• The Korean Journal of Applied Statistics
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• v.36 no.4
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• pp.279-294
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• 2023

It is well known to generate synthetic data sets by the sequential regression multiple imputation (SRMI) method. The R-package synthpop are widely used for generating synthetic data by the SRMI approaches. In this paper, I suggest generating synthetic data based on the probabilistic principal component analysis (PPCA) method. Two simple data sets are used for a simulation study to compare the SRMI and PPCA approaches. Simulation results demonstrate that pairwise coefficients in synthetic data sets by PPCA can be closer to original ones than by SRMI. Furthermore, for the various data types that PPCA applications are well established, such as time series data, the PPCA approach can be extended to generate synthetic data sets.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1283-1287
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• 2003

Reverse engineering technology refers to the process that creates a CAD model of an existing part using measuring devices. Recently, non-contact scanning devices have become more accurate and the speed of data acquisition has increased drastically. However, they generate thousands of points per second and various types of point data. Therefore. it becomes a important to handle the huge amount and various types of point data to generate a surface model efficiently. This paper proposes a new triangular mesh generation method using 3D grids. The geometric information of a part can be obtained from point cloud data by estimating normal values of the points. In our research, the non-uniform 3D grids are generated first for feature based data reduction based on the geometric information. Then, triangulation is performed with the reduced point data. The grid structure is efficiently used not only for neighbor point search that can speed up the mesh generation process but also for getting surface connectivity information to result in same topology surface with the point data. Through this integrated approach, it is possible to create surface models from scanned point data efficiently.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.22 no.5
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• pp.475-485
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• 2009

In this paper a two-step algorithm is proposed to efficiently generate rebar elements from 3D CAD data in the context of CAD/CAE data transfer. The first step is an algorithm to identify various type rebar objects and their attributes by analyzing 3D CAD data in STEP format, which is one of the international data standards. The second algorithmic step is a procedure to generate one-dimensional rebar elements from the object data made through the first step for finite element analysis or other CAE tasks. Successful rebar element data generation from real 3D CAD data for a reinforced concrete structure shows the efficacy of the proposed algorithm.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.5 s.182
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• pp.143-148
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• 2006

It is still very difficult to generate a geometry model and finite element model, which has complex and many free surface, even though 3D CAD solutions are applied. Furthermore, in the medical field, which is a big growth area of recent years, there is no drawing. For these reasons, making a geometry model, which is used in finite element analysis, is very difficult. To resolve these problems and satisfy the requests of the need to create a 3D digital file for an object where none had existed before, new technologies are appeared recently. Among the recent technologies, there is a growing interest in the availability of fast, affordable optical range laser scanning. The development of 3D laser scan technology to obtain 3D point cloud data, made it possible to generate 3D model of complex object. To generate CAD and finite element model using point cloud data from 3D scanning, surface reconstruction applications have widely used. In the early stage, these applications have many difficulties, such as data handling, model creation time and so on. Recently developed point-based surface generation applications partly resolve these difficulties. However there are still many problems. In case of large and complex object scanning, generation of CAD and finite element model has a significant amount of working time and effort. Hence, we concerned developing a good direct finite element model generation method using point cloud's location coordinate value to save working time and obtain accurate finite element model.

• Journal of the Korean Operations Research and Management Science Society
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• v.3 no.1
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• pp.81-91
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• 1978

Decision models are an attempt to reduce uncertainty in the decision making process. The models describe the relationships of variables and given proper input data generate solutions to managerial problems. These solutions may not be answers to the problems for one of two reasons. First, the data input into the model may not be consistant with the underlying assumptions of the model being used. Frequently parameters are assumed to be deterministic when in fact they are probabilistic in nature. The second failure is that often the decision maker recognizes that the data available are not appropriate for the model being used and begins to collect the required data. By the time these data has been compiled the solution is no longer an answer to the problem. This relates to the timeliness of decision making. The authors point out throught the use of an illustrative problem that stocastic models are well developed and that they do not suffer from any lack of mathematical exactiness. The primary problem is that generally accepted procedures for data generation are historical in nature and not relevant for probabilistic decision models. The authors advocate that management information system designers and accountants must become more familiar with these decision models and the input data required for their effective implementation. This will provide these professionals with the background necessary to generate data in a form that makes it relevant and timely for the decision making process.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.3
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• pp.329-338
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• 2016

As the need of indoor spatial information has grown, many applications have been developed. Nevertheless, the major representations of indoor spatial information are on the 2D or 3D, recently, the service based on omni-directional image has increased. Current service based on omni-directional image is used just for viewer. To provide various applications which can serve the identifying the attribute of indoor space, query based services and so on, topological data which can define the spatial relationships between spaces is required. For developing diverse applications based on omni-directional image, this study proposes the method to generate IndoorGML data which is the international standard of indoor topological data model. The proposed method is consist of 3 step to generate IndoorGML data; 1) Analysis the core elements to adopt IndoorGML concept to image, 2) Propose the method to identify the element of ‘Space’ which is the core element of IndoorGML concept, 3) Define the connectivity of indoor spaces. The proposed method is implemented at the 6-floor of 21centurybuilding of the University of Seoul to generate IndoorGML data and the demo service is implemented based on the generated data. This study has the significance to propose a method to generate the indoor topological data for the indoor spatial information services based on the IndoorGML.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2007.02a
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• pp.116-119
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• 2007

In a keyword search scheme a user stores encrypted data on an untrusted server and gives a database manager a capability for a keyword which enables a database manager to find encrypted data containing the keyword without revealing the keyword to the database manager. Conjunctive keyword search scheme enables a user to obtain data containing all of several keywords through only one query. One of the security requirements of conjunctive keyword search schemes is that a malicious adversary should not be able to generate new valid capabilities from the observed capabilities. In U:5 paper we show that conjunctive keyword search schemes are not secure. In particular, given two capabilities corresponding two sets of keywords, an adversary is able to generate a new capability corresponding to the dierence set of two keywords sets.

• Journal of the Korean Data and Information Science Society
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• v.17 no.1
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• pp.63-76
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• 2006

Marine production forecasting in fisheries is a crucial factor for managing and maintaining fishery resources. Thus this paper aims to generate a forecasting model of total marine production. The most generally method of time series model is to generate the most optimal single forecasting model. But the method could induce a different forecasting results when it does not properly infer a model To overcome the defect, I am trying to propose a single forecasting through multiple time series model. In other word, by comparing and integrating the output resulted from ARIMA and VAR model (which are typical method in a forecasting methodology), I tried to draw a forecasting. It is expected to produce more stable and delicate forecasting prospect than a single model. Through this, I generated 3 models on a yearly and monthly data basis and then here I present a forecasting from 2006 to 2010 through comparing and integrating 3 models. In conclusion, marine production is expected to show a decreasing tendency for the coming years.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.923-926
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• 2003

In this paper we propose a scheme to generate DTM and detect buildings on DSM generated from LIDAR data. Two stages are performed. The first stage is to perform object segmentation by using two morphology operations namely, flattening and H-Dome transformation. After filtering out the object points above the ground, we used the non-object points to generate DTM. The second stage is to detect buildings from the objects by analyzing differential slopes. The test data is in raster form with 1m spacing around Hsin-Chu Scientific Area in Taiwan. The mean error is -0.16m and the RMSE is 0.45m for DTM generation. The successful rate for building detection is 87.7%.

• ETRI Journal
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• v.44 no.2
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• pp.327-338
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• 2022

Classification models for human action recognition require robust features and large training sets for good generalization. However, data augmentation methods are employed for imbalanced training sets to achieve higher accuracy. These samples generated using data augmentation only reflect existing samples within the training set, their feature representations are less diverse and hence, contribute to less precise classification. This paper presents new data augmentation and action representation approaches to grow training sets. The proposed approach is based on two fundamental concepts: virtual video generation for augmentation and representation of the action videos through robust features. Virtual videos are generated from the motion history templates of action videos, which are convolved using a convolutional neural network, to generate deep features. Furthermore, by observing an objective function of the genetic algorithm, the spatiotemporal features of different samples are combined, to generate the representations of the virtual videos and then classified through an extreme learning machine classifier on MuHAVi-Uncut, iXMAS, and IAVID-1 datasets.