检索结果-内蒙古大学图书馆

2019第十一届数字图像处理国际会议

作者： Jinxi Peng Yuanqi Su Xiaorong Xue Donghong Song Xiaoyong Xue Department of Computer Science and Technology & AI Xi'an JiaoTong University Department of AI & software Engineering South China institute of software Engineering Guangzhou University The School of Computer and Information Engineering Anyang Normal University The Middle School of Luonan County

[Purpose]To take the advantages of a variety of remote sensing data,the application of remote sensing image classification is a very important *** sensing image classification is large in computing capacity and time-consuming,and with the development of modern remote sensing technology,the amount of various remote sensing data obtained is getting larger and larger,the issue of how to fuse remote sensing image quickly and accurately and of getting useful information is becoming more and more urgent especially in some remote sensing applications such as disaster monitoring,prevention and relief,*** this paper,in order to fuse remote sensing image quickly and accurately,a parallel classification algorithm of multi-spectral image and panchromatic image based on wavelet transform is proposed.[Methods]In the method,based on parallel computing,the low-frequency components of wavelet decomposition are fused with the classification rule based on the feature matching,and the high-frequency components of wavelet decomposition are fused with the classification rule based on the sub-region *** the low-frequency components and the high-frequency components after classification are processed with the inverse wavelet transform,and the fused image is *** to the statistical characteristics of SAR images and the semantics of fuzzy neural networks analysis,an efficient image segmentation method based on Deep Learning Semantic analysis and wavelet transform is proposed to achieve precision of classification.[Results] The experiment results show that the proposed method can get better classification results and faster computing speed for multi-spectral image and panchromatic *** the proposed classification algorithm of multispectral image and panchromatic image,wavelet transform and different proper classification rules for low-frequency components and high-frequency components of wavelet decomposition are *** get a high speed,pa

关键词： remote sensing image classification panchromatic image multi-spectral image wavelet transform parallel computing Data clustering

来源：评论

学校读者我要写书评

暂无评论

Modeling of Layered Supply Chain Network Considering Similarity

Modeling of Layered Supply Chain Network Considering Similar...

引用

第32届中国控制与决策会议

作者： Yi Zhang Yurong Song Guoping Jiang Bin Yu School of Computer Science Software and Network Space SecurityNanjing University of Posts and Telecommunications School of Automation Nanjing University of Posts and Telecommunications Jiangsu Engineering Lab for IOT Intelligent Robots(IOTRobot) Hengyang Key Laboratory of Information Secure and its application research

The supply chain network is a complex network with the risk of cascading *** study the cascading failure in it,an accurate supply chain network model needs to be *** this paper,we construct a layered supply chain network model according to the types of companies in real supply chain *** first define the similarity between companies in the same layer by studying real-world scenarios in supply chain ***,considering both the node degree and the similarity between nodes in the same layer,we propose preferential attachment probability formulas for the new nodes to join the exist ***,the evolution steps of the model are *** analyze the structural characteristics of the new *** results show that the new model has scale-free property and small-world property,which conform to the structural characteristics of the known supply chain *** with the other network models,it is found that the new model can better describe the actual supply chain network.

关键词： Supply Chain Network Cascading Failure Similarity BA Model Structural Characteristics

来源：评论

学校读者我要写书评

暂无评论

Generating Reliable Friends via Adversarial Training to Improve Social Recommendation

Generating Reliable Friends via Adversarial Training to Impr...

引用

IEEE International Conference on Data Mining (ICDM)

作者： Junliang Yu Min Gao Hongzhi Yin Jundong Li Chongming Gao Qinyong Wang School of Information Technology and Electrical Engineering The University of Queensland School of Big Data and Software Engineering Chongqing University Department of Electrical and Computer Engineering University of Virginia Department of Computer Science and School of Data Science University of Virginia

Most of the recent studies of social recommendation assume that people share similar preferences with their friends and the online social relations are helpful in improving traditional recommender systems. However, this assumption is often untenable as the online social networks are quite sparse and a majority of users only have a small number of friends. Besides, explicit friends may not share similar interests because of the randomness in the process of building social networks. Therefore, discovering a number of reliable friends for each user plays an important role in advancing social recommendation. Unlike other studies which focus on extracting valuable explicit social links, our work pays attention to identifying reliable friends in both the observed and unobserved social networks. Concretely, in this paper, we propose an end-to-end social recommendation framework based on Generative Adversarial Nets (GAN). The framework is composed of two blocks: a generator that is used to produce friends that can possibly enhance the social recommendation model, and a discriminator that is responsible for assessing these generated friends and ranking the items according to both the current user and her friends' preferences. With the competition between the generator and the discriminator, our framework can dynamically and adaptively generate reliable friends who can perfectly predict the current user' preference at a specific time. As a result, the sparsity and unreliability problems of explicit social relations can be mitigated and the social recommendation performance is significantly improved. Experimental studies on real-world datasets demonstrate the superiority of our framework and verify the positive effects of the generated reliable friends.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Density peaks based clustering for single-cell interpretation via multikernel learning

引用

Procedia computer science 2019年 147卷 71-76页

作者： Samina Kausar Rashid Mehmood Muhammad Shahid Iqbal Rongfang Bie Shujaat Ali Yasir Shabir School of Computer Engineering and Science Shanghai University Shanghai 200444 China Department of Software Engineering University of Kotli AJ & K Pakistan School of Computer Science and Technology Anhui University Hefei China College of Information Science and Technology Beijing Normal University Beijing 100875 China

The development in single-cell technology has enabled to quantify the high throughput gene expressions of individual cell, and it became possible to discover heterogeneity at cell level. To detect heterogeneity within cell population remains challenging in presence of outliers, biological noise, and dropouts. SIMLR (single-cell interpretation via multikernel learning) has been proposed to measure cell to cell similarity, dimensional reduction, clustering, and visualization of scRNA-seq data. SIMLR uses K-means to organize the cells into the predefined number of types, which is a significant drawback of SIMLR toward adaptive analysis of scRNA-seq data. In this paper, we introduced density peaks based clustering for single-cell interpretation via multikernel learning (DP-SIMLR), an adaptive approach to discover biological meaningful heterogeneity within the individual cell population. The DP-SIMLR is an extension of SIMLR, where the concept of density peaks is employed to discover heterogeneity within the cell population, adaptively. We have evaluated the DP-SIMLR on four scRNA-seq datasets and the results are compared with SIMLR.

关键词： Data mining single cell RNA-seq clustering

来源：评论

学校读者我要写书评

暂无评论

Secure high capacity data hiding scheme based on reference matrix

引用

International Journal of Network Security 2019年第6期21卷 918-929页

作者： Li, Xiao-Shuang Chang, Chin-Chen He, Ming-Xing Lin, Chia-Chen School of Computer and Software Engineering Xi Hua University Taichung433 Taiwan Department of Information Engineering and Computer Science Feng Chia University Taichung433 Taiwan Department of Computer Science and Information Management Providence University Taichung433 Taiwan

In this paper, a secure high-capacity data hiding scheme based on a reference matrix is proposed. With the help of the numbering reference matrix and a look-up table, each pixel pair of a cover image can conceal 6 secret bits, which offers 2 extra secret bits than Liu et al.'s scheme, while maintaining the average PSNR up to 41.97 dB. Experimental results confirm that our proposed scheme out-performs previous data hiding schemes in visual quality and hiding capacity. Moreover, statistical analysis confirms that the hidden data can be securely protected. © 2019 Femto Technique Co., Ltd.

关键词： Table lookup

来源：评论

学校读者我要写书评

暂无评论

An approach to deep learning service provision with elastic remote interfaces 5th

An approach to deep learning service provision with elastic ...

引用

5th International Conference on Artificial Intelligence and Security, ICAIS 2019

作者： Sun, Mingxu Yang, Zhiyun Wu, Hao Liu, Qi Liu, Xiaodong School of Electrical Engineering University of Jinan Jinan China Nanjing University of Information Science and Technology Nanjing210044 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China School of Computing Edinburgh Napier University 10 Colinton Road EdinburghEH10 5DT United Kingdom

ISBN: (纸本)9783030242701

Deep learning has been widely applied for computer vision, natural language processing, and information retrieval etc. Using a deep learning framework can reduce learning curve of beginners facilitating them to get involved with deep learning algorithms. Current deep learning frameworks can mainly be divided into traditional local deployment and cloud-based platforms. However, the two forms cannot be considered at the same time in terms of debugging and remote access. This paper focuses on the logical isolation between deep learning algorithm design and actual business execution, and it proposes an elastic framework that can resolve the contradiction between internal improvement and external access, which can improve the efficiency of both algorithm design researchers and business requirements department engineers. © Springer Nature Switzerland AG 2019.

关键词： Learning algorithms

来源：评论

学校读者我要写书评

暂无评论

CorrDetector: A framework for structural corrosion detection from drone images using ensemble deep learning

arXiv

引用

arXiv 2021年

作者： Mohammad Forkana, Abdur Rahim Kang, Yong-Bin Jayaraman, Prem Prakash Liao, Kewen Kaul, Rohit Morgan, Graham Ranjan, Rajiv Sinha, Samir Department of Computer Science and Software Engineering Swinburne University of Technology MelbourneVIC Australia Department of Media and Communication Swinburne University of Technology MelbourneVIC Australia Discipline of Information Technology Peter Faber Business School Australian Catholic University SydneyNSW Australia Newcastle University Newcastle upon TyneNE1 7RU United Kingdom Robonomics AI York Street SydneyNSW Australia

In this paper, we propose a new technique that applies automated image analysis in the area of structural corrosion monitoring and demonstrate improved efficacy compared to existing approaches. Structural corrosion monitoring is the initial step of the risk-based maintenance philosophy and depends on an engineer’s assessment regarding the risk of building failure balanced against the fiscal cost of maintenance. This introduces the opportunity for human error which is further complicated when restricted to assessment using drone captured images for those areas not reachable by humans due to many background noises. The importance of this problem has promoted an active research community aiming to support the engineer through the use of artificial intelligence (AI) image analysis for corrosion detection. In this paper, we advance this area of research with the development of a framework, CorrDetector. CorrDetector uses a novel ensemble deep learning approach underpinned by convolutional neural networks (CNNs) for structural identification and corrosion feature extraction. We provide an empirical evaluation using real-world images of a complicated structure (e.g. telecommunication tower) captured by drones, a typical scenario for engineers. Our study demonstrates that the ensemble approach of CorrDetector significantly outperforms the state-of-the-art in terms of classification accuracy. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Analysis of the Efficiency-Energy with Regression and Classification in Household Using K-NN 1

引用

5th International Conference on Artificial Intelligence and Security, ICAIS 2019

作者： Sun, Mingxu Liu, Xiaodong Mbonihankuye, Scholas School of Electrical Engineering University of Jinan Jinan China School of Computing Edinburgh Napier University 10 Colinton Road EdinburghEH10 5DT United Kingdom Nanjing University of Information Science and Technology Nanjing210044 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China

ISBN: (数字)9783030242657

ISBN: (纸本)9783030242640

This paper aims to study energy consumption in a house. Home energy management system (HEMS) has become very important, because energy consumption of a residential sector accounts for a significant amount of total energy consumption. However, a conventional HEMS has some architectural limitations among dimensional variables reusability and interoperability. Furthermore, the cost of implementation in HEMS is very expensive, which leads to the disturbance of the spread of a HEMS. Therefore, this study proposes an Internet of Things (IoT) based HEMS with lightweight photovoltaic (PV) system over dynamic home area networks (DHANs), which enables the construction of a HEMS to be scalable reusable and interoperable. The study suggests a technique for decreasing cost of energy that HEMS is using and various perspectives in system. The method that proposed is K-NN (K-Nearest Neighbor) which helps us to analyze the classification and regression datasets. This paper has the result from the data relevant in October 2018 from some buildings of Nanjing University of information science and Technology. That dataset allowed us to make analysis of electric energy consumption of each home equipment used and to make a simulation of the energy needed for each apparatus. Finally, we succeeded to find the algorithm which is suitable for efficiency-electric energy. © 2019, Springer Nature Switzerland AG.

关键词： Energy utilization

来源：评论

学校读者我要写书评

暂无评论

Cell quantization tool for anterior chamber OCT images

引用

Transactions of the Korean Institute of Electrical Engineers 2020年第7期69卷 1073-1080页

作者： Lee, Yeongseop Kang, Taeseen Han, Yongseop Kim, Jinhyun Kim, Kyong Hoon Lee, Seongjin Dept. of Aerospace and Software Engineering Gyeongsang National University Korea Republic of Dept. of Informatics Gyeongsang National University Korea Republic of Dept of Ophthalmology Gyeongsang National University Changwon Hospital Changwon Korea Republic of Dept of Ophthalmology College of Medicine Gyeongsang National University Jinju Korea Republic of Dept of Information and Communication Engineering Gyeongsang University Korea Republic of School of Computer Science and Engineering Kyungpook National University Korea Republic of

In general, ophthalmologists visually grade the state of a patient by counting the cells within the anterior chamber OCT image. The manual cell counting method is highly inaccurate and spends a lot of time to determine the progress of the patient. In this work, we develop a new tool to count cells in anterior chamber OCT images to aid doctors in analyzing the state of patients. We exploit image processing to remove noises from images, segment the anterior chamber, and quantize the cells in OCT images. We also provide statistics to aid the doctors in determining the progress of the patients. © Korean Institute of Electrical Engineers

关键词： Cells

来源：评论

学校读者我要写书评

暂无评论

A histogram specification technique for dark image enhancement using a local transformation method

引用

IPSJ Transactions on computer Vision and Applications 2018年第1期10卷 1页

作者： Hussain, Khalid Rahman, Shanto Rahman, Md. Mostafijur Khaled, Shah Mostafa Abdullah-Al Wadud, M. Hossain Khan, Muhammad Asif Shoyaib, Mohammad Institute of Information Technology University of Dhaka Dhaka1000 Bangladesh Department of Software Engineering King Saud University Riyadh Saudi Arabia Department of Computer Science and Engineering University of Dhaka Dhaka1000 Bangladesh

Traditional image enhancement techniques produce different types of noise such as unnatural effects, over-enhancement, and artifacts, and these drawbacks become more prominent in enhancing dark images. To overcome these drawbacks, we propose a dark image enhancement technique where local transformation of the pixels have been performed. Here, we apply a transformation method of different parts of the histogram of an input image to get a desired histogram. Afterwards, histogram specification technique has been done on the input image using this transformed histogram. The performance of the proposed technique has been evaluated in both qualitative and quantitative manner, which shows that the proposed method improves the quality of the image with minimal unexpected artifacts as compared to the other techniques. © 2018, The Author(s).

关键词： Image enhancement

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：