• 대한물리치료과학회지
• /
• 제20권1호
• /
• pp.1-7
• /
• 2013

Purpose : Investigate the effects of Horse-back riding Simulation Machine training on the Balance ability in Patients with Stroke. Method : The patients were divided to control group(n=18) with conventional rehabilitation conventional rehabilitation 60min/day and experimental group(n=17) with hippotherapy simulator 15 min/day after conventional rehabilitation 45min/day, 5 time/week for 4 weeks. Balance ability of both groups was assessed using Timed Up and Go(TUG), Berg balabce scale(BBS) and Center of pressure area(COPA). In the present result, there was a no significant(P>0.05) Results : The results of this study showed that Horse-back riding Simulation Machine training, after training, had meaningful difference of TUG, BBS and COPA. Conclusion : This study showed that Horse-back riding Simulation Machine training increased balance ability that resulted in enhancement of motor performance.

• Industrial Engineering and Management Systems
• /
• 제11권1호
• /
• pp.26-29
• /
• 2012

In order to deal with the classification problems of type-2 fuzzy training samples on generalized credibility space. Firstly the type-2 fuzzy training samples are reduced to ordinary fuzzy samples by the mean reduction method. Secondly the definition of strong fuzzy linear separable data for type-2 fuzzy samples on generalized credibility space is introduced. Further, by utilizing fuzzy chance-constrained programming and classic support vector machine, a support vector machine based on type-2 fuzzy training samples and established on generalized credibility space is given. An example shows the efficiency of the support vector machine.

• 정보처리학회논문지:소프트웨어 및 데이터공학
• /
• 제9권1호
• /
• pp.25-32
• /
• 2020

다양한 분야에서 활용되는 상황인지 시스템은 상황정보를 획득하기 위한 추상화 과정에서 규칙 기반의 인공기능 기술이 기존에 사용되었다. 그러나 서비스에 대한 사용자의 요구사항이 다양해지고 사용되는 데이터의 증대로 규칙이 복잡해지면서 규칙 기반 모델의 유지보수와 비정형 데이터를 처리하는데 어려움이 있다. 이러한 한계점을 극복하기 위해 많은 연구들에서는 상황인지 시스템에 기계학습 기술을 적용하였으며, 이러한 기계학습 기반의 모델을 상황인지 시스템에 사용하기 위해서는 주기적으로 학습 데이터를 제공해야 한다. 이에 기계학습 기반 상황인지 시스템에 대한 선행연구에서는 여러 개의 기계학습 모델을 적용하기 위한 학습 데이터 생성, 제공 등의 과정을 보였으나 제한된 종류의 기계학습 모델만을 적용 가능하여 확장성이 고려되어야 한다. 본 논문은 기계학습 기반의 상황인지 시스템의 확장성을 고려한 기계학습 모델의 학습 데이터 생성 방법을 제안한다. 제안하는 방법은 시스템의 확장성을 고려하여 기계학습 모델의 요구사항을 반영할 수 있는 학습 데이터 생성 모델을 정의하고 학습 데이터 생성 모듈을 바탕으로 각각의 기계학습 모델의 학습 데이터를 생성하는 것이다. 시스템의 확장성의 검증을 위해 실험에서는 노인의 건강상태 알림 서비스를 위한 심박상태 분석 모델을 대상으로 한 학습데이터 생성 스키마를 기반으로 학습데이터 생성 모델을 정의하고 실환경에서 정의된 모델을 S/W에 적용하여 학습데이터를 생성한다. 또한 생성된 학습데이터의 유효성을 검증하기 위해 사용되는 기계학습 모델에 생성한 학습데이터를 학습시켜 정확도를 비교하는 과정을 보인다.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2005년도 ICCAS
• /
• pp.1169-1172
• /
• 2005

For elderly people, an advanced training machine that uses actuator and can adjust load according to muscle activity is proposed. The proposed machine allows users to have a safe and effective training through exercise close to ordinal motion appears in daily life such as stretching or stooping motion. A muscle activity sensor real-timely monitors the activation level of user's muscle during the exercise and the training load is adjusted based on the measured data. The training load is exerted and continuously controlled by electric/pneumatic actuator.

• The Journal of Korean Physical Therapy
• /
• 제27권1호
• /
• pp.24-29
• /
• 2015

Purpose: The purpose of this study was to investigate the effect of lower limb training using a sliding rehabilitation machine on the foot motion and stability in stroke patients. Methods: Thirty participants were allocated to two groups: Training group (n=15) and Control group (n=15). Subjects in the control group received physical therapy for 30 minutes, five times per week, and those in the training group received lower limb training using a sliding rehabilitation machine for 30 minutes, five times per week, with physical therapy for 30 minutes, five times per week, during a period of six weeks. Heel rotation, hallux stiffness, foot balance, metatarsal load, toe out angle, and subtalar joint flexibility were measured by RS-scan. Results: Significant improvement of the foot motion (hallux stiffness, meta load) and the foot stability (toe out angle, subtalar joint flexibility) was observed in the training group. Conclusion: This study demonstrated that lower limb training using a sliding rehabilitation machine is an effective intervention to improve the foot motion and stability.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2004년도 ICCAS
• /
• pp.1648-1652
• /
• 2004

An advanced training machine designed for elderly people is proposed. The training machine allows users to have a safe and effective training through exercise close to ordinal motion appears in daily life such as standing up/down motion. The activation level of user's muscle is real timely monitored during the exercise and the training load is adjusted based on the body information. The training load is exerted and continuously controlled by actuation of an air cylinder.

• 대한원격탐사학회지
• /
• 제39권5_1호
• /
• pp.507-519
• /
• 2023

Multi-temporal optical images have been utilized for time-series monitoring of croplands. However, the presence of clouds imposes limitations on image availability, often requiring a cloud removal procedure. This study assesses the applicability of various machine learning algorithms for effective cloud removal in optical imagery. We conducted comparative experiments by focusing on two key variables that significantly influence the predictive performance of machine learning algorithms: (1) land-cover types of training data and (2) temporal variability of land-cover types. Three machine learning algorithms, including Gaussian process regression (GPR), support vector machine (SVM), and random forest (RF), were employed for the experiments using simulated cloudy images in paddy fields of Gunsan. GPR and SVM exhibited superior prediction accuracy when the training data had the same land-cover types as the cloud region, and GPR showed the best stability with respect to sampling fluctuations. In addition, RF was the least affected by the land-cover types and temporal variations of training data. These results indicate that GPR is recommended when the land-cover type and spectral characteristics of the training data are the same as those of the cloud region. On the other hand, RF should be applied when it is difficult to obtain training data with the same land-cover types as the cloud region. Therefore, the land-cover types in cloud areas should be taken into account for extracting informative training data along with selecting the optimal machine learning algorithm.

• 한국전문물리치료학회지
• /
• 제10권3호
• /
• pp.81-90
• /
• 2003

This study was conducted to assess the effects of the gait training method in incomplete spinal cord injured persons using an auto-walking machine. Persons with incomplete spinal cord injury level C or D on the American Spinal Injury Association impairment scale participated for eight weeks in an auto-walking training program. The gait training program was carried out for 15 minutes, three times per day for 8 weeks with an auto-walking machine. The foot rests of the auto-walking machine can be moved forward, downward, backward and upward to make the gait pattern with fixed on crank. The patient's body weight is supported by a harness during waking training. We evaluated the gait speed, physiologic cost index, motor score of lower extremities and the WISCI (walking index for spinal cord injury) level before the training and after the forth and eighth week of walking training. 1. The mean gait speed was significantly increased from .22 m/s at pre-training to .28 m/s after 4 weeks of training and .31 m/s after 8 weeks of training (p=.004). 2. The mean physiologic cost index was decreased from 4.6 beats/min at pre-training to 3.0 beats/min after 4 weeks and 2.0 beats/min after 8 weeks of training, but it was not statistically significant (p=.140). 3. The mean motor score of lower extrernities was significantly increased from 29.8 to 35.8 after 8 weeks of training (p=.043). 4. The mean WISCI level was significantly increased from level 10 to level 19 after 8 weeks of training (p=.007). The results of this study suggest that the gait training program using the auto-walking machine increased the gait speed, muscle strength and galt pattern (WISCI level) in persons with incomplete spinal cord injury. A large, controlled study of this technique is warranted.

• 전기전자학회논문지
• /
• 제22권4호
• /
• pp.948-952
• /
• 2018

Haze removal is an object of scientific desire due to its various practical applications. Existing algorithms are founded upon histogram equalization, contrast maximization, or the growing trend of applying machine learning in image processing. Since machine learning-based algorithms solve problems based on the data, they usually perform better than those based on traditional image processing/computer vision techniques. However, to achieve such a high performance, one of the requisites is a large and reliable training database, which seems to be unattainable owing to the complexity of real hazy and haze-free images acquisition. As a result, researchers are currently using the synthetic database, obtained by introducing the synthetic haze drawn from the standard uniform distribution into the clear images. In this paper, we propose the enhanced equidistribution, improving upon our previous study on equidistribution, and use it to make a new database for training machine learning-based haze removal algorithms. A large number of experiments verify the effectiveness of our proposed methodology.

• 실천공학교육논문지
• /
• 제6권1호
• /
• pp.37-42
• /
• 2014

자동 시각 검사 장비는 전 세계적으로 제조업 기반의 기업들의 공장 자동화 시스템의 주요 장비로 자리 잡고 있다. 반도체, LCD, 철강, 제지 등 다양한 분야에서 품질관리의 자동화를 위하여 필수적으로 활용되고 있다. 그러나 대학, 직업전문학교 등의 교육기관에서는 이에 대한 교육이 거의 이뤄지지 못하고 있다. 본 논문에서는 자동 시각 검사 시스템의 기술훈련을 위하여 라인스캔 카메라 기반의 자동 시각 검사 장비 교육을 위한 실습 장비에 대해서 다루고 있다. 제작된 시스템은 산업현장에 널리 사용되고 있는 X-Y stage 기반으로 구성되었으며, 영상의 픽셀해상도는 $10-30{\mu}m$의 범위에서 가변적으로 조절 가능하다. 또한 조명구조에 따른 영상효과를 확인하기 위하여, 측면 직사조명과 동축조명을 장착하여 활용할 수 있도록 구성되어 있다. 이는 훈련자가 실습환경에서 다양한 조건들을 변경시키며 실습을 수행할 수 있음을 의미하며, 실제 제조 현장에서 활용되는 라인스캔 카메라 기반의 머신비전 시스템과 거의 동일한 기능을 수행하도록 제작되었다.