• Journal of the Korean Magnetic Resonance Society
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• v.19 no.3
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• pp.137-142
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• 2015

NMR-based structural studies on membrane proteins are appreciated quite challenging due to various reasons, generally including the narrow dispersion of NMR spectra, the severe peak broadening, and the lack of long range NOEs. In spite of the poor biophysical properties, structural studies on membrane proteins have got to go on, considering their functional importance in biological systems. In this review, we provide a simple overview of the techniques generally used in structural studies of membrane proteins by solution NMR, with experimental examples of a helical membrane protein, caveolin 3. Detergent screening is usually employed as the first step and the selection of appropriate detergent is the most important for successful approach to membrane proteins. Various tools can then be applied as specialized NMR techniques in solution that include sample deteuration, amino-acid selective isotope labeling, residual dipolar coupling, and paramagnetic relaxation enhancement.

• The KIPS Transactions:PartB
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• v.18B no.4
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• pp.173-180
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• 2011

This paper shows an approach for real-time object segmentation on GPU (Graphics Processing Unit) using CUDA (Compute Unified Device Architecture). Recently, many applications that is monitoring system, motion analysis, object tracking or etc require real-time processing. It is not suitable for object segmentation to procedure real-time in CPU. NVIDIA provide CUDA platform for Parallel Processing for General Computation to upgrade limit of Hardware Graphic. In this paper, we use adaptive Gaussian Mixture Background Modeling in the step of object extraction and CCL(Connected Component Labeling) for classification. The speed of GPU and CPU is compared and evaluated with implementation in Core2 Quad processor with 2.4GHz.The GPU version achieved a speedup of 3x-4x over the CPU version.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.5
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• pp.843-848
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• 2008

Most of face detection technique uses information from the face of the movement. The traditional face detection method is to use difference picture method ate used to detect movement. However, most do not consider this mathematical approach using real-time or real-time implementation of the algorithm is complicated, not easy. This paper, the first to detect real-time facial image is converted YCbCr and RGB video input. Next, you convert the difference between video images of two adjacent to obtain and then to conduct Glassfire Labeling. Labeling value compared to the threshold behavior Area recognizes and converts video extracts. Actions to convert video to conduct face detection, and detection of facial characteristics required for the extraction and use of AdaBoost algorithm.

• Smart Media Journal
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• v.8 no.2
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• pp.74-79
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• 2019

Traditional compound noun decomposition algorithms often face challenges of decomposing compound nouns into separated nouns when unregistered unit noun is included. It is very difficult for those traditional approach to handle such issues because it is impossible to register all existing unit nouns into the dictionary such as proper nouns, coined words, and foreign words in advance. In this paper, in order to solve this problem, compound noun decomposition problem is defined as tag sequence labeling problem and compound noun decomposition method to use syllable unit embedding and deep learning technique is proposed. To recognize unregistered unit nouns without constructing unit noun dictionary, compound nouns are decomposed into unit nouns by using LSTM and linear-chain CRF expressing each syllable that constitutes a compound noun in the continuous vector space.

• The Journal of Korea Robotics Society
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• v.3 no.3
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• pp.212-218
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• 2008

Collision avoidance is a fundamental and important task of an autonomous mobile robot for safe navigation in real environments with high uncertainty. Obstacles are classified into static and dynamic obstacles. It is difficult to avoid dynamic obstacles because the positions of dynamic obstacles are likely to change at any time. This paper proposes a scheme for vision-based avoidance of dynamic obstacles. This approach extracts object candidates that can be considered moving objects based on the labeling algorithm using depth information. Then it detects moving objects among object candidates using motion vectors. In case the motion vectors are not extracted, it can still detect the moving objects stably through their color information. A robot avoids the dynamic obstacle using the dynamic window approach (DWA) with the object path estimated from the information of the detected obstacles. The DWA is a well known technique for reactive collision avoidance. This paper also proposes an algorithm which autonomously registers the obstacle color. Therefore, a robot can navigate more safely and efficiently with the proposed scheme.

• Journal of Korean Society of Transportation
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• v.13 no.3
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• pp.53-65
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• 1995

Proposed are model and its solution algorithm for determining the minimum number of high speed exits and their locations. While the previous researches on exit location aimed to minimize the average runway occuancy time (ROT) of an aircraft mix, the proposed approach is to find the minimum number of exits required to meet maximum allowable ROT. The rationale behind the approach is that the capacity of a runway increases as the ROT decreases down to some value, but not any more even though the ROT keep decreasing below the value. Hence, a maximum allowable ROT might be set up without declining the capacity. The problem is transformed into a shortest path problem on a specially constructed network and Dijkstra's labeling algorithms is employed to solve the problem A hypothetical example is provided to illustrate how the algorithms solves the problem.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.1000-1011
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• 2021

In recent years, using Deep Learning methods to apply for medical and biomedical image analysis has seen many advancements. In clinical, using Deep Learning-based approaches for cancer image analysis is one of the key applications for cancer detection and treatment. However, the scarcity and shortage of labeling images make the task of cancer detection and analysis difficult to reach high accuracy. In 2015, the Unet model was introduced and gained much attention from researchers in the field. The success of Unet model is the ability to produce high accuracy with very few input images. Since the development of Unet, there are many variants and modifications of Unet related architecture. This paper proposes a new approach of using Unet++ with pretrained EfficientNet as backbone architecture for breast tumor cell nuclei segmentation and uses the multi-organ transfer learning approach to segment nuclei of breast tumor cells. We attempt to experiment and evaluate the performance of the network on the MonuSeg training dataset and Triple Negative Breast Cancer (TNBC) testing dataset, both are Hematoxylin and Eosin (H & E)-stained images. The results have shown that EfficientUnet++ architecture and the multi-organ transfer learning approach had outperformed other techniques and produced notable accuracy for breast tumor cell nuclei segmentation.

• Food Science of Animal Resources
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• v.29 no.5
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• pp.647-652
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• 2009

A duplex PCR technique was applied for specific identification of cow and goat milk in milk products by using primers targeting the mitochondrial 12S rRNA gene. Duplex PCR using primers specific for cow and goat generated specific fragments of 223bp and 326bp from cow and goat milk DNA, respectively. Duplex PCR was applied to 15 milk products purchased from the market to verify label statements. The labeling statements of four market milk products, three yoghurt products, and one whole milk powder product were confirmed in the duplex PCR. The labeling statements of five of seven infant milk powder products were also confirmed by duplex PCR but the other two products were shown to be contaminated with either cow or goat milk. The proposed duplex PCR provides a rapid and sensitive approach to detection of as little as 0.1% cow milk in goat milk and one-step detection of cow or goat milk in milk products.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.4
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• pp.157-166
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• 2022

In the case of a die-casting process, defects that are difficult to confirm by visual inspection, such as shrinkage bubbles, may occur due to an error in maintaining a vacuum state. Since these casting defects are discovered during post-processing operations such as heat treatment or finishing work, they cannot be taken in advance at the casting time, which can cause a large number of defects. In this study, we propose an approach that can predict the occurrence of casting defects by defect type using machine learning technology based on casting parameter data collected from equipment in the die casting process in real time. Die-casting parameter data can basically be collected through the casting equipment controller. In order to perform classification analysis for predicting defects by defect type, labeling of casting parameters must be performed. In this study, first, the defective data set is separated by performing the primary clustering based on the total defect rate obtained during the post-processing. Second, the secondary cluster analysis is performed using the defect rate by type for the separated defect data set, and the labeling task is performed by defect type using the cluster analysis result. Finally, a classification learning model is created by collecting the entire labeled data set, and a real-time monitoring system for defect prediction using LabView and Python was implemented. When a defect is predicted, notification is performed so that the operator can cope with it, such as displaying on the monitoring screen and alarm notification.

• English Language & Literature Teaching
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• no.6
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• pp.1-24
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• 2000

Listening comprehension is an integrative and creative process of interaction through which listeners receive speakers' production of linguistic or non-linguistic knowledge. Compared with reading comprehension, it may arouse difficulties and thus impose more burdens on foreign learners. The Audio-Lingual Method focused primarily on speaking. Mimicry, repetition, rote memory, and transformation drills actually interfered with listening comprehension. So learners lost interest and were not highly motivated. Improving listening comprehension requires continual attentiveness and interest. Listening skill can be extended systematically only when students are frequently exposed to a wide range of listening materials with an affective, cultural, social, and psycholinguistic approach. Therefore, teachers should help students learn how to comprehend intactly the overall meaning of intended messages. The literature on teaching listening skill suggests various useful activities: TPR, dictation, role playing, singing, picture recognition, completion, prediction, seeking specific information, summarizing, labeling, humor, jokes, cartoons, media, and so on. Practical classroom teaching necessitates a systematic procedure in which students should take part in meaningful tasks/activities. In addition to this, learners must practice listening comprehension trough a self-study process.