• 한국위성정보통신학회논문지
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• 제7권1호
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• pp.21-26
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• 2012

우리나라는 지난 20여 년 디지털 정보기술 강국을 지향해 왔지만 선진국에서 이미 투자를 시작한 양자 정보 과학 분야에 대한 연구 및 투자는 거의 이루어지지 않았으며, 양자 정보 통신 기술의 수준 또한 개발 선진국들에 비해 턱없이 부족한 상황이다. 최근, 양자역학에 기반을 두고 있는 양자 정보 처리 및 통신에 대한 연구가 세계적으로 활발히 진행 중이다. 90년대부터 본격화된 양자정보이론의 연구는 양자 컴퓨팅, 양자 통신, 양자 정보이론 등의 분야에서 발전해오고 있으며, 90년대 말에 이르러 양자 암호 통신 및 양자 알고리즘 등의 분야에서 큰 연구 성과를 나타내기 시작하였다. 본 논문에서는, 양자 통신 시스템에서 효율적인 양자 신호 전송 및 검출을 위해 직교 시퀀스를 이용한 효율적인 양자 신호 검출 방안에 대해 논하고자 한다.

• 한국멀티미디어학회논문지
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• 제20권5호
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• pp.769-781
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• 2017

Deep learning networks like Convolutional Neural Networks (CNNs) show successful performances in many computer vision applications such as image classification, object detection, and so on. For implementation of deep learning networks in embedded system with limited processing power and memory, deep learning network may need to be simplified. However, simplified deep learning network cannot learn every possible scene. One realistic strategy for embedded deep learning network is to construct a simplified deep learning network model optimized for the scene images of the installation place. Then, automatic training will be necessitated for commercialization. In this paper, as an intermediate step toward automatic training under fisheye camera environments, we study more precise human localization in fisheye images, and propose an accurate human localization method, Automatic Ground-Truth Labelling Method (AGTLM). AGTLM first localizes candidate human object bounding boxes by utilizing GoogLeNet-LSTM approach, and after reassurance process by GoogLeNet-based CNN network, finally refines them more correctly and precisely(tightly) by applying saliency object detection technique. The performance improvement of the proposed human localization method, AGTLM with respect to accuracy and tightness is shown through several experiments.

• 한국추진공학회지
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• 제11권3호
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• pp.29-34
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• 2007

본 연구에서는 모델 기반(Model-Based) 성능진단에 신경회로망을 적용하였고, SIMULINK를 이용하여 PW206C 터보축 엔진의 모델링을 수행하였다. 비행 고도, 비행 마하수, 가스발생기 회전수에 따른 다양한 운용영역의 성능데이터를 base로 하여 압축기, 압축기터빈, 동력터빈의 성능 저하에 대한 학습데이터를 획득하고 역전파(Back Propagation Network)를 이용하여 훈련하였다. 설계점 및 탈설계 영역에서 압축기, 압축기터빈, 동력터빈의 단일 손상 탐지를 수행한 결과 손상된 구성품을 비교적 잘 탐지함을 확인할 수 있었다.

• Journal of Semiconductor Engineering
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• 제2권3호
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• pp.136-141
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• 2021

This paper analyzes the tree expansion for multiple-input multiple-output (MIMO) detection in the viewpoint of hardware implementation. The tree expansion is to calculate path metrics of child nodes performed in every visit to a node while traversing the detection tree. Accordingly, the tree-expansion unit (TEU), which is responsible for such a task, has been an essential component in a MIMO detector. Despite the paramount importance, the analyses on the TEUs in the literature are not thorough enough. Accordingly, we further investigate the hardware complexity of the TEUs to suggest a guideline for selection. In this paper, we focus on a pair of major ways to implement the TEU: 1) a full parallel realization; 2) a transformation of the formulae followed by common subexpression elimination (CSE). For a logical comparison, the numbers of multipliers and adders are first enumerated. To evaluate them in a more practical manner, the TEUs are implemented in a 65-nm CMOS process, and their propagation delays, gate counts, and power consumptions were measured explicitly. Considering the target specification of a MIMO system and the implementation results comprehensively, one can choose which architecture to adopt in realizing a detector.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권2호
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• pp.421-441
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• 2021

Fruit detection in orchards is one of the most crucial tasks for designing the visual system of an automated harvesting robot. It is the first and foremost tool employed for tasks such as sorting, grading, harvesting, disease control, and yield estimation, etc. Efficient visual systems are crucial for designing an automated robot. However, conventional fruit detection methods always a trade-off with accuracy, real-time response, and extensibility. Therefore, an improved method is proposed based on coarse-to-fine multitask cascaded convolutional networks (MTCNN) with three aspects to enable the practical application. First, the architecture of Fruit-MTCNN was improved to increase its power to discriminate between objects and their backgrounds. Then, with a few manual labels and operations, synthetic images and labels were generated to increase the diversity and the number of image samples. Further, through the online hard example mining (OHEM) strategy during training, the detector retrained hard examples. Finally, the improved detector was tested for its performance that proved superior in predicted accuracy and retaining good performances on portability with the low time cost. Based on performance, it was concluded that the detector could be applied practically in the actual orchard environment.

• Nuclear Engineering and Technology
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• 제53권7호
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• pp.2229-2236
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• 2021

In nuclear-related facilities, such as nuclear power plants, research reactors, accelerators, and nuclear waste storage sites, radiation detection, and mapping are required to prevent radiation overexposure. Sensor network systems consisting of radiation sensor interfaces and wxireless communication units have become promising tools that can be used for data collection of radiation detection that can in turn be used to draw a radiation map. During data collection, malfunctions in some of the sensors can occasionally occur due to radiation effects, physical damage, network defects, sensor loss, or other reasons. This paper proposes a reproduction strategy for radiation maps using a U-net model to compensate for the loss of radiation detection data. To perform machine learning and verification, 1,561 simulations and 417 measured data of a sensor network were performed. The reproduction results show an accuracy of over 90%. The proposed strategy can offer an effective method that can be used to resolve the data loss problem for conventional sensor network systems and will specifically contribute to making initial responses with preserved data and without the high cost of radiation leak accidents at nuclear facilities.

• 드라이브 ㆍ 컨트롤
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• 제19권3호
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• pp.9-15
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• 2022

This paper proposes a method for constructing and verifying datasets used in deep learning technology, to prevent safety accidents in automated construction machinery or autonomous vehicles. Although open datasets for developing image recognition technologies are challenging to meet requirements desired by users, this study proposes the interface of virtual simulators to facilitate the creation of training datasets desired by users. The pixel-level training image dataset was verified by creating scenarios, including various road types and objects in a virtual environment. Detecting an object from an image may interfere with the accurate path determination due to occlusion areas covered by another object. Thus, we construct a database, for developing an occlusion area detection algorithm in a virtual environment. Additionally, we present the possibility of its use as a deep learning dataset to calculate a grid map, that enables path search considering occlusion areas. Custom datasets are built using the RDBMS system.

• 드라이브 ㆍ 컨트롤
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• 제21권2호
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• pp.65-71
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• 2024

In this study, we suggested a key-point detection method for robot harvesting of oriental melon. Our suggested method could be used to detect the detachment part and major composition of oriental melon. We defined four points (harvesting point, calyx, center, bottom) based on tomato with characteristics similar to those of oriental melon. The evaluation of estimated key-points was conducted by pixel error and PDK (percentage of detected key-point) index. Results showed that the average pixel error was 18.26 ± 16.62 for the x coordinate and 17.74 ± 18.07 for the y coordinate. Considering the resolution of raw images, these pixel errors were not expected to have a serious impact. The PDK score was found to be 89.5% PDK@0.5 on average. It was possible to estimate oriental melon specific key-point. As a result of this research, we believe that the proposed method can contribute to the application of harvesting robot system.

• 한국정보통신학회논문지
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• 제14권6호
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• pp.1447-1452
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• 2010

Ubiquitous 사회에서 기본 인프라를 구축하는 전력 공급망과 전력기기의 안전은 중요하다. u-City의 재난을 방지하기 위하여 u-IT전력기기의 모듈별 성능평가와 보안은 u-City 안전을 위해 꼭 필요하다. 본 논문에서는 u-IT전력기기의 모듈에 온도센서, 습도센서, 화재센서들을 탑재한 수배전반, 홈 분전반, Home Network Wall-Pad, 차단기, MPNP 블랙박스, 아크 검출기, 아크 안전기, 아울렛의 모듈별 성능평가 방안과 방법 및 보안방법을 연구한다. u-IT전력기기들은 센서의 정보들을 전달 및 분석하여 위험성을 사전 예방하여 안전성을 확보하고, 접근제어, 인증 등 보안대책으로 u-IT 전기안전통합관리시스템의 보안성을 강화시켜, 안전성과 보안성을 갖춘 u-City건설과 운영에 기여하게 될 것이다.

• 한국구조물진단유지관리공학회 논문집
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• 제11권2호
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• pp.134-144
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• 2007

본 연구에서는 구조물의 모니터링 시스템을 위하여 최근에 급속하게 발전하는 스마트 센서 기술을 이용하여 스마트 센서 장치를 개발하였고 다양한 실험을 통하여 개발한 스마트 센서의 기본적 성능 평가와 모형 구조물을 이용한 손상 검출 실험을 실시하였다. 본 논문은 Part 1로써 스마트 센서의 개발과 성능 평가에 관한 것이고 Part 2에서는 스마트 센서를 이용한 손상 검출 결과를 유선 계측 시스템을 이용한 실험결과와 비교하였다. 스마트 센서는 고 출력의 무선 모뎀과 고 성능 MEMS 센서, AVR 마이크로컨트롤러를 이용하여 개발하였으며 센서의 제어와 운영을 위한 임베디드 프로그램을 개발하였다. 스마트 센서의 성능을 검증하기 위하여 민감도와 분해능 분석 실험과 캔틸레버 보와 가진기를 이용한 데이터 획득 실험, 실 구조물을 이용한 현장 적용 실험을 실시하였다. 실험 결과, 개발한 스마트 센서의 성능에 대한 만족스런 결과를 얻었다.