• The Journal of Korean Society for Radiation Therapy
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• v.28 no.2
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• pp.101-108
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• 2016

Purpose : The purpose of this study was to evaluate the Retro recon in SRS planning using BranLAB when stereotactic location error occurs by metal artifact. Materials and Methods : By CT simulator, image were acquired from head phantom(CIRS, PTW, USA). To observe stereotactic location recognizing and beam hardening, CT image were approved by SRS planning system(BrainLAB, Feldkirchen, Germany). In addition, we compared acquisition image(1.25mm slice thickness) and Retro recon image(using for 2.5 mm, 5mm slice thickness). To evaluate these three images quality, the test were performed by AAPM phantom study. In patient, it was verified stereotactic location error. Results : All the location recognizing error did not occur in scanned image of phantom. AAPM phantom scan images all showed the same trend. Contrast resolution and Spatial resolution are under 6.4 mm, 1.0 mm. In case of noise and uniformity, under 11, 5 of HU were measured. In patient, the stereotactic location error was not occurred at reconstructive image. Conclusion : For BrainLAB planning, using Retro recon were corrected stereotactic error at beam hardening. Retro recon may be the preferred modality for radiation treatment planning and approving image quality.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.6
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• pp.1195-1202
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• 2016

Recently, a lot of studies that applying the big data technology to various fields, are progressing actively. In the maritime domain, the big data is the meaningful information which makes and gathers by the navigation and communication equipment from the many ships on the ocean. Also, importance of the maritime safety is emphasized, because maritime accidents are rising with increasing of maritime traffic. To support prevention of maritime accidents, in this paper, we developed a vessel traffic display and statistic system based on AIS messages from the many vessels of maritime. Also, to verify the developed system, we conducted tests for vessel track display function and vessel traffic statistic function based on two test scenarios. Therefore, we verified the effectiveness of the developed system for vessel tracks display, abnormal navigation patterns, checking failure of AIS equipments and maritime traffic statistic analyses.

• Journal of the Society of Disaster Information
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• v.18 no.4
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• pp.839-846
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• 2022

Purpose: To secure the safety of firefighters who are dispatched to emergency activities for patients with suspected infectious diseases during an epidemic, and to identify the current status of suspected infectious disease patients by region based on the information collected at the site, and manage firefighting infectious diseases that can be controlled and supported I want to develop a system. Method: Develop a smartphone app that can classify suspected infectious disease patients to check whether an infectious disease is suspected, and develop a disposable NFC tag for patient identification to prevent infection from suspected infectious disease patients. Develop a management system that collects and analyzes data related to emergency patients with suspected infectious disease input from the field and provides them to relevant business personnel to evaluate whether the transport of emergency patients with suspected infectious disease is improved. Result: As a result of the experiment, it was possible to determine whether an infectious disease was suspected through the algorithm implemented in the smartphone app, and the retransfer rate was significantly reduced by transferring to an appropriate hospital. Conclusion: Through this study, the possibility of improving emergency medical services by applying ICT technology to emergency medical services was confirmed. It is expected that the safety of paramedics will be actively secured.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.91-107
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• 2012

As the Internet becomes more popular, many people use it to communicate. With the increasing number of personal homepages, blogs, and social network services, people often expose their personal information online. Although the necessity of those services cannot be denied, we should be concerned about the negative aspects such as personal information leakage. Because it is impossible to review all of the past records posted by all of the people, an automatic personal information detection method is strongly required. This study proposes a method to detect or classify online documents that contain personal information by analyzing features that are common to personal information related documents and learning that information based on the Na$\ddot{i}$ve Bayes algorithm. To select the document classification algorithm, the Na$\ddot{i}$ve Bayes classification algorithm was compared with the Vector Space classification algorithm. The result showed that Na$\ddot{i}$ve Bayes reveals more excellent precision, recall, F-measure, and accuracy than Vector Space does. However, the measurement level of the Na$\ddot{i}$ve Bayes classification algorithm is still insufficient to apply to the real world. Lewis, a learning algorithm researcher, states that it is important to improve the quality of category features while applying learning algorithms to some specific domain. He proposes a way to incrementally add features that are dependent on related documents and in a step-wise manner. In another experiment, the algorithm learns the additional dependent features thereby reducing the noise of the features. As a result, the latter experiment shows better performance in terms of measurement than the former experiment does.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.3
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• pp.99-108
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• 2021

There are unrestricted conditions on the driver's face inside the vehicle, such as changes in lighting, partial occlusion and various changes in the driver's condition. In this paper, we propose a face identification system in an unrestricted vehicle environment. The proposed method uses a near-infrared (NIR) camera to minimize the changes in facial images that occur according to the illumination changes inside and outside the vehicle. In order to process a face exposed to extreme light, the normal face image is changed to a simulated overexposed image using mean and variance for training. Thus, facial classifiers are simultaneously generated under both normal and extreme illumination conditions. Our method identifies a face by detecting facial landmarks and aggregating the confidence score of each landmark for the final decision. In particular, the performance improvement is the highest in the class where the driver wears glasses or sunglasses, owing to the robustness to partial occlusions by recognizing each landmark. We can recognize the driver by using the scores of remaining visible landmarks. We also propose a novel robust rejection and a new evaluation method, which considers the relations between registered and unregistered drivers. The experimental results on our dataset, PolyU and ORL datasets demonstrate the effectiveness of the proposed method.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.7
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• pp.271-278
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• 2021

As the current cyber attacks become more intelligent, the existing Intrusion Detection System is difficult for detecting intelligent attacks that deviate from the existing stored patterns. In an attempt to solve this, a model of a deep learning-based intrusion detection system that analyzes the pattern of intelligent attacks through data learning has emerged. Intrusion detection systems are divided into host-based and network-based depending on the installation location. Unlike network-based intrusion detection systems, host-based intrusion detection systems have the disadvantage of having to observe the inside and outside of the system as a whole. However, it has the advantage of being able to detect intrusions that cannot be detected by a network-based intrusion detection system. Therefore, in this study, we conducted a study on a host-based intrusion detection system. In order to evaluate and improve the performance of the host-based intrusion detection system model, we used the host-based Leipzig Intrusion Detection-Data Set (LID-DS) published in 2018. In the performance evaluation of the model using that data set, in order to confirm the similarity of each data and reconstructed to identify whether it is normal data or abnormal data, 1D vector data is converted to 3D image data. Also, the deep learning model has the drawback of having to re-learn every time a new cyber attack method is seen. In other words, it is not efficient because it takes a long time to learn a large amount of data. To solve this problem, this paper proposes the Siamese Convolutional Neural Network (Siamese-CNN) to use the Few-Shot Learning method that shows excellent performance by learning the little amount of data. Siamese-CNN determines whether the attacks are of the same type by the similarity score of each sample of cyber attacks converted into images. The accuracy was calculated using Few-Shot Learning technique, and the performance of Vanilla Convolutional Neural Network (Vanilla-CNN) and Siamese-CNN was compared to confirm the performance of Siamese-CNN. As a result of measuring Accuracy, Precision, Recall and F1-Score index, it was confirmed that the recall of the Siamese-CNN model proposed in this study was increased by about 6% from the Vanilla-CNN model.

• Journal of Korea Multimedia Society
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• v.2 no.2
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• pp.217-229
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• 1999

Framework is the set of interrelated classes, constructing reusable design in specific domain or set of abstracted classes, and defines common architecture among applications included in domain. Developers can reuse not only class code but also wide range of knowledge on domain by reusing framework. In this papers, we present COM(Component-Oriented Methodology) for the reuse of framework, and develop construction environment for framework and domain development. That is, domain is analyzed by input of domain knowledge on real world to create software based on component, and hotspot is identified through analyzed information, and redesigned(refactoring) by putting additional information on users and developers. After that, I will create domain framework and application framework depending on domain. In this Component-oriented methodology, information is searched, understood and extracted or composite through component-pattern library storage internally. Then this information is classified into the information on component and pattern respectively, and used as additional information in redesigning. With this, developer can obtain reusability, easiness and portability by constructing infrastructure environment that allow to register, update and delete component through Component Pattern Management System(CPMS) under the development environment which can be easily applied to his own application using multimedia component, in this thesis, CORBA(Common Object Request Broker Architecture) environment.

• The KIPS Transactions:PartD
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• v.15D no.6
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• pp.861-872
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• 2008

As rapid development of computing environment, field of automatic identification research which interoperates with various physical objects and digital information is making active progress. Although the automatic identification system is widely used in various industries, application of automatic identification system in the field of medical health doesn't reach other industry. Therefore research in medical health supplies such as medical equipment, blood, human tissues and etc is on progress. This paper suggests the application of automatic identification technology for biological resources which is core research material in human genome research. First of all, user environment requirements for the introduction of automatic identification technology are defined and through the experiments and research, barcode is selected as a suitable tag interface. Data Matrix which is 2D barcode symbology is chosen and data schema is designed based on UCC/EAN-128 for international defecto standard. To showapplicability of proposed method when applied to actual environment, we developed, tested and evaluated application as following methods. Experiments of barcode read time at 196 and 75 below zero which is actual temperature where biological resources are preserved resulted read speed of average of 1.6 second and the data schema satisfies requirements for the biological resources application. Therefore suggested method can provide data reliability as well as rapid input of data in biological resources information processing.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.5
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• pp.462-469
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• 2023

In this study, we propose a method for evaluating the risk of collision between ships to support determination on the risk of collision in a situation in which ships encounter each other and to prevent collision accidents. Because several uncertainties are involved in the navigation of a ship, must be considered when evaluating the risk of collision. We apply the Dempster-Shafer theory to manage this uncertainty and evaluate the collision risk of each target vessel in real time. The distance at the closest point approach (DCPA), time to the closest point approach (TCPA), distance from another vessel, relative bearing, and velocity ratio are used as evaluation factors for ship collision risk. The basic probability assignments (BPAs) calculated by membership functions for each evaluation factor are fused through the combination rule of the Dempster-Shafer theory. As a result of the experiment using automatic identification system (AIS) data collected in situations where ships actually encounter each other, the suitability of evaluation was verified. By evaluating the risk of collision in real time in encounter situations between ships, collision accidents caused by human errora can be prevented. This is expected to be used for vessel traffic service systems and collision avoidance systems for autonomous ships.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.6
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• pp.1525-1540
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• 2015

Due to a rapid advancement in the electronic commerce technology, the payment method varies from cash to electronic settlement such as credit card, mobile payment and mobile application card. Therefore, financial fraud is increasing notably for a purpose of personal gain. In response, financial companies are building the FDS (Fraud Detection System) to protect consumers from fraudulent transactions. The one of the goals of FDS is identifying the fraudulent transaction with high accuracy by analyzing transaction data and personal information in real-time. Data mining techniques are providing great aid in financial accounting fraud detection, so it have been applied most extensively to provide primary solutions to the problems. In this paper, we try to provide an overview of the research on data mining based fraud detection. Also, we classify researches under few criteria such as data set, data mining algorithm and viewpoint of research.