• 대한의용생체공학회:의공학회지
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• 제5권2호
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• pp.155-160
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• 1984

In this paper, the patient data back-up system for medical and engineering field was designed. The system consists of 8-bit microprocessor, cassette recorder and data acquisition & control logic. To Inake data reduction, a Marked CORTES Algorythm which can be easily reconstructed was also designed in real time mode. In results, a Marked CORTES Algorythm produced about 35% data reduction rate and reconstructed good original data that are suitable Medician's reading.

• Asian Journal of Atmospheric Environment
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• 제5권4호
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• pp.228-236
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• 2011

Since most Green House Gases (GHGs) and air pollutants are generated from the same sources, it will be cost-effective to develop a GHGs reduction plan in combination with simultaneous removal of air pollutants. However, effects on air pollutants reduction according to implementing any GHG abatement plans have been rarely studied. Reflecting simultaneous removal of air pollutants along with the GHGs emission reduction, this study investigated relative cost effectiveness among GHGs reduction action plans in Busan Metropolitan City. We employed the Data Envelopment Analysis (DEA), a methodology that evaluates relative efficiency of decision-making units (DMUs) producing multiple outputs with multiple inputs, for the investigation. Assigning each GHGs reduction action plan to a DMU, implementation cost of each GHGs reduction action plan to an input, and reduction potential of GHGs and air pollutants by each GHGs reduction action plan to an output, we calculated efficiency scores for each GHGs reduction action plan. When the simultaneous removal of air pollutants with the GHGs reduction were considered, green house supply-insulation improvement and intelligent transportation system (ITS) projects had high efficiency scores for cost-positive action plans. For cost-negative action plans, green start network formation and running, and daily car use control program had high efficiency scores. When only the GHGs reduction was considered, project priority orders based on efficiency scores were somewhat different from those when both the removal of air pollutants and GHGs reduction were considered at the same time. The expected action plan priority difference is attributed to great difference of air pollutants reduction potential according to types of energy sources to be reduced.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권7호
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• pp.3714-3732
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• 2019

With the rapid development of network, Intrusion Detection System(IDS) plays a more and more important role in network applications. Many data mining algorithms are used to build IDS. However, due to the advent of big data era, massive data are generated. When dealing with large-scale data sets, most data mining algorithms suffer from a high computational burden which makes IDS much less efficient. To build an efficient IDS over big data, we propose a classification algorithm based on data clustering and data reduction. In the training stage, the training data are divided into clusters with similar size by Mini Batch K-Means algorithm, meanwhile, the center of each cluster is used as its index. Then, we select representative instances for each cluster to perform the task of data reduction and use the clusters that consist of representative instances to build a K-Nearest Neighbor(KNN) detection model. In the detection stage, we sort clusters according to the distances between the test sample and cluster indexes, and obtain k nearest clusters where we find k nearest neighbors. Experimental results show that searching neighbors by cluster indexes reduces the computational complexity significantly, and classification with reduced data of representative instances not only improves the efficiency, but also maintains high accuracy.

• Journal of Multimedia Information System
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• 제6권4호
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• pp.225-234
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• 2019

Deep learning has become increasingly popular in both academic and industrial areas nowadays. Various domains including pattern recognition, Computer vision have witnessed the great power of deep neural networks. However, current studies on deep learning mainly focus on quality data sets with balanced class labels, while training on bad and imbalanced data set have been providing great challenges for classification tasks. We propose in this paper a method of data analysis-based data reduction techniques for selecting good and diversity data samples from a large dataset for a deep learning model. Furthermore, data sampling techniques could be applied to decrease the large size of raw data by retrieving its useful knowledge as representatives. Therefore, instead of dealing with large size of raw data, we can use some data reduction techniques to sample data without losing important information. We group PCB characters in classes and train deep learning on the ResNet56 v2 and SENet model in order to improve the classification performance of optical character recognition (OCR) character classifier.

• 대한교통학회지
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• 제32권2호
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• pp.93-105
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• 2014

본 연구는 자료포락분석(Data Envelopment Analysis: DEA) 기법을 활용하여 도로이동오염원 저감대책의 상대적 효율성 평가와 그에 기반하여 우선순위를 결정하였다. 현재 시행 중이거나 장래 계획 가능한 도로이동오염원 저감 대책들을 근거로 실효성 높은 자동차 온실가스와 대기오염물질 저감 대책 10개를 선정하여 시나리오를 구성하였으며, 대기오염물질 4개(CO, HC, NOX, PM), 온실가스 3개($CO_2$, $CH_4$, $N_2O$)물질에 대해 장래 통행패턴을 고려한 교통수요예측모형과 가변적 복합배출계수를 활용하여 2027년도를 최종 목표년도로 저감량을 산정하였다. 저감 대책들 간의 상대적 효율성을 평가하기 위해 DEA모형 중 초효율성 분석을 수행한 결과, 승용차 요일제 참여 확대 대책이 효율성 점수 1.879로 가장 우선순위가 높은 저감대책으로 선정되었으며, 버스전용차로 확대, CNG버스 보급 대책의 효율성이 높은 것으로 분석되었다. 본 연구의 결과는 자동차 온실가스와 대기오염물질 저감대책 우선순위 선정 결정 시 절대적인 자료로 활용될 수는 없지만 저감대책의 방향성을 제시하고 있으므로 향후 자동차 배출량 저감 정책방향 설정 및 체계적인 중장기 저감대책 수립에 기여할 수 있을 것으로 기대된다.

• 대한영양사협회학술지
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• 제19권2호
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• pp.173-181
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• 2013

This study used survey data to identify the use of salimeters and the prevalence of sodium reduction education in the Gyeonggi region. A survey with 211 dietitians working in school foodservice (106 in elementary schools, 69 in middle schools, and 36 in high schools) was conducted from August 6 to August 17, 2012. The data were analyzed using the SPSS program. Though 86.6% of school kitchens had salimeters, the rate for checking the sodium content of soup or stew was just 62.7% and the rate for checking the sodium content of kimchi and solid food was very low. Since salimeters are mostly used to measure sodium in liquid foods, it is urgent to provide an education and manual on using salimeters and to promote salimetry for kimchi and side dishes. It is also important to provide students with nutritional information by clearly posting the sodium content of food on menus and compelling students to notice them. Sodium reduction education for cooks was conducted in the 70.3% of the kitchens; however, the dietitians perceived that the cooks did not understand the importance of the education. Also, sodium reduction education for students was mostly provided through indirect methods, rather than face-to-face education, resulting in poor educational data (only 36.4% comprehending). By providing detailed guidelines for sodium reduction and labelling accurate content of sodium of the menus, we will be able to enforce practices for sodium reduction in school lunches.

• 한국멀티미디어학회논문지
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• 제14권8호
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• pp.1010-1019
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• 2011

This was a preliminary research for the goal of understanding between internal structure of Osteogenesis Imperfecta Murine (OIM) bone and its fragility. 54 hyperspectral bone data sets were captured by using JASCO 2000 Raman spectrometer at UMKC-CRISP (University of Missouri-Kansas City Center for Research on Interfacial Structure and Properties). Each data set consists of 1,091 data points from 9 OIM bones. The original captured hyperspectral data sets were noisy and base-lined ones. We removed the noise and corrected the base-lined data for the final efficient classification. High dimensional Raman hyperspectral data on OIM bones was reduced by Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA) and efficiently classified for the first time. We confirmed OIM bones could be classified such as strong, middle and weak one by using the coefficients of their PCA or LDA. Through experiment, we investigated the efficiency of classification on the reduced OIM bone data by the Bayesian classifier and K -Nearest Neighbor (K-NN) classifier. As the experimental result, the case of LDA reduction showed higher classification performance than that of PCA reduction in the two classifiers. K-NN classifier represented better classification rate, compared with Bayesian classifier. The classification performance of K-NN was about 92.6% in case of LDA.

• Communications for Statistical Applications and Methods
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• 제26권5호
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• pp.497-506
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• 2019

Forecasting the U.S. employment level is made using machine learning methods of the artificial neural network: deep neural network, long short term memory (LSTM), gated recurrent unit (GRU). We consider the big data of the federal reserve economic data among which 105 important macroeconomic variables chosen by McCracken and Ng (Journal of Business and Economic Statistics, 34, 574-589, 2016) are considered as predictors. We investigate the influence of the two statistical issues of the dimension reduction and time series differencing on the machine learning forecast. An out-of-sample forecast comparison shows that (LSTM, GRU) with differencing performs better than the autoregressive model and the dimension reduction improves long-term forecasts and some short-term forecasts.

• International Journal of Computer Science & Network Security
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• 제21권3호
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• pp.31-36
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• 2021

RNA sequencing (RNA-Seq) is a technology that facilitates transcriptome analysis using next-generation sequencing (NSG) tools. Information on the quantity and sequences of RNA is vital to relate our genomes to functional protein expression. RNA-Seq data are characterized as being high-dimensional in that the number of variables (i.e., transcripts) far exceeds the number of observations (e.g., experiments). Given the wide range of dimensionality reduction techniques, it is not clear which is best for RNA-Seq data analysis. In this paper, we study the effect of three dimensionality reduction techniques to improve the classification of the RNA-Seq dataset. In particular, we use PCA, SVD, and SOM to obtain a reduced feature space. We built nine classification models for a cancer dataset and compared their performance. Our experimental results indicate that better classification performance is obtained with PCA and SOM. Overall, the combinations PCA+KNN, SOM+RF, and SOM+KNN produce preferred results.

• 진공이야기
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• 제2권3호
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• pp.4-10
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• 2015

We developed a real-time three-polarizer spectroscopic ellipsometer based on a new data acquisition algorithm and a general data reduction (the process of extracting the ellipsometric sample parameters from the Fourier coefficients). The data acquisition algorithm measures Fourier coefficients of radiant flux waveform accurately and precisely. The general data reduction is introduced to represent the analytic functions of the standard uncertainties of the ellipsometric sample parameters, and the extracted theoretical values closely agree with the corresponding experimental data. Our approach can be used for optimization of measurement conditions, instrumentation, simulation, standardization, laboratory accreditation, and metrology.