• Journal of Arbitration Studies
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• v.30 no.2
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• pp.69-90
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• 2020

The arbitration system has many advantages, including resilience, speed, ease of approval, and enforcement of foreign arbitration in international disputes, and it plays an important role in today's international business. As the world's economic activities increase, China's trade disputes are intensifying. In 2017, China emphasized the international cooperation and commercial expansion of foreign investment at "One Belt, One Road." Therefore, it is expected that international business will become more active, with the issue of how to recognize and enforce the foreign arbitration awards in China becoming highly important. In addition, South Korea and China maintained deep trade relations after establishing diplomatic relations in 1992 and concluding the Korea-China Free Trade Agreement, which will inevitably increase trade disputes. As far as South Korea is concerned, China is South Korea's largest trading partner, so it is important for South Korea to analyze how foreign arbitration awards are recognized and enforced in China. China's accession to the New York Convention in 1987 was the beginning of the enforcement of foreign arbitrators. However, since China has begun to recognize and enforce foreign arbitrators relatively late, there are many problems in terms of recognizing and enforcing foreign arbitral awards in China. This study introduces the concept and scope of foreign arbitral awards, as well as the legal basis and procedures for recognizing and enforcing foreign arbitral awards, and examines relevant cases and the denial of recognition and enforcement of a foreign arbitration award. In the end, some issues and remedies for the recognition and enforcement of the foreign arbitral awards system in China were concluded.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1299-1311
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• 2017

Sounds based machine fault diagnosis recovers all the studies that aim to detect automatically faults or damages on machines using the sounds emitted by these machines. Conventional methods that use mathematical models have been found inaccurate because of the complexity of the industry machinery systems and the obvious existence of nonlinear factors such as noises. Therefore, any fault diagnosis issue can be treated as a pattern recognition problem. We present here an automatic fault diagnosis system of hand drills using discrete wavelet transform (DWT) and pattern recognition techniques such as principal component analysis (PCA) and artificial neural networks (ANN). The diagnosis system consists of three steps. Because of the presence of many noisy patterns in our signals, we first conduct a filtering analysis based on DWT. Second, the wavelet coefficients of the filtered signals are extracted as our features for the pattern recognition part. Third, PCA is performed over the wavelet coefficients in order to reduce the dimensionality of the feature vectors. Finally, the very first principal components are used as the inputs of an ANN based classifier to detect the wear on the drills. The results show that the proposed DWT-PCA-ANN method can be used for the sounds based automated diagnosis system.

• The KIPS Transactions:PartB
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• v.9B no.3
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• pp.327-336
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• 2002

Texture analysis is an important technique in many image understanding areas, such as perception of surface, object, shape and depth. But the previous works are intend to the issue of only texture segment, that is not capable of acquiring recognition information. No unsupervised method is basased on the recognition of texture in image. we propose a novel approach for efficient texture image analysis that uses unsupervised learning schemes for the texture recognition. The self-organization neural network for multiple texture image identification is based on block-based clustering and merging. The texture features used are the angle and magnitude in orientation-field that might be different from the sample textures. In order to show the performance of the proposed system, After we have attempted to build a various texture images. The final segmentation is achieved by using efficient edge detection algorithm applying to block-based dilation. The experimental results show that the performance of the system Is very successful.

• Korean Journal of Human Ecology
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• v.16 no.6
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• pp.1083-1095
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• 2007

This study had been done to investigate that early childhood teacher's recognition and demand on children's language education and 20 early childhood teachers were interviewed individually who work at state-owned/ public-owned/ private-owned kindergardens residing G, I, and K cities in Jeollabuk-do. First, the purpose of language education was recognized on the formations of essence, concept, expertise, technique and attitude toward language. Second, the contents of language education must be selected by children's experience that they encounter in ordinary life based on oral language and written language. Besides, early childhood teachers strongly felt the necessity of new contents of language education, although they thought of insufficiency of their knowledge on the issue. Third, the method of language education was mainly accomplished by teaching material and objects. Besides, they were aware of looking for new organized teaching methods and also concerned of the importances of teacher's attitude and group formation method. Fourth, the evaluation of language education must be acquired by desirable evaluation method that was based on the recognition of children's unrealistic language capabilities, even though they had recognized the difficulty to do because of knowledge insufficiency. They also showed the tendency of negligence on the evaluation of language education. Fifth, the required facts for early childhood teachers on language education were development and supply of teaching materials, demand on teacher's education and appropriate evaluation method, and cognitive changes on language education by public toward the written language.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.6
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• pp.551-557
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• 2013

In intelligent robot research, hand gesture recognition has been an important issue. And techniques that recognize simple gestures are commercialized in smart phone, smart TV for swiping screen or volume control. For gesture recognition, robust hand detection is important and necessary but it is challenging because hand shape is complex and hard to be detected in cluttered background, variant illumination. In this paper, we propose efficient hand detection algorithm for detecting pointing hand for recognition of place where user pointed. To minimize false detections, ROIs are generated within the compact search region using skin color detection result. The ROIs are verified by HOG-SVM and pointing direction is computed by both detection results of head-shoulder and hand. In experiment, it is shown that proposed method shows good performance for hand detection.

• Journal of Internet Computing and Services
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• v.10 no.4
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• pp.55-70
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• 2009

Vision-based Human computer interaction is an emerging field of science and industry to provide natural way to communicate with human and computer. In that sense, inferring the emotional state of the person based on the facial expression recognition is an important issue. In this paper, we present a novel approach to recognize facial expression from a sequence of input images using emotional specific HMM (Hidden Markov Model) and facial motion tracking based on optical flow. Conventionally, in the HMM which consists of basic emotional states, it is considered natural that transitions between emotions are imposed to pass through neutral state. However, in this work we propose an enhanced transition framework model which consists of transitions between each emotional state without passing through neutral state in addition to a traditional transition model. For the localization of facial features from video sequence we exploit template matching and optical flow. The facial feature displacements traced by the optical flow are used for input parameters to HMM for facial expression recognition. From the experiment, we can prove that the proposed framework can effectively recognize the facial expression in real time.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.2
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• pp.78-83
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• 2019

Sounds based machine fault diagnosis recovers all the studies that aim to detect automatically abnormal sound on machines using the acoustic emission by these machines. Conventional methods that use mathematical models have been found inaccurate because of the complexity of the industry machinery systems and the obvious existence of nonlinear factors such as noises. Therefore, any fault diagnosis issue can be treated as a pattern recognition problem. We propose here an automatic fault diagnosis method of hand drills using discrete wavelet transform(DWT) and pattern recognition techniques such as artificial neural networks(ANN). We first conduct a filtering analysis based on DWT. The power spectral density(PSD) is performed on the wavelet subband except for the highest and lowest low frequency subband. The PSD of the wavelet coefficients are extracted as our features for classifier based on ANN the pattern recognition part. The results show that the proposed method can be effectively used not only to detect defects but also to various automatic diagnosis system based on sound.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.6
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• pp.1419-1427
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• 2012

This paper deals with the online privacy policy, which is designed to solve the information asymmetry problem between websites and internet users. We empirically analyze the recognition, confirmation of the online privacy policy, and its effects on online transaction behavior using a rich survey data representing 5,422 Korean internet users. Major results are as follows. First, there exists a significant difference between recognition and confirmation, and confirmation behavior is positively related with the importance of privacy issue and the experience of privacy invasion. Second, binary variable regressions show that internet user tends to participate in online transaction if he/she confirms the online privacy policy positively. Finally, if websites would make online privacy policy easy and short, a yearly online transaction market size of Korea would increase by 0.46 million participants and 22.4 billion KRW.

• Journal of Advanced Navigation Technology
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• v.25 no.4
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• pp.299-304
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• 2021

Existing human activity recognition systems detect activities through devices such as wearable sensors and cameras. However, these methods require additional devices and costs, especially for cameras, which cause privacy issue. Using WiFi signals that are already installed can solve this problem. In this paper, we propose a CNN-based human activity recognition system using channel state information of WiFi signals, and present results of designing and implementing accelerated hardware structures. The system defined four possible behaviors during studying in indoor environments, and classified the channel state information of WiFi using convolutional neural network (CNN), showing and average accuracy of 91.86%. In addition, for acceleration, we present the results of an accelerated hardware structure design for fully connected layer with the highest computation volume on CNN classifiers. As a result of performance evaluation on FPGA device, it showed 4.28 times faster calculation time than software-based system.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.101-108
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• 2024

AI-OCR (Artificial Intelligence Optical Character Recognition) combines OCR technology with Artificial Intelligence to overcome limitations that required human intervention. To enhance the performance of AI-OCR, training on diverse data sets is essential. However, the recognition rate declines when image colors have similar brightness levels. To solve this issue, this study employs Homomorphic filtering as a preprocessing step to clearly differentiate color levels, thereby increasing text recognition rates. While Homomorphic filtering is ideal for text extraction because of its ability to adjust the high and low frequency components of an image separately using a gamma value, it has the downside of requiring manual adjustments to the gamma value. This research proposes a range for gamma threshold values based on tests involving image contrast, brightness, and entropy. Experimental results using the proposed range of gamma values in Homomorphic filtering suggest a high likelihood for effective AI-OCR performance.