• Journal of the Institute of Convergence Signal Processing
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• v.11 no.2
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• pp.169-176
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• 2010

Nonstationary power quality(PQ) signals which the Sag, Swell, Impulsive Transients, and Harmonics make sometimes the operations of the industrial power electronics equipment, speed and motion controller, plant process control systems in the undesired environments. So, this PQ problem might be critical issues between power suppliers and consumers. Therefore, We have studied the PQ recognition system in order to acquire, analyze, and recognize the PQ signals using the software, i.e, MATLAB, Simulink, and CCS, and the hardware. i.e., TMS320C6713DSK(TI), The algorithms of the PQ recognition system in the Wavelet transforms and Backpropagation algorithms of the neural networks. Also, in order to verify the real-time performances of the PQ recognition system under the environments of software and hardware systems, SIL(Software In the Loop) and PIL(Processor In the Loop) were carried out, resulting in the excellent recognition performances of average 99%.

• Journal of Digital Contents Society
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• v.19 no.2
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• pp.363-370
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• 2018

Ransomware detection has become a hot topic in computer security for protecting digital contents. Unfortunately, current signature-based and static detection models are often easily evadable by compress, and encryption. For overcoming the lack of these detection approach, we have proposed the dynamic ransomware detection system using data mining techniques such as RF, SVM, SL and NB algorithms. We monitor the actual behaviors of software to generate API calls flow graphs. Thereafter, data normalization and feature selection were applied to select informative features. We improved this analysis process. Finally, the data mining algorithms were used for building the detection model for judging whether the software is benign software or ransomware. We conduct our experiment using more suitable real ransomware samples. and it's results show that our proposed system can be more effective to improve the performance for ransomware detection.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1216-1221
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• 2005

The GPS software receiver based on the SDR(Software Defined Radio) technology provides the ability to easily adopt other signal processing algorithms without changing or modifying the hardware of the GPS receiver. However, it is difficult to implement the GPS software receiver using a commercial processor because of heavy computation load for processing the GPS signals in real time. This paper proposes an efficient GPS signal processing scheme and correlator structure to reduce the computation load for processing the GPS signal in the GPS software receiver, which uses a patterned look-up table method to generate the correlation value between the GPS signals and the replica signals. In this paper, it is explained that the computation load of the proposed scheme is much smaller than that of the previous GPS signal processing scheme. Finally, the processing time of the proposed scheme is compared with that of the previous scheme, and the improvement is shown from the viewpoint of the computation load.

• Journal of Korean Society for Geospatial Information Science
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• v.12 no.2 s.29
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• pp.53-59
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• 2004

In this paper, we investigated a method for feature-based classification to develop a software which is suitable for the classification of high resolution satellite imagery. We developed algorithms for image segmentation and fuzzy-based classification required for feature-based classification and designed user interfaces to support interaction with user, considering various elements required for the feature-based classification. Evaluation of the software was accomplished using real image. Classification results were compared and analysed with eCognition software which is unique commercial software for feature-based classification. The classification results from both softwares showed essentially same results and the developed software showed better result in the processing speed.

• Journal of Internet Computing and Services
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• v.23 no.2
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• pp.1-7
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• 2022

Machine learning (ML) algorithms have been intended to seamlessly collaborate for enabling intelligent networking in terms of massive service differentiation, prediction, and provides high-accuracy recommendation systems. Mobile edge computing (MEC) servers are located close to the edge networks to overcome the responsibility for massive requests from user devices and perform local service offloading. Moreover, there are required lightweight methods for handling real-time Internet of Things (IoT) communication perspectives, especially for ultra-reliable low-latency communication (URLLC) and optimal resource utilization. To overcome the abovementioned issues, this paper proposed an intelligent scheme for traffic steering based on the integration of MEC and lightweight ML, namely support vector machine (SVM) for effectively routing for lightweight and resource constraint networks. The scheme provides dynamic resource handling for the real-time IoT user systems based on the awareness of obvious network statues. The system evaluations were conducted by utillizing computer software simulations, and the proposed approach is remarkably outperformed the conventional schemes in terms of significant QoS metrics, including communication latency, reliability, and communication throughput.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.479-480
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• 2022

고속으로 차량이 주행하는 도로에서 정지 차량이나 낙하물은 큰 사고를 유발하기에 이에 대한 대처 방안이 요구되고 있다. 갑작스런 정지 차량의 경우 예상 불가능하며, 낙하물은 순찰대를 편성하여 주기적으로 수거하고 있으나 즉각적인 대응이 어렵다. 해당 문제 해결을 위해 본 논문에서는 딥러닝 실시간 객체인식기술을 적용하여 정지 차량 및 도로 위 낙하물을 인식하며 이에 대한 정보를 제공하는 시스템을 개발하였다. 실시간 객체인식 알고리즘인 YOLOX와 실시간 객체추적기술인 deepSORT 알고리즘을 데스크톱 PC에 적용하여 구현하였다. 개발한 시스템은 정지 차량 및 낙하물에 대한 인식 결과를 제공한다. 기존 설치된 CCTV 영상을 대상으로 시스템 적용이 가능하여 저비용으로 넓은 지역에 대한 도로 위험 상황 인식을 기대할 수 있다.

• 한국전산유체공학회:학술대회논문집
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• 2006.05a
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• pp.288-291
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• 2006

• Journal of KIISE
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• v.43 no.12
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• pp.1325-1333
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• 2016

Software classification has several applications such as copyright infringement detection, malware classification, and software automatic categorization in software repositories. It can be also employed by software filtering systems to prevent the transmission of illegal software. If illegal software is identified by measuring software similarity in software filtering systems, the average number of comparisons can be reduced by shrinking the search space. In this study, we focused on the classification of Windows executables using API call information and machine learning. We evaluated the classification performance of machine learning-based classifier according to the refinement method for API information and machine learning algorithm. The results showed that the classification success rate of SVM (Support Vector Machine) with PolyKernel was higher than other algorithms. Since the API call information can be extracted from binary executables and machine learning-based classifier can identify tampered executables, API call information and machine learning-based software classifiers are suitable for software filtering systems.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.42-47
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• 2010

Many contests by micro mouse was celebrated of which maze search algorithms performance are compared. That is used in various forms based on left(right) weight method, euclidean algorithm method, hill climbing method. However we feel uncomfortable to test algorithms performance through direct development of programs or hardwares as no software platform to test in maze search algorithms. In this research we develop of a platform for maze search algorithms that is easily to produce various forms of maze that are hard to be realized by hardware, to apply algorithms, and evaluate the seek time, operation count, steps and performance. The platform is consist of main layer, interface layer, user layer which has merit to apply and replace easily algorithms. We verified that the maze search algorithm can be applied even in the development and experiment of algorithm by evaluating and analyzing its performance through the experiment of platform.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.171-176
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• 2020

Reinforcement learning is a well-known method for training an artificial software agent for a video game. Even though many reinforcement learning algorithms have been proposed, their performance was varies depending on an application area. This paper compares the performance of the algorithms when we train our reinforcement learning agent for a 2D racing game. We defined performance metrics to analyze the results and plotted them into various graphs. As a result, we found ACER (Actor Critic with Experience Replay) achieved the best rewards than other algorithms. There was 157% gap between ACER and the worst algorithm.