• Proceedings of the Korea Database Society Conference
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• 1999.10a
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• pp.183-189
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• 1999

오늘의 기업에게 정보기술이란 필수요소이다. 정보기술을 효과적으로 활용하기 위해서는 IT 인프라가 체계적으로 구축되어 있어야 한다. 해당 조직에 적합한 정보기술 기반구조를 설계하고 이의 도입을 위한 구체적인 계획을 수립하기 위해서는 체계적이고 효과적인 정보계획 수립(Information System Planning: ISP)이 필요하다. 그러나 정보계획수립 프로젝트의 상당수가 실패로 그치고 있다. 특히 정보기술의 지속적인 변화 때문에 수립한 정보기술 기반구조 계획안이 실제 구현되지 못하고 계획으로만 남는 경향이 있다. 이러한 ISP의 어려움을 해결하기 위해서는 정보기술 참조모델(reference model)을 적극적으로 활용할 필요가 있다. 즉, 조직의 정보시스템에 공통적으로 적용할 수 있는 IT 인프라나 표준 아키텍쳐를 바탕으로 정보계획수립을 수행해 나가는 방식이 필요하다. 이와 같은 참조모델 기반의 정보계획 수립은 새로운 아키텍쳐를 추출하고 표준화를 이룸으로써 프로젝트의 생산성을 높일 수 있다는 장점을 가지고 있다. 기존의 ISP 연구는 ISP의 필요성, 과정, 성공요인 등에 국한되어 왔으며, 방법론에 대한 연구는 미비한 편이다. 최근들어 ISP의 체계적인 분류나 참조모델 기반 계획수립의 필요성이 제기되었다. 그러나 아직까지 이와같은 접근에서 참조 모델을 어떻게 구축하고 활용해 나갈 것인가에 대한 연구는 부족한 실정이다. 따라서 본 논문에서는 참조모델을 구축하기 위한 다양한 접근방법과 각각의 특징을 제시한다. 나아가서 해당 조직의 상황이나 요구수준에 따라 적합한 접근방법을 선택할 수 있게 해주는 방안을 제시한다.타냈으며, 평가결과에 대해 여러 가지 방법으로 분석하였다. 첫째, 동종제품간 평가분석을 통하여 각각의 제품을 비교하였으며, 둘째 소프트웨어 종류별 평가로 제품을 응용소프트웨어, 응용개발도구, 시스템 소프트웨어로 분류하여 평균값으로 비교하였다. 셋째, 국내외 제품별 평가분석으로 전체 제품을 국내제품과 국외제품으로 분류하여 비교하였으며, 마지막으로 총괄분석을 통해 가중치를 적용하여 전 제품의 점수를 비교하였다. 여기에서는 각 제품의 평균점수에 대한 차이를 95%의 유의수준으로 T-Test를 실시하였다.uted to the society, and what the socioeconomic impacts are resulted from the program. It would be useful for the means of (ⅰ) fulfillment of public accountability to legitimate the program and to reveal the expenditure of pubic fund, and (ⅱ) managemental and strategical learning to give information necessary to improve the making. program and policy decision making, The objectives of the study are to develop the methodology of modeling the socioeconomic evaluation, and build up the practical socioeconomic evaluation mod

• Journal of the Korea Society of Computer and Information
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• v.26 no.9
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• pp.13-25
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• 2021

In the age of digital innovation based on the Internet, Information and Communication and Artificial Intelligence technologies, huge amounts of datasets are being generated, collected, accumulated, and opened on the web by various public institutions providing useful and public information. In order to analyse, gain useful insights and information from data, Formal Concept Analysis(FCA) has been successfully used for analyzing, classifying, clustering and visualizing data based on the binary relation between objects and attributes in the dataset. In this paper, we present an approach for enhancing the analysis of relational attributes of data within the extended framework of FCA, which is designed to classify, conceptualize and visualize sets of objects described not only by attributes but also by relations between these objects. By using the proposed tool, RCA wizard, several experiments carried out on some public open datasets demonstrate the validity and usability of our approach on generating and visualizing conceptual hierarchies for extracting more useful knowledge from datasets. The proposed approach can be used as an useful tool for effective data analysis, classifying, clustering, visualization and exploration.

• Journal of IKEEE
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• v.26 no.3
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• pp.364-372
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• 2022

This paper proposes the design and implementation results for human and object classification systems utilizing frequency modulated continuous wave (FMCW) radar sensor. Such a system requires the process of radar sensor signal processing for multi-target detection and the process of deep learning for the classification of human and object. Since deep learning requires such a great amount of computation and data processing, the lightweight process is utmost essential. Therefore, binary neural network (BNN) structure was adopted, operating convolution neural network (CNN) computation in a binary condition. In addition, for the real-time operation, a hardware accelerator was implemented and verified via FPGA platform. Based on performance evaluation and verified results, it is confirmed that the accuracy for multi-target classification of 90.5%, reduced memory usage by 96.87% compared to CNN and the run time of 5ms are achieved.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.113-119
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• 2022

In software fault prediction, a multi classification model that predicts the fault severity category of a module can be much more useful than a binary classification model that simply predicts the presence or absence of faults. A small number of severity-based fault prediction models have been proposed, but no classifier using deep learning techniques has been proposed. In this paper, we construct MLP models with 3 or 5 hidden layers, and they have a structure with a fixed or variable number of hidden layer nodes. As a result of the model evaluation experiment, MLP-based deep learning models shows significantly better performance in both Accuracy and AUC than MLPs, which showed the best performance among models that did not use deep learning. In particular, the model structure with 3 hidden layers, 32 batch size, and 64 nodes shows the best performance.

• Convergence Security Journal
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• v.23 no.5
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• pp.125-133
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• 2023

This paper proposes an AI model that determines parking and non-parking zones based on return authentication photos to address parking issues that may arise in shared electric scooter systems. In this study, we used a pre-trained Segformer_b0 model on ADE20K and fine-tuned it on tactile blocks and electric scooters to extract segmentation maps of objects related to parking and non-parking areas. We also presented a method to perform binary classification of parking and non-parking zones using the Swin model. Finally, after labeling a total of 1,689 images and fine-tuning the SegFomer model, it achieved an mAP of 81.26%, recognizing electric scooters and tactile blocks. The classification model, trained on a total of 2,817 images, achieved an accuracy of 92.11% and an F1-Score of 91.50% for classifying parking and non-parking areas.

• The Transactions of the Korea Information Processing Society
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• v.2 no.6
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• pp.937-946
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• 1995

We proposed and efficient block segmentation and classification method for the document analysis using SGLDM(spatial gray level dependence matrix) and BP (back Propagation) neural network. Seven texture features are extracted directly from the SGLDM of each gray-level block image, and by using the nonlinear classifier of neural network BP, we can classify document blocks into 9 categories. The proposed method classifies the equation block, the table block and the flow chart block, which are mostly composed of the characters, out of the blocks that are conventionally classified as non-character blocks. By applying Sobel operator on the gray-level document image beforebinarization, we can reduce the effect of the background noises, and by using the additional horizontal-vertical smoothing as well as the vertical-horizontal smoothing of images, we can obtain an effective block segmentation that does not lead to the segmentation into small pieces. The result of experiment shows that a document can be segmented and classified into the character blocks of large fonts, small fonts, the character recognigible candidates of tables, flow charts, equations, and the non-character blocks of photos, figures, and graphs.

• FLOWER RESEARCH JOURNAL
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• v.19 no.4
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• pp.225-230
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• 2011

This research was performed for making data-base of cross-breeding between Cymbidium goeringii cultivars. Morphological characteristics were investigated and then genetic relationship was analyzed. Collected 20 Cymbidium goeringii cultivars were clustered into 2 groups. Seven cultivars were clustered into group I, and thirteen cultivars were clustered into group II. Group I doesn't have leaf pattern. Group have leaf pattern. The genetic relationship among collected 20 Cymbidium goeringii cultivars was anaylzed using RAPD with ten 10-mers random primer. Eighty-nine bands were generated by RAPD. Among the rest, three bands were monomorphic and eight-six bands were polymorphic. Overall similarity degree ranged from 0.521 to 0.862. The result of RAPD analysis was clustered into 2 groups, too. Sixteen cultivars were clustered into GroupX, and four cultivars were clustered into GroupY. Result of classification with morphological characteristics and RAPD showed different pattern, but 4 cultivars of GroupY by RAPD analysis were included in groupby morphological characteristics. Crossbreeding combination among low related coltivars in RAPD analysis may get more efficient result.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.651-658
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• 2021

Because bolts with anti-loosening coatings are used mainly for joining safety-related components in automobiles, accurate automatic screening of these coatings is essential to detect defects efficiently. The performance of the convolutional neural network (CNN) used in a previous study [Identification of bolt coating defects using CNN and Grad-CAM] increased with increasing number of data for the analysis of image patterns and characteristics. On the other hand, obtaining the necessary amount of data for coated bolts is difficult, making training time-consuming. In this paper, resorting to the same VGG16 model as in a previous study, transfer learning was applied to decrease the training time and achieve the same or better accuracy with fewer data. The classifier was trained, considering the number of training data for this study and its similarity with ImageNet data. In conjunction with the fully connected layer, the highest accuracy was achieved (95%). To enhance the performance further, the last convolution layer and the classifier were fine-tuned, which resulted in a 2% increase in accuracy (97%). This shows that the learning time can be reduced by transfer learning and fine-tuning while maintaining a high screening accuracy.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.2
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• pp.107-114
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• 2022

In order to classify 3D film images as right or wrong, it is necessary to detect the pattern in a 3D film image. However, if the contrast of the pixels in the 3D film image is low, it is not easy to classify as the right and wrong 3D film images because the pattern in the image might not be clear. In this paper, we propose a method of classifying 3D film images as right or wrong by comparing the width at a specific frequency of each histogram after obtaining the histogram. Since, it is classified using the width of the histogram, the analysis process is not complicated. From the experiment, the histograms of right and wrong 3D film images were distinctly different, and the proposed algorithm reflects these features, and showed that all 3D film images were accurately classified at a specific frequency of the histogram. The performance of the proposed algorithm was verified to be the best through the comparison test with the other methods such as image subtraction, otsu thresholding, canny edge detection, morphological geodesic active contour, and support vector machines, and it was shown that excellent classification accuracy could be obtained without detecting the patterns in 3D film images.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.6
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• pp.1283-1293
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• 2014

In ubiquitous environment, data are changing rapidly and new data is coming as times passes. And sometimes all of the past data will be lost if there is not sufficient space in memory. Therefore, there is a need to make rules and combine it with new data not to lose all the past data or to deal with large amounts of data. In making decision trees and extracting rules, the weight of each of rules is generally determined by the total number of the class at leaf. The computational problem of finding a minimum finite state acceptor compatible with given data is NP-hard. We assume that rules extracted are not correct and may have the loss of some information. Because of this precondition. this paper presents a new approach for refining rules. It controls their weight of rules of previous knowledge or data. In solving rule refinement, this paper tries to make a variety of rules with pruning method with majority and minority properties, control weight of each of rules and observe the change of performances. In this paper, the decision tree classifier with extended data expression having static weight is used for this proposed study. Experiments show that performances conducted with a new policy of refining rules may get better.