• Journal of Communications and Networks
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• 제11권1호
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• pp.20-25
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• 2009

The maximum likelihood detection with QR decomposition and M-algorithm (QRM-MLD) has been presented as a suboptimum multiple-input multiple-output (MIMO) detection scheme which can provide almost the same performance as the optimum maximum likelihood (ML) MIMO detection scheme but with the reduced complexity. However, due to the lack of parallelism and the regularity in the decoding structure, the conventional QRM-MLD which uses the tree-structure still has very high complexity for the very large scale integration (VLSI) implementation. In this paper, we modify the tree-structure of conventional QRM-MLD into trellis-structure in order to obtain high operational parallelism and regularity and then apply the Viterbi algorithm to the QRM-MLD to ease the burden of the VLSI implementation.We show from our selected numerical examples that, by using the QRM-MLD with our proposed trellis-structure, we can reduce the complexity significantly compared to the tree-structure based QRM-MLD while the performance degradation of our proposed scheme is negligible.

• Smart Structures and Systems
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• 제5권4호
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• pp.415-426
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• 2009

This study focuses on the concept of multi-scale wireless sensor networks for damage detection in civil infrastructure systems by first over viewing the general network philosophy and attributes in the areas of data acquisition, data reduction, assessment and decision making. The data acquisition aspect includes a scalable wireless sensor network acquiring acceleration and strain data, triggered using a Restricted Input Network Activation scheme (RINAS) that extends network lifetime and reduces the size of the requisite undamaged reference pool. Major emphasis is given in this study to data reduction and assessment aspects that enable a decentralized approach operating within the hardware and power constraints of wireless sensor networks to avoid issues associated with packet loss, synchronization and latency. After over viewing various models for data reduction, the concept of a data-driven Bivariate Regressive Adaptive INdex (BRAIN) for damage detection is presented. Subsequent examples using experimental and simulated data verify two major hypotheses related to the BRAIN concept: (i) data-driven damage metrics are more robust and reliable than their counterparts and (ii) the use of heterogeneous sensing enhances overall detection capability of such data-driven damage metrics.

• Smart Structures and Systems
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• 제5권4호
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• pp.483-494
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• 2009

Damage detection has been proven to be a challenging task in structural health monitoring (SHM) due to the fact that damage cannot be measured. The difficulty associated with damage detection is related to electing a feature that is sensitive to damage occurrence and evolution. This difficulty increases as the damage size decreases limiting the ability to detect damage occurrence at the micron and submicron length scale. Damage detection at this length scale is of interest for sensitive structures such as aircrafts and nuclear facilities. In this paper a new photonic sensor based on photonic crystal (PhC) technology that can be synthesized at the nanoscale is introduced. PhCs are synthetic materials that are capable of controlling light propagation by creating a photonic bandgap where light is forbidden to propagate. The interesting feature of PhC is that its photonic signature is strongly tied to its microstructure periodicity. This study demonstrates that when a PhC sensor adhered to polymer substrate experiences micron or submicron damage, it will experience changes in its microstructural periodicity thereby creating a photonic signature that can be related to damage severity. This concept is validated here using a three-dimensional integrated numerical simulation.

• 한국항해항만학회지
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• 제37권5호
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• pp.527-533
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• 2013

본 논문은 구조물의 동적특성인 모드형상과 고유진동수를 이용한 손상탐지와 유효 물성치 추정을 통하여 콘크리트 축소모형과 실제 콘크리트 부유식 구조물 함체의 건전성을 평가하였다. 손상탐지의 경우 콘크리트 축소모형에 대한 동적실험을 수행하여 모드형상을 추출한 후 손상탐지기법에 적용하여 실용성을 증명하였다. 또한 실제 콘크리트 부유식 구조물 함체의 모드형상 및 고유진동수를 실험을 통하여 구한 후 구조계추정기법을 이용하여 콘크리트의 유효 물성치를 추정하였다. 손상탐지기법을 이용하여 축소모형의 손상부재를 정확히 찾아내었으며, 구조계추정기법을 이용하여 실제 콘크리트 부유식 함체의 현재 유효 물성치를 추정하였다.

• 한국컴퓨터정보학회논문지
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• 제27권12호
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• pp.77-84
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• 2022

본 논문은 객체 위치식별 알고리즘의 성능을 향상하기 위한 레이블 재할당 방법을 제안한다. 제안한 방법은 추론 단계와 재할당 단계로 구분한다. 추론 단계에서는 학습된 모델로부터 사전 지정된 크기에 따라 다중 스케일 추론을 수행한 뒤, 이를 마스킹한 영상을 다시 한번 추론하여 강인한 클래스 종류의 추론 결과를 얻는다. 재할당 단계에서는 박스간의 IoU를 계산하여 중복 박스를 제거하고, 박스와 클래스의 빈도를 계산하여 지배적 클래스를 다시 할당하였다. 제안한 방법을 검증하기 위하여 공사현장 안전장비 인식 영상 데이터 세트에 레이블 재할당 방법을 적용하고 이를 YOLOX-L 객체 탐지 모델에서 학습하였다. 실험 결과 적용 전 대비 mAP가 3.9% 향상하여 51.07%를 달성하였으며 AP_S를 3배 이상 향상하여 14.53%를 달성하였다. 실험 결과를 통해 레이블 재할당 알고리즘이 더 우수한 성능의 모델을 훈련해 냄을 확인하였다.

• 한국임상수의학회지
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• 제23권3호
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• pp.258-262
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• 2006

This study was performed to evaluate the usefulness of gray-scale and power Doppler ultrasonography, and to compare with radiography for detection of the repairing in experimentally induced bone defects in dogs. In 4 adult beagle dogs bilateral bone defects were created in 8 canine femurs as sized as 5 mm diameter. Mean detection time of the ultrasonographic endosteal callus formations(mean $14.25{\pm}2.31$ days) was significantly shorter than that of the radiographic opacity chanees(mean $23.50{\pm}2.27$ days) in the defected region. Mean time of the neovascularizd flow signal(mean $6.00{\pm}3.59$ days) from the power Doppler ultrasonographic examination was significantly shorter than that of gray-scale ultrasonographic findings. With these results, gray-scale ultransonography and power Doppler ultrasonography can be used for an early detection modality for bone healing.

• 한국ITS학회 논문지
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• 제11권4호
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• pp.63-69
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• 2012

본 논문에서는 용량성 지문센서의 회색조 이미지를 얻기 위한 새로운 회로를 제안하고 있다. 기존의 회로는 회색조 이미지를 얻기 위해 많은 칩 면적을 차지하는 DAC를 적용하거나 전력소모가 많고 전역 클럭을 적용하는 비휘발성 메모리에 적용되는 승압회로를 픽셀별로 적용하였다. 개선된 전하분할 방식의 용량성 지문센서 감지회로는 뉴런모스(vMOS) 기반의 DLC(down literal circuit) 회로와 단순화된 아날로그 MUX(multiplexor)를 적용하였다. 설계된 감지회로는 0.3V, $0.35{\mu}m$ CMOS공정을 적용하여 동작을 검증하였다. 제안된 회로는 기존의 비교기와 주변회로를 필요로하지 않으므로 단위 픽셀의 레이아웃 면적을 줄이고 이미지의 해상도를 향상 시킬 수 있다.

• Smart Structures and Systems
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• 제23권4호
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• pp.359-371
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• 2019

As part of a structural health monitoring system, the relative geometric relationship between a ship and bridge has been recognized as important for bridge authorities and ship owners to avoid ship-bridge collision. This study proposes a novel computer vision method for the real-time geometric parameter identification of moving ships based on a single shot multibox detector (SSD) by using transfer learning techniques and monocular vision. The identification framework consists of ship detection (coarse scale) and geometric parameter calculation (fine scale) modules. For the ship detection, the SSD, which is a deep learning algorithm, was employed and fine-tuned by ship image samples downloaded from the Internet to obtain the rectangle regions of interest in the coarse scale. Subsequently, for the geometric parameter calculation, an accurate ship contour is created using morphological operations within the saturation channel in hue, saturation, and value color space. Furthermore, a local coordinate system was constructed using projective geometry transformation to calculate the geometric parameters of ships, such as width, length, height, localization, and velocity. The application of the proposed method to in situ video images, obtained from cameras set on the girder of the Wuhan Yangtze River Bridge above the shipping channel, confirmed the efficiency, accuracy, and effectiveness of the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1689-1694
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• 2005

Face detection plays an important role in face recognition, video surveillance, and human computer interface. In this paper, we present a face detection system using eye detection with progressive thresholding from a digital camera. The face candidate is detected by using skin color segmentation in the YCbCr color space. The face candidates are verified by detecting the eyes that is located by iterative thresholding and correlation coefficients. Preprocessing includes histogram equalization, log transformation, and gray-scale morphology for the emphasized eyes image. The distance of the eye candidate points generated by the progressive increasing threshold value is employed to extract the facial region. The process of the face detection is repeated by using the increasing threshold value. Experimental results show that more enhanced face detection in real time.

• 한국공간정보시스템학회:학술대회논문집
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• 한국공간정보시스템학회 2007년도 GIS 공동춘계학술대회 논문집
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• pp.139-144
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• 2007

Recording data in hundreds of narrow contiguous spectral intervals, hyperspectral images have provided the opportunity to detect small differences in material composition. But a limitation of a hyperspectral image is the signal to noise ratio (SNR) lower than that of a multispectral image. This paper presents the efficiency of Spectral Similarity Scale (SSS) in change detection of hyperspectral image and the experiment was performed with Hyperion data. SSS is an algorithm that objectively quantifies differences between reflectance spectra in both magnitude and direction dimensions. The thresholds for detecting the change area were determined through Expectation-Maximization (EM) algorithm. The experimental result shows that the SSS algorithm and EM algorithm are efficient enough to be applied to the unsupervised change detection of hyperspectral images.