• 대기
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• 제22권4호
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• pp.449-454
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• 2012

This study presents the long-term variability of spring precipitation over the Korean peninsula. It is found that the significant interdecadal change in the spring precipitation has occurred around year 1991. Over the Korean peninsula the precipitation for the post-1991 period increased by about 30 mm per year in CMAP and station-measured data compared to the precipitation prior to year 1991. Due to an increased baroclinicity during the later period, the low-level negative pressure anomaly has developed with its center over northern Japan. Korea is situated at the western end of the negative pressure anomaly, receiving moisture from westerly winds and producing more precipitation. Also, we estimate the change in the near future (years 2020~2040) spring precipitation using six best performing Coupled Model Intercomparison Project 3 (CMIP3) models. These best model ensemble mean shows that spring precipitation is anticipated to increase by about 4% due to the strengthened westerlies accompanied by the northwestern enhancement of the North Pacific subtropical high.

• 대한조선학회논문집
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• 제39권3호
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• pp.41-47
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• 2002

선박용 프로펠러 후류의 유동 특성을 적응형 하이브리드 2-frame PTV(Particle Tracking Velocimetry)기법을 적용하여 실험적으로 해석하였다. 프로펠러 위상각에 대해 위상평균하여 하류로 나아감에 따른 후류 유동의 발달과정을 연구하였으며, 주 유동 방향으로 날개의 뒷날로부터 프로펠러 직경만큼의 거리까지를 측정하였다. 하나의 날개에 대해 4개의 다른 위상각 각각에서 얻은 위상평균 속도장 결과는 프로펠러 날개의 압력 차이로 인해 발생하는 주기적인 날개끝 보오텍스가 하류로 이동해 나감을 보여주고 있다. 또한, 프로펠러 날개 표면을 따라 발달하는 경계층에 기인한 점성 후류는 축방향 속도성분의 결손을 가진다. 프로펠러 날개 뒷날에서 발생하는 후연 보오텍스는 하류로 나아감에 따라 수축되며 점성 소산으로 인해 그 세기 및 크기는 점차 작아졌다.

• Advances in Energy Research
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• 제6권2호
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• pp.91-101
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• 2019

This paper proposes an efficient data-driven approach to build models for predicting energy consumption in buildings. Data used in this research is collected by installing humidity and temperature sensors at different locations in a building. In addition to this, weather data from nearby weather station is also included in the dataset to study the impact of weather conditions on energy consumption. One of the main emphasize of this research is to make feature selection independent of domain knowledge. Therefore, to extract useful features from data, two different approaches are tested: one is feature selection through principal component analysis and second is relative importance-based feature selection in original domain. The regression model used in this research is gradient boosting regression and its optimal parameters are chosen through a two staged coarse-fine search approach. In order to evaluate the performance of model, different performance evaluation metrics like r2-score and root mean squared error are used. Results have shown that best performance is achieved, when relative importance-based feature selection is used with gradient boosting regressor. Results of proposed technique has also outperformed the results of support vector machines and neural network-based approaches tested on the same dataset.

• 방송공학회논문지
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• 제8권2호
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• pp.153-162
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• 2003

본 논문은 우리나라의 지상파 디지털 TV방식인 ATSC (Advanced Television System Committee) 8-VSB (Vestigial Sideband) 시스템의 수신부 가운데 등화부에 해당되는 DFE (Decision Feedback Equalizer)에 관한 것으로서, DFE 구조의 전반적인 개요와 블라인드 알고리즘에 대한 성능 분석결과를 서술한다. 특히, 다중경로, 도플러 천이(Doppler Shift), 건물 벽에 의한 신호의 감쇄 등의 영향으로 수신이 어려운 실내수신 환경에서의 수신 성능개선을 위해 최적화된 등화기의 구조를 제시하고, 등화기와 TCM(Trellis Coded Modulation)연동방법 및 필터계수 초기화 알고리즘 등을 구현한 후 모의 실험을 통한 성능 분석결과를 제시한다.

• 대한조선학회논문집
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• 제42권5호
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• pp.427-434
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• 2005

The free surface influenced the wake behind a rotating propeller and its effects were investigated experimentally in a circulating water channel with the variation of water depth. Instantaneous velocity fields were measured using two-frame PIV technique and ensemble-averaged to study the phase-averaged flow structure in the wake region. For an isolated propeller, the flow behind the propeller is affected only by the propeller rotation speed, the leading on the blades and the proximity of the propeller to the free surface. The phase-averaged mean velocity fields show that the potential wake and the viscous wake developed on the blade surfaces. The interaction between the tip vortices and the slipstream causes the oscillating trajectory of tip vortices. The presence of the free surface greatly affected the wake structure, especially for propeller immersion depth of 0.6D. At small immersion depths, the free surface modified the tip and trailing vortices and the slipstream flow structure downstream of X/D = 0.3 in the propeller wake.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권6호
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• pp.3144-3164
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• 2019

Face recognition systems have several potential applications, such as security and biometric access control. Ongoing research is focused to develop a robust face recognition algorithm that can mimic the human vision system. Face pose, non-uniform illuminations, and low-resolution are main factors that influence the performance of face recognition algorithms. This paper proposes a novel method to handle the aforementioned aspects. Proposed face recognition algorithm initially uses 68 points to locate a face in the input image and later partially uses the PCA to extract mean image. Meanwhile, the AdaBoost and the LDA are used to extract face features. In final stage, classic nearest centre classifier is used for face classification. Proposed method outperforms recent state-of-the-art face recognition algorithms by producing high recognition rate and yields much lower error rate for a very challenging situation, such as when only frontal ($0^{\circ}$) face sample is available in gallery and seven poses ($0^{\circ}$, ${\pm}30^{\circ}$, ${\pm}35^{\circ}$, and ${\pm}45^{\circ}$) as a probe on the LFW and the CMU Multi-PIE databases.

• Steel and Composite Structures
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• 제43권5호
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• pp.625-637
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• 2022

Axial compression capacity (P_u) is a significant yet complex parameter of concrete-filled steel tube (CFST) columns. This study offers a novel ensemble tool, adaptive neuro-fuzzy inference system (ANFIS) supervised by equilibrium optimization (EO), for accurately predicting this parameter. Moreover, grey wolf optimization (GWO) and Harris hawk optimizer (HHO) are considered as comparative supervisors. The used data is taken from earlier literature provided by finite element analysis. ANFIS is trained by several population sizes of the EO, GWO, and HHO to detect the best configurations. At a glance, the results showed the competency of such ensembles for learning and reproducing the P_u behavior. In details, respective mean absolute errors along with correlation values of 4.1809% and 0.99564, 10.5947% and 0.98006, and 4.8947% and 0.99462 obtained for the EO-ANFIS, GWO-ANFIS, and HHO-ANFIS, respectively, indicated that the proposed EO-ANFIS can analyze and predict the behavior of CFST columns with the highest accuracy. Considering both time and accuracy, the EO provides the most efficient optimization of ANFIS and can be a nice substitute for experimental approaches.

• 한국재료학회지
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• 제33권4호
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• pp.164-174
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• 2023

The bandgap characteristics of semiconductor materials are an important factor when utilizing semiconductor materials for various applications. In this study, based on data provided by AFLOW (Automatic-FLOW for Materials Discovery), the bandgap of a semiconductor material was predicted using only the material's compositional features. The compositional features were generated using the python module of 'Pymatgen' and 'Matminer'. Pearson's correlation coefficients (PCC) between the compositional features were calculated and those with a correlation coefficient value larger than 0.95 were removed in order to avoid overfitting. The bandgap prediction performance was compared using the metrics of R² score and root-mean-squared error. By predicting the bandgap with randomforest and xgboost as representatives of the ensemble algorithm, it was found that xgboost gave better results after cross-validation and hyper-parameter tuning. To investigate the effect of compositional feature selection on the bandgap prediction of the machine learning model, the prediction performance was studied according to the number of features based on feature importance methods. It was found that there were no significant changes in prediction performance beyond the appropriate feature. Furthermore, artificial neural networks were employed to compare the prediction performance by adjusting the number of features guided by the PCC values, resulting in the best R² score of 0.811. By comparing and analyzing the bandgap distribution and prediction performance according to the material group containing specific elements (F, N, Yb, Eu, Zn, B, Si, Ge, Fe Al), various information for material design was obtained.

• Asian Journal of Business Environment
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• 제12권1호
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• pp.11-16
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• 2022

Purpose: This study is aimed to map the provinces in Indonesia based on the education and ICT indicators using several unsupervised learning algorithms. Research design, data, and methodology: The education and ICT indicators such as student-teacher ratio, illiteracy rate, net enrolment ratio, internet access, computer ownership, are used. Several approaches to get deeper understanding on provincial strength and weakness based on these indicators are implemented. The approaches are Ensemble K-Mean and Fuzzy C Means clustering. Results: There are at least three clusters observed in Indonesia the education quality, participation, facilities and ICT Access. Cluster with high education quality and ICT access are consist of DKI Jakarta, Yogyakarta, Riau Islands, East Kalimantan and Bali. These provinces show rapid economic growth. Meanwhile the other cluster consisting of six provinces (NTT, West Kalimantan, Central Sulawesi, West Sulawesi, North Maluku, and Papua) are the cluster with lower education quality and ICT development which impact their economic growth. Conclusions: The provinces in Indonesia are clustered into three group based on the education attainment and ICT indicators. Some provinces can directly implement e-learning; however, more provinces need to improve the education quality and facilities as well as the ICT infrastructure before implementing the e-learning.

• 대기
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• 제28권1호
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• pp.25-35
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• 2018

This study presents future potential sea level change over the seas surrounding Korea using Climate Model Intercomparison Project Phase 5 9 model ensemble result from Representative Concentration Pathways (RCPs), downloaded from icdc.zmaw.de. At the end of 21st century, regional sea level changes are projected to rise 37.8, 48.1, 47.7, 65.0 cm under RCP2.6, RCP4.5, RCP6.0 and RCP8.5 scenario, respectively with the large uncertainty from about 40 to 60 cm. The results exhibit similar tendency with the global mean sea level rise (SLR) with small differences less than about 3 cm. For the East Sea, the Yellow Sea, and the southern sea of Korea, projected SLR in the Yellow Sea is smaller and SLR in the southern sea is larger than the other coastal seas. Differences among the seas are small within the range of 4 cm. Meanwhile, Commonwealth Scientific and Industrial Research Organization (CSIRO) data in 23 years shows that the mean rate of sea level changes around the Yellow Sea is high relative to the other coastal seas. For sea level change, contribution of ice and ocean related components are important, at local scale, Glacial Isostatic Adujstment also needs to be considered.