• Journal of the Korean Data and Information Science Society
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• 제22권5호
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• pp.857-866
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• 2011

입학사정관제는 그 본질 상 정량적인 평가보다는 정성적인 형태의 다양한 평가요소를 대상으로 지원자의 정보를 주관적, 종합적으로 평가하는 것이 주된 내용을 이루므로, 무엇보다도 입학사정관이 다양한 전형요소를 종합적으로 판단하여 선발할 수 있는 전문가적 능력의 구비와 공정한 선발을 기할 수 있는 선발의 공정성 확보가 중요시 되는 제도이다. 그런데 공정성을 확보하기 위해서는 전문성이 필요하며, 이를 위해서는 전문성 강화 교육이 이루어져야 한다. 본 연구에서는 J대학교 입학사정관 교육.훈련을 위한 교육과정을 개발해 보았다. 그 결과 9개의 영역으로 이루어진 교육내용 및 주요 교과목을 제시하였다. 나아가 제시된 교육 훈련 프로그램이 소기의 목적을 달성하는데 필요한 사항들을 제안하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권3호
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• pp.1243-1263
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• 2018

The two-stage linear discrimination analysis (TSLDA) is a feature extraction technique to solve the small size sample problem in the field of image recognition. The TSLDA has retained all subspace information of the between-class scatter and within-class scatter. However, the feature information in the four subspaces may not be entirely beneficial for classification, and the regularization procedure for eliminating singular metrics in TSLDA has higher time complexity. In order to address these drawbacks, this paper proposes an improved two-stage linear discriminant analysis (Improved TSLDA). The Improved TSLDA proposes a selection and compression method to extract superior feature information from the four subspaces to constitute optimal projection space, where it defines a single Fisher criterion to measure the importance of single feature vector. Meanwhile, Improved TSLDA also applies an approximation matrix method to eliminate the singular matrices and reduce its time complexity. This paper presents comparative experiments on five face databases and one handwritten digit database to validate the effectiveness of the Improved TSLDA.

• International Journal of Computer Science & Network Security
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• 제21권12spc호
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• pp.526-538
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• 2021

Machine and deep learning-based models are emerging techniques that are being used to address prediction problems in biomedical data analysis. DNA sequence prediction is a critical problem that has attracted a great deal of attention in the biomedical domain. Machine and deep learning-based models have been shown to provide more accurate results when compared to conventional regression-based models. The prediction of the gene sequence that leads to cancerous diseases, such as prostate cancer, is crucial. Identifying the most important features in a gene sequence is a challenging task. Extracting the components of the gene sequence that can provide an insight into the types of mutation in the gene is of great importance as it will lead to effective drug design and the promotion of the new concept of personalised medicine. In this work, we extracted the exons in the prostate gene sequences that were used in the experiment. We built a Deep Neural Network (DNN) and Bi-directional Long-Short Term Memory (Bi-LSTM) model using a k-mer encoding for the DNA sequence and one-hot encoding for the class label. The models were evaluated using different classification metrics. Our experimental results show that DNN model prediction offers a training accuracy of 99 percent and validation accuracy of 96 percent. The bi-LSTM model also has a training accuracy of 95 percent and validation accuracy of 91 percent.

• Journal of information and communication convergence engineering
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• 제12권4호
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• pp.199-207
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• 2014

A new cooperative MAC protocol called the busy tone cooperative medium access control (BT-COMAC) protocol is proposed to overcome the drawbacks and maximize the advantages of existing schemes. This scheme uses a new metric called decibel power to decide an appropriate helper node. Using received power strength is more efficient in selecting an appropriate helper node, especially in a densely populated network, than the effective transmission rates used in conventional schemes. All communication nodes in a communication service area are assumed to move independently. Two performance metrics are used: System throughput and channel access delay. A performance evaluation of the BT-COMAC protocol is conducted using a computer simulation over a slow fading wireless channel, and its performance results are compared with those of four existing schemes. The numerical results show that the BT-COMAC protocol improves the system throughput by approximately 15% as compared to the best existing scheme.

• 방송공학회논문지
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• 제7권2호
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• pp.168-180
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• 2002

This paper proposes a novel method to detect unauthorized copies of digital images. This copy detection scheme can be used as either an alternative approach or a complementary approach to watermarking. A test image is reduced to 8$\times$8 sub-image by intensity averaging, and the AC coefficients of its discrete cosine transform (DCT) are used to compute distance from those generated from the query image, of which a user wants to find copies. Copies may be Processed to avoid copy detection or enhance image quality. We show ordinal measure of DCT coefficients, which is based on relative ordering of AC magnitude values and using distance metrics between two rank permutations, are robust to various modifications of the original image. The optimal threshold selection scheme using the maximum a posteriori (MAP) criterion is also addressed.

• 디자인학연구
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• 제19권3호
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• pp.155-166
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• 2006

Color selection plays a vitally important role in creating impressions of individuals or companies. This is largely because colors have sensibility aspects, which relate, in part, to images and, in part, to associations. Based on theories of color harmony and sensibility ergonomics, we have developed quantitative and systematic metrics for color images. In this paper, we suggest a color coordinate system that supports color analysis and color harmony functions using color images, which can be captured by corresponding adjectival words. We focus on a prototype system for graphical logo design to exemplify our concepts. The system can be applied to a wide variety of design domains.

• Journal of information and communication convergence engineering
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• 제7권2호
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• pp.185-192
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• 2009

In wireless mesh networks multi-rate technology environment, a mesh node can dynamically modify the data transmission rate on a particular link, in response to link distance, or more accurately, the perceived SNR ratio between neighbor nodes. In such networks, existing route selection schemes use a link quality metric. Thus, these schemes may easily result in the network being overloaded. In this paper, a new route metric is proposed; it considers both per-hop service delay and link quality at mesh nodes. In addition, the Load-Aware AODV (LA-AODV) protocol using the proposed metric is presented. The performance evaluation is performed by simulation using the OPNET simulator. It is demonstrated that the LA-AODV protocol outperforms the existing routing protocols using other existing route metrics in multi-rate WMN environment.

• Journal of the Korean Data and Information Science Society
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• 제8권1호
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• pp.9-20
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• 1997

밀도함수를 추정하는 방법에 있어서 퓨리에(Fourier) 급수기법과 핵(kernel) 기법, 스플라인(spline)평활기법들이 많은 통계학자들의 관심의 대상이 되어 왔다. 이 연구는 확률밀도함수의 추정에 있어서 전통적으로 각각 독립적으로 사용하여 왔던 정진규칙(stopping rule)과 승수규칙(selection multiplier)을 조합하여 퓨리에 급수기법을 이용한 새로운 추정기법을 연구하였다. 모의 실험을 통해 제시된 추정기법이 기존의 연구기법들보다 다소 우월 하다는 결론을 얻었다.

• 한국항공운항학회지
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• 제24권3호
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• pp.10-18
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• 2016

This paper deals with the Performance Based Logistics(PBL) application scheme pertaining to optimal maintenance program for logistics of Korean Fighter Experimental(KF-X) Project. For enhancement of the performance based logistics system application to KF-X program the selection of appropriate standards fit to maximize cost-cutting, a set of performance metrics fit for the purpose of the contract, foreign technology dependence of core equipments and parts were considered. Thus, selecting appropriate standards fit for Korean logistics environment, domestic maintenance enterprise for stable rate of operation of KF-X, a systematic reliability task that is able to measure quantitative combat capability are suggested.

• 한국컴퓨터정보학회논문지
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• 제22권11호
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• pp.111-116
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• 2017

This paper proposes a data mining approach to predicting stock price direction. Stock market fluctuates due to many factors. Therefore, predicting stock price direction has become an important issue in the field of stock market analysis. However, in literature, there are few studies applying data mining approaches to predicting the stock price direction. To contribute to literature, this paper proposes comparing single classifiers and ensemble classifiers. Single classifiers include logistic regression, decision tree, neural network, and support vector machine. Ensemble classifiers we consider are adaboost, random forest, bagging, stacking, and vote. For the sake of experiments, we garnered dataset from Korea Stock Exchange (KRX) ranging from 2008 to 2015. Data mining experiments using WEKA revealed that random forest, one of ensemble classifiers, shows best results in terms of metrics such as AUC (area under the ROC curve) and accuracy.