• The Korean Journal of Emergency Medical Services
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• v.19 no.2
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• pp.99-115
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• 2015

Purpose: This study aimed to provide basic data for clinical training program development by analyzing the operating conditions and satisfaction in a clinical training program for 119 emergency medical technicians (EMTs) in South Korea. Methods: Data from 84 EMTs were collected on June 19, 2014. We administered a 64-item questionnaire about operating conditions and satisfaction in the clinical training program, and analyzed data (SPSS v 21.0). Results: The degree of performance in the field, importance of the item in the field, and level of difficulty were 3.36, 4.23, and 3.21, respectively. In the number of times that an item was directly performed according to the subjects' general characteristics a statistically difference in sex (p = .000), duty (p =.021), and total working time of trainees (p = .002). The subjects' total satisfaction score was 3.77. The difference in satisfaction according to the subjects' characteristics was a statistically significant in terms of sex (p = .016) and clinical training area (p = .005). Conclusion: A more efficient training system for hospital clinical training courses should be developed. The operation condition analyzed in this research may contribute to the improvement of the performance of EMTs.

• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.965-974
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• 2003

In this paper we propose an incremental classification of multi-class data set by LS-SVM. By encoding the output variable in the training data set appropriately, we obtain a new specific output vectors for the training data sets. Then, online LS-SVM is applied on each newly encoded output vectors. Proposed method will enable the computation cost to be reduced and the training to be performed incrementally. With the incremental formulation of an inverse matrix, the current information and new input data are used for building another new inverse matrix for the estimation of the optimal bias and lagrange multipliers. Computational difficulties of large scale matrix inversion can be avoided. Performance of proposed method are shown via numerical studies and compared with artificial neural network.

• Korean Journal of Remote Sensing
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• v.18 no.6
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• pp.369-376
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• 2002

This paper presents a method to enhance the performance of supervised classification by separating the spectral signature of the training data sets for each class. Using clustering technique, a training data set is divided into several subsets which show a pattern of the normal distribution with small value of spectral variances. Then a supervised classification is applied with the divided training data set as training data for the temporary subclasses of the original class. The proposed method is applied to a Landsat TM image of Busan area for the applicability test. The result shows that the proposed method produces better classified results than the conventional statistical classification methods. It is expected that the proposed method will reduce the effort and expense for selecting the training data set for each class in an area which has spectrally homogeneous signature.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.507-519
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• 2023

Multi-temporal optical images have been utilized for time-series monitoring of croplands. However, the presence of clouds imposes limitations on image availability, often requiring a cloud removal procedure. This study assesses the applicability of various machine learning algorithms for effective cloud removal in optical imagery. We conducted comparative experiments by focusing on two key variables that significantly influence the predictive performance of machine learning algorithms: (1) land-cover types of training data and (2) temporal variability of land-cover types. Three machine learning algorithms, including Gaussian process regression (GPR), support vector machine (SVM), and random forest (RF), were employed for the experiments using simulated cloudy images in paddy fields of Gunsan. GPR and SVM exhibited superior prediction accuracy when the training data had the same land-cover types as the cloud region, and GPR showed the best stability with respect to sampling fluctuations. In addition, RF was the least affected by the land-cover types and temporal variations of training data. These results indicate that GPR is recommended when the land-cover type and spectral characteristics of the training data are the same as those of the cloud region. On the other hand, RF should be applied when it is difficult to obtain training data with the same land-cover types as the cloud region. Therefore, the land-cover types in cloud areas should be taken into account for extracting informative training data along with selecting the optimal machine learning algorithm.

• Journal of Engineering Education Research
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• v.18 no.4
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• pp.3-12
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• 2015

The purpose of this study was to develop a pre-training program of engineering college students for maximizing the effectiveness of internship or field training. To pursue this goal, literature review was conducted for data collection about college and corporate pre-training program for internship or field training and pre-training program(draft) was proposed. A questionnaire survey was conducted with engineering professors, students and graduates to identify the needs for pre-training program(draft) for internship or field training. Based on the results, the contents of pre-training program for internship or field training were composed of basic liberal arts, basic competency, real information related with corporation or job, and information exchange network. And key consideration for operating the pre-training program for internship or field training were proposed with the management department, regulation for the obligatory participation, meaningful organizing content, feedback of needs.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.13 no.2
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• pp.99-106
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• 2003

Intrusions pose a serious security risk in a network environment. For detecting the intrusion effectively, many researches have developed data mining framework for constructing intrusion detection modules. Traditional anomaly detection techniques focus on detecting anomalies in new data after training on normal data. To detect anomalous behavior, Precise normal Pattern is necessary. This training data is typically expensive to produce. For this, the understanding of the characteristics of data on network is inevitable. In this paper, we propose to use clustering and association rules as the basis for guiding anomaly detection. For applying entropy to filter noisy data, we present a technique for detecting anomalies without training on normal data. We present dynamic transaction for generating more effectively detection patterns.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.1075-1087
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• 2020

Aviation ICT technology is a convergence technology between aviation and electronics, and has a wide variety of applications, including navigation and education. Among them, in the field of aerial pilot training, there are many problems such as the possibility of accidents during training and the lack of coping skills for various situations. This raises the need for a simulated pilot training system similar to actual training. In this paper, pilot training data were collected in pilot training system using VR/AR to increase immersion in flight training, and Customized Pilot Training Platform with Collaborative Deep Learning in VR/AR Environment that can recommend effective training courses to pilots is proposed. To verify the accuracy of the recommendation, the performance of the proposed collaborative deep learning algorithm with the existing recommendation algorithm was evaluated, and the flight test score was measured based on the pilot's training data base, and the deviations of each result were compared. The proposed service platform can expect more reliable recommendation results than previous studies, and the user survey for verification showed high satisfaction.

• Korean Journal of Computational Design and Engineering
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• v.14 no.4
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• pp.271-280
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• 2009

An experience based training system concerning various fire situations which may result many casualties has been required to make rapid decision and improve the responsiveness. Recently, the necessity of virtual reality (VR) based training system which can replace a dangerous full-scale fire training and be easily adopted to the training or evaluation process is increasing. This study constructed tile virtual environment according to pre-defined scenarios, utilized the FDS(Fire Dynamics Simulator), three dimensional computational fire analysis program, to derive numerically simulated data on the propagation of fire. Finally, by visualizing the realistic fire and smoke behavior through virtual reality technique and implementing real-time interaction, we developed a VR-based fire training simulator. Also, in order to ensure the sense for tile real of a virtual world and reaI-time performance at the same time, we proposed appropriate data processing and space search algorithms, demonstrate d the value of proposed method through experiments.

• Journal of Information Processing Systems
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• v.2 no.1
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• pp.23-27
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• 2006

This paper presents a feature selection technique based on distributional differences for efficient machine learning. Initial training data consists of data including many features and a target value. We classified them into positive and negative data based on the target value. We then divided the range of the feature values into 10 intervals and calculated the distribution of the intervals in each positive and negative data. Then, we selected the features and the intervals of the features for which the distributional differences are over a certain threshold. Using the selected intervals and features, we could obtain the reduced training data. In the experiments, we will show that the reduced training data can reduce the training time of the neural network by about 40%, and we can obtain more profit on simulated stock trading using the trained functions as well.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.3
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• pp.127-133
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• 2019

Most studies of software fault prediction have been about supervised learning models that use only labeled training data. Although supervised learning usually shows high prediction performance, most development groups do not have sufficient labeled data. Unsupervised learning models that use only unlabeled data for training are difficult to build and show poor performance. Semi-supervised learning models that use both labeled data and unlabeled data can solve these problems. Self-training technique requires the fewest assumptions and constraints among semi-supervised techniques. In this paper, we implemented several models using self-training algorithms and evaluated them using Accuracy and AUC. As a result, YATSI showed the best performance.