• Journal of the Korean Society of Industry Convergence
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• v.20 no.4
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• pp.333-338
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• 2017

In recent years, there has been an increasing demand for healthcare services that can periodically monitor health status and maintain health by increasing the weight training population. However, injuries in the absence of trainer are increasing with the increase in the number of members in the fitness training center. Therefore, there is a need for a system that can periodically monitor the user's exercise state and assist in systematic and safe exercise even when the trainer is absent. In this study, we developed an auto training system that can effectively manage the exerciser while supporting the strength movement. The auto training system consists of a cable mount module, a control module, and a training information monitoring module. In order to evaluate performance of the developed system, the assistant force tests are carried out. Experimental results showed that the assistant force works well when the exerciser is out of power.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.59 no.1
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• pp.1-9
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• 2010

This paper summarizes an education and training system for the auto-reclosing of power transmission system using a real time digital simulator. The system is developed to understand the principle of reclosing and the sequence of automatic reclosing schemes, and practice the effects of reclosing actions to power system in real-time simulator. This study is concentrated into the following two parts. One is the development of real time education and training system of automatic reclosing schemes. For this, we use the RTDS(real time digital simulator) and the actual digital protective relay. The mathematical relay model of RTDS and the actual distance relay which is equipped automatic reclosing function are also used. The other is the user friendly interface between trainee and trainer. The various interface displays are used for user handing and result display. The conditions of automatic reclosing which is a number of reclosing, reclosing dead time, reset time, and so on, can be changed by the user interface panel. A number of scenario cases are reserved for the education and training. Through the test, we verified that the proposed system can be effectively used to accomplish the education and training of automatic reclosing.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.32 no.2
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• pp.11-19
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• 2024

Flight simulators are crucial devices for aircraft piloting training and simulation, requiring regular inspections to maintain performance and operational quality. This study explores the development of an automated inspection system for flight simulators to automate quality inspections of control loading systems (CLS). While quality inspection of the control loading system (CLS) is essential for flight simulators, manual inspections are common practice. To address this, we developed an Auto Qualification Test Guide (Auto QTG) using artificial control logic and sensor data and applied it to the militarily simulator. Experimental results demonstrate that Auto QTG successfully automates quality inspections of CLS, enhancing accuracy and efficiency. This automated inspection system is expected to contribute to improving the operation and maintenance of flight simulators.

• Journal of Auto-vehicle Safety Association
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• v.15 no.4
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• pp.32-38
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• 2023

With the transition from internal combustion engine vehicles to eco-friendly cars, it has become essential to systematically construct an education system for electric vehicles based on high-voltage electric devices. In this study, we discussed the establishment of an educational system for electric vehicles based on high-voltage electric devices and proposed methods for qualifications after completing the education. To ensure systematic education, we presented a classification of learners according to their levels and job competencies. Additionally, we emphasized the importance of providing adequate practical training equipment for courses that require higher qualifications. Finally, to distinguish between the levels of completion of training and practical skills, we highlighted the necessity of implementing a system to certificates to individuals who have successfully completed the systematic training program.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.8
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• pp.2881-2894
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• 2014

In complicated environment, context information plays an important role in image segmentation/labeling. The recently proposed auto-context algorithm is one of the effective context-based methods. However, the standard auto-context approach samples the context locations utilizing a fixed radius sequence, which is sensitive to large scale-change of objects. In this paper, we present a scale invariant auto-context (SIAC) algorithm which is an improved version of the auto-context algorithm. In order to achieve scale-invariance, we try to approximate the optimal scale for the image in an iterative way and adopt the corresponding optimal radius sequence for context location sampling, both in training and testing. In each iteration of the proposed SIAC algorithm, we use the current classification map to estimate the image scale, and the corresponding radius sequence is then used for choosing context locations. The algorithm iteratively updates the classification maps, as well as the image scales, until convergence. We demonstrate the SIAC algorithm on several image segmentation/labeling tasks. The results demonstrate improvement over the standard auto-context algorithm when large scale-change of objects exists.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.701-706
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• 1998

A design method of rule -based fuzzy modeling is presented for the model identification of complex and nonlinear systems. The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient form of " IF..., THEN,," statements. using the theories of optimization and linguistic fuzzy implication rules. The improved complex method, which is a powerful auto-tuning algorithm, is used for tuning of parameters of the premise membership functions in consideration of the overall structure of fuzzy rules. The optimized objective function, including the weighting factors, is auto-tuned for better performance of fuzzy model using training data and testing data. According to the adjustment of each weighting factor of training and testing data, we can construct the optimal fuzzy model from the objective function. The least square method is utilized for the identification of optimum consequence parameters. Gas furance and a sewage treatment proce s are used to evaluate the performance of the proposed rule-based fuzzy modeling.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.342-345
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• 2005

This paper describes dynamics analysis of a small training boat and a new type of ship's autopilot not only to keep her course but also to reduce her roll motion. Firstly, statistical analysis through multi-variate auto regressive model is carried out using the real data collected from the sea trial on an actual small training boat Sazanami after the navigational system of the boat was upgraded. It is shown that the roll motion is strongly influenced by the rudder motion and it is suggested that there is a possibility of reducing the roll motion by controlling the rudder order properly. Based on this observation, a new type of ship's autopilot that takes the roll motion into account is designed using the muti-variate modern control theory. Lastly, digital simulations by white noise are carried out in order to evaluate the proposed system and a typical result is demonstrated. As results of simulations, the proposed autopilot had good performance compared with the original data.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.5
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• pp.30-37
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• 2015

The study for enhancing the data transmission rate of the next generation wireless communication system using MIMO system operating in the frequency selective fading environment is currently actively conducted. Mixed signal from each transmitted antennas are received at antennas. The training signal with orthogonal property is needed to separate the mixed signal and enable to estimate channel and time synchronization. In this paper we introduce several training sequences used in MIMO communication system and proposed the modified WeCAN sequence with good auto-correlation property in interested area. We compared auto-correlation property of each sequence via simulation and compared the performance of sequences in doppler shift and multipath fading channel.

• Journal of the Semiconductor & Display Technology
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• v.17 no.1
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• pp.71-75
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• 2018

Image enhancement is a necessary end essential step after taking a picture with a digital camera. Many different photo software packages attempt to automate this process with various auto enhancement techniques. This paper provides and implements a system that can learn a user's preferences and apply the preferences into the process of image enhancement. Five major components are applied to the implemented system, which are computing a distance metric, finding a training set, finding an optimal parameter set, training and finally enhancing the input image. To estimate the validity of the method, we carried out user studies, and the fact that the implemented system was preferred over the method without learning user preferences.

• Journal of Korean Society of Water and Wastewater
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• v.38 no.2
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• pp.83-93
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• 2024

In this study, we present a sewer pipe inspection technique through a combination of active sonar technology and deep learning algorithms. It is difficult to inspect pipes containing water using conventional CCTV inspection methods, and there are various limitations, so a new approach is needed. In this paper, we introduce a inspection method using active sonar, and apply an auto encoder deep learning model to process sonar data to distinguish between normal and abnormal pipelines. This model underwent training on sonar data from a controlled environment under the assumption of normal pipeline conditions and utilized anomaly detection techniques to identify deviations from established standards. This approach presents a new perspective in pipeline inspection, promising to reduce the time and resources required for sewer system management and to enhance the reliability of pipeline inspections.