• Annual Conference of KIPS
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• 2014.11a
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• pp.913-916
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• 2014

본 논문의 수중로봇 도미(Domi) ver1.0는 관상어용 물고기 로봇 개발을 목표로 연구 개발되었다. 물고기 로봇은 머리, 1단, 2단 몸체와 꼬리부분과 2개의 구동 관절로 구성되어 있다. 물고기 로봇의 추력에 적합한 구동부 선정을 위하여 물고기 로봇 모델링과 유영 해석을 통하여 관절 구동부가 설계되었다. 또한 물고기 로봇의 유영알고리즘은 Lighthill 운동학 해석을 기초로 생체 모방의 유영 근사화 방법을 적용하였다. 설계된 물고기는 수동유영 및 자율운영모드로 동작된다. 수동유영모드는 RF 송수신에 의하여 구현된다. 본 설계된 물고기로봇 도미 ver1.0은 수중 현장시험 평가을 통하여 추력, 내구성, 방수성 등의 성능이 우수함을 확인하였다.

• Journal of the Korean Wood Science and Technology
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• v.21 no.2
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• pp.23-30
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• 1993

As the demand of wood furniture becomes diverse and large-sized, artificial intelligence technique is required to design the expert system which can promote the efficiency of the operations management in wood furniture industry. This study was carried out to develop the expert scheduler, which was applied to the scheduling in chair-manufacturing process to evaluate its validity. The expert scheduler could show the results of scheduling must faster than Gantt chart method with ease. Maximum tardiness in the current chair-manufacturing process could be reduced from 29 seconds to 5 seconds by the addition of a spindle sander, a 12 spindle universal boring machine, and a moulding sander to sanding, boring, and moulding process, respectively.

• Annual Conference of KIPS
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• 2014.04a
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• pp.757-760
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• 2014

프로그래밍 능력은 21세기 정보화 사회를 살아가는데 있어 인간이 컴퓨터를 활용하여 다양한 문제를 해결할 수 있도록 도움을 준다. 효과적인 프로그래밍 교육이 이루어지기 위해서는 학습자들에게 순차적 수행, 조건적 수행, 반복적 수행과 같은 기본적인 프로그래밍 개념을 습득하도록 할 필요가 있다. 따라서 본 연구는 스도쿠 인공지능 프로그래밍 교육에서 프로그래밍의 기본 개념을 바탕으로 알고리즘을 설계하는 방법을 학습시키는 방안을 모색하기 위한 목적이 있다. 연구의 목적을 달성하기 위해 중학생 10명을 대상으로 실험 연구를 진행하였다. 연구 결과, 학습자는 연구자가 제안한 활동지가 알고리즘 설계 학습에 도움이 된 것으로 인식한 것을 확인할 수 있었다. 본 연구는 프로그래밍 교육에서 초보 학습자가 이해하기 어려워하는 프로그래밍 개념을 학습하는데 도움이 되는 학습 방법을 제시하였다는데 의의가 있다.

• Annual Conference of KIPS
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• 2019.05a
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• pp.250-252
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• 2019

국내의 전력 산업은 부정확한 전력수요 예측으로 전력부족과 공급과잉의 주기적 반복이 발생하여 전력 과생산, 에너지 낭비, 전력 과소비와 요금 체납 등의 문제가 발생하고 있다. 이를 해결하기 위해 본 논문에서는 LSTM 알고리즘을 사용하여 전력사용량 예측하고, 정량의 전력을 선구입 할 수 있도록 설계하였다. 제안하는 시스템은 스마트그리드 환경과 인공지능으로 정량의 전기를 구입 할 수 없는 기존의 전력 산업 문제를 보완하여 소비자의 전기요금 절감과 에너지 절약이 가능하다.

• Nuclear Engineering and Technology
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• v.53 no.9
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• pp.2899-2908
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• 2021

The laser transport system of the high power laser facility is mainly composed of large-aperture laser transport mirrors (TMs). Obtaining the high-resolution online damage images during the operation, which is of great significance for operating safely of the mirrors and the facility. Based on wavefront coding, pan-tilt scanning and image stitching technologies, an online laser-damage images detection system is designed, and it can achieve high-precision detection of surface characteristics of large-aperture laser transport mirrors. The preliminary simulation proves that the system can solve the depth of field matching problem caused by pan-tilt tilt imaging and achieve higher resolution.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.132-140
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• 2021

Under the mobile ad-hoc network system, the main reason for causing congestion is because of the limited availability of resources. On the other hand, the standardised TCP based congestion controlling mechanism is unable to control and handle the major properties associated with the shared system of wireless channels. It creates an effect on the design associated with suitable protocols along with protocol stacks through the process of determining the mechanisms of congestion on a complete basis. Moreover, when bringing a comparison with standard TCP systems the major environment associated with mobile ad hoc network is regraded to be more problematic on a complete basis. On the other hand, an agent-based mobile technique for congestion is designed and developed for the part of avoiding any mode of congestion under the ad-hoc network systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.713-728
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• 2021

This paper proposes an augmented reality technique to generate acrobatic scenes from hitting motion videos. After a user shoots a motion that mimics hitting an object with hands or feet, their pose is analyzed using motion tracking with deep learning to track hand or foot movement while hitting the object. Hitting position and time are then extracted to generate the object's moving trajectory using physics optimization and synchronized with the video. The proposed method can create videos for hitting objects with feet, e.g. soccer ball lifting; fists, e.g. tap ball, etc. and is suitable for augmented reality applications to include virtual objects.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.351-354
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• 2016

The competition of Go between AlphaGo and Lee Sedol attracted global interest leading AlphaGo to victory. The core function of AlphaGo is deep-learning system, studying by computer itself. Afterwards, the utilization of deep-learning system using artificial intelligence is said to be verified. Recently, the government passed the loT Act and developing its business model to promote loT. This study is on analyzing IoT business environment using deep-learning AI and constructing specialized business models.

• Proceedings of the KIPE Conference
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• 2019.07a
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• pp.543-545
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• 2019

Recently, the demand for smart farms is increasing due to the increase in the cultivation area such as horticulture, fruit trees and special crops. However, due to the irregular weather changes and the cultivation method of the crops due to the different cultivation environment, there are frequent occurrence of diseases and insect pests and infectious diseases due to system error or carelessness, and the cycle is also very short. In addition, the Smart Farm business has been built by combining various sensors (temperature, humidity, CO2, illumination) and LED lighting, but it is costly in terms of frequent errors, lack of power supply, And thus the management can not be efficiently managed. Therefore, this paper combines real time sensing technology based on IoT Platform and high performance control technology to control pests and equipment errors and monitor the growth status of crops in real time based on big data analysis and Artificial Intelligence System.

• Journal of Korea Multimedia Society
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• v.24 no.4
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• pp.593-600
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• 2021

In this paper, we present the FlappyBird Competition System (FCS) implementation, a competition-based automated assessment system used in an entry-level artificial intelligence (AI) course at a university. The proposed system provides an evaluation method suitable for AI courses while taking advantage of automated assessment methods. Students are to design a neural network structure, train the weights, and tune hyperparameters using the given reinforcement learning code to improve the overall performance of game AI. Students participate using the resulting trained model during the competition, and the system automatically calculates the final score based on the ranking. The user evaluation conducted after the semester ends shows that our competition-based automated assessment system promotes active participation and inspires students to be interested and motivated to learn AI. Using FCS, the instructor significantly reduces the amount of time required for assessment.