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2011 International Conference on Internet Computing and Information Services, ICICIS 2011

作者： Fang, Xie Junwu, Xu Hubei Province Key Laboratory of Intelligent Robot School of Computer Science and Engineering Wuhan Institute of Technology Wuhan 430073 China Hubei Province Key Laboratory of Intelligent Robot School of Computer Science and Engineering Wuhan Institute of Technology Wuhan 430070 China

ISBN: (纸本)9780769545394

The Business intelligence(BI)is to appear on various mature technology bases such as computer soft hardware,network,communication,decision-making,is one item technology used for handling magnanimous *** requires to discovering thelaw from the various data resource(Database,Data Warehouse,Web etc . ),and this will be mainly dependent on the data storehouse(DW),On-Line AnalyticalProcessing(OLAP),datamining(DM). This paper presents the working state of the BI system in a retail enterprise, and elaborates the realization of a BI system and the effect of the practical application under the data environment of a complex business system. This paper also introduces the whole process of the BI project , including the proof of the BI system, the plan shaping, the design of data warehouses, the data mining and the frontier data presentation. © 2011 IEEE.

关键词： Data warehouses

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Infrared and Visible Image Fusion Based on Region Semantic Discrimination Optimization 5

Infrared and Visible Image Fusion Based on Region Semantic D...

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5th International Seminar on Artificial Intelligence, Networking and Information technology, AINIT 2024

作者： Li, Juan Zhou, Huabing College of Computer Science and Engineering Wuhan Institute of Technology Hubei Key Laboratory of Intelligent Robot Wuhan China

ISBN: (纸本)9798350385557

The fusion of infrared and visible images holds significant application value in military, security, environmental monitoring, and other fields. However, infrared images exhibit distinct target features, while visible images contain clear texture details. The fused image should preserve the corresponding features of the infrared or visible source images based on different semantic regions. In this study, based on the semantic discrimination network module, we propose a generative adversarial network with dual discriminators to improve the fusion effect of infrared and visible images. This method extracts semantic information from images through a semantic segmentation network and combines no-reference image quality assessment and entropy to estimate the information content in each semantic object, thereby guiding the fusion process. Experimental results demonstrate that this method can effectively achieve the fusion of infrared and visible images, achieving significant improvements in feature preservation. © 2024 IEEE.

关键词： Image fusion

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Multitasking Elastic Scheduling Cluster in Tensorflow 5

Multitasking Elastic Scheduling Cluster in Tensorflow

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5th International Conference on Control, robotics and Cybernetics, CRC 2020

作者： Zhou, Qin Zhang, Yanduo Li, Xun Hubei Key Laboratory of Intelligent Robot School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (数字)9781728186245

ISBN: (纸本)9781728186245

Deep learning has always been widely concerned and has a good development prospect. More and more people join in. How to use the limited training resources, do more calculations, and enable more users to join the training, has always been a concern of everyone. This paper studies the distributed training cluster and realizes its efficient training mode. On this basis, a distributed training cluster with multitasking elastic scheduling is established. Furthermore, this article designs new functional modules: job management, GPU expansion, video memory control and improvements. Finally completed the design of a multitasking elastic scheduling cluster based on tensor flow. © 2020 IEEE.

关键词： Scheduling

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Trajectory tracking of 4-DOF assembly robot based on quantification factor and proportionality factor self-tuning fuzzy PID control

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International Journal of Wireless and Mobile Computing 2020年第4期18卷 361-370页

作者： Li, Cuiqiao Sun, Ying Li, Gongfa Jiang, Du Zhao, Haoyi Jiang, Guozhang Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan Hubei China Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan Hubei China Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Institute of Precision Manufacturing Wuhan University of Science and Technology Wuhan China Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Institute of Precision Manufacturing Wuhan University of Science and Technology Wuhan Hubei China Research Centre for Biomimetic Robot and Intelligent Measurement and Control Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan Hubei China Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan Hubei China

When the manipulator performs the assembling task, the performance of the system is easily affected by external interference and parameter changes, resulting in slow tracking response speed and low tracking accuracy. In order to improve the stability and robustness of the controller, a self-tuning fuzzy PID controller with quantification factor and proportionality factor is designed based on the fuzzy PID control. The parameters of the PID controller are dynamically fine-tuned by the fuzzy controller 1, and the quantification factor and proportionality factor are adjusted online by the modifying fuzzy controller 2 to fine-tune the parameters of PID controller. The control effect and robustness of the improved fuzzy PID controller to the manipulator system are simulated and verified on the MATLAB/Simulink platform. The simulation results show that the proposed method significantly improves the transient response speed, tracking accuracy and follower characteristics of the system, and has good dynamic performance. Copyright © 2020 Inderscience Enterprises Ltd.

关键词： Manipulators

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Research on Semantic Sentiment Analysis Based on BiLSTM 4

Research on Semantic Sentiment Analysis Based on BiLSTM

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4th International Conference on Artificial Intelligence and Big Data, ICAIBD 2021

作者： Zhang, Chenyue Liu, Lizhi Hubei Key Laboratory of Intelligent Robot School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9780738131702

This paper proposes an emotion classification model based on attention mechanism and Bi-LSTM. Firstly, the texts were trained into word vectors by word2vec model. Secondly, Bi-LSTM network was used to learn the semantic information of context. Then attention mechanism was added to extract the relatively important part of the texts for emotion classification. Finally, the activation function was used to classify the texts. This paper made an experiment of emotion classification based on IMDB data sets. The experimental results show that the proposed model is better than the traditional machine learning method and recurrent neural network in accuracy, recall and F1value. This proves that the AT-BiLSTM model proposed in this paper can effectively improve the effect of emotion classification, and has certain practicability. © 2021 IEEE.

关键词： Sentiment analysis

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FAM: Fully Attention Module for Medical Image Segmentation

FAM: Fully Attention Module for Medical Image Segmentation

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Xu, Guoping Wu, Xinglong Wuhan Institute of Technology School of Computer Science Engineering Hubei Key Laboratory of Intelligent Robot Wuhan China

ISBN: (纸本)9781665401265

Semantic segmentation plays crucial role in image analysis, which needs rich spatial information and contextual representations. Recently, deep convolution neural networks have achieved much progress in semantic segmentation. However, it still faces many challenges due to the complexity of images, like the blur, noise, and similarity, which imped the progress of the image segmentation. In this paper, we rethink the feature fusion attention methods, such as spatial attention and channel attention, and propose a novel Fully Attention Module (named FAM) based on every pixel of feature map without reducing the size of the input feature maps. We integrate the proposed FAM into U-Net and Fast-SCNN to assess its effectiveness on Synapse dataset. The extensive experiments show that FAM could improve the performance of both two architectures with acceptable cost in terms of speed and total number of parameters. Specifically, we improve the average Dice Similarity Coefficient (DSC) 4.31% / 1.07% and average Hausdorff Distance (HD) 17.62mm / 8.21mm comparing to U-Net and FastSCNN, respectively. In summary, our proposed FAM could boost the segmentation performance by extracting the fully attention of each pixel in the feature maps. The code will be made publicly available at https://***/apple1986/FAM. © 2021 IEEE.

关键词： Semantic Segmentation

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PFCA-Net: a post-fusion based cross-attention model for predicting PCa Gleason Group using multiparametric MRI

PFCA-Net: a post-fusion based cross-attention model for pred...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Cao, Xinyu Jiang, Yan Fang, Yin Wu, Peiyan Song, Wenbo Xing, Hanshuo Wu, Xinglong Xu, Guoping Wuhan Institute of Technology School of Computer Science & Engineering Hubei Key Laboratory of Intelligent Robot Wuhan China

ISBN: (纸本)9798350337488

Prostate cancer (PCa) is a malignancy originating from epithelial cells within the prostate gland. The gold standard for diagnosing PCa is typically based on the Gleason score. However, the inherent variability in biopsy sampling and its potential discordance with radical prostatectomy outcomes can result in the misclassification of the International Society of Urological Pathology (ISUP) Gleason Group (GG). Furthermore, the employment of prostate-specific antigen (PSA) for screening, guiding biopsy decisions, and the reduction in PSA biopsy thresholds has led to a notable increase in unwarranted biopsies among patients with PCa. Consequently, developing an efficient and accurate method to predict ISUP GG is imperative. Currently, most studies focus on ISUP GG binary classification for specific GG. In this study, we leverage multiparametric Magnetic Resonance Imaging (mpMRI) images, encompassing T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC), to introduce a post-fusion based cross-attention model named PFCA-Net. The model is designed to predict ISUP GG categories, specifically GG 0/1, GG 2, GG 3, and GG 4/5. We validate our approach using three distinct deep learning classification models and a mpMRI pre-fusion classification model. Our proposed approach demonstrates exceptional performance, attaining an accuracy (ACC) of 0.9692 and an area under the curve (AUC) of 0.9986, over a dataset comprising 107 PCa patients and encompassing a total of 927 MRI images. This study emphasizes the practical value of mpMRI in distinguishing various ISUP GG categories and expediting the evaluation of PCa GG by clinicians. © 2023 IEEE.

关键词： Deep learning Gleason group mpMRI Multiparametric fusion Prostate cancer

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Motion Blur Image Restoration Based on Generative Adversarial Networks

Motion Blur Image Restoration Based on Generative Adversaria...

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2022 International Symposium on Artificial Intelligence Control and Application technology, AICAT 2022

作者： Wang, Feng Cai, Qiong Hubei Key Laboratory of Intelligent Robot School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9781510656710

During the image capture process, it is difficult to maintain relative stillness between the camera and the subject, resulting in blurred images. In order to solve this problem, based on the generative adversarial network, this paper introduces a residual module to solve the gradient dispersion problem generated during the training process. The multi-scale theory is introduced to process the image to better extract the detailed information of the image. Finally, the experiment shows that the scheme has achieved good results. © 2022 SPIE.

关键词： Generative adversarial networks

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GeoSAM: A Sampling and Attention Mechanism for Cross-View Geo-Localisation 6

GeoSAM: A Sampling and Attention Mechanism for Cross-View Ge...

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6th International Conference on Frontier Technologies of Information and Computer, ICFTIC 2024

作者： Wu, Fan Zhou, Huabing College of Computer Science and Engineering Wuhan Institute of Technology Hubei Key Laboratory of Intelligent Robot Wuhan China

ISBN: (纸本)9798331541750

Cross-view geo-localisation aims to match street-view images with satellite images, but this task remains challenging due to significant appearance and viewpoint discrepancies. This paper proposes a novel framework, GeoSAM, which enhances feature alignment and matching accuracy by integrating a dynamic sampling strategy and attention mechanism. GeoSAM employs a dual-branch network with Convolutional Block Attention Modules (CBAM) to strengthen feature extraction for both street-view and satellite-view images. Furthermore, the framework incorporates a dynamic hard negative sampling strategy, combining geographic proximity and visual similarity, to ensure robustness in contrastive learning. Experiments on the CVUSA dataset demonstrate that GeoSAM significantly outperforms existing methods in terms of Top-1 accuracy and recall, offering an efficient and generalisable solution for cross-view geo-localisation tasks. © 2024 IEEE.

关键词： Image matching

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Query Optimization for Air Quality Big Data based on Hive-ORC 5

Query Optimization for Air Quality Big Data based on Hive-OR...

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5th International Conference on Control, robotics and Cybernetics, CRC 2020

作者： Peng, Bei Liu, Lizhi Wuhan Institute of Technology School of Computer Science and Engineering Hubei Key Laboratory of Intelligent Robot Wuhan China

ISBN: (数字)9781728186245

ISBN: (纸本)9781728186245

To improve the efficiency of analyzing the massive amount of monitoring data collected from an air quality monitoring system, a method based on Hive data warehouse to store data as ORC file format then create Row Group Index and Bloom Filter Index is proposed to optimize the query of air quality big data. The air quality monitoring data from the environmental monitoring center of Hubei province was taken as a research object. After the data was transferred to Hive, Spark was used to query and analyze data. Five queries were carried out on three datasets to conduct the comparison experiment between the method proposed in this paper and the method of storing data as TextFile by default on Hive. The results show that the optimization method based on Hive-ORC and its indexes reduces the storage space of big data by 90% and reduces its query time by 86%. What's more, better optimization effect can be achieved with the increase of data volume. © 2020 IEEE.

关键词： Big data

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