• 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.

• Journal of Korean Society for Geospatial Information Science
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• v.14 no.4 s.38
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• pp.19-26
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• 2006

This study assessed the accuracies of the ground information extracting methods from the LiDAR data. Especially, it compared two kinds of method, one of them is using directly the raw LiDAR data which is point type vector data and the other is using changed data to DSM type as the normal grid type. The methods using Local Maxima and Entropy methods are applied as a former case, and for the other case, this study applies the method using edge detection with filtering and the generated reference surface by the mean filtering. Then, the accuracy assessment are performed with these results, DEM constructed manually and the error permitted limit in scale of digital map. As a results, each DEM mean errors of methods using edge detection with filtering, reference surface, Local Maxima and Entropy are 0.27m, 2.43m, 0.13m and 0.10m respectively. Hence, the method using entropy presented the highest accuracy. And an accuracy from a method directly using the raw LiDAR data has higher accuracy than the method using changed data to DSM type relatively.

• Journal of Positioning, Navigation, and Timing
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• v.1 no.1
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• pp.15-21
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• 2012

For a GNSS receiver's robustness against RFI and the high accuracy of navigation solution in GNSS, interference source detection and mitigation are needed. In this paper, an adaptive lattice IIR notch filter is employed to track single-tone continuous wave and swept continuous wave interference signals, and an interference detection method is proposed. Furthermore, this paper presents interference source characterization algorithm using multiple ground stations' interference detection results. The measurement of the signal powers from each ground station is used to build weighting factors to estimate the type of the interference. The performance of interference detection algorithm is simulated for scenarios of GPS signal in the presence of single-tone continuous wave interference and swept continuous wave interference.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.9
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• pp.699-704
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• 2016

In this paper, we propose a development method for a drowsiness detection system using retinex theory and edge information for vehicle safety. Detection of a drowsy state of a driver is very important because the drowsiness of driver is often the main cause of many car accidents. After acquiring an image of the entire face, we executed the pre-process step using the retinex theory. We then applied a technique for the detection of the white pixels using edge information. Experimental results showed that the proposed method improved the accuracy of detecting drowsiness to nearly 98%, and can be used to prevent a car accident caused by the driver's drowsiness.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.569-572
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• 2008

The possibility to extent the solution in human detection problem for plug-in on vision-based Human Computer Interaction domain is very attractive, since the successful of the machine leaning theory and computer vision marriage. Bayesian logistic regression is a powerful classifier performing sparseness and high accuracy. The difficulties of finding people in an image will be conquered by implementing this Bavesian model as classifier. The comparison with other massive classifier e.g. SVM and RVM will introduce acceptance of this method for human detection problem. Our experimental results show the good performance of Bavesian logistic regression in human detection problem, both in trade-off curves (ROC, DET) and real-implementation compare to SVM and RVM.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.7
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• pp.865-870
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• 2018

A machine vision based industrial inspection includes defects detection and classification. Fast inspection is a fundamental problem for many applications of real-time vision systems. It requires little computation time and localizing defects robustly with high accuracy. Deep learning technique have been known not to be suitable for real-time applications. Recently a couple of fast region-based CNN algorithms for object detection are introduced, such as Faster R-CNN, and YOLOv2. We apply these methods for an industrial inspection problem. Three CNN based detection algorithms, VOV based CNN, Faster R-CNN, and YOLOv2, are experimented for defect detection on metal surface. The results for inspection time and various performance indices are compared and analysed.

• Journal of Korea Multimedia Society
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• v.20 no.6
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• pp.873-881
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• 2017

Prevention of drowsy driving is one of the important issues for safe driving. In this study, the algorithm for detection of drowsy driving has been developed. The algorithm was implemented by applying template matching and line profile, which detects eye blink. The accuracy of eye detection and blink detection was $97.45{\pm}3.67%$ and $98.50{\pm}0.92%$, which was resulted from the verification experiment that 21 subjects participated. Consequently, the algorithm is expected to be used to prevent sleep-deprived driving.

• Journal of information and communication convergence engineering
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• v.18 no.2
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• pp.75-81
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• 2020

Approximately 1.3 million people die from traffic accidents each year, and smartphone usage while driving is one of the main causes of such accidents. Therefore, detection of smartphone usage by drivers has become an important part of distracted driving detection. Previous studies have used single camera-based methods to collect the driver images. However, smartphone usage detection by employing a single camera can be unsuccessful if the driver occludes the phone. In this paper, we present a driver smartphone usage detection system that uses multiple cameras to collect driver images from different perspectives, and then processes these images with ensemble convolutional neural networks. The ensemble method comprises three individual convolutional neural networks with a simple voting system. Each network provides a distinct image perspective and the voting mechanism selects the final classification. Experimental results verified that the proposed method avoided the limitations observed in single camera-based methods, and achieved 98.96% accuracy on our dataset.

• Electronics and Telecommunications Trends
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• v.33 no.4
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• pp.23-32
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• 2018

Object detection is a challenging field in the visual understanding research area, detecting objects in visual scenes, and the location of such objects. It has recently been applied in various fields such as autonomous driving, image surveillance, and face recognition. In traditional methods of object detection, handcrafted features have been designed for overcoming various visual environments; however, they have a trade-off issue between accuracy and computational efficiency. Deep learning is a revolutionary paradigm in the machine-learning field. In addition, because deep-learning-based methods, particularly convolutional neural networks (CNNs), have outperformed conventional methods in terms of object detection, they have been studied in recent years. In this article, we provide a brief descriptive summary of several recent deep-learning methods for object detection and deep learning architectures. We also compare the performance of these methods and present a research guide of the object detection field.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.5159-5178
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• 2018

Network Intrusion detection is a rapidly growing field of information security due to its importance for modern IT infrastructure. Many supervised and unsupervised learning techniques have been devised by researchers from discipline of machine learning and data mining to achieve reliable detection of anomalies. In this paper, a deep convolutional neural network (DCNN) based intrusion detection system (IDS) is proposed, implemented and analyzed. Deep CNN core of proposed IDS is fine-tuned using Randomized search over configuration space. Proposed system is trained and tested on NSLKDD training and testing datasets using GPU. Performance comparisons of proposed DCNN model are provided with other classifiers using well-known metrics including Receiver operating characteristics (RoC) curve, Area under RoC curve (AuC), accuracy, precision-recall curve and mean average precision (mAP). The experimental results of proposed DCNN based IDS shows promising results for real world application in anomaly detection systems.