• Journal of The Korean Association of Information Education
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• v.23 no.2
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• pp.107-116
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• 2019

The purpose of this study is to develop a Python sorting algorithm training program based on execution time as an educational method for enhancing the logical thinking power of elementary students and then to verify the effect. The education program was developed based on the results of the pre-demand analysis conducted on 100 elementary school teachers. In order to verify the effectiveness of the developed educational program, I teached 25 students of the volunteer sample of the elementary school education donation program conducted at ${\bigcirc}{\bigcirc}$ University conducted 42 hours, 7 days. The results of the pre-test and post-test were analyzed using the 'Group Assessment of Logical Thinking(GALT)' developed by the Korea Educational Development Institute. The results showed that the Python sorting algorithm training centered on execution time was effective in improving the logical thinking ability of elementary school students.

• Journal of the Society of Disaster Information
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• v.15 no.3
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• pp.347-357
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• 2019

Purpose: The purpose of this paper is to present a method for effectively conducting discussion-based training in disaster response safety training. Method: To this end, we analyzed the disaster response training of developed countries and suggested the training scenarios by applying the war-game process that is currently applied in the operation planning of our military. Result: In one disaster situation, several contingencies could be identified, and supplementary requirements for the manual could be derived. Conclusion: Therefore, in conclusion, if the military war-game process is applied to the discussion-based training in disaster response safety training, effective training can be carried out.

• International Journal of Railway
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• v.2 no.2
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• pp.50-59
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• 2009

Numerous train operating companies provide training by means of driving simulators. A detailed analysis in the course of the rail research project 2TRAIN has shown that the simulation technology, the purposes of training and the overall concept of simulator-based training are rather diverse (Schmitz & Maag, 2008). A joint factor however are weak assessment capabilities and the fact that the simulator training is often not embedded into the overall competence management. This fact hinders an optimal use of the simulators. Therefore, 2TRAIN aims at the development of enhanced training and assessment tools. Taking into account that several simulators are already in use, the focus lays on the extension of existing simulation technology instead of developing entirely new systems. This extension comprises (1) a common data simulation interface (CDSI), (2) a rule-based expert system (ExSys), (3) a virtual instructor (VI), and (4) an _assessment database (AssDB). The foundation of this technical development is an assessment concept (PERMA concept) that is based on performance markers. The first part of the paper presents this assessment concept and a process model for the two major steps of driver performance assessment, i.e. (1) the specification of exercise and assessment and (2) the assessment algorithm and execution of the assessment. The second part describes the rationale and the functionalities of the simulator add-on tools. Finally, recommendations for further technical improvement and appropriate usage are given. based on the results of a pilot study.

• The KIPS Transactions:PartB
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• v.13B no.6 s.109
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• pp.615-624
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• 2006

The objective of this paper is to implement parallel multi-layer ANN(Artificial Neural Network) simulator based on the mobile agent system which is executed in parallel in the virtual parallel distributed computing environment. The Multi-Layer Neural Network is classified by training session, training data layer, node, md weight in the parallelization-level. In this study, We have developed and evaluated the simulator with which it is feasible to parallel the ANN in the training session and training data parallelization because these have relatively few network traffic. In this results, we have verified that the performance of parallelization is high about 3.3 times in the training session and training data. The great significance of this paper is that the performance of ANN's execution on virtual parallel computer is similar to that of ANN's execution on existing super-computer. Therefore, we think that the virtual parallel computer can be considerably helpful in developing the neural network because it decreases the training time which needs extra-time.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.6
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• pp.161-169
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• 2011

Recent intelligent video surveillance system asks for development of more advanced technology for analysis and recognition of video data. Especially, machine learning algorithm such as Support Vector Machine (SVM) is used in order to recognize objects in video. Because SVM training demands massive amount of computation, parallel processing technique is necessary to reduce the execution time effectively. In this paper, we propose a parallel processing method of SVM training with a multi-core processor. The results of parallel SVM on a 4-core processor show that our proposed method can reduce the execution time of the sequential training by a factor of 2.5.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.6
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• pp.797-805
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• 2019

Threats targeting cyberspace are becoming more intelligent and increasing day by day. To cope with such cyber threats, it is essential to improve the coping ability of system security officers. In this paper, we propose a simulated threat generator that automatically generates cyber threats for cyber defense training. The proposed Simulated Threat Generator is designed with MITRE ATT & CK(Adversarial Tactics, Techniques and Common Knowledge) framework to easily add an evolving cyber threat and select the next threat based on the threat execution result.

• Physical Therapy Rehabilitation Science
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• v.2 no.1
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• pp.7-11
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• 2013

Objective: Final goal of nerve advancement therapy is to provide maximum ability to function independently in life to patients. This paper appraises and describes basic concepts of the virtual reality (VR) based exercise program to improve functional movement for neurologically impaired patients. Design: Review article. Methods: Stroke patients from the physical therapy department while wearing comfortable clothing receive therapy and also VR based motion therapy administered by the therapist in charge. After evaluation of stroke patients, therapy includes an exercise program that is suitable for use with stroke patients; stroke patients wear head-mounted display while in front of the computer, where the camera is located; they follow the action on the screen and the computer perceives the operation of the stroke patients according to subject accomplishment. Results: According to obstacle condition of stroke patients using the method, which is various environments after setting, in stroke patients, there is a possibility of presenting suitable therapeutic environments. The display presentation of the method, which is identical, causes difficulty for all stroke patients. According to subject accomplishment; stroke patients result in execution of repetition training and deepening study, which leads to mobility. Conclusions: The VR based rehabilitation training programs is a difference of the existing video training program, is immediate feedback and compensation method. It will provide rehabilitation training services for the family of the patient whose condition could be improved with rehabilitative therapy where it is a continuous circumstance as a matter of the social welfare facility therapy.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.23 no.11 s.170
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• pp.1929-1939
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• 1999

This paper presents a neural network approach, which was named PRONET, to 3D object recognition and pose calculation. 3D objects are represented using a set of centroidal profile patterns that describe the boundary of the 2D views taken from evenly distributed view points. PRONET consists of the training stage and the execution stage. In the training stage, a three-layer feed-forward neural network is trained with the centroidal profile patterns using an error back-propagation method. In the execution stage, by matching a centroidal profile pattern of the given image with the best fitting centroidal profile pattern using the neural network, the identity and approximate orientation of the real object, such as a workpiece in arbitrary pose, are obtained. In the matching procedure, line-to-line correspondence between image features and 3D CAD features are also obtained. An iterative model posing method then calculates the more exact pose of the object based on initial orientation and correspondence.

• Korean Journal of Oriental Medicine
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• v.13 no.2 s.20
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• pp.127-133
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• 2007

Objectives : This study were aimed to estimate the needs for clinical research educational program development focused on oriental medicine. Methods : Department Medical Research of Korea Institute of Oriental Medicine surveyed 204 certified subjects in total by using web-based questionnaires through e-mail from Jan. 17th to Jan. 31th, 2007. Reply from 62 on-line correspondents were collected and statistically analyzed. Results : The number of clinical trial involving oriental medicine continues to increase. According to the survey, many Issues were raised as problems such as difficulty of recruitment, lack of fund and lack of stepped program. Emphasized issues were clinical research methodologies, development of protocols and case report form (CRF), and Regulations including Institutional Review Boards (IRBs) in these three consecutive education training program. Conclusion : The results of this study may contribute to the development of an educational program for oriental medicine, a program that should be taken into consideration for developing practical items, such as, problem-based learnings which reflects participants' actual needs in their works. Also this report be used for future strategy plans and execution of training program for oriental medical clinical trial.

• The KIPS Transactions:PartA
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• v.11A no.4
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• pp.243-250
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• 2004

Monitoring is used to see if a real-time system provides a service on time. Generally, monitoring for real-time focuses on investigating the current status of a real-time system. To support a stable performance of a real-time system, it should have not only a function to see the current status of real-time process but also a function to predict executions of real-time processes, however. The legacy prediction model has some limitation to apply it to a real-time monitoring. First, it performs a static prediction after a real-time process finished. Second, it needs a statistical pre-analysis before a prediction. Third, transition probability and data about clustering is not based on the current data. We propose the execution prediction model based on learning algorithm to solve these problems and apply it to real-time monitoring. This model gets rid of unnecessary pre-processing and supports a precise prediction based on current data. In addition, this supports multi-level prediction by a trend analysis of past execution data. Most of all, We designed the model to support dynamic prediction which is performed within a real-time process' execution. The results from some experiments show that the judgment accuracy is greater than 80% if the size of a training set is set to over 10, and, in the case of the multi-level prediction, that the prediction difference of the multi-level prediction is minimized if the number of execution is bigger than the size of a training set. The execution prediction model proposed in this model has some limitation that the model used the most simplest learning algorithm and that it didn't consider the multi-regional space model managing CPU, memory and I/O data. The execution prediction model based on a learning algorithm proposed in this paper is used in some areas related to real-time monitoring and control.