• Applied Microscopy
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• v.43 no.2
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• pp.88-97
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• 2013

Technical investigation to figure out the problems arising during in-situ heating electron backscatter diffraction (EBSD) analysis inside scanning electron microscopy (SEM) was carried out. EBSD patterns were successfully acquired up to $830^{\circ}C$ without degradation of EBSD pattern quality in steels. Several technical problems such as image drift and surface microstructure pinning were taking place during in-situ experiments. Image drift problem was successfully prevented in constant current supplying mode. It was revealed that the surface pinning problem was resulted from the $TiO_2$ oxide particle formation during heating inside SEM chamber. Surface pinning phenomenon was fairly reduced by additional platinum and carbon multi-layer coating before in-situ heating experiment, furthermore was perfectly prevented by improvement of vacuum level of SEM chamber via leakage control. Plane view in-situ observation provides better understanding on the overall feature of recrystallization phenomena and cross sectional in-situ observation provides clearer understanding on the recrystallization mechanism.

• Phonetics and Speech Sciences
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• v.6 no.3
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• pp.131-138
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• 2014

A close-copy stylization of intonation curve was used for an acoustical analysis of emotional speech. For the analysis, 408 utterances of five emotions (happiness, anger, fear, neutral and sadness) were processed to extract acoustical feature values. The results show that certain pitch point features (pitch point movement time and pitch point distance within a sentence) and sentence level features (pitch range of a final pitch point, pitch range of a sentence and pitch slope of a sentence) are affected by emotions. Pitch point movement time, pitch point distance within a sentence and pitch slope of a sentence show no significant difference between male and female participants. The emotions with high arousal (happiness and anger) are consistently distinguished from the emotion with low arousal (sadness) in terms of these acoustical features. Emotions with higher arousal show steeper pitch slope of a sentence. They have steeper pitch slope at the end of a sentence. They also show wider pitch range of a sentence. The acoustical analysis in this study implies the possibility that the measurement of these acoustical features can be used to cluster and identify emotions of speech.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.1264-1274
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• 2015

Human action recognition received many interest in the computer vision community. Most of the existing methods focus on either construct robust descriptor from the temporal domain, or computational method to exploit the discriminative power of the descriptor. In this paper we explore the idea of using local action attributes to form an action descriptor, where an action is no longer characterized with the motion changes in the temporal domain but the local semantic description of the action. We propose an novel framework where introduces local action attributes to represent an action for the final human action categorization. The local action attributes are defined for each body part which are independent from the global action. The resulting attribute descriptor is used to jointly model human action to achieve robust performance. In addition, we conduct some study on the impact of using body local and global low-level feature for the aforementioned attributes. Experiments on the KTH dataset and the MV-TJU dataset show that our local action attribute based descriptor improve action recognition performance.

• Earthquakes and Structures
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• v.18 no.1
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• pp.129-145
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• 2020

Seismic resilience is a key feature for buildings that play a strategic role within the community. In this framework, not only the structural and non-structural elements damage but also the protracted structural dysfunction can contribute significantly to overall seismic damage and post-seismic crisis situations. Reduction of the residual and peak displacements and energy dissipation by replaceable elements are some effective aspects to pursue in order to enhance the resilience. Control systems able to adapt their response based on the nature of events, such as active or semi-active, can achieve the best results, but also require higher costs and their complexity jeopardizes their reliability; on the other hand, a passive control system is not able to adapt but its functioning is more reliable and characterized by lower costs. In this study it is proposed a strategy for the optimization of the dissipative capacity of a seismic resistant system obtained placing in parallel two different groups dissipative Re-Centering Devices, specifically designed to enhance the energy dissipation, one for the low and the other for the high intensity earthquakes. In this way the efficiency of the system in dissipating the seismic energy is kept less sensitive to the seismic intensity compared to the case of only one group of dissipative devices.

• ETRI Journal
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• v.37 no.3
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• pp.541-550
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• 2015

In this paper, we propose a classification-based approach for hybridizing statistical machine translation and rulebased machine translation. Both the training dataset used in the learning of our proposed classifier and our feature extraction method affect the hybridization quality. To create one such training dataset, a previous approach used auto-evaluation metrics to determine from a set of component machine translation (MT) systems which gave the more accurate translation (by a comparative method). Once this had been determined, the most accurate translation was then labelled in such a way so as to indicate the MT system from which it came. In this previous approach, when the metric evaluation scores were low, there existed a high level of uncertainty as to which of the component MT systems was actually producing the better translation. To relax such uncertainty or error in classification, we propose an alternative approach to such labeling; that is, a cut-off method. In our experiments, using the aforementioned cut-off method in our proposed classifier, we managed to achieve a translation accuracy of 81.5% - a 5.0% improvement over existing methods.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.11
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• pp.1445-1454
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• 1995

In this study, we have developed a prototype of the automatic defects detection system for capsule inspection using the computer vision techniques. The subjects for inspection are empty hard capsules of various sizes which are made of gelatine. To inspect both sides of a capsule, 2-stage recognition is performed. Features we have used are various lengths of a capsule, area, linearity, symmetricity, head curvature and so on. Decision making is performed based on average value which is computed from 20 good capsules in training and permission bounds in factories. Most of time-consuming process for feature extraction is computed by hardware to meet the inspection speed of more than 20 capsules/sec. The main logic for control and arithmetic computation is implemented using EPLD for the sake of easy change of design and reduction in time for developement. As a result of experiment, defects on size or contour of binary images are detected over 95%. Because of dead zone in imaging system, detection ratio of defects on surface, such as bad joint, chip, speck, etc, is lower than the former case. In this case, detection ratio is 50-85%. Defects such as collet pinch and mashed cap/body seldom appear in binary image, and detection ratio is very low. So we have to process the gray-level image directly in partial region.

• Journal of Information Science Theory and Practice
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• v.2 no.1
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• pp.48-61
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• 2014

Using statistic and bibliometric methods to characterize scientific cooperation between China (excluding Hong Kong, Macao, and Taiwan) and South Korea through their bilateral co-authored papers covered by the Science Citation Index CD-ROM, 1991-2010, in our paper we exploit the feature of their cooperation in four levels: time sequence, academic community, key fields, and institution distribution. From the time sequence we know that collaboration between China and Korea starts in 1991, reaching the first peak during 2004-2007. As for the academic community, the number of Chinese corresponding authors (2414) is slightly lower than that of Korea (2700). Regarding the 27 high yield authors, there are only 4 coming from China. Korea has a higher active level than Chinese authors. China and Korea tend to cooperate with each other on strong disciplines such as physics, chemistry, material science, engineering, mathematics, pharmaceutical, computer science and biology. Furthermore, they also attach great importance to basic research and high-tech cooperation. Besides, Chinese Academy of Sciences ranks at the top 1 among the distribution of institutions. As a majority of the collaborative institutions are universities, the participation of non-university institutions is relatively low. There are 7 Korean universities among the top ten institutions, while Yanbian University and Tsinghua University in China rank respectively as third and fourth. Seoul National University, accompanied by Korea University and Yonsei University as the three top Korean universities, is also among the top among the cooperating institutions.

• International Journal of Contents
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• v.12 no.2
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• pp.49-57
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• 2016

This paper aims to explore instructional design of a cyber classroom for secondary students' basic English language competence. A paucity of support for low or under achieving students' English learning exists particularly at the secondary level. In order to bridge the gap, there has been demand for online educational resources considered to be an effective tool in improving students' self-directed learning and motivation. This study employs a comprehensive approach to instructional design for the asynchronous cyber classroom with the underlying premise that different learning theories can be applied in a complementary manner to serve different pedagogical purposes best. Gagné's conditions of learning theory, Bruner's constructivist theory, Carroll's minimalist theory, and Vygotsky's social cognitive development theory serve as the basis for designing instruction and selecting appropriate media. The ADDIE model is used to develop online teaching and learning materials. Twenty-five key grammatical features were selected through the analysis of the national curriculum of English, being grouped into five units. Each feature is covered in one cyber asynchronous class. An Integration Class is given at the end of every five classes for synthesis, where students can practice grammatical features in a communicative context. Related theories, pedagogical practices, and practical web-design strategies for cyber Basic English classes are discussed with suggestions for research, practice and policy to support self-directed learning through a cyber class.

• Korean Journal of Remote Sensing
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• v.21 no.1
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• pp.31-49
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• 2005

We first review some history of the Global Geodynamics Project (GGP), particularly in the progress of ground-satellite gravity comparisons. The GGP Satellite Project has involved the measurement of ground-based superconducting gravimeters (SGs) in Europe for several years and we make quantitative comparisons with the latest satellite GRACE data and hydrological models. The primary goal is to recover information about seasonal hydrology cycles, and we find a good correlation at the microgal level between the data and modeling. One interesting feature of the data is low soil moisture resulting from the European heat wave in 2003. An issue with the ground-based stations is the possibility of mass variations in the soil above a station, and particularly for underground stations these have to be modeled precisely. Based on this work with a regional array, we estimate the effectiveness of future SG arrays to measure co-seismic deformation and silent-slip events. Finally we consider gravity surveys in volcanic areas, and predict the accuracy in modeling subsurface density variations over time periods from months to years.

• International Journal of Contents
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• v.18 no.2
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• pp.68-80
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• 2022

The health of the human heart is commonly measured using ECG (Electrocardiography) signals. To identify any anomaly in the human heart, the time-sequence of ECG signals is examined manually by a cardiologist or cardiac electrophysiologist. Lightweight anomaly detection on ECG signals in an embedded system is expected to be popular in the near future, because of the increasing number of heart disease symptoms. Some previous research uses deep learning networks such as LSTM and BiLSTM to detect anomaly signals without any handcrafted feature. Unfortunately, lightweight LSTMs show low precision and heavy LSTMs require heavy computing powers and volumes of labeled dataset for symptom classification. This paper proposes an ECG anomaly detection system based on two level BiLSTM for acceptable precision with lightweight networks, which is lightweight and usable at home. Also, this paper presents a new threshold technique which considers statistics of the current ECG pattern. This paper's proposed model with BiLSTM detects ECG signal anomaly in 0.467 ~ 1.0 F₁ score, compared to 0.426 ~ 0.978 F₁ score of the similar model with LSTM except one highly noisy dataset.