• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.928-931
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• 2017

네트워크 트래픽 데이터를 정제하여, Convolutional Neural Network Model 훈련에 적합한 데이터 세트로 변환하는데, 그 방법은 패킷 단위의 트래픽 데이터를 이미지 형태로 만드는 것이다. 완성된 데이터 세트를 훈련데이터로 하여 Convolutional Neural Network Model에 훈련하고, 훈련데이터의 이미지 크기를 변환해가며 훈련시킨 결과에 대해 비교 분석 및 평가를 진행한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3416-3435
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• 2023

Task offloading in vehicular networks is hot topic in the development of autonomous driving. In these scenarios, due to the role of vehicles and pedestrians, task characteristics are changing constantly. The classical deep learning algorithm always uses a pre-trained neural network to optimize task offloading, which leads to system performance degradation. Therefore, this paper proposes a neural network fine-tuning task offloading algorithm, combining with location prediction for pedestrians and vehicles by the Payne model of fluid dynamics and the car-following model, respectively. After the locations are predicted, characteristics of tasks can be obtained and the neural network will be fine-tuned. Finally, the proposed algorithm continuously predicts task characteristics and fine-tunes a neural network to maintain high system performance and meet low delay requirements. From the simulation results, compared with other algorithms, the proposed algorithm still guarantees a lower task offloading delay, especially when congestion occurs.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.129-134
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• 2024

Object Detection is an emerging technology in the field of Computer Vision and Image Processing that deals with detecting objects of a particular class in digital images. It has considered being one of the complicated and challenging tasks in computer vision. Earlier several machine learning-based approaches like SIFT (Scale-invariant feature transform) and HOG (Histogram of oriented gradients) are widely used to classify objects in an image. These approaches use the Support vector machine for classification. The biggest challenges with these approaches are that they are computationally intensive for use in real-time applications, and these methods do not work well with massive datasets. To overcome these challenges, we implemented a Deep Learning based approach Convolutional Neural Network (CNN) in this paper. The Proposed approach provides accurate results in detecting objects in an image by the area of object highlighted in a Bounding Box along with its accuracy.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06a
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• pp.60-61
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• 2010

• Journal of the Korea Society of Computer and Information
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• v.21 no.8
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• pp.41-48
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• 2016

In this paper, we propose an election protocol based on mobile ad-hoc network. In distributed systems, a group of computer should continue to do cooperation in order to finish some jobs. In such a system, an election protocol is especially practical and important elements to provide processes in a group with a consistent common knowledge about the membership of the group. Whenever a membership change occurs, processes should agree on which of them should do to accomplish an unfinished job or begins a new job. The problem of electing a leader is very same with the agreeing common predicate in a distributed system such as the consensus problem. Based on the termination detection protocol that is traditional one in asynchronous distributed systems, we present the new election protocol in distributed systems that are based on MANET, i.e. mobile ad hoc network.

• 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.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.41-50
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• 2021

Cloud computing is one of the most expanding technologies nowadays; it offers many benefits that make it more cost-effective and more reliable in the business. This paper highlights the various benefits of cloud computing and discusses different cryptography algorithms being used to secure communications in cloud computing environments. Moreover, this thesis aims to propose some improvements to enhance the security and safety of cloud computing technologies.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.590-593
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• 2012

현재 인터넷은 하드웨어 종속적인 특징을 가지고 있어 급변하는 환경에 적응하기 힘들다. 이러한 제약사항은 관련 산업 발전을 더디게 하고 있다. 이와 같은 네트워크 환경에서 산업 발전을 위하여 네트워크 인프라에 유연성을 제공할 수 있는 기술의 개발이 필요하다. 그러한 문제를 해결하기 위해 오픈프로토콜인 OpenFlow의 Programmable Network의 특성을 이용하여 네트워크 가상화를 구현하였으며, 응용 서비스별 Virtual Network를 제공하는 방안에 대해 연구하였다. 이를 위하여 OpenFlow 기반의 Programmable Network를 구축하였으며, 동적으로 구성이 가능한 네트워크에서 가상화를 제공하기 위해 VNAPI를 개발하였다. 또한, VNAPI를 통하여 신뢰성 있고 효율적인 응용 서비스의 전달을 위하여 Virtual Network Topology에 대한 설계를 같이 수행하였다.

• Journal of Communications and Networks
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• v.18 no.5
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• pp.713-724
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• 2016

Energy consumption has become a main problem of sustainable development in communication networks and how to communicate with high energy efficiency is a significant topic that researchers and network operators commonly concern. In this paper, an energy-efficient multicast algorithm in multi-hop wireless networks is proposed aiming at new generation wireless communications. Traditional multi-hop wireless network design only considers either network efficiency or minimum energy consumption of networks, but rarely the maximum energy efficiency of networks. Different from previous methods, the paper targets maximizing energy efficiency of networks. In order to get optimal energy efficiency to build network multicast, our proposed method tries to maximize network throughput and minimize networks' energy consumption by exploiting network coding and sleeping scheme. Simulation results show that the proposed algorithm has better energy efficiency and performance improvements compared with existing methods.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.362-364
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• 2018

Predicting network traffic volume has become a popular topic recently due to its support in many situations such as detecting abnormal network activities and provisioning network services. Especially, predicting the volume of the next upcoming traffic from the series of observed recent traffic volume is an interesting and challenging problem. In past, various techniques are researched by using time series forecasting methods such as moving averaging and exponential smoothing. In this paper, we propose a long short-term memory neural network (LSTM) based network traffic volume prediction method. The proposed method employs the changing rate of observed traffic volume, the corresponding time window index, and a seasonality factor indicating the changing trend as input features, and predicts the upcoming network traffic. The experiment results with real datasets proves that our proposed method works better than other time series forecasting methods in predicting upcoming network traffic.