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International Automatic Control Conference

作者： Bo Sun Xunfang Tao Ji li Xiaonan Luo Guangxi Key Laboratory of Intelligent processing of Computer Images and Graphics Guilin University of Electronic Technology Guilin China Guilin Huigu Institute of Artificial Intelligence Industrial Technology Guilin University of Electronic Technology Guilin China

ISBN: (数字)9781728171982

ISBN: (纸本)9781728171999

When collecting palm vein images, it is easy to be affected by external factors such as light source and placement angle, which result in poor recognition accuracy. In this paper, a new method, which involve a new method of region of interest segmentation and an improved palm recognition method of VGG16 deep convolutional neural network, was proposed to promote the recognition accuracy and be well adapted to the actual application scenarios. Firstly, the original palm vein image is obtained through profile original image, positioning key point of original image, and extract region of interest image. Afterwards, the adaptive histogram equalization technique and Gaussian Filters are utilized to improve image quality. Secondly, for palm vein image recognition application scenarios, the output of the convolutional layer of the VGG-16 convolutional neural network is standardized in batches, and the attention mechanism is introduced to optimize the VGG-16 neural network. The optimized network is used for feature extraction and recognition of palm vein images. Thirdly, data enhancement was performed on the public Polyu multispectral palm vein data set, and then a large number of experiments were carried out, and the best recognition rate was 99.57%.

关键词： image segmentation image recognition Veins Neural networks Feature extraction Convolutional neural networks Light sources

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A Novel Biometric Method for Blurred Palm Vein images

A Novel Biometric Method for Blurred Palm Vein Images

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International Automatic Control Conference

作者： Xunfang Tao Bo Sun Ji Li Xiaonan Luo Guangxi Key Laboratory of Intelligent Processing of Computer Images and Graphics Guilin University of Electronic Technology Guilin China Guilin Huigu Institute of Artificial Intelligence Industrial Technology Guilin University of Electronic Technology Guilin China

ISBN: (数字)9781728171982

ISBN: (纸本)9781728171999

This paper proposes a palm vein recognition system based on compressed sensing theory to solve the problem of blurred vein image recognition, and establishes a more accurate palm vein recognition system. This method is mainly composed of two parts: First, the compressed sensing is utilized to reconstruct the palm vein image extracted by the infrared sensor to obtain a clearer image than the original blurred image. Second, geometry-based method for processing palm vein images to extract image feature information easier. This paper solve the problems of blurred veins, difficult segmentation and feature information extraction in palm vein images. The accurate recognition experiment of palm vein images is conducted on the polyu database which processed by gaussian noise show that our method has advantages in accuracy compared to traditional palm vein recognition systems.

关键词： image recognition Databases Veins Feature extraction Security Compressed sensing image reconstruction

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Parameter training methods for convolutional neural networks with adaptive adjustment method based on Caputo fractional-order differences

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Chaos, Solitons & Fractals 2025年 199卷

作者： Haiming Zhao Honggang Yang Jiejie Chen Ping Jiang Zhigang Zeng School of Computer and Information Engineering Hubei Normal University Huangshi 435000 China School of Automation Wuhan University of Technology Wuhan 430000 China School of Computer Hubei Polytechnic University Huangshi 435000 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430000 China Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education of China Wuhan 430000 China

As deep learning technologies continue to permeate various sectors, optimization algorithms have become increasingly crucial in neural network training. This paper introduces two adaptive momentum algorithms based on Grünwald–Letnikov and Caputo fractional-order differences—Fractional Order Adagrad (FAdagrad) and Fractional Order Adam (FAdam)—to update parameters more flexibly by adjusting momentum information. Commencing from the definitions of fractional derivatives, we propose integrating fractional-order differences with gradient algorithms in convolutional neural networks (CNNs). These adaptive momentum algorithms, leveraging Grünwald–Letnikov and Caputo fractional-order differences, offer enhanced flexibility, thereby accelerating convergence. Our nonlinear parameter tuning method for CNNs demonstrates superior performance compared to traditional integer-order momentum algorithms and the standard Adam algorithm. Experimental results on the BraTS2021 dataset and CIFAR-100 dataset reveal that the proposed fractional-order optimization algorithms significantly outperform their integer-order counterparts in model optimization. They not only expedite convergence but also improve the accuracy of image recognition and segmentation.

关键词： Adaptive momentum algorithms Convolutional neural networks Deep learning Fractional-order differences

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Multi-Scale Recurrent Network for Single image Deraining 8

Multi-Scale Recurrent Network for Single Image Deraining

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8th International Conference on Digital Home, ICDH 2020

作者： Li, Youxing Lan, Rushi Sun, Long Li, Ji Luo, Xiaonan Pang, Cheng Guilin University of Electronic Technology School of Computer Science and Information Security Guilin541004 China Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China Guilin University of Electronic Technology National Local Joint Engineering Research Center of Satellite Navigation and Location Service Guilin541004 China Guilin Huigu Institute of Artificial Intelligence Industrial Technology Guilin541004 China

ISBN: (纸本)9781728192345

Rain streaks noise is one of the greatest influence of outdoor scene tasks. In order to improve the effect of the outdoor scene tasks, we need to reduce the impact of the rain streaks noise while ensuring that other important details are preserved. To handle this issue, we proposed a multi-scale recurrent network (MSReNet) for single image rain removal. By divided MSReNet into two stages: (i) removal rain streaks in the first stage and (ii) restoring background-truth details in the second stage, our network can availably remove huge rain streaks and restore some important background details. Extensive experiments on both synthetic and real images demonstrated that the proposed MSReNet prominently exceeds many recent state-of-the-art methods. Taking the proposed method effectiveness, it is also attractive as a preprocessing process for some visual tasks. © 2020 IEEE.

关键词： Rain

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The Design of Logic Gate Based on Triplex Structures 14th

The Design of Logic Gate Based on Triplex Structures

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14th International Conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2019

作者： Hu, Yingxin Wang, Zhiyu Chen, Zhekun Liao, Sulin Huang, Yabing Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology WuhanHubei430074 China College of Information Science and Technology Shijiazhuang Tiedao University ShijiazhuangHebei050043 China School of Computer Science and Technology Huazhong University of Science and Technology WuhanHubei430074 China Department of Pathology Renmin Hospital of Wuhan University WuhanHubei430060 China

ISBN: (纸本)9789811534140

Molecular carch hotspots in the area of DNA nanotechnology due to its inherent large storage capacity, high parallelism, and energy efficiency. The assembled structures in the existing computing units mainly adopt a double-helix structure. However, besides duplex, DNA is known to adopt other helical forms such as triplex form. The triplex structure has introduced a novel paradigm into the DNA engineer owing to their unique characteristic sequence, structural and assembly requirements. Based on the assembly and disassembly of the triplex structure, a YES logic gate was constructed via strand displacement reaction. The experiment results showed that GC content and stem length would influence the displacement reaction. Then AND logic gate was also established through cooperative effect by two DNA strands. The construction of the cascading circuit indicated triplex-based logic gate could be integrated into larger circuits and enrich the toolbox of the computing devices. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Logic gates

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RTM3D: Real-time monocular 3D detection from object keypoints for autonomous driving

arXiv

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arXiv 2020年

作者： Li, Peixuan Zhao, Huaici Liu, Pengfei Cao, Feidao Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences University of Chinese Academy of Sciences Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Key Lab of Image Understanding and Computer Vision Liaoning Province

In this work, we propose an efficient and accurate monocular 3D detection framework in single shot. Most successful 3D detectors take the projection constraint from the 3D bounding box to the 2D box as an important component. Four edges of a 2D box provide only four constraints and the performance deteriorates dramatically with the small error of the 2D detector. Different from these approaches, our method predicts the nine perspective keypoints of a 3D bounding box in image space, and then utilize the geometric relationship of 3D and 2D perspectives to recover the dimension, location, and orientation in 3D space. In this method, the properties of the object can be predicted stably even when the estimation of keypoints is very noisy, which enables us to obtain fast detection speed with a small architecture. Training our method only uses the 3D properties of the object without the need for external networks or supervision data. Our method is the first real-time system for monocular image 3D detection while achieves state-ofthe-art performance on the KITTI benchmark. Code will be released at https://***/Banconxuan/RTM3D. Copyright © 2020, The Authors. All rights reserved.

关键词： Real time systems

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Mem Brain: An Easy-to-Use Online Webserver for Transmembrane Protein Structure Prediction

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Nano-Micro Letters 2018年第1期10卷 12-19页

作者： Xi Yin Jing Yang Feng Xiao Yang Yang Hong-Bin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Computer Science Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering

Membrane proteins are an important kind of proteins embedded in the membranes of cells and play crucial roles in living organisms, such as ion channels,transporters, receptors. Because it is difficult to determinate the membrane protein's structure by wet-lab experiments,accurate and fast amino acid sequence-based computational methods are highly desired. In this paper, we report an online prediction tool called Mem Brain, whose input is the amino acid sequence. Mem Brain consists of specialized modules for predicting transmembrane helices, residue–residue contacts and relative accessible surface area of a-helical membrane proteins. Mem Brain achieves aprediction accuracy of 97.9% of ATMH, 87.1% of AP,3.2 ± 3.0 of N-score, 3.1 ± 2.8 of C-score. Mem BrainContact obtains 62%/64.1% prediction accuracy on training and independent dataset on top L/5 contact prediction,respectively. And Mem Brain-Rasa achieves Pearson correlation coefficient of 0.733 and its mean absolute error of13.593. These prediction results provide valuable hints for revealing the structure and function of membrane *** Brain web server is free for academic use and available at ***/bioinf/Mem Brain/.

关键词： Transmembrane a-helices Structure prediction Machine learning Contact map prediction Relative accessible surface area

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Control-oriented fault detection of solid oxide fuel cell system unknown input on fuel supply

Control-oriented fault detection of solid oxide fuel cell sy...

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作者： Wu, Xiao-long Xu, Yuan-wu Xue, Tao Shuai, Junchao Jiang, Jianhua Deng, Zhonghua Fu, Xiaowei Li, Xi School of Automation Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science & Technology Wuhan China College of Computer Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China Wuhan Industrial Economic Development Research Center Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China

As the solid oxide fuel cell (SOFC) system work environment is a high-temperature environment for a long time, it is difficult to obtain the SOFC stack internal state change directly. When the fault occurs, it is difficult to determine where the fault occurs. Moreover, the existing literature ignores the impact of faults, which creates many problems for SOFC system control. Therefore, a state observer-based fault detection method, which is used to detect the input flow sensor fault and the fuel input fault, is proposed. Their advantage is that they do not need data processing. To realize the fault detection, the observer is used to track the changes of SOFC stack chamber temperature. To obtain the observer estimation parameter, an approach from the actual stack structure parameters is employed to approximate the observer parameters. The results show the proposed fault detect method can judge fuel input fault type quickly and shield the disturbances signals from the sensor effectively. The proposed method also can be used to other operating points or air input fault. © 2018 Chinese Automatic Control Society and John Wiley & Sons Australia, Ltd

关键词： Fault detection

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Synaptic learning with augmented spikes

arXiv

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arXiv 2020年

作者： Yu, Qiang Song, Shiming Ma, Chenxiang Pan, Linqiang Tan, Kay Chen Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Key Laboratory of Image Information Processing and Intelligent Control Institute of Artificial Intelligence School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China The Department of Computer Science City University of Hong Kong Hong Kong

Traditional neuron models use analog values for information representation and computation, while all-or-nothing spikes are employed in the spiking ones. With a more brain-like processing paradigm, spiking neurons are more promising for improvements on efficiency and computational capability. They extend the computation of traditional neurons with an additional dimension of time carried by all-or-nothing spikes. Could one benefit from both the accuracy of analog values and the time-processing capability of spikes? In this paper, we introduce a concept of augmented spikes to carry complementary information with spike coefficients in addition to spike latencies. New augmented spiking neuron model and synaptic learning rules are proposed to process and learn patterns of augmented spikes. We provide systematic insight into the properties and characteristics of our methods, including classification of augmented spike patterns, learning capacity, construction of causality, feature detection, robustness and applicability to practical tasks such as acoustic and visual pattern recognition. The remarkable results highlight the effectiveness and potential merits of our methods. Importantly, our augmented approaches are versatile and can be easily generalized to other spike-based systems, contributing to a potential development for them including neuromorphic computing. Copyright © 2020, The Authors. All rights reserved.

关键词： Neurons

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Relationship between pulmonary nodule malignancy and surrounding pleurae, airways and vessels: a quantitative study using the public LIDC-IDRI dataset

arXiv

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arXiv 2021年

作者： Qin, Yulei Gu, Yun Zhang, Hanxiao Yang, Jie Wang, Lihui Wang, Zhexin Yao, Feng Zhu, Yue-Min Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China CREATIS INSA Lyon CNRS UMR 5220 INSERM U1206 Université de Lyon Villeurbanne69621 France Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province School of Computer Science and Technology Guizhou University Guiyang550025 China Department of Thoracic Surgery Shanghai Chest Hospital Shanghai Jiao Tong University Shanghai200025 China

Objectives: To investigate whether the pleurae, airways and vessels surrounding a nodule on non-contrast computed tomography (CT) can discriminate benign and malignant pulmonary nodules. Materials and Methods: The LIDC-IDRI dataset, one of the largest publicly available CT database, was exploited for study. A total of 1556 nodules from 694 patients were involved in statistical analysis, where nodules with average scorings 3 were respectively denoted as benign and malignant. Besides, 339 nodules from 113 patients with diagnosis ground-truth were independently evaluated. computer algorithms were developed to segment pulmonary structures and quantify the distances to pleural surface, airways and vessels, as well as the counting number and normalized volume of airways and vessels near a nodule. Odds ratio (OR) and Chi-square (χ2) testing were performed to assess the correlation between features of surrounding structures and nodule malignancy. A non-parametric receiver operating characteristic (ROC) analysis was conducted in logistic regression to evaluate discrimination ability of each structure. Results: For the benign and malignant groups, the average distances from nodules to pleural surface, airways and vessels are respectively (6.56, 5.19), (37.08, 26.43) and (1.42, 1.07) mm. The correlation between nodules and the counting number of airways and vessels that contact or project towards nodules are respectively (OR=22.96, χ2=105.04) and (OR=7.06, χ2=290.11). The correlation between nodules and the volume of airways and vessels are (OR=9.19, χ2=159.02) and (OR=2.29, χ2=55.89). The areas-under-curves (AUCs) for pleurae, airways and vessels are respectively 0.5202, 0.6943 and 0.6529. Conclusion: Our results show that malignant nodules are often surrounded by more pulmonary structures compared with benign ones, suggesting that features of these structures could be viewed as lung cancer biomarkers. © 2021, CC BY.

关键词： computerized tomography

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