• Proceedings of the Korean Society for Bioinformatics Conference
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• 2000.11a
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• pp.61-68
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• 2000

Now that the complete genomes of numerous organisms have been ascertained, key problems in molecular biology include determining the functions of the genes in each organism, the relationships that exist among these genes, and the regulatory mechanisms that control their operation. These problems can be partially addressed by using machine learning methods to induce predictive models from available data. My group is applying and developing machine learning methods for several tasks that involve characterizing gene regulation. In one project, for example, we are using machine learning methods to identify transcriptional control elements such as promoters, terminators and operons. In another project, we are using learning methods to identify and characterize sets of genes that are affected by tumor promoters in mammals. Our approach to these tasks involves learning multiple models for inter-related tasks, and applying learning algorithms to rich and diverse data sources including sequence data, microarray data, and text from the scientific literature.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.5
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• pp.462-476
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• 2014

The Korean government has been implementing diverse policies with programs to generate better outcomes and results of university-industry collaboration since 1990s. In this paper, we analyze the effect of universities' competency factors on the performance of technology commercialization and start-ups respectively. We employ multiple regression models using 154 data sets from university information posting system of the Korean Council for University Education. Through conducting statistical analyses with diverse data manipulations, we obtained a high degree of significance on hypotheses, and also could compare mutual differences between the effects of university competence on technology commercialization and start-ups. The technology transfer and commercialization specifically depends on professors' patent applications and technology holding company, while start-ups does professionals in industry-university cooperation. We suggest government to spur on the ongoing customization of university-industry collaboration policy, and university to properly cope with global atmosphere changing from ivory tower to academic capitalism and start-ups promotion.

• Korean Journal of Remote Sensing
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• v.17 no.2
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• pp.121-129
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• 2001

A DEM(digital elevation model) is generated from a SPOT panchromatic stereo-pair using automated algorithms over a 8 km$\times$10 km region around Mokpo city. The aims are to continue the accuracy assessment over diverse conditions and to examine the applicability of a SPOT DEM for coastal disaster monitoring. The accuracy is assessed with respect to three reference data sets: 10 global positioning system records, 19 leveling data, and 1:50,000 topography map. The planimetric error is 10.6m r.m.s. and the elevation erroer ranges from 12.4m to 14.4m r.m.s.. The DEM accuracy of the flat Mokpo region is consistent with that over a mountainous area, which supports the robustness of the algorithms. It was found that coordinate transformation errors are significant at a few meters when using the data from leveling and topographic maps. The error budget is greater than the requirements for coastal disaster monitoring. Exploiting that a sub-scene is used, the affine transformation improves the accuracy by 50% during the camera modeling.

• Investigative Magnetic Resonance Imaging
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• v.21 no.4
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• pp.223-232
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• 2017

Purpose: To report the use of multiband accelerated echo-planar imaging (EPI) for resting-state functional MRI (rs-fMRI) to achieve rapid high temporal resolution at 3T compared to conventional EPI. Materials and Methods: rs-fMRI data were acquired from 20 healthy right-handed volunteers by using three methods: conventional single-band gradient-echo EPI acquisition (Data 1), multiband gradient-echo EPI acquisition with 240 volumes (Data 2) and 480 volumes (Data 3). Temporal signal-to-noise ratio (tSNR) maps were obtained by dividing the mean of the time course of each voxel by its temporal standard deviation. The resting-state sensorimotor network (SMN) and default mode network (DMN) were estimated using independent component analysis (ICA) and a seed-based method. One-way analysis of variance (ANOVA) was performed between the tSNR map, SMN, and DMN from the three data sets for between-group analysis. P < 0.05 with a family-wise error (FWE) correction for multiple comparisons was considered statistically significant. Results: One-way ANOVA and post-hoc two-sample t-tests showed that the tSNR was higher in Data 1 than Data 2 and 3 in white matter structures such as the striatum and medial and superior longitudinal fasciculus. One-way ANOVA revealed no differences in SMN or DMN across the three data sets. Conclusion: Within the adapted metrics estimated under specific imaging conditions employed in this study, multiband accelerated EPI, which substantially reduced scan times, provides the same quality image of functional connectivity as rs-fMRI by using conventional EPI at 3T. Under employed imaging conditions, this technique shows strong potential for clinical acceptance and translation of rs-fMRI protocols with potential advantages in spatial and/or temporal resolution. However, further study is warranted to evaluate whether the current findings can be generalized in diverse settings.

• The KIPS Transactions:PartA
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• v.12A no.3 s.93
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• pp.243-252
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• 2005

This paper introduces a noble modeling technique to build data communication prediction models in multiprocessors, using Least-Squares and Robust Estimation methods. A set of sample communication rates are collected by using a few small input data sets into workload programs. By applying estimation methods to these samples, we can build analytic models that precisely estimate communication rates for huge input data sets. The primary advantage is that, since the models depend only on data set size not on the specifications of target systems or workloads, they can be utilized to various systems and applications. In addition, the fact that the algorithmic behavioral characteristics of workloads are reflected into the models entitles them to model diverse other performance metrics. In this paper, we built models for cache miss rates which are the main causes of data communication in shared memory multiprocessor systems. The results present excellent prediction error rates; below $1\%$ for five cases out of 12, and about $3\%$ for the rest cases.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.1
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• pp.31-36
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• 2010

Many clustering algorithms have been used in diverse fields. When we need to group given data set into clusters, many clustering algorithms based on similarity or distance measures are considered. Most clustering works have been based on hierarchical and non-hierarchical clustering algorithms. Generally, for the clustering works, researchers have used clustering algorithms case by case from these algorithms. Also they have to determine proper clustering methods subjectively by their prior knowledge. In this paper, to solve the subjective problem of clustering we make empirical comparisons of popular clustering algorithms which are hierarchical and non hierarchical techniques using Silhouette measure. We use silhouette information to evaluate the clustering results such as the number of clusters and cluster variance. We verify our comparison study by experimental results using data sets from UCI machine learning repository. Therefore we are able to use efficient and objective clustering algorithms.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.2
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• pp.116-120
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• 2008

Clustering is a method for unsupervised learning. General clustering tools have been depended on statistical methods and machine learning algorithms. One of the popular clustering algorithms based on machine learning is the self organizing map(SOM). SOM is a neural networks model for clustering. SOM and extended SOM have been used in diverse classification and clustering fields such as data mining. But, SOM has had a problem determining optimal number of clusters. In this paper, we propose an improvement of SOM using gap statistic and probability distribution. The gap statistic was introduced to estimate the number of clusters in a dataset. We use gap statistic for settling the problem of SOM. Also, in our research, weights of feature nodes are updated by probability distribution. After complete updating according to prior and posterior distributions, the weights of SOM have probability distributions for optima clustering. To verify improved performance of our work, we make experiments compared with other learning algorithms using simulation data sets.

• Journal of the Ergonomics Society of Korea
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• v.29 no.4
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• pp.405-422
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• 2010

Occupational musculoskeletal disorders currently account for the largest proportion of the occupational illnesses in Korea. In this research, status of musculoskeletal disorders among the occupational illnesses was examined through workers' compensation claims data. Types and characteristics of musculoskeletal disorders were looked at other data set as well. The data sets included epidemiological investigation data reported by Occupational Safety and Health Research Institute, and data collected from occupational disease surveillance reports and Korean occupational health-related scientific journals. Number of cases, incidence rate and insurance benefits for occupational musculoskeletal disorders in Korea are increasing every year. In addition, musculoskeletal disorders occurrence is shifted from large enterprises group to small-and-medium group, from manufacturing to service sector, and from production workers to office and professional workers. Although low back pain is still most common, its occurrence characteristics is gradually shifted from traumatic to cumulative while musculoskeletal disorders are somewhat seemingly moved from lumbar to upper limb body part. Musculoskeletal disorders were observed to be more diverse and prevalent in epidemiological investigations or surveillance data rather in workers' compensation claims data. Musculoskeletal disorders occurrence is related to demographic factors, occupational psychosocial factors, and ergonomic risk factors at workplace for which appropriate preventive measures needed to be made accordingly.

• Journal of KIISE
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• v.43 no.3
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• pp.380-388
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• 2016

Graph clustering is widely used to analyze a graph and identify the properties of a graph by generating clusters consisting of similar vertices. Recently, large graph data is generated in diverse applications such as Social Network Services (SNS), the World Wide Web (WWW), and telephone networks. Therefore, the importance of graph clustering algorithms that process large graph data efficiently becomes increased. In this paper, we propose an effective clustering algorithm which generates clusters for large graph data efficiently. Our proposed algorithm effectively estimates similarities between clusters in graph data using Min-Hash and constructs clusters according to the computed similarities. In our experiment with real-world data sets, we demonstrate the efficiency of our proposed algorithm by comparing with existing algorithms.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.2
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• pp.91-102
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• 2017

Backpack-mounted mapping system is firstly introduced for flexible movement in indoor spaces where satellite-based localization is not available. With the achieved advances in miniaturization and weight reduction, use of LiDAR (Light Detection and Ranging) sensors in mobile platforms has been increasing, and indeed, they have provided high-precision information on indoor environments and their surroundings. Previous research on the development of backpack-mounted mapping systems, has concentrated mostly on the improvement of data processing methods or algorithms, whereas practical system components have been determined empirically. Thus, in the present study, a simulator for a LiDAR sensor (Velodyne VLP-16), was developed for comparison of the effects of diverse conditions on the backpack system and its operation. The simulated data was analyzed by visual inspection and comparison of the data sets' statistics, which differed according to the LiDAR arrangement and moving speed. Also, the data was used as input to a point-cloud registration algorithm, ICP (Iterative Closest Point), to validate its applicability as pre-analysis data. In fact, the results indicated centimeter-level accuracy, thus demonstrating the potentials of simulation data to be utilized as a tool for performance comparison of pointdata processing methods.