检索结果-内蒙古大学图书馆

Ship Trajectory Prediction Based on BP Neural Network

Journal on Artificial Intelligence 2019年第1期1卷 29-36页

作者： Hai Zhou Yaojie Chen Sumin Zhang School of Computer Science and Technology Wuhan University of Science and TechnologyWuhan430065China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and TechnologyWuhan430065China School of Information Systems and Technology Drexel UniversityPhiladelphia19104The United States

In recent years,with the prosperity of world trade,the water transport industry has developed rapidly,the number of ships has surged,and ship safety accidents in busy waters and complex waterways have become more *** the movement of the ship and analyzing the trajectory of the ship are of great significance for improving the safety level of the *** at the multi-dimensional characteristics of ship navigation behavior and the accuracy and real-time requirements of ship traffic service system for ship trajectory prediction,a ship navigation trajectory prediction method combining ship automatic identification system information and Back Propagation(BP)neural network are *** to the basic principle of BP neural network structure,the BP neural network is trained by taking the characteristic values of ship navigation behavior at three consecutive moments as input and the characteristic values of ship navigation behavior at the fourth moment as output to predict the future ship navigation *** on the Automatic Identification System(AIS)information of the waters near the Nanpu Bridge in Pudong New Area,Shanghai,the results show that the method is used to predict the ship's navigational behavior eigenvalues accurately and in real *** with the traditional kinematics prediction trajectory method,the model can effectively predict ship *** trajectory improves the accuracy of the ship's motion situation prediction,and has the advantages of high computational efficiency and strong versatility,and the error is within an acceptable range.

关键词： Maritime safety back propagation neural network automatic identification system track prediction

来源：评论

学校读者我要写书评

暂无评论

Synchronization of complex dynamic networks with coupling delays via quantized pinning control

Synchronization of complex dynamic networks with coupling de...

引用

第三十八届中国控制会议

作者： Hui Chen Ming-Feng Ge Teng-Fei Ding Kai Zhang Juan-Juan He 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 School of Mechanical Engineering and Electronic Information China University of Geosciences

In this paper, in the case of quantized communication and coupling delays, a novel quantized pinning control(QPC)law is designed to deal with the synchronization problem of complex dynamic networks(CDNs). Utilizing the Lyapunov method,non-smooth analysis tools and convex combination techniques, some sufficient criteria for achieving synchronization of CDNs with coupling delays are given. Finally, the effectiveness of the main results is demonstrated by presenting a simulation example.

关键词： Complex dynamic network Coupling delay Quantized pinning control Synchronization

来源：评论

学校读者我要写书评

暂无评论

Research on computer Network Teaching Reform Based on Simulation Software

引用

Journal of Physics: Conference Series 2020年第1期1654卷

作者： Jiong Wang Hua Zhang Zhengyuan Wu Ge Xu College of Computer and Control Engineering Minjiang University Fuzhou 350108 China and Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University)

This paper first analyzes the concept and development status of computer network technology, then expounds the problems existing in computer network practice teaching, and finally makes an in-depth analysis on how to formulate strategies to solve the problems existing in computer network practice teaching, including the detailed discussion and planning on the reform of course examination methods, the improvement of course setting and the study of software and hardware. Based on the current situation of computer network development in China, the practical teaching of computer network is continuously improved, which makes this course carry out smoothly in teaching.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Multisynchronization of a class of delayed memristor-based neural networks

Multisynchronization of a class of delayed memristor-based n...

引用

第三十八届中国控制会议

作者： Ya-Qi Lin Ming-Feng Ge Teng-Fei Ding Ziqi Zhu Juanjuan He 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 School of Mechanical Engineering and Electronic Information China University of Geosciences

This paper investigates the multisynchronization problem of a new class of delayed memristor-based neural networks(DMNNs). To address this problem, a class of feedback controller is designed. By employing the Lyapunov stability theory,the corresponding sufficient conditions for achieving multisynchronization of DMNNs with the presented feedback control are derived. It implies that multiple agreements of system responses are obtained via the dynamical evolution of the controlled DMNNs. Finally, a simulation experiment is presented to verify the validity and feasibility of the main result of the DMNNs.

关键词： Delayed memristor-based neural networks Multisynchronization Multiple agreement systems Feedback controller

来源：评论

学校读者我要写书评

暂无评论

New Pruning Methods for Mining Minimal Functional Dependencies from Large-Scale Distributed Data

New Pruning Methods for Mining Minimal Functional Dependenci...

引用

International Conference on Advanced Cloud and Big Data (CBD)

作者： Feng Cheng Zhe Yang Information Processing Technology Provincial Key Laboratory for Computer Soochow University Suzhou Jiangsu China

Extracting minimal functional dependencies (MFDs) from relational databases is an import database analysis technique. With the advent of big data era, it is challenging to discover MFDs from big data, especially large-scale distributed data stored in many different sites. The key to discovering MFDs as fast as possible is pruning the useless candidate MFDs. And in most existed algorithms, it usually prunes candidate MFDs from top to bottom or from bottom to top. We present a new algorithms FastMFDs for discovering all MFDs from large-scale distributed data both from top to bottom and from bottom to top in parallel. We experimented our algorithm in real-life datasets, and our algorithm is more efficient and faster than the existed discovering algorithms.

关键词： Distributed databases Big Data Search problems Partitioning algorithms Relational databases

来源：评论

学校读者我要写书评

暂无评论

Optimal protein structure extraction with clustering algorithm 13

Optimal protein structure extraction with clustering algorit...

引用

13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018

作者： Zhao, Jiafu Zhang, Xiaolong Hubei, Min Cao Key Laboratory of Intelligent Information Processing and Real-time Industrial System School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China

ISBN: (纸本)9781538637579

In this paper, we use K-means++ and AP algorithm to cluster the five protein similarity measures of RMSD, TM, MaxSub, GDT-TS and GDT-HA. As for the selection of the number of clusters, using the measures of Scikit-learn to value the cluster result to attain an optimal number of clusters. Also, we optimize the AP algorithm through the change of the preference value, whose value changes from the general mean value, max value, min value to mean value of 4 neighbour at the corresponding position. Moreover, we propose a cluster center selection algorithm based on mean distance from data points to cluster center and number of data points in a cluster, which could automatically delete exception value, thus improve the accuracy of selection cluster center. After the cluster and selection of cluster center, we get better similarity between protein structure and natural protein compare to the earlier results, which means a lot to the protein structure prediction. © 2018 IEEE.

关键词： Proteins

来源：评论

学校读者我要写书评

暂无评论

Salient Object Detection in Low Contrast Images via Global Convolution and Boundary Refinement

Salient Object Detection in Low Contrast Images via Global C...

引用

IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Nan Mu Xin Xu Xiaolong Zhang School 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 Wuhan University of Science and Technology China

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Benefit from the powerful features created by using deep learning technology, salient object detection has recently witnessed remarkable progresses. However, it is difficult for a deep network to achieve satisfactory results in low contrast images, due to the low signal to noise ratio property, thus previous deep learning based saliency methods may output maps with ambiguous salient objects and blurred boundaries. To address this issue, we propose a deep fully convolutional framework with a global convolutional module (GCM) and a boundary refinement module (BRM) for saliency detection. Our model drives the network to learn the local and global information to discriminate pixels belonging to salient objects or not, thus can produce more uniform saliency map. To refine the localization and classification performance of the network, five GCMs are integrated to preserve more spatial knowledge of feature maps and enable the densely connections with classifiers. Besides, to propagate saliency information with rich boundary content, a BRM is embed behind each convolutional layer. Experiments on six challenging datasets show that the proposed saliency model achieves state-of-the-art performance compared to nine existing approaches in terms of nine evaluation metrics.

关键词： Convolution Feature extraction Object detection Machine learning Semantics Kernel Visualization

来源：评论

学校读者我要写书评

暂无评论

Visual Saliency Based Ship Extraction Using Improved Bing

Visual Saliency Based Ship Extraction Using Improved Bing

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yihua Tan Hao Liang Zengrong Guan Airong Sun National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial of Automation Huazhong University of Science and Technology Wuhan China School of Computer Science Wuhan Institute of Technology Wuhan China

In this paper, a novel ship extraction algorithm is proposed to acquire the precise segmentation. First, Binarized Normed Gradients (BING) is improved to locate the potential ship regions according to the remote sensing application. Second, the visual saliency computation by combining Hypercomplex Frequency Domain Transform (HFT) and Phase Quaternion Fourier Transform (PQFT) presented to analyze the located regions. Third, segmentation on the computed saliency map is conducted to extract precise blobs of the ship candidates. Finally, the prior knowledge about ship is utilized to remove the false alarms in the discrimination stage. The experimental results of typical remote sensing images show that the algorithm is very effective.

关键词： Marine vehicles Proposals Remote sensing Visualization Microsoft Windows Object detection Fourier transforms

来源：评论

学校读者我要写书评

暂无评论

Prediction of σ54 promoters in prokaryotes based on SVM–Adaboost

Prediction of σ54 promoters in prokaryotes based on SVM–Ad...

引用

Chinese Automation Congress (CAC)

作者： Yongxian Fan Qingqi Zhu Chengwei Lv Xianyong Pan School of Computer and Information Security Guilin University of Electronic Technology Guilin Guangxi Key Laboratory of System Control and Information Processing Ministry of Education of China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

σ 54 promoters are responsible for transcriptional carbon and nitrogen in prokaryotes. However, it is costly and difficult by experimental identification of them, especially in the postgenomic era with avalanche of sequencing data. Thus, it is imperative to develop efficiently and rapidly computational algorithms to identify the σ 54 promoters. In this study, a novel predictor named SVM-Adaboost was developed to predict σ 54 promoters from sequences alone, it used the Adaboost algorithm as the core, and support vector machine (SVM) as weak base predictors. SVM-Adaboost integrated SVM predictors to construct a more powerful and robust ensemble predictor. In SVM-Adaboost, we used pseudo k-tuple nucleotide composition method to encode DNA sequences, and then a feature selection method was used to further select the discriminate features for subsequent classification. We strictly evaluate the SVM-Adaboost on a constructed gold-standard σ 54 promoter dataset using ten-fold cross validation 100 times, and achieved an average accuracy of 96.06%.

关键词： DNA Support vector machines Feature extraction Training Prediction algorithms Benchmark testing Computational modeling

来源：评论

学校读者我要写书评

暂无评论

License Plate Recognition in Diversified Situations Using Robust L-GEM-Based RBFNN

License Plate Recognition in Diversified Situations Using Ro...

引用

International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)

作者： Yi Zhu Wendi Li Ting Wang Junwen Li Wing W.Y. N.G. Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information School of Computer Science and Engineering South China University of Technology Guangzhou China South China University of Technology Guangzhou China

The most critical step in license plate recognition tasks is the identification of individual character image from the license plate image segments. Conventional methods of recognizing a character including Support Vector Machine (SVM) and neural network require the training using many license plate images. However, the amount of training data is limited and there are many unseen situations, where the generalization capability of a trained classifier is usually limited. If the license plate image distortion is serious due to either weather conditions or technical reasons of photographing, accuracy of these methods will be greatly reduced. Therefore a robust license plate recognition method is proposed using a Radial Basis Function Neural Network (RBFNN) trained via a minimization of the localized generalization error model (L-GEM). The L-GEM provides the upper bound of the generalization capability of an RBFNN with respect to a given training data set. Therefore, the trained RBFNN yields a better generalization capability and a higher recognition rate for new unseen samples. Experimental results show that RBFNNs trained by minimizing the L-GEM always yield the highest accuracy in diversified situations, such as rainy and snowy conditions.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：