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8th International Conference on Intelligent Computing and Signal Processing, ICSP 2023

作者： Zhang, Chunhui Gu, Shenming Zhejiang Ocean University School of Information Engineering Zhoushan316022 China Zhejiang Ocean University Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhoushan316022 China

ISBN: (纸本)9798350302455

Object detection, as an important research direction of artificial intelligence technology, has a wide scope of application in several fields. At the same time, object detection technology shows a trend of multidisciplinary integration, which frees related researchers and some laborers from repetitive labor, thus improving the efficiency and accuracy of their work. Fishery resources play a pivotal role as an important agricultural resource, and the realization of different kinds of fish detection is of great help to the rational use and protection of fishery resources. However, there are some problems in the application of object detection technology to this field, such as the lack of datasets, and the quantity and quality of samples in the data set cannot satisfy the increasingly large neural network models. In this paper, we created a dataset dedicated to fish target detection through various channels such as photography and the web. The dataset contains 16 categories and 1038 images, including common fish (e.g., carp, hairtail, etc.) and also some precious fish such as grouper. We improved the quality of the images to enable the network to learn more accurate information about the characteristics of different fish. Meanwhile, we propose BIAS-YOLO, which improves the original SPP (Spatial Pyramid Pooling) module based on the original YOLOv5 network, and introduced the idea of spatial bias, which can encode the global information and thus improve the network to make full use of the whole image information. On our proposed dataset, our network works better compared to the baseline. © 2023 IEEE.

关键词： Object detection

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Deep Convolutional Neural Network Based Sperm Detection in Microscopic Videos

Deep Convolutional Neural Network Based Sperm Detection in M...

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International Conference on Image, Vision and Intelligent Systems, ICIVIS 2023

作者： Ma, Shiyi Li, Jindong Zhang, Wenbo Yang, Jinzhu Grzegorzek, Marcin Li, Chen Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China Institute of Medical Informatics University of Luebeck Luebeck Germany Department of Knowledge Engineering University of Economics in Katowice Katowice Poland

ISBN: (纸本)9789819708543

In recent years, with the increasingly serious quality problems of human sperm, the performance of traditional sperm analysis methods has been unable to meet the growing demand for diagnosis and treatment. The purpose of this study was to explore the application of deep convolutional neural network microvideo in computer assisted sperm analysis (CASA). For the sperm detection task, the model structure, loss function and non-maximum suppression algorithm were optimized to improve the performance of the model, reflecting the advantages of deep learning. To avoid the impact of data differences, cross-validation test was adopted in this study, and multiple indexes such as F1-score and AP were compared with the original model after training. These improvements significantly improved the average accuracy and F1 scores of the models. The performance of the model not only has an important impact on the indicators of the target detection model, but also has an important impact on the visualization results of the model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Computer aided diagnosis

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Plankton detection based on improved YOLOv5s

Plankton detection based on improved YOLOv5s

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2023 International Conference on Optics and Machine Vision, ICOMV 2023

作者： Li, Xiaohui Gu, Shenming Wei, Dan School of Information Engineering Zhejiang Ocean University Zhoushan316022 China Key Laboratory of Oceanographic Big Data Mining Application of Zhejiang Province Zhejiang Ocean University Zhoushan316022 China

ISBN: (纸本)9781510664777

With the rapid development of marine resource exploitation, planktonic microorganisms have gradually become one of the research directions in the field of machine vision. In order to optimize the detection of small targets in the images of planktonic microorganisms, this paper proposes an improved YOLOv5s model to enhance the detection of planktonic microorganisms. The SE attention mechanism allows the network to pay more attention to the target feature area and suppress useless feature information. The PANet feature module is improved into a weighted bidirectional pyramidal BiFPN feature fusion network to achieve high-efficiency bidirectional cross-scale connectivity and weighted feature map fusion. The results show that the combination of the SE attention mechanism and BiFPN feature fusion improves the mAP value by 6.96%, increases the precision by 11.45%, and reduces the loss rate by 1.62%. Our proposed method effectively solves the problems of false detection, missed detection, and low detection accuracy of the existing models for small target detection. © 2023 SPIE. All rights reserved.

关键词： Computer vision

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Prediction of Ionospheric TEC Based on BLS-LSTM-GRU Hybrid Model 14th

Prediction of Ionospheric TEC Based on BLS-LSTM-GRU Hybrid M...

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14th china Satellite Navigation Conference, CSNC 2024

作者： Li, Dengao Zhang, Xinfang Zhao, Jumin Wu, Fanming Feng, Ran Zhao, Jinhua Shi, Danyang College of Data Science Taiyuan University of Technology Taiyuan030024 China College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan030024 China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan030024 China Shanxi Province Engineering Technology Research Center of Spatial Information Network Taiyuan030024 China

ISBN: (纸本)9789819969272

In satellite communications, global navigation satellite systems(GNSS) and other important space activities, the value of total electron content (TEC) in the ionosphere directly affects the size of ionospheric delay. Achieving accurate prediction of ionospheric total electron content through modeling can effectively improve the reliability and accuracy of GNSS positioning. Considering the high prediction accuracy of long short-term memory neural network (LSTM) and the fast training speed of gated recurrent unit network (GRU), but it is easy to fall into the local optimum, while the width learning system(BLS) can effectively avoid this drawback. Therefore, this paper proposes a model for predicting the total electron content of the ionosphere, which consists of a broad learning system, an LSTM network and a GRU networks. Six parameters of TEC, geomagnetic index Dst, Kp, Ap, solar activity index F10.7, and hour (HD) are selected as input features from the observed data to predict the variation of the total ionospheric electron content. The results generate prediction maps of the total ionospheric electron content and are compared with empirical models and conventional neural network models at different times and solar activity conditions. The experimental results show that the root mean square error (RMSE) of the prediction results of this BLS-LSTM-GRU model is decreased by 23.07%, 17.48% and 9.46% compared with the IRI-2016, NeQuick and CNN-LSTM prediction models, respectively, with higher prediction accuracy. © 2024, Aerospace Information Research Institute.

关键词： Long short-term memory

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VProChart: Answering Chart Question Through Visual Perception Alignment Agent and Programmatic Solution Reasoning 39

VProChart: Answering Chart Question Through Visual Perceptio...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Huang, Muye Zhang, Lingling Han, Lai Wu, Wenjun Zhang, Xinyu Liu, Jun School of Computer Science and Technology Xi’an Jiaotong University China MOE KLINNS Lab Xi’an Jiaotong University China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering China

ISBN: (纸本)157735897X

Charts are widely used for data visualization across various fields, including education, research, and business. Chart Question Answering (CQA) is an emerging task focused on the automatic interpretation and reasoning of data presented in charts. However, chart images are inherently difficult to interpret, and chart-related questions often involve complex logical and numerical reasoning, which hinders the performance of existing models. This paper introduces VProChart, a novel framework designed to address these challenges in CQA by integrating a lightweight Visual Perception Alignment Agent (VPAgent) and a Programmatic Solution Reasoning approach. VPAgent aligns and models chart elements based on principles of human visual perception, enhancing the understanding of chart context. The Programmatic Solution Reasoning approach leverages large language models (LLMs) to transform natural language reasoning questions into structured solution programs, facilitating precise numerical and logical reasoning. Extensive experiments on benchmark datasets such as ChartQA and PlotQA demonstrate that VProChart significantly outperforms existing methods, highlighting its ability to understand and reason with charts. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Research on Zenith Tropospheric Delay Model Based on TCN Improving HGPT2 Model 14th

Research on Zenith Tropospheric Delay Model Based on TCN Imp...

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14th china Satellite Navigation Conference, CSNC 2024

作者： Li, Dengao Shi, Danyang Zhao, Jumin Wu, Fanming Yan, Liangquan Feng, Ran Zhang, Xinfang Zhao, Jinhua College of Data Science Taiyuan University of Technology Taiyuan030024 China College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan030024 China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan030024 China Shanxi Province Engineering Technology Research Center of Spatial Information Network Taiyuan030024 China

ISBN: (纸本)9789819969272

The zenith tropospheric delay (ZTD) obtained by global navigation satellite system (GNSS) atmospheric sounding is a pivotal data source for water vapor monitoring. Meteorological changes in Antarctica play an important role in analysis of the global climate, but factors such as complex climatic conditions can limit the collection of meteorological data needed for ZTD retrieval. Therefore, it is necessary to establish high-precision ZTD models that do not rely on measured meteorological data. The existing global pressure and temperature 3 (GPT3) model has limited ability to capture complex weather variations and cannot obtain high-precision GPT3_ZTD. To address the above issues, this research proposes a high-precision ZTD model through using temporal convolutional network (TCN) for improving the hourly global pressure and temperature 2 (HGPT2) model. The HGTP2 model based on Fourier analysis and the time-segmentation concept can consider the linear trend of climate variations, simultaneously the TCN is introduced to simulate the long-term temporal dependence of HGPT2_ZTD, so as to achieve the goal of obtaining high-precision ZTD without relying on measured meteorological data. The experimental results show that the precision of TCN_ZTD obtained from the proposed model is higher than GPT3_ZTD and HGPT2_ZTD when using the GNSS_ZTD observations obtained from 35 stations in Antarctica as a reference, and the improvement of the model is obvious. © 2024, Aerospace Information Research Institute.

关键词： Fourier analysis

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Identification of Phtato Leaf Disease Based on The TS-Resnet Deep Learing Method 21

Identification of Phtato Leaf Disease Based on The TS-Resnet...

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21st International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2023

作者： Duan, Xiao-Yue Gu, Shen-Ming School of information Engineering Zhejiang Ocean University Zhoushan316022 China Zhejiang Ocean University Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhoushan316022 China

ISBN: (纸本)9798350303810

At present, the incidence of potato disease is large, the disease is complex, and it is difficult to control, which seriously affects the potato yield and quality. Therefore, in order to realize the potato disease image recognition technology under small sample data, by using attention mechanism, transfer learning and data enhancement technology, the potato leaf disease image is accurately analyzed. This paper proposes a potato leaf disease recognition method based on TS-ResNet(Transform learning and SE) deep learning method. Based on the original ResNet101 model, its full connectivity layer is modified to add transfer learning and attention mechanism. In order to increase the number of potato disease part of the data set, the original graph can be flipped, horizontal flip and other operations to avoid the phenomenon of overfitting in the original model. The results of this study can provide some support for the intelligent research of potato leaf disease identification. © 2023 IEEE.

关键词： Disease recognition Potato ResNet101 SE attention mecha-nism Transfer learning

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Regional Winter Wheat Yield Estimation Based on CASA Model and Improved Harvest Index Using Remote Sensing Images

Regional Winter Wheat Yield Estimation Based on CASA Model a...

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2023 International Conference on Remote Sensing, Mapping, and Geographic Systems, RSMG 2023

作者： Li, Qian Zhao, Jianhui Yang, Huijin Li, Ning School of Computer and Information Engineering Henan University Kaifeng475004 China Henan Province Engineering Research Center of Spatial Information Processing Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Kaifeng475004 China

ISBN: (纸本)9781510668898

Accurately and timely grasping agricultural information at the regional scale helps to solve food security issues and formulate agricultural policies. Remote sensing images have the advantages of wide monitoring range and the ability to eliminate human interference. In recent years, they have been increasingly valued in crop yield estimation. The CASA model is used to estimate the net primary productivity (NPP) of crops and then combine it with the harvest index (HI) to estimate crop yields. However, most studies use a fixed HI for yield estimation, which can lead to low accuracy. In this context, the HI is studied and improved for winter wheat yield prediction in this research. The main contents are as follows: Firstly, time-series Sentinel-2 optical data and meteorological data were used as inputs of the CASA model to calculate the NPP of winter wheat. Then, HI was calculated using the fieldwork data and a method was proposed to improve it due to its geographic location-related characteristics. Finally, the calculated NPP and improved HI were used to estimate the winter wheat yield. The experimental results prove that the proposed method has higher accuracy than original methods, with determination coefficient(R2) of 0.595, root mean square error(RMSE) of 793.4 kg/ha, and mean absolute error(MAE) of 659.53 kg/ha. © 2023 SPIE.

关键词： Crops

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iCREPCP:A deep learning-based web server for identifying base-resolution cis-regulatory elements within plant core promoters

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Plant Communications 2023年第1期4卷 213-216页

作者： Kaixuan Deng Qizhe Zhang Yuxin Hong Jianbing Yan Xuehai Hu College of Informatics Hubei Engineering Technology Research Center of Agricultural Big DataHuazhong Agricultural UniversityWuhanHubeiP.R.China National Key Laboratory of Crop Genetic Improvement Huazhong Agricultural UniversityWuhan 430070P.R.China

Dear Editor,A central question of plant biology is how to specify the temporal and spatial patterns as well as the quantitative levels of gene expression,which are significantly associated with important agronomic *** has been a growing consensus in the past decade that the two key factors determining gene expression level are cis-regulatory modules(CRMs)and trans-acting factors(TAFs)(Schmitz et al.,2022).Common CRMs include gene-proximal promoters and distal enhancers,which are both considered to be complex assemblies of cis-regulatory elements(CREs).It is the binding or interaction between CREs and TAFs(often transcription factors[TFs])in a ubiquitous or cell-specific manner that determines in which cell,at what time,and at what level a gene is ***,the identification of plant CRMs or critical CREs will not only help us understand transcriptional regulatory mechanisms in plants but also serve as an essential prerequisite for plant breeding 4.0—breeding by genome editing(Gao,2021).

关键词： server web breeding

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Modeling viral evolution:A novel SIRSVIDE framework with application to SARS-CoV-2 dynamics

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hLife 2024年第5期2卷 227-245页

作者： Kaichun Jin Xiaolu Tang Zhaohui Qian Zhiqiang Wu Zifeng Yang Tao Qian Chitin Hon Jian Lu State Key Laboratory of Protein and Plant Gene Research Center for BioinformaticsSchool of Life SciencesPeking UniversityBeijingChina NHC Key Laboratory of Systems Biology of Pathogens Institute of Pathogen BiologyChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina State Key Laboratory of Respiratory Disease National Clinical Research Center for Respiratory DiseaseGuangzhou Institute of Respiratory HealthThe First Affiliated Hospital of Guangzhou Medical UniversityGuangdongChina Respiratory Disease AI Laboratory on Epidemic and Medical Big Data Instrument Applications Department of Engineering ScienceFaculty of Innovation EngineeringMacao University of Science and TechnologyMacaoChina Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases KingMed School of Laboratory MedicineGuangzhou Medical UniversityGuangdongChina Macao Center for Mathematical Sciences Macao University of Science and TechnologyMacaoChina Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacaoChina

Understanding evolutionary trends in emerging viruses,such as severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),is crucial for effective public health management and ***,extensive debates have arisen concerning viral evolutionary trends,particularly the interplay between transmissibility,pathogenicity,and immune *** this context,we have developed a novel computational model named SIRSVIDE(Susceptible-Infected-Recovered-Susceptible-Variation-Immune Decay-Immune Escape)to simulate the transmission and evolutionary dynamics of viral *** simulation results indicate that under conditions of high mutation rates,elevated transmission rates,and larger susceptible host populations,viral populations exhibit prolonged increases in transmissibility and immune escape,accompanied by reductions in pathogenicity and noticeable short-term fluctua***,when the total susceptible population size and mutation rate decrease,substantial uncertainty in the evolutionary trends of viral populations becomes *** summary,the SIRSVIDE model establishes a comprehensive framework for generating both short-and long-term viral epidemiological and evolutionary *** simulation outcomes align with existing evidence indicating that SARS-CoV-2 is undergoing selection for heightened transmissibility,decreased pathogenicity,and enhanced immune ***,the model sheds light on the possible evolutionary dynamics of other viruses.

关键词： viral evolution severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) transmissibility-pathogenicity trade-off immune escape

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