检索结果-内蒙古大学图书馆

IET International Radar Conference 2020, IET IRC 2020

作者： Chang, Jiahui Fu, Zewen Li, Ning Chang, Jike College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng475004 China

ISBN: (纸本)9781839535635

Synthetic Aperture Radar (SAR) Systems can be used for discovery and surveillance. According to the current requirements, accurate ship detection is becoming more and more important. However, the accuracy of traditional ship detection algorithm is not satisfying in complex scene. As a consequence, this paper proposes a novel method for detecting ships within SAR imagery using Gabor feature and Two Parameter Constant False Alarm Rate (2P-CFAR) detection technique. First, feature extraction was performed by using Gabor filter. Then, combined with the extracted orientation feature, the ship recognition of SAR image is obtained by 2P-CFAR detector. Finally, false alarms among the candidate targets were removed by geometric features of the ships. To prove the rationality of this method, experiments were carried with two high-resolution Gaofen-3 satellite SAR images in open sea background. The experimental results show that the presented method has preferable performance in the accuracy of ship identification. © 2020 Institution of Engineering and Technology. All rights reserved.

关键词： Synthetic aperture radar

来源：评论

学校读者我要写书评

暂无评论

Boosting Fine-Grained Visual Anomaly Detection with Coarse-Knowledge-Aware Adversarial Learning

arXiv

引用

arXiv 2024年

作者： Fang, Qingqing Su, Qinliang Lv, Wenxi Xu, Wenchao Yu, Jianxing School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Department of Computing The Hong Kong Polytechnic University Hong Kong School of Artificial Intelligence Sun Yat-sen University Guangdong China Pazhou Lab Guangzhou510330 China

Many unsupervised visual anomaly detection methods train an auto-encoder to reconstruct normal samples and then leverage the reconstruction error map to detect and localize the anomalies. However, due to the powerful modeling and generalization ability of neural networks, some anomalies can also be well reconstructed, resulting in unsatisfactory detection and localization accuracy. In this paper, a small coarsely-labeled anomaly dataset is first collected. Then, a coarse-knowledge-aware adversarial learning method is developed to align the distribution of reconstructed features with that of normal features. The alignment can effectively suppress the auto-encoder’s reconstruction ability on anomalies and thus improve the detection accuracy. Considering that anomalies often only occupy very small areas in anomalous images, a patch-level adversarial learning strategy is further developed. Although no patch-level anomalous information is available, we rigorously prove that by simply viewing any patch features from anomalous images as anomalies, the proposed knowledge-aware method can also align the distribution of reconstructed patch features with the normal ones. Experimental results on four medical datasets and two industrial datasets demonstrate the effectiveness of our method in improving the detection and localization performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Convolutional Long-Short Term Memory Network for convective weather prediction based on 3D Doppler radar data

Convolutional Long-Short Term Memory Network for convective ...

引用

2020 International Conference on Cyberspace Innovation of Advanced Technologies, CIAT 2020

作者： Shen, Xiajiong Meng, Kunying Guo, Zhongli Zhang, Lei Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China Southwest Regional Air Traffic Management Bureau Caac Senior Engineer of Aviation Meteorology

ISBN: (纸本)9781450387828

Convectional weather is one of the weather which often occurs during the warm season, the scope of this kind of weather is normally small, short duration, etc, so it is difficult to predicted, the effects of convectional weather are very large, from the national economy to military activities will be threatened by it, so how to achieve more accurate of convection weather forecast will be of great significance. Doppler radar is one of the main tools for monitoring and early warning severe convective weather. It can provide important strong convective information for prediction. through its observed real-time echo intensity (reflectivity factor Z), radial velocity (V) and velocity spectrum width (W). Echo intensity is an important basis for judging whether severe, convective weather occurs, and radial velocity can identify wind damage and is also an important basis for judging the occurrence of severe convective weather. From the radial velocity, we can see the convergence, divergence and rotation characteristics of airflow, which are closely related to the occurrence and development of severe convective weather. Therefore, Doppler radar data plays a very important role in the detection and prediction of severe weather. At present, there are still problems about how to effectively extract these information from Doppler radar data. This paper proposes a method of near prediction based on deep learning. The neural network is used to automatically learn data features for prediction. Convolution neural network is combined with long-short-time memory network. Firstly, a three-dimensional convolution is proposed to extract the spatial features of three-dimensional original data, and then the long-short-time memory network is used to extract the features of data in time dimension, thus improving the prediction accuracy. Deep learning method avoids the process of manually extracting data features, and uses historical 3D Doppler radar image data to predict convective weather f

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Load balance and time delay efficient algorithm for VNF service chain deployment based on DQN

Load balance and time delay efficient algorithm for VNF serv...

引用

International Conference on Computational Intelligence and Security

作者： Hejun Xuan Lidan Lin Zhenghui Liu Xuelin Zhao Department of Computer and Information Technology Xinyang Normal University Henan Key Laboratory of Analysis and Applications of Education Big Data Xinyang Normal University Henan Xinyang China Department of Information Engineering Luo He Vocational Technology College Henan Luohe China

In mobile edge computing, computing and storage resources are close to the network of the user device, which can effectively reduce network access and computing service delay. However, network function virtualization for mobile edge computing networks is more challenging due to the strong coupling of CPU resources, storage resources, and wireless resources. In order to solve the problems of load balance and long response time of user-oriented service function chain deployment algorithm in mobile edge computing networks, this paper proposed a load balance and time delay efficient algorithm (denoted as LBDQN) for virtual network function service chain deployment problem based Deep Q-LearningFirst, a global optimization model, which minimizes time delay and maximizes the load balance, is established. Then, a new real-time algorithm for VNF service chain deployment based on DQN is proposed. In this algorithm, an adaptive ε-greedy method is designed. In addition, a particle swarm optimization is used to optimize the neural network parameters. To demonstrate the efficiency of the proposed algorithm, some simulation experiments are conducted and the experimental results show that the algorithm can effectively reduce the time delay and improve the load balance than the compared algorithms.

关键词： Wireless communication Adaptation models Multi-access edge computing Service function chaining Delay effects Computational modeling Neural networks

来源：评论

学校读者我要写书评

暂无评论

A Metaheuristic with Learning Mechanism for Solving the Multi-school Heterogeneous School Bus Routing Problem

IAENG International Journal of Computer Science

引用

IAENG International Journal of Computer Science 2021年第4期48卷 884页

作者： Xie, Yi Kong, Yunfeng Xiang, Hongzhen Hou, Yan-e Han, Daojun Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions Henan University China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475000 China Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions Henan University Kaifeng475000 China College of Computer and Information Engineering Henan University Kaifeng475000 China

This paper presents a metaheuristic with online learning (MOL) to solve the multi-school heterogeneous-fleet school bus routing problem (MHSBRP). In the iterated local search (ILS) metaheuristic framework, an online learning mechanism is integrated with the neighborhood search heuristic. It evaluates the performance of search operators according to the historical search information, and then adjusts the selection probability of the operators in the following loops of search. The proposed algorithm is tested on a set of benchmark instances. The solution results show that MOL solves both mixed-load and single-load instances of MHSBRP effectively, and outperforms the ILS significantly. Compared with the algorithm without learning mechanism, MOL could improve the convergence of neighborhood search and the quality of solution. © 2021. IAENG International Journal of Computer Science. All Rights Reserved.

关键词： Benchmarking

来源：评论

学校读者我要写书评

暂无评论

Quantized Tensor Neural Network

引用

ACM/IMS Transactions on data Science 2021年第4期2卷 1–18页

作者： Gao, Yuan Yang, Laurence T. Zheng, Dehua Yang, Jing Zhao, Yaliang School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Computer Science and Technology Huazhong University of Science and Technology and Department of Computer Science St. Francis Xavier University AntigonishNSB2G2W5 Canada Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Henan University and with Department of Computer Science St. Francis Xavier University Antigonish Canada

Tensor network as an effective computing framework for efficient processing and analysis of high-dimensional data has been successfully applied in many fields. However, the performance of traditional tensor networks still cannot match the strong fitting ability of neural networks, so some data processing algorithms based on tensor networks cannot achieve the same excellent performance as deep learning models. To further improve the learning ability of tensor network, we propose a quantized tensor neural network in this article (QTNN), which integrates the advantages of neural networks and tensor networks, namely, the powerful learning ability of neural networks and the simplicity of tensor networks. The QTNN model can be further regarded as a generalized multilayer nonlinear tensor network, which can efficiently extract low-dimensional features of the data while maintaining the original structure information. In addition, to more effectively represent the local information of data, we introduce multiple convolution layers in QTNN to extract the local features. We also develop a high-order back-propagation algorithm for training the parameters of QTNN. We conducted classification experiments on multiple representative datasets to further evaluate the performance of proposed models, and the experimental results show that QTNN is simpler and more efficient while compared to the classic deep learning models. © 2022 Association for Computing Machinery.

关键词： Tensors

来源：评论

学校读者我要写书评

暂无评论

Cascaded Aggregation Convolution Network for Saliency Detection Of Stored-Grain Pests

SSRN

引用

SSRN 2023年

作者： Yu, Junwei Zhai, Fupin Liu, Nan Key Laboratory of Grain Information Processing and Control Ministry of Education Zhengzhou450001 China School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou450001 China College of Information Scinence and Engineering Henan University of Technology Zhengzhou450001 China PLA SSF Information Engineering University Zhengzhou450001 China

The behaviors exhibited by stored grain pests, including feeding, respiration, excretion, and reproduction, engender multifarious threats to grain storage. Consequently, it is imperative to diligently undertake the task of detecting and effectively controlling these pests. At present, Great progress has made in both traditional methods and deep learning techniques for salient object detection. It is still a challenge, especially for small objects and non-salient images, biological vision. This paper presents a grain pests detection method based on cascaded strous convolution . We collet(collect) a novel dataset GrainPest, which comprises 500 images covering various salient pests in grains. We develop a simple and effective framework named cascaded aggregation convolution network(CACNet). CACNet outperforms various state-of-the-art object detection baselines on all datasets tested, making it a robust, general framework that can help facilitate future research in COD. Finally, we conduct a large-scale COD study, evaluating 13 cutting-edge models, providing some interesting findings, and showing several potential applications. Our research offers the community an opportunity to explore more in this new field. © 2023, The Authors. All rights reserved.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

A Blockchain-Based Multi-Cloud Storage data Consistency Verification Scheme

A Blockchain-Based Multi-Cloud Storage Data Consistency Veri...

引用

IEEE International Conference on big data and Cloud Computing (BdCloud)

作者： Feiyu Wang Jian-tao Zhou Hao Wang Xu Guo College of Computer Science Inner Mongolia University Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China

Users worldwide widely use cloud storage because of its efficiency, convenience, and high availability. Multi-cloud storage is usually selected to ensure the high availability of data. Unfortunately, when data is migrated and replicated between multi-cloud data centers, it is not easy to guarantee data con-sistency. This paper proposes an efficient, secure, and new data consistency verification scheme using blockchain technology. In order to reduce the computation and communication overhead in the verification process, our scheme uses encrypted tags to build Merkle hash tree to generate unique and lightweight verification proofs and does not use third-party auditors. The final theoretical and experimental analysis shows that our scheme has higher security and a faster verification process in multi-cloud storage.

关键词： Cloud computing data centers Costs Blockchains Behavioral sciences

来源：评论

学校读者我要写书评

暂无评论

Design of Safety Production Supervision and data Management System Based on Cloud Platform

Design of Safety Production Supervision and Data Management ...

引用

International Conference on Applications and Techniques in Cyber Intelligence, ATCI 2019

作者： Xu, Hongsheng Fan, Ganglong Luoyang Normal University Luoyang471934 China Henan Key Laboratory for Big Data Processing and Analytics of Electronic Commerce Luoyang471934 China

ISBN: (纸本)9783030251277

Safety production refers to the accident prevention and control measures in the production and operation activities, in order to avoid causing personnel injury and property damage accident. This paper firstly analyzes the performance and function of safety production supervision and management system, and points out the problems existing in the existing security supervision and management system. big data analysis technology is an effective way to solve these problems and can be timely and accurate detection of hidden dangers, improve the ability of the investigation and management. This paper presents design of safety production supervision and data management system based on cloud platform. The system is the unified support system platform based on cloud computing and intelligent terminal technology, and can improve the efficiency of data collection and realize the prediction and early warning of the real by big data technology. © 2020, Springer Nature Switzerland AG.

关键词： big data

来源：评论

学校读者我要写书评

暂无评论

Extraction Method of Classification Rules in Decision Tree Based on Attribute Selection Metric of Rough Set

Extraction Method of Classification Rules in Decision Tree B...

引用

International Conference on Applications and Techniques in Cyber Intelligence, ATCI 2019

作者： Wang, Lan Xu, Hongsheng Luoyang Normal University Luoyang471934 China Henan Key Laboratory for Big Data Processing & Analytics of Electronic Commerce Luoyang471934 China

ISBN: (纸本)9783030251277

In this paper, the kernel algorithm of decision tree is used as a tool, and on the basis of constructing a good data source group for classification, the characteristics of decision tree classification algorithm are analyzed and experimented. This paper introduces a rough set attribute selection metric, which selects attributes for classification from the point of view of improving the accuracy of classification and the purity of sub-databases. Association rule mining is the mining of interesting association rules or relationships between indexes (items) in the database. The paper present extraction method of classification rules in decision tree based on attribute selection metric of rough set. © 2020, Springer Nature Switzerland AG.

关键词： Association rules

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：