• Journal of the Korean Chemical Society
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• v.53 no.6
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• pp.653-662
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• 2009

Developing effective tools for predicting absorption, distribution, metabolism, excretion properties and toxicity (ADME/T) of new chemical entities in the early stage of drug design is one of the most important tasks in drug discovery and development today. As one of these attempts, support vector machines (SVM) has recently been exploited for the prediction of ADME/T related properties. However, two problems in SVM modeling, i.e. feature selection and parameters setting, are still far from solved. The two problems have been shown to be crucial to the efficiency and accuracy of SVM classification. In particular, the feature selection and optimal SVM parameters setting influence each other, which indicates that they should be dealt with simultaneously. In this account, we present an integrated practical solution, in which genetic-based algorithm (GA) is used for feature selection and grid search (GS) method for parameters optimization. hERG ion-channel inhibitor classification models of ADME/T related properties has been built for assessing and testing the proposed GA-GS-SVM. We generated 6 different models that are 3 different single models and 3 different ensemble models using training set - 1891 compounds and validated with external test set - 175 compounds. We compared single model with ensemble model to solve data imbalance problems. It was able to improve accuracy of prediction to use ensemble model.

• Geophysics and Geophysical Exploration
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• v.1 no.1
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• pp.49-56
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• 1998

Seismic tomography has been widely used as high resolution subsurface imaging techniques in engineering applications. Although most of the techniques have been using travel time inversion, waveform method is being driven forward owing to the progress of computational environments. Although full-waveform inversion method has been known as the best method in terms of model resolving power without high-frequency restriction and weak scattering approximation, it has practical disadvantage that it is apt to get stuck in local minimum if the initial guess is far from the actual model and it consumes so much time to calculate. In this study, 2-D full-waveform inversion algorithm in acoustic medium is developed, which uses result of traveltime tomography as initial model. From the application on synthetic data, it is proved that this approach can efficiently reduce the problem of conventional approaches: our algorithm shows much faster convergence rate and improvement of model resolution. Result of application on physical modeling data also shows much improvement. It is expected that this algorithm can be applicable to real data.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.82-98
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• 2019

The importance of spatial information is rapidly rising. In particular, 3D spatial information construction and modeling for Real World Objects, such as smart cities and digital twins, has become an important core technology. The constructed 3D spatial information is used in various fields such as land management, landscape analysis, environment and welfare service. Three-dimensional modeling with image has the hig visibility and reality of objects by generating texturing. However, some texturing might have occlusion area inevitably generated due to physical deposits such as roadside trees, adjacent objects, vehicles, banners, etc. at the time of acquiring image Such occlusion area is a major cause of the deterioration of reality and accuracy of the constructed 3D modeling. Various studies have been conducted to solve the occlusion area. Recently the researches of deep learning algorithm have been conducted for detecting and resolving the occlusion area. For deep learning algorithm, sufficient training data is required, and the collected training data quality directly affects the performance and the result of the deep learning. Therefore, this study analyzed the ability of detecting the occlusion area of the image using various image quality to verify the performance and the result of deep learning according to the quality of the learning data. An image containing an object that causes occlusion is generated for each artificial and quantified image quality and applied to the implemented deep learning algorithm. The study found that the image quality for adjusting brightness was lower at 0.56 detection ratio for brighter images and that the image quality for pixel size and artificial noise control decreased rapidly from images adjusted from the main image to the middle level. In the F-measure performance evaluation method, the change in noise-controlled image resolution was the highest at 0.53 points. The ability to detect occlusion zones by image quality will be used as a valuable criterion for actual application of deep learning in the future. In the acquiring image, it is expected to contribute a lot to the practical application of deep learning by providing a certain level of image acquisition.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.183-203
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• 2018

News articles are the most suitable medium for examining the events occurring at home and abroad. Especially, as the development of information and communication technology has brought various kinds of online news media, the news about the events occurring in society has increased greatly. So automatically summarizing key events from massive amounts of news data will help users to look at many of the events at a glance. In addition, if we build and provide an event network based on the relevance of events, it will be able to greatly help the reader in understanding the current events. In this study, we propose a method for extracting event networks from large news text data. To this end, we first collected Korean political and social articles from March 2016 to March 2017, and integrated the synonyms by leaving only meaningful words through preprocessing using NPMI and Word2Vec. Latent Dirichlet allocation (LDA) topic modeling was used to calculate the subject distribution by date and to find the peak of the subject distribution and to detect the event. A total of 32 topics were extracted from the topic modeling, and the point of occurrence of the event was deduced by looking at the point at which each subject distribution surged. As a result, a total of 85 events were detected, but the final 16 events were filtered and presented using the Gaussian smoothing technique. We also calculated the relevance score between events detected to construct the event network. Using the cosine coefficient between the co-occurred events, we calculated the relevance between the events and connected the events to construct the event network. Finally, we set up the event network by setting each event to each vertex and the relevance score between events to the vertices connecting the vertices. The event network constructed in our methods helped us to sort out major events in the political and social fields in Korea that occurred in the last one year in chronological order and at the same time identify which events are related to certain events. Our approach differs from existing event detection methods in that LDA topic modeling makes it possible to easily analyze large amounts of data and to identify the relevance of events that were difficult to detect in existing event detection. We applied various text mining techniques and Word2vec technique in the text preprocessing to improve the accuracy of the extraction of proper nouns and synthetic nouns, which have been difficult in analyzing existing Korean texts, can be found. In this study, the detection and network configuration techniques of the event have the following advantages in practical application. First, LDA topic modeling, which is unsupervised learning, can easily analyze subject and topic words and distribution from huge amount of data. Also, by using the date information of the collected news articles, it is possible to express the distribution by topic in a time series. Second, we can find out the connection of events in the form of present and summarized form by calculating relevance score and constructing event network by using simultaneous occurrence of topics that are difficult to grasp in existing event detection. It can be seen from the fact that the inter-event relevance-based event network proposed in this study was actually constructed in order of occurrence time. It is also possible to identify what happened as a starting point for a series of events through the event network. The limitation of this study is that the characteristics of LDA topic modeling have different results according to the initial parameters and the number of subjects, and the subject and event name of the analysis result should be given by the subjective judgment of the researcher. Also, since each topic is assumed to be exclusive and independent, it does not take into account the relevance between themes. Subsequent studies need to calculate the relevance between events that are not covered in this study or those that belong to the same subject.

• Journal of KIISE:Software and Applications
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• v.37 no.7
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• pp.518-535
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• 2010

Cloud Computing is emerged as an effective reuse paradigm, where service providers operate hardware and software and as a service, and service consumers invoke the service through Internet. Software-as-a-Service (SaaS) is a type of cloud services, where the whole software is designed as a service so that several consumers can reuse the SaaS. While tradition software applications are developed for a specific organization, SaaS is developed for multiple users in the various organizations. Hence, reusability is very essential characteristic of SaaS. Reusability is defined as a metric of how effective and efficient software functionalities can be used by various users. Reusability in SaaS is evaluated by considering three sub-characteristics; applicability, adaptability, and scalability. Since such a SaaS has considerable differences and characteristics from traditional software applications, conventional methods including object-oriented modeling, component-based development method, and service-oriented architecture (SOA) service development method would be limited in developing services which can fulfill these three sub-characteristics related to reusability as well as SaaS-intrinsic characteristics. Hence, there is a great demand for effective processes for developing SaaS cloud services. In this paper, we present a practical process for developing SaaS, which focuses on ensuring reusability. And by performing a case study with our proposed SaaS development process, we evaluate applicability of our proposed process and explain how the process is used in a real domain. Then, we compare our proposed process with others for verifying our study. Through the proposed process, cloud services with high quality can be more effectively developed.

• Korean Journal of Social Welfare
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• v.64 no.1
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• pp.31-52
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• 2012

This study has three objectives. First, it examines the relationship between organizational fairness and organizational commitment perceived by social workers in Korea. Second, it simultaneously examines mediating effects of organizational trust and organizational cynicism in the relationship between organizational fairness and organizational commitment. Third, it also examines the effect of social workers' perceived work value on the combined model by considering the unique characteristics of social work profession. This study employs the stratified cluster sampling method on social workers with more than two year work experiences in their current social service agencies that are located in Seoul and Kyungki province; finally it analyzes the responses from 564 social workers by using the method of structural equation modeling. This study has the following results: (1) there is a positive causal relationship between organizational fairness and organizational commitment perceived by social workers; (2) there is also a positive causal relationship between social workers' perceived work value and organizational commitment; and (3) in the mediating effects of organizational trust and organizational cynicism, there are no mediating effects in the relationship between organizational fairness and organizational commitment. This study discusses the importance of social workers' perceived work value and theoretical and practical implications of the results.

• The KIPS Transactions:PartA
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• v.16A no.6
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• pp.427-436
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• 2009

Digital virtual manufacturing is a technology that aims for the rapid development of products and the verification of production-process in ways that are more efficient by integrating digital models within the entire manufacturing process. These digital models utilize various information technologies, such as 3D CAD and simulations. Mixed reality, which represents graphical objects for only needed parts against real scene, can bring a more enriched sense of reality to an existing virtual manufacturing system that is in a pure virtual environment, and it can reduce the time and money needed for modeling the environment. This paper suggests a method for planning virtual factory layouts based on mixed reality using legacy datathat are already constructed in the real field. To do this, we developed the method to acquire simulation data from legacy data and process this acquired data for visualization based on mixed reality. And then we construct display system based on mixed reality, which can simulate virtual factory layout with processed data. Developed system can reduce errors related with factory layout by verifying the location and application of equipments in advance before arrangement of real ones at the practical job site.

• Korean Journal of Social Welfare
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• v.67 no.3
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• pp.83-105
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• 2015

This study has two objectives. First, it examines the relationship between organizational trust and error management culture. Second, it also examines the effect of social workers' perceived work value on the combined model by considering the unique characteristics of social work profession and social desirability. This study employs the stratified sampling method on social workers with more than two year work experiences in their current social service agencies that are located in Seoul and Kyungki province; finally it analyzes the responses from 564 social workers by using the method of multiple regression modeling. This study has the following results: (1) there is a causal relationship between social desirability and organizational trust perceived by social workers; (2) there is a positive causal relationship between social workers' perceived work value and organizational trust; and (3) there is also a positive causal relationship between factor structure of error management culture and organizational trust. This study discusses the theoretical and practical implications of the results.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.9
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• pp.528-535
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• 2020

Recently, the protection coordination method of DC systems has been presented because renewable energy and distributed resources are being installed and operated in distribution systems. On the other hand, it is difficult to detect ground faults because there is no significant difference compared to a steady-state current in ungrounded IT systems, such as DC load networks and urban railways. Therefore, this paper formulates the detection principle of IMD (Insulation Monitoring Device) to use it as a protection coordination device in a DC system. Based on the signal injection method of IMD, which is analyzed by a wavelet transform, this paper presents an algorithm of detecting ground faults in a DC system in a fast and accurate manner. In addition, this paper modeled an IMD and an ungrounded DC system using the PSCAD/EMTDC S/W and performed numerical analysis of a wavelet transform with the Matlab S/W. The simulation results of a ground fault case in an ungrounded DC system showed that the proposed algorithm and modeling are useful and practical tools for detecting a ground fault in a DC system.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.26 no.1
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• pp.1-7
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• 2013

Structural design and analysis of a coiling arm unloading machine for submarine cable have been originally conducted in this study. Three-dimensional CAD modeling process is practically applied for the structural design in detail. Finite element method(FEM) and multi-body dynamics(MBD) analyses are also used to verify the safety and required motions of the designed coiling arm structure. The effective moving functions of the designed coiling arm with respect to rotational and radial motions are achieved by adopting bearing-roller mechanical parts and hydraulic system. Critical design loading conditions due to its self weight, carrying cables, offshore wind, and hydraulic system over operation conditions are considered for the present structural analyses. In addition, possible inclined ground conditions for the installation of the designed coiling arm are also considered to verify overturn stability. The present hydraulic type coiling arm system is originally designed and developed in this study. The developed coiling arm has been installed at a harbor, successfully tested its operational functions, and finished practical unloading mission of the submarine cable.