• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2014.10a
• /
• pp.845-847
• /
• 2014

In recent years, the mobile OS market, enlargement and, at the same time, Android has mounted various smart phones and feature air conditioning, smart TV, cleaning robot, camera, etc. that the number is being spread at a rapid pace. But a lot of devices to control the modules and applications at once, this is not a device for controlling the dissemination of applications all in one place, and in charge of the devices that allow you to manage applications are lacking. Accordingly, in this paper, use the Web Server registered in the appliance can be controlled by management, Web access, is proposing and designing the system. This is the current research is actively in progress in the field of IoT (Internet of Things) would be utilized.

• The Journal of the Convergence on Culture Technology
• /
• v.7 no.4
• /
• pp.705-710
• /
• 2021

Nowadays, cutting-edge information communication technology is the genuine core technology of the fourth Industrial Revolution and is still making great progress rapidly among various technology fields. The biggest issue in ICT fields is the machine learning based Artificial Intelligence applications using big data in cloud computing environment on the basis of wireless network, and also the technology fields of autonomous control applications such as Autonomous Car or Mobile Robot. Since value of the high-tech ICT technology depends on the surrounded environmental factors and is very flexible, the precise technology valuation method is urgently needed in order to get successful technology transfer, transaction and commercialization. In this research, we analyze the characteristics of the high-tech ICT technology and the main factors in technology transfer or commercialization process, and propose the precise technology valuation method that reflects the characteristics of the ICT technology through phased analysis of the existing technology valuationmodel.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.11
• /
• pp.4084-4104
• /
• 2021

Visual place recognition (VPR) is a fundamental yet challenging task of mobile robot navigation and localization. The existing VPR methods are usually based on some pairwise similarity of image descriptors, so they are sensitive to visual appearance change and also computationally expensive. This paper proposes a simple yet effective four-step method that achieves adaptive convolutional neural network (CNN) landmark matching for VPR. First, based on the features extracted from existing CNN models, the regions with higher significance scores are selected as landmarks. Then, according to the coordinate positions of potential landmarks, landmark matching is improved by removing mismatched landmark pairs. Finally, considering the significance scores obtained in the first step, robust image retrieval is performed based on adaptive landmark matching, and it gives more weight to the landmark matching pairs with higher significance scores. To verify the efficiency and robustness of the proposed method, evaluations are conducted on standard benchmark datasets. The experimental results indicate that the proposed method reduces the feature representation space of place images by more than 75% with negligible loss in recognition precision. Also, it achieves a fast matching speed in similarity calculation, satisfying the real-time requirement.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2003.09b
• /
• pp.105-119
• /
• 2003

일반적인 로봇시스템은 자신이 이동해야 할 목표 지점을 자율적으로 생성할 수 없으므로 어떤 다른 시스템의 정보를 이용하여 주변을 탐색하거나 장애물을 인식하고 식별하여 자신의 제어전략을 수립한다. 그러므로 본 논문에서 제시한 시스템은 초소형 비행체를 이용하여 주위 환경과 자율 이동로봇의 위치 정보를 탐색할 수 있도록 시스템을 구성하였다 이러한 시스템의 성능은 로봇이 위치하고 있는 주위의 불완전한 정보로부터 적절한 결론을 유도해 낼 수 있어야 한다. 그러한 비선형적인 문제는 현재까지도 문제 해결을 위해 많은 연구가 진행되고 있다. 본 연구에서는 자율이동로봇의 행동 환경을 공간상의 제약을 받지 않는 비선형 시스템인 초소형 비행체에 극초단파(UHF16채널) 영상장치를 이용하여 호스트 PC로 전송하고 호스트 PC는 로봇의 현재 위치, 이동해야 할 목표위치, 장애물의 위치와 형태 등을 분석한다. 분석된 결과 파라메타는 RF-Module을 이용해서 로봇에 전송하고, 로봇은 그 데이터를 분석하여 동작하게 된다. 로봇이 오동작 또는 장애물로 인해 정확한 목적지까지 도달하지 못할 때 호스트 PC는 새로운 최단경로를 생성하거나 장애물을 회피 할 새로운 전략을 로봇에게 보내준다. 본 연구에 적용한 알고리즘은 초소형 비행체에서 탐지한 불완전한 영상정보에서도 비교적 신뢰도 놀은 결과를 보이는 A* 알고리즘을 사용하였다 적용한 알고리즘은 실험을 통하여 실시간으로 정보를 처리할 수 있었으며, 자율 이동로봇의 충돌회피나 최단 경로 생성과 같은 문제를 실험을 통하여 그 성능과 타당성을 검토하였다.delta}textitH]$를 도출하였다.rc}C$에서 30 ㎫의 압력으로 1시간동안 행하였다 소결한 시편들은 직사각형 형태로 가공하였으며 표면은 0.5$\mu\textrm{m}$의 다이아몬드 입자로 연마하였다. XRD, SEM 및 TEM을 이용하여 상분석 및 미세조직관찰을 행하였다. 파괴강도는 3중점 굽힘 법으로 (3-point bending test) 측정하였다. 이때 시편 하부의 지지 점간의 거리는 30mm, cross-head 속도는 0.5 mm/min으로 하였고 5개의 시편을 측정하여 평균값을 구하였다.ell/\textrm{cm}^3$, 혼합재료 3은 0.123$\ell/\textrm{cm}^3$, 0.017$\ell/\textrm{cm}^3$, 혼합재료 4는 0.055$\ell/\textrm{cm}^3$, 0.016$\ell/\textrm{cm}^3$, 혼합재료 5는 0.031$\ell/\textrm{cm}^3$, 0.015$\ell/\textrm{cm}^3$, 혼합재료 6은 0.111$\ell/\textrm{cm}^3$, 0.020$\ell/\textrm{cm}^3$로 나타났다. 3. 단일재료의 악취흡착성능 실험결과 암모니아는 코코넛, 소나무수피, 왕겨에서 흡착능력이 우수하게 나타났으며, 황화수소는 펄라이트, 왕겨, 소나무수피에서 다른 재료에 비하여 상대적으로

• KIPS Transactions on Software and Data Engineering
• /
• v.11 no.3
• /
• pp.141-148
• /
• 2022

Recently, reinforcement learning combined with deep neural network technology has achieved remarkable success in various fields such as board games such as Go and chess, computer games such as Atari and StartCraft, and robot object manipulation tasks. However, such deep reinforcement learning describes states, actions, and policies in vector representation. Therefore, the existing deep reinforcement learning has some limitations in generality and interpretability of the learned policy, and it is difficult to effectively incorporate domain knowledge into policy learning. On the other hand, dNL-RRL, a new relational reinforcement learning framework proposed to solve these problems, uses a kind of vector representation for sensor input data and lower-level motion control as in the existing deep reinforcement learning. However, for states, actions, and learned policies, It uses a relational representation with logic predicates and rules. In this paper, we present dNL-RRL-based policy learning for transportation mobile robots in a manufacturing environment. In particular, this study proposes a effective method to utilize the prior domain knowledge of human experts to improve the efficiency of relational reinforcement learning. Through various experiments, we demonstrate the performance improvement of the relational reinforcement learning by using domain knowledge as proposed in this paper.