• Journal of Korean Society of Industrial and Systems Engineering
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• v.31 no.4
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• pp.177-187
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• 2008

It is very important to select optimal investment alternative. The common method of economic evaluation is to compare of NPV, FW, AE by MARR, or the rate of return for the cash flow of alternatives. This method is undergoing by assumption that cash flow can be always evaluated by MARR, but the cash flow is not always increased or discounted like MARR. So this paper suggests a model on an economic analysis and evaluation regarding to various cash pattern, that is helpful for the person in the field to use easily.

• Journal of the Korean Operations Research and Management Science Society
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• v.11 no.1
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• pp.12-23
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• 1986

The minimum attractive rate of return (MARR) has been used for many years as a decision criterion in engineering economic analysis. Typically, inflation has been either ignored in such studies or considered by adjusting each of the individual cash flows associated with a project for inflation, frequently a lengthy process. This research investigates a new decision criterion for economic analysis, the comparative rate of return (CRR). The CRR is defined to be the minimum rate of return earned on uninflated cash flows of proposed expenditures is simplified, since the analysis can be performed on the uninflated cash flows. The research presents a derivation of the CRR and investigates its relationships to the MARR, inflation rate project cash flows and project life.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.31 no.3
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• pp.74-79
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• 2008

The reinvestment assumption of the internal rate of return(IRR) method may not be valid in an engineering economy study. This situation, coupled with the computational demands and possible multiple interest rate associated with the IRR method, has given rise to other rate of return methods, such as the external rate of return(ERR) method, that can remedy some of these weaknesses. But ERRs are not used generally. We present another rate of return including all attributes of the minimum attractive rate of return(MARR).

• Proceedings of the Ginseng society Conference
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• 1974.09a
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• pp.129-136
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• 1974

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2788-2808
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• 2023

Brain tumors are one of the most threatening malignancies for humans. Misdiagnosis of brain tumors can result in false medical intervention, which ultimately reduces a patient's chance of survival. Manual identification and segmentation of brain tumors from Magnetic Resonance Imaging (MRI) scans can be difficult and error-prone because of the great range of tumor tissues that exist in various individuals and the similarity of normal tissues. To overcome this limitation, the Amended Convolutional Neural Network (ACNN) model has been introduced, a unique combination of three techniques that have not been previously explored for brain tumor detection. The three techniques integrated into the ACNN model are image tissue preprocessing using the Kalman Bucy Smoothing Filter to remove noisy pixels from the input, image tissue segmentation using the Isotonic Regressive Image Tissue Segmentation Process, and feature extraction using the Marr Wavelet Transformation. The extracted features are compared with the testing features using a sigmoid activation function in the output layer. The experimental findings show that the suggested model outperforms existing techniques concerning accuracy, precision, sensitivity, dice score, Jaccard index, specificity, Positive Predictive Value, Hausdorff distance, recall, and F1 score. The proposed ACNN model achieved a maximum accuracy of 98.8%, which is higher than other existing models, according to the experimental results.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.12
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• pp.124-131
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• 1994

In this paper, we propose a new approach of solving the stereopsis problem with a discrete-time cellular neural network(DTCNN) where each node has connections only with its local neithbors. Because the matching process of stereo correspondence depends on its geometrically local characteristics, the DTCNN is suitable for the stereo correspondence. Moreover, it can be easily implemented in VLSI. Therefore, we employed a two-layer DTCNN with dual templates, which are determined with the back propagation learning rule. Based on evaluation of the proposed approach for several random-dot stereograms, its performance is better than that of the Marr-Poggio algorithm.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.26 no.9
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• pp.1302-1310
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• 2002

A direct numerical simulation in turbulent curved channel flow is performed. The drifting Taylor-Gortler vortices are identified by applying a conditional averaging. A new algorithm is proposed based on the wavelet transform of the wall information. A continuous wavelet transform with Marr wavelets is employed to decompose the flow signals at a chosen length scale. An active cancellation is applied to attenuate the Taylor-Gortler vortices and to reduce the wall skin friction.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1364-1366
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• 1987

Laplacian of Gaussian, ${\nabla}^2G$ operator proposed by Marr and Hildreth is known as a rough bandpass filter. This paper shows how to detect the change of intensity corresponding to an arbitrary spatial frequency in an image using ${\nabla}^2G$ operator.

• Proceedings of the Korean Society of Fisheries Technology Conference
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• 2001.10a
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• pp.329-330
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• 2001

연안의 오염으로 인한 수생생물의 피해를 파악하기 위하여 여러방면으로 연구가 진행되고 있다. 특히 여러 오염원에 대한 수생생물의 생존과 성장은 중요한 평가요인이 될 수 있다(Marr et al., 1996). 이들 중금속에 대한 여러 연구들이 현재 진행 되 고 있으며 (Gagne' et al.,1990; Castano et al.,1998), 현재 우리나라 연안에 분포하는 중금속에 대한 수생생물의 영향분석의 일환으로 구리 및 카드륨이 넙치의 생존, 성장 및 사료효율에 미치는 만성적 영향을 파악하는 것을 본 연구의 목적으로 한다. (중략)

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.6
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• pp.59-66
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• 1993

Stereo vision is useful in obtaining three dimensional depth information from two images taken from different view points. Neural network modeling for stereo matching, the key step in stereo vision, is defined by an energy function satisfying with three constraints proposed by Marr and Poggio. Stereo matching is then carried out through the network to find minimum energy corresponding to the optimized solution of the problem. An algorithm for stereo matching using an analog neural network is presented here. The network can reduce errors in initial state an early iteration steps by adoption of continuous sigmoid function in stead of binary state. The experimental results show good matching performance for sparse random dot stereogram and real image.