• Structural Engineering and Mechanics
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• v.38 no.6
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• pp.715-732
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• 2011

A novel structural damage detection method with a new damage index has been recently proposed by the authors based on the statistical moments of dynamic responses of shear building structures subject to white noise ground motion. The statistical moment-based damage detection (SMBDD) method is theoretically extended in this paper with general application. The generalized SMBDD method is more versatile and can identify damage locations and damage severities of many types of building structures under various external excitations. In particular, the incomplete measurements can be considered by the proposed method without mode shape expansion or model reduction. Various damage scenarios of two general forms of building structures with incomplete measurements are investigated in consideration of different excitations. The effects of measurement noise are also investigated. The damage locations and damage severities are correctly identified even when a high noise level of 15% and incomplete measurements are considered. The effectiveness and versatility of the generalized SMBDD method are demonstrated.

• Journal of Navigation and Port Research
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• v.36 no.3
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• pp.163-168
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• 2012

Recently, people's desire for the waterfront space has been increasing, and more people want to spend their leisure time close to the water. This paper proposes a damage detection technique using the static strain for the floating structure. An existing damage index, in which the modal strain energy was utilized to identify possible location of damage, is expanded to apply the static strain. The new damage index is expressed in terms of the static strains of undamaged and damaged structures. After calculating damage index, the possible damage locations in the structure are determined by the pattern recognition technique. The accuracy and feasibility of the proposed method is demonstrated by using experimental strain data from a scale model of floating structure.

• Journal of Convergence Society for SMB
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• v.6 no.3
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• pp.37-41
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• 2016

In this paper, we propose a fire alarm system that utilizes Wi-Fi to alarm multiple people at once. This system, based on Arduino, uses smoke, flame and temperature sensor units to sense fire and send detection data to a server via wireless communication system. The server uses stored data to relay current fire situations gathered from nearby sensors to smartphones. It also automatically reports the fire using location data from sensors. Using this system, we were able to retrieve fire alarm from sensors via push notification of our smartphone. We also confirmed the establishment of linkage with sensors and automatic report of fire via SMS. From this result, the possibility of sending real-time notifications via the Internet toward nearby smartphones about disasters such as conflagration has been proven to be feasible.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.41 no.9
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• pp.39-45
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• 2004

A design method is proposed for the sequence detection with fixed decision delay with less hardware complexity using the concept of the Voronoi diagram and its dual, the Delaunay tessellation. This detector design is based on the Fixed Delay Tree Search (FDTS) detection. The FDTS is a computationally efficient sequence detection algerian and has been shown to achieve near-optimal performance in the severe Intersymbol Interference (ISI) channels when combined with decision feedback equalization and the appropriate channel coding. In this approach, utilizing the information contained in the Voronoi diagram or equivalently the Delaunay tessellation, the relative location of the detector input sequence in the multi-dimensional Euclidean space is found without any computational redundancy, which leads to a reduced complexity implementation of the detector.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.3
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• pp.735-741
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• 2014

Edge in images appears when a great difference shows up in light and shade between pixels and includes data of the subject's size, location direction and etc. The edge is generally detected by the methods such as Sobel, Roberts, Laplacian, LoG(Laplacian of Gaussian) and etc. However, in AWGN(additive white Gaussian noise) added images, quality of the edge becomes slightly uncertain. Therefore, this paper proposed edge detection algorithm using modified mask of weighting to improve the quality of the existing methods. And in order to verify the performance efficiency of the proposed method, processed image and PFOM(Pratt's figure of merit) has been used as valuation standard for a comparison with the existing methods.

• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.3
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• pp.261-270
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• 2010

This paper presents an analytical investigation for a baseline-free imaging of a defect in plate-like structures using the time-reversal of Lamb waves. We first consider the flexural wave (A0 mode) propagation in a plate containing a defect, and reception and time reversal process of the output signal at the receiver. The received output signal is then composed of two parts: a directly propagated wave and a scattered wave from the defect. The time reversal of these waves recovers the original input signal, and produces two additional side bands that contain the time-of-flight information on the defect location. One of the side band signals is then extracted as a pure defect signal. A defect localization image is then constructed from a beamforming technique based on the time-frequency analysis of the side band signal for each transducer pair in a network of sensors. The simulation results show that the proposed scheme enables the accurate, baseline-free detection of a defect, so that experimental studies are needed to verify the proposed method and to be applied to real structure.

• Journal of Biomedical Engineering Research
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• v.26 no.1
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• pp.55-63
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• 2005

A novel imaging system for High-resolution Ultrasonic Transmission Tomography (HUTT) and soft tissue differentiation methodology for the HUTT system are presented. The critical innovation of the HUTT system includes the use of sub-millimeter transducer elements for both transmitter and receiver arrays and multi-band analysis of the first-arrival pulse. The first-arrival pulse is detected and extracted from the received signal (i.e., snippet) at each azimuthal and angular location of a mechanical tomographic scanner in transmission mode. Each extracted snippet is processed to yield a multi-spectral vector of attenuation values at multiple frequency bands. These vectors form a 3-D sinogram representing a multi-spectral augmentation of the conventional 2-D sinogram. A filtered backprojection algorithm is used to reconstruct a stack of multi-spectral images for each 2-D tomographic slice that allow tissue characterization. A novel methodology for soft tissue differentiation using spectral target detection is presented. The representative 2-D and 3-D HUTT images formed at various frequency bands demonstrate the high-resolution capability of the system. It is shown that spherical objects with diameter down to 0.3㎜ can be detected. In addition, the results of soft tissue differentiation and characterization demonstrate the feasibility of quantitative soft tissue analysis for possible detection of lesions or cancerous tissue.

• The Journal of Korea Robotics Society
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• v.14 no.4
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• pp.357-362
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• 2019

This paper proposes a model and train method that can real-time detect objects and distances estimation based on a monocular camera by applying deep learning. It used YOLOv2 model which is applied to autonomous or robot due to the fast image processing speed. We have changed and learned the loss function so that the YOLOv2 model can detect objects and distances at the same time. The YOLOv2 loss function added a term for learning bounding box values x, y, w, h, and distance values z as 클래스ification losses. In addition, the learning was carried out by multiplying the distance term with parameters for the balance of learning. we trained the model location, recognition by camera and distance data measured by lidar so that we enable the model to estimate distance and objects from a monocular camera, even when the vehicle is going up or down hill. To evaluate the performance of object detection and distance estimation, MAP (Mean Average Precision) and Adjust R square were used and performance was compared with previous research papers. In addition, we compared the original YOLOv2 model FPS (Frame Per Second) for speed measurement with FPS of our model.

• Earthquakes and Structures
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• v.17 no.1
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• pp.63-73
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• 2019

This paper studies damage detection as an optimization problem. A new objective function based on changes in natural frequencies, and Natural Frequency Vector Assurance Criterion (NFVAC) was developed. Due to their easy and fast acquisition, natural frequencies were utilized to detect structural damages. Moreover, they are sensitive to stiffness reduction. The method presented here consists of two stages. Firstly, Finite Element Model (FEM) is updated. Secondly, damage severities and locations are determined. To minimize the proposed objective function, a new bio-inspired optimization algorithm called salp swarm was employed. Efficiency of the method presented here is validated by three experimental examples. The first example relates to three-story shear frame with two single damage cases in the first story. The second relates to a five-story shear frame with single and multiple damage cases in the first and third stories. The last one relates to a large-scale eight-story shear frame with minor damage case in the first and third stories. Moreover, the performance of Salp Swarm Algorithm (SSA) was compared with Particle Swarm Optimization (PSO). The results show that better accuracy is obtained using SSA than using PSO. The obtained results clearly indicate that the proposed method can be used to determine accurately and efficiently both damage location and severity in multi-story shear frames.

• Structural Engineering and Mechanics
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• v.70 no.3
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• pp.339-350
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• 2019

This research focuses on finite element model updating and damage assessment of structures at element level based on global nondestructive test results. For this purpose, an optimization system is generated to minimize the structural dynamic parameters discrepancies between numerical and experimental models. Objective functions are selected based on the square of Euclidean norm error of vibration frequencies and modal assurance criterion of mode shapes. In order to update the finite element model and detect local damages within the structural members, modern optimization techniques is implemented according to the evolutionary algorithms to meet the global optimized solution. Using a simulated numerical example, application of genetic algorithm (GA), particle swarm (PSO) and artificial bee colony (ABC) algorithms are investigated in FE model updating and damage detection problems to consider their accuracy and convergence characteristics. Then, a hybrid multi stage optimization method is presented merging advantages of PSO and ABC methods in finding damage location and extent. The efficiency of the methods have been examined using two simulated numerical examples, a laboratory dynamic test and a high-rise building field ambient vibration test results. The implemented evolutionary updating methods show successful results in accuracy and speed considering the incomplete and noisy experimental measured data.