• Structural Engineering and Mechanics
• /
• v.26 no.3
• /
• pp.315-341
• /
• 2007

A sudden change of stiffness in a structure, associated with the events such as weld fracture and brace breakage, will cause a discontinuity in acceleration response time histories recorded in the vicinity of damage location at damage time instant. A new damage index is proposed and implemented in this paper to detect the damage time instant, location, and severity of a structure due to a sudden change of structural stiffness. The proposed damage index is suitable for online structural health monitoring applications. It can also be used in conjunction with the empirical mode decomposition (EMD) for damage detection without using the intermittency check. Numerical simulation using a five-story shear building under different types of excitation is executed to assess the effectiveness and reliability of the proposed damage index and damage detection approach for the building at different damage levels. The sensitivity of the damage index to the intensity and frequency range of measurement noise is also examined. The results from this study demonstrate that the damage index and damage detection approach proposed can accurately identify the damage time instant and location in the building due to a sudden loss of stiffness if measurement noise is below a certain level. The relation between the damage severity and the proposed damage index is linear. The wavelet-transform (WT) and the EMD with intermittency check are also applied to the same building for the comparison of detection efficiency between the proposed approach, the WT and the EMD.

• Journal of the Korean Society of Safety
• /
• v.20 no.3 s.71
• /
• pp.64-70
• /
• 2005

In this literature, fault detection and classification of faulty induction motors are carried out through Z-index and frequency analysis. Above frequency analysis refer Fourier transformation and Wavelet transformation. Z-index is defined as the similar form of energy function, also the faulty and healthy conditions are classified through Z-index. For the detection and classification feature extraction for the fault detection of an induction motor is carried out using the information from stator current. Fourier and Wavelet transforms are applied to detect the characteristics under the healthy and various faulty conditions. We can obtain feature vectors from two transformations, and the results illustrate that the feature vectors are complementary each other.

• Journal of Institute of Control, Robotics and Systems
• /
• v.18 no.4
• /
• pp.321-327
• /
• 2012

This paper presents a comparative analysis on three different types of performance indices of obstacle detection for an overlapped ultrasonic sensor ring. Due to beam overlap, the entire sensing zone of each ultrasonic sensor can be divided into three smaller sensing subzones, which leads to significant reduction of positional uncertainty in obstacle detection. First, the positional uncertainty in obstacle detection is expressed in terms of the area of a sensing subzone, and type 1 performance index is then defined as the area ratio of side and center sensing subzones. Second, based on the area of a sensing subzone, type 2 performance index is defined taking into account the size of the entire range of obstacle detection as well as the degree of the positional uncertainty in obstacle detection. Third, the positional uncertainty in obstacle detection is now expressed in terms of the length of the uncertainty arc spanning a sensing subzone, and type 3 performance index is then defined as the average value of the uncertainty arc lengths over the entire range of obstacle detection. Fourth, using a commercial low directivity ultrasonic sensor, the changes of three different performance indices depending on the parameter of an overlapped ultrasonic sensor ring are examined and compared.

• Journal of the Korean Society for Precision Engineering
• /
• v.15 no.5
• /
• pp.100-109
• /
• 1998

In order to increase productivity through unmanned machining in CNC milling process, in-process tool fracture detection is required. In this paper, a new algorithm for tool fracture detection using cutting load variations was developed. For this purpose, developed were tool condition vector which is dimensionless indicator of cutting load and tool fracture index (TFI) which represents magnitude of tool fracture. Through cutting force simulation, tool fracture index was shown to be independent of tool run-outs and cutting condition variations. Using tool fracture index, the ratio of the tool fracture to feed per tooth could be indentified.

• Asia pacific journal of information systems
• /
• v.11 no.4
• /
• pp.99-111
• /
• 2001

The prediction of stock price index is a very difficult problem because of the complexity of stock market data. It has been studied by a number of researchers since they strongly affect other economic and financial parameters. The movement of stock price index has a series of change points due to the strategies of institutional investors. This study presents a two-stage forecasting model of stock price index using change-point detection and artificial neural networks. The basic concept of this proposed model is to obtain intervals divided by change points, to identify them as change-point groups, and to use them in stock price index forecasting. First, the proposed model tries to detect successive change points in stock price index. Then, the model forecasts the change-point group with the backpropagation neural network(BPN). Finally, the model forecasts the output with BPN. This study then examines the predictability of the integrated neural network model for stock price index forecasting using change-point detection.

• Structural Monitoring and Maintenance
• /
• v.1 no.1
• /
• pp.89-110
• /
• 2014

Structural Health Monitoring (SHM) is an effective alternative to conventional inspections which are time-consuming and subjective. SHM can detect damage early and reduce maintenance cost and thereby help reduce the likelihood of catastrophic structural events to infrastructure such as bridges. After reviewing the Damage Index Method (DIM), an Iterative Damage Index Method (IDIM) is proposed to improve the accuracy of damage detection. These two damage detection techniques are compared based on damage on two structures, a simply supported beam and a pedestrian bridge. Compared to the traditional damage detection algorithm, the proposed IDIM is shown to be less arbitrary and more accurate.

• Korean Journal of Remote Sensing
• /
• v.32 no.4
• /
• pp.339-351
• /
• 2016

For the safety of sea, it is important to monitor sea fog, one of the dangerous meteorological phenomena which cause marine accidents. To detect and monitor sea fog, Moderate Resolution Imaging Spectroradiometer (MODIS) data which is capable to provide spatial distribution of sea fog has been used. The previous automatic sea fog detection algorithms were focused on detecting sea fog using Terra/MODIS only. The improved algorithm is based on the sea fog detection algorithm by Wu and Li (2014) and it is applicable to both Terra and Aqua MODIS data. We have focused on detecting spring season sea fog events in the Yellow Sea. The algorithm includes application of cloud mask product, the Normalized Difference Snow Index (NDSI), the STandard Deviation test using infrared channel ($STD_{IR}$) with various window size, Temperature Difference Index(TDI) in the algorithm (BTCT - SST) and Normalized Water Vapor Index (NWVI). Through the calculation of the Hanssen-Kuiper Skill Score (KSS) using sea fog manual detection result, we derived more suitable threshold for each index. The adjusted threshold is expected to bring higher accuracy of sea fog detection for spring season daytime sea fog detection using MODIS in the Yellow Sea.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.16 no.2
• /
• pp.45-52
• /
• 2020

This paper is focused on the modulation scheme detection of the IEEE 802.11 standard. In the IEEE 802.11ac standard, the information of the modulation scheme is indicated by the modulation coding scheme (MCS) included in the VHT-SIG-A of the preamble field. Transmitting end determines the MCS index suitable for the low signal to noise ratio (SNR) situation and transmits the data accordingly. Since data field decoding can take place only when the receiving end acquires the MCS index information of the frame. Therefore, accurate MCS detection must be guaranteed before data field decoding. However, since the MCS index information is the information obtained through preamble field decoding, the detection rate can be affected significantly in a low SNR situation. In this paper, we propose a relatively robust modulation classification method based on deep learning to solve the low detection rate problem with a conventional method caused by a low SNR.

• Journal of the Architectural Institute of Korea Structure & Construction
• /
• v.34 no.2
• /
• pp.3-10
• /
• 2018

Damage location and extent of structure could be detected by the inverse analysis on dynamic response properties such as frequencies and mode shapes. In practice the measured difference of natural frequencies represent the stiffness change reliably, however the measured mode shape is insensitive for stiffness change, but provides spatial information of damage. The damage detection index on shear building structures is formulated in this study. The damage detection index could be estimated from mode shape and srory stiffness of undamaged structure and frequency difference between undamaged and damaged structure. For the verification of the observed damage detection method, the numerical analysis of Matlab and MIDAS and shacking table test were performed. In results, the damage index of damaged story was estimated so higher than undamaged stories that indicates the damaged story apparently.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2000.11a
• /
• pp.427-436
• /
• 2000

The prediction of stock price index is a very difficult problem because of the complexity of the stock market data it data. It has been studied by a number of researchers since they strong1y affect other economic and financial parameters. The movement of stock price index has a series of change points due to the strategies of institutional investors. This study presents a two-stage forecasting model of stock price index using change-point detection and artificial neural networks. The basic concept of this proposed model is to obtain Intervals divided by change points, to identify them as change-point groups, and to use them in stock price index forecasting. First, the proposed model tries to detect successive change points in stock price index. Then, the model forecasts the change-point group with the backpropagation neural network (BPN). Fina1ly, the model forecasts the output with BPN. This study then examines the predictability of the integrated neural network model for stock price index forecasting using change-point detection.