• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1999.06a
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• pp.97.1-102
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• 1999

This paper porposes and efficient coding scheme for remote medical communication systems, or“telemedicine systems”. These systems require a technique which is able to transfer large volume of data such as X-ray images effectively. We have already developed a hierarchical image coding and transmission scheme (HITS), which achieves an efficient transmission of medical images simply[1]. In this paper, a new coding scheme for HITS is proposed, which used hierarchical context modeling for the purpose of improving the coding efficiency. The hierarchical context modeling divides wavelet coefficients into several sets by the value of a correspondent coefficient in their higher class, or“a parent”, optimizes a Golomb-Rice (GR) code parameter in each set, and then encodes the coefficients with the parameter. Computer simulation shows that the proposed scheme is effective with simple implementation. This is due to fact that a wavelet coefficient has dependence on its parent. As a result, high speed data transmission is achieved even if the telemedicine system consists of simple personal computers.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.693-698
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• 2003

The application of object-oriented modeling to develop a powertrain performance simulation program, called P-DYN, is introduced. Powertrain components, such as the engine, transmission, shaft, clutch are modeled as classes which have data and method by using object-oriented modeling methodology. P-DYN, a performance simulation program, based on the object-oriented modeling is made in C++. One powertrain example is simulated through the P-DYN. It is expected that the simulation program or individual class constructed in this paper would be useful for automotive engineers in predicting the performance of powertrains and developing a simulation program.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.5
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• pp.805-811
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• 2015

Floating photovoltaic systems have been developed by the construction process such as design, construction, operation and management. Therefore, the power of floating photovoltaic systems has been calculated by using simple formulas and the optimal tracking interval is set by operation experience. But, flow characteristics have a decisive effect on it unlike land based PV systems. In this paper, a tracking floating photovoltaic system is modeled by using Matlab/simulink. The modeling for the floating photovoltaic system is verified through applying the flow characteristics based on actual operating data of 100㎾ class tracking floating photovoltaic.

• Journal of KIISE:Software and Applications
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• v.34 no.11
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• pp.1001-1010
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• 2007

HMM tends to output high probability for not only the proper class data but confusable class data, since the modeling power increases as the number of parameters increases. Thus it may not be helpful for discrimination to simply increase the number of parameters of HMM. We proposed two methods in this paper. One is a CMC(Confusion Likelihood Model Selection Criterion) using confusion class data probability, the other is a new recognition method, RCM(Recognition Using Confusion Models). In the proposed recognition method, confusion models are constructed using confusable class data, then confusion models are used to depress misrecognition by confusion likelihood is subtracted from the corresponding standard model probability. We found that CMC showed better results using fewer number of parameters compared with ML, ALC2, and BIC. RCM recorded 93.08% recognition rate, which is 1.5% higher result by reducing 17.4% of errors than using standard model only.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.33 no.6
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• pp.411-417
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• 2020

To utilize building information modeling (BIM) technology at the bridge maintenance stage, it is necessary to integrate large quantities of bridge inspection and model data for object-oriented information management. This research aims to establish the benefits of utilizing the extended industry foundation class (IFC)-BIM and ontology for bridge inspection information management. The IFC entities were extended to represent the bridge objects, and a method of generating the extended IFC-based information model was proposed. The bridge inspection ontology was also developed by extraction and classification of inspection concepts from the AASHTO standard. The classified concepts and their relationships were mapped to the ontology based on the semantic triples approach. Finally, the extended IFC-based BIM model was integrated with the ontology for bridge inspection data management. The effectiveness of the proposed framework for bridge inspection information management by integration of the extended IFC-BIM and ontology was tested and verified by extracting bridge inspection data via the SPARQL query.

• 전자공학회논문지 IE
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• v.48 no.2
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• pp.19-25
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• 2011

This paper presented neural network modeling method using vehicle data to predict fuel consumption. To acquire data for training and testing the proposed neural network, medium-class gasoline vehicle drove at downtown and parameters measured include speed, engine rpm, throttle position sensor (TPS), and mass air flow (MAF) as input data, and fuel consumption as target data from OBD-II port. Multi layer perception network was used for nonlinear mapping between the input and the output data. It was observed that the neural network model can predict the vehicle quite well with mean squared error was $1.306{\times}10^{-6}$ for the fuel consumption.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.24 no.5
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• pp.363-373
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• 2014

In this study, computational multi-body dynamic analyses for the drivetrain system of a 5 MW class offshore wind turbine have been conducted using efficient equivalent modeling technique based on the design guideline of GL 2010. The present drivetrain system is originally modeled and its related system data is adopted from the NREL 5 MW wind turbine model. Efficient computational method for the drivetrain system dynamics is proposed based on an international guideline for the certification of wind turbine. Structural dynamic behaviors of drivetrain system with blade, hub, shaft, gearbox, supports, brake disk, coupling, and electric generator have been analyzed and the results for natural frequency and equivalent torsional stiffness of the drivetrain system are presented in detail. It is finally shown that the present multi-body dynamic analysis method gives good agreement with the previous results of the 5 MW class wind turbine system.

• Journal of KIBIM
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• v.7 no.1
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• pp.18-27
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• 2017

Although Industry Foundation Classes (IFC) provide standards for exchanging Building Information Modeling (BIM) data, authoring tools still require manual mapping between BIM entities and IFC classes. This leads to errors and omissions, which results in corrupted data exchanges that are unreliable and thus compromise the validity of IFC. This research explored precedent work by Krijnen and Tamke, who suggested ways to automate the mapping of IFC classes using a machine learning technique, namely anomaly detection. The technique incorporates geometric features of individual components to find outliers among entities in identical IFC classes. This research primarily focused on applying this approach on two architectural BIM models and determining its feasibility as well as limitations. Results indicated that the approach, while effective, misclassified outliers when an IFC class had several dissimilar entities. Another issue was the lack of entities for some specific IFC classes that prohibited the anomaly detection from comparing differences. Future research to improve these issues include the addition of geometric features, using novelty detection and the inclusion of a probabilistic graph model, to improve classification accuracy.

• The Korean Journal of Pesticide Science
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• v.9 no.2
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• pp.140-145
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• 2005

Quantitative Structure Activity Relationship (QSAR) assumes the relatedness between physical property and biological activity. However, activity data measured at single concentration such as percent activity have not been used extensively for modeling purpose. This probably comes from the fact that these values are qualitative instead of quantitative. To utilize percent activity data for molecular modeling, we classified the whole data into two classes. One class represents the active while the other signifies the inactive. The percent activity data of ${\beta}$-Ketoacetoanilides measured for TLB (Tomato Late Blight) were investigated. CoMFA (Comparative Molecular Field Analysis) was used as a discriminant function. Using CoMFA provides 3D (three dimensional) information, which is crucial for chemical insight. It can also serve as a predictive model. The resultant model classified the given data correctly (98%). When LOO (leave-one-out) crossvalidation procedure was applied, the classification accuracy was 69%. Therefore two class approximation of percent activity data with CoMFA can be utilized to understand the relationship between chemical structure and biological activity and design subsequent chemical analogs.

• Journal of Information Processing Systems
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• v.14 no.3
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• pp.569-589
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• 2018

Cloud computing, also known as "country as you go", is used to turn any computer into a dematerialized architecture in which users can access different services. In addition to the daily evolution of stakeholders' number and beneficiaries, the imbalance between the virtual machines of data centers in a cloud environment impacts the performance as it decreases the hardware resources and the software's profitability. Our axis of research is the load balancing between a data center's virtual machines. It is used for reducing the degree of load imbalance between those machines in order to solve the problems caused by this technological evolution and ensure a greater quality of service. Our article focuses on two main phases: the pre-classification of tasks, according to the requested resources; and the classification of tasks into levels ('odd levels' or 'even levels') in ascending order based on the meta-heuristic "Bat-algorithm". The task allocation is based on levels provided by the bat-algorithm and through our mathematical functions, and we will divide our system into a number of virtual machines with nearly equal performance. Otherwise, we suggest different classes of virtual machines, but the condition is that each class should contain machines with similar characteristics compared to the existing binary search scheme.