• Structural Engineering and Mechanics
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• v.78 no.3
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• pp.319-332
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• 2021

The wood-concrete composite is an interesting solution in the field of Civil Engineering to create high performance bending elements for bridges, as well as in the building construction for the design of wood concrete floor systems. The authors of this paper has been working for the past few years on the development of the bonding process as applied to wood-concrete composite structures. Contrary to conventional joining connectors, this assembling technique does ensure an almost perfect connection between wood and concrete. This paper presents a careful theoretical investigation into interfacial stresses at the level of the two interfaces in composite wooden beam- reinforced concrete slab strengthened by external bonding of prestressed composite plate under a uniformly distributed load. The model is based on equilibrium and deformations compatibility requirements in all parts of the strengthened composite beam, i.e., the wooden beam, RC slab, the CFRP plate and the adhesive layer. The theoretical predictions are compared with other existing solutions. This research is helpful for the understanding on mechanical behaviour of the interface and design of the CFRP- wooden-concrete hybrid structures.

• Journal of Engineering Education Research
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• v.24 no.3
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• pp.50-57
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• 2021

Although the mathematics education process in AI education is a very important issue, little cases are reported in developing effective methods on AI and mathematics education at the university level. The universities cover all fields of mathematics in their curriculums, but they lack in connecting and applying the math knowledge to AI in an efficient manner. Students are hardly interested in taking many math courses and it gets worse for the students in humanities, social sciences and arts. But university education is very slow in adapting to rapidly changing new technologies in the real world. AI is a technology that is changing the paradigm of the century, so every one should be familiar with this technology but it requires fundamental math knowledge. It is not fair for the students to study all math subjects and ride on the AI train. We recognize that three key elements, SW knowledge, mathematical knowledge, and domain knowledge, are required in applying AI technology to the real world problems. This study proposes a modular approach of studying mathematics knowledge while connecting the math to different domain problems using AI techniques. We also show a modular curriculum that is developed for using math for AI-driven autonomous driving.

• Journal of the Korea Institute of Military Science and Technology
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• v.24 no.2
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• pp.237-244
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• 2021

In recent years, the maturity of the technology for a high speed underwater vehicle using supercavitation increase, it is entering the stage of applied research for practical use. In this study, hydrodynamic performance of the supercavitating object was evaluated by using a Viscous-Potential based Boundary Element Method(VP-BEM). 27 models with different shape parameters such as body diameter, length and fore-body shape were considered. The process of the supercavity development of each model was simulated, and drag generated according to operating conditions such as changes in water depth was analyzed.

• Journal of the Korean Society of Safety
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• v.37 no.2
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• pp.97-105
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• 2022

Human casualties from fires are increasing worldwide. The majority of human deaths occur during the evacuation process, as occupants panic and are unaware of the location of the fire and evacuation routes. Using an Internet of Things (IoT) sensor and reinforcement learning, we propose a method to find the safest evacuation route by considering the fire location, flame speed, occupant position, and walking conditions. The first step is detecting the fire with IoT-based devices. The second step is identifying the occupant's position via a beacon connected to the occupant's mobile phone. In the third step, the collected information, flame speed, and walking conditions are input into the reinforcement learning model to derive the optimal evacuation route. This study makes it possible to provide the safest evacuation route for individual occupants in real time. This study is expected to reduce human casualties caused by fires.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4308-4325
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• 2021

The importance and necessity of artificial intelligence, particularly machine learning, has recently been emphasized. In fact, artificial intelligence, such as intelligent surveillance cameras and other security systems, is used to solve various problems or provide convenience, providing solutions to problems that humans traditionally had to manually deal with one at a time. Among them, information security is one of the domains where the use of artificial intelligence is especially needed because the frequency of occurrence and processing capacity of dangerous codes exceeds the capabilities of humans. Therefore, this study intends to examine the definition of artificial intelligence and machine learning, its execution method, process, learning algorithm, and cases of utilization in various domains, particularly the cases and contents of artificial intelligence technology used in the field of information security. Based on this, this study proposes a method to apply machine learning technology to the method of classifying and detecting malware that has rapidly increased in recent years. The proposed methodology converts software programs containing malicious codes into images and creates training data suitable for machine learning by preparing data and augmenting the dataset. The model trained using the images created in this manner is expected to be effective in classifying and detecting malware.

• Journal of the Korean Wood Science and Technology
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• v.49 no.3
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• pp.254-266
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• 2021

Using forest resources involves solving complex and diverse tasks. At the same time, one of the key goals in the field is improving the quality of forest infrastructure. This direction requires adequate mathematical and economic justification. Moreover, creating an effective infrastructure will not only increase the accessibility and usage volumes of wood and other forest resources, but also contribute to the development of continuous and sustainable forest management. The existing practice of making decisions in terms of the organizational and technological aspects of logging, based on the personal experiences of managers or leading specialists in enterprises, hinders the achievement of constant optimal efficiency. The paper presents results that are a continuation of the research cycle of the authors' team in the fields of optimization and algorithmization of various logging processes. The focus of the study lies in the processing and movement of wood resources, the most valuable products of the investigated groups of enterprises. To this end, the paper presents a developed algorithm for determining an effective technological chain of transportation in logging operations, and for improving loading and unloading processing operations under dynamic natural and production conditions. This algorithm serves as the methodological basis for designing logging infrastructure in a dynamically changing environment.

• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.264-272
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• 2022

Human-robot cooperative tasks are increasingly required in our daily life with the development of robotics and artificial intelligence technology. Interactive reinforcement learning strategies suggest that robots learn task by receiving feedback from an experienced human trainer during a training process. However, most of the previous studies on Interactive reinforcement learning have required an extra feedback input device such as a mouse or keyboard in addition to robot itself, and the scenario where a robot can interactively learn a task with human have been also limited to virtual environment. To solve these limitations, this paper studies training strategies of robot that learn table balancing tasks interactively using deep reinforcement learning with human's facial expression feedback. In the proposed system, the robot learns a cooperative table balancing task using Deep Q-Network (DQN), which is a deep reinforcement learning technique, with human facial emotion expression feedback. As a result of the experiment, the proposed system achieved a high optimal policy convergence rate of up to 83.3% in training and successful assumption rate of up to 91.6% in testing, showing improved performance compared to the model without human facial expression feedback.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.4
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• pp.24-33
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• 1996

PLCs are vital components of modern automation systems, which have penetrated into almost every industry. Many industries have a demand for facilitation of PLC programming. In this study, a programning system for sequential control is developed on a personal computer. This programming system consists of two main parts, a GRAFCET editor and a GRAFCET compiler. The GRAFCET editor enables us to model an actual sequential process by a GRAFCET diagram. This GRAFCET editor is developed by the menu-driven method based on specific menus and graphic symbols. The GRAFCET compiler consists of two parts, a GRAFCET parser and a code generator. The possible errors in a drawn GRAFCET diagram are first checked by the GRAFCET parser which generates finally an intermediate code from a verified CRAFCET diagram. Then the intermediate code is converted into a control code of an actual sequential controller by the code generator. To show the usefulness of this programming system, this system is applied to a pneumatically controlled handling robot. For this robot, a Z-80 microprocessor is used as the actual sequential controller.

• Annual Conference of KIPS
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• 2011.04a
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• pp.1356-1359
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• 2011

IT 프로젝트는 많은 불확실성을 내포하고 있다. 이러한 불확실성은 프로젝트의 성공적인 수행에 좋지 않은 영향을 미치게 된다. 이처럼 악영향을 미치는 근본적인 이유는 프로젝트가 가지고 있는 잠재적 위험(Risk)이 중요한 원인이라 할 수 있다. CMMI-DEV(Capability Maturity Model Integration for Development, version 1.2)모델을 기반으로 IT 프로젝트를 수행하는 많은 조직에서 소프트웨어 개발시 실제 CMMI-DEV 모델을 적용하여 IT 프로젝트의 위험관리를 수행하고 있다. 그러나 대다수 기업에서 위험을 관리하기 위한 프로세스의 범위(영역)를 선정하는 것에는 많은 노력을 기울이지만 단위 프로세스별로 어떠한 방법(How)과 도구 및 관리기법을 사용하여 위험을 정량적으로 관리하고 완화시킬지에 대한 구체화된 노력은 미흡한 것이 사실이다. 본 논문에서는 CMMI-DEV 모델의 RSKM(Risk Management) 프로세스와 PMBOK(Project Management Body of Knowledge, 4th Edition)의 위험관리 지식영역(Knowledge Area)을 프로세스의 지속적인 개선을 위하여 PDCA(Plan-Do-Check-Action) Cycle의 각 단계별 목적에 맞게 통합(Integration)시켜 IT 프로젝트를 위한 개선된 위험관리 프로세스를 제안하였다. 제안 프로세스의 개선효과를 검증하고 분석하기 위하여 측정지표를 제시하였으며, 개선 프로세스의 적용 전후 결과를 정량적으로 비교 및 분석함으로써 개선효과를 도출하였다.

• Journal of Korean Academy of Nursing
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• v.53 no.6
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• pp.622-634
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• 2023

Purpose: This study aimed to identify the mediating effects of career motivation and job-esteem and the effect of the nursing work environment on intention to stay among hospital nurses. Methods: Data were collected from 289 nurses working at an advanced general hospital. The research model design was based on the PROCESS macro proposed by Hayes and analyzed using SPSS 24.0 program. Results: The results showed a positive correlation between intention to stay and nursing work environment (r = .19, p = .001), career motivation (r = .34, p < .001), and job-esteem (r = .37, p < .001). Nursing work environment (B = 0.34 [.09~.59]) and job-esteem (B = 0.27 [.04~.49]) had a direct effect on intention to stay. There was a two-mediator sereal mediation effect of career motivation and job-esteem. The nursing work environment showed a significant effect on the intention to stay among hospital nurses through career motivation and job-esteem. Conclusion: In order to increase the retention rate of hospital nurses, it is suggested that government and medical institutions provide multifaceted support that can increase nurses' motivation for career development and recognition of the nursing profession through improvement of the nursing work environment.