• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.70.1-70.1
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• 2021

We examine the HI gas kinematics and distributions of galaxy pairs in group or cluster environments from high-resolution Australian Square Kilometer Array Pathfinder (ASKAP) WALLABY pilot observations. We use 32 well-resolved close pair galaxies from the Hydra, Norma, and NGC 4636, two clusters and a group of which are identified by their spectroscopy information and additional visual inspection. We perform profile decomposition of HI velocity profiles of the galaxies using a new tool, BAYGAUD which allows us to separate a line-of-sight velocity profile into an optimal number of Gaussian components based on Bayesian MCMC techniques. Then, we construct super profiles via stacking of individual HI velocity profiles after aligning their central velocities. We fit a model which consists of double Gaussian components to the super profiles, and classify them as kinematically cold and warm HI gas components with respect to their velocity dispersions, narrower or wider 𝜎, respectively. The kinematically cold HI gas reservoir (M_cold/M_HI) of the paired galaxies is found to be relatively higher than that of unpaired control samples in the clusters and the group, showing a positive correlation with the HI mass in general. Additionally, we quantify the gravitational instability of the HI gas disk of the sample galaxies using their Toomre Q parameters and HI morphological disturbances. While no significant difference is found for the Q parameter values between the paired and unpaired galaxies, the paired galaxies tend to have larger HI asymmetry values which are derived using their moment0 map compared to those of the non-paired control sample galaxies in the distribution.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.70.2-71
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• 2021

We examine gas kinematics and star formation activities of NGC 6822, a gas-rich dwarf irregular galaxy in the Local Group at a distance of ~490 kpc. We perform profile decomposition of all the line-of-sight (LOS) HI velocity profiles of the high-resolution (42.4" × 12" spatial; 1.6 km/s spectral) HI data cube of the galaxy, taken with the Australian Telescope Compact Array (ATCA). To this end, we use a novel tool based on Bayesian Markov Chain Monte Carlo (MCMC) techniques, the so-called BAYGAUD, which allows us to decompose a velocity profile into an optimal number of Gaussian components in a quantitative manner. We group all the decomposed components into bulk-narrow, bulk-broad, and non-bulk gas components classified with respect to their velocity dispersions and the amounts of velocity offset from the global kinematics, respectively. Using the surface densities and velocity dispersions of the kinematically decomposed HI gas maps together with the rotation curve of NGC 6822, we derive Toomre-Q parameters for individual regions of the galaxy which quantify the level of local gravitational instability of the gaseous disk. We also measure the local star formation rate (SFR) of the corresponding regions in the galaxy by combining GALEX Far-ultraviolet (FUV) and WISE 22㎛ images. We then relate the gas and SFR surface densities in order to investigate the local Kennicutt-Schmidt (K-S) law of gravitationally unstable regions which are selected from the Toomre Q analysis. Of the three groups, the bulk-narrow, bulk-broad and non-bulk gas components, we find that the lower Toomre-Q values the bulk-narrow gas components have, the more consistent with the linear extension of the K-S law derived from molecular hydrogen (H2) observations.

• Journal of Powder Materials
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• v.30 no.3
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• pp.217-222
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• 2023

Machine learning-based data analysis approaches have been employed to overcome the limitations in accurately analyzing data and to predict the results of the design of Nb-based superalloys. In this study, a database containing the composition of the alloying elements and their room-temperature tensile strengths was prepared based on a previous study. After computing the correlation between the tensile strength at room temperature and the composition, a material science analysis was conducted on the elements with high correlation coefficients. These alloying elements were found to have a significant effect on the variation in the tensile strength of Nb-based alloys at room temperature. Through this process, a model was derived to predict the properties using four machine learning algorithms. The Bayesian ridge regression algorithm proved to be the optimal model when Y, Sc, W, Cr, Mo, Sn, and Ti were used as input features. This study demonstrates the successful application of machine learning techniques to effectively analyze data and predict outcomes, thereby providing valuable insights into the design of Nb-based superalloys.

• Animal Bioscience
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• v.37 no.4
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• pp.622-630
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• 2024

Objective: Pig breeders cannot obtain phenotypic information at the time of selection for sow lifetime productivity (SLP). They would benefit from obtaining genetic information of candidate sows. Genomic data interpreted using deep learning (DL) techniques could contribute to the genetic improvement of SLP to maximize farm profitability because DL models capture nonlinear genetic effects such as dominance and epistasis more efficiently than conventional genomic prediction methods based on linear models. This study aimed to investigate the usefulness of DL for the genomic prediction of two SLP-related traits; lifetime number of litters (LNL) and lifetime pig production (LPP). Methods: Two bivariate DL models, convolutional neural network (CNN) and local convolutional neural network (LCNN), were compared with conventional bivariate linear models (i.e., genomic best linear unbiased prediction, Bayesian ridge regression, Bayes A, and Bayes B). Phenotype and pedigree data were collected from 40,011 sows that had husbandry records. Among these, 3,652 pigs were genotyped using the PorcineSNP60K BeadChip. Results: The best predictive correlation for LNL was obtained with CNN (0.28), followed by LCNN (0.26) and conventional linear models (approximately 0.21). For LPP, the best predictive correlation was also obtained with CNN (0.29), followed by LCNN (0.27) and conventional linear models (approximately 0.25). A similar trend was observed with the mean squared error of prediction for the SLP traits. Conclusion: This study provides an example of a CNN that can outperform against the linear model-based genomic prediction approaches when the nonlinear interaction components are important because LNL and LPP exhibited strong epistatic interaction components. Additionally, our results suggest that applying bivariate DL models could also contribute to the prediction accuracy by utilizing the genetic correlation between LNL and LPP.

• Information Systems Review
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• v.10 no.2
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• pp.137-158
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• 2008

Rapid development of various information technologies creates new opportunities in online and offline markets. In this changing market environment, customers have various demands on new products and services. Therefore, their power and influence on the markets grow stronger each year. Companies have paid great attention to customer relationship management. Especially, personalized product recommendation systems, which recommend products and services based on customer's private information or purchasing behaviors in stores, is an important asset to most companies. CRM is one of the important business processes where reliable information is mined from customer database. Data mining techniques such as artificial intelligence are popular tools used to extract useful information and knowledge from these customer databases. In this research, we propose a recommendation system that predicts customer's purchase intention. Then, customer's purchasing intention of specific product is predicted by using data mining techniques using receipt data set. The performance of this suggested method is compared with that of other data mining technologies.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.37 no.4
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• pp.313-322
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• 2013

Batteries, which are being used as energy sources in various applications, tend to degrade, and their capacity declines with repeated charging and discharging cycles. A battery is considered to fail when it reaches 80% of its initial capacity. To predict this, prognosis techniques are attracting attention in recent years in the battery community. In this study, a method is proposed for estimating the battery health and predicting its remaining useful life (RUL) based on the slope of the charge voltage curve. During this process, a Bayesian framework is employed to manage various uncertainties, and a Particle Filter (PF) algorithm is applied to estimate the degradation of the model parameters and to predict the RUL in the form of a probability distribution. Two sets of test data-one from the NASA Ames Research Center and another from our own experiment-for an Li-ion battery are used for illustrating this technique. As a result of the study, it is concluded that the slope can be a good indicator of the battery health and PF is a useful tool for the reliable prediction of RUL.

• The KIPS Transactions:PartB
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• v.15B no.2
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• pp.137-146
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• 2008

Fault localization plays a significant role in enormous distributed system because it can identify root cause of observed faults automatically, supporting self-managing which remains an open topic in managing and controlling complex distributed systems to improve system reliability. Although many Artificial Intelligent techniques have been introduced in support of fault localization in recent research especially in increasing complex ubiquitous environment, the provided functions such as diagnosis and prediction are limited. In this paper, we propose fault localization for self-managing in performance evaluation in order to improve system reliability via learning and analyzing real-time streams of system performance events. We use probabilistic reasoning functions based on the basic Bayes' rule to provide effective mechanism for managing and evaluating system performance parameters automatically, and hence the system reliability is improved. Moreover, due to large number of considered factors in diverse and complex fault reasoning domains, we develop an efficient method which extracts relevant parameters having high relationships with observing problems and ranks them orderly. The selected node ordering lists will be used in network modeling, and hence improving learning efficiency. Using the approach enables us to diagnose the most probable causal factor with responsibility for the underlying performance problems and predict system situation to avoid potential abnormities via posting treatments or pretreatments respectively. The experimental application of system performance analysis by using the proposed approach and various estimations on efficiency and accuracy show that the availability of the proposed approach in performance evaluation domain is optimistic.

• Structural Monitoring and Maintenance
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• v.2 no.1
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• pp.77-93
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• 2015

The idea of using measured dynamic characteristics for damage detection is attractive because it allows for a global evaluation of the structural health and condition. However, vibration-based damage detection for complex structures such as long-span cable-supported bridges still remains a challenge. As a suspension or cable-stayed bridge involves in general thousands of structural components, the conventional damage detection methods based on model updating and/or parameter identification might result in ill-conditioning and non-uniqueness in the solution of inverse problems. Alternatively, methods that utilize, to the utmost extent, information from forward problems and avoid direct solution to inverse problems would be more suitable for vibration-based damage detection of long-span cable-supported bridges. The auto-associative neural network (ANN) technique and the probabilistic neural network (PNN) technique, that both eschew inverse problems, have been proposed for identifying and locating damage in suspension and cable-stayed bridges. Without the help of a structural model, ANNs with appropriate configuration can be trained using only the measured modal frequencies from healthy structure under varying environmental conditions, and a new set of modal frequency data acquired from an unknown state of the structure is then fed into the trained ANNs for damage presence identification. With the help of a structural model, PNNs can be configured using the relative changes of modal frequencies before and after damage by assuming damage at different locations, and then the measured modal frequencies from the structure can be presented to locate the damage. However, such formulated ANNs and PNNs may still be incompetent to identify damage occurring at the deck members of a cable-supported bridge because of very low modal sensitivity to the damage. The present study endeavors to enhance the damage identification capability of ANNs and PNNs when being applied for identification of damage incurred at deck members. Effort is first made to construct combined modal parameters which are synthesized from measured modal frequencies and modal shape components to train ANNs for damage alarming. With the purpose of improving identification accuracy, effort is then made to configure PNNs for damage localization by adapting the smoothing parameter in the Bayesian classifier to different values for different pattern classes. The performance of the ANNs with their input being modal frequencies and the combined modal parameters respectively and the PNNs with constant and adaptive smoothing parameters respectively is evaluated through simulation studies of identifying damage inflicted on different deck members of the double-deck suspension Tsing Ma Bridge.

• Journal of Acupuncture Research
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• v.23 no.6
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• pp.239-254
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• 2006

The 3rd Japan-Korea Workshop on Acupuncture and EBM was held at Kanazawa on June $16^{th}$. From Korea team, 4 papers were presented. Dr. Hahn introduced a new approach of data analysis on series of n-of-1 trials using the Bayesian statistics. It offered important information for the future n-of-1 trials. Dr. Park clearly demonstrated the significance of various sham devices proposed and stressed the importance of research questions when we choose the control intervention in RCT. Dr. Lee reported the results of survey in Korean Medical Doctors (KMD) for their point selection and techniques to the distal and local points. Dr. Kim presented the results of face to face survey on the KMD with 28 items for acupuncture treatment on the knee OA. Finally, a draft of protocol was introduced by Dr. Kim. The title was "multi-center, a randomized, single blinded, two arms, parallel-group study to compare the effectiveness and safety of 'individualized acupuncture' and 'standardized minimal acupuncture' in Korean and Japanese patients with knee osteoarthritis (Phase IV)". From Japan team, 7 speakers presented their comments and proposals on the protocol. Dr. Takahashi introduced several issues regarding n-of-1 trials and pointed out the importance of obtaining generalizability from n-of-1 trials. Dr. Shichidou pointed the importance of research design, selection of outcome measures and reduction of biases. Dr. Itoh presented the results of point selection for the knee OA based on the literature survey. Dr. Sumiya introduced several differences between KMD and Japanese acupuncturists based on the questionnaire used in KMD survey. Dr. Furuya demonstrated a result of press tack needle and its sham device on shoulder stiffness. Dr. Yamashita introduced the results of literature survey regarding adverse events occurred by acupuncture on knee OA. Dr.Tsukayama stressed the importance of responsibility of Institutional Review Board (IRB) for the conduction of clinical trials. After several issues were discussed, the need of continued meeting for final protocol development was agreed, then the workshop was closed.