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UK/ China Emerging Technologies (UCET)

作者： Christyn Akosua Owusu-Agyei Jin Hou School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu Sichuan P.R. China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu Sichuan P.R. China School of Information Science and Technology Southwest Jiaotong University Chengdu Sichuan P.R. China

ISBN: (纸本)9781665495769

Hands emerge quite often in egocentric video, and their structure and disposition reveal a lot as to what individuals have been engaged and showing interest to. Earlier researches in hand detection, on the other hand, have depended on significant effect that only operate in limited situations, such as lab settings or with minimal social interaction. This work investigates the recently developed YOLO architectural for object recognition in egocentric video, which offers a good balance of precision and performance. We performed an experiment of assessments utilizing various different architecture configurations and training methods. Our experiment leads to all sorts of conclusions about the most cost - effective methodologies to our strategic objectives, and leads us to further research to enhance detection of smart glasses videos. We tested these approaches on an existing dataset that contains 48 first individual footage of people interacting in situations in real life as well as the bounding box on a pixel level for more than 15,000 hand gestures.

关键词： Training Costs data models Object recognition Smart glasses

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Dancing between Threats and Conflicts: How Chinese Energy Companies Invest Amidst Global Geopolitical Risks

SSRN

引用

SSRN 2023年

作者： Huang, Zhuo Ye, Chengfang Lai, Fujun Li, Yunzhong National School of Development Peking University Changsha Institute of Computing and Digital Economy Peking University Beijing100091 China School of Public Administration Xiangtan University Xiangtan411105 China School of Finance Yunnan University of Finance and Economics Kunming650221 China National Engineering Laboratory for Big Data Analysis and Application Peking University Changsha Institute of Computing and Digital Economy Peking University Beijing100091 China

In an increasingly interconnected world, global geopolitical risk (GPR) poses significant challenges to the investment decisions of energy companies. However, few studies have examined the effects of such risk, particularly global geopolitical acts (GPRA) and global geopolitical threat (GPRT), on Chinese energy companies. Utilizing data from publicly listed energy companies in China, this study fills this gap by investigating the effects of GPRA and GPRT on these companies’ investment decisions. The results indicate that GPRT significantly inhibits the investment activities of Chinese energy companies, whereas GPRA promotes them. Capital hedging demand is identified as the primary channel through which GPRA and GPRT affect these companies. Additionally, we determine that factors such as companies’ business credit capability, valuation, operational risk, and degree of internationalization, along with macroeconomic characteristics such as China’s economic opportunities and the business confidence index, moderate the relationships between GPRT, GPRA, and companies’ investment decisions. The findings of this study enhance our understanding of the impact of GPR on the investment decisions of Chinese energy companies and provide valuable insights for both policymakers and industry practitioners navigating the complex global energy landscape. © 2023, The Authors. All rights reserved.

关键词： Investments

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Exploring the influencing factors on demand-responsive customized bus ridership using multi-source data

Exploring the influencing factors on demand-responsive custo...

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International Conference on Transportation Information and Safety (ICTIS)

作者： Yamin Huo Zhongquan Qiu Yi Xin Shideng Dai Renbin Pan 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 China Academy of Urban Planning and Design Beijing China School of Business Administration Faculty of Business Administration Southwestern University of Finance and Economics Chengdu China Department of Civil and Environmental Engineering University of Tennessee Knoxville TN USA

ISBN: (纸本)9781665497145

Demand-responsive customized bus (DRCB) is a specific type of demand-responsive transit (DRT), which can flexibly customize services according to travel demand. However, few papers explored the influence factors on it. The purpose of this paper is to use the DRCB trip data from November 15 to December 15, 2020, in Xiongan New Area, China, to investigate the influence of built environment, weather, and time factors on DRCB ridership by using a negative binomial regression model. According to the spatiotemporal analysis, DRCB ridership is mainly concentrated in the peak hours of 07:00-09:00 and 17:00-20:00; the spatial distribution is primarily centered in areas such as government service center, railway station, and residential area. The model results show that scenic spot, governmental agency, morning peak period, and evening peak period are positively correlated with the DRCB ridership. Company, medical facility, visibility, rain, and weekend have a negative correlation with DRCB ridership. The results of this paper can provide insights for relevant companies to better understand the demand for DRCB and better plan the DRCB systems.

关键词： Rain Graphical models Transportation Companies data models Rail transportation Spatiotemporal phenomena

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Towards Accurate Camouflaged Object Detection with Mixture Convolution and Interactive Fusion

arXiv

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arXiv 2021年

作者： Chen, Geng Chen, Xinrui Dong, Bo Zhuge, Mingchen Wang, Yongxiong Bi, Hongbo Chen, Jian Wang, Peng Zhang, Yanning National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University China Center for Brain Imaging Science and Technology Zhejiang University China School of Computer Science China University of Geo-sciences China Optical Electrical and Computer Engineering University of Shanghai for Science and Technology China School of Electrical Engineering & Information Department Northeast Petroleum University China School of Electronic Electrical Engineering and Physics Fujian University of Technology China

Camouflaged object detection (COD), which aims to identify the objects that conceal themselves into the surroundings, has recently drawn increasing research efforts in the field of computer vision. In practice, the success of deep learning based COD is mainly determined by two key factors, including (i) A significantly large receptive field, which provides rich context information, and (ii) An effective fusion strategy, which aggregates the rich multi-level features for accurate COD. Motivated by these observations, in this paper, we propose a novel deep learning based COD approach, which integrates the large receptive field and effective feature fusion into a unified framework. Specifically, we first extract multi-level features from a backbone network. The resulting features are then fed to the proposed dual-branch mixture convolution modules, each of which utilizes multiple asymmetric convolutional layers and two dilated convolutional layers to extract rich context features from a large receptive field. Finally, we fuse the features using specially-designed multilevel interactive fusion modules, each of which employs an attention mechanism along with feature interaction for effective feature fusion. Our method detects camouflaged objects with an effective fusion strategy, which aggregates the rich context information from a large receptive field. All of these designs meet the requirements of COD well, allowing the accurate detection of camouflaged objects. Extensive experiments on widely-used benchmark datasets demonstrate that our method is capable of accurately detecting camouflaged objects and outperforms the state-of-the-art methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep learning

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Hyperspectral Classification of Coastal Wetland Based on Multi-objective CNN and Decision Fusion Model

Hyperspectral Classification of Coastal Wetland Based on Mul...

引用

Information Systems and Computing technology (ISCTech), International Conference on

作者： Yabin Hu Junfang Yang Guangbo Ren Yi Ma Jianbu Wang Xiaoheng Zhu Lab of Marine Physics and Remote Sensing First Institute of Oceanography Ministry of Natural Resources Qingdao China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xian China College of Oceanography and Space Informatics China University of Petroleum Qingdao China College of Geodesy and Geomatics ShanDong University of Science and Technology Qingdao China

Coastal wetland play an irreplaceable role in maintaining biodiversity and improving climate. In this paper, we proposed a hyperspectral classification algorithm of coastal wetland based on multi-objective Convolutional Neural Networks (CNN) and decision fusion model. The algorithm is named that the multi-objective spatial-spectral CNN classification with binary morphological strategy decision fusion model algorithm (MB2SCNN-BMSDF). The multi-objective hyperspectral feature band selection model of coastal wetland based on spectral deviation algorithm is developed and constructed to effectively obtain hyperspectral sensitive bands for single target surface object types. On this basis, a CNN hyperspectral classification model that integrates multi-objective spatial spectral information, and a binary morphological decision fusion algorithm that combines spatial position information and fuzzy membership degrees are proposed, which solves the problems of salt and pepper noise and sand eye effect in the classification process, and reduces noise information. The results showed that the CNN classification of multi-objective spatial spectral information and the decision fusion model of binary morphology strategy have better classification effect and performance.

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Dynamic OD Prediction for Urban Networks Based on Automatic Number Plate Recognition data: Paramertic vs. Non-parametric Approaches

Dynamic OD Prediction for Urban Networks Based on Automatic ...

引用

International Conference on Intelligent Transportation

作者： Fangfang Zheng Jing Liu Henk van Zuylen Kun Wang Xaobo Liu Jie Li School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University P.R. China School of Transportation and Logistics Southwest Jiaotong University P.R. China Department of Transport and Planning Delft University of Technology Delft the Netherlands Civil Engineering College Hunan University Changsha Hunan Province P.R. China

OD flows provide important information for traffic management and planning. In this paper, we propose four OD prediction models based on the data obtained by Automated Number Plate Recognition (ANPR) cameras. The principal component analysis (PCA) is applied to reduce the dimension of the original OD matrices and to separate the main structure patterns from the noisier components. A state-space model is established for the main structure patterns and the structure deviations, and is incorporated in the Kalman filter framework to make prediction. We further develop three K- Nearest Neighbor (K-NN) based pattern recognition approaches. The proposed four approaches are validated with three days' field ANPR data from Changsha city, P.R. China. The results show that on one hand our proposed approaches are able to make accurate prediction of OD flows under different demand conditions. On the other hand, the prediction accuracy is highly dependent on the quality of the available OD data: the Kalman filter model performs better for regular and periodic OD patterns; while for irregular OD matrices K-NN models could make more accurate prediction.

关键词： Predictive models Principal component analysis Mathematical model data models Kalman filters Market research Covariance matrices

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A cross Transformer for image denoising

引用

Information Fusion 2024年 102卷

作者： Tian, Chunwei Zheng, Menghua Zuo, Wangmeng Zhang, Shichao Zhang, Yanning Lin, Chia-Wen School of Software Northwestern Polytechnical University Xi'anShaanxi710129 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'anShaanxi710129 China School of Computer Science and Technology Harbin Institute of Technology Heilongjiang Harbin150001 China Peng Cheng Laboratory Guangdong Shenzhen518055 China School of Computer Science and Engineering Central South University Hunan Changsha410083 China School of Computer Science Northwestern Polytechnical University Shaanxi Xi'an710129 China Department of Electrical Engineering and the Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan

Deep convolutional neural networks (CNNs) depend on feedforward and feedback ways to obtain good performance in image denoising. However, how to obtain effective structural information via CNNs to efficiently represent given noisy images is key for complex scenes. In this paper, we propose a cross Transformer denoising CNN (CTNet) with a serial block (SB), a parallel block (PB), and a residual block (RB) to obtain clean images for complex scenes. A SB uses an enhanced residual architecture to deeply search structural information for image denoising. To avoid loss of key information, PB uses three heterogeneous networks to implement multiple interactions of multi-level features to broadly search for extra information for improving the adaptability of an obtained denoiser for complex scenes. Also, to improve denoising performance, Transformer mechanisms are embedded into the SB and PB to extract complementary salient features for effectively removing noise in terms of pixel relations. Finally, a RB is applied to acquire clean images. Experiments illustrate that our CTNet is superior to some popular denoising methods in terms of real and synthetic image denoising. It is suitable to mobile digital devices, i.e., phones. Codes can be obtained at https://***/hellloxiaotian/CTNet. © 2023 Elsevier B.V.

关键词： Image denoising

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On the precise quantification of the impact of a single discretionary lane change on surrounding traffic

arXiv

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arXiv 2024年

作者： Hou, Kangning Zou, Jia Zheng, Fangfang Liu, Xiaobo He, Zhengbing School of Transportation and Logistics Southwest Jiaotong University Western Hi-tech Zone Chengdu Sichuan611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Western Hi-tech Zone Chengdu Sichuan611756 China Senseable City Lab Massachusettes Institute of Technology CambridgeMA02421 United States

Lane-changing is a critical maneuver of vehicle driving, and a comprehensive understanding of its impact on traffic is essential for effective traffic management and optimization. Unfortunately, existing studies fail to adequately distinguish the impact of lane changes from those resulting from natural traffic dynamics. Additionally, there is a lack of precise methods for measuring the spatial extent and duration of the impact of a single discretionary lane change, as well as a definitive metric to quantify the overall spatiotemporal impact. To address these gaps, this study introduces a quantitative indicator called the Corrected Travel Distance Bias (CTDB), which accounts for variable speeds due to inherent traffic dynamics, providing a more accurate assessment of lane-changing impacts. A comprehensive methodology is developed to compare vehicle trajectory data before and after lane-changing events, measuring both the magnitude and spatiotemporal extent of the lane-changing impact. The results, based on the Zen traffic data from Japan, indicate that the impact of a lane change in the target lane lasts an average of 23.8 seconds, affecting approximately 5.6 vehicles, with a CTDB value of -10.8 meters. In contrast, in the original lane, the impact lasts 25 seconds, affects 5.3 vehicles, and yields a CTDB value of 4.7 meters. Copyright © 2024, The Authors. All rights reserved.

关键词： Vehicles

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An Eagle-Leg-Inspired Manipulator Composed of a Grasping Mechanism Based on the Motion Law of the Eagle-Claw and an Ankle Joint Mechanism

SSRN

引用

SSRN 2023年

作者： Zhao, Ruiyuan Li, Xiaotong Chen, Jian Wu, Pengxi College of Engineering China Agricultural University Beijing100083 China Key Laboratory of Smart Agricultural Technology in Tropical South China Ministry of Agriculture and Rural Affairs Guangzhou510642 China Key Laboratory of Spatial‐temporal Big Data Analysis and Application of Natural Resources in Megacities MNR Shanghai200063 China Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural Resources Shenzhen518000 China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China

In order to make the multi-rotor UAV more efficient to complete the contact operations such as picking, obstacle cleaning, grasping and inspired by the skeleton structure and motion law of the eagle's leg, this paper proposes a prototype design of a large load, wide range of movement, multiple DOF and collapsible manipulator. It is suitable for medium and larger size multi-rotor UAV. The main structure of the manipulator consists of a planetary reducer and a coupled parallel linkage mechanism. The reducer makes the manipulator more powerful, while the coupled parallel linkage mechanism can increase the movable range of the grasping mechanism. Based on the movement law of eagle’s claw, the grasping mechanism of the manipulator is designed. It can effectively increase the contact area between the target. In order to increase the DOF of the grasping mechanism, an ankle mechanism using a pair of double helical telescopic mechanisms to constrain the DOF of the universal joint is designed. It connects the grasping mechanism and the main structure of the manipulator. This paper shows the development process of the manipulator. The kinematic modeling and control of the manipulator are carried out. The ability of the manipulator system is tested, And the flight test experiment of the UAV carrying the manipulator is also executed. © 2023, The Authors. All rights reserved.

关键词： Manipulators

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From Few to More: Scribble-based Medical Image Segmentation via Masked Context Modeling and Continuous Pseudo Labels

arXiv

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arXiv 2024年

作者： Wang, Zhisong Ye, Yiwen Chen, Ziyang Shu, Minglei Xia, Yong National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University China Shandong Artificial Intelligence Institute Qilu University of Technology Shandong Academy of Sciences China Ningbo Institute of Northwestern Polytechnical University Ningbo315048 China Research & Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen518057 China

Scribble-based weakly supervised segmentation techniques offer comparable performance to fully supervised methods while significantly reducing annotation costs, making them an appealing alternative. Existing methods often rely on auxiliary tasks to enforce semantic consistency and use hard pseudo labels for supervision. However, these methods often overlook the unique requirements of models trained with sparse annotations. Since the model must predict pixel-wise segmentation maps with limited annotations, the ability to handle varying levels of annotation richness is critical. In this paper, we adopt the principle of ‘from few to more’ and propose MaCo, a weakly supervised framework designed for medical image segmentation. MaCo employs masked context modeling (MCM) and continuous pseudo labels (CPL). MCM uses an attention-based masking strategy to disrupt the input image, compelling the model’s predictions to remain consistent with those of the original image. CPL converts scribble annotations into continuous pixel-wise labels by applying an exponential decay function to distance maps, resulting in continuous maps that represent the confidence of each pixel belonging to a specific category, rather than using hard pseudo labels. We evaluate MaCo against other weakly supervised methods using three public datasets. The results indicate that MaCo outperforms competing methods across all datasets, setting a new record in weakly supervised medical image segmentation. Copyright © 2024, The Authors. All rights reserved.

关键词： Pixels

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