• Journal of the Korean Society for Aviation and Aeronautics
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• v.30 no.3
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• pp.109-116
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• 2022

A modular platform development technique was proposed to minimize development cost and development period by utilizing the already developed unmanned Aerial target AVT, which has been operated and verified for many years. New Mission Profile was designed and structural analysis was performed through finite element analysis (FEA) by analyzing mission requirements for visual short-range, non-visible mid-range, and long-range targets. The targets are used for guided missile anti-aircraft training. In addition, avionics systems including flight control computers for autonomous flights were developed to verify their conformance by performing launcher take-off tests with rapid acceleration changes and autonomous flight tests at a maximum speed of 300km per hour.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.5C
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• pp.618-626
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• 2004

In this paper, we describe a robot endeffector recognition system based on a Modular Neural Networks (MNN). The proposed recognition system can be used for vision system which track a given object using a sequence of images from a camera unit. The main objective to achieve with the designed MNN is to precisely recognize the given robot endeffector and to minimize the processing time. Since the robot endeffector can be viewed in many different shapes in 3- D space, a MNN structure, which contains a set of feedforwared neural networks, can be more attractive in recognizing the given object. Each single neural network learns the endeffector with a cluster of training patterns. The training MNN patterns for a neural network share the similar characteristics so that they can be easily trained. The trained UM is les s sensitive to noise and it shows the better performance in recognizing the endeffector. The recognition rate of MNN is enhanced by 14％ over the single neural network. A vision system with the MNN can precisely recognize the endeffector and place it at the center of a display for a remote operator.

• Journal of the Korean housing association
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• v.24 no.1
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• pp.69-78
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• 2013

Recently, due to the casualties and property damage caused by disasters, it became important to evacuate the victims to a safe place and come up with a space for them to inhabit for a certain period of time. Therefore, this study aims to design and develop a temporary housing system that would quickly provide a safe and comfortable living space until the displaced persons can return to a normal life again when a disaster occurs. As a result, a system of a BIM-based modular housing, a modular town through automatic placement, and a method to calculate the capacity and the total cost was developed. As this system provides the temporary housing facilities and the site in 3D, it can be utilized as a training material on a normal basis, as well as the first case material for rapid decision making when there is a disaster.

• The Journal of Information Systems
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• v.1
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• pp.149-178
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• 1992

The field of Artificial Intelligence (AI) is growing, and many firms are investing in expert system, one of AI's subfields. An expert system is defined as a computer program designed to replicate some aspect of the decision making of one or more experts and to be used by nonexperts. The kernel of an expert system is the knowledge base, which consists of the facts and rules that represent the expert's knowledge. Firms need expert systems for training employees to provide competitive advantage. This paper describes the model of an instructional expert training system which interfaces to external programs, such as an ASCII file, a work-sheet program, and a database program. A model for such an expert training system, and its prototype have been developed to demonstrate its functionality. A modular knowledge base has been developed and implemented in support of this study. The modularized knowledge base offers the user an easy and quick maintenance of facts and rules, which are frequently required to change in future.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.496-499
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• 1999

This paper describes a medular neural network(MNN) for a vision system which tracks a given object using a sequence of images from a camera unit. The MNN is used to precisely recognize the given robot endeffector and to minize the processing time. Since the robot endeffector can be viewed in many different shapes in 3-D space, a MNN structure, which contains a set of feedforwared neural networks, co be more attractive in recognizing the given object. Each single neural network learns the endeffector with a cluster of training patterns. The training patterns for a neural network share the similar charateristics so that they can be easily trained. The trained MNN is less sensitive to noise and it shows the better performance in recognizing the endeffector. The recognition rate of MNN is enhanced by 14% over the single neural network. A vision system with the MNN can precisely recognize the endeffector and place it at the center of a display for a remote operator.

• Journal of Fisheries and Marine Sciences Education
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• v.25 no.6
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• pp.1273-1284
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• 2013

The purpose of this study is to develop vocational English textbooks for vocational high school, especially marine production high school. Vocational English is intended to increase English proficiency in the area of specific industry or jobs. Based on the review of the literature, the study established developed vocational English textbooks for marine production high school based on the modular system. The developing process includes analysis for marine production job, marine production high school and current English textbook for marine production. Based on the analysis, this study developed English contents which marine production students utilize in not only school but also workplace. Also, this study suggests future research directions for effective development and use of the textbooks in the vocational high schools.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.248-254
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• 2022

Educational innovations are first created, improved or applied educational, didactic, educative, and managerial systems and their components that significantly improve the results of educational activities. The development of pedagogical technology in the global educational space is conventionally divided into three stages. The role of innovative technologies in Higher School practice is substantiated. Factors of effectiveness of the educational process are highlighted. Technology is defined as a phenomenon and its importance is emphasized, it is indicated that it is a component of human history, a form of expression of intelligence focused on solving important problems of being, a synthesis of the mind and human abilities. The most frequently used technologies in practice are classified. Among the priority educational innovations in higher education institutions, the following are highlighted. Introduction of modular training and a rating system for knowledge control (credit-modular system) into the educational process; distance learning system; computerization of libraries using electronic catalog programs and the creation of a fund of electronic educational and methodological materials; electronic system for managing the activities of an educational institution and the educational process. In the educational process, various innovative pedagogical methods are successfully used, the basis of which is interactivity and maximum proximity to the real professional activity of the future specialist. There are simulation technologies (game and discussion forms of organization); technology "case method" (maximum proximity to reality); video training methodology (maximum proximity to reality); computer modeling; interactive technologies; technologies of collective and group training; situational modeling technologies; technologies for working out discussion issues; project technology; Information Technologies; technologies of differentiated training; text-centric training technology and others.

• Journal of the Korean Society of Industry Convergence
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• v.13 no.1
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• pp.49-54
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• 2010

Lean 6 sigma has recently been used to describe a management system which combines lean management and 6 sigma. The marriage between Lean manufacturing and 6 sigma has proven to be a powerful tool for cutting waste and improving the organization operations. Time and quality are the most important metrics in improving any company's production and profit performance. lean 6 sigma is a management innovation for improving production efficiency, process quality, cost reduction, investment efficiency and customer's satisfaction. in this paper, Advanced cell line is builded the home appliance goods of the LCD TV final assembly line of domestic company line, training the multi-skilled man and controlling the production information system based on Lean 6 sigma.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.59-64
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• 2001

The robot soccer simulation game is a dynamic multi-agent environment. In this paper we suggest a new reinforcement learning approach to each agent's dynamic positioning in such dynamic environment. Reinforcement learning is the machine learning in which an agent learns from indirect, delayed reward an optimal policy to chose sequences of actions that produce the greatest cumulative reward. Therefore the reinforcement learning is different from supervised learning in the sense that there is no presentation of input pairs as training examples. Furthermore, model-free reinforcement learning algorithms like Q-learning do not require defining or learning any models of the surrounding environment. Nevertheless it can learn the optimal policy if the agent can visit every state- action pair infinitely. However, the biggest problem of monolithic reinforcement learning is that its straightforward applications do not successfully scale up to more complex environments due to the intractable large space of states. In order to address this problem. we suggest Adaptive Mediation-based Modular Q-Learning (AMMQL)as an improvement of the existing Modular Q-Learning (MQL). While simple modular Q-learning combines the results from each learning module in a fixed way, AMMQL combines them in a more flexible way by assigning different weight to each module according to its contribution to rewards. Therefore in addition to resolving the problem of large state effectively, AMMQL can show higher adaptability to environmental changes than pure MQL. This paper introduces the concept of AMMQL and presents details of its application into dynamic positioning of robot soccer agents.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.833-835
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• 2014

On this study, we have developed respiratory training system to improve stability of respiration, one of the most important factors of Respiratory Gated Radiation Therapy, RGRT. Respiratory training system that we developed was applied to personal respiratory cycle so that it could provide comfortable respiratory triggering to patients. To give sufficient time for practice, we used modular portable device to practice easily and to be undetered by time and place. We have intended to improve efficiency and accuracy by providing it to patients. We are now planning to conduct experiment of 10 peoples to find out stability, degree of durability betterment and regularity of respiration when patients are using respiratory training system. There are three kinds of breathing style. First is free breathing that Individual patients can breathe freely. Second is guide breathing that patients apply to personal respiration cycle through the guiding sight and hearing program. Third is prediction breathing that patients breathe after respiratory training without guiding sight and hearing program. By using these 3 data of respiration method, we have evaluated usability of respiratory training system by quantitatively analyzing respiration period, amplitude and area's variation.