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Dynamic visual simulation of marine vector field based on LIC——a case study of surface wave field in typhoon condition

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Journal of Oceanology and Limnology 2019年第6期37卷 2025-2036页

作者： LIU Zhendong LIU Haixing SU Tianyun JIA Zhen LI Xinfang ZHOU Lin SONG Zhuanling First Institute of Oceanography(FIO) Ministry of Natural Resources(MNR)Qingdao 266061China Laboratory for Regional Oceanography and Numerical Modelling Qingdao National Laboratory for Marine Science and TechnologyQingdao 266237China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Qingdao 266061China

Line integral convolution(LIC)is a useful visualization technique for a vector ***,the output image produced by LIC has many problems in a marine vector *** focus on the visual quality improvement when LIC is applied in the ocean steady and unsteady flow field in the following *** a white noise is used as the input in a steady flow field,interpolation is used to turn the discrete white noise into continuous white noise to solve the problem of ***"cross"high-pass filtering is used to enhance the textures of streamlines to be more concentrated and continuity strengthened for each *** a sparse noise is used as the input in a steady flow field,we change the directions of background sparse noise according to the directions of vector field to make the streamlines clearer and *** addition,we provide a random initial phase for every streamline to avoid the pulsation effect during *** velocities of vector field are encoded in the speed of the same length streamlines so that the running speed of streamlines can express flow ***,to solve the problem of obvious boundaries when stitching image,we change the streamline tracking *** a white noise is used as an input in an unsteady flow field,double value scattering is used to enhance the contrast of streamlines;moreover,the"cross"high-pass filtering is also adopt instead of two-dimensional high-pass ***,we apply the above methods to a case of the surface wave field in typhoon *** experimental results show that applying the methods can generate high-quality wave images and ***,it is helpful to understand and study waves in typhoon condition to avoid the potential harm of the waves to people's lives and property.

关键词： line integral convolution(LIC) wave data visualization steady and unsteady marine flow field

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An anomaly detection-based dynamic OD prediction framework for urban networks

An anomaly detection-based dynamic OD prediction framework f...

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2020 Forum on Integrated and Sustainable Transportation Systems, FISTS 2020

作者： Liu, Jing Zheng, Fangfang Zuylen, Henk Van Li, Jie Luo, Jie Southwest Jiaotong University School of Transportation and Logistics 611756 China Southwest Jiaotong University School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology 611756 China Delft University of Technology Department of Transport and Planning Delft Netherlands Hunan University Civil Engineering College Changsha410082 China

ISBN: (数字)9781728195032

ISBN: (纸本)9781728195032

The dynamic origin-destination (OD) information is crucial for traffic operations and control. This paper presents a dynamic traffic demand prediction framework based on an anomaly detection algorithm. The Principal Component analysis (PCA) method is applied to extract main demand patterns which are used to detect the abnormal conditions. The proposed approach can select prediction methods (parametric or nonparametric) automatically based on the pattern detection results. Both simulation and field observed Automatic Number Plate Recognition (ANPR) data are used to verify the proposed approach where the Kalman filter model and the K-nearest neighbor model are chosen as the basic prediction methods. The results show that the prediction framework can effectively reduce the noise of a single prediction model particularly in the abnormal conditions and provide more accurate and reliable prediction results. © 2020 IEEE.

关键词： Forecasting

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Neural multi-objective combinatorial optimization with diversity enhancement 23

Neural multi-objective combinatorial optimization with diver...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Jinbiao Chen Zizhen Zhang Zhiguang Cao Yaoxin Wu Yining Ma Te Ye Jiahai Wang School of Computer Science and Engineering Sun Yat-sen University P.R. China School of Computing and Information Systems Singapore Management University Singapore Department of Industrial Engineering & Innovation Sciences Eindhoven University of Technology Netherlands Department of Industrial Systems Engineering & Management National University of Singapore Singapore School of Computer Science and Engineering Sun Yat-sen University P.R. China and Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Sun Yat-sen University P.R. China and Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou P.R. China

Most of existing neural methods for multi-objective combinatorial optimization (MOCO) problems solely rely on decomposition, which often leads to repetitive solutions for the respective subproblems, thus a limited Pareto set. Beyond decomposition, we propose a novel neural heuristic with diversity enhancement (NHDE) to produce more Pareto solutions from two perspectives. On the one hand, to hinder duplicated solutions for different subproblems, we propose an indicator-enhanced deep reinforcement learning method to guide the model, and design a heterogeneous graph attention mechanism to capture the relations between the instance graph and the Pareto front graph. On the other hand, to excavate more solutions in the neighborhood of each subproblem, we present a multiple Pareto optima strategy to sample and preserve desirable solutions. Experimental results on classic MOCO problems show that our NHDE is able to generate a Pareto front with higher diversity, thereby achieving superior overall performance. Moreover, our NHDE is generic and can be applied to different neural methods for MOCO.

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The research of forecasting model of automobile parts recycling cost based on data intelligence 2019

The research of forecasting model of automobile parts recycl...

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2019 International Conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2019

作者： Fan, Bo Cai, Yueshun School of Information Science and Technology Southwest Jiaotong University National Engineering Laboratory of Industrial Big-Data Application Technology CASIC ChengDu China

ISBN: (纸本)9781450372022

In view of the problem of reducing the cost control of the used parts in the after-sales service cost analysis process of the automobile enterprises, the multi-dimensional intelligent analysis of the used business data is carried out by extracting and cleaning the relevant business data resources of the used parts. This paper proposes a method based on data intelligence for cost forecasting, which assists enterprises in mining valuable information and knowledge contained in used parts data, making the used parts business data intelligent and realizing the value-added of used parts data. The management of used parts recycling is an important value link for automotive enterprises to reduce costs and increase efficiency, and is the key to building a reverse supply chain coordination architecture and achieving circular economy. There are many factors influencing the cost of recycling used parts, and too much interference will affect the forecasting effect. Therefore, before establishing the corresponding used parts recovery cost prediction model, the data should be decomposed into a stable time series, so that the prediction results are more accurate. This paper compares several typical prediction algorithms, uses the theory of empirical mode decomposition to preprocess the business data of used parts data, then builds the ARMA model, selects the ARMA model parameters through the AIC information criterion, and designs the supplier's used parts recycling cost prediction model. And the model is validated using the used piece business data piece data to predict the cost of the supplier's used parts recovery and provide support for the actual business system. © 2019 Association for Computing Machinery.

关键词： Cost reduction

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Surface soil moisture inversion based on DEFS-GA-BP model using Sentinel-1/2 remote sensing data 5

Surface soil moisture inversion based on DEFS-GA-BP model us...

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IET International Radar Conference 2020, IET IRC 2020

作者： Zhang, Chenyang Min, Lin Zhao, Jianhui 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 Network Information Center Office Henan University Kaifeng475004 China

ISBN: (纸本)9781839535635

The major work is to retrieve the surface soil moisture under crop cover in this paper. This paper proposes an improved GA-BP model called as DEFS-GA-BP model. This model is proposed to retrieve the surface soil moisture distribution using Sentinel-1 and Sentinel-2 remote sensing data. After extracting nine polarization features and one roughness parameter from Sentinel-1 data, five vegetation indexes from Sentinel-2 data. The differential evolution feature selection (DEFS) algorithm is applied to select the optimal feature subset. By using the genetic algorithm (GA) to optimize the node weights, the back propagation (BP) neural network is trained using the optimal feature subset and then used to generate the surface soil moisture distribution. The results of inversion algorithm indicate that the proposed DEFS-GA-BP model has a better inversion precision than GA-BP model, and demonstrates its application potential in surface soil moisture inversion. © 2020 Institution of engineering and technology. All rights reserved.

关键词： Genetic algorithms

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Interacting multiple model estimator for event-triggered cyber-physical systems against Denial-of-service attacks

Interacting multiple model estimator for event-triggered cyb...

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Chinese Automation Congress (CAC)

作者： Zengwang Jin Shuting Zhang Yanning Zhang Changyin Sun School of Cyberspace Security Northwestern Polytechnical University Xi'an China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Northwestern Polytechnical University Xi'an China School of Automation Southeast University Nanjing China

ISBN: (纸本)9781665426480

This paper investigates the problem of security state estimation of event-triggered cyber-physical systems (CPSs) under constrained bandwidth and random DoS attacks. Two independent Markov chains are introduced to separately describe the transition of system models and the occurrence of DoS attacks. By presenting an event-triggered scheduling mechanism based on the measurement innovation, the impact of bandwidth and power limitations in CPSs is mitigated. For the purpose of reducing the complexity of model sequences, the interacting multiple model (IMM) framework is extended event-triggered CPSs to cut down the the branch of exponential growth assumptions. In addition, the statistical information of event-triggered conditions are utilized to estimate the continuous state, identify the system mode and detect DoS attacks. Finally, the performance of the proposed method is validated by Monte Carlo simulation in a two-dimensional maneuvering target tracking.

关键词： Technological innovation Target tracking Simulation Bandwidth Markov processes Cyber-physical systems Denial-of-service attack

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Tissue Segmentation of Thick-Slice Fetal Brain MR Scans with Guidance from High-Quality Isotropic Volumes

arXiv

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arXiv 2023年

作者： Huang, Shijie Zhang, Xukun Cui, Zhiming Zhang, He Chen, Geng Shen, Dinggang School of Biomedical Engineering ShanghaiTech University Shanghai China Academy for Eng. & Tech. Fudan University Shanghai China Department of Radiology Obstetrics and Gynecology Hospital of Fudan University Shanghai China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China School of Biomedical Engineering ShanghaiTech University Shanghai201210 China Shanghai United Imaging Intelligence company Ltd. Shanghai200030 China Shanghai Clinical Research Trial Center Shanghai201210 China

Accurate tissue segmentation of thick-slice fetal brain magnetic resonance (MR) scans is crucial for both reconstruction of isotropic brain MR volumes and the quantification of fetal brain development. However, this task is challenging due to the use of thick-slice scans in clinically-acquired fetal brain data. To address this issue, we propose to leverage high-quality isotropic fetal brain MR volumes (and also their corresponding annotations) as guidance for segmentation of thick-slice scans. Due to existence of significant domain gap between high-quality isotropic volume (i.e., source data) and thick-slice scans (i.e., target data), we employ a domain adaptation technique to achieve the associated knowledge transfer (from high-quality volumes to thick-slice scans). Specifically, we first register the available high-quality isotropic fetal brain MR volumes across different gestational weeks to construct longitudinally-complete source data. To capture domain-invariant information, we then perform Fourier decomposition to extract image content and style codes. Finally, we propose a novel Cycle-Consistent Domain Adaptation Network (C2DA-Net) to efficiently transfer the knowledge learned from high-quality isotropic volumes for accurate tissue segmentation of thick-slice scans. Our C2DA-Net can fully utilize a small set of annotated isotropic volumes to guide tissue segmentation on unannotated thick-slice scans. Extensive experiments on a large-scale dataset of 372 clinically acquired thick-slice MR scans demonstrate that our C2DA-Net achieves much better performance than cutting-edge methods quantitatively and qualitatively. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance

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Chop chop BERT: Visual question answering by Chopping Visualbert's heads

arXiv

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arXiv 2021年

作者： Gao, Chenyu Zhu, Qi Wang, Peng Wu, Qi School of Computer Science Northwestern Polytechnical University Xi'an China School of Software Northwestern Polytechnical University Xi'an China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology China University of Adelaide Australia

Vision-and-Language (VL) pre-training has shown great potential on many related downstream tasks, such as Visual Question Answering (VQA), one of the most popular problems in the VL field. All of these pre-trained models (such as VisualBERT, ViLBERT, LXMERT and UNITER) are built with Transformer, which extends the classical attention mechanism to multiple layers and heads. To investigate why and how these models work on VQA so well, in this paper we explore the roles of individual heads and layers in Transformer models when handling 12 different types of questions. Specifically, we manually remove (chop) heads (or layers) from a pre-trained VisualBERT model at a time, and test it on different levels of questions to record its performance. As shown in the interesting echelon shape of the result matrices, experiments reveal different heads and layers are responsible for different question types, with higher-level layers activated by higher-level visual reasoning questions. Based on this observation, we design a dynamic chopping module that can automatically remove heads and layers of the VisualBERT at an instance level when dealing with different questions. Our dynamic chopping module can effectively reduce the parameters of the original model by 50%, while only damaging the accuracy by less than 1% on the VQA *** Codes 68T45 Copyright © 2021, The Authors. All rights reserved.

关键词： Visual languages

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Revisit the scheduling problem in Hurdle, V.F., 1973: A novel mathematical solution approach and two extensions

arXiv

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arXiv 2020年

作者： Fan, Wenbo School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu China

The scheduling problem in Hurdle (1973) was formulated in a general form that simultaneously concerned the vehicle dispatching, circulating, fleet sizing, and patron queueing. As a constrained variational problem, it remains not fully solved for decades. With technical prowess in graphic analysis, the author unveiled the closed-form solution for the optimal dispatch rates (with key variables undetermined though), but only suggested the lower and upper bounds of the optimal fleet size. Additionally, such a graphic analysis method lacks high efficiency in computing specific scheduling problems, which are often of a large scale (e.g., hundreds of bus lines). In light of this, the paper proposes a novel mathematical solution approach that first relaxes the original problem to an unconstrained one, and then attacks it using calculus of variations. The corresponding Euler-Lagrange equation yields the closed-form solution of the optimal dispatching rates, to which Hurdle's results are a special case. Thanks to the proposed approach, the optimal fleet size can also be solved. This paper completes the work of Hurdle (1973) by formalizing a solution method and generalizing the results. Based on that, we further make two extensions to the scheduling problem of general bus lines with multiple origins and destinations and that of mixed-size or modular buses. Closed-form results are also obtained with new insights. Among others, we find that the solutions for shuttle/feeder lines are a special case to our results of general bus lines. Numerical examples are also provided to demonstrate the effectiveness and efficiency of the proposed approach. Copyright © 2020, The Authors. All rights reserved.

关键词： Scheduling

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Camouflaged Object Detection via Context-aware Cross-level Fusion

arXiv

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arXiv 2022年

作者： Chen, Geng Liu, Si-Jie Sun, Yu-Jia Ji, Ge-Peng Wu, Ya-Feng Zhou, Tao Northwestern Polytechnical University Xi’an710072 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering China Data Processing Center School of Power and Energy China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China School of Computer Science Inner Mongolia University Hohhot010021 China Artificial Intelligence Institute School of Computer Science Wuhan University Wuhan430072 China

Camouflaged object detection (COD) aims to identify the objects that conceal themselves in natural scenes. Accurate COD suffers from a number of challenges associated with low boundary contrast and the large variation of object appearances, e.g., object size and shape. To address these challenges, we propose a novel Context-aware Cross-level Fusion Network (C2F-Net), which fuses context-aware cross-level features for accurately identifying camouflaged objects. Specifically, we compute informative attention coefficients from multi-level features with our Attention-induced Cross-level Fusion Module (ACFM), which further integrates the features under the guidance of attention coefficients. We then propose a Dual-branch Global Context Module (DGCM) to refine the fused features for informative feature representations by exploiting rich global context information. Multiple ACFMs and DGCMs are integrated in a cascaded manner for generating a coarse prediction from high-level features. The coarse prediction acts as an attention map to refine the low-level features before passing them to our Camouflage Inference Module (CIM) to generate the final prediction. We perform extensive experiments on three widely used benchmark datasets and compare C2F-Net with state-of-the-art (SOTA) models. The results show that C2F-Net is an effective COD model and outperforms SOTA models remarkably. Further, an evaluation on polyp segmentation datasets demonstrates the promising potentials of our C2F-Net in COD downstream applications. Our code is publicly available at: https://***/Ben57882/C2FNet-TSCVT. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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