• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1645-1655
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• 2016

A Dynamic Voltage Restorer (DVR) model is proposed to eliminate the short-term voltage disturbances that occur in the grid-connected mode, the switching between grid-connected mode and the stand-alone mode of a Microgrid. The proposed DVR structure is based on a conventional cascaded H-bridge multilevel inverter (MLI) topology; a novel composite control strategy is presented, which could ensure the compensation ability of voltage sag by the DVR. Moreover, the compensation to specified order of harmonic is added to implement effects that zero-steady error compensation to harmonic voltage in specified order of the presented control strategy; utilizing wind turbines-batteries units as DC energy storage components in the Microgrid, the operation cost of the DVR is reduced. When the Microgrid operates under stand-alone mode, the DVR can operate on microsource mode, which could ease the power supply from the main grid (distribution network) and consequently be favorable for energy saving and emission reduction. Simulation results validate the robustness and effective of the proposed DVR system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.4
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• pp.246-253
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• 2016

Infrared (IR) imaging has been researched for various applications such as surveillance. IR radiation has the capability to detect thermal characteristics of objects under low-light conditions. However, automatic segmentation for finding the object of interest would be challenging since the IR detector often provides the low spatial and contrast resolution image without color and texture information. Another hindrance is that the image can be degraded by noise and clutters. This paper proposes multi-level segmentation for extracting regions of interest (ROIs) and objects of interest (OOIs) in the IR scene. Each level of the multi-level segmentation is composed of a k-means clustering algorithm, an expectation-maximization (EM) algorithm, and a decision process. The k-means clustering initializes the parameters of the Gaussian mixture model (GMM), and the EM algorithm estimates those parameters iteratively. During the multi-level segmentation, the area extracted at one level becomes the input to the next level segmentation. Thus, the segmentation is consecutively performed narrowing the area to be processed. The foreground objects are individually extracted from the final ROI windows. In the experiments, the effectiveness of the proposed method is demonstrated using several IR images, in which human subjects are captured at a long distance. The average probability of error is shown to be lower than that obtained from other conventional methods such as Gonzalez, Otsu, k-means, and EM methods.

• Journal of Korean Society of Steel Construction
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• v.12 no.1 s.44
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• pp.93-102
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• 2000

Substructuring-based hierarchical approach for design analysis and optimization of structural frames is presented in this study. The conceptual framework of this method is in the hierarchical modeling for design processes as well as structural systems and the methodology combining substructuring analysis and multilevel optimization. Mathematical models for analysis and synthesis are established on the common basis of substructuring systems. Modularized behavioral analysis, design sensitivity analysis and optimization are linked and integrated on the mathematical and structural basis of substructuring. Substructures are coordinated with the active constraints for system level and the weight ratio criteria. Numerical examples for test frames show the validity and effectiveness of the present approach.

• Journal of Power Electronics
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• v.9 no.2
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• pp.133-145
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• 2009

In this paper a modular high performance MV-to-LV rectifier based on a cascaded H-bridge rectifier is presented. The proposed rectifier can directly connect to the medium voltage levels and provide a low-voltage and highly-stable DC interface with the consumer applications. The input stage eliminates the necessity for heavy and bulky step-down transformers. It corrects the input power factor and maintains the voltage balance among the individual DC buses. The second stage includes the high frequency parallel-output DC/DC converters which prepares the galvanic isolation, regulates the output voltage, and attenuates the low frequency voltage ripple ($2f_{line}$) generated by the first stage. The parallel-output converters can work in interleaving mode and the active load-current sharing technique is utilized to balance the load power among them. The detailed analysis for modeling and control of the proposed structure is presented. The validity and performance of the proposed topology is verified by simulation and experimental results.

• Journal of Power Electronics
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• v.18 no.6
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• pp.1659-1669
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• 2018

An improved circulating current suppression control method is proposed in this paper. In the proposed controller, an outer loop of the average capacitor voltage control model is used to balance the sub-module capacitor voltage. Meanwhile, an individual voltage balance controller and an arm voltage balance controller are also used. The DC and harmonic components of the circulating current are separated using a low pass filter. Therefore, a multiple quasi-proportional-resonant (multi-quasi-PR) controller is introduced in the inner loop to eliminate the circulating harmonic current, which mainly contains second-order harmonic but also contains other high-order harmonics. In addition, the parameters of the multi-quasi-PR controller are designed in the discrete domain and an analysis of the stability characteristic is given in this paper. In addition, a simulation model of a three-phase MMC system is built in order to confirm the correctness and superiority of the proposed controller. Finally, experiment results are presented and compared. These results illustrate that the improved control method has good performance in suppressing circulating harmonic current and in balancing the capacitor voltage.

• Nuclear Engineering and Technology
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• v.55 no.12
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• pp.4408-4425
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• 2023

Alarm flood due to abnormality propagation is the most difficult alarm overloading problem in nuclear power plants (NPPs). Root-cause analysis is suggested to help operators in understand emergency events and plant status. Multilevel Flow Modeling (MFM) has been extensively applied in alarm management by virtue of the capability of explaining causal dependencies among alarms. However, there has never been a technique that can identify the actual root cause for complex alarm situations. This paper presents an automated root-cause analysis system based on MFM. The causal reasoning algorithm is first applied to identify several possible root causes that can lead to massive alarms. A novel root-cause ranking algorithm can subsequently be used to isolate the most likely faults from the other root-cause candidates. The proposed method is validated on a pressurized water reactor (PWR) simulator at HAMMLAB. The results show that the actual root cause is accurately identified for every tested operating scenario. The automation of root-cause identification and ranking affords the opportunity of real-time alarm analysis. It is believed that the study can further improve the situation awareness of operators in the alarm flooding situation.

• Korean Journal of Social Welfare
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• v.67 no.1
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• pp.83-107
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• 2015

The present study examines the development of self-esteem from young adulthood to old age, as well as predictors of change in self-esteem over time. The eight waves of Korean Welfare Panel Data(KOWEPS) were used for analyses, and a nationally representative sample of 15,511 individual aged 19 years and above were included. The multilevel growth curve model was specified to address the research questions. The result shows that the self-esteem trajectory differed across different age groups with those in early adulthood and adulthood showed an increasing linear trajectories while the old age showed a declining slope. Furthermore, predictors of changes in self-esteem also differed across the age groups that while depression and relationship variables were constant in predicting self-esteem change for all three age groups, some variables such as marital status, poverty status, and employment status predicted individuals in certain age groups. Such results demonstrate the need to understand and examine the change in self-esteem at the individual level.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.9
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• pp.5325-5329
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• 2013

Objectives: Although there are many reports about the risk of breast cancer, few have reported clinical factors including history of breast-related or other diseases that affect the prevalence of breast cancer. This study explores these risk factors for breast cancer cases reported in Beijing in 2009. Materials and Methods: Data were derived from a Beijing breast cancer screening performed in 2009, of 568,000 women, from 16 districts of Beijing, all aged between 40 and 60 years. In this study, multilevel statistical modeling was used to identify clinical factors that affect the prevalence of breast cancer and to provide more reliable evidence for clinical diagnostics by using screening data. Results and Conclusion: Those women who had organ transplants, compared with those with none, were associated with breast cancer with an odds ratio (OR)=65.352 [95% confidence interval (CI): 8.488-503.165] and those with solid breast mass compared with none had OR=1.384 (95% CI: 1.022-1.873). Malignant tendency was strongly associated with increased risk of breast cancer, OR=207.999(95% CI: 151.950-284.721). The risk of breast cancer increased with age, $OR_1$=2.759 (95% CI: 1.837-4.144, 56-60 vs. 40-45), $OR_2$=2.047 (95% CI: 1.394-3.077, 51-55 vs. 40-45), $OR_3$=1.668 (95% CI: 1.145-2.431). Normal results of B ultrasonic examination show a lower risk among participants, OR= 0.136 (95% CI: 0.085-0.218). Those women with ductal papilloma compared with none were associated with breast cancer, OR=6.524 (95% CI: 1.871-22.746). Therefore, this study suggests that clinical doctors should pay attention to these high-risk factors.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.7
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• pp.299-306
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• 2022

In recent, sensors embedded in robots, equipment, and circuits have become common, and research for diagnosing device failures by learning measured sensor data is being actively conducted. This failure diagnosis study is divided into a classification model for predicting failure situations or types and a regression model for numerically predicting failure conditions. In the case of a classification model, it simply checks the presence or absence of a failure or defect (Class), whereas a regression model has a higher learning difficulty because it has to predict one value among countless numbers. So, the reason that regression modeling is more difficult is that there are many irregular situations in which it is difficult to determine one output from a similar input when predicting by matching input and output. Therefore, in this paper, we focus on input and output data with periodicity, analyze the input/output relationship, and secure regularity between input and output data by performing sliding window-based input data patterning. In order to apply the proposed method, in this study, current and temperature data with periodicity were collected from MMC(Modular Multilevel Converter) circuit system and learning was carried out using ANN. As a result of the experiment, it was confirmed that when a window of 2% or more of one cycle was applied, performance of 97% or more of fit could be secured.

• Korean Journal of School Psychology
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• v.18 no.2
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• pp.249-267
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• 2021

This study examined how social withdrawal as an individual factor and discrimination as a contextual factor contributed to depression caused by peer victimization among middle school students. Self-reported data of 1,611 students from 86 classrooms in 7 middle schools was analyzed, using multilevel path analysis. The results indicate that peer victimization had a significant partial mediating effect on the relation between social withdrawal and depression at the individual level. Social withdrawal had a direct positive effect on depression as well as an indirect positive effect on depression via high levels of peer victimization. Discrimination also positively predicted peer victimization at the classroom level. Moreover, classroom-level discrimination moderated the individual-level relations between social withdrawal and peer victimization. The relation between social withdrawal and peer victimization was much stronger as the levels of discrimination in the classroom were higher. These findings shed light on the importance of considering both individual and contextual factors when intervening to prevent peer victimization.