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Optical polarization imaging for underwater target detection with non-scatter background

Journal of Measurement Science and Instrumentation 2020年第4期11卷 335-342页

作者： GUAN Jin-ge ZHAO Yong ZHENG Yong-qiu MA Miao SUN Peng XUE Chen-yang School of Information and Communication Engineering North University of ChinaTaiyuan 030051China Beijing Institute of Computer Application Technology China North Industries Group Corporation LimitedBeijing 100089China Key Laboratory of Instrumentation Science and Dynamic Measurement(North University of China) Ministry of EducationTaiyuan 030051China Department of Science Taiyuan Institute of TechnologyTaiyuan 030008China

For conventional optical polarization imaging of underwater target,the polarization degree of backscatter should be pre-measured by averaging the pixel intensities in the no target region of the polarization images,and the polarization property of the target is assumed to be completely *** the scattering background is unseen in the field of view or the target is polarized,conventional method is helpless in detecting the *** improvement is to use lots of co-polarization and cross polarization detection *** propose a polarization subtraction method to estimate depolarization property of the scattering noise and target *** experiment in a quartz cuvette container is performed to demonstrate the effectiveness of the proposed *** results show that the proposed method can work without scattering background reference,and further recover the target along with smooth surface for polarization preserving *** study promotes the development of optical polarization imaging systems in underwater environments.

关键词： polarization imaging underwater optical scattering optical information processing target detection

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Nonlinear Cross-Domain Feature Representation Learning Method Based on Dual Constraints

Nonlinear Cross-Domain Feature Representation Learning Metho...

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IEEE International Conference on Big Knowledge (ICBK)

作者： Han Ding Yuhong Zhang Shuai Yang Yaojin Lin Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Data Science and Intelligence Application Fujian Province University Zhangzhou China

Feature representation learning is a research focus in domain adaptation. Recently, due to the fast training speed, the marginalized Denoising Autoencoder (mDA) as a standing deep learning model has been widely utilized for feature representation learning. However, the training of mDA suffers from the lack of nonlinear relationship and does not explicitly consider the distribution discrepancy between domains. To address these problems, this paper proposes a novel method for feature representation learning, namely Nonlinear cross-domain Feature learning based Dual Constraints (NFDC), which consists of kernelization and dual constraints. Firstly, we introduce kernelization to effectively extract nonlinear relationship in feature representation learning. Secondly, we design dual constraints including Maximum Mean Discrepancy (MMD) and Manifold Regularization (MR) in order to minimize distribution discrepancy during the training process. Experimental results show that our approach is superior to several state-of-the-art methods in domain adaptation tasks.

关键词： Noise reduction Manifolds Training Adaptation models Kernel Deep learning Feature extraction

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FrepJoin:an efficient partition-based algorithm for edit similarity join

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Frontiers of Information technology & Electronic Engineering 2017年第10期18卷 1499-1510页

作者： Ji-zhou LUO Sheng-fei SHI Hong-zhi WANG Jian-zhong LI School of Computer Science and Technology Harbin Institute of Technology Guangdong Key Laboratory of Popular High Performance Computers Key Laboratory of Service Computing and Application

String similarity join(SSJ) is essential for many applications where near-duplicate objects need to be found. This paper targets SSJ with edit distance constraints. The existing algorithms usually adopt the filter-andrefine framework. They cannot catch the dissimilarity between string subsets, and do not fully exploit the statistics such as the frequencies of characters. We investigate to develop a partition-based algorithm by using such *** frequency vectors are used to partition datasets into data chunks with dissimilarity between them being caught easily. A novel algorithm is designed to accelerate SSJ via the partitioned data. A new filter is proposed to leverage the statistics to avoid computing edit distances for a noticeable proportion of candidate pairs which survive the existing filters. Our algorithm outperforms alternative methods notably on real datasets.

关键词： String similarity join Edit distance Filter and refine Data partition Combined frequency vectors

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Research and Prospect of Digital Economy Development in Shandong Province

Research and Prospect of Digital Economy Development in Shan...

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2019 5th International Conference on Humanities and Social Science Research (ICHSSR 2019)

作者： Zhengqian Feng Hao Wang Liping Wei Shandong Information Resources Application Association Qilu University of Technology(Shandong Academy of Sciences) Shandong Computer Science Center(National Supercomputer Center in Jinan) Shandong Provincial Key Laboratory of Computer Networks Shandong University of Science and Technology

Digital economy refers to the economic model that takes digital technology as the core to drive the whole economic activity process and create *** the future, all economic links may be driven by digital technology, digital technology will be the main driving force for world economic development, and digital economy will be the new engine for world economic *** great transformation of digital economy is a new opportunity for economic *** development level of digital economy in Shandong province has entered the first echelon in China, but there are still some factors restricting its *** digital economy has become the core force leading the scientific and technological revolution and industrial transformation, and human society is entering a new stage marked by digital *** province must correctly grasp the general trend of the development of digital economy, do a good job in planning and creating momentum and taking advantage of it, accelerate the construction of a strong digital province, make the digital economy become an important starting point to promote the conversion of old and new drives and an important supporting force of "lead the way".

关键词： Digital Economy Development Status Evaluation Index

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Single Image Super Resolution Based on Generative Adversarial Networks

Single Image Super Resolution Based on Generative Adversaria...

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2019第十一届数字图像处理国际会议

作者： Kai Li Ye Liang Shenghao Yang Jinfang Jia Jianqiang Huang Xiaoying Wang State Key Laboratory of Plateau Ecology and Agriculture Department of Computer Technology and ApplicationQinghai University Rocket Force University of Engineering Basic Course DepForeign language Office

Deep neural networks based on SRGAN single image super-resolution reconstruction can generate more realistic images than CNN-based super-resolution deep neural ***,when the network is deeper and more complex,unpleasant artifacts can *** a lot of experiments,we can use the ESRGAN model to avoid such *** using the ESRGAN model for super-resolution reconstruction,the perceived index of the resulting results does not reach a lower *** are two reasons for this:（1）ESRGAN does not expand the feature *** uses 128*128 to obtain the feature information of the image by default,and can’t get more image information better.（2） ESRGAN did not re-optimize the generated ***,we propose ESRGAN-Pro to optimize ESRGAN for the above two aspects,combined with a large amount of training data,and get a better perception index and texture.

关键词： Generative Adversarial Networks super resolution deep learning framework image processing

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An embedding-based approach to constructing OWL ontologies

An embedding-based approach to constructing OWL ontologies

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2018 ISWC Posters and Demonstrations, Industry and Blue Sky Ideas Tracks, ISWC-P and D-Industry-BlueSky 2018

作者： Zhang, Lijing Zhang, Xiaowang Zhao, Leyuan Tian, Jiachen Chen, Shizhan Wu, Hong Wang, Kewen Feng, Zhiyong School of Computer Science and Technology Tianjin University Tianjin China School of Computer Software Tianjin University Tianjin China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China

This paper presents a novel system OWLearner for automatically extracting axioms for OWL ontologies from RDF data using embedding models. In this system, ontology construction is transformed to the classification problem in machine learning and thus off-the-shelf tools can be employed to learn axioms in OWL. There are mainly three modules, namely, embedding, sampling, and training & learning. Large ontologies DBpedia and YAGO are used to validate the proposed approach. The experimental results show that OWLearner is able to learn high-quality expressive OWL axioms automatically and efficiently. © 2018 CEUR-WS. All rights reserved.

关键词： Embeddings

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An ontology-based approach to adaptive data processing

An ontology-based approach to adaptive data processing

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2018 ISWC Posters and Demonstrations, Industry and Blue Sky Ideas Tracks, ISWC-P and D-Industry-BlueSky 2018

作者： Chen, Haokun Zhang, Xiaowang Feng, Zhiyong School of Computer Science and Technology Tianjin University Tianjin China School of Computer Software Tianjin University Tianjin China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China

In this paper, we present an ontology-based approach to generate workflows which are core in adaptive data processing by taking advantage of ontologies in characterizing implicit relations among tasks of data processing. Moreover, compared with manual configurations of current approaches, ontology reasoning can automatically infer preference orders of tasks and overcome the limitation of manual configurations. Experimental results show that our proposal is effective and efficient. © 2018 CEUR-WS. All rights reserved.

关键词： Ontology

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A Wavelet-CNN-LSTM Model for Tailings Pond Risk Prediction

arXiv

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arXiv 2020年

作者： Yang, Jun Li, Qing Sun, Yixuan Wang, Wei Li, Linchao Wang, Xuwei Jia, Shengyao Tong, Renyuan National and Local Joint Engineering research center for Disaster Monitoring Technologies and Instruments China Jiliang University Hangzhou310051 China Zhejiang Provincial Key Laboratory of Intelligent Manufacturing Quality Big Data Traceability Analysis and Application China Jiliang University Hangzhou310051 China Zhejiang PeckerAI Technology. Ltd Hangzhou310018 China College of Computer Science and Technology Zhejiang University Hangzhou310027 China

Tailings ponds are places for storing industrial waste. The saturation line is the key factor of quantifying the safety of tailings pond. Existing saturation line time-series prediction methods are mainly based on statistical models or shallow machine learning models. Although these models aim to capture the time dependence of the sequence data, the channel and temporal are even unavailable in principle. In this paper, we presents a two-stage forecasting method, which embeds the channel and temporal attention into a hybrid CNN-LSTM model to predict the saturation line. In the first stage, the discrete wavelet transform (DWT) is applied to capture the refined sequence information. In the second stage, the CNN-LSTM model is utilized to learn the basic spatial and temporal features in the time series. Furthermore, the channel and temporal attention model are embedded into CNN-LSTM model to enhance the feature extracting ability in channel and temporal dimension. Consequently, our proposed model is shown to outperform classic models on five real-world datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

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Solder joint image adaptive block compressive sensing with convex optimisation and Gini index

Solder joint image adaptive block compressive sensing with c...

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7th International Symposium on Test Automation and Instrumentation, ISTAI 2018

作者： Zhao, Huihuang Wang, Yaonan Zheng, Jinhua Qiao, Zhijun Zhang, Yun Department of Computer Science and Technology College of Computer Science and Technology Hengyang Normal University Hengyang Hunan China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang China Department of Electrical and Engineering College of Electrical and Information Engineering Hunan University Changsha China School of Mathematical and Statistical Sciences University of Texas Rio Grande ValleyTX United States Institute of Radio and TV Technology Communication University of Zhejiang Hangzhou310018 China

This study aims to improve the performance in solder joint image compression and reconstruction. A novel adaptive block compressive sensing with convex optimisation and Gini index (Ad_BCSGB_Gini) methodology for solder joint image compression and reconstruction is proposed. At first, the image is split into square blocks and each block is resized into a row which consists of a new image. Then, the new image is transformed into a sparse signal by an orthogonal basis matrix, and the image reconstruction is handled as a convex optimisation problem. Moreover, a gradient-based method which has fast computational speed is used to reconstruct image. There is a control factor which controls a norm l1 in the optimisation problem. To achieve the best performance, at last, the proposed method adaptively selects the best result by comparing Gini index of the reconstruction results based on different control factor values. Experimental results with different methods indicate that the Ad_BCSGB_Gini method is able to achieve the best performance in quantisation comparison than several classical algorithms, and Ad_BCSGB_Gini has a good robustness. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Convex optimization

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Waterline mapping of inland great lake with subpixel accuracy from GF-3 SAR images 6

Waterline mapping of inland great lake with subpixel accurac...

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6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Li, Ning Niu, Shilin Wang, Robert Wu, Lin Guo, Zhengwei 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 Kaifeng475004 China Department of Space Microwave Remote Sensing System Institute of Electronics Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781728129129

High-accuracy waterline mapping with Synthetic Aperture Radar (SAR) images is a challenging task because of the inhomogeneities of SAR imagery caused by the speckle noise and complex terrain. This paper presents a novel method for waterline mapping with subpixel accuracy. The proposed method mainly consists of three steps. Firstly, an improved Non-Local filter is adapted to suppress the speckle noise. Secondly, a Fuzzy C-Means (FCM) clustering algorithm is used to extract the pixel-level waterline. Thirdly, along the pixel-level waterline, a novel subpixel-scale waterline extraction method based on bicubic convolution and Geometric Active Contour (GAC) model is presented to further improve the accuracy of the waterline. Without loss of generality, the Danjiangkou reservoir (DJKR), which is the largest artificial freshwater lake in Asia, is selected as a study case. SAR images from Chinese GaoFen-3 (GF-3) satellite working on different modes are processed to demonstrate the effectiveness of the proposed method. © 2019 IEEE.

关键词： Synthetic aperture radar

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