• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2011년도 춘계학술발표대회
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• pp.576-579
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• 2011

무선센서네트워크는 여러 분야에 사용되고 있으며 그 특성상 에너지를 효율적으로 사용할 수 있도록 설계되어야 한다. 무선 센서들은 한 번 설치되면 다시 교환할 수 없으며 제한된 배터리를 활용하여 운영된다. 따라서 네트워크 수명을 늘리기 위해서는 이러한 센서들의 효율적 활용이 필수적이다. BCDCP 에서는 CH(클러스터 헤드)가 BS(베이스스테이션)에 모든 데이터를 전송한다. BCDCP는 적은 규모의 네트워크에서는 잘 동작하지만 큰 규모에서는 무선 통신을 위한 에너지 소모가 많아 적절하지가 았다. TBRP 는 큰 규모의 네트워크에서는 잘 동작하지만 다중 홉 전송에 따는 에너지 고갈 현상이 빨리 발생한다. 본 논문에서는 균형화된 에너지 소모를 통해 네트워크 수명을 늘리기 위한 기법인 CETRP 를 제안하였다. CETRP 는 클러스터 헤드를 트리구조로 선정하여 에너지 효율을 극대화하였으며 다른 기법과 성능을 비교하였다.

• 한국콘텐츠학회논문지
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• 제10권12호
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• pp.101-108
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• 2010

센서네트워크에서 노드의 에너지 제약 특성을 고려하여 군집 이벤트를 위한 에너지 효율적인 탐지 기법에 대한 다양한 연구들이 진행되고 있다. 기존에 제안된 군집 이벤트 탐지 기법들은 이벤트를 탐지한 센서 중 군집의 경계에 위치한 노드의 정보만을 추출하여 기지국으로 전송하는 방식을 취한다. 하지만 군집 이벤트의 범위가 넓어지고 센서의 배포 밀도가 높아지면 이벤트 경계에 위치한 노드들의 수 또한 증가하여 많은 전송 비용을 필요로 한다. 본 논문에서는 이벤트 경계 노드들의 정보를 압축/요약하여 나타낼 수 있는 인-네트워크 최소 경계 다각형을 이용한 에너지 효율적인 군집 이벤트 탐지 기법을 제안한다. 제안하는 기법은 대규모 센서 네트워크 환경에서 MBP 생성기법을 통해 군집 이벤트의 경계 정보를 표현한다. 제안하는 기법의 우수성을 보이기 위해 제안하는 기법과 기존 기법과의 성능평가를 수행하였다. 성능평가 결과, 최대 92%이상의 정확도를 유지하며 80.13% 에너지 소모량이 감소하였다.

• 한국전산구조공학회논문집
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• 제33권1호
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• pp.55-61
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• 2020

국내 고도성장기 이후 본격 건설되기 시작한 사회 기반 시설물은 노후화가 빠르게 진행되고 있다. 특히 사고 발생 시 대량 인명 피해로 직결될 수 있는 교량, 터널 등의 대형 구조물에 대한 안전성 평가가 필요하다. 하지만 기존의 유선 센서 기반의 Structural Health Monitoring(SHM)을 개선한 무선 스마트 센서 네트워크는 짧은 신호 도달거리로 인해 경제적이고 효율적인 시스템 구축이 힘들다. 따라서 LoRa LPWAN 시스템은 사물인터넷의 확산과 더불어 저전력 장거리 통신이 각광을 받고 있으며, 이를 구조 건전성 모니터링에 응용함으로써 경제적이면서도 효율적인 모니터링 시스템 구축이 가능하다. 본 연구에서는 LoRa LPWAN 기반의 무선 계측센서 기술동향을 조사하였으며, LoRa LPWAN 기반의 무선 계측센서 설치 및 유지관리 방안을 제안한다.

• Smart Structures and Systems
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• 제3권3호
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• pp.321-340
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• 2007

Structural control technologies have attracted great interest from the earthquake engineering community over the last few decades as an effective method of reducing undesired structural responses. Traditional structural control systems employ large quantities of cables to connect structural sensors, actuators, and controllers into one integrated system. To reduce the high-costs associated with labor-intensive installations, wireless communication can serve as an alternative real-time communication link between the nodes of a control system. A prototype wireless structural sensing and control system has been physically implemented and its performance verified in large-scale shake table tests. This paper introduces the design of this prototype system and investigates the feasibility of employing decentralized and partially decentralized control strategies to mitigate the challenge of communication latencies associated with wireless sensor networks. Closed-loop feedback control algorithms are embedded within the wireless sensor prototypes allowing them to serve as controllers in the control system. To validate the embedment of control algorithms, a 3-story half-scale steel structure is employed with magnetorheological (MR) dampers installed on each floor. Both numerical simulation and experimental results show that decentralized control solutions can be very effective in attaining the optimal performance of the wireless control system.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2007년도 춘계종합학술대회
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• pp.351-353
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• 2007

최근 유비쿼터스 컴퓨팅 기술의 연구 동향 및 발전 추세는 다양한 센서노드들이 상황인지 및 추론, 협업을 통해 사용자들에게 상황에 맞는 최적의 서비스를 제공하는 방향으로 나아가고 있다. 이를 위해, 다양한 이기종 센서 네트워크에 대하여 상호 호환성, 통합된 통신 환경, 모니터링 그리고 데이터 처리 기능을 갖춘 WSN 미들웨어 플랫폼 개발의 필요성이 점차 부각되고 있다. 본 논문에서는 대규모 그린 하우스 내의 다양한 응용 서비스로부터 주어지는 질의들에 대한 응답을 신속히 제공하고 상황 인지를 통하여 해당 이벤트에 상응하는 action을 actuator가 수행하도록 한다.

• Smart Structures and Systems
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• 제6권5_6호
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• pp.579-593
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• 2010

Low-power radio frequency (RF) chip transceiver technology and the associated structural health monitoring platforms have matured recently to enable high-rate, lossless transmission of measurement data across large-scale sensor networks. The intrinsic value of these advanced capabilities is the allowance for high-quality, rapid operational modal analysis of in-service structures using distributed accelerometers to experimentally characterize the dynamic response. From the analysis afforded through these dynamic data sets, structural identification techniques can then be utilized to develop a well calibrated finite element (FE) model of the structure for baseline development, extended analytical structural evaluation, and load response assessment. This paper presents a case study in which operational modal analysis is performed on a three-span prestressed reinforced concrete bridge using a wireless sensor network. The low-power wireless platform deployed supported a high-rate, lossless transmission protocol enabling real-time remote acquisition of the vibration response as recorded by twenty-nine accelerometers at a 256 Sps sampling rate. Several instrumentation layouts were utilized to assess the global multi-span response using a stationary sensor array as well as the spatially refined response of a single span using roving sensors and reference-based techniques. Subsequent structural identification using FE modeling and iterative updating through comparison with the experimental analysis is then documented to demonstrate the inherent value in dynamic response measurement across structural systems using high-rate wireless sensor networks.

• Journal of Communications and Networks
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• 제14권1호
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• pp.51-62
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• 2012

The exponential growth in wireless services has resulted in an overly crowded spectrum. The current state of spectrum allocation indicates that most usable frequencies have already been occupied. This makes one pessimistic about the feasibility of integrating emerging wireless services such as large-scale sensor networks into the existing communication infrastructure. Cognitive radio is an emerging dynamic spectrum access technology that can be used for flexibly and efficiently achieving open spectrum sharing. Cognitive radio is an intelligent wireless communication system that is aware of its radio environment and that is capable of adapting its operation to statistical variations of the radio frequency. In ad hoc cognitive radio networks, a common control channel (CCC) is usually used for supporting transmission coordination and spectrum-related information exchange. Determining a CCC in distributed networks is a challenging research issue because the spectrum availability at each ad hoc node is quite different and dynamic due to the interference between and coexistence of primary users. In this paper, we propose a novel CCC selection protocol that is implemented in a distributed way according to the appearance patterns of primary systems and connectivity among nodes. The proposed protocol minimizes the possibility of CCC disruption by primary user activities and maximizes node connectivity when the control channel is set up. It also facilitates adaptive recovery of the control channel when the primary user is detected on that channel.

• Nuclear Engineering and Technology
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• 제55권2호
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• pp.493-505
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• 2023

Comprehensive condition monitoring of large industry systems such as nuclear power plants (NPPs) is essential for safety and maintenance. In this study, we developed novel system-scale diagnostic technology based on deep-learning and IR thermography that can efficiently and cost-effectively classify system conditions using compact Raspberry Pi and IR sensors. This diagnostic technology can identify the presence of an abnormality or accident in whole system, and when an accident occurs, the type of accident and the location of the abnormality can be identified in real-time. For technology development, the experiment for the thermal image measurement and performance validation of major components at each accident condition of NPPs was conducted using a thermal-hydraulic integral effect test facility with compact infrared sensor modules. These thermal images were used for training of deep-learning model, convolutional neural networks (CNN), which is effective for image processing. As a result, a proposed novel diagnostic was developed that can perform diagnosis of components, whole system and accident classification using thermal images. The optimal model was derived based on the modern CNN model and performed prompt and accurate condition monitoring of component and whole system diagnosis, and accident classification. This diagnostic technology is expected to be applied to comprehensive condition monitoring of nuclear power plants for safety.

• International Journal of Aeronautical and Space Sciences
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• 제11권4호
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• pp.326-337
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• 2010

This work addresses problems regarding trajectory planning for unmanned aerial vehicle sensors. Such sensors are used for taking measurements of large nonlinear systems. The sensor investigations presented here entails methods for improving estimations and predictions of large nonlinear systems. Thoroughly understanding the global system state typically requires probabilistic state estimation. Thus, in order to meet this requirement, the goal is to find trajectories such that the measurements along each trajectory minimize the expected error of the predicted state of the system. The considerable nonlinearity of the dynamics governing these systems necessitates the use of computationally costly Monte-Carlo estimation techniques, which are needed to update the state distribution over time. This computational burden renders planning to be infeasible since the search process must calculate the covariance of the posterior state estimate for each candidate path. To resolve this challenge, this work proposes to replace the computationally intensive numerical prediction process with an approximate covariance dynamics model learned using a nonlinear time-series regression. The use of autoregressive time-series featuring a regularized least squares algorithm facilitates the learning of accurate and efficient parametric models. The learned covariance dynamics are demonstrated to outperform other approximation strategies, such as linearization and partial ensemble propagation, when used for trajectory optimization, in terms of accuracy and speed, with examples of simplified weather forecasting.

• 제어로봇시스템학회논문지
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• 제6권12호
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• pp.1113-1119
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• 2000

To operate a process plant safely and economically, process monitoring is very important. Process monitoring is the task to identify the state of the system from sensor data. Process monitoring includes data acquisition, regulatory control, data reconciliation, fault detection, etc. This research focuses on the data recon-ciliation using scale-space filtering and fault detection using functional-link associative neural networks. Scale-space filtering is a multi-resolution signal analysis method. Scale-space filtering can extract highest frequency factors(noise) effectively. But scale-space filtering has too large calculation costs and end effect problems. This research reduces the calculation cost of scale-space filtering by applying the minimum limit to the gaussian kernel. And the end-effect that occurs at the end of the signal of the scale-space filtering is overcome by using extrapolation related with the clustering change detection method. Nonlinear principal component analysis methods using neural network have been reviewed and the separately expanded functional-link associative neural network is proposed for chemical process monitoring. The separately expanded functional-link associative neural network has better learning capabilities, generalization abilities and short learning time than the exiting-neural networks. Separately expanded functional-link associative neural network can express a statistical model similar to real process by expanding the input data separately. Combining the proposed methods-modified scale-space filtering and fault detection method using the separately expanded functional-link associative neural network-a process monitoring system is proposed in this research. the usefulness of the proposed method is proven by its application a boiler water supply unit.