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arXiv 2020年

作者： Meng, Lubin Huang, Jian Zeng, Zhigang Jiang, Xue Yu, Shan Jung, Tzyy-Ping Lin, Chin-Teng Chavarriaga, Ricardo 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 Wuhan China Brainnetome Center National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China La Jolla CA United States Center for Advanced Neurological Engineering Institute of Engineering in Medicine Ucsd La Jolla CA United States Centre of Artificial Intelligence Faculty of Engineering and Information Technology University of Technology Sydney Australia Zhaw DataLab Zürich University of Applied Sciences Winterthur8401 Switzerland

Research and development of electroencephalogram (EEG) based brain-computer interfaces (BCIs) have advanced rapidly, partly due to deeper understanding of the brain and wide adoption of sophisticated machine learning approaches for decoding the EEG signals. However, recent studies have shown that machine learning algorithms are vulnerable to adversarial attacks. This article proposes to use narrow period pulse for poisoning attack of EEG-based BCIs, which is implementable in practice and has never been considered before. One can create dangerous backdoors in the machine learning model by injecting poisoning samples into the training set. Test samples with the backdoor key will then be classified into the target class specified by the attacker. What most distinguishes our approach from previous ones is that the backdoor key does not need to be synchronized with the EEG trials, making it very easy to implement. The effectiveness and robustness of the backdoor attack approach is demonstrated, highlighting a critical security concern for EEG-based BCIs and calling for urgent attention to address it. Copyright © 2020, The Authors. All rights reserved.

关键词： Electroencephalography

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DNA Strand Displacement Based on Nicking Enzyme for DNA Logic Circuits 1

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13th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2018

作者： Wang, Gaiying Wang, Zhiyu Yan, Xiaoshan Liu, Xiangrong School of Information Science and Technology Xiamen University XiamenFujian361005 China Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Automation Huazhong University of Science and Technology WuhanHubei430074 China

ISBN: (数字)9789811328268

ISBN: (纸本)9789811328251

DNA strand displacement is widely used in the construction of DNA molecule computational models. In this work, nicking enzyme is used as the input of the logic calculation model for it can cut one strand of a double-stranded DNA at a specific recognition nucleotide sequences known as a restriction site. Based on this, a variety of logic gates are designed and implemented, and a multi-person voting circuit is constructed. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Enzymes

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Heterogeneous belief formation method based on BP neural network 2

Heterogeneous belief formation method based on BP neural net...

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2nd International Conference on Electronic Information Technology and Computer Engineering, EITCE 2018

作者： Zhuang, Yifan Wang, Hongwei Chen, Xi School of Automation Huazhong University of Science and Technology Wuhan430074 China School of Management Huazhong University of Science and Technology Wuhan430074 China Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Wuhan430074 China

The BDI model has always been the focus of subject modeling research, which includes three kinds of thinking states of the rational subject: Belief, Desire and Intention. Belief is the cognition of agent to the world;it is a collection of environmental information, other agent information, and its own information that the agent has;and it is also the basis of the agent's thinking activity. Due to differences in the individual's living environment and experience, the formation of heterogeneous beliefs is an important issue in the BDI model study. This article divides individual belief set into two parts: knowledge belief and achievable belief. This article proposes an overall framework for the formation of individual heterogeneity beliefs: First, the individual's knowledge experience is modeled, and the empirical knowledge is structured and quantified into binary propositions;then the BP neural network learn and memory propositions of different combinations to form heterogeneous beliefs. Experiments show that this method can simulate the heterogeneity of individual beliefs caused by the individual's own experience, and can realize the belief generation mechanism of gradual information flow, limited attention and heterogeneous priors. © The Authors, published by EDP Sciences, 2018.

关键词： Neural networks

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LIGHT YOLO FOR HIGH-SPEED GESTURE RECOGNITION

LIGHT YOLO FOR HIGH-SPEED GESTURE RECOGNITION

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IEEE International Conference on image processing

作者： Zihan Ni Jia Chen Nong Sang Changxin Gao Leyuan Liu Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China

This paper proposes an efficient model named Light YOLO for hand gesture recognition on the embedded platforms. Light YOLO improves accuracy, speed, and model size, in three aspects. To deal with the small scale gestures in practical applications, we strengthen the YOLOv2 with a spatial refinement module to obtain fine-grained features. To accelerate the refined network, we propose a selective-dropout channel pruning approach to prune the redundancy convolution kernels in the network. Moreover, we introduce a dataset for hand gesture recognition in complex scenes. The experimental results on this dataset show that the proposed Light YOLO significantly improve the YOLOv2 network, i.e., accuracy from 96.80% to 98.06%, speed form 40PFS to 125FPS, and size form 250M to 4MB.

关键词： Gesture recognition Feature extraction Detectors Acceleration Taylor series Kernel Cost function

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Multi-Scale YOLOv2 for Hand Detection in Complex Scenes

Multi-Scale YOLOv2 for Hand Detection in Complex Scenes

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International Conference on control, Automation, Robotics and Vision (ICARCV)

作者： Jia Chen Zihan Ni Nong Sang Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China

This paper presents a model named Multi-Scale YOLOv2 (MS-YOLOv2) for hand detection in complex scenes. The proposed MS-YOLOv2 is implemented by introducing three modules to YOLOv2, including a Multi-Scale Feature Refinement Module to acquire fine-grained features, a Channel Importance Evaluation Module to recalibrate feature channels and a Hard Example Punishment Module to get rid of hand interference areas. Experiment results show that the proposed MS-YOLOv2 makes much performance improvement to YOLOv2, but with little computational complexity gain. On our dataset, the proposed MS-YOLOv2 can achieve 98.2% of AP and 97.9% of AR. Moreover, on the VIVA challenge, the proposed MS-YOLOv2 achieves AP/AR of 85.1%/45.8% at Level-1 and 80.1%/45.9% at Level-2.

关键词： Feature extraction Detectors Object detection Skin Color Gesture recognition

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Deep Learning for Sleep Stage Classification

Deep Learning for Sleep Stage Classification

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Chinese Automation Congress (CAC)

作者： Yang Wang Dongrui Wu Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China

Scoring of sleep stages plays an important role in the diagnosis of sleep-related diseases. Scoring by visual inspection is time-consuming and heavily depends on the experience of experts. Thus, there is an urgent need for an automatic sleep stage classification system. This paper proposes a novel compact convolutional neural network (C-CNN) using only single-channel EEG signal. Compared with traditional machine learning approaches based on hand-engineered features, our approach provides an end-to-end solution that requires almost no prior knowledge and preprocessing while achieving better performance. Experiments on the expanded Sleep-EDF database verified its effectiveness and efficiency. In addition, we notice the issue of class imbalance in sleep stages, and a class-imbalance metric, the balanced classification accuracy (BCA), is introduced. At the cost of a little drop in accuracy, which is still higher than existing classification methods, the introduction of class-imbalance weights can significantly increase the BCA metric and result in a higher recall for each sleep stage. This paper also proposes a recurrent neural network based on the attention mechanism and bidirectional long short-term memory (LSTM), which provides better performance than C-CNN but requires more training time.

关键词： Feature extraction Sleep Electroencephalography Training Convolution Brain modeling Deep learning

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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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In Vitro Fertilization (IVF) cumulative pregnancy rate prediction from basic patient characteristics

arXiv

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arXiv 2019年

作者： Zhang, Bo Cui, Yuqi Wang, Meng Li, Jingjing Jin, Lei Wu, Dongrui Reproductive Medicine Center Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan Hubei430030 China Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan Hubei430074 China

Tens of millions of women suffer from infertility worldwide each year. In vitro fertilization (IVF) is the best choice for many such patients. However, IVF is expensive, time-consuming, and both physically and emotionally demanding. The first question that a patient usually asks before the IVF is how likely she will conceive, given her basic medical examination information. This paper proposes three approaches to predict the cumulative pregnancy rate after multiple oocyte pickup cycles. Experiments on 11,190 patients showed that first clustering the patients into different groups and then building a support vector machine model for each group can achieve the best overall performance. Our model could be a quick and economic approach for reliably estimating the cumulative pregnancy rate for a patient, given only her basic medical examination information, well before starting the actual IVF procedure. The predictions can help the patient make optimal decisions on whether to use her own oocyte or donor oocyte, how many oocyte pickup cycles she may need, whether to use embryo frozen, etc. They will also reduce the patient's cost and time to pregnancy, and improve her quality of life. Copyright © 2019, The Authors. All rights reserved.

关键词： Obstetrics

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Anode Effect prediction based on Expectation Maximization and XGBoost model

Anode Effect prediction based on Expectation Maximization an...

引用

Data Driven control and Learning Systems (DDCLS)

作者： Zhixin Zhang Gaofeng Xu Hongting Wang Kaibo Zhou School of Automation MOE Key Laboratory of Image Processing and Intelligence Control Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781538626191

Anode Effect Prediction problem has been drawing great research interest of scientists, due to its significant values in reducing energy consumption and improving the efficiency of aluminum electrolysis. However, a large number of missing values contained in the collected data from the aluminum reduction cell are always neglected in the works, resulting in a decline in prediction accuracy and generalization ability. To solve this problem, a combined model of Expectation Maximization and XGBoost (EM-XGBoost) is proposed. Firstly, the original incomplete samples collected from the aluminum cells are recovered by Expectation Maximization (EM) algorithm. Afterwards, the XGBoost model trains on the recovered data, and then predicts the result for new samples. The more comprehensive metrics accuracy and F1 Score are introduced for evaluation. The results in the experiment show that the proposed model improves the accuracy to 99.7% and the F1 Score can achieve 99.8% under the premise of forecasting 30 minutes in advance. The proposed model not only has a high prediction accuracy, but also owns an excellent generalization ability.

关键词： Anodes Aluminum Data models Predictive models Electrochemical processes Measurement Filling

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$H_{\infty}$ consensus control of fractional-order multi-agent systems over the directed graph

$H_{\infty}$ consensus control of fractional-order multi-age...

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Chinese control Conference (CCC)

作者： Lin Chen Wen-Sen Wang Hao Wu Yan Lei Jiang-Wen Xiao Key Laboratory of Image Processing and Intelligent Control Ministry of Education State grid shanxi electric power research institute

In this paper, the H ∞ consensus of fractional-order multi-agent systems with directed communication graph is investigated. It's the first time to introduce the H ∞ control to investigate the consensus problem of the fractional-order multi-agent systems. In view of Mittag-Leffler stability theory and fractional Lyapunov directed method, a sufficient condition is presented to guarantee all the agents reach consensus with the desired H ∞ performance. Finally, the results are verified by several numerical simulations.

关键词： Multi-agent systems control systems Laplace equations Eigenvalues and eigenfunctions Power system stability Stability analysis Graph theory

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