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A new runoff calculation model based on multi-source radar remote sensing technology 6

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A new runoff calculation model based on multi-source radar r...

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

作者： Zhao, Jianhui Wang, Ning Wu, Lin Min, Lin College of Computer and Information Engineering Henan University Kaifeng475004 China College of Environment and Planning Henan University Kaifeng475004 China Network Information Center Office 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

ISBN: (纸本)9781728129129

River runoff changing will directly affect the safety of the surrounding areas. In theory, river runoff can be calculated from water depth, river width and flow velocity. But in the actual monitoring, the observation of water depth, river width and flow velocity is complex. Radar remote sensing can observe target all-time and all-weather, and has the ability to penetrate clouds, rain and fog. The common Radar remote sensing runoff calculation model is depth-runoff relationship model, which is constructed by fitting water depth and in-situ runoff. The river depth is derived from Radar Altimetry (RA) data. But this model has only one input parameter, the accuracy of this model inverted runoff is not high. In view of the above problems, this paper proposed the Multi-source Radar Remote Sensing Runoff Calculation Model (MRRS-RCM). Firstly, river width and relative water depth were derived from Synthetic Aperture Radar (SAR) images and RA data. Secondly, based on the Manning's equation, the flow velocity were described from river width and relative water depth. Thirdly, river width relative water depth, and in-situ runoff were fitted to construct MRRS-RCM. Experiments show the accuracy of MRRS-RCM estimated runoff is better than depth-runoff relationship model, the RRMSE values of MRRS-RCM reach 13%. © 2019 IEEE.

关键词： Synthetic aperture radar

Exploiting cognitive structure for adaptive learning

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arXiv 2019年

作者： Liu, Qi Tong, Shiwei Liu, Chuanren Zhao, Hongke Chen, Enhong Ma, Haiping Wang, Shijin Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology School of Data Science University of Science and Technology of China Business Analytics and Statistics University of Tennessee College of Management and Economics Tianjin University IFLYTEK Research IFLYTEK CO. LTD. State Key Laboratory of Cognitive Intelligence

Adaptive learning, also known as adaptive teaching, relies on learning path recommendation, which sequentially recommends personalized learning items (e.g., lectures, exercises) to satisfy the unique needs of each learner. Although it is well known that modeling the cognitive structure including knowledge level of learners and knowledge structure (e.g., the prerequisite relations) of learning items is important for learning path recommendation, existing methods for adaptive learning often separately focus on either knowledge levels of learners or knowledge structure of learning items. To fully exploit the multifaceted cognitive structure for learning path recommendation, we propose a Cognitive Structure Enhanced framework for Adaptive Learning, named CSEAL. By viewing path recommendation as a Markov Decision Process and applying an actor-critic algorithm, CSEAL can sequentially identify the right learning items to different learners. Specifically, we first utilize a recurrent neural network to trace the evolving knowledge levels of learners at each learning step. Then, we design a navigation algorithm on the knowledge structure to ensure the logicality of learning paths, which reduces the search space in the decision process. Finally, the actor-critic algorithm is used to determine what to learn next and whose parameters are dynamically updated along the learning path. Extensive experiments on real-world data demonstrate the effectiveness and robustness of CSEAL. Copyright © 2019, The Authors. All rights reserved.

关键词： Markov processes

Remote real-time wastewater surveillance by the optical sensor supported by machine learning

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Sensors and Actuators Reports 2025年 10卷

作者： Małgorzata Szczerska Kacper Cierpiak Monika Kosowska Paweł Wityk Sebastián García-Galán Patryk Sokołowski Sylwia Fudala-Książek Michał T. Tomczak Bei Ye Aneta Łuczkiewicz Department of Metrology and Optoelectronics Faculty of Electronics Telecommunications and Informatics Gdańsk University of Technology 11/12 Narutowicza Street 80-233 Gdansk Poland Faculty of Telecommunications Computer Science and Electrical Engineering Bydgoszcz University of Science and Technology Al. Prof. S. Kaliskiego 7 85-796 Bydgoszcz Poland Department of Biotechnology and Microbiology Faculty of Chemistry Gdańsk University of Technology 11/12 Narutowicza Street 80-233 Gdansk Poland Department of Biopharmaceutics and Pharmacodynamics Medical University of Gdańsk Hallera 107 Pharmacy Faculty Gdańsk Poland Telecommunications Engineering Department Linares Higher Polytechnic School Jaén University Avenida de la Universidad S/N 23700 Linares Jaén Spain Department of Water and Wastewater Technology Faculty of Civil and Environmental Engineering Gdańsk University of Technology 11/12 Narutowicza Street 80-233 Gdańsk Poland Faculty of Management and Economics Gdańsk University of Technology 11/12 Narutowicza Street 80-233 Gdańsk Poland Shenzhen Key Laboratory of Ecological Remediation and Carbon Sequestration Environmental Protection Key Laboratory of Microorganism Application and Risk Control Institute of Environment and Ecology Shenzhen International Graduate School Tsinghua University Shenzhen 518055 PR China

In this study, a remote monitoring sewage system based on optical method is presented for the first time. The built-in wastewater surveillance system can perform autonomous monitoring with no requirement of sample collection and its transport to the laboratory. The measurement results can be obtained in real time, the work operation allows continuous or on-demand mode. The study includes design, development and application of a biofunctionalized fiber-optic sensor, as well as engaging machine learning algorithms for measured signals classification. The validated SARS-CoV-2 antibodies sensor has a measurement range of 10 −12 mg/mL to 10 −1 mg/mL. Known concentrations of Immunoglobulin G (IgG) from 10 −6 mg/mL to 10 −1 mg/mL were added to the tested wastewater samples and then detected by the prepared optical probe. The data obtained were then processed and classified by traditional method and selected machine learning algorithms; the results obtained for the KNeighbors algorithm are Balanced Accuracy of 92.97 % and F1-score of 94.19 %. This study is of significance to establish a system that can effectively monitor the outbreak of potential infections in society.

关键词： Remote wastewater surveillance Real-time monitoring SARS-CoV-2 Optical sensors Fiber optic sensor Machine learning

A Diverse Biases Non-negative Latent Factorization of Tensors Model for Dynamic Network Link Prediction

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A Diverse Biases Non-negative Latent Factorization of Tensor...

IEEE International Conference on Networking, Sensing and Control

作者： Xuke Wu Hang Gou Hao Wu Juan Wang Minzhi Chen Siyu Lai Computer School China West Normal University Nanchong China Computer School of China West Normal University Nanchong Sichuan China University of Chinese Academy of Sciences Beijing China Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China North Sichuan Medical College Nanchong China Medical College Nanchong Sichuan China

ISBN: (数字)9781728168555

ISBN: (纸本)9781728168562

Dynamic networks vary over time, making it vital to capture networks temporal patterns for predicting missing links with high accuracy. A biased non-negative latent factorization of tensors (BNLFT) model is very effective in extracting such patterns from dynamic data. However, a BNLFT model only integrates single bias, which cannot adequately represents the volatility of the dynamic data. To address this issue, this paper presents a Diverse Biases Non-negative Latent Factorization of Tensors (DBNT) model for accurate prediction of missing links in dynamic networks. Meanwhile, for further prediction accuracy improvement, the preprocessing bias is integrated into the DBNT model. Empirical studies on two dynamic networks datasets from real applications show that compared with state of the art predictors, a DBNT model achieves higher prediction accuracy.

关键词： Tensors Conferences Predictive models Data models Sensors Data mining

Li, An, and Morozov Reply:

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Physical Review Letters 2019年第11期123卷 119602-119602页

作者： Guodong Li Qi An Sergey I. Morozov Hubei Key Laboratory of Theory and Application of Advanced Materials Mechanics School of Science Wuhan University of Technology Wuhan 430070 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of Technology Wuhan 430070 China Department of Chemical and Materials Engineering University of Nevada Reno Reno Nevada 89557 USA Department of Computer Simulation and Nanotechnology South Ural State University Chelyabinsk 454080 Russia

Magnetic connectivity between the light bridge and penumbra in a sunspot

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arXiv 2020年

作者： Feng, Song Miao, Yuhu Yuan, Ding Qu, Zhongquan Nakariakov, Valery M. Yunnan Key Laboratory of Computer Technology Application Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Yunnan Astronomical Observatory Chinese Academy of Sciences PO Box 110 Kunming650011 China Institute of Space Science and Applied Technology Harbin Institute of Technology Shenzhen Guangdong518055 China Centre for Fusion Space and Astrophysics Department of Physics University of Warwick CV4 7AL United Kingdom St Petersburg Branch Special Astrophysical Observatory Russian Academy of Sciences St Petersburg196140 Russia

A light bridge is a prominent structure commonly observed within a sunspot. Its presence usually triggers a wealth of dynamics in a sunspot, and has a lasting impact on sunspot evolution. However, the fundamental structure of light bridges is still not well understood. In this study, we used the high-resolution spectropolarimetry data obtained by the Solar Optical Telescope onboard the Hinode satellite to analyze the magnetic and thermal structure of a light bridge at AR 12838. We also combined the high-cadence 1700 Å channel data provided by the Atmospheric Imaging Assembly onboard the Solar Dynamic Observatory to study the dynamics on this bridge. We found that a pair of blue and red Doppler shift patches at two ends of this bridge, this pattern appears to be the convective motion directed by the horizontal component of the magnetic field aligned with the spine of this bridge. Paired upward and downward motions implies that the light bridge could have a two-legged or undulate magnetic field. Significant four minute oscillations in the emission intensity of the 1700 Å bandpass were detected at two ends, which had overlap with the paired blue and red shift patches. The oscillatory signals at the light bridge and the penumbra were highly correlated with each other. Although they are separated in space at the photosphere, the periodicity seems to have a common origin from the underneath. Therefore, we infer that the light bridge and penumbra could share a common magnetic source and become fragmented at the photosphere by magneto-convection. Copyright © 2020, The Authors. All rights reserved.

关键词： Magnetohydrodynamics

A performance evaluation of correspondence grouping methods for 3D rigid data matching

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arXiv 2019年

作者： Yang, Jiaqi Xian, Ke Wang, Peng Zhang, Yanning School of Computer Science Northwestern Polytechnical University National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'an710129 China National Key Laboratory of Science and Technology on Multi-spectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

Seeking consistent point-to-point correspondences between 3D rigid data (point clouds, meshes, or depth maps) is a fundamental problem in 3D computer vision. While a number of correspondence selection methods have been proposed in recent years, their advantages and shortcomings remain unclear regarding different applications and perturbations. To fill this gap, this paper gives a comprehensive evaluation of nine state-of-the-art 3D correspondence grouping methods. A good correspondence grouping algorithm is expected to retrieve as many as inliers from initial feature matches, giving a rise in both precision and recall as well as facilitating accurate transformation estimation. Toward this rule, we deploy experiments on three benchmarks with different application contexts including shape retrieval, 3D object recognition, and point cloud registration together with various perturbations such as noise, point density variation, clutter, occlusion, partial overlap, different scales of initial correspondences, and different combinations of keypoint detectors and descriptors. The rich variety of application scenarios and nuisances result in different spatial distributions and inlier ratios of initial feature correspondences, thus enabling a thorough evaluation. Based on the outcomes, we give a summary of the traits, merits, and demerits of evaluated approaches and indicate some potential future research directions. Copyright © 2019, The Authors. All rights reserved.

关键词： Benchmarking

Robust environmental sound recognition with sparse key-point encoding and efficient multi-spike learning

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arXiv 2019年

作者： Yu, Qiang Yao, Yanli Wang, Longbiao Tang, Huajin Dang, Jianwu Tan, Kay Chen Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China College of Computer Science Sichuan University Chengdu China Japan Advenced Institute of Science and Technology Japan Department of Computer Science City University of Hong Kong Hong Kong

The capability for environmental sound recognition (ESR) can determine the fitness of individuals in a way to avoid dangers or pursue opportunities when critical sound events occur. It still remains mysterious about the fundamental principles of biological systems that result in such a remarkable ability. Additionally, the practical importance of ESR has attracted an increasing amount of research attention, but the chaotic and non-stationary difficulties continue to make it a challenging task. In this study, we propose a spike-based framework from a more brain-like perspective for the ESR task. Our framework is a unifying system with a consistent integration of three major functional parts which are sparse encoding, efficient learning and robust readout. We first introduce a simple sparse encoding where key-points are used for feature representation, and demonstrate its generalization to both spike and non-spike based systems. Then, we evaluate the learning properties of different learning rules in details with our contributions being added for improvements. Our results highlight the advantages of the multi-spike learning, providing a selection reference for various spike-based developments. Finally, we combine the multi-spike readout with the other parts to form a system for ESR. Experimental results show that our framework performs the best as compared to other baseline approaches. In addition, we show that our spike-based framework has several advantageous characteristics including early decision making, small dataset acquiring and ongoing dynamic processing. Our framework is the first attempt to apply the multi-spike characteristic of nervous neurons to ESR. The outstanding performance of our approach would potentially contribute to draw more research efforts to push the boundaries of spike-based paradigm to a new horizon. Copyright © 2019, The Authors. All rights reserved.

关键词： Neural networks

Collaborative filtering recommendation algorithm integrating user similarity and trust

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Journal of Physics: Conference Series 2021年第1期2010卷

作者： Mengge Huang Kai Cao Jingyi Zhang Chuanlin Zhang Tao Deng Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University Lanzhou 730030 P. R. China Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University Lanzhou 730030 P. R. China School of Mathematics and Computer Science Northwest Minzu University Lanzhou 730030 P. R. China

The rapid development of the Internet has brought convenience to people and has also produced the problem of 'information overload'. In view of the traditional collaborative filtering algorithm facing some bottlenecks to be solved, this study proposes a collaborative filtering algorithm that combines similarity and trust. First of all, in view of the large deviation of traditional similarity calculation and prediction of user ratings, this study proposes an optimized Pearson correlation coefficient calculation method; secondly, the trust relationship is established based on the user's rating of the common project, and the trust relationship between users who do not have a direct trust relationship is established through the transfer of trust; then find the nearest neighbor set of the target user through the fusion of user similarity and trust; finally, the item is scored and predicted to generate a recommendation list. Experimental results show that the algorithm proposed in this study can effectively improve the accuracy of recommendation.

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