• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.213-217
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• 1998

This study overviews existing evaluation methods of classification accuracy using confusion matrix proposed by Cohen in 1960's, and proposes ISDd(Index of Spatial Distribution by distance) and ISDs(Index of Spatial Distribution by scatteredness) for the evaluation of spatial distribution of satellite image classification errors, which has not been tried yet. Index of spatial distribution offers the basis of decision on adoption/rejection of classification results at sub-image level by evaluation of distribution, such as status of local aggregation of misclassified pixels. So, users can understand the spatial distribution of misclassified pixels and, can have the basis of judgement of suitability and reliability of classification results.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.274-279
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• 1998

For the accurate generation of DEMs, the determination of match quality in adaptive least square matching algorithm is significantly important. Traditionally, only the degree of convergence of a solution matrix in least squares estimation has been considered for the determination of match quality. It is, however, not enough to determine the true match quality. This paper reports two approaches of match quality determination based on adaptive least square correlation : the conventional if-then logic approaches with scene geometry and correlation as additional quality measures; and, the fuzzy logic approaches. Through these, accurate decision of match quality will minimize the number of blunder and maximize the number of exact match. The proposed methods have been tested on JERS and SPOT images and the results show good performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.9
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• pp.1998-2016
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• 2012

Reliable spectrum sensing algorithm is a fundamental component in cognitive radio. In this paper, a non-cooperative spectrum sensing algorithm which needs only one cognitive radio node named CORDIC (Coordinate Rotation Digital Computer) Jacobi based method is proposed. The algorithm computes the eigenvalues of the sampled covariance of received signal mainly by shift and additional operations, which is suitable for hardware implementation. Based the latest random matrix theory (RMT) about the distribution of the limiting maximum and minimum eigenvalue ratio, the relationship between the probability of false alarm and the decision threshold is derived. Simulations and discussions show the method is effective. Real captured digital television (DTV) signals and Universal Software Radio Peripheral (USRP) are also employed to evaluate the performance of the algorithm, which prove the proposed algorithm can be applied in practical spectrum sensing applications.

• Journal of the Korean Operations Research and Management Science Society
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• v.20 no.3
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• pp.105-122
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• 1995

Based on linear models, the inference about the true measurement x$_{f}$ and the optimal designs x (nx1) for the calibration experiments are considered via Baysian statistical decision analysis. The posterior distribution of x$_{f}$ given the observation y$_{f}$ (qxl) and the calibration experiment is obtained with normal priors for x$_{f}$ and for themodel parameters (.alpha., .betha.). This posterior distribution is not in the form of any known distributions, which leads to the use of a numerical integration or an approximation for the calculation of the overall expected loss. The general structure of the expected loss function is characterized in the form of a conjecture. A near-optimal design is obtained through the approximation nof the conditional covariance matrix of the joint distribution of (x$_{f}$ , y$_{f}$ $^{T}$ )$^{T}$ . Numerical results for the univariate case are given to demonstrate the conjecture and to evaluate the approximation.n.

• Journal of the Korea Safety Management & Science
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• v.11 no.3
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• pp.11-18
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• 2009

Unless certain limitation is increasing the number of the job type will be inevitable in the future because of development of industry, adaptation on speedy life style, and leisure oriented nuclear family style. In this paper, a prospective model of supply and demand of work force has been developed basing on various categories of industries and patterns about employees to look over efficient supply and demand of work force suiting employment of work force policies. In this paper, after Analyzing job preference, we have noticed a more stable job system and the results showed significant improvements over the existing job system.

• Electronics and Telecommunications Trends
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• v.32 no.6
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• pp.57-65
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• 2017

Neural network based AI computing is a promising technology that reflects the recognition and decision operation of human beings. Early AI computing processors were composed of GPUs and CPUs; however, the dramatic increment of a floating point operation requires an energy efficient AI processor with a highly parallelized architecture. In this paper, we analyze the trends in processor architectures for AI computing. Some architectures are still composed using GPUs. However, they reduce the size of each processing unit by allowing a half precision operation, and raise the processing unit density. Other architectures concentrate on matrix multiplication, and require the construction of dedicated hardware for a fast vector operation. Finally, we propose our own inAB processor architecture and introduce domestic cutting-edge processor design capabilities.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.10
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• pp.960-966
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• 2011

In this paper, an $\mathfrak{H}$_/$\mathfrak{H}_{\infty}$ fault detection and isolation (FDI) observer design problem is investigated for discrete-time delayed systems. To that end, a bank consisting of the sensor's number of observers is introduced. Each residual should be sensitive to a certain partial group of faults, but robust against the disturbance as far as possible. We formulate this multiobjective FDI problem as $\mathfrak{H}$_/$\mathfrak{H}_{\infty}$ observers design problem. Sufficient design condition is expressed as iterative linear matrix inequalities. The fault is then detected and isolated by evaluating the residuals through an FDI decision logic. A computer simulation is provided for verification of the proposed technique.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.67-84
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• 2010

In terms of business, forecasting is a work of what is expected to happen in the future to make managerial decisions and plans. Therefore, the accurate forecasting is very important for major managerial decision making and is the basis for making various strategies of business. But it is very difficult to make an unbiased and consistent estimate because of uncertainty and complexity in the future business environment. That is why we should use scientific forecasting model to support business decision making, and make an effort to minimize the model's forecasting error which is difference between observation and estimator. Nevertheless, minimizing the error is not an easy task. Case-based reasoning is a problem solving method that utilizes the past similar case to solve the current problem. To build the successful case-based reasoning models, retrieving the case not only the most similar case but also the most relevant case is very important. To retrieve the similar and relevant case from past cases, the measurement of similarities between cases is an important key factor. Especially, if the cases contain symbolic data, it is more difficult to measure the distances. The purpose of this study is to improve the forecasting accuracy of case-based reasoning approach using fuzzy relation and composition. Especially, two methods are adopted to measure the similarity between cases containing symbolic data. One is to deduct the similarity matrix following binary logic(the judgment of sameness between two symbolic data), the other is to deduct the similarity matrix following fuzzy relation and composition. This study is conducted in the following order; data gathering and preprocessing, model building and analysis, validation analysis, conclusion. First, in the progress of data gathering and preprocessing we collect data set including categorical dependent variables. Also, the data set gathered is cross-section data and independent variables of the data set include several qualitative variables expressed symbolic data. The research data consists of many financial ratios and the corresponding bond ratings of Korean companies. The ratings we employ in this study cover all bonds rated by one of the bond rating agencies in Korea. Our total sample includes 1,816 companies whose commercial papers have been rated in the period 1997~2000. Credit grades are defined as outputs and classified into 5 rating categories(A1, A2, A3, B, C) according to credit levels. Second, in the progress of model building and analysis we deduct the similarity matrix following binary logic and fuzzy composition to measure the similarity between cases containing symbolic data. In this process, the used types of fuzzy composition are max-min, max-product, max-average. And then, the analysis is carried out by case-based reasoning approach with the deducted similarity matrix. Third, in the progress of validation analysis we verify the validation of model through McNemar test based on hit ratio. Finally, we draw a conclusion from the study. As a result, the similarity measuring method using fuzzy relation and composition shows good forecasting performance compared to the similarity measuring method using binary logic for similarity measurement between two symbolic data. But the results of the analysis are not statistically significant in forecasting performance among the types of fuzzy composition. The contributions of this study are as follows. We propose another methodology that fuzzy relation and fuzzy composition could be applied for the similarity measurement between two symbolic data. That is the most important factor to build case-based reasoning model.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.3
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• pp.397-411
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• 2023

The number of deteriorated bridges with a service period of more than 30 years has been rapidly increasing in Korea. Accordingly, the importance of advanced maintenance technologies through the predictions of age-induced deterioration degree, condition, and performance of bridges is more and more noticed. The prediction method of the safety grade of bridges was proposed in this study using the classification models of the Decision Tree and the Random Forest based on machine learning. As a result of analyzing these models for the 8,850 bridges located in national roads with various evaluation indexes such as confusion matrix, balanced accuracy, recall, ROC curve, and AUC, the Random Forest largely showed better predictive performance than that of the Decision Tree. In particular, random under-sampling in the Random Forest showed higher predictive performance than that of other sampling techniques for the C and D grade bridges, with the recall of 83.4%, which need more attention to maintenance because of the significant deterioration degree. The proposed model can be usefully applied to rapidly identify the safety grade and to establish an efficient and economical maintenance plan of bridges that have not recently been inspected.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.2
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• pp.139-150
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• 2019

Though, fishing vessel accidents account for 70 % of all maritime accidents in Korean waters, most research has focused on identifying causes and developing mitigation policies in an attempt to reduce this rate. However, predicting and evaluating accident risk needs to be done before the implementation of such reduction measures. For this reasons, we havve performed a risk analysis to calculate the risk of accidents and propose a risk criteria matrix with 4 quadrants, within one of which forecasted risk is plotted for the relative comparison of risks. For this research, we considered 9 types of fishing vessel accidents as reported by Korea Maritime Safety Tribunal (KMST). Given that no risk evaluation criteria have been established in Korea, we established a two-dimensional frequency-consequence grid consisting of four quadrants into which paired frequency and consequence for each type of accident are presented. With the simple structure of the evaluation model, one can easily verify the effect of frequency and consequence on the resulting risk within each quadrant. Consequently, these risk evaluation results will help a decision maker employ more realistic risk mitigation measures for accident types situated in different quadrants. As an application of the risk evaluation matrix, accident types were further analyzed using accident causes including human error (factor) and appropriate risk reduction options may be established by comparing the relative frequency and consequence of each accident cause.