• 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.

• Journal of Korean Neurosurgical Society
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• v.49 no.5
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• pp.292-295
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• 2011

A rare case of intraventricular meningioma that arose in the atrium of the left lateral ventricle was identified in a 51-year-old woman. Gross total removal was performed by transcortical approach. Histopathological findings showed meningothelial meningioma with a focal atypical area which had 8% of Ki-67 labeling index (LI). A large recurrence extending into the ipsilateral quadrigeminal cistern and opposite medial occipital lobe developed approximately 41 months after the first operation. The specimens obtained from the second resection showed atypical meningioma with 20% of Ki-67 LI but there were no anaplastic area. The patient underwent fractionated stereotactic radiotherapy. However, multiple local distant metastases were found in the occipital and cerebellar cortex suggesting cerebrospinal fluid dissemination apparently 24 months after the second operation. This report presents chronological progression of a rare intraventricular atypical meningioma with more aggressive transformation.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.34 no.8
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• pp.301-307
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• 1985

The first four generations of computers are all based on a single basic design: the Von Neuman Processor, which is sequential and does one operation at a time. Efforts to develop concurrent or parallel computers have been carried on for many years. Data flow approach is significant in these efforts to make high speed parallel machines and expected a great deal of parallelism. In this paper we propose a preliminary data Flow Machine Model operating asynchronously on the base of Node Labelling. We introduce a concept of Node Labeling for this purpose which is relevant to the Data dependency and Parallelism. And we explain how the Node Tokens are fired in the proposed system.

• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.154-158
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• 2002

This paper describes a path tracking method for vision-based and autonomous mobile robot in a corridor. At first, we extract the ceiling-lamp of the corridor through simple preprocessing (gray, thresholding, labeling, etc.) for robot position and orientation. Then, we design the controller for path-tracking. Simulations conducted, and acceptable vehicle localization results were obtained to prove the feasibility of the proposed approach.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.3
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• pp.209-215
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• 2014

Sequence tagging is the task of predicting frame-wise labels for a given input sequence and has important applications to diverse domains. Conventional methods such as maximum likelihood (ML) learning matches global features in empirical and model distributions, rather than local features, which directly translates into frame-wise prediction errors. Recent probabilistic sequence models such as conditional random fields (CRFs) have achieved great success in a variety of situations. In this paper, we introduce a novel discriminative CRF learning algorithm to minimize local feature mismatches. Unlike overall data fitting originating from global feature matching in ML learning, our approach reduces the total error over all frames in a sequence. We also provide an efficient gradient-based learning method via gradient forward-backward recursion, which requires the same computational complexity as ML learning. For several real-world sequence tagging problems, we empirically demonstrate that the proposed learning algorithm achieves significantly more accurate prediction performance than standard estimators.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.4092-4107
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• 2015

We propose a background subtraction method for moving cameras based on trajectory classification, image segmentation and label inference. In the trajectory classification process, PCA-based outlier detection strategy is used to remove the outliers in the foreground trajectories. Combining optical flow trajectory with watershed algorithm, we propose a trajectory-controlled watershed segmentation algorithm which effectively improves the edge-preserving performance and prevents the over-smooth problem. Finally, label inference based on Markov Random field is conducted for labeling the unlabeled pixels. Experimental results on the motionseg database demonstrate the promising performance of the proposed approach compared with other competing methods.

• Journal of Institute of Convergence Technology
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• v.11 no.1
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• pp.1-5
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• 2021

Deep learning based methods achieve state-of-the-art accuracy, however, they typically rely on supervised training with large labeled datasets. It is known in many medical applications that labeling medical images requires significant expertise and much time, and typical hand-tuned approaches for data augmentation fail to capture the complex variations in such images. This paper proposes a 3D image augmentation method to overcome these difficulties. It allows us to enrich diversity of training data samples that is essential in medical image segmentation tasks, thus reducing the data overfitting problem caused by the fact the scale of medical image dataset is typically smaller. Our numerical experiments demonstrate that the proposed approach provides significant improvements over state-of-the-art methods for 3D medical image segmentation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.293-297
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• 2007

This thesis suggests a new algorithm to detects multiple moving objects using a CMODE(Correct Multiple Object DEtection) method in the color images acquired in real-time and to track the interested pedestrian using motion and hue information. The multiple objects are detected, and then shaking trees or moving cars are removed using structural characteristics and shape information of the man , the interested pedestrian can be detected, The first similarity judgment for tracking an interested pedestrian is to use the distance between the previous interested pedestrian's centroid and the present pedestrian's centroid. For the area where the first similarity is detected, three feature points are calculated using k-mean algorithm, and the second similarity is judged and tracked using the average hue value for the $3{\times}3$ area of each feature point. The zooming of camera is adjusted to track an interested pedestrian at a long distance easily and the FOV(Field of View) of camera is adjusted in case the pedestrian is not situated in the fixed range of the screen. As a experiment results, comparing the suggested CMODE method with the labeling method, an average approach rate is one fourth of labeling method, and an average detecting time is faster three times than labeling method. Even in a complex background, such as the areas where trees are shaking or cars are moving, or the area of shadows, interested pedestrian detection is showed a high detection rate of average 96.5%. The tracking of an interested pedestrian is showed high tracking rate of average 95% using the information of situation and hue, and interested pedestrian can be tracked successively through a camera FOV and zooming adjustment.

• Journal of the Korean Society of Food Science and Nutrition
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• v.32 no.8
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• pp.1390-1393
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• 2003

Measuring in vivo rate of bone collagen synthesis has so far been technically difficult and often subject to quite large errors. In the present study, bone collagen synthesis rate was measured using a precursor-product method, based on the exchange of $^2$$H_2O$ into amino acids. Mass isotopomer abundance in hydroxyproline from bone collagen was analyzed by gas chromatography/mass spectrometry. The $^2$$H_2O$ labeling protocol consisted of an initial intraperitoneal injection of 99.9% $^2$$H_2O$, to achieve approximately 2.5% body water enrichment followed by administration of 4% $^2$$H_2O$ in drinking water for 9 weeks. Body $^2$$H_2O$ enrichments were stable at 2.7 ∼ 3.0% over labeling Period. In growing rats, the fractional synthesis rate ( $k_{s}$) of bone collagen was 0.066 $\pm$ 0.049 w $k^{-1}$ . The unique features of stable $^2$$H_2O$ pools and label incorporation allowed the precursor-product approach to be used for measuring bone collagen synthesis rate..

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.1
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• pp.15-26
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• 2022

3D open-field farm model developed from UAV (Unmanned Aerial Vehicle) data could make crop monitoring easier, also could be an important dataset for various fields like remote sensing or precision agriculture. It is essential to separate crops from the non-crop area because labeling in a manual way is extremely laborious and not appropriate for continuous monitoring. We, therefore, made a 3D open-field farm model based on UAV images and developed a crop segmentation model using a supervised machine learning algorithm. We compared performances from various models using different data features like color or geographic coordinates, and two supervised learning algorithms which are SVM (Support Vector Machine) and KNN (K-Nearest Neighbors). The best approach was trained with 2-dimensional data, ExGR (Excess of Green minus Excess of Red) and z coordinate value, using KNN algorithm, whose accuracy, precision, recall, F1 score was 97.85, 96.51, 88.54, 92.35% respectively. Also, we compared our model performance with similar previous work. Our approach showed slightly better accuracy, and it detected the actual crop better than the previous approach, while it also classified actual non-crop points (e.g. weeds) as crops.