• Journal of radiological science and technology
• /
• v.40 no.1
• /
• pp.35-40
• /
• 2017

The purpose of this study was to investigate the relationship between abdominal subcutaneous fat thickness(ASFT) and maternal gestational diabetes mellitus(GDM) measured by ultrasound at period of pregnancy. We compared maternal age, pre-pregnancy body mass index, and weight gain during pregnancy in 286 pregnant women who were diagnosed with early pregnancy ASFT and high GDM screening test(50 g OGTT) of more than 140 mg/dL. ROC curve analysis was used to determine the cut-off value of ASFT for GDM prediction. Maternal age and weight gain during pregnancy were not related to GDM in the mid-trimester and pre-pregnancy body mass index and earely pregnancy ASFT were significantly different between normal and GDM high risk groups. The cut-off value of ASFT for GDM prediction was 2.23 cm(AUC 0.913. Sensitivity 76.19%, Specificity 93.72%). ASFT measured by ultrasound in early pregnancy was useful as an important index for predicting mid-trimester GDM prediction. Therefore, ASFT can be used as an auxiliary diagnostic index for early recognition of GDM.

• Journal of Korean Society of Forest Science
• /
• v.96 no.2
• /
• pp.170-177
• /
• 2007

The objectives of this study were to select early, late, and abundant flowering trees of black locust from domesticated and introduced cultivars, and prediction of flowering period by calculation of accumulated temperature in spring. Four cultivars (Debreceni-2, Pusztavacs, Jaszkiseri, and Rozsaszin AC) from Hungary and a cultivar from Beijing, China, were introduced, propagated by seed and planted in a seed orchard. For domesticated black locust, 63 cultivars from 10 locations throughout the country were selected and propagated by root cutting. Criteria for selection of domesticated cultivars were abundant flowering, long flowering period, or abundant nectar production with, if possible, straight stems. Accumulated temperature was calculated from data of a nearby weather station by accumulating daily maximum temperature minus 5 degree Celsius from January 1 up to the date reaching 880 degrees. Daily mean temperature was also used to calculate accumulated temperature up to the date reaching 450 degrees. The percentages of two-year and three-year-old flowering trees propagated by root cutting were higher than that of trees propagated by seeds, while four-year-old trees all flowered regardless of propagation methods. Among the domesticated cultivars, all the cultivars from Ganghwa showed abundant flowering with highest nectar production of 6.5 ul per flower, which was 100% more than other domesticated cultivars and 50% more than Debreceni-2 cultivar with highest nectar production among the introduced cultivars from Hungary. At the end of the eight years of observations, two trees of Debreceni-2 cultivars and a tree from Beijing, China were selected for early flowering trees which flowered 2 to 3 days earlier than average trees, while a tree of Debeceni-2 and three trees from Bejing were selected for late flowering trees which flowered 2 to 3 days later than average trees. It is possible to extend the flowering period of black locust by 4 to 6 days by planting early and late flowering cultivars together. Abundant flowering trees were unable to be selected due to severe damages by leaf gall midges which killed many trees and reduced the crown size of the remaining trees in the seed orchard, and which were first found in Korea in 2001 and now damaging most of the black locust forests in Korea. The prediction of flowering period by accumulated temperature indicated that black locust flowered to a peak when accumulated daily maximum temperature reached 880 degrees Celsius, and when daily mean temperature reached 450 degrees.

• Korean Journal of Construction Engineering and Management
• /
• v.24 no.5
• /
• pp.22-34
• /
• 2023

Predicting accurately the construction cost budget in the early stages of construction projects is crucial to support the client's decision-making and achieve the objectives of the construction project. This holds true for public construction projects as well. However, the current methods for predicting construction cost budgets in the early stages of public construction projects are not sophisticated enough in terms of accuracy and reliability, indicating a need for improvement. The objective of this study is to develop a construction cost budget prediction model that can be utilized in the early stages of public building projects using an artificial neural network (ANN). In this study, an artificial neural network model was developed using the SPSS Statistics program and the data provided by the Public Procurement Service. The level of construction cost budget prediction was analyzed, and the accuracy of the model was validated through additional testing. The validation results demonstrated that the developed artificial neural network model exhibited an error range for estimates that can be utilized in the early stages of projects, indicating the potential to predict construction cost budgets more accurately by incorporating various project conditions.

• Proceedings of the Korean Society for Agricultural Machinery Conference
• /
• 2017.04a
• /
• pp.42-42
• /
• 2017

Prediction of rice yield during a growing season would be very helpful to magnify rice yield as it also allows better farm practices to maximize yield with greater profit and lesser costs. UAV imagery based automatic detection of rice can be a relevant solution for early prediction of yield. So, we propose an image processing technique to predict rice yield using low altitude UAV images. We proposed $L^*a^*b^*$ color space based image segmentation algorithm. All images were captured using UAV mounted RGB camera. The proposed algorithm was developed to find out rice grain area from the image background. We took RGB image and applied filter to remove noise and converted RGB image to $L^*a^*b^*$ color space. All color information contain in both $a^*$ and $b^*$ layers and by using k-mean clustering classification of these colors were executed. Variation between two colors can be measured and labelling of pixels was completed by cluster index. Image was finally segmented using color. The proposed method showed that rice grain could be segmented and we can recognize rice grains from the UAV images. We can analyze grain areas and by estimating area and volume we could predict rice yield.

• Magazine of the Korea Concrete Institute
• /
• v.10 no.6
• /
• pp.191-202
• /
• 1998

The maturity method in which the strength increase of cement concrete is expressed as a function of an intergral of the curing period and temperature of the concrete has often been applied to its strength prediction. For the purpose of the application of the maturity method to the compressive strength prediction for lightweight polymer mortars using an unsaturated polyester resin as a binder, the lightweight polymer mortars with various catalyst and accelerator contents, are prepared. tested for compressive strength, and the datum temperatures for the maturity equations are estimated. The maturity is calculated by using the maturity equations with the estimated datum temperature. The compressive strengths of the lighweight polymer mortars are predicted from the maturity-compressive strength relationships.

• Journal of the Korean Solar Energy Society
• /
• v.36 no.3
• /
• pp.33-43
• /
• 2016

Daylight is an important factor which influences building energy efficiency and visual comfort for occupants. It is important to predict precise sky luminance at the early stages of design to reduce light energy in the building. This study predicted sky luminance distributions of standard sky model(CIE overcast sky, CIE clear sky) that was provided from the CIE(Commission internationale de $l^{\prime}{\acute{e}}clairage$). Afterward, result of sky luminance was compared and verified with simulation value of Radiance program. From the CIE overcast sky, zenith and horizon ratio is about 3:1. From the CIE clear sky, luminance value gets most high value around the sun. On the other hand, luminance value is the lowest in the opposite direction of the sun when angle is $90^{\circ}$ between the sun and sky element. As a result of comparing the calculation results with Radiance program, sky luminance prediction error rate is 0.4~1.3% when it is CIE overcast sky. Also, sky luminance prediction error rate is 0.3~1.5% when it is CIE clear sky. When compared with the results of radiance simulation, it was evaluated as fairly accurate.

• 한국방재학회:학술대회논문집
• /
• 2008.02a
• /
• pp.369-372
• /
• 2008

Recently, landslides have frequently occurred on natural slopes during periods of intense rainfall. With a rapidly increasing population on or near steep terrain in Korea, landslides have become one of the most significant natural hazards. Thus, it is necessary to protect people from landslides and to minimize the damage of houses, roads and other facilities. To accomplish this goal, many landslide prediction methods have been developed in the world. In this study, a simple landslide prediction system that enables people to escape the endangered area is introduced. The system is focused to debris flows which happen frequently during periods of intense rainfall. The system is based on the wireless sensor network (WSN) that is composed of sensor nodes, gateway, and server system. Sensor nodes comprising a sensing part and a communication part are developed to detect ground movement. Sensing part is designed to measure inclination angle and acceleration accurately, and communication part is deployed with Bluetooth (IEEE 802.15.1) module to transmit the data to the gateway. To verify the feasibility of this landslide prediction system, a series of experimental studies was performed at a small-scale earth slope equipped with an artificial rainfall dropping device. It is found that sensing nodes installed at slope can detect the ground motion when the slope starts to move. It is expected that the landslide prediction system by wireless senor network can provide early warnings when landslides such as debris flow occurs.

• International Journal of Computer Science & Network Security
• /
• v.24 no.2
• /
• pp.196-202
• /
• 2024

Currently, the second most devastating form of cancer in people, particularly in women, is Breast Cancer (BC). In the healthcare industry, Machine Learning (ML) is commonly employed in fatal disease prediction. Due to breast cancer's favorable prognosis at an early stage, a model is created to utilize the Dataset on Wisconsin Diagnostic Breast Cancer (WDBC). Conversely, this model's overarching axiom is to compare the effectiveness of five well-known ML classifiers, including Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN), and Naive Bayes (NB) with the conventional method. To counterbalance the effect with conventional methods, the overarching tactic we utilized was hyperparameter tuning utilizing the grid search method, which improved accuracy, secondary precision, third recall, and finally the F1 score. In this study hyperparameter tuning model, the rate of accuracy increased from 94.15% to 98.83% whereas the accuracy of the conventional method increased from 93.56% to 97.08%. According to this investigation, KNN outperformed all other classifiers in terms of accuracy, achieving a score of 98.83%. In conclusion, our study shows that KNN works well with the hyper-tuning method. These analyses show that this study prediction approach is useful in prognosticating women with breast cancer with a viable performance and more accurate findings when compared to the conventional approach.

• The Journal of Korean Association of Computer Education
• /
• v.6 no.2
• /
• pp.33-40
• /
• 2003

Criticality prediction models that determine whether a design entity is fault-prone or non fault-prone play an important role in reducing system development costs because the problems in early phases largely affect the quality of the late products. Real-time systems such as telecommunication systems are so large that criticality prediction is mere important in real-time system design. The current models are based on the technique such as discriminant analysis, neural net and classification trees. These models have some problems with analyzing causes of the prediction results and low extendability. This paper builds a new prediction model, GAM, based on Genetic Algorithm. GAM is different from other models because it produces a criticality function. So GAM can be used for comparison between entities by criticality. GAM is implemented and compared with a well-known prediction model, BackPropagation neural network Model(BPM), considering Internal characteristics and accuracy of prediction.

• Journal of KIISE:Software and Applications
• /
• v.34 no.7
• /
• pp.595-602
• /
• 2007

This paper proposes a method that discriminates signal peptide and predicts the cleavage site of the secretory proteins cleaved by the signal peptidase I. The preprocessing stage uses hydrophobicity scales of amino acids in order to predict the presence of signal sequence and the cleavage site. The preprocessing enhances the performance of the prediction method by eliminating the non-secretory proteins in the early stage of prediction. for the effective use of support vector machine for the signal sequence prediction, the biologically relevant distance between the amino acid sequences is defined by using the hydrophobicity and substitution matrix; the hydrophobicity can be used to Predict the location of amino acid in a cell and the substitution matrix represents the evolutionary relationships of amino acids. The proposed method showed 98.9% discrimination rates from signal sequences and 88% correct rate of the cleavage site prediction on Swiss-Prot release 50 protein database using the 5-fold-cross-validation. In the comparison tests, the proposed method has performed significantly better than other prediction methods.