• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.9
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• pp.1175-1182
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• 2013

The lock-in mode infrared thermography (IRT) technique has been developed to improve the detection capability of defects in materials with high thermal conductivity, and it has been shown to provide better detection capability than conventional active IRT. Therefore, to investigate the application of this technique to nuclear piping components, lock-in mode IRT tests were conducted on pipe specimens containing simulated wall-thinning defects. Phase images of the wall-thinning defects were obtained from the tests, and they were compared with thermal images obtained from conventional active IRT tests. It showed that the ability to size the detected wall-thinning defects in piping components was improved by using lock-in mode IRT. The improvement was especially apparent when detecting short and narrow defects and defects with slanted edges. However, the detection capability for shallow wall-thinning defects did not improve much when using lock-in mode IRT.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.1
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• pp.130-140
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• 2010

SIP (Session Initiation Protocol) is a signaling protocol to provide IP-based VoIP (Voice over IP) service. However, many security vulnerabilities exist as the SIP protocol utilizes the existing IP based network. The SIP Malformed message attacks may cause malfunction on VoIP services by changing the transmitted SIP header information. Additionally, there are several threats such that an attacker can extract personal information on SIP client system by inserting malicious code into SIP header. Therefore, the alternative measures should be required. In this study, we analyzed the existing research on the SIP anomaly message detection mechanism against SIP attack. And then, we proposed a Co-occurrence based SIP packet analysis mechanism, which has been used on language processing techniques. We proposed a association rule generation and an attack detection technique by using the actual SIP session state. Experimental results showed that the average detection rate was 87% on SIP attacks in case of using the proposed technique.

• Journal of KIISE
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• v.45 no.3
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• pp.311-320
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• 2018

Recently, the use of digital audio and video as proof in criminal and all kinds of litigation is increasing, and scientific investigation using digital forensic technique is developing. With the development of computing and file editing technologies, anyone can simply manipulate video files, and the number of cases of manipulating digital data is increasing. As a result, the integrity of the evidence and the reliability of the evidence Is required. In this paper, we propose a technique for extracting the Electrical Network Frequency (ENF) through a grid of power grids according to the geographical environment for power supply, and then performing signal processing for peak detection using QIFFT. Through the detection algorithm using the standard deviation, it was confirmed that the video file was falsified with 73% accuracy and the forgery point was found.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.4
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• pp.478-484
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• 2009

Automatic speech recognition(ASR) is attractive technique in trend these day that seek convenient life. Although many approaches have been proposed for ASR but the performance is still not good in noisy environment. Now-a-days in the state of art in speech recognition, ASR uses not only the audio information but also the visual information. In this paper, We present a novel lip detection method for visual speech recognition in mobile environment. In order to apply visual information to speech recognition, we need to extract exact lip regions. Because eye-detection is more easy than lip-detection, we firstly detect positions of left and right eyes, then locate lip region roughly. After that we apply K-means clustering technique to devide that region into groups, than two lip corners and lip center are detected by choosing biggest one among clustered groups. Finally, we have shown the effectiveness of the proposed method through the experiments based on samsung AVSR database.

• Korean Journal of Agricultural Science
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• v.47 no.2
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• pp.205-217
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• 2020

In this study, a deep learning-based tillage boundary detection method for autonomous tillage by a tractor was developed, which consisted of image cropping, object classification, area segmentation, and boundary detection methods. Full HD (1920 × 1080) images were obtained using a RGB camera installed on the hood of a tractor and were cropped to 112 × 112 size images to generate a dataset for training the classification model. The classification model was constructed based on convolutional neural networks, and the path boundary was detected using a probability map, which was generated by the integration of softmax outputs. The results show that the F1-score of the classification was approximately 0.91, and it had a similar performance as the deep learning-based classification task in the agriculture field. The path boundary was determined with edge detection and the Hough transform, and it was compared to the actual path boundary. The average lateral error was approximately 11.4 cm, and the average angle error was approximately 8.9°. The proposed technique can perform as well as other approaches; however, it only needs low cost memory to execute the process unlike other deep learning-based approaches. It is possible that an autonomous farm robot can be easily developed with this proposed technique using a simple hardware configuration.

• Management & Information Systems Review
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• v.22
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• pp.211-229
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• 2007

The ISO/TS16949 APQP goal of defect prevention and decrease of spread waste, is the customer satisfaction which leads a continuous improvement and profit creation. The quality expense where the most is caused by but with increase of production initial quality problem occurrence is increasing to is actuality. Like this confirmation amendment. with the problem which is forecast in the place development at the initial stage which it does completeness it does not confront not to be able, production phase to be imminent, the problem accumulates and it talks the development shedding of which occurs. In opposition, prediction confrontation. is forecast in development early stage to and it is a structure which does not occur a problem to production early stage. Like this development is a possibility of accomplishing competitive company from production phase. Which attains an goal of, chance cause it leads a APQP activity (common cause) with special cause prevention & detection the connection characteristic of the focus technique against a interaction is important. And the customer requirement satisfaction and must convert a APQP goal of attainment at the key characteristics action step. (1) The Prevention - with Design FMEA application prevention of the present design management/detection, (2) the Detection (prevention/detection) - with Process FMEA application prevention of the present process control/detection, (3) Special Cause - statistical process control (SPC) 4M cause spread removal, (4) Common Cause - statistical process control (SPC) the nothing zero defect which leads the continuous improvement back of spread with application it will be able to attain with application.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.2
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• pp.449-455
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• 2004

This paper presents a novel defect detection method for one chip RF front end with fault detection circuits using input matching measurement. We present a BIST circuit using 40.25{\mu}m$ CMOS technology. We monitor the input transient voltage of the RF front end to differentiate faulty and fault-free RF front end. Catastrophic as well as parametric variation fault models are used to simulate the faulty response of the RF front end. This technique has several advantages with respect to the standard approach based on current test stimulus and frequency domain measurement. Because DUT and fault detection circuits are implemented in the same chip, this test technique only requires use of digital voltmeter (RMS meter) and RF voltage source generator for simpleand inexpensive testing.

• Journal of KIISE:Databases
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• v.37 no.4
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• pp.197-202
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• 2010

Recent studies on change detection for RDF data are focused on not only the structural difference but also the semantic-aware difference by computing the closure of RDF models. However, since these techniques which take into account the semantics of RDF model require both RDF models to be memory resident, or they use a forward-chaining strategy which computes the entire closure in advance, it is not efficient to apply them directly to detect changes in large RDF data. In this paper, we propose a scalable change detection technique for RDF data, which uses a backward-chaining inference based on relational database. Proposed method uses a new approach for RDF reasoning that computes only the relevant part of the closure for change detection in a relational database. We show that our method clearly outperforms the previous works through experiment using the real RDF from the bioinformatics domain.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.5
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• pp.389-398
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• 2019

In this paper, we propose a method to detect small unmanned aerial vehicles(UAVs) flying in a real-world environment. Small UAV signals are frequently obscured by clutter signals because UAVs usually fly at low altitudes over urban or mountainous terrain. Therefore, to obtain a desirable detection performance, clutter signals must be considered in addition to noise, and thus, a performance analysis of each clutter removal technique is required. The proposed detection process uses clutter removal and pulse integration methods to suppress clutter and noise signals, and then detects small UAVs by utilizing a constant false alarm rate detector. After applying three clutter removal techniques, we analyzed the performance of each technique in detecting small UAVs. Based on experimental data acquired in a real-world outdoor environment, we found it was possible to derive a clutter removal method suitable for the detection of small UAVs.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.10
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• pp.1311-1319
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• 2019

Currently, the web environment is a commonly used area for sharing information and conducting business. It is becoming an attack point for external hacking targeting on personal information leakage or system failure. Conventional signature-based detection is used in cyber threat but signature-based detection has a limitation that it is difficult to detect the pattern when it is changed like polymorphism. In particular, injection attack is known to the most critical security risks based on web vulnerabilities and various variants are possible at any time. In this paper, we propose a novelty detection technique to detect abnormal state that deviates from the normal state on web-server log dataset(WSLD). The proposed method is a machine learning-based technique to detect a minor anomalous data that tends to be different from a large number of normal data after replacing strings in web-server log dataset with vectors using machine learning-based embedding algorithm.