• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.6
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• pp.1419-1429
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• 2017

Recently, environment based authentication technique had proposed reinforced authentication, which generating statistical model per user after user login history classifies into account takeover or legitimate login. But reinforced authentication is likely to be attacked if user was not attacked in past. To improve this problem in this paper, we propose unconsciousness authentication technique that generates 2-Class user model, which trains user's environmental information and others' one using machine learning algorithms. To evaluate performance of proposed technique, we performed evasion attacks: non-knowledge attacker that does not know any information about user, and sophisticated attacker that only knows one information about user. Experimental results against non-knowledge attacker show that precision and recall of Class 0 were measured as 1.0 and 0.998 respectively, and experimental results against sophisticated attacker show that precision and recall of Class 0 were measured as 0.948 and 0.998 respectively.

• KIISE Transactions on Computing Practices
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• v.22 no.8
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• pp.357-362
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• 2016

A social network or bio network is one of the complex networks that are formed by connecting specific relationships between interacting objects. Usually, these networks consist of community structures. Automatically detecting the structures is an important technique to understand and control the interaction objects. However, the topologies and structures of the networks change by interactions of the objects, with respect to time. Conventional techniques for finding the community structure have a high computational complexity. Additionally, the methods inefficiently deal with repeated computation concerning graph operation. In this paper, we propose an incremental technique for detecting interaction-based communities in dynamic networks. The proposed technique is able to efficiently find the communities, since there is an awareness of changed objects from the previous network, and it can incrementally reuse the previous community structure.

• Journal of the Korean Society for Nondestructive Testing
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• v.22 no.1
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• pp.53-58
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• 2002

New nondestructive inspection (NDI) technique to detect the defect in metal was developed in which an electromagnetic field is induced in a metal by AC current flowing in the magnetic coil and the leak magnetic-flux disturbed by defects is measured using a tape-recorder head with air gap. This technique can be applied in evaluating the location and sizing of surface defects in components of the ferromagnetic body by means of the non-contacting measurement. In this paper, we have applied this technique to the evaluation of two-dimensional surface cracks in ferromagnetic metal, and also investigated the influence of the various frequencies and lift-off. Defects were detected with maximum values in the distribution of voltage and it was found that the maximum values tend to increase with the defect depth. Although the maximum values for defects are affected by the frequency and lift-off, the depth of small defects can be estimated from the linear relationship between the depth and voltage rate$(V_0/V_{ave})$.

• Journal of the Korean Society for Nondestructive Testing
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• v.23 no.3
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• pp.205-211
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• 2003

Acoustic emission (AE) technique has been applied to not only mechanical property testing but also on-line monitoring of the el)tire structure or a limit zone only. Although several AE devices have already been developed for the on-line monitoring, the price of these systems is very high and it is difficult for the field to apply yet. In this study, wc developed a specially designed PC-based source location system using the A/D board. The source location technique is very important to identify the source, such as crack, leak detection. However, since the AE waveforms obtained from transducers are very difficult to distinguish the defect signals, therefore, it is necessary to consider the signal analyses of the transient waveform. Wavelet Transform (WT) is a powerful tool for processing transient signals with temporally varying spectra that helps to resolve high and low frequency transients components effectively In this study, the analyses of the AE signals are presented by employing the WT analyses. AE results are compared the PC-based source location system using A/D board with the commercial AE system.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.7
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• pp.1088-1097
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• 2021

Vibration data of mechanical equipment inevitably have noise. This noise adversely af ects the maintenance of mechanical equipment. Accordingly, the performance of a learning model depends on how effectively the noise of the data is removed. In this study, the noise of the data was removed using the Denoising Auto Encoder (DAE) technique which does not include the characteristic extraction process in preprocessing time series data. In addition, the performance was compared with that of the Wavelet Transform, which is widely used for machine signal processing. The performance comparison was conducted by calculating the failure detection rate. For a more accurate comparison, a classification performance evaluation criterion, the F-1 Score, was calculated. Failure data were detected using the One-Class SVM technique. The performance comparison, revealed that the DAE technique performed better than the Wavelet Transform technique in terms of failure diagnosis and error rate.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.93-108
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• 2014

To support business decision making, interests and efforts to analyze and use transaction data in different perspectives are increasing. Such efforts are not only limited to customer management or marketing, but also used for monitoring and detecting fraud transactions. Fraud transactions are evolving into various patterns by taking advantage of information technology. To reflect the evolution of fraud transactions, there are many efforts on fraud detection methods and advanced application systems in order to improve the accuracy and ease of fraud detection. As a case of fraud detection, this study aims to provide effective fraud detection methods for auction exception agricultural products in the largest Korean agricultural wholesale market. Auction exception products policy exists to complement auction-based trades in agricultural wholesale market. That is, most trades on agricultural products are performed by auction; however, specific products are assigned as auction exception products when total volumes of products are relatively small, the number of wholesalers is small, or there are difficulties for wholesalers to purchase the products. However, auction exception products policy makes several problems on fairness and transparency of transaction, which requires help of fraud detection. In this study, to generate fraud detection rules, real huge agricultural products trade transaction data from 2008 to 2010 in the market are analyzed, which increase more than 1 million transactions and 1 billion US dollar in transaction volume. Agricultural transaction data has unique characteristics such as frequent changes in supply volumes and turbulent time-dependent changes in price. Since this was the first trial to identify fraud transactions in this domain, there was no training data set for supervised learning. So, fraud detection rules are generated using outlier detection approach. We assume that outlier transactions have more possibility of fraud transactions than normal transactions. The outlier transactions are identified to compare daily average unit price, weekly average unit price, and quarterly average unit price of product items. Also quarterly averages unit price of product items of the specific wholesalers are used to identify outlier transactions. The reliability of generated fraud detection rules are confirmed by domain experts. To determine whether a transaction is fraudulent or not, normal distribution and normalized Z-value concept are applied. That is, a unit price of a transaction is transformed to Z-value to calculate the occurrence probability when we approximate the distribution of unit prices to normal distribution. The modified Z-value of the unit price in the transaction is used rather than using the original Z-value of it. The reason is that in the case of auction exception agricultural products, Z-values are influenced by outlier fraud transactions themselves because the number of wholesalers is small. The modified Z-values are called Self-Eliminated Z-scores because they are calculated excluding the unit price of the specific transaction which is subject to check whether it is fraud transaction or not. To show the usefulness of the proposed approach, a prototype of fraud transaction detection system is developed using Delphi. The system consists of five main menus and related submenus. First functionalities of the system is to import transaction databases. Next important functions are to set up fraud detection parameters. By changing fraud detection parameters, system users can control the number of potential fraud transactions. Execution functions provide fraud detection results which are found based on fraud detection parameters. The potential fraud transactions can be viewed on screen or exported as files. The study is an initial trial to identify fraud transactions in Auction Exception Agricultural Products. There are still many remained research topics of the issue. First, the scope of analysis data was limited due to the availability of data. It is necessary to include more data on transactions, wholesalers, and producers to detect fraud transactions more accurately. Next, we need to extend the scope of fraud transaction detection to fishery products. Also there are many possibilities to apply different data mining techniques for fraud detection. For example, time series approach is a potential technique to apply the problem. Even though outlier transactions are detected based on unit prices of transactions, however it is possible to derive fraud detection rules based on transaction volumes.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.7
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• pp.387-392
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• 2019

A technique was researched to detect bolt looseness using a transfer impedance technique (the dual piezoelectric material technique) for monitoring the structural health of a bolt joint. In order to use the single piezoelectric material technique, an expensive impedance analyzer should be used. However, in the transfer impedance technique, low-cost fault detection can be performed using a general function generator and a digital multimeter. A steel plate frame test specimen composed of bolt joints was fabricated, and the tightening torques of the bolts were loosened step by step. By using the transfer impedance method, the damage index was obtained. It was found that the presence of faults could be reasonably estimated using the damage index, which increased with the degree of bolt looseness. An experiment was performed on the same specimen using the single piezoelectric material technique, and the results showed a similar tendency. It could be possible to estimate the damage of a bolt joint at low cost by eliminating the expensive impedance analyzer. This method could be used effectively for structural health monitoring after carrying out a study to estimate the fault location and severity.

• International Journal of Control, Automation, and Systems
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• v.5 no.4
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• pp.410-418
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• 2007

For the purpose of fault detection of the primary control surface, real-time estimation of the longitudinal stability and control derivatives of the DURUMI-II using the flight data is considered in this paper. The DURUM-II, a research UAV developed by KARI, is designed to have split control surfaces for the redundancy and to guarantee safety during the fault mode flight test. For fault mode analysis, the right elevator was deliberately fixed to the specified deflection condition. This study also mentions how to implement the multi-step control input efficiently, and how to switch between the normal mode and the fault mode during the flight test. As a realtime parameter estimation technique, Fourier transform regression method was used and the estimated data was compared with the results of the analytical method and the other available method. The aerodynamic derivatives estimated from the normal mode flight data and the fault mode data are compared and the possibility to detect the elevator fault by monitoring the control derivative estimated in real time by the computer onboard was discussed.

• BMB Reports
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• v.42 no.2
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• pp.81-85
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• 2009

Early and accurate detection of drug resistant Mycobacterium tuberculosis can improve both the treatment outcome and public health control of tuberculosis. A number of molecular-based techniques have been developed including ones using probe molecules that target drug resistance-related mutations. Although these techniques are highly specific and sensitive, mixed signals can be obtained when the drug resistant isolates are mixed with drug susceptible isolates. In order to overcome this problem, we developed a new drug susceptibility test (DST) for one of the most effective anti-tuberculosis drug, isoniazid. This technique employed a microplate hybridization assay that quantified signals from each probe molecule, and was evaluated using clinical isolates. The evaluation analysis clearly showed that the microplate hybridization assay was an accurate and rapid method that overcame the limitations of DST based on conventional molecular techniques.

• Journal of the Korean Chemical Society
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• v.28 no.1
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• pp.41-46
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• 1984

To preconcentrate trace elements, microgram amounts of 5 heavy metals (Cu, Co, Ni, Zn and Cd) were precipitated with 8-hydroxyquinoline (oxine) and metal oxinates were extracted with stearic acid. And then each of the molten stearic acid extract with stearic acid. And then each of the molten stearic acid extract was poured into a glass ring and cooled for specimen preparation. The obtained specimens were analyzed by X-ray fluorescene spectrometry. And then conditions of precipitation formation and extraction, reproducibility, sensitivity and detection limit were observed. The relative standard deviation of specimen preparation was 1.0~5.7% and the detection limit was 5~$50{\mu}g$/100ml. The proposed preconcentration procedure exhibited a considerable inhancement and simplicity in preparing specimens.