• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.6
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• pp.219-225
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• 2019

There is a problem with the existing method of selecting the difficulty levels of Hanja characters. Some Hanja characters selected by the existing methods are different from Sino-Korean words used in real life and it is impossible to know how many times the Hanja characters are used. To solve this problem, we measure the difficulty of Hanja characters using the multiple regression analysis with the frequency as the features. Based on the elementary textbooks, FWS and FHU are counted. A questionnaire is written using the two frequencies and stroke together to answer the appropriate timing of learning the Hanja characters and use them as target variables for regression. Use stepwise regression to select the appropriate features and perform multiple linear regression. The R² score of the model was 0.1105 and the RMSE was 0.1105.

• Journal of the Korean Society of Clothing and Textiles
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• v.25 no.9
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• pp.1593-1602
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• 2001

본 연구는 스포츠 아우터웨어용 나일론 직물의 소리에 대한 주관적 감각과 이에 관련된 객관적 측정치를 규명하기 위하여, 서로 다른 8종의 나일론 직물의 소리의 스펙트럼 파형을 고찰하였으며, 소리 파라미터로 총음압(level pressure of total sound, LPT),세 가지 AR (autoregressive)계수, Zwicker의 심리음향학적 모델에 따른 크기(Z)와 날카로움(Z)를 계산하였고, Kawabata Evaluation System(KES)으로 직물의 물리적 성질을 측정하였다. 주관적 감각 평가를 위하여 피험자에게 녹음된 각 직물소리를 들려주어 7개 소리 감각 (부드러움, 시끄러움, 날카로움, 맑음, 거 침, 높음, 유쾌함)을 의미분별척도로 답하게 한 후, 단계적 선형 회귀식을 이용하여 직물 소리의 주관적 감각에 대한 예측 모델을 제시하였다. 울트라스웨이드를 제외한 태피터 나일론 직물들은 스펙트럼 파형 에서 다른 조성 섬유의 직물들보다 음압 값이 높고, 총음압이 60dB 안팎의 값을 보여, 착용자에게 불쾌감을 줄 것으로 예상되었으며, 주관적 감각 평가에서도 소리의 부드러움과 맑음, 유쾌함에서 음의 점수를, 시끄러움과 날카로움, 거침, 높음에서 양의 점수를 얻었다. 주관적 감각의 예측모델에서 총음압은 시끄러움과 거침에 정적 영향을, 유쾌함에 부적 영향을 미쳐서 나일론 직물 소리의 총음압이 50dB 이하일 때 주관적으로 유쾌하게 느껴지는 것으로 나타났다.

• Journal of Preventive Medicine and Public Health
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• v.30 no.4 s.59
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• pp.729-740
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• 1997

To determine the relationship between hypertension and nutrient intake cross-sectional study were performed in a rural area. Adult resident over 30-year-old age were measured blood pressure and body mass index(BMI), and interviewed about food in-take for the previous 24 hours. 250 men and 297 women participated the survey. Significant correlation was showen in men between mean systolic blood pressure and protein density. Significant correlation with mean diastolic blood pressure was showen on protein density, protein energy(%), calcium density and energy-adjusted protein in men. We analysed risk factor for hypertension adjust the effect of age, BMI, sex and family history by multiple logistic regression. Protein density(odds ratio=3.18), fat density(odds ratio=1.94) and energy-adjusted protein(odds ratio=1.01) intake were positively associated with hypertension but sodium density(odds ratio=0.73) was showen to have inverse relationship.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.121-131
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• 2014

Traffic speed is an important measure in transportation. It can be employed for various purposes, including traffic congestion detection, travel time estimation, and road design. Consequently, accurate speed prediction is essential in the development of intelligent transportation systems. In this paper, we present an analysis and speed prediction of a certain road section in Busan, South Korea. In previous works, only historical data of the target link are used for prediction. Here, we extract features from real traffic data by considering the neighboring links. After obtaining the candidate features, linear regression, model tree, and k-nearest neighbor (k-NN) are employed for both feature selection and speed prediction. The experiment results show that k-NN outperforms model tree and linear regression for the given dataset. Compared to the other predictors, k-NN significantly reduces the error measures that we use, including mean absolute percentage error (MAPE) and root mean square error (RMSE).

• Advanced Industrial SCIence
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• v.1 no.1
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• pp.7-15
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• 2022

To adopt the development in the medical scenario IoT developed towards the advancement with the processing of a large amount of medical data defined as an Internet of Medical Things (IoMT). The vast range of collected medical data is stored in the cloud in the structured manner to process the collected healthcare data. However, it is difficult to handle the huge volume of the healthcare data so it is necessary to develop an appropriate scheme for the healthcare structured data. In this paper, a machine learning mode for processing the structured heath care data collected from the IoMT is suggested. To process the vast range of healthcare data, this paper proposed an MTGPLSTM model for the processing of the medical data. The proposed model integrates the linear regression model for the processing of healthcare information. With the developed model outlier model is implemented based on the FinTech model for the evaluation and prediction of the COVID-19 healthcare dataset collected from the IoMT. The proposed MTGPLSTM model comprises of the regression model to predict and evaluate the planning scheme for the prevention of the infection spreading. The developed model performance is evaluated based on the consideration of the different classifiers such as LR, SVR, RFR, LSTM and the proposed MTGPLSTM model and the different size of data as 1GB, 2GB and 3GB is mainly concerned. The comparative analysis expressed that the proposed MTGPLSTM model achieves ~4% reduced MAPE and RMSE value for the worldwide data; in case of china minimal MAPE value of 0.97 is achieved which is ~ 6% minimal than the existing classifier leads.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.127-142
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• 2023

The management of size and weight, which are the growth information of aquaculture fish in fish-farms, is the most basic goal. In this study, the epoch is defined in fish-farms from the time of stocking or dividing to the time of shipment, and the growth data for a total of three epoch is analyzed from a time series perspective. Growth information such as the size and weight of aquaculture fish that occur over time in fish-farms is compared and analyzed with water quality environmental information and feeding information, and a model is presented using the analysis results. In this study, linear, exponential, and logarithmic regression models are presented using the Box-Jenkins method for size and weight by epoch using data obtained in the field.

• Journal of the Korea Convergence Society
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• v.9 no.6
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• pp.45-52
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• 2018

In this paper, we propose a linear interpolation method based on patch information generated from a low - resolution image for generating a super resolution image in a single image. Using the regression model of the global space, which is a conventional super resolution generation method, results in poor quality in general because of lack of information to be referred to a specific region. In order to compensate for these results, we propose a method to extract meaningful information by dividing the region into patches in the process of super resolution image generation, analyze the constituents of the image matrix region extended for super resolution image generation, We propose a method of linear interpolation based on optimal patch information that is searched by correlating patch information based on the information gathered before the interpolation process. For the experiment, the original image was compared with the reconstructed image with PSNR and SSIM.

• Journal of Biomedical Engineering Research
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• v.16 no.2
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• pp.223-230
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• 1995

This study investigates a combined mathematical model for the quantitative estimation of regional myocardial blood flow in experimental canine coronary artery occlusion and in patients with ischemic myocardial diseases using Rb-82 dynamic myocardial positron emission tomography. The coronary thrombosis was induced using the new catheter technique by narrowing the lumen of coronary vessel gradually, which finally led to partial obstruction of coronary artery. Thirty four Rb-82 dynamic myocardial PET scans were performed sequentially for each experiment using our 5, 10 and 20 second acquisition protocol, respectively, and six to seven regions of interest were drawn on each transaxial slices, one on left ventricular chamber for input function and the others on normal and decreased perfusion myocardial segments for the flow estimation in those regions. Two compartment model and graphical analysis method have been applied to the measured sets of regional PET data, and the rate constants of influx to myocardial tissue were calculated for regional myocardial flow estimates with the two parameter fits of raw data by the Levenberg-Marquardt method. The results showed that, (I) two compartment model suggested by Kety-Schmidt, with proper modification of the measured data and volume of distribution, could be used for the simple estimation of regional myocardial blood flow, (2) the calculated regional myocardial blood flow estimates were dependent on the selection of input function, which reflected partial volume effect and left ventricular wall motion in previously used graphical analysis, and (3) mathematically fitted input and tissue time activity curves were more suitable than the direct application of the measured data in terms of convergence.

• Journal of the Korea Society of Computer and Information
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• v.11 no.3
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• pp.281-289
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• 2006

Even though software developing environment has been changing to Web basis very fast, there are just few studies of quality metric or estimation model for Web software. In this study after analyzing the correlation between the risk level and property of objects using linear regression, six middle sized industrial system has been used to propose the correlation model of size and Number of Classes(NOC), size and Number of Methods(NOM), complexity and NOC, and complexity and NOM. Among of six systems 5 systems(except S06) have high correlation between size(LOC) and NOM, and four systems(except S04 & S06) have high correlation between complexity and NOC / NOM. As Web software architecture with three sides of Server, Client and HTML, complexity of each sides has been compared, two system(S04, S06) has big differences of each sides compleity values and one system(S06) has very higher complexity value of HTML, So the risk level could be estimated through NOM to improve maintenance in case of that the system has no big differences of each sides complexity.

• Journal of the Korean Geosynthetics Society
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• v.21 no.2
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• pp.21-30
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• 2022

Shear strength is the most important indicator in the evaluation of rock slope stability. It is generally estimated by comparing the results of existing literature data, back analysis, experiments and etc. There are additional variables related to the state of discontinuity to consider in the shear strength of the rock slope. It is difficult to determine whether these variables exist through drilling, and it is also difficult to find an exact relationship with shear strength. In this study, the data calculated through back analysis were used. The relationship between previously considered variables was applied to deep learning and the possibility for estimating shear strength of rock slope was explored. For comparison, an existing simple linear regression model and a deep learning algorithm, a deep neural network(DNN) model, were used. Although each analysis model derived similar prediction results, the explanatory power of DNN was improved with a small differences.