• Title/Summary/Keyword: grey clustering method

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Risk assessment of water inrush in karst tunnels based on a modified grey evaluation model: Sample as Shangjiawan Tunnel

  • Yuan, Yong-cai;Li, Shu-cai;Zhang, Qian-qing;Li, Li-ping;Shi, Shao-shuai;Zhou, Zong-qing
    • Geomechanics and Engineering
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    • v.11 no.4
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    • pp.493-513
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    • 2016
  • A modified grey clustering method is presented to systematically evaluate the risk of water inrush in karst tunnels. Based on the center triangle whitenization weight function and upper and lower limit measure whitenization weight function, the modified grey evaluation model doesn't have the crossing properties of grey cluster and meets the standard well. By adsorbing and integrating the previous research results, seven influence factors are selected as evaluation indexes. A couple of evaluation indexes are modified and quantitatively graded according to four risk grades through expert evaluation method. The weights of evaluation indexes are rationally distributed by the comprehensive assignment method. It is integrated by the subjective factors and the objective factors. Subjective weight is given based on analytical hierarchy process, and objective weight obtained from simple dependent function. The modified grey evaluation model is validated by Jigongling Tunnel. Finally, the water inrush risk of Shangjiawan Tunnel is evaluated by using the established model, and the evaluation result obtained from the proposed method is agrees well with practical situation. This risk assessment methodology provides a powerful tool with which planners and engineers can systematically assess the risk of water inrush in karst tunnels.

Assessing the Impact of Advanced Technologies on Utilization Improvement of Substations

  • Han, Dong;Yan, Zheng;Zhang, Dao-Tian;Song, Yi-Qun
    • Journal of Electrical Engineering and Technology
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    • v.10 no.5
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    • pp.1921-1929
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    • 2015
  • The smart substation is the heart of a transmission system, which is particularly emphasized as the most significant composition of smart grids in China. In order to assess the functionality performance of substation technologies, this paper presents methods used to identify the most promising solutions for smart substation design and to evaluate the technical levels of available technologies. The multi-index optimization model is presented to address the issue of smart substation planning. A mathematical model of the planning decision problem is established with multiple objectives consisting of economic, reliability, and green key indices, and many kinds of concerns including physical and environmentally friendly operations are formulated as a set of constraints. With respect to the assessment of the technical level regarding integration of advanced technologies into a substation, a modified grey whitenization weight function is adopted to structure a novel grey clustering method. The proposed grey clustering approach is used to overcome the difficulty of insufficient quantitative assessment capacity for traditional methods. The evaluation of technical effects provides the classification definition for the development phase and the maturity level of the smart substation. The effectiveness of the proposed approaches in planning decision-making and evaluation of construction efforts is demonstrated with case studies involving the actual smart substation projects of Wenchongkou substation in China Southern Power Grid (CSG) and Mengzi substation in State Grid Corporation of China (SGCC).

Research on Characterizing Urban Color Analysis based on Tourists-Shared Photos and Machine Learning - Focused on Dali City, China - (관광객 공유한 사진 및 머신 러닝을 활용한 도시 색채 특성 분석 연구 - 중국 대리시를 대상으로 -)

  • Yin, Xiaoyan;Jung, Taeyeol
    • Journal of the Korean Institute of Landscape Architecture
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    • v.52 no.2
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    • pp.39-50
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    • 2024
  • Color is an essential visual element that has a significant impact on the formation of a city's image and people's perceptions. Quantitative analysis of color in urban environments is a complex process that has been difficult to implement in the past. However, with recent rapid advances in Machine Learning, it has become possible to analyze city colors using photos shared by tourists. This study selected Dali City, a popular tourist destination in China, as a case study. Photos of Dali City shared by tourists were collected, and a method to measure large-scale city colors was explored by combining machine learning techniques. Specifically, the DeepLabv3+ model was first applied to perform a semantic segmentation of tourist sharing photos based on the ADE20k dataset, thereby separating artificial elements in the photos. Next, the K-means clustering algorithm was used to extract colors from the artificial elements in Dali City, and an adjacency matrix was constructed to analyze the correlations between the dominant colors. The research results indicate that the main color of the artificial elements in Dali City has the highest percentage of orange-grey. Furthermore, gray tones are often used in combination with other colors. The results indicated that local ethnic and Buddhist cultures influence the color characteristics of artificial elements in Dali City. This research provides a new method of color analysis, and the results not only help Dali City to shape an urban color image that meets the expectations of tourists but also provide reference materials for future urban color planning in Dali City.

Copyright Protection for Fire Video Images using an Effective Watermarking Method (효과적인 워터마킹 기법을 사용한 화재 비디오 영상의 저작권 보호)

  • Nguyen, Truc;Kim, Jong-Myon
    • KIPS Transactions on Software and Data Engineering
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    • v.2 no.8
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    • pp.579-588
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    • 2013
  • This paper proposes an effective watermarking approach for copyright protection of fire video images. The proposed watermarking approach efficiently utilizes the inherent characteristics of fire data with respect to color and texture by using a gray level co-occurrence matrix (GLCM) and fuzzy c-means (FCM) clustering. GLCM is used to generate a texture feature dataset by computing energy and homogeneity properties for each candidate fire image block. FCM is used to segment color of the fire image and to select fire texture blocks for embedding watermarks. Each selected block is then decomposed into a one-level wavelet structure with four subbands [LL, LH, HL, HH] using a discrete wavelet transform (DWT), and LH subband coefficients with a gain factor are selected for embedding watermark, where the visibility of the image does not affect. Experimental results show that the proposed watermarking approach achieves about 48 dB of high peak-signal-to-noise ratio (PSNR) and 1.6 to 2.0 of low M-singular value decomposition (M-SVD) values. In addition, the proposed approach outperforms conventional image watermarking approach in terms of normalized correlation (NC) values against several image processing attacks including noise addition, filtering, cropping, and JPEG compression.