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855 results on '"Wang, Shengjin"'

207. Service Quality Evaluation Method of City Shared Bicycles Based on SERVQUAL Model

Author

Wang Shengjin and Wang Jichuan

Subjects
Transport engineering, lcsh:GE1-350, Service quality, SERVQUAL, Index system, Computer science, media_common.quotation_subject, Evaluation methods, Quality (business), Last mile, Bicycle sharing, lcsh:Environmental sciences, media_common
Abstract

Bicycle sharing is an effective way to solve the problem of residents’ last mile travel. Based on the SERVQUAL model. Build a service quality evaluation index system and evaluation model for shared bicycles. Taking Harbin’s bicycle as an example, the quantitative evaluation of the service quality of shared bicycles was carried out, and the existing problems of the service quality of shared bicycles were identified and targeted improvement strategies were proposed. The research results can provide a scientific basis for improving the quality of city shared bicycle services.

Published
2021

208. Research on Architecture Design of Urban Comprehensive Management Service Platform

Author

Wang Shengjin and Wang Jichuan

Subjects
lcsh:GE1-350, Service (systems architecture), Process management, Computer science, media_common.quotation_subject, Information processing, Shared resource, Public space, Resource (project management), Key (cryptography), Quality (business), Spatial analysis, lcsh:Environmental sciences, media_common
Abstract

Information platform can connect all the urban management links and it is a key tool to improve the efficiency and quality of urban comprehensive management. In recent years, the construction of the urban management information platform has progressed rapidly, but problems such as insufficient resource sharing within the platform, poor information communication between departments, active supervision and monitoring are still outstanding and difficult to adapt to the city. From the perspective of urban comprehensive management needs, it is proposed that the ideal platform should consist of a city management sub-platform responsible for the necessary administrative permits, a city operation sub-platform responsible for information processing, and a city service sub-platform that serves citizens, with comprehensive information and rights. Features such as clear responsibilities, functional linkage, and real-time active monitoring serve the maintenance of urban basic functions and public space management. It is proposed that the guarantee mechanism should be straightened out, the rights and responsibilities between various departments should be clarified, and various resource data should be incorporated on the basis of the spatial data base map to activate and integrate three sub-platforms of urban management, operation and service to support urban management decision-making.

Published
2021

213. Adaptive Affinity for Associations in Multi-Target Multi-Camera Tracking.

Author

Hou, Yunzhong, Wang, Zhongdao, Wang, Shengjin, and Zheng, Liang

Subjects
EXPERIMENTAL design, MULTIPLE target tracking, CAMERAS, ADAPTIVE optics
Abstract

Data associations in multi-target multi-camera tracking (MTMCT) usually estimate affinity directly from re-identification (re-ID) feature distances. However, we argue that it might not be the best choice given the difference in matching scopes between re-ID and MTMCT problems. Re-ID systems focus on global matching, which retrieves targets from all cameras and all times. In contrast, data association in tracking is a local matching problem, since its candidates only come from neighboring locations and time frames. In this paper, we design experiments to verify such misfit between global re-ID feature distances and local matching in tracking, and propose a simple yet effective approach to adapt affinity estimations to corresponding matching scopes in MTMCT. Instead of trying to deal with all appearance changes, we tailor the affinity metric to specialize in ones that might emerge during data associations. To this end, we introduce a new data sampling scheme with temporal windows originally used for data associations in tracking. Minimizing the mismatch, the adaptive affinity module brings significant improvements over global re-ID distance, and produces competitive performance on CityFlow and DukeMTMC datasets. [ABSTRACT FROM AUTHOR]

Published
2022
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215. Visual tracker using sequential Bayesian learning: discriminative, generative, and hybrid

Author

Lei, Yun, Ding, Xiaoqing, and Wang, Shengjin

Subjects
Algorithm, Algorithms -- Analysis
Abstract

This paper presents a novel solution to track a visual object under changes in illumination, viewpoint, pose, scale, and occlusion. Under the framework of sequential Bayesian learning, we first develop a discriminative model-based tracker with a fast relevance vector machine algorithm, and then, a generative model-based tracker with a novel sequential Gaussian mixture model algorithm. Finally, we present a three-level hierarchy to investigate different schemes to combine the discriminative and generative models for tracking. The presented hierarchical model combination contains the learner combination (at level one), classifier combination (at level two), and decision combination (at level three). The experimental results with quantitative comparisons performed on many realistic video sequences show that the proposed adaptive combination of discriminative and generative models achieves the best overall performance. Qualitative comparison with some state-of-the-art methods demonstrates the effectiveness and efficiency of our method in handling various challenges during tracking. Index Terms--Discriminative, generative, model combination, particle filtering, sequential learning, visual tracking.

Published
2008

228. AT-GAN

Author

Xie, Xinxiong, primary, Dong, Yuhan, additional, Li, Yali, additional, and Wang, Shengjin, additional

Published
2020
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