• Journal of the Korean Society for Aviation and Aeronautics
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• v.6 no.1
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• pp.31-50
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• 1998

한국의 항공산업은 짧은 역사에도 불구하고 괄목할 만한 성장을 이루었으나, 그 규모에 비해 행해진 사회과학적 연구는 부족한 실정이다. 본 연구는 한국 군 출신 조종사들이 민간 항공조종사로 전환시 그 적응을 돕기 위한 교육 프로그램에 있어서 주요 고려사항들에 중점을 두었다. 이러한 고려사항들을 찾아내고 선별하는 것을 돕기 위하여 국내 두 항공사의 현역 조종사들 중 군 경력자들을 대상으로 설문을 실시하였으며 그 설문 결과를 SPSS에 의해 분석을 하였다. 분석 결과 국내 항공사의 전환 교육 프로그램이 군 출신 조종사들이 민간 항공 조종사로 적응하는데 필요한 임무의 차이점이나 CRM, 영어 교육 및 기술적 환경변화 등을 효과적으로 준비시키는데 적절치 못한 것으로 나타났다. 이는 군 경력 조종사들의 민간 항공 전환시 그 적응을 돕기 위한 개별적인 교육 프로그램이 필요함을 예시해 주고 있다.

• Journal of Intelligence and Information Systems
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• v.11 no.3
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• pp.69-81
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• 2005

As the competition in business becomes severe, companies are focusing their capacity on customer relationship management (CRM) for survival. One of the important issues in CRM is to build a purchase prediction model, which classifies customers into either purchasing or non-purchasing groups. Until now, various techniques for building purchase prediction models have been proposed. However, they have been criticized because their performances are generally low, or it requires much effort to build and maintain them. Thus, in this study, we propose the support vector machine (SVM) a tool for building a purchase prediction model. The SVM is known as the technique that not only produces accurate prediction results but also enables training with the small sample size. To validate the usefulness of SVM, we apply it and some of other comparative techniques to a real-world purchase prediction case. Experimental results show that SVM outperforms all the comparative models including logistic regression and artificial neural networks.

• Journal of Information Processing Systems
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• v.9 no.4
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• pp.633-650
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• 2013

All the imputation techniques proposed so far in literature for data imputation are offline techniques as they require a number of iterations to learn the characteristics of data during training and they also consume a lot of computational time. Hence, these techniques are not suitable for applications that require the imputation to be performed on demand and near real-time. The paper proposes a computational intelligence based architecture for online data imputation and extended versions of an existing offline data imputation method as well. The proposed online imputation technique has 2 stages. In stage 1, Evolving Clustering Method (ECM) is used to replace the missing values with cluster centers, as part of the local learning strategy. Stage 2 refines the resultant approximate values using a General Regression Neural Network (GRNN) as part of the global approximation strategy. We also propose extended versions of an existing offline imputation technique. The offline imputation techniques employ K-Means or K-Medoids and Multi Layer Perceptron (MLP)or GRNN in Stage-1and Stage-2respectively. Several experiments were conducted on 8benchmark datasets and 4 bank related datasets to assess the effectiveness of the proposed online and offline imputation techniques. In terms of Mean Absolute Percentage Error (MAPE), the results indicate that the difference between the proposed best offline imputation method viz., K-Medoids+GRNN and the proposed online imputation method viz., ECM+GRNN is statistically insignificant at a 1% level of significance. Consequently, the proposed online technique, being less expensive and faster, can be employed for imputation instead of the existing and proposed offline imputation techniques. This is the significant outcome of the study. Furthermore, GRNN in stage-2 uniformly reduced MAPE values in both offline and online imputation methods on all datasets.

• Resources Recycling
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• v.27 no.1
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• pp.84-91
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• 2018

In this study, a novel soft information based most probable classification scheme is proposed for sorting recyclable metal alloys with laser induced breakdown spectroscopy (LIBS). Regression analysis with LIBS captured spectrums for estimating concentrations of common elements can be efficient for classifying unknown arbitrary metal alloys, even when that particular alloy is not included for training. Therefore, partial least square regression (PLSR) is employed in the proposed scheme, where spectrums of the certified reference materials (CRMs) are used for training. With the PLSR model, the concentrations of the test spectrum are estimated independently and are compared to those of CRMs for finding out the most probable class. Then, joint soft information can be obtained by assuming multi-variate normal (MVN) distribution, which enables to account the probability measure or a prior information and improves classification performance. For evaluating the proposed schemes, MVN soft information is evaluated based on PLSR of LIBS captured spectrums of 9 metal CRMs, and tested for classifying unknown metal alloys. Furthermore, the likelihood is evaluated with the radar chart to effectively visualize and search the most probable class among the candidates. By the leave-one-out cross validation tests, the proposed scheme is not only showing improved classification accuracies but also helpful for adaptive post-processing to correct the mis-classifications.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.16 no.1
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• pp.18-28
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• 2008

The purpose of this study is leading to prevent the major causes of commercial-aviation fatalities about controlled-flight-into-terrain(CFIT) in approach-and-landing accidents. The paper of major analysis for controlled flight into terrain(CFIT) was Guam accident, Mokpo accident and Gimhae accident in commercial transport-aircraft accidents from 1993 through 2002. CFIT occurs when an airworthy aircraft under the control of the flight crew is flown unintentionally into terrain, obstacles or water, usually with no prior awareness by the crew. This type of accident can occur during most phases of flight, but CFIT is more common during the approach-and-landing phase. Ninety-five percent of the Guam accident, Mokpo accident, and Gimhae accident where weather was known involved IMC, fog, and rain. The paper believed that prevention for CFIT accident was education and training for flying crew and upgrade for equipment such as EGPWS, and need more research for professional organizations of airlines, and accomplishing precision approaches should be a high priority.

• Analytical Science and Technology
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• v.32 no.5
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• pp.210-216
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• 2019

Concentrations of Cu, Zn, Fe, Mn, Ni and Cr have been estimated in sediments of the Shitalakhya River at Polash-Ghorashal area, Narsingdi, Bangladesh. 36 samples of sediments from nine sampling point at different locations of Shitalakhya River were collected to determine the concentration of Cu, Zn, Fe, Mn, Ni, Cr and the samples were analyzed by atomic absorption spectrophotometer (AAS). The obtained results were compared with national and international guidelines. The levels of heavy metal concentrations in sediments were found to decrease in the order of Fe > Mn > Zn > Ni > Cu > Cr, respectively. The heavy metal concentration in sediment of Shitalakhya was below the recommended safe limits of heavy metals by WHO, FAO and other international standards. Contamination factor (CF) of Zn and Cu at sampling point Fsd2 show higher (> 1) values due to the influence of external discrete sources like wastage catalysts of ZnO and CuO. Geo-accumulation index values of the study indicate as non-contaminated to moderately contaminate.

• The Korean Journal of Air & Space Law and Policy
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• v.26 no.2
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• pp.149-179
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• 2011

Statistics of aviation accident in Korea show that safety level of training flights is high. However, more than 80% of aviation accidents happen owing to human factors. And because most reasons of them are concerned with pilot error, it is very important for student pilots who will transport a lot of passengers to develop the knowledge of safety and abilities of risk management for preventing accidents. In this study, in order to investigate the Human Factors which affect safety in training student pilots for flight, verified the correlationbetween experiences of accident, the differences according to the experience level of training flight and the differences between college student pilots and ordinary student pilots on the basis of human factors that composes the SHELL models. For the study, Using SPSS 17.0, conducted Correlation Analysis, Analysis of Variance(ANOVA) and t-test. To sum up the result of this study, student pilot's ability and equipment in the cockpit are the important factors for safety when pilots are training flight. Also the analysis of the differences between human factors according to the characters of student pilots' groups shows that college student pilots are affected by immanent factors and organizational cultures. So far, there haven't been any accidents which is related with human casualties when training at the ATO(Approved Training Organization). But accidents can occur at any time and anywhere. Especially the human factors which comprises most of aviation accident have a wide reach and are impossible to be eliminated, therefore, it is best to minimize them. Because ATO is the starting point to lead the aviation industry of Korea, we will have to be aware of problems and improve education/training of human factors.

• Information Systems Review
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• v.10 no.2
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• pp.137-158
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• 2008

Rapid development of various information technologies creates new opportunities in online and offline markets. In this changing market environment, customers have various demands on new products and services. Therefore, their power and influence on the markets grow stronger each year. Companies have paid great attention to customer relationship management. Especially, personalized product recommendation systems, which recommend products and services based on customer's private information or purchasing behaviors in stores, is an important asset to most companies. CRM is one of the important business processes where reliable information is mined from customer database. Data mining techniques such as artificial intelligence are popular tools used to extract useful information and knowledge from these customer databases. In this research, we propose a recommendation system that predicts customer's purchase intention. Then, customer's purchasing intention of specific product is predicted by using data mining techniques using receipt data set. The performance of this suggested method is compared with that of other data mining technologies.

• Journal of the Ergonomics Society of Korea
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• v.36 no.5
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• pp.467-484
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• 2017

Objective: The purpose of this paper is to review, with the main focus on aviation safety technology and management program, how human factors are currently taken into consideration within transportation sectors, especially aviation, and to further share related information. Background: Human factors account for the majority of aviation accidents/incidents. Thus, the aviation sector has been comparatively quick in developing and applying technologies and management programs that deal with human factors. This paper reviews the latest safety technologies and management programs regarding human factors and aims to identify the trend. Method: This paper, based on literature research and practical experience, examines the latest international standards on technologies and management programs, those that deal with human factors and are adopted by international and domestic aviation organization. The main focus of discussion is how human factors are reflected during the system design and operation process. Results: The current most important issue in designing is the consideration of human factors in Cockpit, Automation, and Safety system technology design. From an operational point of view, the issues at hand are screening and training aviation workers to promote aviation safety, providing education on human factors and CRM/TEM, and running a safety management program to implement SMS. They were discussed based on the operational experience within the aviation sector. Conclusion: Major examples of a human factors approach to promote aviation safety are safety programs and various safety and monitoring technologies applied to aviation personnel for error management. These programs must be managed in an integrated manner that takes both the system designing and operational point of view into account. Application: It is thought that the human factors approach for promoting aviation safety reviewed in this paper can be extended and applied to safety management programs in other transportation sectors such as the railroad, maritime, road traffic etc.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.241-254
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• 2011

Financial time-series forecasting is one of the most important issues because it is essential for the risk management of financial institutions. Therefore, researchers have tried to forecast financial time-series using various data mining techniques such as regression, artificial neural networks, decision trees, k-nearest neighbor etc. Recently, support vector machines (SVMs) are popularly applied to this research area because they have advantages that they don't require huge training data and have low possibility of overfitting. However, a user must determine several design factors by heuristics in order to use SVM. For example, the selection of appropriate kernel function and its parameters and proper feature subset selection are major design factors of SVM. Other than these factors, the proper selection of instance subset may also improve the forecasting performance of SVM by eliminating irrelevant and distorting training instances. Nonetheless, there have been few studies that have applied instance selection to SVM, especially in the domain of stock market prediction. Instance selection tries to choose proper instance subsets from original training data. It may be considered as a method of knowledge refinement and it maintains the instance-base. This study proposes the novel instance selection algorithm for SVMs. The proposed technique in this study uses genetic algorithm (GA) to optimize instance selection process with parameter optimization simultaneously. We call the model as ISVM (SVM with Instance selection) in this study. Experiments on stock market data are implemented using ISVM. In this study, the GA searches for optimal or near-optimal values of kernel parameters and relevant instances for SVMs. This study needs two sets of parameters in chromosomes in GA setting : The codes for kernel parameters and for instance selection. For the controlling parameters of the GA search, the population size is set at 50 organisms and the value of the crossover rate is set at 0.7 while the mutation rate is 0.1. As the stopping condition, 50 generations are permitted. The application data used in this study consists of technical indicators and the direction of change in the daily Korea stock price index (KOSPI). The total number of samples is 2218 trading days. We separate the whole data into three subsets as training, test, hold-out data set. The number of data in each subset is 1056, 581, 581 respectively. This study compares ISVM to several comparative models including logistic regression (logit), backpropagation neural networks (ANN), nearest neighbor (1-NN), conventional SVM (SVM) and SVM with the optimized parameters (PSVM). In especial, PSVM uses optimized kernel parameters by the genetic algorithm. The experimental results show that ISVM outperforms 1-NN by 15.32%, ANN by 6.89%, Logit and SVM by 5.34%, and PSVM by 4.82% for the holdout data. For ISVM, only 556 data from 1056 original training data are used to produce the result. In addition, the two-sample test for proportions is used to examine whether ISVM significantly outperforms other comparative models. The results indicate that ISVM outperforms ANN and 1-NN at the 1% statistical significance level. In addition, ISVM performs better than Logit, SVM and PSVM at the 5% statistical significance level.