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检索条件"任意字段=IEEE Information Visualization Conference/IEEE Visualization Conference"

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Development of Poisson's Ratio Measurement Software Based on Digital Twin Technology

Development of Poisson's Ratio Measurement Software Based on...

引用

ieee information Technology and Mechatronics Engineering conference (ITOEC)

作者： Shang Wang Zhe Sun Jinru Ma Shaomin Wang School of Automotive Engineering Beijing Polytechnic Beijing China College of Automation Engineering Beijing Polytechnic Beijing China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

This paper presents the development of an innovative software tool that utilizes digital twin technology to measure the Poisson's ratio. Traditional measurement methods often face challenges due to the small magnitude of axial and radial strains, leading to inaccuracies and insufficient data representation. To overcome these limitations, we propose a digital twin-based approach that enables the creation of a virtual replica of physical systems, allowing for real-time simulation and analysis of material behavior. This software not only facilitates precise measurement of the Poisson's ratio but also generates interactive visual representations of the data, thereby enhancing the understanding of engineers and stakeholders. Our research findings indicate that integrating digital twin technology into Poisson's ratio testing is not only feasible but also beneficial. This approach significantly improves measurement reliability and showcases visually enriched plots, making it easier for engineers and researchers to interpret complex data. The utilization of digital twins allows for real-time simulations and comprehensive data analysis, which are crucial for understanding material behavior under external loads.

关键词： visualization Data analysis Data visualization Real-time systems Digital twins Software measurement Stress Strain Testing Stress measurement

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TVConv: Efficient Translation Variant Convolution for Layout-aware Visual Processing

TVConv: Efficient Translation Variant Convolution for Layout...

引用

ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Chen, Jierun He, Tianlang Zhuo, Weipeng Ma, Li Ha, Sangtae Chan, S-H Gary Hong Kong Univ Sci & Technol Hong Kong Peoples R China Univ Colorado Boulder Boulder CO USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

A convolution has empowered many smart applications, dynamic convolution further equips it with the ability to adapt to diverse inputs. However, the static and dynamic convolutions are either layout-agnostic or computation-heavy, making it inappropriate for layout-specific applications, e.g., face recognition and medical image segmentation. We observe that these applications naturally exhibit the characteristics of large intra-image (spatial) variance and small cross-image variance. This observation motivates our efficient translation variant convolution (TVConv) for layout-aware visual processing. Technically, TVConv is composed of affinity maps and a weight-generating block. While affinity maps depict pixel-paired relationships gracefully, the weight-generating block can be explicitly overparameterized for better training while maintaining efficient inference. Although conceptually simple, TVConv significantly improves the efficiency of the convolution and can be readily plugged into various network architectures. Extensive experiments on face recognition show that TVConv reduces the computational cost by up to 3.1x and improves the corresponding throughput by 2.3x while maintaining a high accuracy compared to the depthwise convolution. Moreover, for the same computation cost, we boost the mean accuracy by up to 4.21%. We also conduct experiments on the optic disc/cup segmentation task and obtain better generalization performance, which helps mitigate the critical data scarcity issue. Code is available at https://***/JierunChen/TVConv.

关键词： Training visualization Convolution Face recognition Microscopy Computer architecture Network architecture

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Multi-label Iterated Learning for Image Classification with Label Ambiguity

Multi-label Iterated Learning for Image Classification with ...

引用

ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Rajeswar, Sai Rodriguez, Pau Singhal, Soumye Vazquez, David Courville, Aaron ServiceNow Res Montreal PQ Canada Montreal Inst Learning Algorithms Montreal PQ Canada Univ Montreal Montreal PQ Canada

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Transfer learning from large-scale pre-trained models has become essential for many computer vision tasks. Recent studies have shown that datasets like ImageNet are weakly labeled since images with multiple object classes present are assigned a single label. This ambiguity biases models towards a single prediction, which could result in the suppression of classes that tend to co-occur in the data. Inspired by language emergence literature, we propose multi-label iterated learning (MILe) to incorporate the inductive biases of multi-label learning from single labels using the framework of iterated learning. MILe is a simple yet effective procedure that builds a multi-label description of the image by propagating binary predictions through successive generations of teacher and student networks with a learning bottleneck. Experiments show that our approach exhibits systematic benefits on ImageNet accuracy as well as ReaL F1 score, which indicates that MILe deals better with label ambiguity than the standard training procedure, even when fine-tuning from self-supervised weights. We also show that MILe is effective reducing label noise, achieving state-of-the-art performance on real-world large-scale noisy data such as WebVision. Furthermore, MILe improves performance in class incremental settings such as IIRC and it is robust to distribution shifts. Code: https://***/rajeswar18/MILe

关键词： Training Computer vision visualization Systematics Computational modeling Transfer learning Predictive models

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Show, Deconfound and Tell: Image Captioning with Causal Inference

Show, Deconfound and Tell: Image Captioning with Causal Infe...

引用

ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Liu, Bing Wang, Dong Yang, Xu Zhou, Yong Yao, Rui Mao, Zhiwen Zhao, Jiaqi China Univ Min & Technol Sch Comp Sci & Technol Beijing Peoples R China Southeast Univ Sch Comp Sci & Engn Dhaka Bangladesh

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The transformer-based encoder-decoder framework has shown remarkable performance in image captioning. However;most transformer-based captioning methods ever overlook two kinds of elusive confounders: the visual confounder and the linguistic confounder, which generally lead to harmful bias, induce the spurious correlations during training, and degrade the model generalization. In this paper, we first use Structural Causal Models (SCMs) to show how two confounders damage the image captioning. Then we apply the backdoor adjustment to propose a novel causal inference based image captioning (CHC) framework, which consists of an interventional object detector (IOD) and an interventional transformer decoder (ITD) to jointly confront both confounders. In the encoding stage, the IOD is able to disentangle the region-based visual features by deconfounding the visual confounder. In the decoding stage, the ITD introduces causal intervention into the transformer decoder and deconfounds the visual and linguistic confounders simultaneously. Two modules collaborate with each other to alleviate the spurious correlations caused by the unobserved confounders. When tested on MSCOCO, our proposal significantly outperforms the state-of-the-art encoder-decoder models on Karpathy split and online test split. Code is published in https://***/CUMTGG/CIIC.

关键词： Training visualization Correlation Object detection Linguistics Transformers Encoding

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LNC Assisted Localization and Mapping in Pipe Environment

LNC Assisted Localization and Mapping in Pipe Environment

引用

ieee/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Zhang, Hongkai Yuan, Jianjun Guo, Shijie Wang, Hesheng Ma, Shugen Bao, Sheng Du, Liang Shanghai Univ Sch Mechatron Engn & Automat Shanghai Key Lab Intelligent Mfg & Robot Shanghai Peoples R China Shanghai Univ Shanghai Robot Inst Shanghai Peoples R China Fudan Univ Acad Engn & Technol Shanghai Peoples R China Heibei Univ Technol Sch Mech Engn Tianjin Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai Peoples R China Ritsumeikan Univ Dept Robot Kusatsu Shiga Japan

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

Regular maintenance of pipelines is an important task to ensure oil transportation and other operation (sewers, nature gas). Precise localization of pipeline damage can greatly improve the efficiency of maintenance work. Since the texture similarity and illumination change of pipe, traditional local descriptors for image matching like SIFT, SURF and ORB are easy to suffer from false correspondences. As to remove the false matches, the local neighborhood constraints (LNC) that contain spatial constructs around feature points are proposed. Good correspondences are essential for the high-accuracy localization and mapping solution given the limited textures and illumination in the pipes. The LNC method is also integrated into the state-of-the-art visual SLAM system. The proposed LNC image matching method and the SLAM system are evaluated on datasets gathered from the pipe environment. Compared with other state-of-the-art methods, our LNC image matching method achieves similar or better performance in precision, recall and runtime. The SLAM system provides state estimation and map reconstruction of the pipe in real-time, and the localization error is within 1%.

关键词： Location awareness visualization Simultaneous localization and mapping Image matching Pipelines Lighting Maintenance engineering

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Spatial Analysis of Turkey 2023 Seismic Activity: Geographical Distribution Research

Spatial Analysis of Turkey 2023 Seismic Activity: Geographic...

引用

Smart information Systems and Technologies (SIST), ieee International conference on

作者： A.Y. Zhakupova A.A. Abilova K.K. Assylkhan L.N. Gumilev Eurasian National University Astana Kazakhstan

ISBN: (数字)9798350374865

ISBN: (纸本)9798350374872

Pre-crisis management involves selecting the most suitable sites for construction and emergency response centers to minimize risk and vulnerability. The southeastern part of Turkey sits at the intersection of the Anatolian and Arabian lithospheric plates. Prolonged, consistent pressure has led to a devastating earthquake. To optimally locate future buildings and aid centers, as well as reduce risks to affected populations, the underlying ground for construction should be thoroughly investigated. To address this, the study utilized geographic information systems (GIS). ArcGIS was employed to overlay layers and epicenters of seismic activity in Turkey, illustrating the methods and tools used for earthquake data analysis and visualization on a map. The article covers key data processing steps, including data collection and preprocessing, selection of pertinent geographic data, and creation of geospatial visualizations based on data analysis. The outcomes of this analysis offer valuable insights for geologists, seismologists, and urban planners, aiding in a better understanding of regional seismic activity and informing measures to reduce risks, enhance safety, and guide future development.

关键词： Interpolation Seismic measurements Data analysis Geology Sociology Earthquakes Data visualization

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Discovery and Replication of Time-Resolved Functional Network Connectivity Differences in Adolescence and Adulthood in over 50K fMRI Datasets 44

Discovery and Replication of Time-Resolved Functional Networ...

引用

44th Annual International conference of the ieee-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Abrol, Anees Calhoun, Vince Georgia State Univ Ctr Translat Res Neuroimaging & Data Sci TReNDS Atlanta GA 30303 USA Georgia Inst Technol Atlanta GA 30332 USA Emory Univ Atlanta GA 30322 USA

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

There remains an open question about whether and in what context brain function varies in adolescence and adulthood. In this work, we systematically study the functional brain networks of adolescents and adults, outlining the significant differences in the developing brain detected via time-resolved functional network connectivity (trFNC) derived from a fully automated independent component analysis pipeline applied to resting-state fMRI data in over 50K individuals. We then statistically analyze the transient, recurrent, and robust brain state profiles in both groups. We confirmed the results in independent replication datasets for both groups. Our findings indicate a strengthening of a state reflecting functional coupling within the visual, motor, and auditory domains and anticorrelation with all other domains in a unique adult state profile, a pattern consistently less modular in adolescents. This new insight into possible integration, strengthening, and modularization of resting-state brain connections beyond childhood convergently indicates that the highlighted temporal dynamics likely reflect robust differences in brain function in adolescents versus adults.

关键词： Couplings visualization Pipelines Independent component analysis Functional magnetic resonance imaging Biomarkers Biology

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Towards the Co-Design of a Teachers' Dashboards in a Hybrid Learning Environment 2

Towards the Co-Design of a Teachers' Dashboards in a Hybrid ...

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2nd International conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)

作者： Ouatiq, Amina Riyami, Bouchaib Mansouri, Khalifa Qbadou, Mohammed Aoula, Es-Saadia Hassan II Univ Casablanca ENSET Mohammedia M2S2I Lab Mohammadia Morocco IGA Inst Super Genie Appl Casablanca Morocco

ISBN: (纸本)9781665422093

Learning Analytic Dashboards support learning, and try to empower stakeholders to make better-informed decisions. While creating dashboards for teachers is common in Learning analytics, designing it with them is not that common, especially in hybrid systems and using data from both the online and face-to-face sessions. This paper will be discussing co-designing a teacher dashboard with teachers through participatory workshop and interview, after getting their goals and expectation for dashboard in hybrid system. To arrive to a dashboard prototype, conform with teachers' needs and expectations.

关键词： co-design Human-centered design Teacher's dashboard Learning analytics visualization

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Finding the Signal: Near Real-time Data Analysis for Urban Traffic Monitoring on a Distributed Bluetooth Sensor Network 18

Finding the Signal: Near Real-time Data Analysis for Urban T...

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ieee 18th International conference on E-Science (E-Science)

作者： Sichani, Mohsen Arnold, Richard Bubendorfer, Kris Victoria Univ Wellington Sch Engn & Comp Sci Wellington New Zealand Victoria Univ Wellington Sch Math & Stat Wellington New Zealand

ISBN: (数字)9781665461245

ISBN: (纸本)9781665461245

The development of pervasive personal digital devices such as phones, watches, and headphones, interconnected by technologies such as Bluetooth, has led to a disruptive change in the ways in which local governments can monitor both vehicular and pedestrian traffic flows within their cities. In modern vehicles, navigation systems interconnect the personal devices of drivers and passengers typically via Bluetooth. By continuously monitoring such devices when they are in discover mode, traffic flows can be estimated almost in real-time. This paper examines traffic data collected from a Bluetooth Traffic Monitoring Systems installed by the Wellington City Council. Potentially such an installation could assist a local authority in real-time monitoring of normal traffic, as well as events including concerts and sport events, or in reaction to unanticipated events such as disasters. The limitation in this technology is that the data collected is of low fidelity, that is: not every vehicle has a detectable device, some have many, and there are devices carried by pedestrians and non-motor vehicles as well as stationary devices. This paper enumerates and investigates these challenges through statistical modelling, cleaning and data analysis. We present two novel algorithms for the processing of Bluetooth traffic data and validate our algorithms against a physical road counter. A case study is of a major earthquake is then presented as a proof of concept. The earthquake led to road closures, building collapse and other infrastructure damage and we examine three weeks of BTMS data and visualise how this earthquake impacted daily traffic flows.

关键词： Bluetooth Local government Roads Earthquakes Urban areas Data visualization Traffic control

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A Review of Evolutionary Optimization Methods for information visualization and Feature Space Exploration

A Review of Evolutionary Optimization Methods for Informatio...

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ieee International Symposium on Innovations in Intelligent Systems and Applications (INISTA)

作者： Marius Gavrilescu Florin Leon Lavinia-Eugenia Ferariu Cristian-Nicolae Buțincu Faculty of Automatic Control and Computer Engineering “Gheorghe Asachi” Technical University of Iasi Romania

ISBN: (数字)9798350368130

ISBN: (纸本)9798350368147

Evolutionary optimization plays an important role in the representation of information from data sets originating from various technical fields and natural sciences, as it helps to explore parameter spaces for meaningful representations. Quality-Diversity (QD) methods, notably MAP-Elites variants, prove effective in diverse fields, emphasizing their ability to provide sets of high-performing solutions. This review discusses challenges in single- and multi-objective optimization, with applications in multiple directions such as image processing, visualization, and medical imaging. It also reviews QD algorithms and highlights advancements in algorithmic adaptations, user-driven optimization and the potential to explore complex feature spaces. The presented works contribute to understanding and applying evolutionary optimization for solving visualization and feature space exploration problems in various domains.

关键词： visualization Technological innovation Uncertainty Costs Reviews Image processing Optimization methods

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