• Spatial Information Research
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• v.22 no.5
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• pp.9-18
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• 2014

According to the recent development of the ubiquitous computing environment, the use of spatio-temporal data from sensors with GPS is increasing, and studies on the Semantic Sensor Web using spatio-temporal data for providing different kinds of services are being actively conducted. Especially, the W3C developed the SSNO(Semantic Sensor Network Ontology) which uses sensor-related standards such as the SWE(Sensor Web Enablement) of OGC and defines classes and properties for expressing sensor data. Since these studies are available for the query processing about non-spatio-temporal sensor data, it is hard to apply them to spatio-temporal sensor data processing which uses spatio-temporal data types and operators. Therefore, in this paper, we developed the SWE based on SSNO which supports the spatio-temporal sensor data types and operators expanding spatial data types and operators in "OpenGIS Simple Feature Specification for SQL" by OGC. The system receives SensorML(Sensor Model Language) and O&M (Observations and Measurements) Schema and converts the data into SSNO. It also performs the efficient query processing which supports spatio-temporal operators and reasoning rules. In addition, we have proved that this system can be utilized for the web service by applying it to a virtual scenario.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.4
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• pp.153-163
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• 2017

This research is based on the previous research that personally preferred safe distance, rotating angle and speed are differentiated. Thus, we use machine learning model for recognizing maneuvering modes trained per personal or per similar driving pattern groups, and we evaluate automatic driving according to maneuvering modes. By utilizing driving knowledge, we subdivided 8 kinds of longitudinal modes and 4 kinds of lateral modes, and by combining the longitudinal and lateral modes, we build 21 kinds of maneuvering modes. we train the labeled data set per time stamp through RNN, LSTM and Bi-LSTM models by the trips of drivers, which are supervised deep learning models, and evaluate the maneuvering modes of automatic driving for the test data set. The evaluation dataset is aggregated of living trips of 3,000 populations by VTTI in USA for 3 years and we use 1500 trips of 22 people and training, validation and test dataset ratio is 80%, 10% and 10%, respectively. For recognizing longitudinal 8 kinds of maneuvering modes, RNN achieves better accuracy compared to LSTM, Bi-LSTM. However, Bi-LSTM improves the accuracy in recognizing 21 kinds of longitudinal and lateral maneuvering modes in comparison with RNN and LSTM as 1.54% and 0.47%, respectively.

• Bulletin of the Korean Chemical Society
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• v.30 no.5
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• pp.1152-1156
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• 2009

Silent information regulator 2 (Sir2) or sirtuins are NAD(+)-dependent deacetylases, which hydrolyze the acetyllysine residues. In mammals, sirtuins are classified into seven different classes (SIRT1-7). SIRT1 was reported to be involved in age related disorders like obesity, metabolic syndrome, type II diabetes mellitus and Parkinson’s disease. Activation of SIRT1 is one of the promising approaches to treat these age related diseases. In this study, we have used HipHop module of CATALYST to identify a series of pharmacophore models to screen SIRT1 enhancing molecules. Three molecules from Sirtris Pharmaceuticals were selected as training set and 607 sirtuin activator molecules were used as test set. Five different hypotheses were developed and then validated using the training set and the test set. The results showed that the best pharmacophore model has four features, ring aromatic, positive ionization and two hydrogen-bond acceptors. The best hypothesis from our study, Hypo2, screened high number of active molecules from the test set. Thus, we suggest that this four feature pharmacophore model could be helpful to screen novel SIRT1 activator molecules. Hypo2-virtual screening against Maybridge database reveals seven molecules, which contains all the critical features. Moreover, two new scaffolds were identified from this study. These scaffolds may be a potent lead for the SIRT1 activation.

• The Journal of the Acoustical Society of Korea
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• v.35 no.6
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• pp.480-490
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• 2016

As wind turbines become larger and lighter, they are likely to respond sensitively by dynamic loads applied on them. Since the responses at resonances are particularly interested, it is required to be able to predict natural frequencies of wind turbines reliably at early design stage. To achieve this, the foundation-soil analysis is needed to be carried out and a finite element approach is adopted in general. However, the finite element approach would not be appropriate in early design stage because it demands heavy efforts in pile-soil modelling and computing facilities. On the contrary, theoretical approaches adopting linear approximations for soils are relatively simple and easy to handle. Therefore, they would be a useful tool in predicting a pile-soil interaction, particularly in early design stage. In this study an analysis for a pile inserted in soil is performed. The pile and soil are modelled as a beam and continuum medium, respectively, within an elastic range. In this analysis, influence factors at the pile head for lateral loads are predicted by means of this continuum approach for various length-diameter ratios of the pile. The influence factors predicted are validated with those reported in literature, proposed from a finite element analysis.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.6
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• pp.67-78
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• 2017

Traffic congestion cost is increasing annually. Specifically congestion caused by the CDB traffic contains more than a half of the total congestion cost. Recent advancement in the field of Big Data, AI paved the way to industry revolution 4.0. And, these new technologies creates tremendous changes in the traffic information dissemination. Eventually, accurate and timely traffic information will give a positive impact on decreasing traffic congestion cost. This study, therefore, focused on developing both recurrent and non-recurrent congestion prediction models on urban roads by adopting Recurrent Neural Network(RNN), a tribe in machine learning. Two hidden layers with scaled conjugate gradient backpropagation algorithm were selected, and tested. Result of the analysis driven the authors to 25 meaningful links out of 33 total links that have appropriate mean square errors. Authors concluded that RNN model is a feasible model to predict congestion.

• Geophysics and Geophysical Exploration
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• v.18 no.2
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• pp.74-84
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• 2015

To yield physically meaningful images through elastic reverse-time migration, the wavefield separation which extracts P- and S-waves from reconstructed vector wavefields by using elastic wave equation is prerequisite. For expanding the application of the elastic reverse-time migration to anisotropic media, not only the anisotropic modelling algorithm but also the anisotropic wavefield separation is essential. The anisotropic wavefield separation which uses pseudo-derivative filters determined according to vertical velocities and anisotropic parameters of elastic media differs from the Helmholtz decomposition which is conventionally used for the isotropic wavefield separation. Since applying these pseudo-derivative filter consumes high computational costs, we have developed the efficient anisotropic wavefield separation algorithm which has capability of parallel computing by using GPUs (Graphic Processing Units). In addition, the highly efficient anisotropic elastic reverse-time migration algorithm using MPI (Message-Passing Interface) and incorporating the developed anisotropic wavefield separation algorithm with GPUs has been developed. To verify the efficiency and the validity of the developed anisotropic elastic reverse-time migration algorithm, a VTI elastic model based on Marmousi-II was built. A synthetic multicomponent seismic data set was created using this VTI elastic model. The computational speed of migration was dramatically enhanced by using GPUs and MPI and the accuracy of image was also improved because of the adoption of the anisotropic wavefield separation.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.4
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• pp.457-467
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• 2002

In order to simulate cyber attacks and predict network behavior by attacks, we should represent attributes of network components in the simulation model, and should express characteristics of systems that carry out various cyber attacks and defend from these attacks. To simulate how network load may change under the cyber attacks, we extended SSF[9, 10] that is process-based event-oriented simulation system. We added a firewall class and a packet manipulator into the SSFNet that is a component of SSF. The firewall class, which is related to the security, is to simulate cyber attacks, and the packet manipulator is a set of functions to write attack programs for the simulation. The extended SSFNet enables to simulate a network with the security systems and provides advantages that make easy to port already exsiting attack programs and apply them to the simulation evironment. We made a vitual network model to verify operations of the added classes, and simulated a smurf attack that is a representative denial of sevive attack, and observed the network behavior under the smurf attack. The results showed that the firewall class and packet manipulator developed in this paper worked normaly.

• Archives of design research
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• v.12 no.4
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• pp.294-303
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• 1999

With the breakthrough development of computing environment, the design phases have been changed a lot nowadays. In the case of prior phases of transportation design such as cars and forklift-trucks design, designers have depended on surveys and 2D line drawings for fixing a product layout and extracting ergonomic data. In this method, designers don't meet only the problem of reliability of measuring data but also, the problems of unknown situation of operators' fatigue and comfort in work situation. In these methods, it has much less creditability to have a 2D human model to check the real world motion due to the limitation of the 3 Dimension. Even though with a 2D human model, perfect layout is determined, it is still difficult to measure about comfort and fatigue for a user because it measuring an analysing method is static. The development of computer hardware and software have not only changed the flow in the social-wide range but also immerged design into Virtual Environment. In conventional design method, visualization and data transferring have been the main issues but, in virtual environment, determining of design layout and analysing ergonomic data with sophisticated feeling about comfort and fatigue are possible by using 3D virtual human. In this study, the general characteristics of virtual environment was discussed and the possibility of digital process of design was treated. For these studies, layout design for forklift-trucks was tested. Eventually, the merits of each design phase applied virtual environment are discussed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.10
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• pp.1277-1286
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• 2018

In this paper, we provide flexible scalability of computing resources in cloud environment and Apache Hadoop based cloud environment for analysis of public medical information big data. In fact, it includes the ability to quickly and flexibly extend storage, memory, and other resources in a situation where log data accumulates or grows over time. In addition, when real-time analysis of accumulated unstructured log data is required, the system adopts Hadoop-based analysis module to overcome the processing limit of existing analysis tools. Therefore, it provides a function to perform parallel distributed processing of a large amount of log data quickly and reliably. Perform frequency analysis and chi-square test for big data analysis. In addition, multivariate logistic regression analysis of significance level 0.05 and multivariate logistic regression analysis of meaningful variables (p<0.05) were performed. Multivariate logistic regression analysis was performed for each model 3.

• Journal of KIISE:Software and Applications
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• v.29 no.1_2
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• pp.30-41
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• 2002

We focus on the mobile agents which are considered as new paradigm to solve information retrieval of large volumes of data in the distributed and heterogeneous environment. The mobile agent moves the computation to data instead of large volumes of data to computations. In this paper, we propose an information retrieval model, which can effectively search data in the distributed and heterogeneous environment, using mobile agents. Our model is applied to the design and implementation of an Q&A(Question and Answer) retrieval system. Our Q&A retrieval system, called QASSMA(Q&A Search System using Mobile Agents), uses mobile agents to retrieve articles from Q&A boards and newsgroups that exist in the heterogeneous and distributed environment. QASSMA has the following features and advantages. First, the mobile retrieval agent moves to the destination server to retrieve articles to reduce the retrieval time by eliminating data traffics from the server to the client host. Also it can reduce the traffic that was occurred in the centralized network system, and reduce the usage of resources by sending its agent and running in the destination host. Finally, the mobile retrieval agent of QASSMA can add and update dynamically the class file according to its retrieval environment, and support other retrieval manner. In this paper, we have shown that our Q&A retrieval system using mobile agents is more efficient than the retrieval system using static agents by our experiments.