• Korean Journal of Materials Research
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• v.28 no.12
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• pp.748-757
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• 2018

Inorganic semiconductor compounds, e.g., CIGS and CZTS, are promising materials for thin film solar cells because of their high light absorption coefficient and stability. Research on thin film solar cells using this compound has made remarkable progress in the last two decades. Vacuum-based processes, e.g., co-evaporation and sputtering, are well established to obtain high-efficiency CIGS and/or CZTS thin film solar cells with over 20 % of power conversion. However, because the vacuum-based processes need high cost equipment, they pose technological barriers to producing low-cost and large area photovoltaic cells. Recently, non-vacuum based processes, for example the solution/nanoparticle precursor process, the electrodeposition method, or the polymer-capped precursors process, have been intensively studied to reduce capital expenditure. Lately, over 17 % of energy conversion efficiency has been reported by solution precursors methods in CIGS solar cells. This article reviews the status of non-vacuum techniques that are used to fabricate CIGS and CZTS thin films solar cells.

• Investigative Magnetic Resonance Imaging
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• v.25 no.3
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• pp.156-163
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• 2021

Recently, neuromelanin and nigrosome imaging techniques have been developed to evaluate the substantia nigra in Parkinson's disease. Previous studies have shown potential benefits of quantitative analysis of neuromelanin and nigrosome images in the substantia nigra, although visual assessments have been performed to evaluate structures in most studies. In this study, we investigate the potential of using deep learning based automatic region segmentation techniques for quantitative analysis of the substantia nigra. The deep convolutional neural network was trained to automatically segment substantia nigra regions on 3D nigrosome and neuromelanin sensitive MR images obtained from 30 subjects. With a 5-fold cross-validation, the mean calculated dice similarity coefficient between manual and deep learning was 0.70 ± 0.11. Although calculated dice similarity coefficients were relatively low due to empirically drawn margins, selected slices were overlapped for more than two slices of all subjects. Our results demonstrate that deep convolutional neural network-based method could provide reliable localization of substantia nigra regions on neuromelanin and nigrosome sensitive MR images.

• ETRI Journal
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• v.44 no.5
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• pp.733-745
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• 2022

In software-defined wireless networking (SDWN), the optimal routing technique is one of the effective solutions to improve its performance. This routing technique is done by many different methods, with the most common using integer linear programming problem (ILP), building optimal routing metrics. These methods often only focus on one routing objective, such as minimizing the packet blocking probability, minimizing end-to-end delay (EED), and maximizing network throughput. It is difficult to consider multiple objectives concurrently in a routing algorithm. In this paper, we investigate the application of machine learning to control routing in the SDWN. An intelligent routing algorithm is then proposed based on the machine learning to improve the network performance. The proposed algorithm can optimize multiple routing objectives. Our idea is to combine supervised learning (SL) and reinforcement learning (RL) methods to discover new routes. The SL is used to predict the performance metrics of the links, including EED quality of transmission (QoT), and packet blocking probability (PBP). The routing is done by the RL method. We use the Q-value in the fundamental equation of the RL to store the PBP, which is used for the aim of route selection. Concurrently, the learning rate coefficient is flexibly changed to determine the constraints of routing during learning. These constraints include QoT and EED. Our performance evaluations based on OMNeT++ have shown that the proposed algorithm has significantly improved the network performance in terms of the QoT, EED, packet delivery ratio, and network throughput compared with other well-known routing algorithms.

• Journal of Radiation Industry
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• v.17 no.4
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• pp.551-557
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• 2023

In recent years, radiation has become a socially important issue, increasing the need for accurate prediction of radiation levels. In this study, machine learning-based models such as Multiple Linear Regression (MLR), Random Forest (RF), XGBoost, and LightGBM, which predict the dose rate by time(nSv h^-1) by selecting only important variables, were used, and the correlation between temperature, humidity, cumulative precipitation, wind direction, wind speed, local air pressure, sea pressure, solar radiation, and radiation dose rate (nSv h^-1) was analyzed by collecting weather data and radiation dose rate for about 6 months in Jangseong, Jeollanam-do. As a result of the evaluation based on the RMSE (Root Mean Squared Error) and R-Squared (R-Squared coefficient of determination) scores, the RMSE of the XGBoost model was 22.92 and the R-Squared was 0.73, showing the best performance among the models used. As a result of optimizing hyperparameters of all models using the GridSearch method and comparing them by adding variables inside the measuring instrument, it was confirmed that the performance improved to 2.39 for RMSE and 0.99 for R-Squared in both XGBoost and LightGBM.

• The Journal of the Acoustical Society of Korea
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• v.21 no.3
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• pp.285-292
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• 2002

One of the problems that should be solved in Text-To-Speech (TTS) is discontinuities at unit-joining points. To cope with this problem, a smoothing method using a low-pass filter is employed in this paper, In the proposed soothing method, a filter coefficient that controls the amount of smoothing is determined according to contort information to be synthesized. This method efficiently reduces both discontinuities at unit-joining points and artifacts caused by undesired smoothing. The amount of smoothing is determined with discontinuities around unit-joins points in the current synthesized speech and discontinuities predicted from context. The discontinuity predictor is implemented by CART that has context feature variables. To evaluate the performance of the proposed method, a corpus-based concatenative TTS was used as a baseline system. More than 6075 of listeners realized that the quality of the synthesized speech through the proposed smoothing is superior to that of non-smoothing synthesized speech in both naturalness and intelligibility.

• Journal of Power Electronics
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• v.16 no.3
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• pp.1110-1121
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• 2016

The mathematical model of a three phase PWM converter under the stationary αβ reference frame is deduced and constructed based on a Proportional-Resonant (PR) regulator, which can replace trigonometric function calculation, Park transformation, real-time detection of a Phase Locked Loop and feed-forward decoupling with the proposed accurate calculation of the inductance voltage vector. To avoid the parallel resonance of the LCL topology, the active damping method of the proportional capacitor-current feedback is employed. As to current vector error elimination, an optimized PR controller of the inner current loop is proposed with the zero-pole matching (ZPM) and cancellation method to configure the regulator. The impacts on system's characteristics and stability margin caused by the PR controller and control parameter variations in the inner-current loop are analyzed, and the correlations among active damping feedback coefficient, sampling and transport delay, and system robustness have been established. An equivalent model of the inner current loop is studied via the pole-zero locus along with the pole placement method and frequency response characteristics. Then, the parameter values of the control system are chosen according to their decisive roles and performance indicators. Finally, simulation and experimental results obtained while adopting the proposed method illustrated its feasibility and effectiveness, and the inner current loop achieved zero static error tracking with a good dynamic response and steady-state performance.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.7 no.4
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• pp.151-158
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• 2014

In this paper, we introduce an inference system using hard scatter partition method and model the nonlinear process. To do this, we use the hard scatter partition method that partition the input space in the scatter form with the value of the membership degree of 0 or 1. The proposed method is implemented by C-Means clustering algorithm. and is used for the initial center values by means of binary split. by applying the LBG algorithm to compensate for shortcomings in the sensitive initial center value. Hard-scatter-partitioned input space forms the rules in the rule-based system modeling. The premise parameters of the rules are determined by membership matrix by means of C-Means clustering algorithm. The consequence part of the rules is expressed in the form of polynomial functions and the coefficient parameters of each rule are determined by the standard least-squares method. The data widely used in nonlinear process is used to model the nonlinear process and evaluate the characteristics of nonlinear process.

• Journal of Climate Change Research
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• v.6 no.4
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• pp.331-344
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• 2015

A long-term gridded historical data at 3 km spatial resolution has been generated for practical regional applications such as hydrologic modelling. However, overly high or low values have been found at some grid points where complex topography or sparse observational network exist. In this study, the Inverse Distance Weighting (IDW) method was applied to properly smooth the overly predicted values of Improved GIS-based Regression Model (IGISRM), called the IDW-IGISRM grid data, at the same resolution for daily precipitation, maximum temperature and minimum temperature from 2001 to 2010 over South Korea. We tested various effective distances in the IDW method to detect an optimal distance that provides the highest performance. IDW-IGISRM was compared with IGISRM to evaluate the effectiveness of IDW-IGISRM with regard to spatial patterns, and quantitative performance metrics over 243 AWS observational points and four selected stations showing the largest biases. Regarding the spatial pattern, IDW-IGISRM reduced irrational overly predicted values, i. e. producing smoother spatial maps that IGISRM for all variables. In addition, all quantitative performance metrics were improved by IDW-IGISRM; correlation coefficient (CC), Index Of Agreement (IOA) increase up to 11.2% and 2.0%, respectively. Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) were also reduced up to 5.4% and 15.2% respectively. At the selected four stations, this study demonstrated that the improvement was more considerable. These results indicate that IDW-IGISRM can improve the predictive performance of IGISRM, consequently providing more reliable high-resolution gridded data for assessment, adaptation, and vulnerability studies of climate change impacts.

• Science of Emotion and Sensibility
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• v.19 no.4
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• pp.95-110
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• 2016

The purpose of this study was to propose a proper method for the multilevel mediation analysis, for which the hierarchical method should be utilized, then MLM (multilevel modeling) approach as a hierarchical method has been popularly utilized until MSEM (multilevel structural equation modeling) approach was not proposed. This purpose was covered by three research questions about statistical methods, analytic procedure, and real example. First, MSEM statistical method was preferred to MLM method for its estimation accuracy and analytic flexibility. Second, the four-step procedures of model building, assumption examination, model comparison, and coefficient testing were proposed for the multilevel mediation analysis. Third, the real data of 2695 students of elementary and secondary schools and 89 teachers were analyzed in the multilevel directions of $2{\rightarrow}2{\rightarrow}1$ and $1{\rightarrow}1{\rightarrow}2$. Out of these directions of $2{\rightarrow}2{\rightarrow}1$, and $1{\rightarrow}1{\rightarrow}2$ model, only the coefficient of $2{\rightarrow}2{\rightarrow}1$ model was significant at the 95% CI. Mplus programs used for the real example are attached on the Appendix. Based on the results, significance and limitations of this study, were discussed in detail.

• Journal of dental hygiene science
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• v.9 no.2
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• pp.219-224
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• 2009

The purpose of this study was to investigate the relationship among the stress on clinical practice, stress coping method and the somatization symptom of dental hygiene students and propose strategies for effective instruction of clinical practice. The survey was performed by self-reported questionnaires for 268 dental hygiene students who have recently experienced clinical practice. Collected data were analyzed using by t-test and Pearson correlation coefficient with the SPSS Win 12.0 program. Its results are as follows. 1. In the satisfaction of clinical practice, 'Satisfaction', 'Average' and 'Unsatisfactory' were 32.5%, 42.2% and 25.4%, respectively. The reason for dissatisfaction of clinical practice were 'insufficient education of college'(29.8%), 'environment of clinical practice institute'(23,4%), 'interpersonal relationship'(21.3%) in order. 2. Somatization symptoms was positive correlation(0.307, p < 0.01) associated with stress of clinical practice. Also it was negative correlation associated with satisfaction level of the subjects and stress of clinical practice. Correlation coefficient of the interpersonal relation factor was found the most significantly high as much as 0.331, according to the analysis carried out between subordinate factors of clinical practice stress and somatization. 3. According to stress coping method. Subjects were distributed into two group of active method and passive method. Thereafter as a result of verifying the difference of somatization symptoms, there was statistically significant difference between active method group and passive method group. Based on the study results, we suggests that effective management program of clinical practice should be developed and applied to the dental hygiene students to make them cope with stress and somatic symptom during their clinical practice.