• Smart Structures and Systems
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• 제5권4호
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• pp.457-468
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• 2009

A novel approach for integrating active sensing data interrogation algorithms for structural health monitoring (SHM) applications is presented. These algorithms cover Lamb wave propagation, impedance methods, and sensor diagnostics. Contrary to most active-sensing SHM techniques, which utilize only a single signal processing method for damage identification, a suite of signal processing algorithms are employed and grouped into one package to improve the damage detection capability. A MATLAB-based user interface, referred to as HOPS, was created, which allows the analyst to configure the data acquisition system and display the results from each damage identification algorithm for side-by-side comparison. By grouping a suite of algorithms into one package, this study contributes to and enhances the visibility and interpretation of the active-sensing methods related to damage identification. This paper will discuss the detailed descriptions of the damage identification techniques employed in this software and outline future issues to realize the full potential of this software.

• Smart Structures and Systems
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• 제20권2호
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• pp.175-180
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• 2017

The load-carrying capacity and structural behavior of concrete-filled steel tube (CFST) structures is highly influenced by the grouting compactness in the steel tube. Due to the invisibility of the grout in the steel tube, monitoring of the grouting progress in such a structure is still a challenge. This paper develops an active sensing approach with combined piezoceramic-based smart aggregates (SA) and piezoceramic patches to monitor the grouting compactness of CFST bridge structure. A small-scale steel specimen was designed and fabricated to simulate CFST bridge structure in this research. Before casting, four SAs and two piezoceramic patches were installed in the pre-determined locations of the specimen. In the active sensing approach, selected SAs were utilized as actuators to generate designed stress waves, which were detected by other SAs or piezoceramic patch sensors. Since concrete functions as a wave conduit, the stress wave response can be only detected when the wave path between the actuator and the sensor is filled with concrete. For the sake of monitoring the grouting progress, the steel tube specimen was grouted in four stages, and each stage held three days for cement drying. Experimental results show that the received sensor signals in time domain clearly indicate the change of the signal amplitude before and after the wave path is filled with concrete. Further, a wavelet packet-based energy index matrix (WPEIM) was developed to compute signal energy of the received signals. The computed signal energies of the sensors shown in the WPEIM demonstrate the feasibility of the proposed method in the monitoring of the grouting progress.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.610-614
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• 2006

In this paper the advanced method of geodesic active contours was developed for the task of building detection from aerial and satellite images. Automatic extraction of man-made structures including buildings, building blocks or roads from remote sensing data is useful for land use mapping, scene understanding, robotic navigation, image retrieval, surveillance, emergency management procedures, cadastral etc. A level set method based on a region-driven segmentation model was implemented with which building boundaries were detected, through this curve propagation technique. The essence of this approach is to optimize the position and the geometric form of the curve by measuring information along that curve, and within the regions that compose the image partition. To this end, one can consider uniform intensities inside objects and the background. Thus, given an initial position of the curve, one can determine global, region-driven functions and provide a statistical description of the inside and outside object area. The calculus of variations and a gradient descent method was used to optimize the variational functional by an iterative steady state process. Experimental results demonstrate the potential of the proposed processing scheme.

• Smart Structures and Systems
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• 제12권2호
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• pp.97-119
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• 2013

This paper presents numerical and experimental results on the use of guided waves for structural health monitoring (SHM) of crack growth during a fatigue test in a thick steel plate used for civil engineering application. Numerical simulation, analytical modeling, and experimental tests are used to prove that piezoelectric wafer active sensor (PWAS) can perform active SHM using guided wave pitch-catch method and passive SHM using acoustic emission (AE). AE simulation was performed with the multi-physic FEM (MP-FEM) approach. The MP-FEM approach permits that the output variables to be expressed directly in electric terms while the two-ways electromechanical conversion is done internally in the MP-FEM formulation. The AE event was simulated as a pulse of defined duration and amplitude. The electrical signal measured at a PWAS receiver was simulated. Experimental tests were performed with PWAS transducers acting as passive receivers of AE signals. An AE source was simulated using 0.5-mm pencil lead breaks. The PWAS transducers were able to pick up AE signal with good strength. Subsequently, PWAS transducers and traditional AE transducer were applied to a 12.7-mm CT specimen subjected to accelerated fatigue testing. Active sensing in pitch catch mode on the CT specimen was applied between the PWAS transducers pairs. Damage indexes were calculated and correlated with actual crack growth. The paper finishes with conclusions and suggestions for further work.

• 산업기술연구
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• 제12권
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• pp.61-67
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• 1992

Two control algorithms of active suspension system for improving ride quality are described and their effectiveness is assessed using a quarter car model. Optimal control approach demonstrates great flexibility to meet various running conditions of a vehicle. However, in order to fully utilize the power of optimal control apporach, accurate estimation of the state variables is essential. Simple, yet effective sky-hook algorithm seems to be well suited for real application because of its much relaxed requirements on sensing the stste variables and relative easiness to implment.

• 전기학회논문지P
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• 제59권2호
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• pp.135-140
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• 2010

This paper presents a sensorless speed control of BLDC Motor using terminal voltage of the one phase. Rotor position information is extracted by indirectly sensing the back EMF from only one of the three terminal voltages for a three-phase BLDC motor. Depending on the terminal voltage sensing rotor position, active filter is used for position information. This leads to a significant reduction in the component device of the sensorless circuit. Therefore this is a advantage for the cost saving and size reduction. With indirect sensing methods based on detection of the terminal voltage that require active filtering, the position information needs the six divider section by PLL circuit, the binary counter and johnson counter by the EPLD. Finally, this algorithm can estimate the rotor position information similar to Hall-sensor sticked the three-phase BLDC motor. As a result, the method described that it is not sensitive to filtering delays, allowing the motor to achieve a good performance over a wide speed range. In addition, a simple starting method and a speed estimation approach are also proposed. Experimental and simulation results are included to verify the proposed scheme.

• 대한원격탐사학회지
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• 제37권1호
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• pp.111-122
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• 2021

Building footprint extraction is an active topic in the domain of remote sensing, since buildings are a fundamental unit of urban areas. Deep convolutional neural networks successfully perform footprint extraction from optical satellite images. However, semantic segmentation produces coarse results in the output, such as blurred and rounded boundaries, which are caused by the use of convolutional layers with large receptive fields and pooling layers. The objective of this study is to generate visually enhanced building objects by directly extracting the vertices of individual buildings by combining instance segmentation and keypoint detection. The target keypoints in building extraction are defined as points of interest based on the local image gradient direction, that is, the vertices of a building polygon. The proposed framework follows a two-stage, top-down approach that is divided into object detection and keypoint estimation. Keypoints between instances are distinguished by merging the rough segmentation masks and the local features of regions of interest. A building polygon is created by grouping the predicted keypoints through a simple geometric method. Our model achieved an F1-score of 0.650 with an mIoU of 62.6 for building footprint extraction using the OpenCitesAI dataset. The results demonstrated that the proposed framework using keypoint estimation exhibited better segmentation performance when compared with Mask R-CNN in terms of both qualitative and quantitative results.

• International Journal of Aeronautical and Space Sciences
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• 제9권1호
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• pp.42-47
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• 2008

One of the main cases of the desertification impact is the negative contribution of human activities that worsen environment. There are historical facts of inability and excessive activities which destroyed the civilizations. The basic difference is only in the tempo and scales of civilization collapse. Human pressure was accumulated within the centuries and millennia due to the extremely active economic activities. But today it covers only the decades. Presently the process of desertification has a global scale. There are huge factors of Earth aridization as an increase of the quantity of C02 and atmospheric dust and bloom. This process related not only to the arid areas. Obviously a comprehensive approach of development of territories, particularly arid areas is very important. The use of the satellite information and technologies of remote sensing data processing can take a significant place for decision-makers for calculation and estimation of the environment impacts.

• 대한원격탐사학회지
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• 제34권6_2호
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• pp.1239-1249
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• 2018

광학 위성영상은 레이더 영상에 비해 시각적으로 친숙한 영상을 제공한다. 하지만해빙종류에 대한 구분은 분광학적으로 쉽지 않아 기존 기계학습에서 주로 사용하는 분광정보를 이용한 분류기법을 이용했을 경우 광학영상에서 해빙종류의 구분은 매우 어렵다. 본 연구에서는 분광정보 기반의 분류모델이 아닌 딥러닝 기반 분류기법인 semantic segmentation을 이용하여 계층적, 공간적 패턴을 학습하여 해빙종류 분류를 수행하였다. 또한 주기적으로 획득되는 광학위성자료에 비해 감독분류에서 매우 중요한 양질의 레이블 자료는 수집하는데 있어 높은 시간 및 노동 비용이 소모된다. 본 연구에서는 부족한 레이블 자료로 인해 어려운 다중영상에 대한 감독분류 문제를 준지도학습과 능동학습의 결합을 통해 해결을 시도 하였다. 이를 통해 레이블 되지 않은 새로운 영상자료로부터 추가적인 레이블을 스스로 학습하여 분류모델을 강화할 수 있었으며, 이는 향후 광학영상 기반의 운영 가능한 해빙종류 산출물 개발에도 적용될 수 있을 것으로 기대된다.

• Structural Monitoring and Maintenance
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• 제4권4호
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• pp.381-396
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• 2017

Structural health monitoring (SHM) is a necessity for reliable and efficient functioning of engineering systems. Damage detection (DD) is a crucial component of any SHM system. Lamb waves are a popular means to DD owing to their sensitivity to small damages over a substantial length. This typically involves an active sensing paradigm in a pitch-catch setting, that involves two piezo-sensors, a transmitter and a receiver. In this paper, we propose a data-intensive DD approach for beam structures using high frequency signals acquired from beams in a pitch-catch setting. The key idea is to develop a statistical learning-based approach, that harnesses the inherent sparsity in the problem. The proposed approach performs damage detection, localization in beams. In addition, quantification is possible too with prior calibration. We demonstrate numerically that the proposed approach achieves 100% accuracy in detection and localization even with a signal to noise ratio of 25 dB.