• Journal of the Korean Institute of Gas
• /
• v.16 no.1
• /
• pp.26-32
• /
• 2012

The autoignition temperature is one of the most important physical properties used to determine the flammability characteristics of chemical substances. Despite the needs of the experimental autoignition temperature data for the design of chemical plants, it is not easy to get the data. This study have built and compared partial least squares (PLS) and support vector machine (SVM) models to predict the autoignition temperatures of 503 organic compounds out of DIPPR 801. As the independent variables of the models, 59 functional groups were chosen based on the group contribution method. The prediction errors calculated from cross-validation were employed to determine the optimal parameters of two models. And, particle swarm optimization was used to get three parameters of SVM model. The PLS and SVM results of the average absolute errors for the whole data range from 58.59K and 29.11K, respectively, indicating that the predictive ability of the SVM is much superior than PLS.

• Investigative Magnetic Resonance Imaging
• /
• v.24 no.3
• /
• pp.123-131
• /
• 2020

Purpose: Joint label fusion (JLF) is a popular multi-atlas-based segmentation algorithm, which compensates for dependent errors that may exist between atlases. However, in order to get good segmentation results, it is very important to set the several free parameters of the algorithm to optimal values. In this study, we first investigate the feasibility of a JLF algorithm for prostate segmentation in MR images, and then suggest the optimal set of parameters for the automatic prostate segmentation by validating the results of each parameter combination. Materials and Methods: We acquired T2-weighted prostate MR images from 20 normal heathy volunteers and did a series of cross validations for every set of parameters of JLF. In each case, the atlases were rigidly registered for the target image. Then, we calculated their voting weights for label fusion from each combination of JLF's parameters (r_pxy, r_pz, r_sxy, r_sz, β). We evaluated the segmentation performances by five validation metrics of the Prostate MR Image Segmentation challenge. Results: As the number of voxels participating in the voting weight calculation and the number of referenced atlases is increased, the overall segmentation performance is gradually improved. The JLF algorithm showed the best results for dice similarity coefficient, 0.8495 ± 0.0392; relative volume difference, 15.2353 ± 17.2350; absolute relative volume difference, 18.8710 ± 13.1546; 95% Hausdorff distance, 7.2366 ± 1.8502; and average boundary distance, 2.2107 ± 0.4972; in parameters of r_pxy = 10, r_pz = 1, r_sxy = 3, r_sz = 1, and β = 3. Conclusion: The evaluated results showed the feasibility of the JLF algorithm for automatic segmentation of prostate MRI. This empirical analysis of segmentation results by label fusion allows for the appropriate setting of parameters.

• KIISE Transactions on Computing Practices
• /
• v.22 no.12
• /
• pp.681-686
• /
• 2016

Software defect prediction is an important factor in efficient project management and success. The severity of the defect usually determines the degree to which the project is affected. However, existing studies focus only on the presence or absence of a defect and not the severity of defect. In this study, we proposed an ensemble model using FCM based on defect severity. The severity of the defect of NASA data set's PC4 was reclassified. To select the input column that affected the severity of the defect, we extracted the important defect factor of the data set using Random Forest (RF). We evaluated the performance of the model by changing the parameters in the 10-fold cross-validation. The evaluation results were as follows. First, defect severities were reclassified from 58, 40, 80 to 30, 20, 128. Second, BRANCH_COUNT was an important input column for the degree of severity in terms of accuracy and node impurities. Third, smaller tree number led to more variables for good performance.

• Asian-Australasian Journal of Animal Sciences
• /
• v.30 no.11
• /
• pp.1540-1549
• /
• 2017

Objective: Increasing food safety demands in the animal product market have created a need for a system to trace the food distribution process, from the manufacturer to the retailer, and genetic traceability is an effective method to trace the origin of animal products. In this study, we successfully achieved the farm tracing of 6,018 multi-breed pigs, using single nucleotide polymorphism (SNP) markers strictly selected through least absolute shrinkage and selection operator (LASSO) feature selection. Methods: We performed farm tracing of domesticated pig (Sus scrofa) from SNP markers and selected the most relevant features for accurate prediction. Considering multi-breed composition of our data, we performed feature selection using LASSO penalization on 4,002 SNPs that are shared between breeds, which also includes 179 SNPs with small between-breed difference. The 100 highest-scored features were extracted from iterative simulations and then evaluated using machine-leaning based classifiers. Results: We selected 1,341 SNPs from over 45,000 SNPs through iterative LASSO feature selection, to minimize between-breed differences. We subsequently selected 100 highest-scored SNPs from iterative scoring, and observed high statistical measures in classification of breeding farms by cross-validation only using these SNPs. Conclusion: The study represents a successful application of LASSO feature selection on multi-breed pig SNP data to trace the farm information, which provides a valuable method and possibility for further researches on genetic traceability.

• Journal of Nutrition and Health
• /
• v.49 no.4
• /
• pp.223-232
• /
• 2016

Purpose: We investigated the potential interaction between the G-protein beta-3 subunit gene (GNB3) C825T polymorphism, a risk factor for chronic disease in various ethnicities, and obesogenic environments in overweight/obese Korean children. Methods: The present study was conducted as a cross-sectional study using measures of anthropometry, blood pressure (BP), and fasting blood samples as well as 3-day food records. Subjects were recruited from seven elementary schools in an urban district in Seoul, South Korea, between 2007 and 2008. A total of 1,260 children aged 8-9 years were recruited in the study, including 633 boys (50.3%) and 627 girls (49.7%). Results: The allele frequencies of the GNB3 polymorphism were C allele = 49.7% and T allele = 50.3% in subjects. In general, boys with T allele had higher BMI, systolic BP (SBP), and triglycerides, although their energy intake was not significantly different from boys with C allele. In contrast to boys, girls with T allele had lower BMI but higher SBP and energy intake than those with C allele. The girls with T allele had a significantly lower BMI and waist circumference in both the normal weight group and obese group (OB). T allele carriers in both genders had significantly higher TC than C allele carriers in the OB group. At last, girls with T allele in OB appeared to have significantly lower HOMA-IR than those with C allele. Conclusion: Unlike higher risk for negative health outcomes by the GNB3 polymorphism in various ethnicities, GNB3 polymorphism did not influence obesogenic environments in overweight/obese Korean children.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.35 no.12
• /
• pp.1325-1333
• /
• 2011

An analytical, numerical, and experimental comparison of the hydraulic and thermal performance of new vascular channels with semicircular cross sections was conducted. The following conditions were employed in the study: Reynolds number, 30-2000; cooling channels with a volume fraction of the cooling channels, 0.04; and pressure drop, $30-10^5$ Pa. Three flow configurations were considered: first, second, and third constructal structures with diameters optimized for hydraulic operations. To validate the proposed vascular designs by an analytical approach, 3-D numerical analysis was performed. The numerical model was also validated by the experimental data, and the comparison results were in excellent agreement in all cases. The validation study against the experimental data showed that compared to traditional channels, the optimized structure of the cooling plates could significantly enhance heat transfer and decrease pumping power.

• Journal of Information Technology Services
• /
• v.13 no.3
• /
• pp.221-233
• /
• 2014

Because people's interest of the stock market has been increased with the development of economy, a lot of studies have been going to predict fluctuation of stock prices. Latterly many studies have been made using scientific and technological method among the various forecasting method, and also data using for study are becoming diverse. So, in this paper we propose stock prices prediction models using sentiment analysis and machine learning based on news articles and SNS data to improve the accuracy of prediction of stock prices. Stock prices prediction models that we propose are generated through the four-step process that contain data collection, sentiment dictionary construction, sentiment analysis, and machine learning. The data have been collected to target newspapers related to economy in the case of news article and to target twitter in the case of SNS data. Sentiment dictionary was built using news articles among the collected data, and we utilize it to process sentiment analysis. In machine learning phase, we generate prediction models using various techniques of classification and the data that was made through sentiment analysis. After generating prediction models, we conducted 10-fold cross-validation to measure the performance of they. The experimental result showed that accuracy is over 80% in a number of ways and F1 score is closer to 0.8. The result can be seen as significantly enhanced result compared with conventional researches utilizing opinion mining or data mining techniques.

• Journal of KIISE
• /
• v.42 no.4
• /
• pp.468-474
• /
• 2015

Corporations should make efforts to recognize the importance of projects, identify their failure factors, prevent risks in advance, and raise the success rates, because the corporations need to make quick responses to rapid external changes. There are some previous studies on success and failure factors of projects, however, most of them have limitations in terms of objectivity and quantitative analysis based on data gathering through surveys, statistical sampling and analysis. This study analyzes the failure factors of projects based on data mining to find problems with projects in an audit report, which is an objective project evaluation report. To do this, we identified the texts in the paragraph of suggestions about improvement. We made use of the superior classification algorithms in this study, which were NaiveBayes, SMO and J48. They were evaluated in terms of data of Recall and Precision after performing 10-fold-cross validation. In the identified texts, the failure factors of projects were analyzed so that they could be utilized in project implementation.

• Korean Journal of Cognitive Science
• /
• v.21 no.4
• /
• pp.669-696
• /
• 2010

Assuming that the whole meaning of a document is a composition of the meanings of each part, this paper proposes to study the automatic grading of movie reviews which contain sentimental expressions. This will be accomplished by calculating the values of semantic segments and performing data classification for each review. The ARSSA(The Automatic Rating System for Sentiment analysis using an Appraisal dictionary) system is an effort to model decision making processes in a manner similar to that of the human mind. This aims to resolve the discontinuity between the numerical ranking and textual rationalization present in the binary structure of the current review rating system: {rate: review}. This model can be realized by performing analysis on the abstract menas extracted from each review. The performance of this system was experimentally calculated by performing a 10-fold Cross-Validation test of 1000 reviews obtained from the Naver Movie site. The system achieved an 85% F1 Score when compared to predefined values using a predefined appraisal dictionary.

• Ocean and Polar Research
• /
• v.38 no.4
• /
• pp.339-345
• /
• 2016

Stable water vapor isotopes have been utilized as a tracer for studying atmospheric global circulations, climate change and paleoclimate with ice cores. Recently, since laser spectroscopy has been available, water vapor isotopes can be measured more precisely and continuously. Studies of water vapor isotopes have been conducted over the world, but it is the early stage in south Korea. For vapor isotopes study, a cryogenic sampling device for water vapor isotopes has been developed. The cryogenic sampling device consists of the dewar bottle, filled with extremely low temperature material and impinger connected with a vacuum pump. Impinger stays put in the dewar bottle to change the water vapor which passes through the inside of impinger into the solid phase as ice. The fact that water vapor has not sampled completely leads to isotopic fractionation in the impinger. To minimize the isotopic fractionation during sampling water vapor, we have tested the method using a serial connection with two sets of impinger device in the laboratory. We trapped 98.02% of water vapor in the first trap and the isotopic difference of the trapped water vapor between two impinger were about 20‰ and 6‰ for hydrogen and oxygen, respectively. Considering the amount of water vapor trapped in each impinger, the isotopic differences for hydrogen and oxygen were 0.33‰ and 0.06‰, respectively, which is significantly smaller than the precision of isotopic measurements. This work can conclude that there is no significant fractionation during water vapor trapping.