• Journal of Korean Physical Therapy Science
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• v.20 no.1
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• pp.1-7
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• 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
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• v.11 no.1
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• pp.26-29
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• 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.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.1
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• pp.25-32
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• 2020

In the context-aware system, rule-based AI technology has been used in the abstraction process for getting context information. However, the rules are complicated by the diversification of user requirements for the service and also data usage is increased. Therefore, there are some technical limitations to maintain rule-based models and to process unstructured data. To overcome these limitations, many studies have applied machine learning techniques to Context-aware systems. In order to utilize this machine learning-based model in the context-aware system, a management process of periodically injecting training data is required. In the previous study on the machine learning based context awareness system, a series of management processes such as the generation and provision of learning data for operating several machine learning models were considered, but the method was limited to the applied system. In this paper, we propose a training data generating method of a machine learning model to extend the machine learning based context-aware system. The proposed method define the training data generating model that can reflect the requirements of the machine learning models and generate the training data for each machine learning model. In the experiment, the training data generating model is defined based on the training data generating schema of the cardiac status analysis model for older in health status notification service, and the training data is generated by applying the model defined in the real environment of the software. In addition, it shows the process of comparing the accuracy by learning the training data generated in the machine learning model, and applied to verify the validity of the generated learning data.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1169-1172
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• 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
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• v.27 no.1
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• pp.24-29
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• 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
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• 2004.08a
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• pp.1648-1652
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• 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.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.507-519
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• 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.

• Physical Therapy Korea
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• v.10 no.3
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• pp.81-90
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• 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.

• Journal of IKEEE
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• v.22 no.4
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• pp.948-952
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• 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.

• Journal of Practical Engineering Education
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• v.6 no.1
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• pp.37-42
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• 2014

The automated visual inspection system (machine vision system) for quality assurance is important factory automation equipment in the manufacturing industries, such as display, semiconductor, etc. There is a lot of demand for the machine vision engineers. However, there are no technical training courses for machine vision technologies in vocational schools, colleges and universities. In this paper, we present the implementation of line scan camera based equipment for technical training of the automated visual inspection system. The training system consists of the X-Y stage which is widely used in machine vision industries and its variable image resolution are set to $10-30{\mu}m$. Additionally, this training system can attach the industrial illumination, either the direct illuminator or coaxial illuminator, for verifying the effect of illuminations. This means that the trainee can have a practical training in various equipment conditions and the training system is similar to the automated visual inspection system in industries.