• 디자인학연구
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• 16호
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• pp.17-25
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• 1996

, \l!ernatives in multi dimensional decision prohlems generally possess numerous attrihutes by which they can be describ('d and compared, The ('\';dllation factors include all attributes that have Ic\'(']s specified by quantitative and qualitativc objectil'l'S, Howev'('f since qualitative factors arc difficul! to quantify as num('ral estimates, these factors have tended to bl' ignored without regard for their importance to human contrnl. In this study, the author adapted :j ('va]uation methods with critrria which have qualitative and qualitative attributes: the Intuitive Evaluation ~1cthods the Accumulativc' Evaluation Model the Benchmarking Evaluation Methods, and studied the corrC'iation between them, The results show that Ill(' :j Mrthods have reciprocal relationships under reliability (r=O, (XX)]] In order to removl' obstacles of desi!;n ev'aluation ( lots of timl' l'llnsumption, constr;lints of placc" difficulties of hu!;!' data procc'ssin!;), it is necessary to be developed a new ('va]uation syst('rn which could prov'idc' effective rat in!; of desi!;n v'alm's 10 make value judw'rnents, , \l!ernatives in multi dimensional decision prohlems generally possess numerous attrihutes by which they can be describ('d and compared, The ('\';dllation factors include all attributes that have Ic\'(']s specified by quantitative and qualitativc objectil'l'S, Howev'('f since qualitative factors arc difficul! to quantify as num('ral estimates, these factors have tended to bl' ignored without regard for their importance to human contrnl. In this study, the author adapted :j ('va]uation methods with critrria which have qualitative and qualitative attributes: the Intuitive Evaluation ~1cthods the Accumulativc' Evaluation Model the Benchmarking Evaluation Methods, and studied the corrC'iation between them, The results show that Ill(' :j Mrthods have reciprocal relationships under reliability (r=O, (XX)]] In order to removl' obstacles of desi!;n ev'aluation ( lots of timl' l'llnsumption, constr;lints of placc" difficulties of hu!;!' data procc'ssin!;), it is necessary to be developed a new ('va]uation syst('rn which could prov'idc' effective rat in!; of desi!;n v'alm's 10 make value judw'rnents,alm's 10 make value judw'rnents,

• 한국정보통신학회논문지
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• 제23권7호
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• pp.852-860
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• 2019

본 논문에서는 실내 환경에서 blind 노드가 이동하거나 움직이는 장애물 (ex. 사람)로 인하여 RSSI가 급격히 변하더라도 정확한 blind 노드 측위를 가능하게 하는 exponential moving average (EMA) 필터 적용 적응적 신호 모델 기반 삼변측량기법을 제안한다. 제안된 EMA 필터 적용 적응적 신호 모델 기반 삼변측량기법은 고정된 세 개의 전파 송신 노드와 blind 노드 간 얻어진 RSSI를 통해 blind 노드의 위치를 측정한다. 또한 외부 환경 요인으로 인해 RSSI가 급격히 변화할 경우 non-LOS (NLOS) 환경인 것인지 혹은 blind 노드의 이동으로 인한 RSSI 변화인지를 판별한다. Blind 노드와 전파 송신 노드 사이 경로가 NLOS 환경이 되었다고 판단될 경우 LOS 환경에서 측정된 RSSI를 기반으로 NLOS 환경에서 측정된 RSSI를 보정하여 blind 노드의 좌표를 도출하고, blind 노드가 이동하였다고 판단된다면 실시간 측정된 RSSI를 이용하여 blind 노드의 좌표를 도출한다. 제안 기법은 ZigBee 기반 testbed를 통해 검증하였으며, NLOS 환경 혹은 blind 노드가 이동하는 환경 하에서 기존 기법 대비 개선된 위치 인식 정확도를 가짐을 증명하였다.

• 해양환경안전학회지
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• 제28권4호
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• pp.499-506
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• 2022

자율운항선박이 상용화되어 연안을 항해하기 위해서는 해상의 장애물을 탐지할 수 있어야 한다. 연안에서 가장 많이 볼 수 있는 장애물 중의 하나는 양식장의 부표이다. 이에 본 연구에서는 YOLO 알고리즘을 이용하여 해상의 부표를 탐지하고, 카메라 영상의 기하학적 해석을 통해 선박으로부터 떨어진 부표의 거리와 방위를 계산하여 장애물을 시각화하는 해상물체탐지시스템을 개발하였다. 1,224장의 양식장 부표 사진으로 해양물체탐지모델을 훈련시킨 결과, 모델의 Precision은 89.0 %, Recall은 95.0 % 그리고 F1-score는 92.0 %이었다. 얻어진 영상좌표를 이용하여 카메라로부터 떨어진 물체의 거리와 방위를 계산하기 위해 카메라 캘리브레이션을 실시하고 해상물체탐지시스템의 성능을 검증하기 위해 Experiment A, B를 설계하였다. 해상물체탐지시스템의 성능을 검증한 결과 해상물체탐지시스템이 레이더보다 근거리 탐지 능력이 뛰어나서 레이더와 더불어 항행보조장비로 사용이 가능할 것으로 판단된다.

• 한국측량학회지
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• 제39권6호
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• pp.599-607
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• 2021

스트리트뷰(Street-view) 영상은 도로의 특정 위치를 중심으로 한 전방위 영상을 제공하며, 보행 환경에 대한 다양한 장애물 정보를 포함한다. 보행자용 길안내 서비스에 활용하기 위한 보행 네트워크(Pedestrian network) 데이터는 교통약자를 비롯한 보행자의 이동 편의성을 보장하기 위하여 보행 장애물에 대한 최신 정보를 반영해야 한다. 본 연구에서는 스트리트뷰 영상과 딥러닝 기반의 객체탐지 알고리즘을 활용하여 서울 전역에 위치한 주요 보행 장애물인 볼라드(Bollard)를 학습하였다. 또한, 탐지된 볼라드 정보와 보행 네트워크 간의 공간매칭을 통해 횡단보도 노드를 대상으로 볼라드의 유무와 개수 정보를 장애물 속성으로 입력하고, 동시에 누락된 횡단보도 정보를 갱신하기 위한 프로세스를 정의하였다. 스트리트뷰 영상으로 학습된 모델은 보행 상황에서 스마트폰으로 촬영한 사진에 대해서도 적용이 가능하며, 향후 스트리트뷰 영상에 포함된 다양한 보행 장애물에 대한 추가 학습을 통해 효율적인 보행 장애 정보 갱신이 가능할 것으로 기대된다.

• Nuclear Engineering and Technology
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• 제55권8호
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• pp.3030-3038
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• 2023

Nowadays, mobile robots have been used to search for uncontrolled radioactive source in indoor environments to avoid radiation exposure for technicians. However, in the indoor environments, especially in the presence of obstacles, how to make the robots with limited sensing capabilities automatically search for the radioactive source remains a major challenge. Also, the source search efficiency of robots needs to be further improved to meet practical scenarios such as limited exploration time. This paper proposes an automatic source search strategy, abbreviated as ACA: the location of source is estimated by a convolutional neural network (CNN), and the path is planned by the A-star algorithm. First, the search area is represented as an occupancy grid map. Then, the radiation dose distribution of the radioactive source in the occupancy grid map is obtained by Monte Carlo (MC) method simulation, and multiple sets of radiation data are collected through the eight neighborhood self-avoiding random walk (ENSAW) algorithm as the radiation data set. Further, the radiation data set is fed into the designed CNN architecture to train the network model in advance. When the searcher enters the search area where the radioactive source exists, the location of source is estimated by the network model and the search path is planned by the A-star algorithm, and this process is iterated continuously until the searcher reaches the location of radioactive source. The experimental results show that the average number of radiometric measurements and the average number of moving steps of the ACA algorithm are only 2.1% and 33.2% of those of the gradient search (GS) algorithm in the indoor environment without obstacles. In the indoor environment shielded by concrete walls, the GS algorithm fails to search for the source, while the ACA algorithm successfully searches for the source with fewer moving steps and sparse radiometric data.

• International Journal of Computer Science & Network Security
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• 제24권5호
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• pp.1-10
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• 2024

Individuals with visual impairments face numerous challenges in their daily lives, with navigating streets and public spaces being particularly daunting. The inability to identify safe crossing locations and assess the feasibility of crossing significantly restricts their mobility and independence. Globally, an estimated 285 million people suffer from visual impairment, with 39 million categorized as blind and 246 million as visually impaired, according to the World Health Organization. In Saudi Arabia alone, there are approximately 159 thousand blind individuals, as per unofficial statistics. The profound impact of visual impairments on daily activities underscores the urgent need for solutions to improve mobility and enhance safety. This study aims to address this pressing issue by leveraging computer vision and deep learning techniques to enhance object detection capabilities. Two models were trained to detect objects: one focused on street crossing obstacles, and the other aimed to search for objects. The first model was trained on a dataset comprising 5283 images of road obstacles and traffic signals, annotated to create a labeled dataset. Subsequently, it was trained using the YOLOv8 and YOLOv5 models, with YOLOv5 achieving a satisfactory accuracy of 84%. The second model was trained on the COCO dataset using YOLOv5, yielding an impressive accuracy of 94%. By improving object detection capabilities through advanced technology, this research seeks to empower individuals with visual impairments, enhancing their mobility, independence, and overall quality of life.

• 한국가금학회지
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• 제44권2호
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• pp.103-111
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• 2017

The quail (Coturnix japonica) has been used as a model animal in many research fields and its application is still expanding in other fields. Compared to the chicken, the quail is quicker to reach sexually maturity, has short generation intervals, is easy to handle, requires less space and feed, and is sturdy. In addition, it produces many eggs and the research tools developed for chicken can be applied directly to quail or with some modifications. Due to recent advances in next-generation sequencing, abundant sequence data for the quail genome and transcripts have been generated. These sequence data are valuable sources for studying functional genomics using quail, which is one of the model animal used to investigate gene function and networks. Although there are some obstacles to be removed, the quail is the best optimized model to study the functional genomics of poultry. In many research fields, functional genomics study using the quail model will provide the best opportunity to understand the phenomena and principles of life. We review why, among many other birds, the quail is the best model for studying poultry functional genomics.

• 인터넷정보학회논문지
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• 제24권6호
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• pp.81-89
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• 2023

현대사회에서 시각장애인들은 도보, 승강기, 횡단보도 등 일반적인 환경에서 보행을 하는데 어려움이 있다. 시각장애인의 불편 해소를 위한 연구로 영상이나 음성을 이용한 연구가 있으며, 이런 연구는 고비용의 웨어러블 장치, 고성능 CCTV, 음성 센서 등을 사용하여 실생활에 적용하는 데는 한계가 있다. 본 논문에서 시각장애인이 보행 중에 안전한 이동을 위해서 스마트폰에 포함된 저비용의 영상 센서를 활용하여 주변 도보 공간을 인지하는 인공지능 융합 알고리즘을 제안한다. 제안된 알고리즘은 이동 중인 사람 탐지를 위해서 모션 캡처 알고리즘과 장애물 탐지를 위한 객체 탐지 알고리즘을 융합하여 개발하였다. 모션 캡처 알고리즘으로 mediapipe을 사용하여 이동 중에 있는 주변 보행자들을 모델링 및 탐지하였다. 객체 탐지 알고리즘을 사용했으며 도보 중에 발생하는 다양한 장애물을 모델링 하였다. 실험을 통하여 인공지능 융합 알고리즘을 검증했으며, 정확도 0.92, 정밀도 0.91, 재현율 0.99. F1 score 0.95로 결과를 얻어서 알고리즘의 성능을 확인하였다. 본 연구로 보행 중에 발생하는 볼라드, 공유 킥보드, 자동차 등의 주변 장애물 및 이동 중인 보행자 회피하여 시각장애인들의 통행에 도움을 줄 수 있다.

• Journal of Electrical Engineering and Technology
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• 제13권6호
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• pp.2168-2177
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• 2018

The unavoidable forecast error of wind power is one of the biggest obstacles for wind farms to participate in day-ahead electricity market. To mitigate the deviation from forecast, installation of energy storage system (ESS) is considered. An accurate model of wind power forecast error is fundamental for ESS sizing. However, previous study shows that the error distribution has variable kurtosis and fat tails, and insufficient measurement data of wind farms would add to the difficulty of modeling. This paper presents a comprehensive way that makes the use of mixed skewness model (MSM) and copula theory to give a better approximation for the distribution of forecast error, and it remains valid even if the dataset is not so well documented. The model is then used to optimize the ESS power and capacity aiming to pay the minimal extra cost. Results show the effectiveness of the new model for finding the optimal size of ESS and increasing the economic benefit.

• 한국디지털건축인테리어학회논문집
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• 제12권3호
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• pp.67-79
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• 2012

The purpose of this study is to suggest the interior architecture design studio through the pedagogical method of educational technology for college students who lack self-directed learning. The pedagogical method has been organized to make a student-centered class based on the operation of existing architectural design studios. This teaching and learning method emphasizes the role of teachers as facilitators to help students lacking in self-directed learning in the design process, the BIM visualization to give students an expression of design project and the critics to give students an experience of working circumstances. The results of this study can be summarized as follows. First, This pedagogical model can improve the self-directed learning of students, accomplish the design process well through teamwork, and provide problem based learning (PBL) to settle obstacles that come up during the project. Second, through this model, students can improve their field design capacity by instructor, design feedback and criticism. Finally, This model can suggest new pedagogical methods for interior architectural design studios and management of student-centered studios.