• The Journal of the Korean dental association
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• v.49 no.8
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• pp.461-471
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• 2011

The detection of carious lesions is a key point to apply appropriate preventive measures or operative treatment of dental caries. A laser fluorescence device DIAGNOdent$^{(R)}$ (KaVo, Biberach, Germany) has also been shown to be of additional clinical value in the detection of initial caries. This report focus on the DIAGNOdent$^{(R)}$ for caries detection. DIAGNOdent$^{(R)}$ irradiate visible red light at a wavelength of 655 nm to elicit near-infrared fluorescence from caries lesion. This device is known as a reproducible method for caries detection, with good sensitivity and specificity especially for caries detection on occlusal and accessible smooth surfaces. DIAGNOdent$^{(R)}$ tended to be more sensitive method of detecting occlusal dentinal caries, however, showed more false-positive diagnoses than the visual inspection. So Clinician should not use the device as a clinician's primary diagnostic method and it is recommended that the device should be used in the decision-making process in relation to the diagnosis of caries as a second opinion in cases of doubt after visual inspection. The trend of modern dentistry would be a preventive approach rather than invasive treatment of the disease. This is possible only with early detection and respective preventive measures, DIAGNOdent$^{(R)}$ can help the changes.

• Applied Chemistry for Engineering
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• v.30 no.4
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• pp.399-407
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• 2019

The development of explosive detection technology in a security environment and fear of terrorism at homeland and abroad has been one of the most important issues. Moreover, research works on the explosive detection are highly required to achieve domestic production technology due to the implementation of aviation security performance certification system. Traditionally, explosives are detected by using classical chemical analyses. However, in the view of high sensitivity, rapid analysis, miniaturization and portability electrochemical methods are considered as promising. Most of electrochemical explosive detection technologies are developed in USA, China, Israel, etc. This review highlights the principle and research trend of electrochemical explosive detection technologies carried out overseas in addition to the research direction for future exploration.

• Journal of Wetlands Research
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• v.6 no.4
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• pp.35-43
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• 2004

The non-parametric Mann-Kendall(MK) statistical test has been widely used to assess the significance of trend in hydrologic time series. The test requires sample data should be serially independent. If sample data is serially correlated, the presence of serial correlation in a time series will affect the test ability for trend analysis. So, we would like to use the modified MK test which uses the effective sample size(ESS) to eliminate the effect of serial correlation in a series. This study investigates the ability of ESS to eliminate the influence of serial correlation of MK test by Monte Carlo simulation and by real series. As the results, MK test shows the increase of trend rate as the serial correlation is increased but the modified MK test shows ESS can eliminate the serial correlation for trend analysis. Therefore we confirmed the modified MK test is a very useful tool for trend analysis.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.1
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• pp.21-29
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• 2019

This study analyzed the variability of drought risk based on trend analysis of dry-seasonal dam inflow located in upper river basins. To this, we used areal averaged precipitation and dam inflow of three upper river dams such as Soyang dam, Chungju dam, and Andong dam. We employed Mann-Kendall trend analysis and change point detection method to identify the significant trends and changing point in time series. Our results showed that significant decreasing trends (95% confidence interval) in dry-seasonal runoff rates (= dam inflow/precipitation) in three-dam basins. We investigated potential causes of decreasing runoff rates trends using changes in potential evapotranspiration (PET) and precipitation indices. However, there were no clear relation among changes in runoff rates, PET, and precipitation indices. Runoff rate reduction in the three dams may increase the risk of dam operational management and long-term water resource planning. Therefore, it will be necessary to perform a multilateral analysis to better understand decreasing runoff rates.

• Journal of Internet Computing and Services
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• v.12 no.2
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• pp.85-101
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• 2011

The recommender system analyzes users' preference and predicts the users' preference to items in order to recommend various items such as book, movie and music for the users. The collaborative filtering method is used most widely in the recommender system. The method uses rating information of similar users when recommending items for the target users. Performance of the collaborative filtering-based recommendation is lowered when attacker maliciously manipulates the rating information on items. This kind of malicious act on a recommender system is called 'Recommendation Attack'. When the evaluation data that are in continuous change are analyzed in the perspective of data stream, it is possible to predict attack on the recommender system. In this paper, we will suggest the method to detect attack on the recommender system by using the stream trend of the item evaluation in the collaborative filtering-based recommender system. Since the information on item evaluation included in the evaluation data tends to change frequently according to passage of time, the measurement of changes in item evaluation in a fixed period of time can enable detection of attack on the recommender system. The method suggested in this paper is to compare the evaluation stream that is entered continuously with the normal stream trend in the test cycle for attack detection with a view to detecting the abnormal stream trend. The proposed method can enhance operability of the recommender system and re-usability of the evaluation data. The effectiveness of the method was verified in various experiments.

• Journal of the Korea Society of Computer and Information
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• v.23 no.12
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• pp.153-161
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• 2018

In this paper, we propose a text-centered approach to identify the research trend of thyroid cancer in Korea. We incorporate statistical analysis, text mining and machine learning techniques with our clinical insights to find connective associations between terminologies and to discover informative clusters of literatures. The incidence of thyroid cancer in Korea increased rapidly in the 2000s, which fueled the debate regarding overdiagnosis, but recently the number of patients undergoing surgery has decreased significantly due to conscious reform efforts from various circles. We analyzed the abstracts and keywords of related research papers from DBpia. It was found that most were case reports in the 1980s, and some papers in the 1990s discussed the early detection of thyroid cancer by mass screening. While many papers focused on different diagnostic techniques and the detection of small cancers in the 2000s, many emphasized more on the quality of life of patients in the 2010s. There was an apparent change in the topics of thyroid cancer research over past decades. The results of this study would serve as a reference guide for current and future research directions.

• Transactions of the Korean Society of Automotive Engineers
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• v.7 no.5
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• pp.268-277
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• 1999

Impulsive acoustic and vibration signals within gear are often induced by impacting of fault tooths in gear. Thus the detection of these impulses can be useful for fault diagnosis. Recently there is an increasing trend towards the use of higher order statistics for fault detection within mechanical systems based on the observation that impulsive signals then to increase the kurtosis values. We show that the fourth order Wigner Moment Spectrum, called the Wigner Trispectrum, has found superior detection performance to second order Wigner distribution for typical impulsive signals in a condition monitoring application. These methods are also applied to data sets measured within an industrial gear box.

• Proceedings of the KIEE Conference
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• 1993.07b
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• pp.588-590
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• 1993

This paper presents a diagnostic technique using ultrasonic for operating power transformer. Two methods are used as a base for detecting a partial discharge. One is a analysis of PD trend using counted ultrasonic signal, the other is a estimation of PD location source using cross-correlation. In this paper we implement detection equipment of partial discharge in power transformer. The desired system is utilized to two methods for PD detection and operated by graphical user interface(GUI). The results of test showed that the system can be adopted to real power transformer.

• Preventive Nutrition and Food Science
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• v.5 no.2
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• pp.93-99
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• 2000

A study was performed to establish the detection method of irradiated cereals. A drastic reduction of the apparent viscosity of suspensions with heat treatment was observed up to 1∼2 kGy in brown rice, Job's-tears, polished barley and polished rice. They were gently reduced to samples irradiated at 15 kGy. This trend was similar for all stirring speeds. The viscosity of unirradiated brown rice, Job's-tears, polished barley and polished rice reduced with in-creasing stirring speeds and this tendency was similar for irradiation doses. Regression expressions and coefficients of brown rice, Job's s-tears, polished barley and polished rice on different doses were 0.9399($y=3408.0e^{-0.2338x}$), 0.8855($y=3597.8e^{-0.6864x}$), 0.9343($y=7554.0e^{-0.4998x}$) and 0.9714($y=3228.2e^{-0.5312x}$), respectively, at 120 rpm. These results sug-gest that detection of irradiation for cereals could be possible by viscometric methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.198-201
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• 2022

Recently, new cyber threats targeting new technologies are increasing, and hackers' attack targets are becoming broader and more intelligent. To counter these attacks, major security companies are using traditional EDR (Endpoint Detection and Response) solutions. However, the conventional method does not consider the context, so there is a limit to the accuracy and efficiency of responding to an advanced attack. In order to improve this problem, the need for a security solution centered on XDR (Extended Detection and Response) has recently emerged. In this study, we present effective threat detection and countermeasures in a changing environment through XDR trends and development roadmaps using machine learning-based context analysis.