• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.32-48
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• 2023

Autonomous driving technology is shaping the future of personalized travel, encouraging personalized travel, and traffic impact could be influenced by individualized travel behavior during the transition of driving entity from human to machine. In order to evaluate traffic impact, it is necessary to estimate the total number of trips based on an understanding of individual travel characteristics. The Activity-based model(ABM), which allows for the reflection of individual travel characteristics, deals with all travel sequences of an individual. Understanding the relationship between travel and travel must be important for assessing traffic impact using ABM. However, the ABM has a limitation in the data hunger model. It is difficult to adjust in the actual demand forecasting. Therefore, we utilized a Tour-based model that can explain the relationship between travels based on household travel survey data instead. After that, vehicle registration and population data were used for correction. The result showed that, compared to the KTDB one, the traffic generation exhibited a 13% increase in total trips and approximately 9% reduction in working trips, valid within an acceptable margin of error. As a result, it can be used as a generation correction method based on Tour, which can reflect individual travel characteristics, prior to building an activity-based model to predict demand due to the introduction of autonomous vehicles in terms of road operation, which is the ultimate goal of this study.

• Journal of Power System Engineering
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• v.4 no.3
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• pp.72-80
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• 2000

In order to design a stable robust controller, nominal model, and the upper bound about the uncertainty which is the error of the model are needed. The problem to estimate the nominal model of controlled system and the upper bound of uncertainty at the same time is called robust identification. When the nominal model of controlled system and the upper bound of uncertainty in relation to robust identification are given, the evaluation of the validity of the model and the upper bound makes it possible to distinguish whether there is a model which explains observation data including disturbance among the model set. This paper suggests a method to identity the uncertainty which removes disturbance and expounds observation data by giving a probable postulation and plural data set to disturbance. It also examines the suggested method through a numerical computation simulation and validates its effectiveness.

• Magazine of the Korean Society of Agricultural Engineers
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• v.30 no.1
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• pp.50-62
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• 1988

This study refers to the development of a hydrologic model simulating daily inflow and release rates for irrigation reservoirs. A daily - based model is needed for adequate operation of an irrigation reservoir sufficing the water demand for paddy fields which is closely related to meteorological conditions. Inflow rates to a reservoir need to be accurately described, which may be simulated using a hydrologic model from daily rainfall data. And the objective of this paper is to develop, test, and apply a hydrologic model for daily runoff simmulation. A well - known tank model was selected and modified to simulate daily inflow rates. The model parameters were calibrated using observed runoff data from twelve watersheds, Relationships between the parameters and the watershed characteristics were derived by a multiple regression analysis. The simulation results were in agreement with the data. The inflow model was found to simulate low flow conditions more accurately than high flow conditions, which may be adequate for water resources utilization.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2690-2692
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• 2000

In order to design a stable robust controller, nominal model, and the upper bound about the uncertainty which is the error of the model are needed. The problem to estimate the nominal model of controlled system and the upper bound of uncertainty at the same time is called robust identifcation. When the nominal model of controlled system and the upper bound of uncertainty in relation to robust identifcation are given, the evaluation of the validity of the model and the upper bound makes it possible to distinguish whether there is a model which explains observation data including disturbance among the model set. This paper suggests a method to identify the uncertainty which removes disturbance and expounds observation data by giving a probable postulation and plural data set to disturbance. It also examines the suggested method through a numerical computation simulation and validates its effectiveness.

• Structural Engineering and Mechanics
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• v.87 no.6
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• pp.601-614
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• 2023

In this study, the uniaxial ratcheting effect of Z2CND18.12N austenitic stainless steel at room and elevated temperatures is firstly simulated based on the Ahmadzadeh-Varvani hardening rule (A-V model), which is embedded into the finite element software ABAQUS by writing the user material subroutine UMAT. The results show that the predicted results of A-V model are lower than the experimental data, and the A-V model is difficult to control ratcheting strain rate. In order to improve the predictive ability of the A-V model, the parameter γ2 of the A-V model is modified using the isotropic hardening criterion, and the extended A-V model is proposed. Comparing the predicted results of the above two models with the experimental data, it is shown that the prediction results of the extended A-V model are in good agreement with the experimental data.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.449-455
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• 2004

Support vector machine (SVM) has been very successful in pattern recognition and function estimation problems for crisp data. This paper proposes a new method to evaluate interval regression models for crisp input-output data. The proposed method is based on quadratic loss SVM, which implements quadratic programming approach giving more diverse spread coefficients than a linear programming one. The proposed algorithm here is model-free method in the sense that we do not have to assume the underlying model function. Experimental result is then presented which indicate the performance of this algorithm.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.1388-1395
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• 2009

In March 2007, Korea's Ministry of Construction & Transportation (MOCT) established "Guidelines on the Computerization and Use of Geotechnical Investigation Results," which took effect as official instructions. The 2007 Geotechnical Information DB Construction Project is underway as a model project for a stable geotechnical information distribution system based on the MOCT guidelines, accompanied by user education on the geotechnical data distribution system. This study introduces a geotechnical data entry system characterized by the standardization of the geotechnical investigation form, the standardization of metadata for creating the geotechnical data to be distributed, and the creation of borehole space data based on the world geodetic system according to the changes in the national coordinate system, to define a unified DB structure and the items for the geotechnical data entry system and to computerize the field geotechnical investigation results using the MOCT guidelines. In addition, the present operating status of the geotechnical data entry system and entry data processing statistics are introduced through an analysis of the model project, and the problems of the project are analyzed to suggest improvements. Education on, and the implementation of, the model project for the geotechnical data entry system, which was developed via the standardization of the geotechnical investigation results form and the metadata for institutions showed that most users can use the system easily. There were problems, however, including those related to the complexity of metadata creation, partial errors in moving to the borehole data window, partial recognition errors in the installation program for different computer operating systems, etc. Especially, the individual standard form usage and the specificity of the person who enters the geotechnical information for the Korea National Housing Corporation, among the institutions under MOCT, required partial improvement of the geotechnical data entry system. The problems surfaced from this study will be promptly addressed in the operation and management of the geotechnical data DB center in 2008.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.10
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• pp.1445-1453
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• 2017

In this paper, we propose a fault detection model based on multi-layer neural network using data mining technique for faults due to boiler tube leakage in a thermal power plant. Major measurement data related to faults are analyzed using statistical methods. Based on the analysis results, the number of input data of the proposed fault detection model is simplified. Then, each input data is clustering with normal data and fault data by applying K-Means algorithm, which is one of the data mining techniques. fault data were trained by the neural network and tested fault detection for boiler tube leakage fault.

• Smart Structures and Systems
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• v.33 no.5
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• pp.325-332
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• 2024

This study proposes a novel long short-term memory (LSTM)-based approach for predicting carbonation depth, with the aim of enhancing the durability evaluation of concrete structures. Conventional carbonation depth prediction relies on statistical methodologies using carbonation influencing factors and in-situ carbonation depth data. However, applying in-situ data for predictive modeling faces challenges due to the lack of time-series data. To address this limitation, an LSTM-based carbonation depth prediction technique is proposed. First, training data are generated through random sampling from the distribution of carbonation velocity coefficients, which are calculated from in-situ carbonation depth data. Subsequently, a Bayesian theorem is applied to tailor the training data for each target bridge, which are depending on surrounding environmental conditions. Ultimately, the LSTM model predicts the time-dependent carbonation depth data for the target bridge. To examine the feasibility of this technique, a carbonation depth dataset from 3,960 in-situ bridges was used for training, and untrained time-series data from the Miho River bridge in the Republic of Korea were used for experimental validation. The results of the experimental validation demonstrate a significant reduction in prediction error from 8.19% to 1.75% compared with the conventional statistical method. Furthermore, the LSTM prediction result can be enhanced by sequentially updating the LSTM model using actual time-series measurement data.

• Journal of Information Technology Applications and Management
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• v.13 no.1
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• pp.137-147
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• 2006

Spatiotemporal data are records of the spatial changes of moving objects over time. Most data in corporate databases have a spatiotemporal nature, but they are typically treated as merely descriptive semantic data without considering their potential visual (or cartographic) representation. Businesses such as geographical CRM, location-based services, and technologies like GPS and RFID depend on the storage and analysis of spatiotemporal data. Effectively handling the data analysis process may be accomplished through spatiotemporal data warehouse and spatial OLAP. This paper proposes a multidimensional model for spatiotemporal data analysis, and cartographically represents the results of the analysis.