• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.844-847
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• 2014

본 논문에서는 다양한 형태의 어플리케이션 중 스마트폰에 탑재된 카메라를 이용하여 명함을 인식할 때 발생하는 문제점을 해결하기 위한 기법을 제시하고자 한다. 스마트폰의 카메라를 이용하여 이미지를 얻을 경우 카메라의 각도에 따라 객체의 모양이 변형된다. 명함인식에서 이러한 이미지 왜곡문제는 인식률에 많은 영향을 미친다. 본 논문에서는 카메라의 각도에 따른 이미지의 왜곡 문제를 해결하기 위해 캐니 에지를 이용하여 객체를 검출하고, 검출된 객체의 간단한 정보를 이용하여 명함 영역을 검출하고, 영역 내에 명함을 검출하는 기법을 제안한다.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.97-108
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• 2001

Frauds detection is a difficult problem, requiring huge computer resources and complicated search activities Researchers have struggled with the problem. Even though a fee research approaches have claimed that their solution is much better than others, research community has not found 'the best solution'well fitting every fraud. Because of the evolving nature of the frauds. a novel and self-adapting method should be devised. In this research a new approach is suggested to solving frauds in insurance claims credit card transaction. Based on evolutionary computing approach, the method is itself self-adjusting and evolving enough to generate a new self of decision-makin rules. We believe that this new approach will provide a promising alternative to conventional ones, in terms of computation performance and classification accuracy.

• International Journal of Contents
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• v.8 no.1
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• pp.30-38
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• 2012

Text extraction and binarization are the important pre-processing steps for text recognition. The performance of text binarization strongly related to the accuracy of recognition stage. In our proposed method, the first stage based on line detection and shape feature analysis applied to locate the position of a business card and detect the shape from the complex environment. In the second stage, several local regions contained the possible text components are separated based on the projection histogram. In each local region, the pixels grouped into several connected components based on the connected component labeling and projection histogram. Then, classify each connect component into text region and reject the non-text region based on the feature information analysis such as size of connected component and stroke width estimation.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.839-842
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• 2009

It is used to differential image for moving object detection in general. But it is difficult to detect the accurate detection which uses differential image between frames. In this paper, the proposed method overcome the noise that is generated by camera, grabber card, or weather condition. It extract to moving big object such as human or vehicle. The proposed method process morphological filtering and binary for the image with noise, reduce error. We are expect to apply to a real-time moving object detection system at fog condition, pass the limit of the object detection method using the differential image.

• International Journal of Automotive Technology
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• v.7 no.7
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• pp.827-832
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• 2006

Occupant classification and position detection have been significant research areas in intelligent safety systems in the automotive field. The detection and classification of seat occupancy open up new ways to control the safety system. This paper deals with a novel algorithm development, hardware implementation and testing of a prototype intelligent safety system for occupant classification and position detection for in-vehicle environment. Borland C++ program is used to develop the novel algorithm interface between the sensor and data acquisition system. MEMS strain gauge hermatic pressure sensor containing micromachined integrated circuits is installed inside the passenger seat. The analog output of the sensor is connected with a connector to a PCI-9111 DG data acquisition card for occupancy detection, classification and position detection. The algorithm greatly improves the detection of whether an occupant is present or absent, and the classification of either adult, child or non-human object is determined from weights using the sensor. A simple computation algorithm provides the determination of the occupant's appropriate position using centroidal calculation. A real time operation is achieved with the system. The experimental results demonstrate that the performance of the implemented prototype is robust for occupant classification and position detection. This research may be applied in intelligent airbag design for efficient deployment.

• Journal of Korea Multimedia Society
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• v.21 no.5
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• pp.617-625
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• 2018

Phishing website has become a crucial concern in cyber security applications. It is performed by fraudulently deceiving users with the aim of obtaining their sensitive information such as bank account information, credit card, username, and password. The threat has led to huge losses to online retailers, e-business platform, financial institutions, and to name but a few. One way to build anti-phishing detection mechanism is to construct classification algorithm based on machine learning techniques. The objective of this paper is to compare different classifier ensemble approaches, i.e. random forest, rotation forest, gradient boosted machine, and extreme gradient boosting against single classifiers, i.e. decision tree, classification and regression tree, and credal decision tree in the case of website phishing. Area under ROC curve (AUC) is employed as a performance metric, whilst statistical tests are used as baseline indicator of significance evaluation among classifiers. The paper contributes the existing literature on making a benchmark of classifier ensembles for web phishing detection.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.06a
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• pp.293-304
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• 2001

frauds detection is a difficult problem, requiring huge computer resources and complicated search activities. researchers have struggled with the problem. Even though a flew research approaches have claimed that their solution is much bettor than others, research community has not found 'the best solution'well fitting every fraud. Because of the evolving nature of the frauds, a Revel and self-adapting method should be devised. In this research a new approach is suggested to solving frauds in insurance claims and credit card transaction. Based on evolutionary computing approach, the method is itself self-adjusting and evolving enough to generate a new set of decision-making rules. We believe that this new approach will provide a promising alternative to conventional ones, in terms of computation performance and classification accuracy.

• Journal of Multimedia Information System
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• v.5 no.2
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• pp.99-104
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• 2018

Phishing website has become a crucial concern in cyber security applications. It is performed by fraudulently deceiving users with the aim of obtaining their sensitive information such as bank account information, credit card, username, and password. The threat has led to huge losses to online retailers, e-business platform, financial institutions, and to name but a few. One way to build anti-phishing detection mechanism is to construct classification algorithm based on machine learning techniques. The objective of this paper is to compare different classifier ensemble approaches, i.e. random forest, rotation forest, gradient boosted machine, and extreme gradient boosting against single classifiers, i.e. decision tree, classification and regression tree, and credal decision tree in the case of website phishing. Area under ROC curve (AUC) is employed as a performance metric, whilst statistical tests are used as baseline indicator of significance evaluation among classifiers. The paper contributes the existing literature on making a benchmark of classifier ensembles for web phishing detection.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.200-206
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• 2021

Fraud in e-commerce transaction increased in the last decade especially with the increasing number of online stores and the lockdown that forced more people to pay for services and groceries online using their credit card. Several machine learning methods were proposed to detect fraudulent transaction. Neural networks showed promising results, but it has some few drawbacks that can be overcome using optimization methods. There are two categories of learning optimization methods, first-order methods which utilizes gradient information to construct the next training iteration whereas, and second-order methods which derivatives use Hessian to calculate the iteration based on the optimization trajectory. There also some training refinements procedures that aims to potentially enhance the original accuracy while possibly reduce the model size. This paper investigate the performance of several NN models in detecting fraud in e-commerce transaction. The backpropagation model which is classified as first learning algorithm achieved the best accuracy 96% among all the models.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.8 no.1
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• pp.1-12
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• 1997

The acousto-optical spectrometer as a new type backend of the receiver system for radio astronomical observations is manufactured for radio signal analysis. We studied on the effect of Acousto-Optic and Acousto-Optic devices and designed the optics system. We manufactured the optical mount and the CCD detector for deflected beam and interface card. This acousto-optical spectrometer consisted of a laser, optics, acousto-optic deflector, CCD detector and Interface card. This system use He-Ne laser as a light source and use optics to get parallel beam and to focus the deflected beam. Acousto-optic deflector converts IF signal to ultrasonic wave and deflect the laser beam according to the Bragg deflection. The ultra wide band acousto-optic deflector has 1 GHz bandwidth and a total of 2,048 channel Charge Coupled Device for signal detection. In this study, we discuss the theoretical description on the effect of Acousto-optics, the design of the optics, manufacture of optical mount, CCD detector, interface card and we presented the results of experiment. As a result of measurement, we have 1,000 channels bandwidth from CCD channels.