scholarly journals KONVERGENSI PENDAPATAN INDONESIA DAN MITRA REGIONAL TRADE AGREEMENT (ASEAN+6): APLIKASI METODE CLUSTER FUZZY

2015 ◽  
Vol 9 (1) ◽  
pp. 63-77
Author(s):  
Azis Muslim
Keyword(s):  
Economic Growth ◽  
Clustering Algorithm ◽  
Historical Data ◽  
Trade Agreement ◽  
Developed Countries ◽  
Regional Trade ◽  
Regional Trade Agreement ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Social Capability

Studi ini mengevaluasi pernyataan bahwa Regional Trade Agreement (RTA) mendorong konvergensi pendapatan. Dengan menggunakan data historis dan menerapkan algoritma fuzzy c-means clustering studi ini menguji konvergensi pendapatan Indonesia dan mitra RTA. Hasil studi menunjukkan bahwa dalam dua dasawarsa sejak tahun 1993, meskipun dengan adanya RTA, Indonesia mengalami pertumbuhan ekonomi, tetapi pendapatan Indonesia tidak konvergen ke arah pendapatan negara maju. Perdagangan perlu dipakai sebagai sarana alih pengetahuan dan teknologi serta peningkatan “kapabilitas sosial” untuk mendukung percepatan pertumbuhan ekonomi. This study evaluates the proposition that Regional Trading Agreements (RTA) endorses convergence of income. Using historical data and fuzzy c-means clustering algorithm, this study analyzes the convergence of Indonesia’s income and RTA partners. The results show that in two decades since 1993, with the presence of RTA, Indonesia has experienced economic growth, yet Indonesia’s income did not converge towards the incomes of developed countries. Trade needs to be utilized as a mean to support knowledge and technology transfer and to increase “social capability” to enhance the acceleration of economic growth.

scholarly journals Automatic measurement of traditional Chinese costume from its silhouette through Fuzzy c-means clustering method

2020 ◽  
Vol 15 ◽  
pp. 155892502097832
Author(s):  
Jiaqin Zhang ◽  
Jingan Wang ◽  
Le Xing ◽  
Hui’e Liang
Keyword(s):  
Industrial Application ◽  
Clustering Algorithm ◽  
Color Space ◽  
Automatic Measurement ◽  
Feature Point ◽  
Feature Points ◽  
Point Location ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Environmental Robustness

As the precious cultural heritage of the Chinese nation, traditional costumes are in urgent need of scientific research and protection. In particular, there are scanty studies on costume silhouettes, due to the reasons of the need for cultural relic protection, and the strong subjectivity of manual measurement, which limit the accuracy of quantitative research. This paper presents an automatic measurement method for traditional Chinese costume dimensions based on fuzzy C-means clustering and silhouette feature point location. The method is consisted of six steps: (1) costume image acquisition; (2) costume image preprocessing; (3) color space transformation; (4) object clustering segmentation; (5) costume silhouette feature point location; and (6) costume measurement. First, the relative total variation model was used to obtain the environmental robustness and costume color adaptability. Second, the FCM clustering algorithm was used to implement image segmentation to extract the outer silhouette of the costume. Finally, automatic measurement of costume silhouette was achieved by locating its feature points. The experimental results demonstrated that the proposed method could effectively segment the outer silhouette of a costume image and locate the feature points of the silhouette. The measurement accuracy could meet the requirements of industrial application, thus providing the dual value of costume culture research and industrial application.

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