• Journal of Drive and Control
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• v.17 no.1
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• pp.37-43
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• 2020

An intelligent excavator can be divided into Machine Guidance (MG), semi-automatic, and unmanned by technology. The MG technology excavator is equipped with a tilt sensor on each link of the excavator and a GPS is installed on the excavator body to inform the user of the position of the excavator bucket end. Machine control (MC) technology that assists the user's work can be divided into semi-automatic and fully automatic technology. The semi-automatic MC equipment has already been commercialized by Komatsu and Caterpillar. The MC excavator is equipped with an electro-hydraulic system, sensors and controllers to control the excavator bucket end according to the user's needs. In this study, the semi-automated excavator modified based on manual excavator, is equipped with an electro-hydraulic system, a controller system, multi-sensors and a control algorithm is developed to assist in excavation work such as leveling and grading. By applying the developed technology, it was possible to confirm productivity improvement compared to manual digging and leveling work. In the future, further research to improve the accuracy of the hydraulic precision control and collaborative work with heterogeneous construction equipment such as dump truck and automated collaboration tasks technology could be developed.

• Journal of Oriental Neuropsychiatry
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• v.19 no.3
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• pp.45-54
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• 2008

Objective: To treat psychogenic headache patients, doctors have to amplify on the headache caused by emotional stress to patients, and assist the patients to cope with difficulties. So, we investigated HRV of the headache patients caused by Chiljungsang and would like to apply to the clinical treatment. Method: Our study measured time and frequency domain HRV indicies(5-min resting study) of 123 headache patients caused by emotional stress. Standardized tests of HRV allow a quantitative estimation of autonomic nervous system function. Results & Conclusions: 1. The study classed as aspects of the head pain showed the differences in RMS-SD(square root of mean squared difference of successive NN intervals) band, HF(high frequency) band significantly. 2. The male headache patients showed higher all the indicies except heart rate compared to the female patients, significantly in SDNN(standard deviation of NN interval), TP(total Power), HF band. 3. As the patients grow older, SDNN, RMS-SD band was lower and LF(low frequency) band, LF/HF ratio higher significantly. The beginning age lower, SDNN, RMS-SD band was higher significantly. The duration of the disease longer, LF band, LF/HF ratio was higher significantly.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.72-81
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• 2023

This research proposes a novel approach to tackle the challenge of categorizing unstructured customer complaints in the automotive industry. The goal is to identify potential vehicle defects based on the findings of our algorithm, which can assist automakers in mitigating significant losses and reputational damage caused by mass claims. To achieve this goal, our model uses the Word2Vec method to analyze large volumes of unstructured customer complaint data from the National Highway Traffic Safety Administration (NHTSA). By developing a score dictionary for eight pre-selected criteria, our algorithm can efficiently categorize complaints and detect potential vehicle defects. By calculating the score of each complaint, our algorithm can identify patterns and correlations that can indicate potential defects in the vehicle. One of the key benefits of this approach is its ability to handle a large volume of unstructured data, which can be challenging for traditional methods. By using machine learning techniques, we can extract meaningful insights from customer complaints, which can help automakers prioritize and address potential defects before they become widespread issues. In conclusion, this research provides a promising approach to categorize unstructured customer complaints in the automotive industry and identify potential vehicle defects. By leveraging the power of machine learning, we can help automakers improve the quality of their products and enhance customer satisfaction. Further studies can build upon this approach to explore other potential applications and expand its scope to other industries.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.4
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• pp.349-355
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• 2015

Underwater robots generally show better performances for tasks than humans under certain underwater constraints such as. high pressure, limited light, etc. To properly diagnose in an underwater environment using remotely operated underwater vehicles, it is important to keep autonomously its own position and orientation in order to avoid additional control efforts. In this paper, we propose an efficient method to assist in the operation for the various disturbances of a remotely operated vehicle for the diagnosis of underwater structures. The conventional AHRS-based bearing estimation system did not work well due to incorrect measurements caused by the hard-iron effect when the robot is approaching a ferromagnetic structure. To overcome this drawback, we propose a sensor fusion algorithm with the camera and AHRS for estimating the pose of the ROV. However, the image information in the underwater environment is often unreliable and blurred by turbidity or suspended solids. Thus, we suggest an efficient method for fusing the vision sensor and the AHRS with a criterion which is the amount of blur in the image. To evaluate the amount of blur, we adopt two methods: one is the quantification of high frequency components using the power spectrum density analysis of 2D discrete Fourier transformed image, and the other is identifying the blur parameter based on cepstrum analysis. We evaluate the performance of the robustness of the visual odometry and blur estimation methods according to the change of light and distance. We verify that the blur estimation method based on cepstrum analysis shows a better performance through the experiments.

• Korean Chemical Engineering Research
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• v.48 no.4
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• pp.457-461
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• 2010

After the industrial revolution, quantity production system has made the life of mankind rich and to fulfill the consumer's desire competitive power of the enterprise constantly make has made effort in quality, price and engineering department not remaining simple manufactures. But in such a change production lacked safety. Consequently, the main study studied in return domestic organization which produces a chemistry fiber rope to be used fact vessel or as a athletic equipment safety countermeasure. One of the assesment method on product liability, Checklist is showed that It can be used not as an evaluation but as a development factor of company. Also, checklist that used a study could expect that assist the company of management structure as well as improvement of product safety.

• Journal of the Ergonomics Society of Korea
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• v.33 no.5
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• pp.407-422
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• 2014

Objective: The purpose of the study is to develop quantitative usability evaluation criteria for senior-friendly autonomous transportation robot. Background: The Republic of Korea has become the most rapidly aging society, and is anticipated to enter the post-aged society in 2026. To raise the quality of life of a senior with limited mobility and to reduce the burden of caregivers, many high-tech assistive products with information technologies are developed nowadays. The senior-friendly autonomous transportation robot is one person robot vehicle to move a senior to the destination for hospitals, nursing homes or silver town complex. With built-in navigation system and environmental monitoring censors, it automatically seeks the path to the destination and avoids collision to obstacles and pedestrians on the way. Due to the early stage of the product, few usability studies in this field have been done, mostly on general service robots to assist seniors, power wheelchairs and delivery robots. ISO and KS standards for the service robots are focused on safety. Method: Based on the reference usability index, the early draft of the usability evaluation questionnaires was developed. After small group tests and interviews, the experts modified the initial draft to the Usability Evaluation Criteria for Senior-Friendly Autonomous Transportation Robot (UEC-SFATR). Result: UEC-SFATR consisted of 4 subscales - Safety, Controllability, Efficiency and Satisfaction. All of the 4 subscales of UEC-SFATR were passed the reliability criteria by 4 groups of seniors, divided by gender and familiarity of smart-devices. Conclusion: UEC-SFATR covers wider area of user experiences of the SFATR and is a good measurement tool to help both the users and developers of the robot. Application: This study provides guide to the future product development and product competitiveness evaluation by quantifying user experiences for the SFATR.

• Spatial Information Research
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• v.19 no.3
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• pp.11-22
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• 2011

It is usual for the carbon dioxide emission to be calculated by official energy consumption statistics produced from a number of specialized industrial process such as refinery, power plant etc. The aim of this research was to evaluate potential of cadastral system in monitoring carbon dioxide emitted from land use. An empirical study for a cadastral category was conducted to demonstrate how a on-site measurement can be used to assist in estimating the carbon dioxide emission in terms of land use specific settings. The cadastral category based analysis made it possible to identify area-wide patterns of carbon dioxide emission, which cannot be acquired by traditional Government statistics. It was possible to identify successively increasing trends in the human-related parcels such as housing land while decreasing trends of carbon dioxide in sink parcels(eg. forest). The results indicate that the cadastral parcel could be used not only as a tool to monitor carbon dioxide emission, but also as an evidence to restrict initiation of development activities causing negative influence to carbon dioxide emission such as road construction. As a result, the research findings have established the new concept of "carbon dioxide emission monitoring based on cadastral category", proposed as an initial aim of this paper.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3423-3440
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• 2023

In this work, a multivariate time-series machine learning meta-model is developed to predict the transient response of a typical nuclear power plant (NPP) undergoing a steam generator tube rupture (SGTR). The model employs Recurrent Neural Networks (RNNs), including the Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a hybrid CNN-LSTM model. To address the uncertainty inherent in such predictions, a Bayesian Neural Network (BNN) was implemented. The models were trained using a database generated by the Best Estimate Plus Uncertainty (BEPU) methodology; coupling the thermal hydraulics code, RELAP5/SCDAP/MOD3.4 to the statistical tool, DAKOTA, to predict the variation in system response under various operational and phenomenological uncertainties. The RNN models successfully captures the underlying characteristics of the data with reasonable accuracy, and the BNN-LSTM approach offers an additional layer of insight into the level of uncertainty associated with the predictions. The results demonstrate that LSTM outperforms GRU, while the hybrid CNN-LSTM model is computationally the most efficient. This study aims to gain a better understanding of the capabilities and limitations of machine learning models in the context of nuclear safety. By expanding the application of ML models to more severe accident scenarios, where operators are under extreme stress and prone to errors, ML models can provide valuable support and act as expert systems to assist in decision-making while minimizing the chances of human error.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.10
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• pp.330-338
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• 2018

The proportional-integral-derivative(PID) controller has been widely used in the industry because of its robust performance and simple structure in a wide range of operating conditions. However, the AVR(Automatic Voltage Regulator) as a control system is not robust to variations of the power system parameters. Therefore, it is necessary to use PID controller to increase the stability and performance of the AVR system. In this paper, a novel design method for determining the optimal PID controller parameters of an AVR system using the particle swarm optimization(PSO) algorithm is presented. The proposed approach has superior features, including easy implementation, stable convergence characteristic and good computational efficiency. In order to assist estimating the performance of the proposed PSO-PID controller, a new performance criterion function is also defined. This evaluation function is intended to reflect when the maximum percentage overshoot, the settling time are given as design specifications. The ITAE evaluation function should impose a penalty if the design specifications are violated, so that the PSO algorithm satisfies the specifications when searching for the PID controller parameter. Finally, through the computer simulations, the proposed PSO-PID controller not only satisfies the given design specifications for the terminal voltage step response, but also shows better control performance than other similar recent studies.

• Geophysics and Geophysical Exploration
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• v.13 no.1
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• pp.61-68
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• 2010

A magnetotelluric (MT) survey has been performed to delineate deeply extended fracture systems at the geothermal field in Seokmo Island, Korea. To assist interpretation of the MT data, geological surveying and well logging of existing wells were also performed. The surface geology of the island shows Cretaceous and Jurassic granite in the north and Precambrian schist in the south. The geothermal regime has been found along the boundary between the schist and Cretaceous granite. Because of the deep circulation along the fracture system, geothermal gradient of the target area exceeds $45^{\circ}C/km$, which is much higher than the average geothermal gradient in Korea. 2D and 3D inversions of MT data clearly showed a very conductive anomaly, which is interpreted as a fracture system bearing saline water that extends at least down to 1.5 km depth and is inclined eastwards. After drilling down to the depth of 1280 m, more than 4000 tons/day of geothermal water overflowed with temperature higher than $70^{\circ}C$. This water showed very similar chemical composition and temperature to those from another existing well, so that they can be considered to have the same origin; i.e. from the same fracture system. A new geothermal project for combined heat and power generation was launched in 2009 in Seokmo Island, based on the survey. Additional geophysical investigations including MT surveys to cover a wider area, seismic reflection surveys, borehole surveys, and well logging of more than 20 existing boreholes will be conducted.