• Journal of the Korea Society of Computer and Information
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• v.25 no.11
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• pp.41-50
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• 2020

Sentimental analysis begins with the search for words that determine the sentimentality inherent in data. Managers can understand market sentimentality by analyzing a number of relevant sentiment words which consumers usually tend to use. In this study, we propose exploring performance of feature selection methods embedded with Particle Swarm Optimization Multi Objectives Evolutionary Algorithms. The performance of the feature selection methods was benchmarked with machine learning classifiers such as Decision Tree, Naive Bayesian Network, Support Vector Machine, Random Forest, Bagging, Random Subspace, and Rotation Forest. Our empirical results of opinion mining revealed that the number of features was significantly reduced and the performance was not hurt. In specific, the Support Vector Machine showed the highest accuracy. Random subspace produced the best AUC results.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.64-72
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• 2016

In this paper, we utilize a Gaussian process to predict the power consumption in the air-conditioning system. As the power consumption in the air-conditioning system takes a form of a time-series and the prediction of the power consumption becomes very important from the perspective of the efficient energy management, it is worth to investigate the time-series model for the prediction of the power consumption. To this end, we apply the Gaussian process to predict the power consumption, in which the Gaussian process provides a prior probability to every possible function and higher probabilities are given to functions that are more likely consistent with the empirical data. We also discuss how to estimate the hyper-parameters, which are parameters in the covariance function of the Gaussian process model. We estimated the hyper-parameters with two different methods (marginal likelihood and leave-one-out cross validation) and obtained a model that pertinently describes the data and the results are more or less independent of the estimation method of hyper-parameters. We validated the prediction results by the error analysis of the mean relative error and the mean absolute error. The mean relative error analysis showed that about 3.4% of the predicted value came from the error, and the mean absolute error analysis confirmed that the error in within the standard deviation of the predicted value. We also adopt the non-parametric Wilcoxon's sign-rank test to assess the fitness of the proposed model and found that the null hypothesis of uniformity was accepted under the significance level of 5%. These results can be applied to a more elaborate control of the power consumption in the air-conditioning system.

• Journal of Internet Computing and Services
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• v.15 no.6
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• pp.117-124
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• 2014

Cluttered background is a major obstacle in developing salient object detection and tracking system for mobile device captured natural scene video frames. In this paper we propose a context aware feature vector selection model to provide an efficient noise filtering by machine learning based classifiers. Since the context awareness for feature selection is achieved by searching nearest neighborhoods, known as NP hard problem, we apply a fast approximation method with complexity analysis in details. Separability enhancement in feature vector space by adding the context aware feature subsets is studied rigorously using principal component analysis (PCA). Overall performance enhancement is quantified by the statistical measures in terms of the various machine learning models including MLP, SVM, Naïve Bayesian, CART. Summary of computational costs and performance enhancement is also presented.

• The Korean Journal of Applied Statistics
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• v.18 no.3
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• pp.533-541
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• 2005

We identify the correct chromosome and locate the corresponding markers close to the QTL in the linkage analysis of a quantitative trait by using the SSVS method. We consider several markers linked to the QTL, as well as to each oyher and thus the i.b.d. values at these loci generate collinear predictors to be evaluated when using the SSVS approach. The results on considering only closely linked markers to two QTL simultaneously showed clear evidence in favor of the closest marker to the QTL considered over other markers. The results of the analysis of collinear markers with SSVS showeed high concordance to those obtained using traditional multiple regression. We conclude based on this simulation study that the SSVS is quite useful to identify linkage with multiple linked markers simultaneously for a complex quantitative trait.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.40 no.12
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• pp.39-45
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• 2003

Design verification of VHDL models is getting difficult and has become a critical and time-consuming process in hardware design. Recent]y the methods using Bayesian estimation and stopping rule have been introduced to verify behavioral models and to reduce clock cycles. This paper presents two strategies to reduce clock cycles when using stopping rule in a VHDL model verification. The first method is that a semi-random variable is defined and the data that stay in the range of semi-random variable are skipped when stopping rule is running. The second one is to keep the old values of parameters when phases of stopping rule are changed. 12 VHDL models are examined to observe the effectiveness of strategies, and the simulation results show that more than about 25% of clock cycles is reduced by using the two proposed strategies with 0.6% losses of branch coverage rate.

• Asian-Australasian Journal of Animal Sciences
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• v.19 no.7
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• pp.943-952
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• 2006

The genetic variation in 10 indigenous Caucasian sheep breeds was studied with 14 micro-satellite loci in order to determine the genetic diversity among and between the breeds. Five breeds from Asia, five breeds from Europe and one breed from Africa, were included in order to study any relationships or influences they may have with the Caucasian sheep analyzed. A Karakul population from Uzbekistan was included in the study to see whether there was any Central Asian influence. All the 14 loci were found to be polymorphic in all the breeds, with the exception of ILST0056, which was monomorphic in Imeretian. A total of 231 alleles were generated from all the 688 individuals of the sheep analyzed. The mean number of alleles (MNA) at each locus was 16.5. The total number of alleles detected in all samples ranged from 13 in several loci to 23 in OarJMP029. Out of total 308 Hardy-Weinberg Equilibrium (HWE) tests, 85 gave significant results. After Bonferroni correction for multiple tests, 30 comparisons still remained significant to the experimental levels. The Gala population was the most diverse and Imeretian the least diverse with a MNA of 8.50 and 5.51, respectively. Gene diversity estimates exhibited the same trend and ranged from 0.803 in Gala and 0.623 in Imeretian, but generally there is higher diversity among the Caucasian breeds in comparison to other eference breeds. The closest breeds were Tushin and Bozakh with Da of 0.113 and most distant breeds were $Djallonk{\acute{e}}$ and North Rondalsy with Da of 0.445. Principal Component (PC) analyses were done. PC1 described 14% of the differences. PC2, which described 13% of the differences, further separated the Caucasian breeds from Asian breeds except Karakul and Awasi, and the two British breeds. PC3 described 10% of the differences, allowing better differentiation of the Caucasian breeds. A moderate degree of reliability was observed for individual-breed assignment from the 14 loci using different approaches among which the Bayesian method proved to be the most efficient. About 72% of individuals analyzed were correctly assigned to their respective breeds.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.9
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• pp.1133-1140
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• 2013

A computer model is a useful tool that provides solution via physical modeling instead of expensive testing. In reality, however, it often does not agree with the experimental data owing to simplifying assumption and unknown or uncertain input parameters. In this study, a Bayesian approach is proposed to calibrate the computer model in a probabilistic manner using the measured data. The elasto-plastic analysis of a pyrotechnically actuated device (PAD) is employed to demonstrate this approach, which is a component that delivers high power in remote environments by the combustion of a self-contained energy source. A simple mathematical model that quickly evaluates the performance is developed. Unknown input parameters are calibrated conditional on the experimental data using the Markov Chain Monte Carlo algorithm, which is a modern computational statistics method. Finally, the results are applied to determine the reliability of the PAD.

• The KIPS Transactions:PartD
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• v.10D no.1
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• pp.109-116
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• 2003

In this paper, a CORBA-based collaborative medical image analysis and visualization system, which provides high accessibility and usability of the system for the users on distributed environment is introduced. The system allows us to manage datasets and manipulates medical images such as segmentation and volume visualization of computed geometry from biomedical images in distributed environments. Using Bayesian classification technique and an active contour model the system provides classification results of medical images or boundary information of specific tissue. Based on such information, the system can create real time 3D volume model from medical imagery. Moreover, the developed system supports collaborative work among multiple users using broadcasting and synchronization mechanisms. Since the system is developed using Java and CORBA, which provide distributed programming, the remote clients can access server objects via method invocation, without knowing where the distributed objects reside or what operating system it executes on.

• Asian-Australasian Journal of Animal Sciences
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• v.30 no.7
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• pp.930-937
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• 2017

Objective: The I pig is a long nurtured longstanding breed in Vietnam, and contains excellent indigenous genetic resources. However, after 1970s, I pig breeds have become a small population because of decreasing farming areas and increasing pressure from foreign breeds with a high growth rate. Thus, there is now the risk of the disappearance of the I pigs breed. The aim of this study was to focus on classifying and identifying the I pig genetic origin and supplying molecular makers for conservation activities. Methods: This study sequenced the complete mitochondrial genome and used the sequencing result to analyze the phylogenetic relationship of I pig with Asian and European domestic pigs and wild boars. The full sequence was annotated and predicted the secondary tRNA. Results: The total length of I pig mitochondrial genome (accession number KX094894) was 16,731 base pairs, comprised two rRNA (12S and 16S), 22 tRNA and 13 mRNA genes. The annotation structures were not different from other pig breeds. Some component indexes as AT content, GC, and AT skew were counted, in which AT content (60.09%) was smaller than other pigs. We built the phylogenetic trees from full sequence and D loop sequence using Bayesian method. The result showed that I pig, Banna mini, wild boar (WB) Vietnam and WB Hainan or WB Korea, WB Japan were a cluster. They were a group within the Asian clade distinct from Chinese pigs and other Asian breeds in both phylogenetic trees (0.0004 and 0.0057, respectively). Conclusion: These results were similar to previous phylogenic study in Vietnamese pig and showed the genetic distinctness of I pig with other Asian domestic pigs.

• Journal of KIISE
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• v.42 no.2
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• pp.190-202
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• 2015

Much research has been conducted on location-based intelligent personal assistants that can understand a user's intention by learning the user's route model and then inferring the user's destinations and routes using data of GPS and other sensors in a smartphone. The intelligence of the location-based personal assistant is contingent on the accuracy and efficiency of the real-time predictions of the user's intended destinations and routes by processing movement information based on uncertain sensor data. We propose a robust particle filter based on Dynamic Bayesian Network model to infer the user's routes. The proposed robust particle filter includes a particle generator to supplement the incorrect and incomplete sensor information, an efficient switching function and an weight function to reduce the computation complexity as well as a resampler to enhance the accuracy of the particles. The proposed method improves the accuracy and efficiency of determining a user's routes and destinations.