• 대한기계학회:학술대회논문집
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• 대한기계학회 2004년도 춘계학술대회
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• pp.1682-1686
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• 2004

A slit-flow apparatus with laser diffraction method has been developed with significant advances in ektacytometry design, operation and data analysis. In the slit-flow ektacytometry (or laser-diffractometry), the deformation of red blood cells subjected to continuously decreasing shear stress in slit flow is measured. A laser beam traverses a diluted blood suspension flowing through a slit and is diffracted by RBCs in the volume. The diffraction patterns are captured by a CCD-video camera, linked to a frame grabber integrated with a computer, while the differential pressure variation is measured by a pressure transducer. Both measurements of laser-diffraction image and pressure with respect to time enable to determine deformation index and the shear stress. The range of shear stress of 0 ${\sim}$ 35 Pa and measuring time is less than 2 min. When deforming under decreasing shear stress, RBCs change gradually from the prolate ellipsoid towards a circular biconcave morphology. The Deformation Index (DI) as a measure of RBC deformability is determined from an isointensity curve in the diffraction pattern using an ellipse-fitting program. The advantages of this design are simplicity, i.e., ease of operation and no moving parts, low cost, short operating time, and the disposable kit which is contacted with blood sample.

• 음성과학
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• 제13권3호
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• pp.207-219
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• 2006

The present study was designed to investigate the comprehension of 'who' and 'what' questions in 2- to 3-year-old normal children. Sixty children were divided into 3 groups depending on their ages, i.e., age groups 2;6-2;11, 3;0-3:5, and 3;6-3;11. Three types of 'who' questions and 2 types of 'what' questions were generated depending on the attached case markers, i.e., who-nominative, who-accusative, who-dative, what-nominative, and what-accusative. The children watched 36 cuts of short video recordings. After watching each cut, they were asked to answer one of the 5 types of wh-questions. For the 'who-nominative' and 'what-accusative' questions, even the late 2-year-old children performed with over 70% of accuracy, and the late 3-year-old children performed with over 95% of accuracy. For the 'who-accusative' and 'who-dative' questions, the late 2-year olds exhibited difficulty in comprehension with performance accuracy of 41% and 33%, respectively. However, the late 3-year olds could comprehend those questions correctly with over 90% of accuracy. On the other hand, in answering 'what-nominative' questions, the children did not show rapid development across the age groups, as the mean performance accuracies of the 3 groups were 39%, 49%, and 59%, respectively. The results indicated that children's understanding of a wh- question is largely affected by the case of the interrogative.

• 한국추진공학회:학술대회논문집
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• 한국추진공학회 2004년도 제22회 춘계학술대회논문집
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• pp.85-92
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• 2004

As a research to develop a SCRAM jet engine is actively conducted, a necessity to produce a high-enthalpy flow in a laboratory is increasing. In order to develop the SCRAM-jet engine, stabilized combustion in a supersonic flow-field should be attained, in which a duration time of flow is extremely short. Therefore, a mixing process of breathed air and fuel, which is injected into supersonic flow-fields is one of the most important problem. Since, the flow inside SCRAM jet engine has high-enthalpy, an experimental facility is required to produce such high-enthalpy flow-field. In this study, a detonation-driven shock tunnel was built and was used to produce high-enthalpy flow. Further-more, SCRAM jet engine model equipped backward-facing step was installed at test section and flow-fields were visualized using color-schlieren technique and high speed video camera. The fuel was injected perpendicular to the flow of Mach number three behind backward-facing step. The height of the step, distance of injection and injection pressure were changed to investigate the effects of step on a mixing characteristic between air and fuel. The schlieren photograph and pressure histories show that the fuel was ignited behind the step.

• 정보과학회 논문지
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• 제42권5호
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• pp.671-680
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• 2015

무선 센서 네트워크에서 에너지 효율을 높이기 위해 클러스터링 방법이 제안되었다. 클러스터링 방법은 데이터를 수집하고 전달하는 계층 구조를 형성하고 있다. 그러나 대용량 데이터를 전송하는 무선 멀티미디어 센서 네트워크에서 기존의 무선 센서 네트워크의 클러스터링 기법은 과도한 데이터 전송에너지 소모량과 극도로 짧은 수명으로 인하여 적용하는 데에 어려움이 있다. 본 논문에서는 기존의 문제점을 해결하기 위하여 상황에 따른 협력 통신 기법을 적용한 대용량 데이터 전송 방법으로 EEAC를 제안하여 에너지 효율을 높였다. 실험결과 제안방법은 기존의 방법보다 에너지 효율이 약 2.5배 향상되었음을 보였다.

• 한국운동역학회지
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• 제13권1호
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• pp.51-62
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• 2003

This study was attempted to kinematical characteristics of the El-grip swing with 1turn to el-grip in elite horizontal bar for the purpose of improving performance. The subjects were three males who were 2002 Busan Asian Games in men's team. The three dimensional motion analysis with DLT method was executed using three video cameras of analyzing the actual competition situation. In point of analyzing the actual competition situation, it is expected that gymnastics and coaches have the effective informations, and the following conclusion had resulted. 1. In case of release, It is impotant to make fast horizontal velocity of CM, high vertical position of CM, large hip and shoulder angle. Also It should be performed release motion of trunk rotation angle(+). 2. During LHR the action should be made at higher position than the CM and the shoulder joint is moving within $127{\pm}16.82$. It is important to make large lunk rotation angle. 3. During Hop, the RHR motion should be done in high position with short time and fast twisting action and to reduce the vertical speed is important.

• The Journal of Korean Physical Therapy
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• 제28권6호
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• pp.355-363
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• 2016

Purpose: In this study, we investigated the effectiveness of a 12-week virtual reality exercise program using the Nintendo Wii console (Wii) in improving balance, emotion, and quality of life among patients with cognitive decline. Methods: The study included 30 patients with cognitive decline (12 female, 18 male) who were randomly assigned to an experimental (n=15) and control groups (n=15). All subjects performed a traditional cognitive rehabilitation program and the experimental group performed additional three 40-minute virtual reality based video game (Wii) sessions per week for 12 weeks. The berg balance scale (BBS) was used to assess balance abilities. The short form geriatric depression scale-Korean (GDS-K) and the Korean version of quality of life-Alzheimer's disease (KQOL-AD) scale were both used to assess life quality in patients. Statistical significance was tested within and between groups before and after treatment, using Wilcoxon signed rank and Mann-Whitney u-tests. Results: After 36 training sessions, there were significant beneficial effects of the virtual reality game exercise on balance (BBS), GDS-K, and KQOL-AD in the experimental group when compared to the control group. No significant difference was observed within the control group. Conclusion: These findings demonstrate that a virtual reality-training program could improve the outcomes in terms of balance, depression, and quality of life in patients with cognitive decline. Long-term follow-ups and further studies of more efficient virtual reality training programs are needed.

• 한국어교육
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• 제28권4호
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• pp.1-38
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• 2017

This longitudinal study analyzed syntactic development (Complexity, Accuracy, and Fluency; CAF) of six L2 Korean learners in a natural learning context. The learners recalled the stories of a short animated video through speaking and writing every 3 months, from month 0 to 15. The learners' responses were analyzed for a series of CAF measures and their cognitive, psychological, and social variables were investigated. The results showed that (i) L2 Korean learners' speaking and writing in various time periods showed significant differences in spoken and written accuracy, and complexity; (ii) the correlation between spoken and written complexity, spoken and written accuracy, as well as spoken and written fluency were significant, and (iii) the regression analysis showed that learners' cognitive, social, and psychological variables have significant effect on the L2 Korean syntactic development. The current study reports that L2 Korean learners engaged in self-learning in a natural learning environment without formal instruction made significant syntactic development.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권6호
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• pp.1981-1995
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• 2021

When a person tries to conceal emotions, real emotions will manifest themselves in the form of micro-expressions. Research on facial micro-expression recognition is still extremely challenging in the field of pattern recognition. This is because it is difficult to implement the best feature extraction method to cope with micro-expressions with small changes and short duration. Most methods are based on hand-crafted features to extract subtle facial movements. In this study, we introduce a method that incorporates optical flow and deep learning. First, we take out the onset frame and the apex frame from each video sequence. Then, the motion features between these two frames are extracted using the optical flow method. Finally, the features are inputted into an improved MobileNetV2 model, where SVM is applied to classify expressions. In order to evaluate the effectiveness of the method, we conduct experiments on the public spontaneous micro-expression database CASME II. Under the condition of applying the leave-one-subject-out cross-validation method, the recognition accuracy rate reaches 53.01%, and the F-score reaches 0.5231. The results show that the proposed method can significantly improve the micro-expression recognition performance.

• International Journal of Computer Science & Network Security
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• 제21권2호
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• pp.229-237
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• 2021

Our modern 'information-hungry' age demands delivery of information at unprecedented fast rates. Timely delivery of noteworthy information about recent events can help people from different segments of life in number of ways. As world has become global village, the flow of news in terms of volume and speed demands involvement of machines to help humans to handle the enormous data. News are presented to public in forms of video, audio, image and text. News text available on internet is a source of knowledge for billions of internet users. Urdu language is spoken and understood by millions of people from Indian subcontinent. Availability of online Urdu news enable this branch of humanity to improve their understandings of the world and make their decisions. This paper uses available online Urdu news data to train machines to automatically categorize provided news. Various machine learning algorithms were used on news headline for training purpose and the results demonstrate that Bernoulli Naïve Bayes (Bernoulli NB) and Multinomial Naïve Bayes (Multinomial NB) algorithm outperformed other algorithms in terms of all performance parameters. The maximum level of accuracy achieved for the dataset was 94.278% by multinomial NB classifier followed by Bernoulli NB classifier with accuracy of 94.274% when Urdu stop words were removed from dataset. The results suggest that short text of headlines of news can be used as an input for text categorization process.

• 자동차안전학회지
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• 제14권3호
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• pp.60-64
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• 2022

In the automotive industry, the development of automobiles to meet safety requirements is becoming increasingly complex. This is because quality evaluation agencies in each country are continually strengthening new safety standards for vehicles. Among these various requirements, collision safety must be satisfied by controlling airbags, seat belts, etc., and can be defined as post-crash safety. Apart from this safety system, the Advanced Driver Assistance Systems (ADAS) use advanced detection sensors, GPS, communication, and video equipment to detect the hazard and notify driver before the collision. However, research to improve passenger safety in case of an accident by using the sensor of active safety represented by ADAS in the existing passive safety is limited to the level that utilizes the sudden braking level of the FCA (Forward Collision-avoidance Assist) system. Therefore, this study aims to develop logic that can improve passenger protection in case of an accident by using ADAS information and driving information secured before a collision. The proposed logic was constructed based on LSTM deep learning techniques and trained using crash test data.