• Korean Journal of Computational Design and Engineering
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• v.13 no.3
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• pp.163-174
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• 2008

In this paper, for integrating of data on car parts we model information of parts that PDM system manages. Ontology of car parts applies existing ontology mapping research to integrate into car ontology. We propose a method for semantic integration of PLM object of MEMPHIS based on the integrated ontology. Through our method, we introduce C# ontology model to apply existing C# applications with ontology. We also classify ontology integration into three through examples and explain them. While semantically integrating PLM objects based on the integrated ontology, we explain the need for change of PLM object type and describe the process of change for PLM object type by examples.

• Journal of information and communication convergence engineering
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• v.8 no.1
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• pp.83-88
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• 2010

Recently car navigation systems which have been widely spread and evolved. The systems use various information and techniques such as real time traffic information and augmented reality technique. In order to provide a shortest path with good flow, real-time traffic information provided by DMB is required. Augmented reality technique is also introduced to give a reality to driver by displaying real images captured by camera during driving. However, these operate well when the system receives GPS data normally. Exact information about the positions of vehicles is a base that supports the above function with realities. This paper proposes a model for acquiring exact position of vehicles. When the GPS does not operate normally, the proposed model uses various data which are generated by integrated vehicle networks.

• Asia pacific journal of information systems
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• v.1 no.1
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• pp.63-86
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• 1991

In this paper, our concern is the artificial neural network-based patten classification, when can resolve the difficulties in the Autoregressive Moving Average(ARMA) model identification problem To effectively classify a time series into an approriate ARMA model, we adopt the Multi-layered Backpropagation Network (MLBPN) as a pattern classifier, and Extended Sample Autocorrelation Function (ESACF) as a feature extractor. To improve the classification power of MLBPN's we suggest an integrated neural network system which consists of an AR Network and many small-sized MA Networks. The output of AR Network which will gives the MA order. A step-by-step training strategy is also suggested so that the learned MLBPN's can effectively ESACF patterns contaminated by the high level of noises. The experiment with the artificially generated test data and real world data showed the promising results. Our approach, combined with a statistical parameter estimation method, will provide a way to the automation of ARMA modeling.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.441-452
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• 2021

Federated learning provides an efficient integrated model for distributed data, allowing the local training of different data. Meanwhile, the goal of multi-task learning is to simultaneously establish models for multiple related tasks, and to obtain the underlying main structure. However, traditional federated multi-task learning models not only have strict requirements for the data distribution, but also demand large amounts of calculation and have slow convergence, which hindered their promotion in many fields. In our work, we apply the rank constraint on weight vectors of the multi-task learning model to adaptively adjust the task's similarity learning, according to the distribution of federal node data. The proposed model has a general framework for solving optimal solutions, which can be used to deal with various data types. Experiments show that our model has achieved the best results in different dataset. Notably, our model can still obtain stable results in datasets with large distribution differences. In addition, compared with traditional federated multi-task learning models, our algorithm is able to converge on a local optimal solution within limited training iterations.

• Journal of Intelligence and Information Systems
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• v.15 no.4
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• pp.99-121
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• 2009

Recently, there have been many studies that predict corporate bankruptcy using data mining techniques. Although various data mining techniques have been investigated, some researchers have tried to combine the results of each data mining technique in order to improve classification performance. In this study, we classify 4 types of data mining techniques via their characteristics and select representative techniques of each type then combine them using a genetic algorithm. The genetic algorithm may find optimal or near-optimal solution because it is a global optimization technique. This study compares the results of single models, typical combination models, and the proposed integration model using the genetic algorithm.

• Journal of the military operations research society of Korea
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• v.25 no.1
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• pp.179-198
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• 1999

This study concerns logistics support analysis record(LASR) which provides guaranteed support, and is needed in various weapon systems development and acquisition to develop the optimal factor for integrated logistics support(ILS) by reflecting the results of logistics support analysis(LSA). We observe and analyze the logistics management information(LMI) system used in the United States under the integrated data environment(IDE), and analyze the differences between logistics management information system and the LOADERS(logistic support analysis data entry and retrieval system) which is currently used in Korea. Based on the analyzed results, a improvement model that corresponds to reality and can be applied is presented.

• Journal of KIBIM
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• v.4 no.2
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• pp.1-9
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• 2014

As interest in green building has increased, construction market has evolved through BIM-based architecture also, BIM-based technologies have been developed simultaneously. Due to this aspect, the need of environmental analysis software utilizing BIM data became essential. This study shows that BIM-based integrated module provides objective analysis to proceed quick decision-making for a proposal. In addition to that, this integrated module creates a model through BIM data to analyze and report residential environment and energy consumption such as, daylight, view, ventilation and privacy in order to practically apply the BIM technology from the schematic design.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.13 no.1
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• pp.15-18
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• 2003

High purity single crystalline calcium fluoride ($CaF_2$) has excellent optical transmission characteristics down to deep UV and is therefore selected as the main optical material for the next generation of lithography apparatus operating at wavelength of 157 nm. The growth of large sized $CaF_2$ single crystals with the required properties for this optical application can be achieved only by optimizing the crystal growth process by the aid of numerical simulation. This needs especially a precise calculation of the heat transport and temperature distribution in the solid and liquid $CaF_2$ under crystal growth conditions. As $CaF_2$ is considered to be semitransparent, the internal radiative heat transfer in $CaF_2$ plays an decisive role in the simulation of the heat transport. On the other hand it is very difficult to obtain quantitative experimental data for evaluating numerical models as $CaF_2$ is extremely corrosive at high temperatures. In this work we present a newly developed experimental technique to perform temperature measurements in $CaF_2$-crystal as well as in the melt under conditions of crystal growth process. These experimental results are compared to calculated temperature data, which were obtained by using different numerical models concerning the internal heat transfer in semitransparent $CaF_2$. It will be shown, that an advanced model, which was developed by the authors, gives a much better agreement with experimental data as a standard model, which was taken from the literature.

• Proceedings of the Korea Water Resources Association Conference
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• 1992.07a
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• pp.443-447
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• 1992

This study describes the strategy for advanced decision support system (ADSS) development for integrated management of water resources and quality in reservoir systems. The developed ADSS consists of database that contain hydrologic data, observed operational data, and data to support specific reservoir operations simulation, optimization models, and water quality models. The optimization model, mass balance simulation model and water quality models are used in a general prototype ADSS, menu driven controlling framework that assists the user to specify and evaluate the alternative operational scenarios at one time. These alternative scenarios are evaluated by the models and the results are compared through the use of a graphical based display system. This graphical based system uses an icon based schematic representation of the system to organize the presentation of the results. The ADSS includes the ability to use monthly or weekly time periods of analysis for the models and it can use monthly historical or stochastically generated inflows.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.639-640
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• 2015

Traditional construction planning based on historical data and heuristic adjustment can no longer incorporate all the operational details and guarantee the expected performance. The variation between the expected and the actual production leads to cost overruns or delay. Although predicting reliable productivity on construction site is getting more important, the difficulty of this increases. In this regard, this paper suggested to develop BIM-integrated simulation framework. This framework could predict productivity dynamics by considering factors affecting on construction productivity at operational phase. We developed the following processes; 1) enabling a BIM model to produce input data for simulation; 2) developing the construction operation simulation; 3) running simulation using BIM data and obtaining productivity results. The BIM-integrated simulation framework was tested with structural steel erection model because steel erection work is one of the most critical process influencing on the whole construction budget and duration. We could improve to predict more dynamic productivity from this framework, and this reliable productivity helps construction managers to optimize resource allocation, increase schedule reliability, save storage cost, and reduce material loss.