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TraSculptor: Visual Analytics for Enhanced Decision-Making in Road Traffic Planning

IEEE Transactions on Visualization and Computer Graphics 2025年 PP卷 PP页

作者： Deng, Zikun Liu, Yuanbang Zhu, Mingrui Xiang, Da Yu, Haiyue Su, Zicheng Lu, Qinglong Schreck, Tobias Cai, Yi South China University of Technology School of Software Engineering China South China University of Technology Ministry of Education Key Laboratory of Big Data and Intelligent Robot China Tongji University Key Laboratory of Road and Traffic Engineering of the Ministry of Education China National University of Singapore Department of Civil and Environmental Engineering Singapore Graz University of Technology Austria

The design of urban road networks significantly influences traffic conditions, underscoring the importance of informed traffic planning. Traffic planning experts rely on specialized platforms to simulate traffic systems, assessing the efficacy of the road network across various states of modifications. Nevertheless, a prevailing issue persists: many existing traffic planning platforms exhibit inefficiencies in flexibly interacting with the road network's structure and attributes and intuitively comparing multiple states during the iterative planning process. This paper introduces TraSculptor, an interactive planning decision-making system. To develop TraSculptor, we identify and address two challenges: interactive modification of road networks and intuitive comparison of multiple network states. For the first challenge, we establish flexible interactions to enable experts to easily and directly modify the road network on the map. For the second challenge, we design a comparison view with a history tree of multiple states and a road-state matrix to facilitate intuitive comparison of road network states. To evaluate TraSculptor, we provided a usage scenario where the Braess's paradox was showcased, invited experts to perform a case study on the Sioux Falls network, and collected expert feedback through interviews. © 1995-2012 IEEE.

关键词： Visual analytics

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SoyOmics:A deeply integrated database on soybean multi-omics

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Molecular Plant 2023年第5期16卷 794-797页

作者： Yucheng Liu Yang Zhang Xiaonan Liu Yanting Shen Dongmei Tian Xiaoyue Yang Shulin Liu Lingbin Ni Zhang Zhang Shuhui Song Zhixi Tian State Key Laboratory of Plant Cell and Chromosome Engineering Institute of Genetics and Developmental BiologyChinese Academy of SciencesBeijing 100101China National Genomics Data Center&CAS Key Laboratory of Genome Sciences and Information Beijing Institute of GenomicsChinese Academy of SciencesBeijing 100101China China National Center for Bioinformation Beijing 100101China University of Chinese Academy of Sciences Beijing 100039China

Dear Editor,As one of the most important crops to supply the majority of plant oil and protein for the whole world,soybean is facing an increasing global *** reference genome of accession"Williams82"opened the gate of genomics research in soybean(Schmutz et al.,2010).After that,vast multi-omics data were generated,thereby providing valuable resources for functional study and molecular *** of these data have been collected in different soybean databases(see details in Supplemental Table 1),such as Soybase(Grant et al.,2010)and SoyKB(Joshi et al.,2012),which made valuable efforts to facilitate the wide utility of these ***,these existing databases poorly tackled multi-omics data integration and interactivity for soybean,provoking tremendous challenges for researchers to deal with these big omics data,particularly considering the unprecedented rate of data growth(Yang et al.,2021).Thus,constructing an integrated multi-omics database for soybean that provides a one-stop solution for big data mining with friendly interactivity is highly desired.

关键词： database details thereby

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Improved LS+MAR hybrid method to UT1-UTC ultra-short-term prediction by using first-order-difference UT1-UTC

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Geodesy and Geodynamics 2024年第4期15卷 379-385页

作者： Fei Ye Yunbin Yuan BeiDou High-precision Satellite Navigation and Location Service Hunan Engineering Research Center Hunan Institute of Geomatics Sciences and TechnologyChangsha410007China State Key Laboratory of Geodesy and Earth's Dynamics Innovation Academy for Precision Measurement Science and TechnologyChinese Academy of SciencesWuhan430077China HNCORS Data Center Hunan Institute of Geomatics Sciences and TechnologyChangsha 410007China

Accurate ultra-short-term prediction of the Earth rotation parameters(ERP)holds paramount impor-tance for real-time applications,particularly in reference frame *** them,diurnal rota-tion(UT1-UTC)which cannot be directly estimated through Global Navigation Satellite System(GNSS)techniques,significantly affects the rapid and ultra-rapid orbit determination of GNsS ***-ently,the traditional LS(least squares)+AR(autoregressive)and LS+MAR(multivariate autoregressive)hybrid methods stand as primary approaches for UT1-UTC ultra-short-term predictions(1-10 days).The LS+MAR hybrid method relies on the UT1-UTC and LOD(length of day)***,the correlation between LOD and first-order-difference UT1-UTC is stronger than that between LOD and *** light of this,and with the aid of the first-order-difference UT1-UTC,we propose an enhanced LS+MAR hybrid method to UT1-UTC ultra-short-term *** using the UT1-UTC and LOD data series of the IERS(International Earth Rotation and Reference Systems Service)EOP 14 C04 product,we conducted a thorough analysis and evaluation of the improved method's prediction performance compared to the traditional LS+AR and LS+MAR hybrid *** to the numerical results over more than 210 days,they demonstrate that,when considering the correlation information between the LoD and the first-order-difference UT1-UTC,the mean absolute errors(MAEs)of the improved LS+MAR hybrid method range from 21 to 934μs in 1-10 days *** comparison to the traditional LS+AR hybrid method,the MAEs show a reduction of 7-53μs in 1-10 days predictions,with corresponding improvement percentages ranging from 1 to 28%.Similarly,when compared to the traditional LS+MAR hybrid method,the MAEs have a reduction of 5-42μs in 1-10 days predictions,with corresponding improvement percentages ranging from 4-20%.Additionally,when aided by GNSS-derived LOD data series,the MAEs of improved LS+MAR hybrid method experience further

关键词： UT1-UTCultra-short-termprediction Rapidandultra-rapidproducts LS+AR and LS+MAR Correlation First-order-differenceUT1-UTC

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Improved Sparse Activity Detection Based on Optimized Measurement Matrix in mMTC 100

Improved Sparse Activity Detection Based on Optimized Measur...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Wang, Jianyu Yang, Liu Bai, Chaoyuan Liu, Heng Chengdu611756 China Southwest Jiaotong University Key Lab of Information Coding & Transmission Institude of Mobile Communication Chengdu611756 China Southwest Jiaotong University National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu611756 China

ISBN: (纸本)9798331517786

Device activity detection and channel estimation are central problems in massive Machine Type Communication (mMTC) scenarios. Given the sparse distribution of active devices, compressive sensing (CS) emerges as a viable solution at the gNB to mitigate interference stemming from non-orthogonal preamble sets. This paper proposes an algorithm for optimizing measurement matrix, thereby elevating the performance of signal recovery in CS. Within the proposed algorithm, a optimization model that integrates eigenvalue averaging and equiangular tight frame (ETF) theory is formulated, and an iterative algorithm is devised to approximate the optimal solution utilizing singular value decomposition (SVD). Further, a weight-based strategie is discussed with respect to the feasibility of the proposed algorithm, accompanied by the introduction of iteration termination condition. Experimental results corroborate the algorithm's swift convergence, underpinning the efficient production of high-performance matrices that refine the accuracy of joint device activity detection and channel estimation. © 2024 IEEE.

关键词： Eigenvalues and eigenfunctions

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InMu-Net: Advancing Multi-modal Intent Detection via Information Bottleneck and Multi-sensory Processing 24

InMu-Net: Advancing Multi-modal Intent Detection via Informa...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhu, Zhihong Cheng, Xuxin Chen, Zhaorun Chen, Yuyan Zhang, Yunyan Wu, Xian Zheng, Yefeng Xing, Bowen Peking University Beijing China The University of Chicago Chicago United States Fudan University Shanghai China Jarvis Research Center Tencent YouTu Lab Shenzhen China Medical Artificial Intelligence Lab Westlake University Hangzhou China Beijing Key Laboratory of Knowledge Engineering for Materials Science School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9798400706868

Multi-modal intent detection (MID) aims to comprehend users' intentions through diverse modalities, which has received widespread attention in dialogue systems. Despite the promising advancements in complex fusion mechanisms or architecture designs, challenges remain due to: (1) various noise and redundancy in both visual and audio modalities and (2) long-tailed distributions of intent categories. In this paper, to tackle the above two issues, we propose InMu-Net, a simple yet effective framework for MID from the Information bottleneck and Multi-sensory processing perspective. Our contributions lie in three aspects. First, we devise a denoising bottleneck module to filter out the intent-irrelevant information in the fused feature;Second, we introduce a saliency preservation loss to prevent the dropping of intent-relevant information;Ultimately, kurtosis regulation is introduced to maintain representation smoothness during the filtering process, mitigating the adverse impact of the long tail distribution. Comprehensive experiments on two MID benchmark datasets demonstrate the effectiveness of InMu-Net and its vital components. Impressively, a series of analyses reveal our denoising potential and robustness in low-resource, modality corruption, cross-architecture and cross-task scenarios. © 2024 ACM.

关键词： Multicarrier modulation

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Day Ahead Optimal Scheduling of Smart Community Microgrid Based on Improved NSGA-III 1

Day Ahead Optimal Scheduling of Smart Community Microgrid Ba...

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1st International Conference on Intelligent Transportation and Smart Cities, ICITSC 2024

作者： Zhao, Meixuan Hao, Xueli Pei, Lili Li, Hangying Li, Wei School of Information Engineering Chang'an University Xi'an China Anhui Key Laboratory of Intelligent Transportation Anhui Keli Information Industry Co. Ltd. He'fei China School of Data Science and Artificial Intelligence Chang'an University Xi'an China

ISBN: (纸本)9781643685205

Urban Microgrids have complex topologies and operating environments. This leads to extreme volatility and instability when high percentage of renewable energy is integrated into them. To address these problems, we propose a day-ahead optimal scheduling model for the smart community microgrid on the basis of the improved non-dominated sorting genetic algorithm III (NSGA-III). It minimizes the day-ahead operating cost of the microgrid while balancing electricity load. Firstly, we design a wind/solar/hydrogen storage self-consistent energy system inside the community microgrid to realize the efficient utilization of renewable energy. Secondly, we introduce an adaptive mutation-crossover strategy, and propose a fuzzy inference-based NSGA-III algorithm, to solve the slow convergence speed and poor convergence stability of the current multi-objective optimization algorithms. Finally, we design an optimal microgrid scheduling strategy based on typical day. In the day ahead operation cycle of the community microgrid, it maximizes the absorption of wind/solar unit outputs. This improves the operational efficiency of the microgrid and effectively reducing system operating costs. Extensive experiments demonstrate that the proposed model achieves superior performance compared with the original NSGA-III methods. The total daily operating costs of the proposed microgrid is reduced by 4.5%, and the renewable energy consumption rates is increased by 8.1%. © 2024 The Authors.

关键词： Energy utilization

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VG-Net: Sensor Time Series Anomaly Detection with Joint Variational Autoencoder and Graph Neural Network 10

VG-Net: Sensor Time Series Anomaly Detection with Joint Vari...

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10th IEEE Smart World Congress, SWC 2024

作者： Wang, Shuai Liu, Shenghao Fan, Xiaoxuan Wang, Haijun Zhou, Yinxin Gao, Hanjun Lu, Hongwei Deng, Xianjun Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China Hunan Technical College of Railway High-speed China Nanjing University of Posts and Telecommunications China China National Nuclear Power Co Ltd China

ISBN: (纸本)9798331520861

Intelligent Cyber-Physical Systems typically utilize sensors to gather a significant amount of raw data which often possesses temporal and high-dimensional characteristics. Ensuring the data reliability is crucial for the normal operation of the system. While most existing methods are capable of identifying anomalies in high-dimensional time series data from sensors, they often lack adequate accuracy and fail to adequately consider the correlation between sensor nodes. To address these limitations, we propose an unsupervised framework which is a combination of variational autoencoder and graph neural network(VG-Net). By jointly optimizing the reconstruction module and the prediction module, VG-Net effectively models the correlation between sensor nodes while simultaneously detecting anomalies. The reconstruction module is responsible for modeling the latent representation space and reconstructing the time series data through variational autoencoder. The prediction module leverages bidirectional long short term memory networks and neural tensor network to learn the relationships between sensor nodes, and uses graph neural network to predict the future behavior of the data. Experiments on eight real and synthetic datasets demonstrate that, compared with existing methods, VG-Net can accurately model the relationship between sensor nodes and enhance the anomaly detection performance of high-dimensional time series data. © 2024 IEEE.

关键词： Graph neural networks

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Visualization and simulation of steel metallurgy processes

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International Journal of Minerals,Metallurgy and Materials 2021年第8期28卷 1387-1396页

作者： Te Xu Guang Song Yang Yang Pei-xin Ge Li-xin Tang Key Laboratory of Data Analytics and Optimization for Smart Industry(Northeastern University) Ministry of EducationShenyang 110819China Liaoning Engineering Laboratory of Operations Analytics and Optimization for Smart Industry Northeastern UniversityShenyang 110819China Liaoning Key Laboratory of Manufacturing System and Logistics Shenyang 110819China Institute of Industrial&Systems Engineering Northeastern UniversityShenyang 110819China

Steel production involves the transfer and transformation of material and energy at different levels, structures, and scales, and this process incurs substantial information in the material and energy dimensions. Given the black-box feature of iron and steel production processes, process visualization plays an important role and inevitably benefits parameter correction, technical support decision-making, personnel training, and other aspects of the steel metallurgy industry. The technological characteristics of the entire process in the steel industry were analyzed in this study, a visualization technology route based on virtual reality(VR) was built, and the important components of visual simulation system for steel industry and the important technical points needed to realize the system were proposed. On the foundation, a visual simulation model for the process scheduling of the iron and steel enterprise raw materials' field, slab, and hot rolling processes was built, and a visualization simulation platform of the iron and steel metallurgy plant-wide process, including ironmaking, steelmaking, hot rolling, and cold rolling, was developed. Lastly, the effectiveness of platform was illustrated by practical application.

关键词： virtual reality steel metallurgy plant-wide visualization

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Exceptional point enhanced nanoparticle detection in deformed Reuleaux‑triangle microcavity

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Frontiers of Optoelectronics 2024年第3期17卷 83-91页

作者： Jinhao Fei Xiaobei Zhang Qi Zhang Yong Yang Zijie Wang Chuanlu Deng Yi Huang Tingyun Wang Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced CommunicationShanghai Institute for Advanced Communication and Data ScienceSchool of Communication and Information EngineeringShanghai UniversityShanghai 200444China

In this paper,we propose a deformed Reuleaux-triangle resonator(RTR)to form exceptional point(EP)which results in the detection sensitivity enhancement of *** introducing single nanoparticle to the deformed RTR at EP,frequency splitting obtains an enhancement of more than 6 times compared with non-deformed *** addition,EP induced a result that the far feld pattern of chiral mode responses signifcantly to external perturbation,corresponding to the change in internal ***,single nanoparticle with far distance of more than 4000 nm can be detected by measuring the variation of far feld directional *** to traditional frequency splitting,the far feld pattern produced in deformed RTR provides a cost-efective and convenient path to detect single nanoparticle at a long distance,without using tunable laser and external *** structure indicates great potential in high sensitivity sensor and label-free detector.

关键词： Exceptional point Deformed microcavity Nanoparticle detection Far-feld pattern

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Narrow-linewidth and frequency-agile FMCW laser on the Si3N4 -on-SOI photonic platform

Narrow-linewidth and frequency-agile FMCW laser on the Si3N4...

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CLEO: Applications and Technology in CLEO 2024, CLEO: A and T 2024 - Part of Conference on Lasers and Electro-Optics

作者： Liu, Chuxin Li, Xinhang Guo, Yuyao Lu, Liangjun Chen, Jianping Zhou, Linjie State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Lab of Navigation and Location Services Shanghai Institute for Advanced Communication and Data Science Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu314200 China

We demonstrate a hybrid-integrated self-injection-locked laser on the Si3N4-on-SOI platform to generate FMCW signal with 9 kHz linewidth, 2 MHz repetition rate, and high linearity, supporting FMCW-LiDAR with refresh r... 详细信息

ISBN: (纸本)9781957171395

关键词： Linewidth

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