• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.2
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• pp.120-125
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• 2020

Supply chain managers seek to achieve global optimization by solving problems in the supply chain's business process. However, companies in the supply chain hide the adverse information and inform only the beneficial information, so the information is distorted and cannot be the information that describes the entire supply chain. In this case, supply chain managers can directly collect and analyze supply chain activity data to find and manage the companies described by the data. Therefore, this study proposes a method to collect the order-inventory information from each company in the supply chain and detect the companies whose data characteristics are explained through deep learning. The supply chain consists of Manufacturer, Distributor, Wholesaler, Retailer, and training and testing data uses 600 weeks of time series inventory information. The purpose of the experiment is to improve the detection accuracy by adjusting the parameter values of the deep learning network, and the parameters for comparison are set by learning rate (lr = 0.001, 0.01, 0.1) and batch size (bs = 1, 5). Experimental results show that the detection accuracy is improved by adjusting the values of the parameters, but the values of the parameters depend on data and model characteristics.

• Journal of Power Electronics
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• v.15 no.1
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• pp.216-226
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• 2015

Grid synchronization is one of the key techniques for the grid-connected power converters used in distributed power generation systems. In order to achieve fast and accurate grid synchronization, a new phase locked loop (PLL) is proposed on the basis of the complex filter matrixes (CFM) orthogonal signal generator (OSG) crossing-decoupling method. By combining first-order complex filters with relation matrixes of positive and negative sequence voltage components, the OSG is designed to extract specific frequency orthogonal signals. Then, the OSG mathematical model is built in the frequency-domain and time-domain to analyze the spectral characteristics. Moreover, a crossing-decoupling method is suggested to decouple the fundamental voltage. From the eigenvalue analysis point of view, the stability and dynamic performance of the new PLL method is evaluated. Meanwhile, the digital implementation method is also provided. Finally, the effectiveness of the proposed method is verified by experiments under unbalanced and distorted grid voltage conditions.

• Journal of the Korean Society of Safety
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• v.26 no.4
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• pp.87-95
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• 2011

The urgent VAI(Vital Area Identification) method development is required since 'The Act of Physical Protection and Radiological Emergency' that is established in 2003 requires an evaluation of physical threats in nuclear facilities and an establishment of physical protection in Korea. The KAERI(Korea Atomic Energy Research Institute) has developed the VAI methodology and VAI software called as VIPEX(Vital area Identification Package EXpert) for identifying the vital areas. This study is to demonstrate the applicability of KAERI's VAI methodology to a hypothetical facility, and to identify the importance of information of cable and piping runs when identifying the vital areas. It is necessarily needed to consider cable and piping runs to determine the accurate and realistic TEPS(Top Event Prevention Set). If the information of cable and piping runs of a nuclear power plant is not considered when determining the TEPSs, it is absolutely impossible to acquire the complete TEPSs, and the results could be distorted by missing it. The VIPEX and FTREX(Fault Tree Reliability Evaluation eXpert) properly calculate MCSs and TEPSs using the fault tree model, and provide the most cost-effective method to save the VAI and physical protection costs.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.11
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• pp.1645-1650
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• 2015

This paper presents the 3-dimensional electromagnetic field analysis method and correction of sensor distortion that is used by a motor speed sensor. The magnetic sensors are being expanded due to lower price than the other speed sensors such as resolver and encoder. Magnetic sensor generates sine and cosine waves when the motor rotates. However, the sine and cosine signals are distorted due to magnetic noise, which makes the angle error of the sensor, generated near by the Hall element. This paper defines an optimal design variables by using the Taguchi method to minimize output distortion of the magnetic sensor and permanent magnet. To enhance reliability of the magnetic position sensor from sensitivity error, assembly amplitude mismatch and the electrical angle, 3-Dimensional electromagnetic finite element method and correction algorithm errors were performed in due of the magnetic sensor in order to improve the quality of the initial production model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.9
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• pp.1613-1631
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• 2011

Several metrics have been reported in the literature to assess stereo image quality, mostly based on visual attention or human visual sensitivity based distortion prediction with the help of disparity information, which do not consider the combined aspects of human visual processing. In this paper, visual attention and depth assisted stereo image quality assessment model (VAD-SIQAM) is devised that consists of three main components, i.e., stereo attention predictor (SAP), depth variation (DV), and stereo distortion predictor (SDP). Visual attention is modeled based on entropy and inverse contrast to detect regions or objects of interest/attention. Depth variation is fused into the attention probability to account for the amount of changed depth in distorted stereo images. Finally, the stereo distortion predictor is designed by integrating distortion probability, which is based on low-level human visual system (HVS), responses into actual attention probabilities. The results show that regions of attention are detected among the visually significant distortions in the stereo image pair. Drawbacks of human visual sensitivity based picture quality metrics are alleviated by integrating visual attention and depth information. We also show that positive correlation with ground-truth attention and depth maps are increased by up to 0.949 and 0.936 in terms of the Pearson and the Spearman correlation coefficients, respectively.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.132-138
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• 2020

Worldwide, the number of corona virus disease 2019 (COVID-19) confirmed cases is rapidly increasing. Although vaccines and treatments for COVID-19 are being developed, the disease is unlikely to disappear completely. By attaching a smart sensor to the respirator worn by medical staff, Internet of Things (IoT) technology and artificial intelligence (AI) technology can be used to automatically detect the medical staff's infection symptoms. In the case of medical staff showing symptoms of the disease, appropriate medical treatment can be provided to protect the staff from the greater risk. In this study, we design and develop a system that detects cough, a typical symptom of respiratory infectious diseases, by applying IoT technology and artificial technology to respiratory protection. Because the cough sound is distorted within the respirator, it is difficult to guarantee accuracy in the AI model learned from the general cough sound. Therefore, coughing and non-coughing sounds were recorded using a sensor attached to a respirator, and AI models were trained and performance evaluated with this data. Mel-spectrogram conversion method was used to efficiently classify sound data, and the developed cough recognition system had a sensitivity of 95.12% and a specificity of 100%, and an overall accuracy of 97.94%.

• Advances in Computational Design
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• v.8 no.3
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• pp.237-253
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• 2023

Torque ripple content and variable switching frequency operation of conventional direct torque control (DTC) are reduced by the integration of space vector modulation (SVM) into DTC. Integration of space vector modulation to conventional direct torque control known as SVM-DTC. It had been more frequently used method in renewable energy and machine drive systems. In this paper, SVM-DTC is used to control the rotor side converter (RSC) of a wind driven doubly-fed induction generator (DFIG) because of its advantages such as reduction of torque ripples and constant switching frequency operation. However, flux and torque ripples are still dominant due to distorted current waveforms at different operations of the wind turbine. Therefore, to smoothen the torque profile a Neural Network Controller (NNC) based SVM-DTC has been proposed by replacing the PI controller in the speed control loop of the wind turbine controller. Also, stability analysis and simulation study of DFIG using process reaction curve method (RRCM) are presented. Validation of simulation study in MATLAB/SIMULINK environment of proposed wind driven DFIG system has been performed by laboratory developed prototype model. The proposed NNC based SVM-DTC yields superior torque response and ripple reduction compared to other methods.

• Nuclear Engineering and Technology
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• v.55 no.1
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• pp.37-44
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• 2023

Using the Kalman filtering technique, we propose a novel method for estimating the missing video frames to monitor the activities inside the control room of a nuclear power plant (NPP). The purpose of this study is to reinforce the existing security and safety procedures in the control room of an NPP. The NPP control room serves as the nervous system of the plant, with instrumentation and control systems used to monitor and control critical plant parameters. Because the safety and security of the NPP control room are critical, it must be monitored closely by security cameras in order to assess and reduce the onset of any incidents and accidents that could adversely impact the safety of the NPP. However, for a variety of technical and administrative reasons, continuous monitoring may be interrupted. Because of the interruption, one or more frames of the video may be distorted or missing, making it difficult to identify the activity during this time period. This could endanger overall safety. The demonstrated Kalman filter model estimates the value of the missing frame pixel-by-pixel using information from the frame that occurred in the video sequence before it and the frame that will occur in the video sequence after it. The results of the experiment provide evidence of the effectiveness of the algorithm.

• Molecules and Cells
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• v.45 no.2
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• pp.65-75
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• 2022

Hypothalamus is a brain region that controls food intake and energy expenditure while sensing signals that convey information about energy status. Within the hypothalamus, molecularly and functionally distinct neurons work in concert under physiological conditions. However, under pathological conditions such as in diet-induced obesity (DIO) model, these neurons show dysfunctional firing patterns and distorted regulation by neurotransmitters and neurohormones. Concurrently, resident glial cells including astrocytes dramatically transform into reactive states. In particular, it has been reported that reactive astrogliosis is observed in the hypothalamus, along with various neuroinflammatory signals. However, how the reactive astrocytes control and modulate DIO by influencing neighboring neurons is not well understood. Recently, new lines of evidence have emerged indicating that these reactive astrocytes directly contribute to the pathology of obesity by synthesizing and tonically releasing the major inhibitory transmitter GABA. The released GABA strongly inhibits the neighboring neurons that control energy expenditure. These surprising findings shed light on the interplay between reactive astrocytes and neighboring neurons in the hypothalamus. This review summarizes recent discoveries related to the functions of hypothalamic reactive astrocytes in obesity and raises new potential therapeutic targets against obesity.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.11a
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• pp.63-64
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• 2020

The purpose of this study is to find out how construction sites workers experience adaptation processes in relation to the prevention of safety accidents in the workplace through the method of grounded theory. In order to understand the experience of preventing safety accidents of workers at construction sites, a grounded theory analysis method was chosen. In order to achieve the purpose of this study, the first questionnaire was sent to 17 people working in the construction site by e-mail, and the participants of the study were met one by one for in-depth interviews. As a result of this study, the paradigm model of the experience of preventing safety accidents of workers at construction sites was classified into causal conditions, contextual conditions, central phenomena, intervening conditions, action/interactions, and outcomes. The conclusions of this study are as follows. First, forming a safety culture that can improve the safety awareness of construction sites is a priority. Second, it is necessary to improve self-management capabilities so that construction workers can accurately diagnose their current state such as their own body, emotion, and cognition and provide appropriate safety education. Third, providing safety education for construction workers with negative thoughts or distorted beliefs about safety accidents needs to include psychological treatment and counseling, such as methods of emotional purification, methods of relieving and managing stress, and methods of removing trauma.