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RISS 인기검색어
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- 저자 : Bi Xiaoming (검색결과 3 건)
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Research on Smart Campus Based on the Internet of Things and Virtual Reality
Youliang Huang,Sajid Ali,Xiaoming Bi,Xing Zhai,Renquan Liu,Fengying Guo,Peng Yu 보안공학연구지원센터 2016 International Journal of Smart Home Vol.10 No.12
Smart campus is an inevitable trend in the development of digital campus construction. With the development of the Internet of Things (IoT) and Virtual Reality (VR) technology, these technologies become the key to the construction of smart campus. The major objective of this study put forward a smart campus system prototype based on Internet technology. This paper firstly introduces the definition and characteristics of the IoT and VR technology. Secondly, presents the architecture and implementation methodology of the system prototype, and analyzes the core idea of smart campus. Finally, discuss the problems should be noticed in the smart campus construction.
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A New Kmeans Clustering Algorithm for Point Cloud
Kun Zhang,Weihong Bi,Xiaoming Zhang,Xinghu Fu,Li Zhu,Kunpeng Zhou 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.9
With development of 3D scanner, it becomes more convenient to access point data. However, for processing the large-scale point cloud, it raised a new challenge for computer graphics. This paper places an emphasis on the point data own characteristics, and then the point data have been divided into certain point sets by clustering algorithm, that is will be divided into different clusters. In order to suit for the point data organization or space division, the clustering algorithm would be improved. This paper provided a new Kmeans algorithm with density constraints. Before processing the point cloud by Kmeans algorithm with density constraints, the density of the point cloud have been defined in this paper, the density of the point cloud can be used for quantification of the convergence. Finally, the Kmeans algorithm with density constraints is verified by the experiment results. Our experiment showed that the improved Kmeans can reduce the processing time, especially, As the increase of the value of K, that is number of cluster, the calculating time of the clustering algorithm can be decreased greatly. In addition, with the increases of the the scale of data size, the stability of the improved Kmeans algorithm has been verified.
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Cha Min Jae,Cho Iksung,Hong Joonhwa,Kim Sang-Wook,Shin Seung Yong,Paek Mun Young,Bi Xiaoming,Kim Sung Mok 대한영상의학회 2021 Korean Journal of Radiology Vol.22 No.7
Objective: Motion-corrected averaging with a single-shot technique was introduced for faster acquisition of late-gadoliniumenhancement (LGE) cardiovascular magnetic resonance (CMR) imaging while free-breathing. We aimed to evaluate the image quality (IQ) of free-breathing motion-corrected single-shot LGE (moco-ss-LGE) in patients with hypertrophic cardiomyopathy (HCM). Materials and Methods: Between April and December 2019, 30 patients (23 men; median age, 48.5; interquartile range [IQR], 36.5–61.3) with HCM were prospectively enrolled. Breath-held single-shot LGE (bh-ss-LGE) and free-breathing mocoss-LGE images were acquired in random order on a 3T MR system. Semi-quantitative IQ scores, contrast-to-noise ratios (CNRs), and quantitative size of myocardial scar were assessed on pairs of bh-ss-LGE and moco-ss-LGE. The mean ± standard deviation of the parameters was obtained. The results were compared using the Wilcoxon signed-rank test. Results: The moco-ss-LGE images had better IQ scores than the bh-ss-LGE images (4.55 ± 0.55 vs. 3.68 ± 0.45, p < 0.001). The CNR of the scar to the remote myocardium (34.46 ± 11.85 vs. 26.13 ± 10.04, p < 0.001), scar to left ventricle (LV) cavity (13.09 ± 7.95 vs. 9.84 ± 6.65, p = 0.030), and LV cavity to remote myocardium (33.12 ± 15.53 vs. 22.69 ± 11.27, p < 0.001) were consistently greater for moco-ss-LGE images than for bh-ss-LGE images. Measurements of scar size did not differ significantly between LGE pairs using the following three different quantification methods: 1) full width at half-maximum method; 23.84 ± 12.88% vs. 24.05 ± 12.81% (p = 0.820), 2) 6-standard deviation method, 15.14 ± 10.78% vs. 15.99 ± 10.99% (p = 0.186), and 3) 3-standard deviation method; 36.51 ± 17.60% vs. 37.50 ± 17.90% (p = 0.785). Conclusion: Motion-corrected averaging may allow for superior IQ and CNRs with free-breathing in single-shot LGE imaging, with a herald of free-breathing moco-ss-LGE as the scar imaging technique of choice for clinical practice.
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