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IEEE Transactions on Artificial Intelligence 2024年第1期5卷 104-114页

作者： Lin, Zhijie He, Zhaoshui Yao, Ruoyu Wang, Xu Liu, Taiheng Deng, Yamei Xie, Shengli Guangdong University of Technology School of Automation Guangzhou510006 China Key Laboratory for IoT Intelligent Information Processing and System Integration of Ministry of Education Guangzhou510006 China Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China Third Affiliated Hospital of Guangzhou Medical University Department of Radiology Guangzhou510150 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China

Automatic diagnosis of Coronavirus disease 2019 (COVID-19) using chest computed tomography (CT) images is of great significance for preventing its spread. However, it is difficult to precisely identify COVID-19 due to the following problems: 1) the location and size of lesions can vary greatly in CT images;2) its unique characteristics are often imperceptible in imaging findings. To solve these problems, a Deep Dual Attention Network ($\text {D}^{\text {2}}\text {ANet}$) is proposed for accurate diagnosis of COVID-19 by integrating dual attention modules (DAMs) with different scales of the feature extractor, where DAM can adaptively detect relevant lesion regions to extract discriminative imaging features of COVID-19. Specifically, DAM is implemented by two parallel blocks: 1) global attention block (GAB) and 2) local attention block (LAB), in which GAB is designed to roughly locate the infected regions from the entire image by modeling global contexts while LAB is developed to explicitly highlight subtle differences of COVID-19 from other viral pneumonia in the infected regions by learning detailed lesion information. Experimental results on several public datasets show that $\text {D}^{\text {2}}\text {ANet}$ outperforms the state-of-the-art approaches in various performance metrics. © 2020 IEEE.

关键词： COVID-19

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Beyond animal detection: A benchmark for detecting animal age group 5

Beyond animal detection: A benchmark for detecting animal ag...

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5th International Conference on Artificial Intelligence and Computer Science, AICS 2023

作者： Zhou, Hang Wu, You Li, Jiehong Pan, Liangtao Ye, Hengzhou Li, Shuiwang College of Information Science and Engineering Guilin University of Technology Guangxi Guilin541006 China Guangxi Key Laboratory of Embedded Technology and Intelligent Information Processing Guangxi Guilin541006 China

ISBN: (纸本)9781510668621

There are numerous practical applications for animal age estimation, such as age simulation, population structure estimation, wildlife conservation, biometrics, and more. Most of the current methods either concentrate solely on animal detection or animal age estimation. However, animal detection provides limited information, which is not adequate for more sophisticated computer vision tasks. On the other hand, existing methods for estimating animal age are based primarily on facial images, but capturing an accurate image of an animal’s face in a wild environment is extremely difficult and impractical for animals without discriminative facial features. In this paper, we propose a novel task, called Animal Age Group Detection. This task is a combination of animal detection and animal age estimation, with the primary goal of obtaining more information beyond the location of the animal by also providing crude age information, which is simple to accomplish with computer vision methods in a variety of open-world applications. To facilitate the study of this challenging, we present a benchmark for Animal Age Group Detection (AAGD), which contains a diverse range of 22 animal types, comprising a total of 4, 778 images. These images are well-annotated with axis-aligned bounding boxes and include information about the age groups of the animals. In addition, we establish a baseline detector for this task, called AG-YOLOF. The release of AAGD aims to facilitate future research on the identification of animal age groups and enhance awareness of this significant area of study. © 2023 SPIE.

关键词： Animals

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UAV-Aided Multi-antenna Relay systems with Wireless Power Transfer for Maritime Communications 14th

UAV-Aided Multi-antenna Relay Systems with Wireless Power T...

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14th EAI International Conference on Wireless and Satellite systems, WiSATS 2024

作者： Li, Yuheng Zhou, Zhiquan Wang, Jinlong Wang, Lina Wang, Chenxu School of Information Science and Engineering Harbin Institute of Technology Weihai264209 China Shandong Provincial Key Laboratory of Marine Electronic Information and Intelligent Unmanned Systems Weihai264209 China Key Laboratory of Cross-Domain Synergy and Comprehensive Support for Unmanned Marine Systems Ministry of Industry and Information Technology Weihai264209 China

ISBN: (纸本)9783031862021

With the increase in maritime activities, traditional communication methods can no longer meet the needs of today’s maritime work, and the use of unmanned aerial vehicle(UAV) relay communications in marine scenarios has attracted more and more attention. In this paper, A long-distance three-node two-hop UAV relay system for maritime users is investigated. The UAV aerial relay node first transmits an energy-carrying radio frequency (RF) signal to the maritime user node, which then harvests energy and sends data information back to the UAV aerial relay node, which in turn forwards it to the land node. Considering the peculiarities of maritime communication channels, a novel land-air-maritime communication channel model is proposed. Utilizing Zero-Forcing/Zero-Transmission (ZFR/ZFT) technology, a precoding matrix for the aerial relay node is designed, followed by simulation and emulation, the performance of the channel is tested in the case of different transmission power of relay nodes or different number of antennas of relay nodes. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Unmanned aerial vehicles (UAV)

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Dilation-Supervised Learning: A Novel Strategy for Scale Difference in Retinal Vessel Segmentation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1693-1707页

作者： Wang, Huadeng Zuo, Wenbin Li, Bingbing Pan, Xipeng Liu, Zhenbing Lan, Rushi Luo, Xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Guilin University of Electronic Technology School of Computer Science and Information Security Guilin541004 China Ganzhou Municipal Hospital Department of Pathology Ganzhou341000 China

Retinal fundus image segmentation based on deep learning is an important method for auxiliary diagnosis of ophthalmic diseases. Due to the scale difference of the blood vessels and the imbalance between foreground and background pixels in the fundus image, the deep learning network will inevitably ignore thin vessels when downsampling and feature learning. For the scale difference problem, this article aims to tackle its limitation from two perspectives: changing the supervised approach and adapting the feature learning. Correspondingly, a dilation-supervised learning method and an adaptive scale dimensional attention mechanism which are used to construct a two-stage segmentation model is proposed. Moreover, we introduce a quantitative approach to evaluate the scale difference of the blood vessels. With the help of the proposed weighted loss function, the segmentation results are refined, and the class imbalance problem between foreground and background pixels is resolved. Finally, the proposed adaptive threshold selection method is used in the postprocessing of segmentation results. The experiments on DRIVE, STARE, CHASE_DB1, and HRF datasets show that the proposed method achieves better segmentation performance compared with other state-of-the-art methods, and has good generalization ability and robustness. © 2020 IEEE.

关键词： Semantics

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Hybrid CNN-sLSTM Algorithm for Short-Term Wind Speed Prediction 3

Hybrid CNN-sLSTM Algorithm for Short-Term Wind Speed Predict...

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3rd International Conference on Mechatronics and Automation technology, ICMAT 2024

作者： Jin, Tao Wei, Jianing Zhou, Hongjuan Wang, Chenxu Zhou, Zhiquan Weihai China Shandong Provincial Key Laboratory of Marine Electronic Information and Intelligent Unmanned Systems Weihai China A Key Laboratory of Cross-Domain Synergy and Comprehensive Support for Unmanned Marine Systems Ministry of Industry and Information Technology Weihai China

ISBN: (纸本)9781643685649

Wind speed prediction is of significant scientific and practical importance for optimizing the operation and maintenance of wind power generation systems. This paper proposes a neural network architecture for short-term wind speed forecasting, which combines convolutional neural networks (CNN) with scalar long short-term memory (sLSTM). The first component effectively extracts spatial features by capturing local dependencies and patterns crucial for accurate predictions. The latter component captures temporal dependencies using scalar memory cells, which enhance information retention and updates over time. This combination enables the proposed model to handle the complex and dynamic nature of wind speed data. To evaluate the model performance, a comprehensive comparison and validation were conducted of different wind speed prediction algorithms using public datasets. The results demonstrate that the proposed model outperformed other benchmark models in terms of accuracy and stability. The CNN-sLSTM model, with its enhanced predictive capabilities, demonstrates the ability to improve the operational efficiency and precision of mechatronic systems within the wind power industry. © 2024 The Authors.

关键词： Wind forecasting

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Inspection Method of Track Facilities Using Image Analysis of Images Taken from Front of Trains

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Quarterly Report of RTRI (Railway Technical Research Institute) 2023年第3期64卷 205-210页

作者： Nagamine, Nozomi Goda, Wataru Maeda, Riho Tsubokawa, Yosuke Kato, So Itoi, Kensuke Head of Image Analysis Laboratory Information and Communication Technology Division Research Areas: Computer Vision Image Processing Signalling Systems Japan Image Analysis Laboratory Information and Communication Technology Division Research Areas: Computer Vision Image Processing Japan Head of Track Geometry and Maintenance Laboratory Track Technology Division Research Areas: Track Measuring Japan Track Geometry and Maintenance Laboratory Track Technology Division Research Areas: Track Measuring Japan Research Areas: Track Measuring Japan

Appropriate maintenance of tracks is vital for the safe operation of railways. Properly managing track facilities is necessary to prevent buckling of rails. Changes in society are creating a shortage of workers, a decrease in skilled engineers, a decrease in passenger income, and an increase in the need to develop low-cost inspection methods which do not require experience. Therefore, we have developed a method using only inexpensive camcorders, which can extract information from images to detect joint gaps, wooden sleeper deterioration and ballast shape, and estimate the kilometer point of these images to locate the installations in question. This paper describes the outline of the method and its application results. © 2023 Ken-yusha Inc.. All rights reserved.

关键词： Inspection

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A space-based radar nonlinear tracking method for air targets based on VB-PHD filter 2

A space-based radar nonlinear tracking method for air target...

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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Fang, Linlin Zhong, Lihua Han, Bing Hu, Yuxin Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798331515669

Space-based radar has better coverage and is not limited by the curvature of the earth. Aircraft target tracking is particularly important in space-based radar system. However, the long observation distance and strong nonlinear observation increases the challenge of target tracking. Therefore, we proposed a space-based radar nonlinear tracking method for air targets based on variational Bayesian probability hypothesis density (VB-PHD) filter. The relationship between state and observation is established. Then, the VB method is adopted to deal with uncertainty of measurement noise variances. Besides, the cubature filter is introduced to deal with the strongly nonlinear models, and the recursion process is completed by using the third-order spherical radial volume rule. Finally, simulation scenarios with different measurement noise parameters were constructed, and the tracking result comparisons show that it effectively solves nonlinear problems, and tracking precision can be significantly improved. © 2024 IEEE.

关键词： Space-based radar

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Attention information Fusion Emotion Analysis Model Based on BiLSTM-BERT 4

Attention Information Fusion Emotion Analysis Model Based on...

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4th International Conference on Consumer Electronics and Computer Engineering, ICCECE 2024

作者： Cheng, Zhi-Wei Li, Shun-Xin Wuhan University of Science and Technology Department of Computer Science and Technology Wuhan China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial Wuhan China

ISBN: (纸本)9798350384062

Aiming at the problem that most sentiment analysis methods do not focus on the weight fusion of local context and global context information and the important information in fusion features is not prominent, AIFM-BB (Attention information fusion emotion analysis model based on BiLSTM-BERT) is proposed. The model uses a pre-trained BERT model to process text vectors and a collaborative speech analysis algorithm to process audio vectors, and introduces BiLSTM and Attention mechanisms. The weight of the feature vector is dynamically adjusted by combining the global information and local information of text and audio and the attention information of text and audio modes. The emotion analysis results are obtained through the Softmax layer after the fusion features are self-focused. The experimental results show that the accuracy of the proposed AIFM-BB model on the CMU-MOSI dataset is 85.3%, which is 2.8 percentage points higher than the baseline. In the multi-category emotion classification task, the accuracy of the model is 44.7%, an improvement of 1.1 percentage points compared with baseline. The accuracy is 83.6% on the CMU-MOSEI dataset, an improvement of 1.4 percentage points compared to baseline. © 2024 IEEE.

关键词： Sentiment analysis

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A discrete-time looped functional approach and its application to discrete-time systems with cyclically varying delays

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Science China(information Sciences) 2025年

作者： Wen-Hu CHEN Chuan-Ke ZHANG Chen-Rui WANG Ke-You XIE Yong HE Hong-Bing ZENG School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration School of Electrical and Information Engineering Hunan University of Technology

This paper investigates the stability analysis problem of discrete-time systems with cyclically varying *** previous studies, the delay investigated in this paper cyclically alternates between increasing and decreasing changes within its upper and lower bounds. First, to reasonably describe the cyclic variation characteristics of the delay, a discretetime looped functional(DTLF) approach and a refined allowable delay set(ADS) are proposed. Then, based on the constructed DTLF with delay-product terms and using matrix-separation-based inequality to estimate its forward difference,delay-variation-interval-dependent stability conditions considering two different ADSs are derived. Finally, the effectiveness and superiority of the obtained conditions are demonstrated through two numerical examples.

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Adaptive Multi-Updating Strategy Based Particle Swarm Optimization

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intelligent Automation & Soft Computing 2023年第9期37卷 2783-2807页

作者： Dongping Tian Bingchun Li Jing Liu Chen Liu Ling Yuan Zhongzhi Shi School of Computer Science and Technology Kashi UniversityKashi844006China Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing100190China

Particle swarm optimization(PSO)is a stochastic computation tech-nique that has become an increasingly important branch of swarm intelligence ***,like other evolutionary algorithms,PSO also suffers from premature convergence and entrapment into local optima in dealing with complex multimodal *** this paper puts forward an adaptive multi-updating strategy based particle swarm optimization(abbreviated as AMS-PSO).To start with,the chaotic sequence is employed to generate high-quality initial particles to accelerate the convergence rate of the ***,according to the current iteration,different update schemes are used to regulate the particle search process at different evolution *** be specific,two different sets of velocity update strategies are utilized to enhance the exploration ability in the early evolution stage while the other two sets of velocity update schemes are applied to improve the exploitation capability in the later evolution *** by the unequal weightage of acceleration coefficients is used to guide the search for the global worst particle to enhance the swarm *** addition,an auxiliary update strategy is exclusively leveraged to the global best particle for the purpose of ensuring the convergence of the PSO ***,extensive experiments on two sets of well-known benchmark functions bear out that AMS-PSO outperforms several state-of-the-art PSOs in terms of solution accuracy and convergence rate.

关键词： Particle swarm optimization local optima acceleration coefficients swarm diversity premature convergence

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