• Journal of Korea Society of Digital Industry and Information Management
• /
• v.10 no.2
• /
• pp.75-82
• /
• 2014

MANET has various types of attacks. In particular, routing attacks using characteristics of movement of nodes and wireless communication is the most threatening because all nodes which configure network perform a function of router which forwards packets. Therefore, mechanisms that detect routing attacks and defense must be applied. In this paper, we proposed hierarchical structure attack detection techniques in order to improve the detection ability against routing attacks. Black hole detection is performed using PIT for monitoring about control packets within cluster and packet information management on the cluster head. Flooding attack prevention is performed using cooperation-based distributed detection technique by member nodes. For this, member node uses NTT for information management of neighbor nodes and threshold whether attack or not receives from cluster head. The performance of attack detection could be further improved by calculating at regular intervals threshold considering the total traffic within cluster in the cluster head.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10b
• /
• pp.702-705
• /
• 1996

Recently, induction motors are used more widely because of their low cost and simple structure. Therefore, the importance of fault detection and isolation of induction motors significantly increases. In most case the line current is used for fault detection and isolation. But in case that an induction motor has an inverter for control, it distorts the information of faulty state included in the line current. This paper proposes a new method for fault detection and isolation of induction motors that is speed controlled by the inverter using frequency analysis of the reference current instead of the line current for fault detection and isolation.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
• /
• v.29 no.11
• /
• pp.676-680
• /
• 2016

This paper describes a low voltage detection circuit used in the semiconductor chips. The circuit was composed of a detection part of the CMOS structure as three stages and two inverters. The output of the low voltage detection circuit become to 'high' from 'low', when the power supply voltage falls below 80%. When the power supply voltage is 5 V, it was detected at 4 V point. The proposed low voltage detection circuit can be easily applied only by changing the resister and the capacitor without structural change in a wide range of power supply voltage.

• Structural Engineering and Mechanics
• /
• v.54 no.2
• /
• pp.221-237
• /
• 2015

An integrated method is proposed for structural nonlinear damage detection based on time series analysis and the higher statistical moments of structural responses in this study. It combines the time series analysis, the higher statistical moments of AR model residual errors and the fuzzy c-means (FCM) clustering techniques. A few comprehensive damage indexes are developed in the arithmetic and geometric mean of the higher statistical moments, and are classified by using the FCM clustering method to achieve nonlinear damage detection. A series of the measured response data, downloaded from the web site of the Los Alamos National Laboratory (LANL) USA, from a three-storey building structure considering the environmental variety as well as different nonlinear damage cases, are analyzed and used to assess the performance of the new nonlinear damage detection method. The effectiveness and robustness of the new proposed method are finally analyzed and concluded.

• Structural Engineering and Mechanics
• /
• v.60 no.4
• /
• pp.667-691
• /
• 2016

This study focuses on the damage detection of defect types in plate structures based on wavelet transform (WT) and curvelet transform (CT). In particular, for damage detection of structures these transforms have been developed since the last few years. In recent years, the CT approach has been also introduced in an attempt to overcome inherent limitations of traditional multi-scale representations such as wavelets. In this study, the performance of CT is compared with WT in order to demonstrate the capability of WT and CT in detection of defect types in plate structures. To achieve this purpose, the damage detection of defect types through defect shape in rectangular plate is investigated. By using the first mode shape of plate structure and the distribution of the coefficients of the transforms, the damage existence, the defect location and the approximate shape of defect are detected. Moreover, the accuracy and performance generality of the transforms are verified through using experimental modal data of a plate.

• International journal of advanced smart convergence
• /
• v.8 no.2
• /
• pp.116-125
• /
• 2019

The fire should extinguish as soon as possible because it causes economic loss and loses precious life. In this study, we propose a new atypical fire and smoke detection algorithm using deep learning and color histogram of fire and smoke. First, input frame images obtain from the ONVIF surveillance camera mounted in factory search motion candidate frame by motion detection algorithm and mean square error (MSE). Second deep learning (Faster R-CNN) is used to extract the fire and smoke candidate area of motion frame. Third, we apply a novel algorithm to detect the fire and smoke using color histogram algorithm with local area motion, similarity, and MSE. In this study, we developed a novel fire and smoke detection algorithm applied the local motion and color histogram method. Experimental results show that the surveillance camera with the proposed algorithm showed good fire and smoke detection results with very few false positives.

• Journal of Electrical Engineering and Technology
• /
• v.12 no.1
• /
• pp.363-372
• /
• 2017

This paper presents a novel face recognition algorithm based on the deep convolution neural network and key point detection jointed local binary pattern methodology to enhance the accuracy of face recognition. We firstly propose the modified face key feature point location detection method to enhance the traditional localization algorithm to better pre-process the original face images. We put forward the grey information and the color information with combination of a composite model of local information. Then, we optimize the multi-layer network structure deep learning algorithm using the Fisher criterion as reference to adjust the network structure more accurately. Furthermore, we modify the local binary pattern texture description operator and combine it with the neural network to overcome drawbacks that deep neural network could not learn to face image and the local characteristics. Simulation results demonstrate that the proposed algorithm obtains stronger robustness and feasibility compared with the other state-of-the-art algorithms. The proposed algorithm also provides the novel paradigm for the application of deep learning in the field of face recognition which sets the milestone for further research.

• Journal of Korean Society of Steel Construction
• /
• v.11 no.1 s.38
• /
• pp.1-11
• /
• 1999

The aim of this study is to construct a machine vision monitoring system for an automatic visual inspection of weld joint in steel structure. An image processing algorithm for a visual weld defects detection on weld bead is developed using the intensity image. An optic system for getting four intensity images was set as a fixed camera position and four different illumination directions. The input images were thresholded and segmented after a suitable preprocessing and the features of each region were defined and calculated. The features were used in the detection and the classification of the visual weld defects. It is confirmed that the developed algorithm can detect weld defects that could not be detected by previously developed techniques. The recognized results were evaluated and compared to expert inspectors' results.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.18 no.5
• /
• pp.171-177
• /
• 2018

Recently there are many image datasets which has variety of data class and point to extract general features. But in order to this variety data class and point, deep learning model trained this dataset has not good performance in heterogeneous data feature local area. In this paper, we propose the structure which use sub-category and openset object detection methods to train more robust model, named multi-branch tree using ASSL. By using this structure, we can have more robust object detection deep learning model in heterogeneous data feature environment.

• Journal of the Korean Institute of Telematics and Electronics A
• /
• v.33A no.2
• /
• pp.173-183
• /
• 1996

In this paper, a new structure that can do fault detection and location of digial logic circuits more efficiently using current testing techniques is proposed. In the conventional method, observation point for steady state power supply current was only one, but in the proposed method more fault classes are divided for fault detection and location through the ovservation of steady state power supply current at two points. Also, it is shown that this structure can be easily applied in detection of stuck-open fault which is not easy to do testing with conventional current testing techniques. In the presented mehtod, an extra trasnistor is used, and current path is made compulsorily in the CMOS circuits in which no current path can be established in steady state, then it can be known that stuck-open tault is in the MOS transistor on the considering current path, if this path disappears due to stuck-open fault. The validity and the effectiveness is shwon, thorugh the SPICE simulation of circuits with fault and the current path search experiment using current path search program based on transistor short model wirtten in C language on SUN sparc workstation.