• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.5
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• pp.37-47
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• 2013

Text Mining is a research area of retrieving high quality hidden information such as patterns, trends, or distributions through analyzing unformatted text. Basically, since text mining assumes an unstructured text, it needs to be represented as a simple text model for analyzing it. So far, most frequently used model is VSM(Vector Space Model), in which a text is represented as a bag of words. However, recently much researches tried to apply a graph-based text model for representing semantic relationships between words. In this paper, we survey research trends of graph-based text representation models for text mining. Additionally, we also discuss about future models of graph-based text mining.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.1
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• pp.39-46
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• 2006

We propose an global color model based method for tracking motions of multiple human using a networked multiple-camera system in intelligent space as a human-robot coexistent system. An intelligent space is a space where many intelligent devices, such as computers and sensors(color CCD cameras for example), are distributed. Human beings can be a part of intelligent space as well. One of the main goals of intelligent space is to assist humans and to do different services for them. In order to be capable of doing that, intelligent space must be able to do different human related tasks. One of them is to identify and track multiple objects seamlessly. In the environment where many camera modules are distributed on network, it is important to identify object in order to track it, because different cameras may be needed as object moves throughout the space and intelligent space should determine the appropriate one. This paper describes appearance based unknown object tracking with the distributed vision system in intelligent space. First, we discuss how object color information is obtained and how the color appearance based model is constructed from this data. Then, we discuss the global color model based on the local color information. The process of learning within global model and the experimental results are also presented.

• Korea Journal of Hospital Management
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• v.4 no.2
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• pp.162-191
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• 1999

This study was conducted to establish finance performance evaluation model for physicians in each clinical department, by using factors which determines financial outcome(performance) in each clinical department The ultimate aim of study is to develop effective performance-based pay system for physicians. The system, by motivating physicians, should increase their productivity. To do so, it is critical to establish finance performance evaluation model to achieve final goal of this study. 232 private hospitals were chosen from 693 hospitals which were subject to hospital survey by the Korea Institute of Health Services Management and their revenue and expense-related data during 1997 were collected. By adopting multiple regression method, the study shows that the evaluation model for each clinical department was statistically significant. The study suggest the effective performance-based pay system based on financial performance of each clinical department. The pay system includes the level of compensation, the way of how to allocate profits to each department, and criteria whether the compensation should provide or not. In conclusion, the study has following implications. First, the study suggest finance performance evaluation model for each clinical department Second, the study suggest guidelines and plans to establish qualitative measure of financial performance in each clinical department. Third, the study suggest that adopting performance-based pay for physicians could be impetus to achieve organizational goal by motivating them with fair compensation.

• Nuclear Engineering and Technology
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• v.54 no.8
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• pp.3027-3033
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• 2022

Background: In this study, various types of deep-learning models for predicting in vitro radiosensitivity from gene-expression profiling were compared. Methods: The clonogenic surviving fractions at 2 Gy from previous publications and microarray gene-expression data from the National Cancer Institute-60 cell lines were used to measure the radiosensitivity. Seven different prediction models including three distinct multi-layered perceptrons (MLP), four different convolutional neural networks (CNN) were compared. Folded cross-validation was applied to train and evaluate model performance. The criteria for correct prediction were absolute error < 0.02 or relative error < 10%. The models were compared in terms of prediction accuracy, training time per epoch, training fluctuations, and required calculation resources. Results: The strength of MLP-based models was their fast initial convergence and short training time per epoch. They represented significantly different prediction accuracy depending on the model configuration. The CNN-based models showed relatively high prediction accuracy, low training fluctuations, and a relatively small increase in the memory requirement as the model deepens. Conclusion: Our findings suggest that a CNN-based model with moderate depth would be appropriate when the prediction accuracy is important, and a shallow MLP-based model can be recommended when either the training resources or time are limited.

• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.292-298
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• 2022

The study aimed to measure the effectiveness of using Cognitive virtual tours based on Marzano model to develop the achievement and some of historical research Skills for secondary school students. To achieve the objectives of the study, the experimental method was used and two study groups consisted of (60) students were divided into two groups, one is Control (30) students and the other is in experimental (30) students at Alsalam secondary in Ismailia Governorate. The study used several tools: The experimental processing tool of Cognitive Virtual tours based on Marzano model, Cognitive achievement test for the first unit of Secondary first grade history Course. Historical research skills. The finding of the study showed the effectiveness of using cognitive virtual tours based on Marzano model in developing the achievement and some of historical research skills for first grade secondary students which there are Statistically differences at level (0.01) between the average scores of the students in (experimental and Control) groups in post application for Cognitive achievement test. Practical application of the study can contribute to clarify how to use Cognitive virtual tours based on Marzano model in teaching history, and Draw the attention of history developers to the development of historical research skills.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.676-684
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• 2009

Decision making at the early stages of a construction project has a significant impact on the project, and various scenarios created based on the owner's requirements should be considered for the decision making. At the early stages of a construction project, the information regarding the project is usually limited and uncertain. As such, it is difficult to plan and manage the project (especially cost planning). Thus, in this study, a cost model that could be varied according to the owner's requirements was developed. The cost model that was developed in this study is based on the case-based reasoning (CBR) methodology. The model suggests cost estimation with the most similar historical case as a basis for the estimation. In this study, the optimization process was also conducted, using genetic algorithms that reflect the changes in the number of project characteristics and in the database in the model according to the owner's decision making. Two optimization parameters were established: (1) the minimum criteria for scoring attribute similarity (MCAS); and (2) the range of attribute weights (RAW). The cost model proposed in this study can help building owners and managers estimate the project budget at the business planning stage.

• 한국데이터정보과학회:학술대회논문집
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• 2003.10a
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• pp.57-72
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• 2003

This article provides integrated neural network models for the interest rate forecasting using change-point detection. The model is composed of three phases. The first phase is to detect successive structural changes in interest rate dataset. The second phase is to forecast change-point group with data mining classifiers. The final phase is to forecast the interest rate with BPN. Based on this structure, we propose three integrated neural network models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported neural network model, (2) case based reasoning (CBR)-supported neural network model and (3) backpropagation neural networks (BPN)-supported neural network model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. For interest rate forecasting, this study then examines the predictability of integrated neural network models to represent the structural change.

• Fire Science and Engineering
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• v.17 no.2
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• pp.50-55
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• 2003

The lower flash points for the binary system, n-butanol+n-decane, were measured by Pensky-Martens closed cup tester. The experimental results showed the minimum in the flash point versus composition curve. The experimental data were compared with the values calculated by the reduced model under an ideal solution assumption and the flash point-prediction models based on the Van Laar and Wilson equations. The predictive curve based upon the reduced model deviated form the experimental data for this system. The experimental results were in good agreement with the predictive curves, which use the Van Laar and Wilson equations to estimate activity coefficients. However, the predictive curve of the flash point prediction model based on the Willson equation described the experimentally-derived data more effectively than that of the flash point prediction model based on the Van Laar equation.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.187-188
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• 2011

This paper proposes the hybrid Complex-PSO algorithm based on the complex search method and particle swarm optimization (PSO) for unconstrained optimization. This hybridization intends to produce faster and more accurate convergence to the optimum value. These hybrid will concentrate on determining the dynamic load model parameters, the ZIP model and induction motor model parameters. Measurement-based parameter estimation, which employs measurement data to derive load model parameters, is used. The theoretical foundation of the measurement-based approach is system identification. The main objective of this paper is to demonstrate how the standard particle swarm optimization and complex method can be improved through hybridization of the two methods and the results will be compared with that of their original forms.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.10a
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• pp.268-271
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• 2000

In these days, the technical advances and complexities have generated much of the difficulties in managing the project resources, both time and costing to accomplish the project in the most efficient manner. The project manager is frequently required to render judgements concerning the schedule and resource adjustments. This research develops an analytical model for a schedule-cost and risk analysis based on visual PERT/CPM. We used a two-step approaches ：in the step 1, a deterministic PERT/CPM model for the critical path and estimating the project time schedule and related resource planning, In the second step, we developed a heuristic model for crash and stretch out analysis based upon a time-cost trade-off associated with the crash and stretch out of the project. Computer implementation of this model is provided based on GUI-Type objective-oriented programming for the users and provided displays of all the inputs and outputs in the form of visual graphical. Also developed GUI-type program, Dongeui Visual-PERT/CPM. The results of this research will provide the project managers with an efficient management tool.