• Journal of Arbitration Studies
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• v.27 no.3
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• pp.33-52
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• 2017

This article tried to describe the laws concerning the enforcement of foreign arbitration awards in Indonesia. This issue is relevant in the light of frequent curiosity of foreign commentators, business communities, practicing lawyers, concerning the arbitration in Indonesia, in particular its enforcement of foreign arbitration awards. The main laws on arbitration analyzed were, firstly, the Indonesian law on arbitration, namely Law No 30 of 1999 on Arbitration and Alternative Dispute Resolution and the Presidential Regulation No 34 of 1981 concerning the Ratification of the New York Convention on the Recognition and Enforcement of Foreign Arbitral Awards of 1958. The provisions of Law of 1999 analyzed were confined to its international provisions on arbitration, in particular the requirements for the enforcement of foreign arbitration awards and also the requirement that the awards do not violate Indonesian public policy. The problem with the Indonesian arbitration law (and the courts' practice) were that no provisions which provided guidance or meaning with regard to public policy. The absence or lack of guidance or definition on public policy had some times confused lawyers or the parties in dispute fearing that their arbitration awards would not be enforced due to the violation of public policy. Secondly was the different opinion of two Indonesian arbitration experts, Prof. Sudargo Gautama and Prof. Priyatna Abdurrasyid. Both scholars had rather different opinions with regard to the meaning of public policy in Indonesia. Thirdly was a recent case law, Astro Nusantara Bv et.al., vs PT Ayunda Primamitra Case (2010) decided by the Indonesian Supreme Court with regard to the enforcement of foreign arbitration awards. This article concluded that the Indonesian court, in particular the Central of Jakarta Court, so far have given its support that the execution of foreign awards was duly enforced.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.311-320
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• 2003

The Optimal filter yielding optimal texture feature separation is a most effective technique for extracting the texture objects from multiple textures images. But, most optimal filter design approaches are restricted to the issue of supervised problems. No full-unsupervised method is based on the recognition of texture objects in image. We propose a novel approach that uses unsupervised learning schemes for efficient texture image analysis, and the band-pass feature of Gabor-filter is used for the optimal filter design. In our approach, the self-organizing neural network for multiple texture image identification is based on block-based clustering. The optimal frequency of Gabor-filter is turned to the optimal frequency of the distinct texture in frequency domain by analyzing the spatial frequency. In order to show the performance of the designed filters, after we have attempted to build a various texture images. The texture objects extraction is achieved by using the designed Gabor-filter. Our experimental results show that the performance of the system is very successful.

• Smart Media Journal
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• v.8 no.4
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• pp.33-37
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• 2019

Because the ECG signals are time-series data acquired as time elapses, it is important to obtain comparative data the same in size as the enrolled data every time. This paper suggests a network model of GAN (Generative Adversarial Networks) based on an auxiliary classifier to generate synthetic ECG signals which may address the different data size issues. The Cosine similarity and Cross-correlation are used to examine the similarity of synthetic ECG signals. The analysis shows that the Average Cosine similarity was 0.991 and the Average Euclidean distance similarity based on cross-correlation was 0.25: such results indicate that data size difference issue can be resolved while the generated synthetic ECG signals, similar to real ECG signals, can create synthetic data even when the registered data are not the same as the comparative data in size.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.3
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• pp.53-60
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• 2009

The vision-based driver fatigue detection is one of the most prospective commercial applications of facial expression recognition technology. The facial feature tracking is the primary technique issue in it. Current facial tracking technology faces three challenges: (1) detection failure of some or all of features due to a variety of lighting conditions and head motions; (2) multiple and non-rigid object tracking; and (3) features occlusion when the head is in oblique angles. In this paper, we propose a new active approach. First, the active IR sensor is used to robustly detect pupils under variable lighting conditions. The detected pupils are then used to predict the head motion. Furthermore, face movement is assumed to be locally smooth so that a facial feature can be tracked with a Kalman filter. The simultaneous use of the pupil constraint and the Kalman filtering greatly increases the prediction accuracy for each feature position. Feature detection is accomplished in the Gabor space with respect to the vicinity of predicted location. Local graphs consisting of identified features are extracted and used to capture the spatial relationship among detected features. Finally, a graph-based reliability propagation is proposed to tackle the occlusion problem and verify the tracking results. The experimental results show validity of our active approach to real-life facial tracking under variable lighting conditions, head orientations, and facial expressions.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05b
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• pp.603-607
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• 2004

The vision-based driver fatigue detection is one of the most prospective commercial applications of facial expression recognition technology. The facial feature tracking is the primary technique issue in it. Current facial tracking technology faces three challenges: (1) detection failure of some or all of features due to a variety of lighting conditions and head motions; (2) multiple and non-rigid object tracking and (3) features occlusion when the head is in oblique angles. In this paper, we propose a new active approach. First, the active IR sensor is used to robustly detect pupils under variable lighting conditions. The detected pupils are then used to predict the head motion. Furthermore, face movement is assumed to be locally smooth so that a facial feature can be tracked with a Kalman filter. The simultaneous use of the pupil constraint and the Kalman filtering greatly increases the prediction accuracy for each feature position. Feature detection is accomplished in the Gabor space with respect to the vicinity of predicted location. Local graphs consisting of identified features are extracted and used to capture the spatial relationship among detected features. Finally, a graph-based reliability propagation is proposed to tackle the occlusion problem and verify the tracking results. The experimental results show validity of our active approach to real-life facial tracking under variable lighting conditions, head orientations, and facial expressions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.3
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• pp.418-424
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• 2018

Detection and classification of underwater targets is an important issue for both military and non-military purposes. Recently, many performance improvements are being reported in the field of pattern recognition with the development of deep learning technology. Among the results, DBN showed good performance when used for pre-training of DNN. In this paper, DBN was used for the classification of underwater targets using active sonar, and the results are compared with that of the conventional BPNN. We synthesized active sonar target signals using 3-dimensional highlight model. Then, features were extracted based on FrFT. In the single aspect based experiment, the classification result using DBN was improved about 3.83% compared with the BPNN. In the case of multi-aspect based experiment, a performance of 95% or more is obtained when the number of observation sequence exceeds three.

• Journal of KIISE:Software and Applications
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• v.30 no.12
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• pp.1124-1134
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• 2003

The development of microarray technology has supplied a large volume of data to many fields. In particular, it has been applied to prediction and diagnosis of cancer, so that it expectedly helps us to exactly predict and diagnose cancer. It is essential to efficiently analyze DNA microarray data because the amount of DNA microarray data is usually very large. Since accurate classification of cancer is very important issue for treatment of cancer, it is desirable to make a decision by combining the results of various expert classifiers rather than by depending on the result of only one classifier. Generally combining classifiers gives high performance and high confidence. In spite of many advantages of ensemble classifiers, ensemble with mutually error-correlated classifiers has a limit in the performance. In this paper, we propose the ensemble of neural network classifiers learned from negatively correlated features using three benchmark datasets to precisely classify cancer, and systematically evaluate the performances of the proposed method. Experimental results show that the ensemble classifier with negatively correlated features produces the best recognition rate on the three benchmark datasets.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.3
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• pp.81-88
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• 1996

Cluster analysis is based on partitioning a collection of data points into a number of clusters, where the data points in side a cluster have a certain degree of similarity and it is a fundamental process of data analysis. So, it has been playing an important role in solving many problems in pattern recognition and image processing. For these many clustering algorithms depending on distance criteria have been developed and fuzzy set theory has been introduced to reflect the description of real data, where boundaries might be fuzzy. If fuzzy cluster analysis is tomake a significant contribution to engineering applications, much more attention must be paid to fundamental questions of cluster validity problem which is how well it has identified the structure that is present in the data. Several validity functionals such as partition coefficient, claasification entropy and proportion exponent, have been used for measuring validity mathematically. But the issue of cluster validity involves complex aspects, it is difficult to measure it with one measuring function as the conventional study. In this paper, we propose four performance indices and the way to measure the quality of clustering formed by given learning strategy.

• The Korean Journal of Blood Transfusion
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• v.23 no.2
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• pp.97-106
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• 2012

The Korean Society of Blood Transfusion (KSBT) was established in Seoul in 1982 by a group of physicians of clinical pathology (currently laboratory medicine), internal medicine, paediatrics, anaesthesiology, and surgery, who were members of the Korean Society of Hematology, in which they had been actively engaged as transfusion specialists. However, these physicians were dissatisfied with the lack of public recognition of blood transfusion and the unrealistic blood service system and therefore attempted to build an influential organization for improvement of blood services and transfusion medicine. The mission of the KSBT is to advance the science and practice of transfusion medicine. The KSBT advocates for education and research in the fields of transfusion medicine and for safe blood in blood services. The inaugural KSBT conference, with approximately 50 participants, was held in November, 1982; since then, annual conferences and educational workshops have been held regularly once or twice a year. Currently, the KSBT has approximately 800 members, who are practitioners of blood transfusion (holders of M.D.) or staff members of blood services (holders of other professional degrees). The Society also publishes the Korean Journal of Blood Transfusion (first issue published in 1990) and guidelines for blood services. A Society web site (http://www.transfusion.or.kr) was started in 2001, with the hope of increasingly providing information and feedback to members on a real-time basis. The KSBT has been an active participant in international activities. In 1995, the KSBT hosted the Sixth ISBT Western Pacific Regional Congress in Seoul and will host the $33^{rd}$ ISBT Congress in Seoul in 2014. Since its inauguration, the KSBT has had key roles not only in education and research in the fields of transfusion medicine but also in development of safe-blood services during the last 30 years.

• Korean Journal of Remote Sensing
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• v.15 no.3
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• pp.167-175
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• 1999

So far, the remote sensing technology has widely been used in a variety of application areas such as military, medical imaging, environment, geology and so forth. The microwave remote sensing uses the wavelengths ranging from around one centimeter up to a few tens of centimeters and is known to be very effective regardless of the weather conditions and the day/night time as compared with the reflective InfraRed (IR) remote sensing or the thermal IR remote sensing. There are three generic modes of synthetic aperture radar imaging systems depending on its application, that is, stripmap mode, spotlight mode, or inverse mode. In this article we focus on the issue of imaging of flying aircrafts for the inverse mode of a ground - based, fixed radar with moving objects. The imaging of flying aircrafts is considered to be an important step for the automatic target recognition systems, and therefore a great deal of efforts have recently been made on the subject. Here we review the three representative methods including the Fourier transform processing, the time - frequency processing, and the reconstruction from the projection. Some relative merits and drawbacks are also discussed.