• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.3
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• pp.29-41
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• 2009

The purpose of this paper is to develop a new classification method for predicting consumer choice based on genetic algorithm, and to validate Its prediction power over existing methods. To serve this purpose, we propose a hybrid model, and discuss Its methodological characteristics in comparison with other existing classification methods. Also, we conduct a series of experiments employing survey data of consumer choices of MP3 players to assess the prediction power of the model. The results show that the suggested model in this paper is statistically superior to the existing methods such as logistic regression model, artificial neural network model and decision tree model in terms of prediction accuracy. The model is also shown to have an advantage of providing several strategic information of practical use for consumer choice.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2008.10a
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• pp.1-7
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• 2008

The purpose of this paper is to develop a new classification method for predicting consumer choice based on genetic algorithm, and to validate its prediction power over existing methods. To serve this purpose, we propose a hybrid model, and discuss its methodological characteristics in comparison with other existing classification methods. Also, to assess the prediction power of the model, we conduct a series of experiments employing survey data of consumer choices of MP3 players. The results show that the suggested model in this paper is statistically superior to the existing methods such as logistic regression model, artificial neural network model and decision tree model in terms of prediction accuracy. The model is also shown to have an advantage of providing several strategic information of practical use for consumer choice.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.167-170
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• 2000

This paper describes a novel multi-operand radix-2 signed-digit(SD) adder. The novel multi-operand addition algorithm can eliminate carry propagation chain by dividing the input operands into even place part and odd place part, and adding them each. The multi-operand adder with this algorithm can add six operands in parallel, and is faster than the ordinary method of SD adder binary tree. A hardware model for proposed adder is shown which is implemented by the current-mode MOSFET circuit technology. Simulations have been made by SPICE in order to verify the function of the proposed circuit.

• Journal of Korean Society for Geospatial Information Science
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• v.13 no.4 s.34
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• pp.3-9
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• 2005

The extraction of traffic information based on image processing is under broad research recently because the method based on image processing takes less cost and effort than the traditional method based on physical equipment. The main purpose of the algorithm based on image processing is to extract vehicles from an image correctly. Before the extraction, the algorithm needs the pre-processing such as background subtraction and binary image thresholding. During the pre-processing much noise is brought about because roadside tree and passengers in the sidewalk as well as vehicles are extracted as traffic flow. The noise undermines the overall accuracy of the algorithm. In this research, most of the noise could be removed by extracting the exact road area which does not include sidewalk or roadside tree. To extract the exact road area, traffic lanes in the image were used. Algorithm speed also increased. In addition, with the ratio between the sequential images, the problem caused by vehicles' shadow was minimized.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.32 no.10
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• pp.38-45
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• 2004

In this paper, a parallel visualization algorithm is proposed for efficient visualization of the massive data generated from large-scale parallel finite element analysis through investigating the characteristics of parallel rendering methods. The proposed parallel visualization algorithm is designed to be highly compatible with the characteristics of domain-wise computation in parallel finite element analysis by using the sort-last-sparse approach. In the proposed algorithm, the binary tree communication pattern is utilized to reduce the network communication time in image composition routine. Several benchmarking tests are carried out by using the developed in-house software, and the performance of the proposed algorithm is investigated.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.8
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• pp.21-31
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• 1998

In this paper, a new algorithm is proposed which distributes multicast cells in a copy network. The dual copy network is composed of running adder network, distributor, dummy address encoder, and broadcasting network. It is operated lower input address and higher one simultaneously by the distribution algorithm. As a result, for each input has a better equal opportunity of processing, cell delay and hardware complexity are reduced in copy network. Also, for it adopts the broadcasting network from an expansion Banyan network with binary tree and Banyan network, overflow probability is reduced to a half in that network. As a result of computer simulation, the copy network processed by the distribution algorithm is remarkably improved in cell delay of input buffer according to all input loads.

• Korean Journal of Remote Sensing
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• v.20 no.6
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• pp.409-418
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• 2004

LOD is a widely used technique in 3D game and animation to represent large 3D data sets smoothly in real-time. Most LOD algorithms use a binary tree to keep the ancestor information. A new algorithm proposed in this paper, however, do not keep the ancestor information, thus use the less memory space and rather increase the rendering performance. To verify the efficiency of the proposed algorithm, performance comparison with ROAM is conducted in real-time 3D terrain navigation. Result shows that the proposed algorithm uses about 1/4 of the memory space of ROAM and about 4 times faster than ROAM.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.4C
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• pp.253-261
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• 2005

In this paper, we propose an efficient block-based disparity estimation algorithm fur multiple view image coding in EE2 and EE3 in 3DAV. The proposed method emphasizes on visual quality improvement to satisfy the requirements for multiple view generation. Therefore, we perform an adaptive disparity estimation that constructs variable blocks by considering given image features. Examining neighboring features around desired block search range is set up to decrease complexity and additional information than only using quad-tree coding through applying binary-tree and quad-tree coding by taking into account stereo image feature having big disparity. The experimental results show that the proposed method improves PSNR about 1 to 2dB compared to existing other methods and decreases computational complexity up to maximum 68 percentages than FBMA.

• Korean Journal of Radiology
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• v.22 no.3
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• pp.344-353
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• 2021

Objective: The mitotic count of gastrointestinal stromal tumors (GIST) is closely associated with the risk of planting and metastasis. The purpose of this study was to develop a predictive model for the mitotic index of local primary GIST, based on deep learning algorithm. Materials and Methods: Abdominal contrast-enhanced CT images of 148 pathologically confirmed GIST cases were retrospectively collected for the development of a deep learning classification algorithm. The areas of GIST masses on the CT images were retrospectively labelled by an experienced radiologist. The postoperative pathological mitotic count was considered as the gold standard (high mitotic count, > 5/50 high-power fields [HPFs]; low mitotic count, ≤ 5/50 HPFs). A binary classification model was trained on the basis of the VGG16 convolutional neural network, using the CT images with the training set (n = 108), validation set (n = 20), and the test set (n = 20). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated at both, the image level and the patient level. The receiver operating characteristic curves were generated on the basis of the model prediction results and the area under curves (AUCs) were calculated. The risk categories of the tumors were predicted according to the Armed Forces Institute of Pathology criteria. Results: At the image level, the classification prediction results of the mitotic counts in the test cohort were as follows: sensitivity 85.7% (95% confidence interval [CI]: 0.834-0.877), specificity 67.5% (95% CI: 0.636-0.712), PPV 82.1% (95% CI: 0.797-0.843), NPV 73.0% (95% CI: 0.691-0.766), and AUC 0.771 (95% CI: 0.750-0.791). At the patient level, the classification prediction results in the test cohort were as follows: sensitivity 90.0% (95% CI: 0.541-0.995), specificity 70.0% (95% CI: 0.354-0.919), PPV 75.0% (95% CI: 0.428-0.933), NPV 87.5% (95% CI: 0.467-0.993), and AUC 0.800 (95% CI: 0.563-0.943). Conclusion: We developed and preliminarily verified the GIST mitotic count binary prediction model, based on the VGG convolutional neural network. The model displayed a good predictive performance.

• Journal of the Korea Computer Industry Society
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• v.2 no.6
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• pp.827-836
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• 2001

Data is represented by file structure in Computer System. But the file size is to be larger, it is hard to control and transmit. Therefore, in recent years, many researchers have developed new algorithms for the data compression. And now, we introduce a new Dynamic Compression Technique, making up for the weaknesses of huffman's. The huffman compression technique has two weaknesses. The first, it needs two steps of reading, one for acquiring character frequency and the other for real compression. The second, low compression rate caused by storing tree information. These weaknesses can be solved by our new Dynamic Relocatable Method, reducing the reading pass by relocating data file to dynamic form, and then storing tree information from pipeline structure. The first, it needs two steps of reading, one for acquiring character frequency and the other for real compression. The second, low compression rate caused by storing tree information. These weaknesses can be solved by our new Dynamic Relocatable Method, reducing the reading pass by relocating data file to dynamic form, and then storing tree information from pipeline structure.