• 한국정보과학회:학술대회논문집
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• 한국정보과학회 1998년도 가을 학술발표논문집 Vol.25 No.2 (2)
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• pp.336-338
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• 1998

This paper presents a novel neural network structure to the blind deconvolution task where the input (source) to a system is not available and the source has any type of distribution including sparse distribution. We employ multiple sensors so that spatial information plays a important role. The resulting learning algorithm is linear so that it works for both sub-and super-Gaussian source. Moreover, we can successfully deconvolve the mixture of a sparse source, while most existing algorithms [5] have difficulties in this task. Computer simulations confirm the validity and high performance of the proposed algorithm.

• ETRI Journal
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• 제38권3호
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• pp.579-588
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• 2016

To improve the ability to determine a vehicle's movement information even in a challenging environment, a hybrid approach called non-Gaussian square rootunscented particle filtering (nGSR-UPF) is presented. This approach combines a square root-unscented Kalman filter (SR-UKF) and a particle filter (PF) to determinate the vehicle state where measurement noises are taken as a finite Gaussian kernel mixture and are approximated using a sparse Gaussian kernel density estimation method. During an outage-free GPS period, the updated mean and covariance, computed using SR-UKF, are estimated based on a GPS observation update. During a complete GPS outage, nGSR-UPF operates in prediction mode. Indeed, because the inertial sensors used suffer from a large drift in this case, SR-UKF-based importance density is then responsible for shifting the weighted particles toward the high-likelihood regions to improve the accuracy of the vehicle state. The proposed method is compared with some existing estimation methods and the experiment results prove that nGSR-UPF is the most accurate during both outage-free and complete-outage GPS periods.

• Smart Structures and Systems
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• 제16권4호
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• pp.607-621
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• 2015

Wireless sensor technology has been opened up numerous opportunities to advanced health and maintenance monitoring of civil infrastructure. Compare to the traditional tactics, it offers a better way of providing relevant information regarding the condition of building structure health at a lower price. Numerous domestic buildings, especially longer-span buildings have a low frequency response and challenging to measure using deployed numbers of sensors. The way the sensor nodes are connected plays an important role in providing the signals with required strengths. Out of many topologies, the dense and sparse topologies wireless sensor network were extensively used in sensor network applications for collecting health information. However, it is still unclear which topology is better for obtaining health information in terms of greatest components, node's size and degree. Theoretical and computational issues arising in the selection of the optimum topology sensor network for estimating coverage area with sensor placement in building structural monitoring are addressed. This work is an attempt to fill this gap in high-rise building structural health monitoring application. The result shows that, the sparse topology sensor network provides better performance compared with the dense topology network and would be a good choice for monitoring high-rise building structural health damage.

• Journal of Communications and Networks
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• 제11권4호
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• pp.350-358
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• 2009

In a wireless sensor network (WSN) setting, this paper presents a distributed decision-making framework and illustrates its application in an online structural health monitoring (SHM) system. The objective is to recover a damage severity vector, which identifies, localizes, and quantifies damages in a structure, via distributive and collaborative decision-making among wireless sensors. Observing the fact that damages are generally scarce in a structure, this paper develops a nonlinear 0-norm minimization formulation to recover the sparse damage severity vector, then relaxes it to a linear and distributively tractable one. An optimal algorithm based on the alternating direction method of multipliers (ADMM) and a heuristic distributed linear programming (DLP) algorithm are proposed to estimate the damage severity vector distributively. By limiting sensors to exchange information among neighboring sensors, the distributed decision-making algorithms reduce communication costs, thus alleviate the channel interference and prolong the network lifetime. Simulation results in monitoring a steel frame structure prove the effectiveness of the proposed algorithms.

• 한국음향학회지
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• 제37권6호
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• pp.499-505
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• 2018

서로 떨어져 설치된 두 개의 음향 센서에 도달하는 신호의 상호 지연 시간을 추정하는 것은 실내 음향과 소나 등에서 목표물 위치 추정 문제나 추적 및 동기화에 이르기까지 다방면에서 쓰이고 있다. 시간 지연을 구하는 방법에서는 두 수신 신호 사이의 상호 상관을 이용한 방법이 대표적이다. 그러나 이 방법은 수신 음향 센서에 잡음이 부과 되는 것에 충분한 고려가 없었다. 본 논문은 수신 음향 센서에 모두 잡음이 부과된 경우를 고려한 새로운 시간 지연 추정 방법을 제안한다. 기존의 일반 상호 상관법과 적응 고유치 분석법과 비교를 통해서 새로 제안한 알고리즘이 유색 신호에 부가된 가우시안 잡음환경에서 우수성이 있음을 확인한다.

• Structural Monitoring and Maintenance
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• 제5권3호
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• pp.379-390
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• 2018

In this paper, a novel and effective damage diagnosis algorithm is proposed to detect and estimate damage using two stages least squares support vector machine (LS-SVM) and limited number of attached sensors on structures. In the first stage, LS-SVM1 is used to predict the unmeasured mode shapes data based on limited measured modal data and in the second stage, LS-SVM2 is used to predicting the damage location and severity using the complete modal data from the first-stage LS-SVM1. The presented methods are applied to a three story irregular frame and cantilever plate. To investigate the noise effects and modeling errors, two uncertainty levels have been considered. Moreover, the performance of the proposed methods has been verified through using experimental modal data of a mass-stiffness system. The obtained damage identification results show the suitable performance of the proposed damage identification method for structures in spite of different uncertainty levels.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2009년도 춘계학술대회
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• pp.203-206
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• 2009

기상 데이터는 측정 장소의 산재로 인하여 매우 성긴 형태의 값으로 수집된다. 따라서 특정 건물이나 지하철 등 제한된 도시 공간에서 정밀한 기상 정보를 수집하는 것은 별도의 측정 장비를 설치해야 하는 비용 문제를 야기한다. 이 논문에서는 최근 관심을 끌고 있는 센스 네트워크 기술을 활용하여 휴대폰을 센서로 기상 지도 생성 및 서비스 응용 기법에 대하여 고찰한다.

• Smart Structures and Systems
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• 제15권5호
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• pp.1215-1232
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• 2015

In the present paper, a method for identifying damage in a multi storeyed shear building structure is presented using minimum number of modal parameters of the structure. A damage at any level of the structure may lead to a major failure if the damage is not attended at appropriate time. Hence an early detection of damage is essential. The proposed identification methodology requires experimentally determined sparse modal data of any particular mode as input to detect the location and extent of damage in the structure. Here, the first natural frequency and corresponding partial mode shape values are used as input to the model and results are compared by changing the sensor placement locations at different floors to conclude the best location of sensors for accurate damage identification. Initially experimental data are simulated numerically by solving eigen value problem of the damaged structure with inclusion of random noise on the vibration characteristics. Reliability of the procedure has been demonstrated through a few examples of multi storeyed shear structure with different damage scenarios and various noise levels. Validation of the methodology has also been done using dynamic data obtained through experiment conducted on a laboratory scale steel structure.

• 한국음향학회지
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• 제43권3호
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• pp.361-367
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• 2024

공기중 초음파 측정은 각종 기계 설비류의 이상 발생 예방 활동으로 산업계에서 사용되고 있다. 최근에는 다수의 초음파 센서 배열을 이용하여 설비의 이상 발생 위치를 찾을 수 있는 공기중 초음파 영상화 기법의 활용이 증가하고 있다. 초음파 음원의 위치를 가시화하기 위해 센서 별 위상 차이를 이용하는 빔형성법이 사용된다. 2차원 평면에 분포된 초음파 센서 배열을 이용해 3차원 공간에서 빔형성 파워 분포를 구할 수 있다. 본 논문에서는 관심 파장보다 크기가 큰 초음파 센서로 구성된 랜덤 희소배열(random sparse array)을 사용하고, 초음파 배열이 분포한 평면으로부터 일정한 거리만큼 떨어진 평행한 평면 내에서의 빔형성 파워 분포를 통해서 음원의 위치를 보여주는 영상화 기법을 구현하고자 한다. 기존의 빔형성법은 사용 하는 배열 센서의 개수와 그에 따른 구경의 크기 등에 의해 공간 해상도가 제한될 수 밖에 없다. 본 연구에서는 배열이 가지는 기하학적 제약을 극복할 수 있는 방법으로 기능성 빔형성법을 적용하여 고해상도 초음파 영상화 기법을 구현하였다. 기능성 빔형성법은 수학적으로 일반화된 형태의 빔형성법으로 표현가능하고, 기존의 빔형성법을 통해 얻어진 영상에서 주엽의 폭과 부엽의 크기를 저감시키는 역할을 하여 고해상도 영상화를 얻을 수 있는 장점이 있다. 컴퓨터 시뮬레이션을 통해 제안한 방법에 의한 영상화 성능을 관찰한 결과, 초음파 희소배열을 이용하여 공기중 초음파 음원의 해상도 증대가 성공적으로 구현됨을 확인할 수 있었다.

• 센서학회지
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• 제29권3호
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• pp.141-148
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• 2020

To find food, homes, and mates, some animals have adapted special sensing capabilities. Rather than using a passive method, they discharge a signal and then extract the necessary information from the response. More importantly, they use the slope of the detected signal to find the destination of an object. In this paper, similar strategy is mathematically formulated. A perturbation and correlation-based gradient estimation method is developed and used as a sensing strategy. This method allows us to adaptively sense an object in a given environment effectively. The proposed strategy is based on the use of gradient values; rather than instantaneous measurements. Considering the gradient value, the sampling frequency is planned adaptively, i.e., sparse sampling is performed in slowly varying regions, while dense sampling is conducted in rapidly changing regions. Using a temperature sensor, the proposed strategy is verified and its effectiveness is demonstrated.