• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.2 no.2 s.3
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• pp.85-95
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• 2003

This paper proposes a multiple moving objects tracking system which is adaptable itself to circumstances. Snake model is sensitive to the start position value because it does not accurately express contours of objects in complex image. It can be improved as the proposed system gets background images by using difference images, segments objects using neighborhood pixels and assesses the position feature values acquired on the start position value to deformable Snake model. And also the system can simplify complex background images and reduce search regions by the constituent points of a Snake laid in Positions of object. It is showed that the proposed system can be appBied to multiple moving vehicle racking systems by the experimental results of 30fps AVI file.

• Biomedical Science Letters
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• v.7 no.3
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• pp.103-110
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• 2001

$\alpha$-Fetoprotein(AFP) is a good marker for the detection of several diseases such as hepatocellular carcinoma, gonadal germ cell tumor, gastric tumor, and Down's syndrome. In this study, we developed ELISA, using synthetic peptides corresponding to the epitopes of AFP. Five kinds of peptides were synthesized from AFP to produce antibodies in rats that recognize AFP in human plasma as well as amniotic fluid and do not cross-react with serum albumin. All five kinds of antibodies showed good reactivities with their peptide-keyhole limpet hemocyanin conjugates. Anti-synthetic peptide 1 (R-N-E-Y-G-I-A-S-I-L, 4-13) antibody, in particular, reacted well with AEP as well as synthetic peptide 1-KLH but not with human serum albumin. The binding affinity(Kd) was 2.7$\times$10$^{-9}$M for peptide 1 and 6.8$\times$10$^{-8}$M for AEP. The range for measurement of AFP was 10~1,000 ng/ml. The within-assay and between-assay coefficients of variance(CV) were 4.83% and 10.97%, respectively. In a sample of 31 sera and 33 amniotic fluids, there was a good correlation between AFP values determined in this assay and those in a commercial kit. These results indicate that the antibodies against synthetic peptides corresponding to the epitopes of AFP are highly specific to APP and synthetic peptide-based ELISA would be useful for the measurement of human AFP.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.353-356
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• 2017

앱 인벤터는 GUI 환경에서 블록 편집기를 사용하여 앱을 개발한다. 따라서 누구나 쉽게 앱 프로그래밍을 시작할 수 있다는 장점이 있다. 또한, 앱 인벤터의 공식 사이트의 gallery 공간에 공개된 수많은 공개 앱 소스(aia 파일)를 쉽게 구할 수 있기 때문에 다른 사람이 만든 앱의 소스를 그대로 가져다가 이미지만 바꿔서 자신이 만든 것처럼 앱을 공개할 수도 있다. 그러나 직접 블록 단위로 비교해보지 않고서는 표절이나 도용 여부를 판단하는 것은 쉽지 않다. 따라서 본 논문에서는 앱 인벤터로 개발한 앱들의 유사도를 자동으로 계산해주는 도구를 개발하였다. 원본 프로그램과 도용된 프로그램은 유사도가 높게 계산될 것임을 예상할 수 있기 때문에 유사도 계산 프로그램은 코드 도용을 확인하는 목적으로 활용될 수 있다. 본 논문에서 구현한 도구의 평가를 위해서 다양한 실험을 수행하였고, 실제로 유사도가 높았던 앱들이 서로 공통된 블록을 다수 포함하고 있음을 밝혀내었다. 이러한 실험결과를 바탕으로 우리가 개발한 도구가 앱 인벤터로 개발한 앱에 대해서 소스 표절이나 코드 도용을 탐지하는 목적으로 활용될 수 있을 것으로 기대한다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.1
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• pp.195-203
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• 2019

Mobile applications stores such as Google Play Store and Apple App Store, are widely used to distribute a variety of applications including finance, shopping, and entertainment. Recently, however, vulnerabilities of the mobile applications are likely to violate users' privacy such as personal information leakage. In this paper, we classify mobile applications that can be download from mobile stores, and analyze the personal information that could be leaked when users are using the mobile applications. As a result of analysis, we found that personal information are leaked in some widely used mobile applications in practice. On the basis of our experiment results, we propose some mitigations to enhance security of the mobile applications and prevent leakage of personal information.

• Convergence Security Journal
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• v.18 no.3
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• pp.27-35
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• 2018

The certificate is electronic information issued by an accredited certification body to certify an individual or to prevent forgery and alteration between communications. Certified certificates are stored in PCs and smart phones in the form of encrypted files and are used to prove individuals when using Internet banking and smart banking services. Among the rapidly growing Android-based malicious applications are malicious apps that leak personal information, especially certificates that exist in the form of files. This paper proposes a method for judging whether malicious codes leak certificates by using DroidBox, an Android-based dynamic analysis tool.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.6_1
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• pp.927-939
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• 2022

This study was conducted to develop a Web-GIS based monitoring module of smart city that can effectively respond, manage and improve situation in all stages of illegal dumping management on a city scale. First, five technologies were set for the core technical elements of the module configuration. Five core technical elements are as follows; video screening technology based on motion vector analysis, human behavior detection based on intelligent video analytics technology, mobile app for receiving civil complaints about illegal dumping, illegal dumping risk model and street cleanliness map, Web-GIS based situation monitoring technology. The development contents and results for each set of core technical elements were evaluated. Finally, a Web-GIS based 'illegal dumping monitoring module' was proposed. It is possible to collect and analyze city data at the local government level through operating the proposed module. Based on this, it is able to effectively detect illegal dumpers at relatively low cost and identify the tendency of illegal dumping by systematically managing habitual occurrence areas. In the future, it is expected to be developed in the form of an add-on module of the smart city integration platform operated by local governments to ensure interoperability and scalability.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.2
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• pp.317-324
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• 2022

In this thesis, in consideration of the functions of general carriers, smart carriers with various convenience functions were studied for supplementation and improvement based on the disadvantages of carriers and inconvenience when using them. Based on the app inventory program, LED and motor locks were installed in carriers through smartphone applications, Bluetooth recognition and control functions that easily control on/off, load cell sensors were installed inside the carriers to detect weight, install Bluetooth and alarms on LCD panels, and prevent theft and loss.

• Journal of Drive and Control
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• v.21 no.3
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• pp.36-45
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• 2024

Collaborative robots, designed for direct interaction with humans have limited adaptability to environmental changes. This study addresses this limitation by implementing a barista robot system using AI technology. To overcome limitations of traditional collaborative robots, a model that applies a real-time object detection algorithm to a 6-degree-of-freedom robot arm to recognize and control the position of random cups is proposed. A coffee ordering application is developed, allowing users to place orders through the app, which the robot arm then automatically prepares. The system is connected to ROS via TCP/IP socket communication, performing various tasks through state transitions and gripper control. Experimental results confirmed that the barista robot could autonomously handle processes of ordering, preparing, and serving coffee.

• Journal of Service Research and Studies
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• v.14 no.1
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• pp.13-26
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• 2024

Multi-modal generation is the process of generating results based on a variety of information, such as text, images, and audio. With the rapid development of AI technology, there is a growing number of multi-modal based systems that synthesize different types of data to produce results. In this paper, we present an AI system that uses speech and text recognition to describe a person and generate a montage image. While the existing montage generation technology is based on the appearance of Westerners, the montage generation system developed in this paper learns a model based on Korean facial features. Therefore, it is possible to create more accurate and effective Korean montage images based on multi-modal voice and text specific to Korean. Since the developed montage generation app can be utilized as a draft montage, it can dramatically reduce the manual labor of existing montage production personnel. For this purpose, we utilized persona-based virtual person montage data provided by the AI-Hub of the National Information Society Agency. AI-Hub is an AI integration platform aimed at providing a one-stop service by building artificial intelligence learning data necessary for the development of AI technology and services. The image generation system was implemented using VQGAN, a deep learning model used to generate high-resolution images, and the KoDALLE model, a Korean-based image generation model. It can be confirmed that the learned AI model creates a montage image of a face that is very similar to what was described using voice and text. To verify the practicality of the developed montage generation app, 10 testers used it and more than 70% responded that they were satisfied. The montage generator can be used in various fields, such as criminal detection, to describe and image facial features.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.3230-3253
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• 2017

Android malware steals users' private information, and embedded unsafe advertisement (ad) libraries, which execute unsafe code causing damage to users. The majority of such traffic is HTTP and is mixed with other normal traffic, which makes the detection of malware and unsafe ad libraries a challenging problem. To address this problem, this work describes a novel HTTP traffic flow mining approach to detect and categorize Android malware and unsafe ad library. This work designed AndroCollector, which can automatically execute the Android application (app) and collect the network traffic traces. From these traces, this work extracts HTTP traffic features along three important dimensions: quantitative, timing, and semantic and use these features for characterizing malware and unsafe ad libraries. Based on these HTTP traffic features, this work describes a supervised classification scheme for detecting malware and unsafe ad libraries. In addition, to help network operators, this work describes a fine-grained categorization method by generating fingerprints from HTTP request methods for each malware family and unsafe ad libraries. This work evaluated the scheme using HTTP traffic traces collected from 10778 Android apps. The experimental results show that the scheme can detect malware with 97% accuracy and unsafe ad libraries with 95% accuracy when tested on the popular third-party Android markets.