• 대한통합의학회지
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• 제11권3호
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• pp.195-204
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• 2023

Purpose : This study applied general training (control group) or cranio-cervical flexor training (experimental group) using a pressure biofeedback unit along with general training for 4 weeks to secondary school teachers with moderate to severe neck pain and forward head posture. After that, we tried to compare the effects through differences in neck pain intensity (using numberical rating scale), functional performance (using neck disability index), and cranio-vertebral angle change. Methods : All 50 subjects were randomly assigned to either the "experimental group (n= 25)" or the "control group (n= 25)", and the measurements were evaluated in the same way before the intervention (baseline) and after the intervention (4 weeks). During the intervention period, the subject visited the physiotherapy center and made a reservation three times a week at a fixed time as much as possible, and each training session was thoroughly conducted under the 1:1 guidance of the therapist in charge so that the correct movement and number of times could be performed without compensatory action. Results : As a result of the homogeneity analysis on the general characteristics of the subjects, there were no significant differences between the groups in all variables (p>.05). Compared to the "control group", the "experimental group" showed significant improvement after intervention in all measured variables of neck pain intensity, functional performance, and cranial-vertebral angle (p<.05). Conclusion : For secondary school teachers with forward head accompanied by neck pain, cranio-cervical flexor training using a compression biofeedback unit is an excellent method to show superior pain reduction and functional performance improvement compared to general training alone. In addition, it can be presented as a more effective intervention method that can promote recovery of forward head posture, which is an essential element of the solution.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2018년도 추계학술대회
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• pp.310-312
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• 2018

본 연구에서는 IALA G1139 지침에서 정의한 시스템 파라미터 및 요구 사항들을 바탕으로 한 수신기 물리계층 구현에 있어 주파수 옵셋 처리상의 어려움을 확인하였던 1차 연구에 이어 추가적인 검토와 주파수 옵셋을 효과적으로 처리하기 위하여 Training 심볼 길이 확장 및 차동 변조 방식에 대한 연구를 수행한다. 본 연구는 추후 IALA ENAV22 의제에서 다뤄질 예정이며, 추후 IALA 회의 결과를 바탕으로 주파수 옵셋 처리 방안을 선정할 예정이다.

• 한국가스학회지
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• 제9권2호
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• pp.22-27
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• 2005

본 연구에서는 LNG로부터 냉열을 활용하여 $^{13}CH_4$과 $^{12}CH_4$을 분리하는 초저온 증류 공정에 대한 전산 모사를 분석한 것이다. 사용한 전산 모사 프로그램은 short-cut 방법으로 사용되는 Smoker식과 FUG(Fenske-Underwood-Gilliland) 방법 두가지를 활용하여 실시하였다. 일반적으로 탄소 동위원소 분리에 대한 기술은 많은 방법들이 연구중에 있으며, 특히 초저온 증류 공정에 의한 분리는 많은 장점으로 인해 상업적으로 활용되고 있다.

• 전자공학회논문지CI
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• 제44권2호
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• pp.11-18
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• 2007

신경회로망의 학습에 널리 사용되고 있는 오차역전파 알고리즘은 최급하강법을 기초로 하고 있기 때문에 초기값에 따라서는 극소값에 떨어지거나, 신경회로망을 학습시킬 때 중간층 유닛수를 얼마로 설정하는 등의 문제점이 있다. 따라서 이러한 문제점을 해결하기 위하여, 본 논문에서는 3비트 패리티 판별을 위하여 신경회로망의 학습에 교차법, 돌연변이법에 새로운 기법을 도입한 개량형 유전적 알고리즘을 제안한다. 본 논문에서는 세대차이, 중간층 유닛수의 차이, 집단의 개체수의 차이에 대하여 실험을 실시하여, 본 방식이 학습 속도의 면에서 유효하다는 것을 나타낸다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.637-639
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• 2003

As the number of spectral bands of high spectral resolution data increases, the capability to detect more detailed classes should also increase, and the classification accuracy should increase as well. Often, it is impossible to access enough training pixels for supervise classification. For this reason, the performance of traditional classification methods isn't useful. In this paper, we propose a new model for classification that operates based on decision fusion. In this classifier, learning is performed at two steps. In first step, only training samples are used and in second step, this classifier utilizes semilabeled samples in addition to original training samples. At the beginning of this method, spectral bands are categorized in several small groups. Information of each group is used as a new source and classified. Each of this primary classifier has special characteristics and discriminates the spectral space particularly. With using of the benefits of all primary classifiers, it is made sure that the results of the fused local decisions are accurate enough. In decision fusion center, some rules are used to determine the final class of pixels. This method is applied to real remote sensing data. Results show classification performance is improved, and this method may solve the limitation of training samples in the high dimensional data and the Hughes phenomenon may be mitigated.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 D
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• pp.2079-2081
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• 2002

R/C helicopter has been used to several fields of military affairs, investigation searching and toys because it has small size hovering and vortical take-off characteristics etc. Therefore it needs more realizable control method. The paper introduces simulation and experimental results for control of a helicopter training simulator by self tuning control method. To realize the disturbance rejection and the given reference tracking, a least common multiple polynomial between the reference and disturbance model polynomials is operated to the plant model. The effectiveness of the method is shown by simulation and experimental results for a helicopter training simulator with two degree of freedom.

• 음성과학
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• 제7권4호
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• pp.49-57
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• 2000

This paper proposes an effective decision method focused on evaluating the 'stress position'. Conventional methods usually extract the acoustic parameters and compare them to references in absolute scale, adversely producing unstable results as testing conditions change. To cope with environmental dependency, the proposed method is designed to be model-based and determines the stressed interval by making relative comparison over candidates. The stressed/unstressed models are then induced from normal phone models by adaptive training. The experimental results indicate that the proposed method is promising, and that it is useful for automatic detection of stress positions. The results also show that generating the stressed/unstressed model by adaptive training is effective.

• 한국음향학회:학술대회논문집
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• 한국음향학회 1994년도 FIFTH WESTERN PACIFIC REGIONAL ACOUSTICS CONFERENCE SEOUL KOREA
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• pp.1015-1020
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• 1994

This paper presents the Korean digit recognition method under noise environment using the spectral mapping training based on static supervised adaptation algorithm. In the presented recognition method, as a result of spectral mapping from one space of noisy speech spectrum to another space of speech spectrum without noise, spectral distortion of noisy speech is improved, and the recognition rate is higher than that of the conventional method using VQ and DTW without noise processing, and even when SNR level is 0 dB, the recognition rate is 10 times of that using the conventional method. It has been confirmed that the spectral mapping training has an ability to improve the recognition performance for speech in noise environment.

• Journal of the Korean Physical Society
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• 제73권9호
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• pp.1269-1278
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• 2018

Using the thawed Gaussian wave packets [E. J. Heller, J. Chem. Phys. 62, 1544 (1975)] and the adaptive reinitialization technique employing the frame operator [L. M. Andersson et al., J. Phys. A: Math. Gen. 35, 7787 (2002)], a trajectory-based Gaussian wave packet method is introduced that can be applied to scattering and time-dependent problems. This method does not require either the numerical multidimensional integrals for potential operators or the inversion of nearly-singular matrices representing the overlap of overcomplete Gaussian basis functions. We demonstrate a possibility that the method can be a promising candidate for the time-dependent $Schr{\ddot{o}}dinger$ equation solver by applying to tunneling, high-order harmonic generation, and above-threshold ionization problems in one-dimensional model systems. Although the efficiency of the method is confirmed in one-dimensional systems, it can be easily extended to higher dimensional systems.

• 한국멀티미디어학회논문지
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• 제24권9호
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• pp.1242-1250
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• 2021

Detection of outliers deviating normal data distribution in high dimensional data is an important technique in many application areas. In this paper, a distance-based outlier detection method using landmarks in high dimensional data is proposed. Given normal training data, the k-means clustering method is applied for the training data in order to extract the centers of the clusters as landmarks which represent normal data distribution. For a test data sample, the distance to the nearest landmark gives the outlier score. In the experiments using high dimensional data such as images and documents, it was shown that the proposed method based on the landmarks of one-tenth of training data can give the comparable outlier detection performance while reducing the time complexity greatly in the testing stage.