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Fault Text Classification Based on Convolutional Neural Network

Fault Text Classification Based on Convolutional Neural Netw...

引用

International Conference on Industrial Engineering and Applications (ICIEA)

作者： Lixia Wang Botao Zhang School of Computer Science and Technology Wuhan University of Science and Technology Key Laboratory of Intelligent Information Processing and Real-time Industrial System in Hubei Province Wuhan China

ISBN: (数字)9781728167855

ISBN: (纸本)9781728167862

The fault text records various fault information of the power system operation, and it is an important data source for analyzing the power system operation. The text management of power faults is becoming more and more intelligent, and the task of classification of fault texts has gradually changed from manual operation to automatic classification of the system. In order to realize automatic classification and improve the classification efficiency and accuracy of power fault texts, in view of the characteristics of power fault short texts, this paper proposes a Convolutional Neural Networks (CNN) short text based on a mixture of word vectors and character vectors. Classification model, which inputs the processed data set information to this classification model to classify short texts of power failures. The experimental results show that the accuracy rate of the proposed model on the power fault classification dataset can reach 88.35%. Compared with other classification models, the feature extraction ability is stronger and the classification effect is better.

关键词： Convolutional neural networks Text categorization Power systems Feature extraction Dictionaries Data models

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Ultrasound vascular image segmentation based on full convolutional aggregation neural network 3

Ultrasound vascular image segmentation based on full convolu...

引用

3rd International Conference on Computer Science and Artificial Intelligence, CSAI 2019 and its Workshop - The 11th International Conference on information and Multimedia technology, ICIMT 2019

作者： Li, Yan Liu, Jun Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial Department of Computer Science and Technology Wuhan University of Science and technology Wuhan430070 China

ISBN: (纸本)9781450376273

The concept of ultrasound therapy has been proposed long ago. However, most of the previous methods of ultrasound therapy destroy tissue through thermal effects and cause great damage to patients. In recent years, ultrasound cavitation therapy has caused extensive discussion and research due to its unique noninvasiveness. In order to achieve real-time access to the patient during treatment, the lesion area must be processed simultaneously and accurately. However, ultrasound images mostly have lower signal-to-noise ratio, contrast, and blurred edges. In order to solve the segmentation problem, this paper proposes a new segmentation model Du-net based on the full convolutional neural network. Under the premise of deepening the network depth to obtain more information, the Encoder- Decoder method and the layered aggregation mode are used to prevent the gradient explosion. Solving boundary segmentation problem by constructing joint network, In the case of a small data set, the data enhancement method using a suitable data set effectively increases the usable image features and achieves better results on the ultrasound blood vessel image. © 2019 Association for Computing Machinery.

关键词： Convolution

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Research on Question Answering System Based on Bi-LSTM and Self-attention Mechanism

Research on Question Answering System Based on Bi-LSTM and S...

引用

International Conference on Industrial Engineering and Applications (ICIEA)

作者： Hao Xiang Jinguang Gu School of Computer Science and Technology Wuhan University of Science and Technology Key Laboratory of Intelligent Information Processing and Real-time Industrial System in Hubei Province Wuhan China

ISBN: (数字)9781728167855

ISBN: (纸本)9781728167862

With the development of artificial intelligence technology, intelligent question answering has become a hot research direction in the field of natural language processing. This paper proposes a question answering method based on Bi-LSTM and self-attention mechanism model. This method uses Bi-LSTM to encode and align the question and answer respectively, then uses self-attention to obtain the relationship between keywords, and finally performs softmax through the fully connected layer to obtain the similarity between the question and answer. Finally, in the experiment, compared with the traditional attention model, the accuracy rate of this model was increased by 1.6%, and the recall rate was increased by 1.5%.

关键词： Semantics Machine learning Task analysis Knowledge discovery Feature extraction Standards Training

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An Order Dispatch System Based on Reinforcement Learning for Ride Sharing Services

An Order Dispatch System Based on Reinforcement Learning for...

引用

IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Zeqiang Chen Peng Li Junlei Xiao Lei Nie Yu Liu College of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China

ISBN: (纸本)9781728176499;9781728176505

Ride-sharing has been widely used in many cities, such as Didi and Uber. Ride-sharing is regarded as an effective way to solve urban traffic congestion and pollution. However, most of the existing dispatch methods take the minimization of the travel distance as the optimization goal, without considering other factors. In this paper, we consider not only detour distance, but also consider seat utilization, future profit, and hidden profit. Firstly, we propose a deep evaluation network to evaluate factors that affect vehicle dispatch, and we exploit the reinforcement learning strategy to train the deep evaluation network. Then, we propose the dynamic external factor calculation (DEFC) algorithm to calculate those factors. Secondly, to calculate the driver's future profit, we propose a prediction model based on the K-Nearest Neighbor(KNN) to predict the number of passengers and vehicles. Thirdly, we use a vehicle search method to search vehicles that satisfy passenger spatial-temporal constraint. Based on a real-world dataset in Rome, we evaluate our algorithm to confirm the effectiveness of our method.

关键词： Search methods Heuristic algorithms Urban areas Reinforcement learning Predictive models Prediction algorithms Dispatching

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real-time Remote Sensing Video Compression for On-orbit Heterogeneous Platform

Real-time Remote Sensing Video Compression for On-orbit Hete...

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2019 IEEE International Conference on Signal, information and Data processing, ICSIDP 2019

作者： Duan, Chenhui Tang, Linbo Li, Chen Wu, Chen Li, Cheng Zhao, Baojun Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing100081 China School of Computer Science North China University of Technology Beijing100144 China

ISBN: (纸本)9781728123455

With the rapid advancement of optical remote sensing (RS) satellites, RS video has replaced RS image as the main way to obtain information in orbit environment. However, the storage and transmission of on-orbit RS video faces enormous challenges: the contradiction between limited power, space and bandwidth and massive video data volume is becoming increasingly acute. To solve this problem, this paper proposes an effective implementation of spaceborne real-time RS video compression based on CPU+GPU heterogeneous framework. First, we propose a new rate control method based on HEVC for on-orbit RS video compression, which allocates less bitrate for low complexity code blocks (CBs) and more bitrate for high complexity CBs, which can improve the accuracy of rate control algorithm while ensuring compression quality. Then this paper makes full use of the CPU + GPU heterogeneous framework to speed up the whole compression processing. The experimental results show that the proposed compression framework realizes 200x real-time compression of 1080p spaceborne RS video with a PSNR greater than 38.7 dB. © 2019 IEEE.

关键词： Orbits

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Terminal bulb segmentation of caenorhabditis elegans under small samples based on two-stage u-net network 3

Terminal bulb segmentation of caenorhabditis elegans under s...

引用

3rd International Conference on Computer Science and Artificial Intelligence, CSAI 2019 and its Workshop - The 11th International Conference on information and Multimedia technology, ICIMT 2019

作者： Cao, Si Liu, Jun Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial Department of Computer Science and Technology Wuhan University of Science and technology Wuhan430070 China

ISBN: (纸本)9781450376273

Convolutional neural network based on machine learning has become one of the most popular methods of image recognition and segmentation, but it needs huge data samples to get better results. In this study, based on U-Net network, a two-stage convolution neural network method for automatic segmentation of Terminal bulb of Caenorhabditis elegans in small samples was proposed. The method solves the problem that traditional singlestage network cannot be implemented in small samples. The Dice coefficient reaches 89.5%, which is higher than that of the baseline U-Net method. © 2019 Association for Computing Machinery.

关键词： Computer vision

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A Hybrid Firefly Algorithm Based on Orthogonal Opposition

A Hybrid Firefly Algorithm Based on Orthogonal Opposition

引用

International Joint Conference on Neural Networks (IJCNN)

作者： YingYing Ge Jun Li ChenYing Meng College of Computer Science and Technology Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Firefly Algorithm (FA) may suffer from lower convergence accuracy when solving high-dimensional and complex optimization problems. To solve this problem, a completely new strategy named Hybrid Firefly Algorithm Based on Orthogonal Opposition (OHFA) is proposed. In OHFA, we perform differential evolution (DE) on brighter fireflies (j) and orthogonal opposition-based learning (OOBL) on globally optimal firefly to improve the search ability of the population. Besides, in high-dimensional and large-scale search space, there is an obvious long Euclidean distance between fireflies, which reduces attraction in movement. Therefore, OHFA adopts a new movement to improve the application of the firefly algorithm in high-dimensional space. Computational results show the effectiveness of OOBL and DE. Our findings suggest that OHFA achieves better solutions than other proposed algorithms on most of the test functions.

关键词： Sociology Statistics Convergence Brightness Optimization Computational modeling

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Research on point aggregation algorithm based on WEBGL 3

Research on point aggregation algorithm based on WEBGL

引用

3rd IEEE International Conference on Electronic information technology and Computer Engineering, EITCE 2019

作者： Chen, Jinghan Chen, Jianxun Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System College of Computer Science and Technology Wuhan China

ISBN: (数字)9781728135847

ISBN: (纸本)9781728135847

WebGL has the advantages of online interaction and hardware accelerated rendering to serve web maps. When there are a large number of spatial points in the visible area of the map that need to be visually expressed, the method of extracting and abstracting the distribution rules and intensiveness of the local spatial points can be achieved through technical synthesis of these spatial points, so as to achieve an objective and comprehensive display space by using limited areas. This paper focuses on the related technologies and principles of WebGL-based point aggregation visualization. The advantages and disadvantages of the algorithm are analyzed according to the aggregation process of the algorithm, and the algorithm is optimized. © 2019 IEEE.

关键词： Visualization

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Design and implementation of CNN custom processor based on risc-v architecture 21

Design and implementation of CNN custom processor based on r...

引用

21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Li, Zhenhao Hu, Wei Chen, Shuang College of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing Real-time Industrial System China

ISBN: (纸本)9781728120584

With the rapid development of CNN(convolutional neural networks), the traditional CPU platform can not make full use of the parallelism of CNN. We decide to adopt a new and popular processor architecture: the risc-v architecture for experimental design. In this paper, a new convolutional neural network processor is designed based on risc-v architecture. The processor can take advantage of the parallelism of CNN and is more flexible. This paper completely designed a CNN processor based on the risc-v architecture. The processor uses a classic five-stage pipeline structure, and implements instruction buffer memory and data buffer memory, and adds peripherals such as FLASH, SRAM, and SDRAM. And, this paper designed custom instructions. Given the convolution operation frequently occurring in CNN, vector store instruction, vector load instruction, vector addition instruction, and convolution operation instruction are designed to accelerate the execution of the convolution process. The design has passed the simulation experiment. It can not only complete the general instructions but also run the custom instructions. The final simulation test verified the correctness of the design. © 2019 IEEE.

关键词： Convolution

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Data Analysis and Planning Analysis Management System Based on Tensorflow Machine Learning Algorithms

Data Analysis and Planning Analysis Management System Based ...

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2019 International Conference on Artificial Intelligence Technologies and Applications, ICAITA 2019

作者： Hu, Shuang Huang, Li Bai, Feng Zhang, Jun School of Computer Science and Technology Wuhan University of Science and Technology China Intelligent Information Processing and Real-time Industrial System Hubei Key Laboratory Institute China

At present, machine learning is quite popular, and tensorflow framework is also very popular. This system takes the plan management system as the practice platform, realizes the data analysis function through the machine learning understanding and the key point. Most of the current market are planning management system, but there is no self-analysis ability, need to base on data, there are limitations. Firstly, the system collects training data such as weight, body fat rate and so on, then builds a deep learning neural network, and finally runs the model to realize plan analysis, so as to help users analyze the feasibility of the plan and provide suggestions for users, and the analysis results will be displayed through processing. © 2019 IOP Publishing Ltd. All rights reserved.

关键词： Learning algorithms

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