• The Journal of Korean Physical Therapy
• /
• 제18권3호
• /
• pp.1-7
• /
• 2006

Purpose: This study was performed to determine the difference or temporal-spatial gait asymmetry ratio according to static pelvic inclination level in hemiplegic patients. Methods: The subjects were 25 hemiplegic patients who was experienced stroke on MCA territory. Gait parameters and static pelvic inclination were recorded by $GAITRite^{(R)}$ system and radiologic PACS. The subjects were divided into three group according to static pelvic inclination. In the group I, the subjects have static pelvic inclination below $58^{\circ}$. The group II has $58^{\circ}{\sim}62^{\circ}$ or static pelvic inclination and the group m has over $62^{\circ}$ or static pelvic inclination. The data or three groups were analysed with ANOVA. Results: In comparison or single support time asymmetry ratio among 3 groups, the score or group II was significantly higher than the other groups(p<0.05). But the swing time asymmetry ratio was not significant(p>0.05). Conclusion: Asymmetry ratio of single support time was statistically significant by static pelvic inclination level. But asymmetry ratio or group II was the highest among three groups. It means that the patients or normal range of pelvic inclination was showed the most asymmetry or gait. And swing time asymmetry ratio was not significant among three groups. Even if the patient has normal ranged static pelvic inclination, it doesn't suggest that the patient has low gait asymmetry.

• 대한교통학회지
• /
• 제26권4호
• /
• pp.123-133
• /
• 2008

최근 교통 물류 분야에서도 유비쿼터스 환경의 정보수집체계 및 이를 응용한 서비스 개발의 필요성이 매년 높아지고 있다. 프로브 차량과 무선통신기술을 활용한 교통정보 수집체계는 그 대표적인 사례로 차량의 기종점 자료를 이용하여 시간대별 OD를 산정하는 것이 가능하다. 그러나 프로브 차량 정보를 활용하여 산정된 OD는 시간적 공간적으로 변동되는 표본OD이기 때문에 이를 정적OD로 전환하기 위해서는 수집정보를 집적하여 적정 표본율을 산정하고, 표본OD를 전수화하는 과정이 필요하다. 본 연구는 제주시를 대상으로 수집된 실제 데이터를 표본OD 산정 및 전수화 알고리즘에 적용하여 표본OD를 산정하고 이를 전수화하였다. 각 링크별 관측교통량과 배분교통량과의 오차를 비교 검토한 결과 링크별 관측교통량 과 배분교통량의 평균 오차율은 22.9%, 상 하위 10%의 이상 자료를 제거한 후의 평균 오차율은 17.6%로 각각 나타났다. 본 연구는 기존OD가 존재하지 않는 지역에서 프로브 차량의 경로정보를 활용하여 정적OD를 산정하였다는 점과 적정 오차율 내 수렴을 위한 적정 표본율을 제시하였다는 점에서 그 의의를 찾을 수 있다.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1998년도 추계학술대회 논문집 학회본부 C
• /
• pp.869-871
• /
• 1998

The extensive use of composite insulators for transmission lines can ultimately be justified only on long-term qualification tests. The actual load working on the insulator in the field is not static load but cyclic load. So in this paper, we discussed an examination of aging degradation by mechanical performance of composite insulators under static tension load and cyclic tension load. and also described useful approaches for analyzing their long term performance so as to develop reliable composite insulators. The static and cyclic tension load-time test data were examined by Weibull distribution for their capability of presuming long term performance. It was found that cyclic tension loads were more severe than static tension loads. The results also indicate that it may be relevant for an user to select composite insulators on basis of their performance under cyclic tension loads than static tension loads.

• 한국컴퓨터그래픽스학회논문지
• /
• 제26권5호
• /
• pp.15-24
• /
• 2020

본 연구에서는 주어진 옷감 시료의 정적 드레이프 모양으로부터 해당 옷감을 시뮬레이션하기 위해 필요한 시뮬레이션 파라미터를 추정하는 데이터 기반 학습법을 제시한다. 정적 드레이프의 모양을 형성하기 위해 의류 산업계에서 옷감을 물성에 따라 분류하기 위해 사용하는 쿠식 드레이프 (Cusick's drape)에서 착안한 방법을 사용한다. 학습 모델의 입력 벡터는 특정 옷감의 정적 드레이프 모양에서 추출한 특징 벡터와 옷감의 밀도 값으로 구성되고, 출력 벡터는 해당 드레이프 결과를 도출하는 여섯가지 시뮬레이션 파라미터로 구성된다. 실제에 가깝고 편향되지 않은 학습 데이터를 생성하고자 먼저 400가지의 실제 니트 옷감에 대한 시뮬레이션 파라미터를 수집하고 이로부터 GMM (Gaussian mixture model) 생성 모델을 만든다. 다음, GMM 확률분포에 따라 대량의 시뮬레이션 파라미터를 무작위 샘플링한다. 샘플링된 각각의 시뮬레이션 파라미터에 대해 옷감 시뮬레이션을 수행하여 가상의 정적 드레이프 결과를 만들고 이로부터 특징 벡터를 추출한다. 생성된 데이터를 로그선형회기(log-linear regression) 모델로 피팅한다. 학습의 수치적 정확도를 검증하고 시뮬레이션 결과의 시각적 유사도를 비교하여 제시된 방법의 유용성을 확인한다.

• 한국BIM학회 논문집
• /
• 제10권2호
• /
• pp.21-28
• /
• 2020

As the number of aging bridges increases, more studies are being conducted on developing effective and reliable methods for the assessment and maintenance of bridges. With the advancement in new sensing systems and data learning techniques through AI technology, there is growing interests in how to evaluate bridges using these advanced techniques. This paper presents a CNN(Convolution Neural Network) deep learning based technique for evaluating the damage existence and for estimating the damage location in PSC bridges using static strain data. Simulation studies were conducted to investigate the proposed method with error analysis. Damage was simulated as the reduction in the stiffness of a finite element. A data learning model was constructed by applying the CNN technique as a type of deep learning. The damage status and its location were estimated using data set built through simulation. It was assumed that the strain gauges were installed in a regular interval under the PSC bridge girders. In order to increase the accuracy in evaluating damage, the squared error between the intact and measured strains are computed and applied for training the data model. Considering the damage occurring near the supports, the results of error analysis were compared according to whether strain data near the supports were included.

• Journal of Advanced Marine Engineering and Technology
• /
• 제20권2호
• /
• pp.116-116
• /
• 1996

This paper is concerned with a method for calibrating five-hole probes of both angle-tube and prismatic geometries to measure local total and static pressures and the magnitude and direction of the mean velocity vector. Descriptions of the calibration technique, the typical calibration data, and an accompanying discussion of the interpolation procedure are included. The flow properties are determined explicitly from measured probe pressures using calibration data. Flow angles are obtained within the deviation angle of 1.0 degree and dynamic pressures within 0.03 with 95% certainty. The variations in the calibration data due to Reynolds number are also discussed. For the range of Reynolds number employed, no effect was detected on the pitch, yaw and total pressure coefficients. However, the static pressure coefficient showed change to cause minor variations in the magnitude of the calculated velocity vector. To account for these variations, average correction factors need to be incorporated into the static pressure coefficient.

• Journal of Advanced Marine Engineering and Technology
• /
• 제20권2호
• /
• pp.48-56
• /
• 1996

This paper is concerned with a method for calibrating five-hole probes of both angle-tube and prismatic geometries to measure local total and static pressures and the magnitude and direction of the mean velocity vector. Descriptions of the calibration technique, the typical calibration data, and an accompanying discussion of the interpolation procedure are included. The flow properties are determined explicitly from measured probe pressures using calibration data. Flow angles are obtained within the deviation angle of 1.0 degree and dynamic pressures within 0.03 with 95% certainty. The variations in the calibration data due to Reynolds number are also discussed. For the range of Reynolds number employed, no effect was detected on the pitch, yaw abd total pressure coefficients. However, the static pressure coefficient showed change to cause minor variations in the magnitude of the calculated velocity vector. To account for these variations, average correction factors need to be incorporated into the static pressure coefficient.

• 대한기계학회논문집B
• /
• 제32권5호
• /
• pp.392-399
• /
• 2008

Accurate measurement of speed and altitude of flying vehicles in air data system remains a critical technical issue. A highly reliable Pitot-static probe is required to obtain air data such as total pressure and static pressure. In this study, an analysis of the characteristics of flowfield around the Pitot-static probe was performed by using a Navier-Stokes CFD code. In addition, for the purpose of finding an optimal configuration, a technique based on the response surface method is applied to the problem with design parameters including shape of the nose section and cone angle. It is shown that the optimal configuration fulfills the MIL specification in wider range of high angles of attack.

• 전기학회논문지
• /
• 제66권1호
• /
• pp.171-178
• /
• 2017

This paper studies the state estimation problem for static neural networks with time-varying delay. Unlike other studies, the controller scheme, which involves time-varying sampling and uncertainties, is first employed to design the state estimator for delayed static neural networks. Based on Lyapunov functional approach and linear matrix inequality technique, the non-fragile sampled-data estimator is designed such that the resulting estimation error system is globally asymptotically stable with $H_{\infty}$ performance. Finally, the effectiveness of the developed results is demonstrated by a numerical example.

• Food Science and Biotechnology
• /
• 제17권6호
• /
• pp.1352-1356
• /
• 2008

Bootstrap method, a computer-intensive statistical technique to estimate the distribution of a statistic was applied to deal with uncertainty and variability of the experimental data in stochastic prediction modeling of microbial growth on a chill-stored food. Three different bootstrapping methods for the curve-fitting to the microbial count data were compared in determining the parameters of Baranyi and Roberts growth model: nonlinear regression to static version function with resampling residuals onto all the experimental microbial count data; static version regression onto mean counts at sampling times; dynamic version fitting of differential equations onto the bootstrapped mean counts. All the methods outputted almost same mean values of the parameters with difference in their distribution. Parameter search according to the dynamic form of differential equations resulted in the largest distribution of the model parameters but produced the confidence interval of the predicted microbial count close to those of nonlinear regression of static equation.