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4th Euro-China Conference on Intelligent data analysis and applications, ECC 2017

作者： Meng, S.H. Hu, S.B. Huang, A.C. Huang, T.J. Xie, Zhixuan Jian, Chen School of Information Science and Engineering Fujian University of Technology FuzhouFujian350118 China Key Laboratory of Big Data Mining and Application School of Information Science and Engineering Fujian University of Technology Minhou Fuzhou CityFujian350118 China Institute of Electro-Optical Science and Engineering National Cheng Kung University Tainan70101 Taiwan Department of Electrical Engineering National Sun Yat-sen University Kaohsiung Taiwan Kaohsiung Municipal ChungShan Senior High School Kaohsiung Taiwan

ISBN: (纸本)9783319685267

Utilizing non-contact eye-detection- and image-processing-based fatigue recognition and early warning technology, this study established a human-computer interaction system based on dynamic expression recognition. The degree of fatigue was determined based on the percentage of eyelid closure (PERCLOS). Facial and eye areas in the key frames for facial expression identification were adopted to determine the expression of fatigue. A skin-color technique was used for face detection. A skin model was later established using the hue, saturation, value (HSV) color model, which was then used to detect the skin color of any given face. The improved circle Hough transform algorithm was applied for use in eye detection. Blink rate (pupil region extraction) and PERCLOS were combined in the detection of fatigue. © 2018, Springer International Publishing AG.

关键词： Face recognition

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Research on Benefit-Risk of Vehicle-Cargo Matching Platform Based on Matching Degree

Research on Benefit-Risk of Vehicle-Cargo Matching Platform ...

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IEEE International Conference on Intelligent Transportation engineering (ICITE)

作者： Yongdong Sun Jin Zhang Qiuyu Tang Yan Yan School of Transportation and Logistics (Southwest JiaoTong University) Chengdu China National United Engineering Laboratory of Integrated and Intelligent Transportation National Engineering Laboratory of Integrated Transportation Big Data Application Technology (Southwest JiaoTong University) Chengdu China

ISBN: (数字)9781728194097

ISBN: (纸本)9781728194103

This paper based on matching degree of the vehicle-cargo matching platform with profit and risk, examines the impact of different factors on the revenue and risk cost of the vehicle-cargo matching platform through a multi-objective optimal model to maximize the matching degree and benefit, minimizing the risk cost, through the improved the NSGA- II algorithm to solve bilevel multi-objective optimization problem, and through the sensitivity analysis to obtain the results. The results show that: 1) the relationship between the total revenue and the total risk cost of the platform increases linearly; 2) the preference of vehicle-source and cargo-source also affects the matching of supply demand of both sides; 3) the total revenue and total cost of the platform increase with the increase of consultation fee, but the increase rate gradually decreases; 4) ignoring the impact of blockchain, vehicle-source enterprises need to pay more guarantee costs, and the platform's income and risk are better than the condition with the impact of blockchain.

关键词： Transportation Blockchain Logistics Optimization Contracts Sensitivity analysis Encoding

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

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

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International Conference on Transportation Information and Safety (ICTIS)

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

ISBN: (纸本)9781665497145

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.

关键词： Correlation Employment Urban planning Motorcycles Bicycles Boosting Safety

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Indoor 3D Reconstruction with an Unknown Camera-Projector Pair

arXiv

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arXiv 2024年

作者： Qi, Zhaoshuai Hao, Yifeng Hu, Rui Chang, Wenyou Yang, Jiaqi Zhang, Yanning National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology The College of Computer Science Northwestern Polytechnical University ShaanXi Xi'An710072 China

Structured light-based method with a camera-projector pair (CPP) plays a vital role in indoor 3D reconstruction, especially for scenes with weak textures. Previous methods usually assume known intrinsics, which are pre-calibrated from known objects, or self-calibrated from multi-view observations. It is still challenging to reliably recover CPP intrinsics from only two views without any known objects. In this paper, we provide a simple yet reliable solution. We demonstrate that, for the first time, sufficient constraints on CPP intrinsics can be derived from an unknown cuboid corner (C2), e.g. a room's corner, which is a common structure in indoor scenes. In addition, with only known camera principal point, the complex multi-variable estimation of all CPP intrinsics can be simplified to a simple univariable optimization problem, leading to reliable calibration and thus direct 3D reconstruction with unknown CPP. Extensive results have demonstrated the superiority of the proposed method over both traditional and learning-based counterparts. Furthermore, the proposed method also demonstrates impressive potential to solve similar tasks without active lighting, such as sparse-view structure from motion. © 2024, CC BY.

关键词： Cameras

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A Cooperative Truck Platooning Approach on Ramp Merging Area of Highway in Mixed Traffic

A Cooperative Truck Platooning Approach on Ramp Merging Area...

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International Conference on Transportation Information and Safety (ICTIS)

作者： Xiao Sang Linhan Bai Tao Zhou Fangfang Zheng School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology National United Engineering Laboratory of Integrated and Intelligent Transportation Southwest Jiaotong University Chengdu China Jiangsu Railway Group Nanjing China

Truck platooning has emerged as a promising solution for improving traffic efficiency on highways. This paper proposes an optimization model for connected automated trucks (CATs) that addresses the trajectory optimization problem for CATs as human-driven trucks (HDTs) merge onto highways from on-ramps in a mixed traffic scenario. To maximize the speed of the mixed traffic system, we propose a mixed integer programming (MIP) model based on a rolling horizon algorithm. The algorithm optimizes CATs’ trajectories iteratively, taking into account real-time updates on their driving state, and guides HDTs to follow the trajectories of CATs. The numerical experiments demonstrate that the increasing number of HDTs on the on-ramp negatively impacts the overall system performance, leading to higher traffic delay and reduced system stability. Furthermore, accounting for the stochastic behavior of HDTs can lead to increased instability in the system. Finally, we show that the objective function that comprehensively considers the system speed and platooning time effectively minimizes the delay of all vehicles.

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ADVISor: Automatic Visualization Answer for Natural-Language Question on Tabular data

ADVISor: Automatic Visualization Answer for Natural-Language...

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Pacific (formerly Asia-Pacific APVIS) Visualization Symposium

作者： Can Liu Yun Han Ruike Jiang Xiaoru Yuan Key Laboratory of Machine Perception (Ministry of Education) and School of EECS Peking University Beijing China National Engineering Laboratory for Big Data Analysis and Application Peking University Beijing China Beijing Engineering Technology Research Center of Virtual Simulation and Visualization Peking University Beijing China

We propose an automatic pipeline to generate visualization with annotations to answer natural-language questions raised by the public on tabular data. With a pre-trained language representation model, the input natural language questions and table headers are first encoded into vectors. According to these vectors, a multi-task end-to-end deep neural network extracts related data areas and corresponding aggregation type. We present the result with carefully designed visualization and annotations for different attribute types and tasks. We conducted a comparison experiment with state-of-the-art works and the best commercial tools. The results show that our method outperforms those works with higher accuracy and more effective visualization.

关键词： Annotations Natural languages Pipelines Neural networks data visualization Tools data mining

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Heterogeneous two-Stream Network with Hierarchical Feature Prefusion for Multispectral Pan-Sharpening

Heterogeneous two-Stream Network with Hierarchical Feature P...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Dong Wang Yunpeng Bai Bendu Bai Chanyue Wu Ying Li National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University Xi'an China School of Communication and Information Engineering Xi’an University of Posts and Telecommunications Xi'an China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech & Image Information Processing Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9781728176055;9781728176062

Multispectral (MS) pan-sharpening aims at producing a high spatial resolution (HR) MS image by fusing a single-band HR panchromatic (PAN) image and a corresponding MS image with low spatial resolution. In this paper, we propose a heterogeneous two-stream network (HTSNet) with hierarchical feature prefusion for MS pan-sharpening. The HTSNet employs a heterogeneous group of spatial and spectral streams for spatial and spectral information extraction, respectively. The spatial stream utilizes a 2D CNN for spatial information extraction from the PAN images, and the spectral stream obtains spectral feature cubes from the MS images by a 3D CNN. At the same time, a prefusion module is introduced to prefuse the spatial details with spectral information and transfer information between different streams, which can enhance later processing. In the experiment, the Gaofen-2 satellite dataset is utilized to compare the proposed method with the state-of-the-art MS pan-sharpening methods. Experimental results demonstrate the superiority of our HTSNet in terms of visual effect and quantitative qualities.

关键词： Satellites Three-dimensional displays Fuses Streaming media Signal processing Information retrieval Visual effects

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Semi-supervised Segmentation and Quantization Algorithm for Infarct Size Measurement of Rat Heart Sections

Semi-supervised Segmentation and Quantization Algorithm for ...

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International Conference on Computer and Communications (ICCC)

作者： Xiaoke Zhu Yulong Cai Xiaopan Chen Qi Hu Lingyun Dong Caihong Yuan School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

Segmentation of rat heart section images and infarct area quantization are essential components of infarct size measurement in processing data from animal experiments on ischemic heart disease. Previously, the task of measuring the infarct size of rat heart sections was performed manually by experienced researchers, which was very costly and time-consuming. In this paper, we propose a two-stage semi-supervised approach to solve the task of ischemic heart infarct size calculation in images of rat section images. The approach uses a semi-supervised semantic segmentation model and a semi-automated quantization algorithm to separate the differently stained regions of rat heart sections. We demonstrate the results of the approach on a rat heart section dataset, and experiments on this dataset emphasize the effectiveness of our approach compared to other methods.

关键词： Heart Training Quantization (signal) Semantic segmentation Manuals Rats Size measurement

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Point-Level and Set-Level Deep Representation Learning for Cross-Modal Person Re-identification

Point-Level and Set-Level Deep Representation Learning for C...

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International Conference on Computer and Communications (ICCC)

作者： Jihui Hu Pengfei Ye Danyang Li Lingyun Dong Xiaopan Chen Xiaoke Zhu Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University

In practice, significant modality differences usually exist between visible and infrared images, which makes visible-infrared Person Re-Identification (VI-ReID) a challenging research task. Due to the existing influence of pose variation, background changes, and occlusion, there are often outlier samples within the set of images from the same person. These outlier samples can adversely affect the process of learning the cross-modal matching model. The existing VI-ReID methods mainly focus on learning cross-modal feature representation by using image-level discriminant constraints, i.e., the distance between the truly-matching cross-modal images should be smaller than that between wrong-matching cross-modal images. However, most of these methods ignore the adverse influence caused by outliers. To solve the above problems, we proposed a Point-level and Set-level Deep Representation Learning (PSDRL) approach for VI-ReID in this paper. By using the set-level constraint in the process of deep representation learning, the discrepancy between visible and infrared modalities can be decreased, and the adverse effect of outliers can be weakened. By employing the image-level constraint, the discriminability of the obtained deep feature representations can be improved. Extensive experiments are conducted on the publicly available cross-modal Person Re-Identification datasets, including SYSU-MM01 and RegDB. Experimental results demonstrate the effectiveness of the proposed approach.

关键词： Representation learning Training Image color analysis Computational modeling Task analysis

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Lightweight Transformer Network and Self-supervised Task for Kinship Verification

Lightweight Transformer Network and Self-supervised Task for...

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International Conference on Computer and Communications (ICCC)

作者： Xiaoke Zhu Yunwei Li Danyang Li Lingyun Dong Xiaopan Chen School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

Kinship verification is one of the interesting and critical problems in computer vision research, with significant progress in the past decades. Meanwhile, Vision Transformer (VIT) has recently achieved impressive success in many domains, including object detection, image recognition, and semantic segmentation, among others. Most of the previous work on kinship verification are based on convolutional or recurrent neural networks. Compared with the local processing of images like convolutions, transformers can effectively understand and process images globally. However, due to overuse, there are many Transformer layers of fully connected layers, and VIT speed is still an issue. Therefore, in this paper, inspired by the recent success of Transformer models in vision tasks, we propose a Transformer-based kinship verification for training and optimizing kinship verification models. We first train the basic vision transformer (VIT-B) with 12 transformer layers, then we reduce the transformer layers to 6 layers, namely VIT-S (Small Vit) and 4 layers, namely VIT-T (Tiny Vit), to make a tradeoff between optimization accuracy and efficiency. As the first attempt to apply Transformer to the kinship verification task, it provides a feasible strategy for kinship research topics and verifies the effectiveness of the method in terms of the accuracy of the experimental results.

关键词： Training Knowledge engineering Recurrent neural networks Semantic segmentation Self-supervised learning Object detection Transformers

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