• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.21-21
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• 2019

본 연구에서는 국내 주요 종관기상관측장비(Automated Surface Observing System; ASOS)의 강수 자료에 CSEOF 분석(Cyclo-stationary Empirical Orthogonal Function Analysis)을 적용하여 주요 성분(principle component)을 추출한 후 이를 분석하여 국내 강수의 계절적 순환 특성을 평가고자 하였다. ASOS 자료로는 전국 131개의 ASOS 중에 40년 이상의 월 강수량 자료가 구축되어 있는 47개 지점의 자료를 이용하였다. 수집한 자료의 기간은 1978년부터 2018년까지이다. 강수 자료의 월별 공간적인 강수 분포 특성을 파악하기 위해 시간적인 순환성을 고려한 CSEOF 분석을 수행하였다. 강수자료의 주성분을 추출해본 결과, CSEOF 분석의 경우 첫 번째 CSEOF 외의 다른 CSEOF들의 원자료 설명 비율 또한 작지 않게 나타나 다양한 강수 변동 특성을 평가할 수 있음을 확인하였다. 8월의 2nd CSEOF는 한반도 전체의 강수가 감소하는 것으로 나타났으며, 이는 라니냐가 7-8월 한반도 강수에 미치는 영향과 유사하다. 아울러 9월의 2nd CSEOF 결과 또한 남부를 중심으로 전체적으로 감소하는 경향이 나타남. 이는 엘리뇨 발생 시 9월의 강수 패턴과 비슷한 것으로 확인되었다. 뿐만 아니라, 우리나라에 영향을 미친 주요 태풍과 CSEOF의 상관관계도 검증할 수 있었으며, 장마와의 관계도 발견할 수 있었다. 향후, CSEOF 분석 결과에 해석방법이 개발된다면, 보다 다각적인 측면에서의 강수 계절적 순환 특성 평가가 이루어 질 수 있을 것으로 기대한다.

• The Korean Journal of Applied Statistics
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• v.32 no.4
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• pp.607-618
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• 2019

A new and interesting task in statistics is to effectively analyze functional data that frequently comes from advances in modern science and technology in areas such as meteorology and biomedical sciences. Functional linear regression with scalar response is a popular functional data analysis technique and it is often a common problem to determine a functional association if a functional predictor variable affects the scalar response in the models. Recently, Kong et al. (Journal of Nonparametric Statistics, 28, 813-838, 2016) established classical testing methods for this based on functional principal component analysis (of the functional predictor), that is, the resulting eigenfunctions (as a basis). However, the eigenbasis functions are not generally suitable for regression purpose because they are only concerned with the variability of the functional predictor, not the functional association of interest in testing problems. Additionally, eigenfunctions are to be estimated from data so that estimation errors might be involved in the performance of testing procedures. To circumvent these issues, we propose a testing method based on fixed basis such as B-splines and show that it works well via simulations. It is also illustrated via simulated and real data examples that the proposed testing method provides more effective and intuitive results due to the localization properties of B-splines.

• Communications of Mathematical Education
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• v.37 no.1
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• pp.65-84
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• 2023

The method of Lagrange multipliers, one of the most fundamental algorithms for solving equality constrained optimization problems, has been widely used in basic mathematics for artificial intelligence (AI), linear algebra, optimization theory, and control theory. This method is an important tool that connects calculus and linear algebra. It is actively used in artificial intelligence algorithms including principal component analysis (PCA). Therefore, it is desired that instructors motivate students who first encounter this method in college calculus. In this paper, we provide an integrated perspective for instructors to teach the method of Lagrange multipliers effectively. First, we provide visualization materials and Python-based code, helping to understand the principle of this method. Second, we give a full explanation on the relation between Lagrange multiplier and eigenvalues of a matrix. Third, we give the proof of the first-order optimality condition, which is a fundamental of the method of Lagrange multipliers, and briefly introduce the generalized version of it in optimization. Finally, we give an example of PCA analysis on a real data. These materials can be utilized in class for teaching of the method of Lagrange multipliers.

• Journal of Electrical Engineering and Technology
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• v.9 no.5
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• pp.1511-1519
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• 2014

The objective of this paper is to propose a modelling of a small compressed air energy storage system, which drives an induction generator based on a field-oriented control (FOC) principle for a renewable power generation. The proposed system is a hybrid technology of energy storage and electrification, which is developed to use as a small scale of renewable energy power plant. The energy will be transferred from the renewable energy resource to the compressed air energy by reciprocating air compressor to be stored in a pressurized vessel. The energy storage system uses a small compressed air energy storage system, developed as a small unit and installed above ground to avoid site limitation as same as the conventional CAES does. Therefore, it is suitable to be placed at any location. The system is operated in low pressure not more than 15 bar, so, it easy to available component in country and inexpensive. The power generation uses a variable speed induction generator (IG). The relationship of pressure and air flow of the compressed air, which varies continuously during the discharge of compressed air to drive the generator, is considered as a control command. As a result, the generator generates power in wide speed range. Unlike the conventional CAES that used gas turbine, this system does not have any combustion units. Thus, the system does not burn fuel and exhaust pollution. This paper expresses the modelling, thermodynamic analysis simulation and experiment to obtain the characteristic and performance of a new concept of a small compressed air energy storage power plant, which can be helpful in system designing of renewable energy electrification. The system was tested under a range of expansion pressure ratios in order to determine its characteristics and performance. The efficiency of expansion air of 49.34% is calculated, while the efficiency of generator of 60.85% is examined. The overall efficiency of system of approximately 30% is also investigated.

• Cross-Cultural Studies
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• v.39
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• pp.381-406
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• 2015

Russian nasal consonants / m /, / n / have a feature value not only [+consonant] in common with obstruents, but also [+sonorant] in common with vowels. Nasal / m /(bi-labial) and / n /(dental) have the same place of articulation but different manner of articulation. The feature value of / m / is [+cons, +son, +nas, +ant, -cor, -high, -low, -back, -cont, -del, rel, -strid, +voic], and that of / n / is [+cons, +son, +nas, +ant, +cor, -high, -low, -back, -cont, -del, rel, -strid, + voic]. There is a difference in feature [cor] value of / m / and / n /. In this study it is confirmed that it is a fact that the Russian nasal consonants behave differently from the other consonants in each phonological phenomenon due to their phonological characteristics. The preceding voiced obstruent is changed to an unvoiced one in a process where the last voiceless obstruent in the consonant cluster ' voiced obstruent + nasal /m/ + voiceless obstruent' skips the nasal consonant and spreads its feature value to the preceding voiced obstruent transparently because of the feature [+sonorant] of the nasal consonant. The coronal nasal /n/ participates in a palatalization with the following palatal actively and palatalize preceding plain consonants passively because of markedness hierarchy such as 'Velar > Labial > Coronal'. But the labial nasal /m/ is palatalized with the following velar palatal actively and participates in a palatalization with the following coronal palatal passively. This result helps us confirm the phonological difference of /m/ and /n/ in a palatalization. When the a final consonant is nasal, the unvoicing phenomenon of a final consonant doesn't occur. In such a case as cluster 'obstruent + nasal' the feature value [voiced] of the preceding obstruent doesn't change, but the following nasal can assimilate into the preceding obstruent. When continuing the same nasals / -nn- / in a consonant cluster, the feature value [+cont] of a weak position leads the preceding nasal / n / to be changed into [-cont] / l /. Through the analysis of the frequency of occurrences of consonants in syllabic onsets and codas that should observe the 'Sonority Sequence Principle', the sonority hierarchy of nasal consonants has been confirmed. In a diachronic perspective following nasal / m /, / n / there is a loss of the preceding labial stop and dental stop. But in clusters with the velar stop+nasal, the two-component cluster has been kept phonetically intact.

• Journal of Korean Society of Environmental Engineers
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• v.39 no.11
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• pp.641-649
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• 2017

In the study, the water quality of the Imjin River basin with pollutants of changing characteristics it was determined through statistical analysis, correlation analysis, principle component and factor analysis, and cluster analysis. Among all analyzed data points, the average water quality concentration at the Sincheon 3 site shows high levels of BOD 13.4 mg/L, COD 19.9 mg/L, T-N 11.145 mg/L, T-P 0.336 mg/L, TOC 14.2 mg/L, indicating that Sincheon basin requires intersive water quality management out of the entire drainage basin. The correlational analysis of comprehensive water quality data shows statistically significant correlation between COD, TOC, BOD, T-N water quality factors, as well as finding of high correlation between organic and nutrients. The principal component analysis show that 2 main components being extracted at 81.221% from the measuring station's entire data, while seasonal data show 3 main components being extracted at 96.241%. Factor analysis of the entire data set and the seasonal data identify BOD, COD, T-N, T-P, TOC as the common factors influencing water quality. The spatial and temporal cluster analysis showed 4 groups and 3 groups, respectively, according to seasonal characteristics and land use. By analysing the water quality factors for the Imjin River basins over an 8 year period, with consideration to the spatial and temporal characteristics, this study will become the fundamental analytic data that will help understand the future changes of water quality in the Imjin River basin.

• Journal of Animal Science and Technology
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• v.56 no.5
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• pp.16.1-16.9
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• 2014

Fatty acids (FA) have recently been used in several studies to infer the diet in a number of species. While these studies have been largely successful, most have dealt with predators that have a fairly specialized diet. In this paper, we used FA analysis as a tool to infer the diet of the nearctic river otter (Lontra canadensis). The river otter is an opportunistic predator known to subsist on a wide variety of prey including, fishes, crayfish, molluscs, reptiles and amphibians, among others. We analyzed the principle components of 60 FA from otters and 25 potential prey species in Illinois, USA. Prey species came from 4 major taxonomic divisions: fishes, crayfish, molluscs and amphibians. Within each division, most, but not all, species had significantly different profiles. Using quantitative FA signature analysis, our results suggest that, by mass, fish species are the most significant component of Illinois River otters' diet ($37.7{\pm}1.0%$). Molluscs ranked second ($32.0{\pm}0.8%$), followed by amphibians ($27.3{\pm}4.3%$), and finally, crayfish ($3.0{\pm}0.6%$). Our analysis indicates that molluscs make up a larger portion of the otter diet than previously reported. Throughout much of the Midwest there have been numerous otter reintroduction efforts, many of which appear to be successful. In regions where mollusc species are endangered, these data are essential for management agencies to better understand the potential impact of otters on these species. Our analysis further suggests that quantitative FA signature analysis can be used to infer diet even when prey species are diverse, to the extent that their FA profiles differ. Better understanding of the otter's metabolism of FA would improve inferences of diet from FA analysis.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.11 no.3
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• pp.261-270
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• 2017

Bad sitting postures are known to cause for a variety of diseases or physical deformation. However, it is not easy to fit right sitting posture for long periods of time. Therefore, methods of distinguishing and inducing good sitting posture have been constantly proposed. Proposed methods were image processing, using pressure sensor attached to the chair, and using the IMU (Internal Measurement Unit). The method of using IMU has advantages of simple hardware configuration and free of various constraints in measurement. In this paper, we researched on distinguishing sitting postures with a small amount of data using just one IMU. Feature extraction method was used to find data which contribution is the least for classification. Machine learning algorithms were used to find the best position to classify and we found best machine learning algorithm. Used feature extraction method was PCA(Principal Component Analysis). Used Machine learning models were five : SVM(Support Vector Machine), KNN(K Nearest Neighbor), K-means (K-means Algorithm) GMM (Gaussian Mixture Model), and HMM (Hidden Marcov Model). As a result of research, back neck is suitable position for classification because classification rate of it was highest in every model. It was confirmed that Yaw data which is one of the IMU data has the smallest contribution to classification rate using PCA and there was no changes in classification rate after removal it. SVM, KNN are suitable for classification because their classification rate are higher than the others.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.4 s.310
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• pp.37-45
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• 2006

In this paper, we propose the development of MHLA(Modulatory Hippocampus Learning Algorithm) which remodel a principle of brain of hippocampus. Hippocampus takes charge auto-associative memory and controlling functions of long-term or short-term memory strengthening. We organize auto-associative memory based 3 steps system(DG, CA3, CAl) and improve speed of learning by addition of modulator to long-term memory learning. In hippocampal system, according to the 3 steps order, information applies statistical deviation on Dentate Gyrus region and is labelled to responsive pattern by adjustment of a good impression. In CA3 region, pattern is reorganized by auto-associative memory. In CAI region, convergence of connection weight which is used long-term memory is learned fast by neural networks which is applied modulator. To measure performance of MHLA, PCA(Principal Component Analysis) is applied to face images which are classified by pose, expression and picture quality. Next, we calculate feature vectors and learn by MHLA. Finally, we confirm cognitive rate. The results of experiments, we can compare a proposed method of other methods, and we can confirm that the proposed method is superior to the existing method.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.6
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• pp.477-484
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• 2016

Small industrial appliances such as fan, audio, electric rice cooker mostly consist of ABS, PP, PS materials. In colored plastics, it is possible to classify by near infrared(NIR) spectroscopy, while in black plastics, it is very difficult to classify black plastic because of the characteristic of black material that absorbs the light. So the RBFNNs pattern classifier is introduced for sorting electrical and electronic waste plastics through LIBS(Laser Induced Breakdown Spectroscopy) spectrometer. At the preprocessing part, PCA(Principle Component Analysis), as a kind of dimension reduction algorithms, is used to improve processing speed as well as to extract the effective data characteristics. In the condition part, FCM(Fuzzy C-Means) clustering is exploited. In the conclusion part, the coefficients of linear function of being polynomial type are used as connection weights. PSO and 5-fold cross validation are used to improve the reliability of performance as well as to enhance classification rate. The performance of the proposed classifier is described based on both optimization and no optimization.