• Title/Summary/Keyword: Regression analysis model formula

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Analysis of Chlorophyll Reflectance and Assessment of Trophic State for Daecheong Reservoir Using Remote Sensing (클로로필의 반사특성 분석과 원격탐측을 이용한 대청호의 영양상태 평가)

  • Kim, Tae-Geun;Kim, Tae-Seung;Cho, Gi-Sung;Kim, Hwan-Gi
    • Journal of Korean Society for Geospatial Information Science
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    • v.4 no.2 s.8
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    • pp.35-45
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    • 1996
  • The reflectance of chlorophyll was measured using UV-VIS spectrophotometer with the reflectance integrator in the laboratory in order to define its spectral characteristics. Sharp peaks appear at around 485nm and 655nm due to fluorescence and scattering, and the reflectance of chlorophyll increases at 580nm. With the increase in the chlorophyll concentration, the reflectance also increases. We have applied TM data to the reflectance spectrum of chlorophyll and have developed two formula with which one can estimate the chlorophyll concetration. Satellite re sensing, with its synoptic overage, is used to obtain the chlorophyll concentration in Daecheong reservoir. The approach involved acquisition of water quality samples front boat simultaneous with Landsat 5 satellite overpass. The remotely-sensed data and the ground truth data were obtained oil 20 June 1995 and on 18 March 1996. Regression models have been developed between the chlorophyll concentration and Landsat Thematic Mapper digital data. As the regression model was determined based on the correlation coefficient which was higher than 0.7 and the spectral characteristics of chlorophyll, and we have applied it to the entire study area to genelate a distribution map of trophic state. According to the trophic state map made based upon Aizaki's TSI and chlorophyll a concentration, the area where Okchun stream was flowing into was shown to be polluted the most all over the Daechung reservoir by showing an eutrophic state in June 1995 and a mesotrophic state in March 1996.

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A Study of Anomaly Detection for ICT Infrastructure using Conditional Multimodal Autoencoder (ICT 인프라 이상탐지를 위한 조건부 멀티모달 오토인코더에 관한 연구)

  • Shin, Byungjin;Lee, Jonghoon;Han, Sangjin;Park, Choong-Shik
    • Journal of Intelligence and Information Systems
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    • v.27 no.3
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    • pp.57-73
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    • 2021
  • Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.

Runoff of an Small Urban Area Using DEM Accuracy Analysis (DEM의 정확도 분석에 의한 도시 소유역의 유출해석)

  • Park, Jin-Hyung;Lee, Kwan-Soo;Lee, Sam-No
    • Journal of the Korean Association of Geographic Information Studies
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    • v.7 no.1
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    • pp.28-38
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    • 2004
  • The purpose of this study is to analyze the urban hydrologic state by the use of GIS, resolution and interpolation. The determination coefficient($R^2$) and Regression Formula were derived from the contour of digital map for the accuracy, and DEM data was made by using TIN interpolation by the size of the grid. By using the observed DEM data, topographical factors were extracted from the small basin, size, the width of a basin and the slope, and were applied in the urban runoff model. Through the model, we tried to find out the most suitable runoff model in a small basin of Yosu-Munsu area. As a result of applying models to the drainage considered, the runoff hydrograph estimated by SWMM model was closer to the observed one than that estimated by ILLUDAS model. The difference between the runoff hydrograph by SWMM and the observed one is maximum error of 19%, minimum error of 5% and average error of 13%. The influence of duration in contrast to pick time is insignificant in a urban small basin. As a conclusion of this study, SWMM model was more suitable and applicable for the urban runoff model than ILLUDAS model due to its accuracy and various abilities.

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Clinical and Physical Characteristics That Affect Apnea-Hypopnea Index in Suspected Obstructive Sleep Apnea Patients : The Preliminary Study (폐쇄성수면무호흡증 의심환자에서 무호흡저호흡지수에 영향을 주는 임상적 신체적 요인 : 예비연구)

  • Kang, Seung-Gul;Shin, Seung-Heon;Lee, Yu Jin;Jung, Joo Hyun;Kang, Il Gyu;Park, Insook;Kim, Peter Chanwoo;Ye, Mi Kyung;Hwang, Hee Young;Kim, Seon Tae;Park, Kee Hyung;Kim, Ji-Eun
    • Korean Journal of Biological Psychiatry
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    • v.20 no.2
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    • pp.54-60
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    • 2013
  • Objectives The purpose of this study is to find the influential clinical and physical characteristics which affect apnea-hypopnea index (AHI) in suspected obstructive sleep apnea (OSA) patients. Methods We evaluated the comprehensive factors including sleep related symptoms, clinical scales, medical history, substance use, and anthropometric data of the 119 participants who complained of the symptoms of OSA. All the participants underwent attended-full night laboratory polysomnography. The correlation and multiple regression analysis were conducted to find the influential and predictive factors of AHI. Results A multiple linear regression model 1 showed that higher AHI was associated with higher body mass index (BMI)(p < 0.001) and higher frequency of observed apnea (p = 0.002). In multiple linear regression model 2, AHI was associated with higher BMI (p < 0.001) and loudness of snoring (p = 0.018). Conclusions The present preliminary results suggest that BMI and observed apnea are most influential factors that affect AHI in suspected OSA patients. In the future study we will design the prediction formula for the OSA and AHI, which is useful in the clinical medical field.

Speed-Power Performance Analysis of an Existing 8,600 TEU Container Ship using SPA(Ship Performance Analysis) Program and Discussion on Wind-Resistance Coefficients

  • Shin, Myung-Soo;Ki, Min Suk;Park, Beom Jin;Lee, Gyeong Joong;Lee, Yeong Yeon;Kim, Yeongseon;Lee, Sang Bong
    • Journal of Ocean Engineering and Technology
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    • v.34 no.5
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    • pp.294-303
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    • 2020
  • This study discusses data collection, calculation of wind and wave-induced resistance, and speed-power analysis of an 8,600 TEU container ship. Data acquisition system of the ship operator was improved to obtain the data necessary for the analysis, which was accomplished using SPA (Ship Performance Analysis, Park et al., 2019) in conformation with ISO15016:2015. From a previous operation profile of the container, the standard operating conditions of mean draft were 12.5 m and 13.6 m, which were defined with the mean stowage configuration of each condition. Model tests, including the load-variation test, were conducted to validate new ship performance and for the speed-power analysis. The major part of the added resistance of container ship is due to the wind. To check the reliability of wind-resistance calculation results, the resistance coefficients, added resistance, and speed-power analysis results using the Fujiwara regression formula (ISO15016:2015) and Computational fluid dynamics (Ryu et al., 2016; Jeon et al., 2017) analysis were compared. Wind speed and direction measured using an anemometer were used for wind-resistance calculation and the wave resistance was calculated using the wave-height and direction-data from weather information. Also, measured water temperature was used to calculate the increase in resistance owing to the deviation in water density. As a result, the SPA analysis using measured data and weather information was proved to be valid and able to identify the ship's resistance propulsion performance. Even with little difference in the air-resistance coefficient value, both methods provide sufficient accuracy for speed-power analysis. The differences were unnoticeable when the speed-power analysis results using each method were compared. Also, speed-power analysis results of the 8,600 TEU container ship in two draft conditions show acceptable trends when compared with the model test results and are also able to show power increase owing to hull fouling and aging. Thus, results of speed-power analysis of the existing 8,600 TEU container ship using the SPA program appropriately exhibit the characteristics of speed-power performance in deal conditions.

Monitoring Bacillus cereus and Aerobic Bacteria in Raw Infant Formula and Microbial Quality Control during Manufacturing (영.유아용 식품원료의 Bacillus cereus와 일반세균 모니터링 및 제조공정 중 미생물 품질제어)

  • Jung, Woo-Young;Eom, Joon-Ho;Kim, Byeong-Jo;Ju, In-Sun;Kim, Chang-Soo;Kim, Mi-Ra;Byun, Jung-A;Park, You-Gyoung;Son, Sang-Hyuck;Lee, Eun-Mi;Jung, Rae-Seok;Na, Mi-Ae;Yuk, Dong-Yeon;Gang, Ji-Yeon;Heo, Ok-Sun;Yoon, Min-Ho
    • Korean Journal of Food Science and Technology
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    • v.42 no.4
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    • pp.494-501
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    • 2010
  • The purpose of this study was to examine the presence of Bacillus cereus, aerobic bacteria and coliforms in the raw material of infant formulas and investigate the manufacturing process in terms of microbial safety. Among ten kinds of raw infant formula material samples (n=20), Bacillus cereus appeared in two (n=4). Aerobic bacteria were not detected in raw infant formula material or maximum 4.15 log CFU/g. Eleven species of aerobic bacteria were isolated and 76% of them were Sphingomonas paucimobilis, Pseudomonas fluorescens, Rhizobium radiobactor, or Stenotrophomonas maltophilia. A Pearson's correlation analysis revealed that the most influential factors for detecting Bacillus cereus were aerobic bacteria and coliforms. In other words, when the measured values of aerobic bacteria and coliforms were higher, the possibility that Bacillus cereus would appear increased. In a regression model to predict Bacillus cereus, the rate of appearance was correlated with aerobic bacteria and coliforms, and its contribution rate for effectiveness was 86%. Improving microbial quality control by pasteurization, spray dry, popping and extrusion resulted in a decrease in the numbers of Bacillus cereus, aerobic bacteria and coliforms in the raw materials. The results suggest that a hazard analysis and critical control point system might be effective for reducing microbiological contamination.

Multidimensional Optimization Model of Music Recommender Systems (음악추천시스템의 다차원 최적화 모형)

  • Park, Kyong-Su;Moon, Nam-Me
    • The KIPS Transactions:PartB
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    • v.19B no.3
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    • pp.155-164
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    • 2012
  • This study aims to identify the multidimensional variables and sub-variables and study their relative weight in music recommender systems when maximizing the rating function R. To undertake the task, a optimization formula and variables for a research model were derived from the review of prior works on recommender systems, which were then used to establish the research model for an empirical test. With the research model and the actual log data of real customers obtained from an on line music provider in Korea, multiple regression analysis was conducted to induce the optimal correlation of variables in the multidimensional model. The results showed that the correlation value against the rating function R for Items was highest, followed by Social Relations, Users and Contexts. Among sub-variables, popular music from Social Relations, genre, latest music and favourite artist from Items were high in the correlation with the rating function R. Meantime, the derived multidimensional recommender systems revealed that in a comparative analysis, it outperformed two dimensions(Users, Items) and three dimensions(Users, Items and Contexts, or Users, items and Social Relations) based recommender systems in terms of adjusted $R^2$ and the correlation of all variables against the values of the rating function R.

A Study on the Development of a Simulation Model for Predicting Soil Moisture Content and Scheduling Irrigation (토양수분함량 예측 및 계획관개 모의 모형 개발에 관한 연구(I))

  • 김철회;고재군
    • Magazine of the Korean Society of Agricultural Engineers
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    • v.19 no.1
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    • pp.4279-4295
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    • 1977
  • Two types of model were established in order to product the soil moisture content by which information on irrigation could be obtained. Model-I was to represent the soil moisture depletion and was established based on the concept of water balance in a given soil profile. Model-II was a mathematical model derived from the analysis of soil moisture variation curves which were drawn from the observed data. In establishing the Model-I, the method and procedure to estimate parameters for the determination of the variables such as evapotranspirations, effective rainfalls, and drainage amounts were discussed. Empirical equations representing soil moisture variation curves were derived from the observed data as the Model-II. The procedure for forecasting timing and amounts of irrigation under the given soil moisture content was discussed. The established models were checked by comparing the observed data with those predicted by the model. Obtained results are summarized as follows: 1. As a water balance model of a given soil profile, the soil moisture depletion D, could be represented as the equation(2). 2. Among the various empirical formulae for potential evapotranspiration (Etp), Penman's formula was best fit to the data observed with the evaporation pans and tanks in Suweon area. High degree of positive correlation between Penman's predicted data and observed data with a large evaporation pan was confirmed. and the regression enquation was Y=0.7436X+17.2918, where Y represents evaporation rate from large evaporation pan, in mm/10days, and X represents potential evapotranspiration rate estimated by use of Penman's formula. 3. Evapotranspiration, Et, could be estimated from the potential evapotranspiration, Etp, by introducing the consumptive use coefficient, Kc, which was repre sensed by the following relationship: Kc=Kco$.$Ka+Ks‥‥‥(Eq. 6) where Kco : crop coefficient Ka : coefficient depending on the soil moisture content Ks : correction coefficient a. Crop coefficient. Kco. Crop coefficients of barley, bean, and wheat for each growth stage were found to be dependent on the crop. b. Coefficient depending on the soil moisture content, Ka. The values of Ka for clay loam, sandy loam, and loamy sand revealed a similar tendency to those of Pierce type. c. Correction coefficent, Ks. Following relationships were established to estimate Ks values: Ks=Kc-Kco$.$Ka, where Ks=0 if Kc,=Kco$.$K0$\geq$1.0, otherwise Ks=1-Kco$.$Ka 4. Effective rainfall, Re, was estimated by using following relationships : Re=D, if R-D$\geq$0, otherwise, Re=R 5. The difference between rainfall, R, and the soil moisture depletion D, was taken as drainage amount, Wd. {{{{D= SUM from { {i }=1} to n (Et-Re-I+Wd)}}}} if Wd=0, otherwise, {{{{D= SUM from { {i }=tf} to n (Et-Re-I+Wd)}}}} where tf=2∼3 days. 6. The curves and their corresponding empirical equations for the variation of soil moisture depending on the soil types, soil depths are shown on Fig. 8 (a,b.c,d). The general mathematical model on soil moisture variation depending on seasons, weather, and soil types were as follow: {{{{SMC= SUM ( { C}_{i }Exp( { - lambda }_{i } { t}_{i } )+ { Re}_{i } - { Excess}_{i } )}}}} where SMC : soil moisture content C : constant depending on an initial soil moisture content $\lambda$ : constant depending on season t : time Re : effective rainfall Excess : drainage and excess soil moisture other than drainage. The values of $\lambda$ are shown on Table 1. 7. The timing and amount of irrigation could be predicted by the equation (9-a) and (9-b,c), respectively. 8. Under the given conditions, the model for scheduling irrigation was completed. Fig. 9 show computer flow charts of the model. a. To estimate a potential evapotranspiration, Penman's equation was used if a complete observed meteorological data were available, and Jensen-Haise's equation was used if a forecasted meteorological data were available, However none of the observed or forecasted data were available, the equation (15) was used. b. As an input time data, a crop carlender was used, which was made based on the time when the growth stage of the crop shows it's maximum effective leaf coverage. 9. For the purpose of validation of the models, observed data of soil moiture content under various conditions from May, 1975 to July, 1975 were compared to the data predicted by Model-I and Model-II. Model-I shows the relative error of 4.6 to 14.3 percent which is an acceptable range of error in view of engineering purpose. Model-II shows 3 to 16.7 percent of relative error which is a little larger than the one from the Model-I. 10. Comparing two models, the followings are concluded: Model-I established on the theoretical background can predict with a satisfiable reliability far practical use provided that forecasted meteorological data are available. On the other hand, Model-II was superior to Model-I in it's simplicity, but it needs long period and wide scope of observed data to predict acceptable soil moisture content. Further studies are needed on the Model-II to make it acceptable in practical use.

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Relationship between diet quality and sarcopenia in elderly Koreans: 2008-2011 Korea National Health and Nutrition Examination Survey

  • Na, Woori;Kim, Jiyu;Chung, Bong Hee;Jang, Dai-Ja;Sohn, Cheongmin
    • Nutrition Research and Practice
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    • v.14 no.4
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    • pp.352-364
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    • 2020
  • BACKGROUND/OBJECTIVES: Given the increasing proportion of the Korean population that is aged 65 years and older, the present study analyzed the relationship between diet quality and sarcopenia in elderly persons by using data from the 2008-2011 Korea National Health and Nutrition Examination Survey (KNHANES). SUBJECTS/METHODS: Data for 3,373 persons aged 65 years and over (men: 1,455, 43.1%) were selected from the 2008-2011 KNHANES. Sarcopenia assessments are based on a formula that divides a subject's appendicular skeletal muscle mass (ASM) by their weight (wt) and multiplies that result by 100 ([ASM/wt] × 100). Sarcopenia is present if the subject's result was less than one standard deviation (SD) below the sex-specific mean for a young reference group. For evaluation of diet quality, data obtained via the 24-hour recall method were used to calculate the Diet Quality Index for Koreans (DQI-K). A general linear model was applied in order to analyze general information and nutritional intake according to sarcopenia status. For analysis of the relationship between diet quality and sarcopenia, a binominal logistic regression analysis was undertaken. RESULTS: The sarcopenia prevalence rate among the study subjects aged 65 years and over was 37.6%. The DQI-K of those without sarcopenia was 3.33 ± 0.04 points, while that of those with sarcopenia was 3.45 ± 0.04 points (P < 0.05). The relationship between diet quality and sarcopenia revealed that subjects aged 75 and older had a poor diet quality, and their odds ratio (OR) of sarcopenia presence was significantly higher (OR: 1.807, 95% confidence interval: 1.003-3.254, P < 0.05). CONCLUSIONS: This study revealed that poor diet quality was related to sarcopenia presence in Koreans aged 75 and older. In order to improve the diet quality of the elderly (aged 75 and older), it is necessary to develop dietary improvement guidelines.

A Study of Job Analysis Method using Information Systems (정보체계를 활용한 직무분석 방안 연구)

  • Hwang, Ho-ryang
    • KIISE Transactions on Computing Practices
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    • v.22 no.10
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    • pp.521-531
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    • 2016
  • In this paper, since most business process of D-agency is being performed through some information systems, including Onnara System is a government standard operating management system, computerized accumulated in the system documentation based on, even if there is no independent job analysis system, in a judgment that can be can be tissue diagnosis, it presented a job analysis plan that leverages the existing information system. Most material is passed online in business processing between departments and between colleagues, it is returned. In situations where most information systems for such business processing is built developed, grasp the work procedures and information systems D-agency data accumulated to derive the necessary elements for job analysis quantified, and verified the validity of the element in the regression statistics.In addition, classification system (BRM, Business Reference Model) of the existing functionality that is available only Onnara System, and to establish a job analysis architecture to be able to function diagnostic departments to leverage common also in other information systems, related implement illustrating additional features of the information system, to derive a department duties value calculation formula with it, and present various job analysis plan that can actually be utilized to diagnose and derived elements department appropriate personnel.