptation Network (DKAN) model, utilizing a new Kernel-Aligned Multi-kernel Maximum Mean Discrepancy (KAMMD) method within deep networks. By calculating kernel alignments between source and target domains and adjusting weights of Gaussian kernels accordingly, DKAN emphasizes features conducive to transfer learning, enabling more accurate cross-user gesture recognition. The model achieves a significant average accuracy of 97.3% across 21 gestures, highlighting its potential in practical applications. This advancement addresses challenges in traditional gesture recognition methods and sets a new direction for future research in smart wearable technology. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Adversarial machine learning

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HTRNN: A New Protein Nuclear Localization Prediction Tool 7

HTRNN: A New Protein Nuclear Localization Prediction Tool

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7th IEEE Advanced Information Technology, Electronic and Automation control Conference, IAEAC 2024

作者： Miao, Xinyu Li, Wei Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan China Image Processing and Intelligent Control Key Laboratory of the Education Ministry of China Wuhan China

ISBN: (纸本)9798350339161

Current protein nuclear localization assays encounter multiple challenges that underscore the constraints of conventional biochemical assays and sequence-based procedures. This paper highlights the emerging interest in utilizing artificial intelligence to surmount these limitations. Specifically, a supervised deep learning algorithm, employing the HTRNN model, is presented to identify mechanisms responsible for the nuclear location of proteins. This methodology embraces an entire data-driven end-to-end structure, making expert experience or previous biological knowledge unnecessary. By utilizing amino acid sequence features from both the head and tail regions, the algorithm displays remarkable detection and generalization capabilities, which have been validated by experimental results on present tagged nuclear location protein datasets. Overall, the data-driven algorithm plays a crucial role in enhancing the identification of protein nuclear localization properties. © 2024 IEEE.

关键词： Proteins

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