• Proceedings of the Korean Information Science Society Conference
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• 2006.06b
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• pp.121-123
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• 2006

베이지안 네트워크 병합은 여러 개의 베이지안 네트워크를 하나의 네트워크로 합치는 것을 말한다. 일반적으로 사용되는 병합 알고리즘은 병합 순서에 따라 최종결과 네트워크의 복잡도가 달라지는 문제를 갖고 있고, 최종 병합 네트워크의 에지 수를 최소화하는 병합 순서를 찾는 것은 NP-hard라고 증명되었다. 본 논문에서는 최적의 병합 순서를 결정하기 위해 진화 알고리즘을 사용하는 방법을 제안한다. 해공간 분석을 통해 permutation index 표현방법이 해탐색에 유리함을 보이고 이를 이용한 진화 알고리즘을 제안한다. 실험결과, 기존의 휴리스틱과 greedy 탐색 방법에 비해 제안한 방법이 우수한 성능을 보였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.476-479
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• 2021

DBMS 파라미터 튜닝이란 데이터베이스에서 제공하는 다양한 파라미터의 값을 조율하여, 최적의 성능을 도출하는 과정이다. 데이터베이스 종류에 따라 파라미터 개수가 수십 개에서 수백 개로 다양하며, 각 기능이 모두 다르기 때문에 최적의 조합을 찾는 것은 쉽지 않다. 선행 연구에서는 BO 기법을 사용하여 적절한 파라미터 값을 추출했지만, 파라미터 개수에 비례하여 차원이 커지는 문제가 발생한다. 본 논문에서는 통계적으로 파라미터를 분류하여 탐색 공간을 줄인 다음 단계적으로 BO 를 수행하는 PBO 방식을 제안한다. 파라미터 값을 랜덤하게 할당하여 벤치마킹한 결과값을 군집화한 후, 각 군집별로 파라미터와의 연관성을 분석해 높은 상관관계를 가진 파라미터를 매칭시켜 분류한다. 제안하는 방법론을 검증하기 위하여 8 가지 회귀 모델과의 비교 실험을 통해 제안한 방법론의 우수성을 검증하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.539-541
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• 2023

COVID-19 is a viral pandemic disease that spreads widely all around the world. The only way to identify COVID-19 patients at an early stage is to stop the spread of the virus. Different approaches are used to diagnose, such as RT-PCR, Chest X-rays, and CT images. However, these are time-consuming and require a specialized lab. Therefore, there is a need to develop a time-efficient diagnosis method to detect COVID-19 patients. The proposed machine learning (ML) approach predicts the presence of coronavirus based on clinical symptoms. The clinical dataset is collected from the Israeli Ministry of Health. We used different ML classifiers (i.e., XGB, DT, RF, and NB) to diagnose COVID-19. Later, classifiers are optimized with the Bayesian hyperparameter optimization approach to improve the performance. The optimized RF outperformed the others and achieved an accuracy of 97.62% on the testing data that help the early diagnosis of COVID-19 patients.

• 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.

• The Journal of the Acoustical Society of Korea
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• v.42 no.4
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• pp.329-344
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• 2023

In this study, we comprehensively analyze the generalization performance of various deep learning-based active sonar target classifiers when applied to small and imbalanced active sonar datasets. To generate the active sonar datasets, we use data from two different oceanic experiments conducted at different times and ocean. Each sample in the active sonar datasets is a time-frequency domain image, which is extracted from audio signal of contact after the detection process. For the comprehensive analysis, we utilize 22 Convolutional Neural Networks (CNN) models. Two datasets are used as train/validation datasets and test datasets, alternatively. To calculate the variance in the output of the target classifiers, the train/validation/test datasets are repeated 10 times. Hyperparameters for training are optimized using Bayesian optimization. The results demonstrate that shallow CNN models show superior robustness and generalization performance compared to most of deep CNN models. The results from this paper can serve as a valuable reference for future research directions in deep learning-based active sonar target classification.

• Journal of the Korean Geotechnical Society
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• v.39 no.12
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• pp.37-46
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• 2023

Dynamic soil properties are essential factors for predicting the detailed behavior of the ground. However, there are limitations to gathering soil samples and performing additional experiments. In this study, we used an artificial neural network (ANN) to predict dynamic soil properties based on static soil properties. The selected static soil properties were soil cohesion, internal friction angle, porosity, specific gravity, and uniaxial compressive strength, whereas the compressional and shear wave velocities were determined for the dynamic soil properties. The Levenberg-Marquardt and Bayesian regularization methods were used to enhance the reliability of the ANN results, and the reliability associated with each optimization method was compared. The accuracy of the ANN model was represented by the coefficient of determination, which was greater than 0.9 in the training and testing phases, indicating that the proposed ANN model exhibits high reliability. Further, the reliability of the output values was verified with new input data, and the results showed high accuracy.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1179-1192
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• 2018

In order to detect the Antarctic Polar Front (PF) among the main fronts in the Southern Ocean, this study is based on the combinations of satellite-based sea surface temperature (SST) and height (SSH) observations. For accurate PF detection, we classified the signals as front or non-front grids based on the Bayesian decision theory from daily SST and SSH datasets, and then spatio-temporal synthesis has been performed to remove primary noises and to supplement geographical connectivity of the front grids. In addition, sea ice and coastal masking were employed in order to remove the noise that still remains even after performing the processes and morphology operations. Finally, we selected only the southernmost grids, which can be considered as fronts and determined as the monthly PF by a linear smoothing spline optimization method. The mean positions of PF in this study are very similar to those of the PFs reported by the previous studies, and it is likely to be well represents PF formation along the bottom topography known as one of the major influences of the PF maintenance. The seasonal variation in the positions of PF is high in the Ross Sea sector (${\sim}180^{\circ}W$), and Australia sector ($120^{\circ}E-140^{\circ}E$), and these variations are quite similar to the previous studies. Therefore, it is expected that the detection approach for the PF position applied in this study and the final composite have a value that can be used in related research to be carried out on the long term time-scale.

• Communications for Statistical Applications and Methods
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• v.24 no.4
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• pp.383-396
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• 2017

The model in our approach assumes that computer responses are a realization of a Gaussian processes superimposed on a regression model called a Gaussian process regression model (GPRM). Selecting a subset of variables or building a good reduced model in classical regression is an important process to identify variables influential to responses and for further analysis such as prediction or classification. One reason to select some variables in the prediction aspect is to prevent the over-fitting or under-fitting to data. The same reasoning and approach can be applicable to GPRM. However, only a few works on the variable selection in GPRM were done. In this paper, we propose a new algorithm to build a good prediction model among some GPRMs. It is a post-work of the algorithm that includes the Welch method suggested by previous researchers. The proposed algorithms select some non-zero regression coefficients (${\beta}^{\prime}s$) using forward and backward methods along with the Lasso guided approach. During this process, the fixed were covariance parameters (${\theta}^{\prime}s$) that were pre-selected by the Welch algorithm. We illustrated the superiority of our proposed models over the Welch method and non-selection models using four test functions and one real data example. Future extensions are also discussed.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.600-602
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• 1997

In this study, a hybrid neural net consisting of an Adaptive LVQ(ALVQ) algorithm and MLP is proposed to perform speaker identification task. ALVQ is a new learning procedure using adaptively feature vector sequence instead of only one feature vector in training codebooks initialized by LBG algorithm and the optimization criterion of this method is consistent with the speaker classification decision rule. ALVQ aims at providing a compressed, geometrically consistent data representation. It is fit to cover irregular data distributions and computes the distance of the input vector sequence from its nodes. On the other hand, MLP aim at a data representation to fit to discriminate patterns belonging to different classes. It has been shown that MLP nets can approximate Bayesian "optimal" classifiers with high precision, and their output values can be related a-posteriori class probabilities. The different characteristics of these neural models make it possible to devise hybrid neural net systems, consisting of classification modules based on these two different philosophies. The proposed method is compared with LBG algorithm, LVQ algorithm and MLP for performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.2914-2935
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• 2016

With the increasing popularization of cloud computing, how to reduce physical energy consumption and increase resource utilization while maintaining system performance has become a research hotspot of virtual machine deployment in cloud platform. Although some related researches have been reported to solve this problem, most of them used the traditional heuristic algorithm based on greedy algorithm and only considered effect of single-dimensional resource (CPU or Memory) on energy consumption. With considerations to multi-dimensional resource utilization, this paper analyzed impact of multi-dimensional resources on energy consumption of cloud computation. A multi-dimensional resource constraint that could maintain normal system operation was proposed. Later, a novel virtual machine deployment method (NVMDM) based on improved particle swarm optimization (IPSO) and Euclidean distance was put forward. It deals with problems like how to generate the initial particle swarm through the improved first-fit algorithm based on resource constraint (IFFABRC), how to define measure standard of credibility of individual and global optimal solutions of particles by combining with Bayesian transform, and how to define fitness function of particle swarm according to the multi-dimensional resource constraint relationship. The proposed NVMDM was proved superior to existing heuristic algorithm in developing performances of physical machines. It could improve utilization of CPU, memory, disk and bandwidth effectively and control task execution time of users within the range of resource constraint.