• Journal of the Computational Structural Engineering Institute of Korea
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• v.21 no.3
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• pp.253-263
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• 2008

Extension of IFC data model for steel bridge members is proposed to represent detailed design information. First of all, the design data items and their representation method are classified by analyzing primary references such as design specification, structural calculation documents and shop drawings. Some of the classified items are enough to be represented by the existing IFC model. However, the need of additional model is noted to systematically represent the design information for other items such as stiffener, diaphragm, joint system, and shear connector. An inheritance relations and properties for added model are also defined. The application program based on the proposed data model is developed. In the end, by loading the application program on the AutoCAD 2002 program, end-users can input the design information of steel bridge members. The applicability and efficiency of the proposed data model and the program are verified by checking the section area, intervals, and interferences.

• Journal of the Korean Society for Heat Treatment
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• v.35 no.6
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• pp.327-336
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• 2022

In this study, a model for automatically recognizing several steel parts through a camera before charging materials was developed under the assumption that the temperature distribution in the pre-air atmosphere was known. For model development, datasets were collected in random environments and factories. In this study, the YOLO-v5 model, which is a YOLO model with strengths in real-time detection in the field of object detection, was used, and the disadvantages of taking a lot of time to collect images and learning models was solved through the transfer learning methods. The performance evaluation results of the derived model showed excellent performance of 0.927 based on mAP 0.5. The derived model will be applied to the model development study, which uses the model to accurately recognize the material and then match it with the temperature distribution in the atmosphere to determine whether the material layout is suitable before charging materials.

• Journal of the Korea Society of Computer and Information
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• v.28 no.9
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• pp.81-92
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• 2023

A privacy model is a technique that quantitatively restricts the possibility and degree of privacy breaches through privacy attacks. Representative models include k-anonymity, l-diversity, t-closeness, and differential privacy. While many privacy models have been studied, research on selecting the most suitable model for a given dataset has been relatively limited. In this study, we develop a system for recommending the suitable privacy model to prevent privacy breaches. To achieve this, we analyze the data features that need to be considered when selecting a model, such as data type, distribution, frequency, and range. Based on privacy model background knowledge that includes information about the relationships between data features and models, we recommend the most appropriate model. Finally, we validate the feasibility and usefulness by implementing a recommendation prototype system.

• The Korean Journal of Archival Studies
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• no.80
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• pp.5-41
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• 2024

This study was conducted to develop a provenance representation model for data-type electronic records. It supports the distinction between provenance and context for the creation and management of data-type electronic records. To express both, it aims to design an extensible provenance model. For this purpose, W3C PROV is utilized as a basic model, with P-Plan and ProvONE for designing prospective provenance area. Afterward, the provenance model was extended by mapping the record management requirements. The provenance model proposed in this study is designed to represent and connect both retrospective and prospective provenance of data-type electronic records. Based on this study, it is expected to discussing the concept of provenance in the records management and archival studies area and to extending the model in the future.

• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.523-541
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• 2023

As accessibility to 3D printers increases, there is a growing frequency of exposure to chemicals associated with 3D printing. However, research on the toxicity and harmfulness of chemicals generated by 3D printing is insufficient, and the performance of toxicity prediction using in silico techniques is limited due to missing molecular structure data. In this study, quantitative structure-activity relationship (QSAR) model based on data-centric AI approach was developed to predict the toxicity of new 3D printing materials by imputing missing values in molecular descriptors. First, MissForest algorithm was utilized to impute missing values in molecular descriptors of hazardous 3D printing materials. Then, based on four different machine learning models (decision tree, random forest, XGBoost, SVM), a machine learning (ML)-based QSAR model was developed to predict the bioconcentration factor (Log BCF), octanol-air partition coefficient (Log Koa), and partition coefficient (Log P). Furthermore, the reliability of the data-centric QSAR model was validated through the Tree-SHAP (SHapley Additive exPlanations) method, which is one of explainable artificial intelligence (XAI) techniques. The proposed imputation method based on the MissForest enlarged approximately 2.5 times more molecular structure data compared to the existing data. Based on the imputed dataset of molecular descriptor, the developed data-centric QSAR model achieved approximately 73%, 76% and 92% of prediction performance for Log BCF, Log Koa, and Log P, respectively. Lastly, Tree-SHAP analysis demonstrated that the data-centric-based QSAR model achieved high prediction performance for toxicity information by identifying key molecular descriptors highly correlated with toxicity indices. Therefore, the proposed QSAR model based on the data-centric XAI approach can be extended to predict the toxicity of potential pollutants in emerging printing chemicals, chemical process, semiconductor or display process.

• The Journal of the Acoustical Society of Korea
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• v.19 no.5
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• pp.20-26
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• 2000

In speaker verification system the HMM(hidden Markov model) parameter updating using small amount of data and the priori threshold decision are crucial factor for dealing with long-term variability in people voices. In the paper we present the speaker model updating technique which can be adaptable to the session-to-intra speaker variability and the priori threshold determining technique. The proposed technique decreases verification error rates which the session-to-session intra-speaker variability can bring by adapting new speech data to speaker model parameter through Baum Welch re-estimation. And in this study the proposed priori threshold determining technique is decided by a hybrid score measurement which combines the world model based technique and the cohen model based technique together. The results show that the proposed technique can lead a better performance and the difference of performance is small between the posteriori threshold decision based approach and the proposed priori threshold decision based approach.

• Journal of the Korea Society for Simulation
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• v.20 no.4
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• pp.67-79
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• 2011

The statistical process control (SPC) assumes that observations follow the particular statistical distribution and they are independent to each other. However, the time-series data do not always follow the particular distribution, and most of cases are autocorrelated, therefore, it has limit to adopt the general SPC in tim series process. In this study, we propose a MPBC (Model Parameter Based Control-chart) method for fault detection in time-series processes. The MPBC builds up the process as a time-series model, and it can determine the faults by detecting changes parameters in the model. The process we analyze in the study assumes that the data follow the ARMA (p,q) model. The MPBC estimates model parameters using RLS (Recursive Least Square), and $K^2$-control chart is used for detecting out-of control process. The results of simulations support the idea that our proposed method performs better in time-series process.

• Structural Engineering and Mechanics
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• v.73 no.3
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• pp.287-302
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• 2020

This investigation deals with a size-dependent coupled thermoelasticity analysis based on Green-Naghdi (GN) theory in nano scale using a new modified nonlocal model of heat conduction, which is based on the GN theory and nonlocal Eringen theory of elasticity. In the analysis based on the proposed model, the nonlocality is taken into account in both heat conduction and elasticity. The governing equations including the equations of motion and the energy balance equation are derived using the proposed model in a nano beam resonator. An analytical solution is proposed for the problem using the Laplace transform technique and Talbot technique for inversion to time domain. It is assumed that the nano beam is subjected to sinusoidal thermal shock loading, which is applied on the one of beam ends. The transient behaviors of fields' quantities such as lateral deflection and temperature are studied in detail. Also, the effects of small scale parameter on the dynamic behaviors of lateral deflection and temperature are obtained and assessed for the problem. The proposed GN-based model, analytical solution and data are verified and also compared with reported data obtained from GN coupled thermoelasticity analysis without considering the nonlocality in heat conduction in a nano beam.

• Journal of Internet Computing and Services
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• v.4 no.6
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• pp.33-42
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• 2003

A XML Instance repository model based on the Edge-Labeled Graph is suggested for storing the XML instance in Relational Databases, This repository model represents the XML instance as a data graph based on the Edge-Labeled Graph, extracts the defined value based on the structure of data path, element, attribute, and table index table presented as database schema, and stores these values using the Mapper module, In order to support querry, XML repository model offers the module translating XQL which is a query language under XPATH to SQL, and has DBtoXML generator module restoring the stored XML instance. As a result, it is possible to represent the storage relationship between the XML instances and the proposed repository model in terms of Graph-based Path, and it shows the possibility of easy search of specific element and attribute information.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.225-227
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• 2003

Traditional photogrammetry and satellite image rectification technique have been developed based on control-points for many decades. These techniques are driven from linked points in image space and the corresponding points in the object space in rigorous colinearity or coplanarity conditions. Recently, digital imagery facilitates the opportunity to use features as well as points for images rectification. These implementations were mainly based on rigorous models that incorporated geometric constraints into the bundle adjustment and could not be applied to the new high-resolution satellite imagery (HRSI) due to the absence of sensor calibration and satellite orbit information. This research is an attempt to establish a new Line Based Transformation Model (LBTM), which is based on linear features only or linear features with a number of ground control points instead of the traditional models that only use Ground Control Points (GCPs) for satellite imagery rectification. The new model does not require any further information about the sensor model or satellite ephemeris data. Synthetic as well as real data have been demonestrated to check the validity and fidelity of the new approach and the results showed that the LBTM can be used efficiently for rectifying HRSI.