• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.8
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• pp.1442-1449
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• 2016

This paper proposes a diagnosis to detecting poor contact fault and fault location. Electrical fire by poor contact fault of power cable occupied a large proportion in the total electrical installations. The proposed method has an object to prevent electrical fault in advance. But detecting poor contact fault is difficult to detect fault type and fault location by using conventional reflectometry due to faults generated intermittently and repeatedly on the time change. Therefore, in this paper poor contact fault and fault conditions were defined. System generating poor contact fault produced for the experimental setup. SSTDR and algorithm of reference signal elimination heighten performance detecting poor contact fault on live power cable. The diagnosis methods of signal process and analysis of reflected signal was proposed for detecting poor contact fault and fault location. The poor contact fault and location had been detected through proposed diagnosis methods. The fault location and error rate of detection were verified detecting accuracy by experiment results.

• Journal of the Korea Society of Computer and Information
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• v.22 no.11
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• pp.17-23
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• 2017

In this paper, we implemented a respiration measurement system consisting of piezoelectric sensor, respiration signal processing device, and a viewer on a notebook. We tried an experiment for measuring respiration and detecting sleep apnea syndrome when a subject lay on a bed. We applied the respiration measurement algorithm to sensor data obtained from four subjects. In order to get a good graph shape, data manipulation methods such as moving averages and maximum values were applied. The window size for moving average was chosen as N=70, and the threshold value for each subject was customized. In this case, the proposed system showed 96.0% accuracy. When the maximum value among 90 data was applied instead of moving average, our system achieved 95.1% accuracy. In an experiment for detecting sleep apnea syndrome, the system showed that sleep apnea occurred correctly and calculated the average interval of sleep apnea. While infants or the elderly as well as patients with sleep apnea syndrome are lying down on a bed, our results are also expected to be able to cope with some accidental emergency situation by observing their respiration and detecting sleep apnea.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.494-510
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• 2024

Internet users are exposed to sophisticated cyberattacks that intrusion detection systems have difficulty detecting. Therefore, research is increasing on intrusion detection methods that use artificial intelligence technology for detecting novel cyberattacks. Unsupervised learning-based methods are being researched that learn only from normal data and detect abnormal behaviors by finding patterns. This study developed an anomaly-detection method based on unsupervised machines and deep learning for a network intrusion detection system (NIDS). We present a hybrid anomaly detection approach based on unsupervised learning techniques using the autoencoder (AE), Isolation Forest (IF), and Local Outlier Factor (LOF) algorithms. An oversampling approach that increased the detection rate was also examined. A hybrid approach that combined deep learning algorithms and traditional machine learning algorithms was highly effective in setting the thresholds for anomalies without subjective human judgment. It achieved precision and recall rates respectively of 88.2% and 92.8% when combining two AEs, IF, and LOF while using an oversampling approach to learn more unknown normal data improved the detection accuracy. This approach achieved precision and recall rates respectively of 88.2% and 94.6%, further improving the detection accuracy compared with the hybrid method. Therefore, in NIDS the proposed approach provides high reliability for detecting cyberattacks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2348-2360
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• 2023

As science and technology evolve, object detection of moving objects has been widely used in the context of machine learning and artificial intelligence. Traditional moving object detection algorithms, however, are characterized by relatively poor real-time performance and low accuracy in detecting moving objects. To tackle this issue, this manuscript proposes a modified Kalman filter algorithm, which aims to expand the equations of the system with the Taylor series first, ignoring the higher order terms of the second order and above, when the nonlinear system is close to the linear form, then it uses standard Kalman filter algorithms to measure the situation of the system. which can not only detect moving objects accurately but also has better real-time performance and can be employed to predict the trajectory of moving objects. Meanwhile, the accuracy and real-time performance of the algorithm were experimentally verified.

• Proceedings of the Korea Concrete Institute Conference
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• 2005.11a
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• pp.229-232
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• 2005

More than effectively judging the existence of voids behind concrete tunnel linings or under concrete pavements, this research aims to develop the analysis algorithm of radar capable of estimation of the shape of specific voids. To detect or estimate void shapes in non-reinforced concrete, the simulation analysis model of transmission and reflection wave of electromagnetic radar is used. This radar simulation model is carried out with various void shapes. As the results, a proposed method in this study has a possibility of detecting or estimating void shapes with good accuracy.

• Imaging Science in Dentistry
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• v.47 no.1
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• pp.25-31
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• 2017

Purpose: The aim of this study was to compare the diagnostic accuracy of previously trained endodontists in the detection of artificially created periapical lesions using cone-beam computed tomography (CBCT) and digital periapical radiography (DPR). Materials and Methods: An ex vivo model using dry skulls was used, in which simulated apical lesions were created and then progressively enlarged using #1/2, #2, #4, and #6 round burs. A total of 11 teeth were included in the study, and 110 images were obtained with CBCT and with an intraoral digital periapical radiographic sensor (Instrumentarium dental, Tuusula, Finland) initially and after each bur was used. Specificity and sensitivity were calculated. All images were evaluated by 10 previously trained, certified endodontists. Agreement was calculated using the kappa coefficient. The accuracy of each method in detecting apical lesions was calculated using the chisquare test. Results: The kappa coefficient between examiners showed low agreement (range, 0.17-0.64). No statistical difference was found between CBCT and DPR in teeth without apical lesions (P=.15). The accuracy for CBCT was significantly higher than for DPR in all corresponding simulated lesions(P<.001). The correct diagnostic rate for CBCT ranged between 56.9% and 73.6%. The greatest difference between CBCT and DPR was seen in the maxillary teeth (CBCT, 71.4%; DPR, 28.6%; P<.01) and multi-rooted teeth (CBCT, 83.3%; DPR, 33.3%; P<.01). Conclusion: CBCT allowed higher accuracy than DPR in detecting simulated lesions for all simulated lesions tested. Endodontists need to be properly trained in interpreting CBCT scans to achieve higher diagnostic accuracy.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.9
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• pp.1644-1651
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• 2010

VLF $tan{\delta}$ diagnosis technology is introduced in IEEE Std 400 and proposed as evaluation criterion in an effective way of detecting water tree which mainly causes the failure of XLPE insulated cables. In order to inspect the accuracy of the VLF $tan{\delta}$ method for XLPE insulated power cables in Korean distribution system, diagnosis for 41 cables which were being serviced in the fields has been carried out and they were removed for AC breakdown voltage test after. Regarding the 41 cables, it was hard to confirm any relation between the VLF $tan{\delta}$ values and AC breakdown voltages and also water tree in the insulation was not detected. However, the other cables were failed several days after the diagnosis of the 41 cables. Water trees were found and their VLF $tan{\delta}$ values were also much higher than the criterion of IEEE standard. It has been ascertained that we need to change the IEEE criteria in order to improve the accuracy of detecting water trees by additional analyzing of field examples of failure and case studies from overseas countries and therefore amended VLF $tan{\delta}$ test voltage and evaluation criteria have been proposed.

• Journal of Korean Society for Geospatial Information Science
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• v.5 no.1 s.9
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• pp.139-145
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• 1997

Rapid development of city has made a lots of urban facilities buried under ground, therefore how to bury underground facilities and how to operate them becomes more and more important. However, due to shortage of composite operation data for burying the facilities under ground, a lots of individual and nation's properties have been destroted and even many people killed. under the circumstances, we need to detect the facilities in detail and in accuracy and we can surgest for underground facilities detecting accuracy as below.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.9B
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• pp.1322-1329
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• 2010

As body sensor network (BSN) research becomes mature, the need for managing power consumption of sensor nodes has become evident since most of the applications are designed for continuous monitoring. Real time Electrocardiograph (ECG) analysis on sensor nodes is proposed as an optimal choice for saving power consumption by reducing data transmission overhead. Smart sensor nodes with the ability to categorize lately detected ECG cycles communicate with base station only when ECG cycles are classified as abnormal. In this paper, ECG classification algorithms are described, which categorize detected ECG cycles as normal or abnormal, or even more specific cardiac diseases. Our Euclidean distance (ED) based classification method is validated to be most power efficient and very accurate in determining normal or abnormal ECG cycles. A close comparison of power efficiency and classification accuracy between our ED classification algorithm and generalized linear model (GLM) based classification algorithm is provided. Through experiments we show that, CPU cycle power consumption of ED based classification algorithm can be reduced by 31.21% and overall power consumption can be reduced by 13.63% at most when compared with GLM based method. The accuracy of detecting NSR, APC, PVC, SVT, VT, and VF using GLM based method range from 55% to 99% meanwhile, we show that the accuracy of detecting normal and abnormal ECG cycles using our ED based method is higher than 86%.

• Journal of the Semiconductor & Display Technology
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• v.20 no.1
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• pp.99-103
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• 2021

Warpage defect detecting of scaffold is very important in biosensor production. Because warpaged scaffold cause problem in cell culture. Currently, there is no detection equipment to warpaged scaffold. In this paper, we produced detection model for shape warpage detection using deep learning based CNN. We confirmed the shape of the scaffold that is widely used in cell culture. We produced scaffold specimens, which are widely used in biosensor fabrications. Then, the scaffold specimens were photographed to collect image data necessary for model manufacturing. We produced the detecting model of scaffold warpage defect using Densenet among CNN models. We evaluated the accuracy of the defect detection model with mAP, which evaluates the detection accuracy of deep learning. As a result of model evaluating, it was confirmed that the defect detection accuracy of the scaffold was more than 95%.