• Title/Summary/Keyword: 심상정보처리

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A Study on the Trade Risk Management of Korean Companies in Incheon Area Trading with China (인천지역 무역업체의 중국과의 무역리스크 관리에 관한 연구)

  • Shim, Sang-Ryul;Bae, Sang-Pil;Wang, Tian-Jiao
    • International Commerce and Information Review
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    • v.14 no.3
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    • pp.513-536
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    • 2012
  • This study aims to examine the status and problems of trade risk management of Korean companies in Incheon area trading with China and to suggest some improvement measures. On the survey with twenty five questions on company profiles, business process and transactions, claims and trade risks, etc. with Chinese trading partners, the following facts are found. In general, Chinese policies on foreign trade, finance, labour, investment, etc. and China's logistics system have caused great worries to Korean companies in Incheon area. This kind of risks from Chinese government policies and China's economic structure are beyond control of each company. Korean government should take more effective measures to negotiate with Chinese government. In the stage of contract, procurement and transportation, settlement, disputes resolution and etc. Korean companies in Incheon area also have many problems with relatively high risks with Chinese trading partners. Based on these survey results, some suggestions for better trade risk management are given.

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Track-Before-Detect Algorithm for Multiple Target Detection (다수 표적 탐지를 위한 Track-Before-Detect 알고리듬 연구)

  • Won, Dae-Yeon;Shim, Sang-Wook;Kim, Keum-Seong;Tahk, Min-Jea;Seong, Kie-Jeong;Kim, Eung-Tai
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.39 no.9
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    • pp.848-857
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    • 2011
  • Vision-based collision avoidance system for air traffic management requires a excellent multiple target detection algorithm under low signal-to-noise ratio (SNR) levels. The track-before-detect (TBD) approaches have significant applications such as detection of small and dim targets from an image sequence. In this paper, two detection algorithms with the TBD approaches are proposed to satisfy the multiple target detection requirements. The first algorithm, based on a dynamic programming approach, is designed to classify multiple targets by using a k-means clustering algorithm. In the second approach, a hidden Markov model (HMM) is slightly modified for detecting multiple targets sequentially. Both of the proposed approaches are used in numerical simulations with variations in target appearance properties to provide satisfactory performance as multiple target detection methods.