• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.39-40
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• 2018

본 논문에서는 IIoT(Industrial IoT) 환경의 분산 게이트웨이 시스템(Distributed-gateway System)에서 하위의 수 천 개 이상의 센서로부터 데이터를 전송받는 각 게이트웨이의 작업부하(Workload)를 감소시키고 데이터 처리 속도를 향상시키기 위하여 신경망(Neural network) 알고리즘을 이용한 센서 데이터 할당 기법을 소개한다. 각 센서의 중요도에 따른 Weight와 측정 간격에 따른 Bias를 설정하고 학습과정을 통해 Output weight를 산출하여 데이터를 효율적으로 게이트웨이에 할당시킴으로써 신뢰성과 정확성, 신속성을 확보한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.47-48
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• 2018

본 논문에서는 NP-hard 문제로 알려진 잡샵 스케쥴링에 대하여 강화학습적 측면에서 접근하는 방식에 대해 제안한다. 다양한 시간이 소요되는 업무들이 가지는 특징들을 최대한 state space aggregation에 고려하고, 이를 neural network를 통해 최적화 시간을 줄이는 방식이다. 잡샵 스케쥴링에 대한 솔루션은 미래에 대한 예측이 불가능하고 다양한 시간이 소요되는 스케쥴링 문제를 최적화하는 것에 대한 가능성을 제시할 것으로 기대된다.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.1-10
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• 2024

In the era of big data, the growth of e-commerce transactions brings forth both opportunities and risks, including the threat of data theft and fraud. To address these challenges, an automated real-time fraud detection system leveraging machine learning was developed. Four algorithms (Decision Tree, Naïve Bayes, XGBoost, and Neural Network) underwent comparison using a dataset from a clothing website that encompassed both legitimate and fraudulent transactions. The dataset exhibited an imbalance, with 9.3% representing fraud and 90.07% legitimate transactions. Performance evaluation metrics, including Recall, Precision, F1 Score, and AUC ROC, were employed to assess the effectiveness of each algorithm. XGBoost emerged as the top-performing model, achieving an impressive accuracy score of 95.85%. The proposed system proves to be a robust defense mechanism against fraudulent activities in e-commerce, thereby enhancing security and instilling trust in online transactions.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.67-76
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• 2024

Diabetic retinopathy is a threatening complication of diabetes, caused by damaged blood vessels of light sensitive areas of retina. DR leads to total or partial blindness if left untreated. DR does not give any symptoms at early stages so earlier detection of DR is a big challenge for proper treatment of diseases. With advancement of technology various computer-aided diagnostic programs using image processing and machine learning approaches are designed for early detection of DR so that proper treatment can be provided to the patients for preventing its harmful effects. Now a day machine learning techniques are widely applied for image processing. These techniques also provide amazing result in this field also. In this paper we discuss various machine learning and deep learning based techniques developed for automatic detection of Diabetic Retinopathy.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.14 no.4
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• pp.149-162
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• 2004

Until now the study of computer forensics has been focused only system forensics which carried on keeping, processing and collecting the remained evidence on computer. Recently the trend of forensic study is proceeding about the network forensics which analyze the collected information in entire networks instead of analyzing the evidence on a victim computer. In particular network forensics is more important in Automated Computer Emergency Response System because the system deals with the intrusion evidence of entire networks. In this paper we defined the information of network forensics that have to be collected in Automated Computer Emergency Response System and verified the defined information by comparing with the collected information in experimental environments.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.699-705
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• 2009

This paper reviews methods used to perform reliability and availability analysis of the network system composed by nodes and links. The combination of nodes and links forms virtual connections (VC). The failure of several VCs cause failure of whole network system. Petri Net models are used to analyze the reliability and availability. Stochastic reward nets (SRN) is an extension of stochastic Petri nets provides modelling facilities for network system analysis.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.6
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• pp.366-371
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• 2014

This paper reports a machine learning approach for image object detection. Object detection and localization in a wild image, such as a STL-10 image dataset, is very difficult to implement using the traditional computer vision method. A convolutional neural network is a good approach for such wild image object detection. This paper presents an object detection application using a convolutional neural network with pretrained feature vector. This is a very simple and well organized hierarchical object abstraction model.

• 전기의세계
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• v.26 no.1
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• pp.63-70
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• 1977

This paper deals with computer analysis technique of the network having 3-terminal elements whose input and output characteristics are defined by nonuniform spacing function group on the volt-ampere space. Developing the algorithms to obtain the solutions of the network mentioned above by computer, we propose optimization technique, which can solve the normal form equations of the network defined in this paper and which involves mode analysis technique to be able to analyze the case that the function group has negative resistance characteristics.

• Communications for Statistical Applications and Methods
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• v.9 no.1
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• pp.167-174
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• 2002

Computer network provides a powerful infrastructure for information sharing and the development of the statistical software with new concepts. In this paper, we discuss the design concepts of the statistical software in the network environment.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.1
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• pp.79-89
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• 1993

This paper deals with a computer aided control of an adjustment process for the complete electronic devices by means of an application of artificial neural network and an implementation of neuro-controller for intelligent control. Multi-layer neural network model is employed as artificial neural network with the learning method of the error back propagation. Information initially available from real plant under control are the initial values of plant output, and the augmented plant input and its corresponding plant output at that time. For the intelligent control of adjustment process utilizing artificial neural network, the neural network emulator (NNE) and the neural network controller(NNC) are developed. The initial weights of each neural network are determined through off line learning for the given product and it is also employed to cope with environments of the another product by on line learning. Computer simulation, as well as the application to the real situation of proposed intelligent control system is investigated.