• Journal of Positioning, Navigation, and Timing
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• v.8 no.1
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• pp.13-18
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• 2019

This paper analyzes the signal acquisition performance of the legacy GNSS spreading codes and a polyphase code. The code length and chip rate of a polyphase code are assumed to be same as those of the GPS L1 C/A and Galileo E1C codes. The autocorrelation and cross correlation characteristics are analyzed. In addition, a way to calculate a more accurate probability of false alarm for a code with sidelobe non-zero auto-correlation function is proposed. Finally, we estimate the probability of detection and the mean acquisition time for a given signal strength and the probability of false alarm.

• Smart Structures and Systems
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• v.30 no.3
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• pp.263-271
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• 2022

In this paper we present an overview of damage diagnosis algorithms that have been developed over the past two decades using vibration signals obtained from structures. Then, the paper focuses primarily on algorithms that can be used following an extreme event such as a large earthquake to identify structural damage for responding in a timely manner. The algorithms presented in the paper use measurements obtained from accelerometers and gyroscope to identify the occurrence of damage and classify the damage. Example algorithms are presented include those based on autoregressive moving average (ARMA), wavelet energies from wavelet transform and rotation models. The algorithms are illustrated through application of data from test structures such as the ASCE Benchmark structure and laboratory tests of scaled bridge columns and steel frames. The paper concludes by identifying needs for research and development in order for such algorithms to become viable in practice.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.11a
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• pp.240-243
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• 2022

본 논문에서는 네일 아트를 한 손 이미지가 주어졌을 때 손톱에 있는 네일 아트의 컬러를 자동으로 분류해주기 위한 시스템을 제안한다. 네일 아트 컬러 자동 분류기는 Object Detection 모델을 이용하여 인풋으로 들어오는 손 이미지에서 손톱 영역을 찾고, 각 손톱에 대하여 13 가지 컬러 중 하나로 분류한 결과를 아웃풋으로 반환한다. 본 프로젝트에서는 사용자가 요청하는 네일 아트 손 이미지에 대하여 컬러 라벨링 결과를 반환해주는 API 형태의 서비스를 제안하며, 반응형 웹을 통해 시연 가능하도록 시스템을 설계 및 구현하였다.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.3
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• pp.42-48
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• 1996

In this paper, a position correction system of industrial robot for door-chassis assembly tast is developed in connection with the position correction algorithm shown in Part I. Tow notches and a hole of auto chassis are selected as the reference measure points and a vision based error detection algorithm is devised to measure in accuracy of less than 0.07mm. And also, the transformation between base and tool coordinates of the robot is shown to send the suitable correction quantities caaording to robot's option. The obtained algorithms were satisfactorily implemented for a real door-chassis model such that the system could accomplish visually acceptable door-chassis assembly task.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.1
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• pp.183-193
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• 2024

Recurrent Neural Network technology that learns past patterns and predicts future patterns using technology for recognizing and classifying objects is being applied to various industries, economies, and languages. And research for practical use is making a lot of progress. However, research on the application of Recurrent Neural Networks for evaluating and predicting the safety of mechanical structures is insufficient. Accurate detection of external load applied to the outside is required to evaluate the safety of mechanical structures. Learning of Recurrent Neural Networks for this requires a large amount of load data. This study applied the Gated Recurrent Unit technique to examine the possibility of load learning and investigated the possibility of applying a stacked Auto Encoder as a way to secure load data. In addition, the usefulness of learning mechanical loads was analyzed with the Gated Recurrent Unit technique, and the basic setting of related functions and parameters was proposed to secure accuracy in the recognition and prediction of loads.

• Journal of Korea Game Society
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• v.9 no.5
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• pp.53-61
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• 2009

Game industry, especially MMORPG (Massively Multiplayer Online Role Playing Game) has rapidly been expanding in these days. In this background, lots of online game security incidents have been increasing and getting more diversity. One of the most critical security incidents is 'Bots', mimics human player's playing behaviors. Bots performs the task without any manual works, it is considered unfair with other players. So most game companies try to block Bots by analyzing the packets between clients and servers. However this method can be easily attacked, because the packets are changeable when it is send to server. In this paper, we propose a Bots detection method by observing the playing patterns of game characters with data on server. In this method, Bots developers cannot handle the data, because it is working on server. Therefore Bots cannot avoid it and we can find Bots users more completely.

• Journal of the Korea Society of Computer and Information
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• v.25 no.11
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• pp.131-138
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• 2020

Malware is generally recognized as a computer program that penetrates another computer system and causes malicious behavior intended by the developer. In cyberspace, it is also used as a cyber weapon to attack adversary. The most important factor that a malware must have as a cyber weapon is that it must achieve its intended purpose before being detected by the other's detection system. It requires a lot of time and expertise to create a single malware to avoid the other's detection system. We propose the framework that automatically generates variant malware when a binary code type malware is input using the DCM technique. In this framework, the sample malware was automatically converted into variant malware, and it was confirmed that this variant malware was not detected in the signature-based malware detection system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.10B
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• pp.1233-1239
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• 2011

The visible light convergence communication technology is suitable for indoor wireless communication and digital lighting fixtures, it could be used as lighting devices as well as a communication device. However, because that VLC is the technology of came to world a few years ago, there are many problems which had to solve. The signal sensing of VLC transmitter is one of the most challenging issue in VLC systems. Therefore in this paper, we analysis the performance of various sensing scheme for efficient detection of VLC systems. The signal of user is OFDM signal and the wirelss channel between a user and VLC system is modeled as indoor VLC channel. From the simulation results, it is confirmed that the proposed scheme is very effective to signal sensing for VLC systems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.63-68
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• 2021

Recently there is sharply increasing interest in watching and creating video contents such as YouTube. However, creating such video contents without privacy protection technique can expose other people in the background in public, which is consequently violating their privacy rights. This paper seeks to remedy these problems and proposes a technique that identifies faces and protecting portrait rights by blurring the face. The key contribution of this paper lies on our deep-learning technique with low detection error and high computation that allow to protect portrait rights in real-time videos. To reduce errors, an efficient tracking algorithm was used in this system with face detection and face recognition algorithm. This paper compares the performance of the proposed system with and without the tracking algorithm. We believe this system can be used wherever the video is used.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.4008-4023
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• 2022

Since machine learning was introduced into cross-site scripting (XSS) attack detection, many researchers have conducted related studies and achieved significant results, such as saving time and labor costs by not maintaining a rule database, which is required by traditional XSS attack detection methods. However, this topic came across some problems, such as poor generalization ability, significant false negative rate (FNR) and false positive rate (FPR). Moreover, the automatic clustering property of graph convolutional networks (GCN) has attracted the attention of researchers. In the field of natural language process (NLP), the results of graph embedding based on GCN are automatically clustered in space without any training, which means that text data can be classified just by the embedding process based on GCN. Previously, other methods required training with the help of labeled data after embedding to complete data classification. With the help of the GCN auto-clustering feature and labeled data, this research proposes an approach to detect XSS attacks (called GCNXSS) to mine the dependencies between the units that constitute an XSS payload. First, GCNXSS transforms a URL into a word homogeneous graph based on word co-occurrence relationships. Then, GCNXSS inputs the graph into the GCN model for graph embedding and gets the classification results. Experimental results show that GCNXSS achieved successful results with accuracy, precision, recall, F1-score, FNR, FPR, and predicted time scores of 99.97%, 99.75%, 99.97%, 99.86%, 0.03%, 0.03%, and 0.0461ms. Compared with existing methods, GCNXSS has a lower FNR and FPR with stronger generalization ability.