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arXiv 2023年

作者： Liu, Can Zhang, Yu Wu, Cong Li, Chen Yuan, Xiaoru School of Intelligence Science and Technology Peking University Beijing100871 China University of Oxford Oxford United Kingdom Central Academy of Fine Arts Beijing China School of Intelligence Science and Technology National Engineering Laboratory for Big Data Analysis and Application Peking University Beijing China

We introduce a spatial constraint model to characterize the positioning and interactions in visualizations, thereby facilitating the activation of static visualizations. Our model provides users with the capability to manipulate visualizations through operations such as selection, filtering, navigation, arrangement, and aggregation. Building upon this conceptual framework, we propose a prototype system designed to activate pre-existing visualizations by imbuing them with intelligent interactions. This augmentation is accomplished through the integration of visual objects with forces. The instantiation of our spatial constraint model enables seamless animated transitions between distinct visualization layouts. To demonstrate the efficacy of our approach, we present usage scenarios that involve the activation of visualizations within real-world contexts. © 2023, CC BY.

关键词： Visualization

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analysis of parallel helix tension measurement based on frequency domain reflection method 5

Analysis of parallel helix tension measurement based on freq...

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5th International Conference on Artificial Intelligence and Computer Science, AICS 2023

作者： Li, Chaopeng Li, Qing Chen, Huamin Xu, Xiajie Miao, Xinyu Zhang, Yangke College of Mechanical and Electrical Engineering China Jiliang University Zhejiang Hangzhou310018 China National and Local Joint Engineering Research Center for Disaster Monitoring Technology and Instruments China Jiliang University Zhejiang Hangzhou310018 China Zhejiang Key Laboratory of Intelligent Manufacturing Quality Big Data Traceability and Application Zhejiang Hangzhou310061 China Jinhua Geological Environment Monitoring Station Zhejiang Jinhua321000 China

ISBN: (纸本)9781510668621

Distributed measurement of underground rock and soil deformation is an important part of geological hazard monitoring. Using parallel helical transmission lines as sensors and combining frequency domain reflection (FDR) analysis enables continuous distributed measurement of larger deformation quantities. It is known that the characteristic impedance of the parallel helical transmission line increases with the increase of tensile force. Through FDR, the measurement and location of the tensile point can be determined. This paper explains the principle of FDR measurement of tensile deformation in parallel helical transmission lines. In response to practical application issues of FDR in parallel helical transmission lines, this paper analyzes the influence of sweep range, frequency, and number of sampling points on measurement results through experimental data. The paper also measures different incident signals under different circumstances, identifies and analyzes factors that may affect measurement results, and selects a better measurement scheme. The proposed method is used to simulate the actual use of parallel helical transmission lines and obtain actual measurement data, which is analyzed for better understanding. © 2023 SPIE.

关键词： Electric lines

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An Optimized Variational Bayesian Point Set Matching Approach using Coordinate Ascent and Simulated Annealing

An Optimized Variational Bayesian Point Set Matching Approac...

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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Fangjun Luan Shitong Liu Shuai Yuan Shenyang Branch of National Special Computer Engineering Technology Research Center Shenyang China School of Computer Science and Engineering Shenyang Jianzhu University Shenyang China Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang China

ISBN: (数字)9798331506100

ISBN: (纸本)9798331506117

Aiming at the interference problem of outliers in point set matching, we propose an optimized variational Bayesian point set matching algorithm using coordinate ascent and simulated annealing. For the complex terms in the objective function, we introduce the coordinate ascending strategy and use the Monte Carlo integral stochastic approximation and control variable method to directly optimize the parameters. To enhance the generalization ability of the algorithm and avoid falling into the local optimal solution, we use the simulated annealing scheme to optimize the spatial mapping of the point set. Finally, the affine transform point set matching experiment and the non-rigid point set matching experiment represent our algorithm have a good matching effect and high accuracy.

关键词： Monte Carlo methods Annealing Integral equations Simulated annealing Transforms Interference Approximation algorithms

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Study on Lix54-Tbp Extraction for Li/Na Separation and its Kinetics and Thermodynamics

SSRN

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SSRN 2024年

作者： Ma, Yu Ma, Liwen Xi, Xiaoli Nie, Zuoren Collaborative Innovation Center of Capital Resource-Recycling Material Technology College of Materials Science and Engineering Beijing University of Technology Beijing100124 China Key Laboratory of Advanced Functional Materials Ministry of Education Beijing University of Technology Beijing100124 China National Engineering Laboratory for Industrial Big-data Application Technology Beijing University of Technology Beijing100124 China

To improve the Li+ recovery efficiency and the Li/Na separation of low-grade lepidolite, a Lix54-TBP extractant system with sec-octyl alcohol as dehalogenation and sulfonated kerosene as diluent was proposed. In a high Na+ and low Li+ solution, the primary extraction efficiency of Li+ reached 98.29% with Li/Na separation coefficient of 5606.25 under the optimal extraction conditions and the stripping efficiency was 99.75%. The kinetic equation of the extraction was obtained as R(Li) = 9.397 × 10-7 × [Li+]0.7882·[Lix54]0.2337·[TBP]0.2999. The thermodynamic parameters were calculated as ΔH=-2.41kJ/mol, ΔG=-1.129kJ/mol, ΔS=3.778J/mol and Ea =19.73kJ/mol, indicated that the extraction reaction was exothermic, spontaneous and probably mixed controlled by diffusion and chemical reaction. The reaction mechanism was ion exchange reaction, and extractants had a higher selectivity for Li+ than Na+ during the extraction process. This study provides an important technical reference for the efficient separation and recovery of Li from low-grade lepidolite, which is helpful for the utilization of strategic mineral resources. © 2024, The Authors. All rights reserved.

关键词： Extraction

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Graph Convolution Synthetic Transformer for Chronic Kidney Disease Onset Prediction 9th

Graph Convolution Synthetic Transformer for Chronic Kidney ...

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19th International Conference on Advanced data Mining and applications, ADMA 2023

作者： Zhu, Di Liu, Yi Chen, Weitong Wang, Yanda Huang, Yefan Wang, Xiaoli Cai, Ken Li, Bohan Nanjing University of Aeronautics and Astronautics Nanjing211106 China Adelaide University AdelaideSA 5005 Australia Nanjing Normal University Nanjing210023 China Xiamen University Xiamen361005 China Zhongkai University of Agriculture and Engineering Guangzhou510225 China Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing211106 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xian710072 China

ISBN: (纸本)9783031466700

Effective disease prediction based on electronic health records (EHR) is an important topic in health informatics. The current methods usually use common deep-learning models for disease prediction. However, it is difficult to fully learn the graphic encounter structure of EHR to improve prediction performance. Moreover, in prediction tasks, chronic kidney disease (CKD) has a poor prognosis due to excessive risk factors and complex comorbidities. Therefore, we propose a CKD onset prediction model called Graph Convolution Synthetic Transformer (GCST) based on EHR, using Fusion Attention Mechanism to solve these challenges. By modifying Transformer, GCST uses Factorized Dense Attention and Medical Local Attention to learn global and local attention, generating Synthetic Attention to learn the potential encounter structure and meaningful medical knowledge of EHR. In addition, we also propose a transfer learning strategy based on sample weighted correction to guide the prediction of GCST in specific low-resource EHR. We conduct sufficient experiments on three datasets to test the performance of GCST. Experiments show that GCST has significant improvement over state-of-the-art models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Forecasting

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Exploring the Non-linear Relationship between E-scooter Ridership and the Built Environment 6

Exploring the Non-linear Relationship between E-scooter Ride...

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6th International Conference on Transportation Information and Safety, ICTIS 2021

作者： Qiu, Zhongquan Yuan, Hongxia Wang, Chao Dai, Shideng Huo, Yamin Wen, Yi Southwest Jiaotong University Hi-Tech Industrial Development Zone School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology National United Engineering Laboratory of Integrated and Intelligent Transportation Chengdu611756 China Southwest Jiaotong University Hi-Tech Industrial Development Zone School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology National United Engineering Laboratory of Transportation Chengdu611756 China University of Tennessee Department of Civil and Environmental Engineering KnoxvilleTN37996 United States

ISBN: (纸本)9781665497138

Shared e-scooter has become a popular mode of transportation in the United States. Despite the increasing attention paid to the e-scooter patterns and the influencing factors of e-scooter trips, few studies have explored the relative importance of the built environment and its threshold effect on e-scooters. Using the trip records of all the shared e-scooters in Austin, Texas, USA from August 2018 to March 2021, the origins and destinations of which are recorded by census tract, this study employs gradient boosting decision trees (GBDT) to examine the non-linear effects of the built environment on the e-scooter ridership. The results show that the relative importance of all built environment factors, with a contribution of 70.45%, is higher than that of socio-demographics (29.54%), and the distance from census tract to city center and bike lane density are the two most essential attributes to e-scooter ridership. Besides, all built environment factors have salient non-linear and threshold effects on the e-scooter ridership. For example, distance to the city center has a significant negative impact on e-scooter trips with an effective range of 6.5 kilometers, and the bike lane density is positively associated with the e-scooter ridership in a certain range. By quantifying each built environment factor's relative importance and threshold effect on e-scooters, this paper can provide e-scooter operators with fleet deployment and operation management suggestions and guide planning departments to build an e-scooter-friendly environment. © 2021 IEEE.

关键词： Decision trees

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A TSICN-based Inferential Synthesis Method for Class Imbalance in Credit Scoring

A TSICN-based Inferential Synthesis Method for Class Imbalan...

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IEEE International Conference on data Mining (ICDM)

作者： Dongxu Fanab Xuanzhi Feng Jinghe Jiang Yuming Jiang Le Zhang Dasha Hu College of Computer Science Sichuan University Chengdu China Big Data Analysis and Fusion Application Technology Engineering Laboratory of Sichuan Province Chengdu China Department of Economics College of Letters and Science University of California Los Angeles USA

lass imbalance and data obsolescence are two major issues in the field of credit scoring, leading to excessive bias and inaccuracies in the classification process of credit scoring models. In order to augment minority class samples and balance time dependencies, this paper proposes a credit scoring model based on Temporal Sample Interaction Convolutional Network (TSICN) to facilitate better credit risk assessment for financial institutions. It relies on the intrinsic features of the minority class samples to synthesize new data, thereby increasing the quantity of the minority class samples. Through inference and synthesis, It greatly mitigates the loss of information from minority class samples. The synthesized minority class samples, blended with the original data, are inputted into the causal convolutional layers and dilated convolutional layers. The information flow and memory updates are regulated through reset gate and update gate, The reset gate determines how to combine past information with the current input, while the update gate determines how to combine the previous hidden state with the current candidate hidden state. Compared to common methods, TSICN can integrate information features from minority class samples into the synthesis data, focusing more on short-term dependencies while reducing the capture of long-term dependencies. Experimental results show that TSICN achieves excellent credit scoring classification performance on real-world datasets. This enables more accurate prediction of applicant credit risk, thus reducing the risk of loan default.

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A weakly supervised pavement crack segmentation based on adversarial learning and transformers

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Multimedia Systems 2025年第3期31卷 1-21页

作者： Apedo, Yvon Tao, Huanjie School of Computer Science Northwestern Polytechnical University Xi’an People’s Republic of China Engineering and Research Center of Embedded Systems Integration (Northwestern Polytechnical University) Ministry of Education Xi’an People’s Republic of China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’an People’s Republic of China

While pixel-level crack segmentation has demonstrated significant potential in infrastructure inspections, its reliance on detailed annotations poses challenges for widespread adoption due to the time-consuming and expensive nature of such labeling. In response, weakly supervised crack segmentation has garnered attention as it eliminates the need for pixel-level annotations. However, existing methods, primarily based on class activation maps (CAM), involve complex training processes and suffer from poor performance due to misalignment between CAM-generated labels and the true target in the image. To address these challenges, we propose a weakly supervised approach for crack segmentation that simultaneously generates synthetic crack images and performs segmentation through adversarial learning. Unlike traditional methods that rely on coarse labels, our approach leverages synthetic crack images with corresponding labels, effectively eliminating issues of misalignment and noisy pseudo-labels. Our method introduces an encoder-decoder architecture for the segmentation model, incorporating a Transformer-based Feature Enhancement module (TFE) in the encoder. This module is specifically designed to capture long-range dependencies and efficiently integrate both high- and low-level features. Additionally, the model includes a Hilo block to extract both high- and low-frequency information from the images, along with a Progressive Shrinking Decoder (PSD) to aggregate and refine adjacent feature maps. Extensive experiments were conducted, and our model achieved an ODS of 67.12% on the CrackForest dataset, 60.11% on the Crack500 dataset, and 44.80% on the AEL dataset. These results demonstrate that our model outperforms several existing weakly supervised pavement segmentation methods across these datasets.

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Blockchain-Based Multi-Cloud data Storage System Disaster Recovery

Blockchain-Based Multi-Cloud Data Storage System Disaster Re...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Feiyu Wang Jian-tao Zhou College of Computer Science Inner Mongolia University Hohhot Inner Mongolia 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

Cloud storage services have been used by most businesses and individual users. However, data loss, service interruptions and cyber attacks often lead to cloud storage services not being provided properly, and these incidents have caused financial losses to users. Second, traditional and single-cloud model disaster recovery services are no longer suitable for the current complex cloud storage systems. Therefore, a scheme to provide disaster recovery for cloud storage services in a multi-cloud storage environment is needed in real production. In this paper, we propose a disaster recovery scheme based on blockchain technology. The proposed scheme outlined in this study aims to address the issue of data availability within the cloud storage landscape. The proposed scheme achieves this goal by dividing data into hot and cold categories, verifying the integrity of copy data via blockchain technology, and utilizing blockchain networks to manage multi-cloud storage systems. Experimental findings demonstrate that the proposed scheme yields superior results in terms of computation and time overheads.

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A Joint Representation Using Continuous and Discrete Features for Cardiovascular Diseases Risk Prediction on Chest CT Scans

arXiv

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arXiv 2024年

作者： Xu, Minfeng Fan, Chen-Chen Zhou, Yan-Jie Guo, Wenchao Liu, Pan Qi, Jing Lu, Le Chao, Hanqing He, Kunlun DAMO Academy Alibaba Group China Department of Electronic Engineering Tsinghua University Beijing China Hupan Laboratory Hangzhou China Beijing Key Laboratory of Precision Medicine for Chronic Heart Failure Beijing China National Engineering Research Center for Medical Big Data Application Technology Beijing China

Cardiovascular diseases (CVD) remain a leading health concern and contribute significantly to global mortality rates. While clinical advancements have led to a decline in CVD mortality, accurately identifying individuals who could benefit from preventive interventions remains an unsolved challenge in preventive cardiology. Current CVD risk prediction models, recommended by guidelines, are based on limited traditional risk factors or use CT imaging to acquire quantitative biomarkers, and still have limitations in predictive accuracy and applicability. On the other hand, end-to-end trained CVD risk prediction methods leveraging deep learning on CT images often fail to provide transparent and explainable decision grounds for assisting physicians. In this work, we proposed a novel joint representation that integrates both discrete quantitative biomarkers and continuous deep features extracted from chest CT scans. Our approach initiated with a deep CVD risk classification model by capturing comprehensive continuous deep learning features while jointly obtaining currently clinical-established quantitative biomarkers (as discrete features) via segmentation models. In the feature joint representation stage, we use an instance-wise feature-gated mechanism to align the continuous and discrete features, followed by a soft instance-wise feature interaction mechanism fostering independent and effective feature interaction for the final CVD risk prediction. Our method substantially improves CVD risk predictive performance and offers individual contribution analysis of each biomarker, which is important in assisting physicians' decision-making processes. We validated our method on a public chest low-dose CT dataset (LDCT-NLST) and a private external chest standard-dose CT patient cohort of 17,207 CT volumes from 6,393 unique subjects, and demonstrated superior predictive performance, achieving AUCs of 0.875 and 0.843 for two cohorts, respectively. Copyright © 2024, The Authors. Al

关键词： Cardiology

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