• JSTS:Journal of Semiconductor Technology and Science
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• v.10 no.2
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• pp.79-99
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• 2010

Pulsed current-voltage (I-V) methods are introduced to evaluate the impact of fast transient charge trapping on the performance of high-k dielectric transistors. Several pulsed I-V measurement configurations and measurement requirements are critically reviewed. Properly configured pulsed I-V measurements are shown to be capable of extracting such device characteristics as trap-free mobility, trap-induced threshold voltage shift (${\Delta}V_t$), as well as effective fast transient trap density. The results demonstrate that the pulsed I-V measurements are an essential technique for evaluating high-$\kappa$ gate dielectric devices.

• Journal of the Korean Data and Information Science Society
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• v.17 no.3
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• pp.753-762
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• 2006

The Snakes and GVF used to find object edges dynamically have assigned their initial contour arbitrarily. If the initial contours are located in the neighboring regions of object edges, Snakes and GVF can be close to the true boundary. If not, these will likely to converge to the wrong result. Therefore, this paper proposes a new initialization of Snakes and GVF using convex hull approximation, which initializes the vertex of Snakes and GVF as a convex polygonal contour near object edges. In simulation result, we show that the proposed algorithm has a faster convergence to object edges than the existing methods. Our algorithm also has the advantage of extracting whole edges in real images.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.3
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• pp.654-661
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• 2011

This paper is about a personal identification algorithm using an ECG that has been studied by a few researchers recently. Previously published algorithm can be classified as two methods. One is the method that analyzes of ECG features and the other is the morphological analysis of ECG. The main characteristic of proposed algorithm can be classified the method of analysis ECG features. Proposed algorithm adopts DSTW(Down Slope Trace Wave) for extracting ECG features, and applies SVM(Support Vector Machine) to training and testing as a classifier algorithm. We choose 18 ECG files from MIT-BIH Normal Sinus Rhythm Database for estimating of algorithm performance. The algorithm extracts 100 heartbeats from each ECG file, and use 40 heartbeats for training and 60 heartbeats for testing. The proposed algorithm shows clearly superior performance in all ECG data, amounting to 93.89% heartbeat recognition rate and 100% ECG recognition rate.

• Journal of Power Electronics
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• v.6 no.3
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• pp.195-204
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• 2006

This paper presents a new compensating system, consisting of series and shunt active filters, for mitigating voltage and current disturbances. The shunt filter is used to compensate for unbalanced and distorted load currents. The series filter comprises two inverters, used to suppress voltage disturbances and handle source currents independently. This configuration is devised to reduce the overall cost of active compensators by using low-frequency high-current switches for the latter inverter. The filters are controlled separately using a novel control strategy. Since voltages at the point of common coupling contain interharmonics, conventional methods cannot be used for extracting voltage references. Therefore, voltage references are obtained from generated sinusoidal waveforms by a phase-locked loop. Current references are detected based on rotating frame vector mapping. Simulation results are presented to verify the system.

• The Transactions of the Korea Information Processing Society
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• v.6 no.5
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• pp.1410-1417
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• 1999

In this paper, we propose the method of extracting the caption regions from news video and the method of recognizing the captions that can be used mainly for content-based indexing and retrieving the MPEG-2 compressed news for NOD(News On Demand). The proposed method can reduce the searching time on detecting caption frames with minimum MPEG-2 decoding, and effectively eliminate the noise in caption regions by deliberately devised preprocessing. Because the kind of fonts that are used for captions is not various in the news video, an enhanced template matching method is used for recognizing characters. We could obtain good recognition result in the experiment of sports news video by the proposed methods.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.81-81
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• 2002

Classification is considered as one of the processes of extracting attributes from satellite imagery and is one of the usual functions in the commercial satellite image processing software. Accuracy of classification plays a key role in deciding the usage of its results. Many tremendous efforts far the higher accuracy have been done in such fields; training area selection, classification algorithm. Our research is one of these effort in different manners. In this research, we conduct classification using multiple satellite image data and evidential approach. We statistically consider the posterior probabilities and certainty in maximum likelihood classification and methodologically Dempster's orthogonal sums. Unfortunately, accuracy for the whole data sets has not assessed yet, but accuracy assessments in training fields and check fields shows accuracy improvement over 10% in overall accuracy and over 0.1 in kappa index.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.153-155
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• 2003

Laser scanning has become a viable technique for the collection of a large amount of accurate 3D point data densely distributed on the scanned object surface. The inherent 3D nature of the sub-randomly distributed point cloud provides abundant spatial information. To explore valuable spatial information from laser scanned data becomes an active research topic, for instance extracting digital elevation model, building models, and vegetation volumes. The sub-randomly distributed point cloud should be segmented and classified before the extraction of spatial information. This paper investigates some exist segmentation methods, and then proposes an octree-based split-and-merge segmentation method to divide lidar data into clusters belonging to 3D planes. Therefore, the classification of lidar data can be performed based on the derived attributes of extracted 3D planes. The test results of both ground and airborne lidar data show the potential of applying this method to extract spatial features from lidar data.

• Journal of Information Processing Systems
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• v.16 no.4
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• pp.882-895
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• 2020

The amount of malware increases exponentially every day and poses a threat to networks and operating systems. Most new malware is a variant of existing malware. It is difficult to deal with numerous malware variants since they bypass the existing signature-based malware detection method. Thus, research on automated methods of detecting and processing variant malware has been continuously conducted. This report proposes a method of extracting feature data from files and detecting malware using machine learning. Feature data were extracted from 7,000 malware and 3,000 benign files using static and dynamic malware analysis tools. A malware classification model was constructed using multiple DNN, XGBoost, and RandomForest layers and the performance was analyzed. The proposed method achieved up to 96.3% accuracy.

• Journal of Information Processing Systems
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• v.10 no.2
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• pp.256-270
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• 2014

A new morphed steganographic algorithm is proposed in this paper. Image security is a challenging problem these days. Steganography is a method of hiding secret data in cover media. The Least Significant Bit is a standard Steganographic method that has some limitations. The limitations are less capacity to hide data, poor stego image quality, and imperceptibility. The proposed algorithm focuses on these limitations. The morphing concept is being used for image steganography to overcome these limitations. The PSNR and standard deviation are considered as a measure to improve stego image quality and morphed image selection, respectively. The stego keys are generated during the morphed steganographic embedding and extracting process. Stego keys are used to embed and extract the secret image. The experimental results, which are based on hiding capacity and PSNR, are presented in this paper. Our research contributes towards creating an improved steganographic method using image morphing. The experimental result indicates that the proposed algorithm achieves an increase in hiding capacity, stego image quality, and imperceptibility. The experimental results were compared with state of the art steganographic methods.

• Journal of the Korean Society of Safety
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• v.34 no.5
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• pp.95-102
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• 2019

Non-destructive methods such as rebound hardness method and ultrasonic method are widely studied for evaluating the physical properties, condition and damage of concrete, but are not suitable for detecting delamination and cracks near the surface due to various constraints of the site as well as the accuracy. Therefore, in this study, the impact resonance method was applied to detect the separation cracks occurring near the surface of the concrete slab and bridge deck. As a next step, the principal component analysis were performed by extracting various features using the FFT data. As a result of principal component analysis, it was analyzed that the reliability was high in distinguishing defects in concrete. This feature extraction and application of principal component analysis can be used as basic data for future use of machine learning technique for the better accuracy.