• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권10호
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• pp.2731-2749
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• 2012

Sybil attack has been proved effective in mitigating the P2P botnet, but the impacts of some important parameters were not studied, and no model to estimate the effectiveness was proposed. In this paper, taking Kademlia-based botnets as the example, the model which has the upper and lower bound to estimate the mitigating performance of the Sybil attack is proposed. Through simulation, how three important factors affect the performance of the Sybil attack is analyzed, which is proved consistent with the model. The simulation results not only confirm that for P2P botnets in large scale, the Sybil attack is an effective countermeasure, but also imply that the model can give suggestions for the deployment of Sybil nodes to get the ideal performance in mitigating the P2P botnet.

• 한국컴퓨터정보학회논문지
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• 제22권2호
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• pp.29-35
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• 2017

In this paper, we present a 5W1H programming model for IT non-experienced people who are not familiar with computer programming and those who need programming education. Based on this model, we can design a development tool that can be easily programmed by beginners. This development tool is a programming method applying the 5W1H concept and constructs a sentence to satisfy the control condition of 'Who, When, Where, What, and How', which is the sentence element of 5W1H. Therefore, the user can easily develop the target system as if constructing the sentence without learning the programming language of the target system. In this paper, to verify the effectiveness of the 5W1H programming model proposed in this paper, we applied the concept of 5W1H programming to Arduino and developed the development tool and performed the first verification and applied the second verification to the speech recognition smart home development platform.

• International Journal of Computer Science & Network Security
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• 제21권3호
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• pp.229-234
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• 2021

The explosive growth of video-based services is considered as the dominant contributor to Internet traffic. Hence it is very important for video service providers to meet the quality expectations of end-users. In the past, the Quality of Service (QoS) was the key performance of networks but it considers only the network performances (e.g., bandwidth, delay, packet loss rate) which fail to give an indication of the satisfaction of users. Therefore, Quality of Experience (QoE) may allow content servers to be smarter and more efficient. This work is motivated by the inherent relationship between the QoE and the QoS. We present a no-reference (NR) prediction model based on Deep Neural Network (DNN) to predict video QoE. The DNN-based model shows a high correlation between the objective QoE measurement and QoE prediction. The performance of the proposed model was also evaluated and compared with other types of neural network architectures, and three known machine learning methodologies, the performance comparison shows that the proposed model appears as a promising way to solve the problems.

• 스마트미디어저널
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• 제9권4호
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• pp.17-25
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• 2020

In the image object classification problem, low-resolution images may have a negative impact on the classification result, especially when the classification method, such as a convolutional neural network (CNN) model, is trained on a high-resolution (HR) image dataset. In this paper, we analyze the behavior of applying a classical super-resolution (SR) method such as bicubic interpolation, and a deep CNN model such as SRCNN to enhance low-resolution (LR) weeds images used for classification. Using an HR dataset, we first train a CNN model for weeds image classification with a default input size of 128 × 128. Then, given an LR weeds image, we rescale to default input size by applying the bicubic interpolation or the SRCNN model. We analyze these two approaches on the Chonnam National University (CNU) weeds dataset and find that SRCNN is suitable for the image size is smaller than 80 × 80, while bicubic interpolation is convenient for a larger image.

• International Journal of Computer Science & Network Security
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• 제22권6호
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• pp.319-331
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• 2022

The latest global COVID-19 pandemic has made the use of facial masks an important aspect of our lives. People are advised to cover their faces in public spaces to discourage illness from spreading. Using these face masks posed a significant concern about the exactness of the face identification method used to search and unlock telephones at the school/office. Many companies have already built the requisite data in-house to incorporate such a scheme, using face recognition as an authentication. Unfortunately, veiled faces hinder the detection and acknowledgment of these facial identity schemes and seek to invalidate the internal data collection. Biometric systems that use the face as authentication cause problems with detection or recognition (face or persons). In this research, a novel model has been developed to detect and recognize faces and persons for authentication using scale invariant features (SIFT) for the whole segmented face with an efficient local binary texture features (DLBP) in region of eyes in the masked face. The Fuzzy C means is utilized to segment the image. These mixed features are trained significantly in a convolution neural network (CNN) model. The main advantage of this model is that can detect and recognizing faces by assigning weights to the selected features aimed to grant or provoke permissions with high accuracy.

• Journal of Biosystems Engineering
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• 제8권2호
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• pp.86-98
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• 1983

본 연구는 미국의 사료공장 중 주종을 이루고 있는 형식을 분석하여 일반적인 Model로 개발하였고 이 Model을 설계할 수 있는 Computer Program을 개발하였다. Model에 적용된 규모는 10ton/hr로부터 50ton/hr이었고 종류는 돼지 및 닭 사료공장과 완전히 pellet화된 소 사료공장이었다. 개발된 Computer Program은 1개의 Main Program과 3개의 Subprogram으로 구성되어 있고 29개의 Input data을 사용하게 되어 있다. 이 Computer Program의 output은 다음과 같다. 1. 공장의 Dimension 2. 곡물저장 및 원료 bin의 수량 및 규모 3. 각 기계의 규모 및 성능 4. 총 작업시간 및 전기 소요량 위와같은 사료공장의 일반화 된 Model 및 이를 설계하기 위한 Computer Program은 사료 생산의 소요동력, 노동력 및 투자자본까지 추정할 수 있게 하였으며 이 분야에서 학문적으로 처음 시도하여 본 것이다.

• 스마트미디어저널
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• 제4권2호
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• pp.9-16
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• 2015

Fluids appear in innumerable phenomena; therefore, it is interesting to reproduce those phenomena by computer graphics techniques. However, this process is not trivial. We work with a fluid simulation that uses Navier-Stokes equations to model the fluid, a semi-Lagrangian approach to solve it and the level set method to track the surface of the fluid. Modified versions of the Navier-Stokes equations for computer graphics allow us to create a wide diversity of effects. In this paper, we propose a technique that allows us to integrate a force inspired by surface tension into the model. We describe which information we need and how to modify the model with this new approach. We end up with a modified simulation that has additional effects that might be suitable for computer graphics purposes. The effects that we are able to recreate are small waves and droplet-like formations close to the surface of the fluid. This model preserves the overall behavior governed by the Navier-Stokes equations.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2001년도 추계학술발표논문집 (하)
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• pp.977-980
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• 2001

This paper is a study on the AC and PMI model for the defense computer network. It is suggested that the organization plan of PMI model is a proper model for the characteristics of military system and military defense network security demands based on defense PKI system. Futhermore, it will be presented both various types of defense AC and AC according to the role and clearance in PMI. Defense AC will provide strong users' authentication and Role Based Access Control to give more secured and trusted authentication service by using users' attribute such as role and clearance.

• 한국컴퓨터정보학회논문지
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• 제23권10호
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• pp.181-188
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• 2018

In this paper, we tried to investigate whether the influence of 'I' in a heterogeneous network of physical network and virtual network can be quantitatively measurable. To do this, we used Networked Community(NC) methodology to devise a concrete model of influence assessment in heterogeneous network. In order to test the model, we conducted an experiment with Donald J. Trump and his surroundings to evaluate the effectiveness of this influence assessment model. Experimentation included the measurement of impacts on the physical and virtual networks, and the impact on the networked community. Using Trump's case, we found that analyzing only one of the two networks can not accurately analyze the impact on others.

• 한국컴퓨터정보학회논문지
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• 제24권6호
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• pp.21-28
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• 2019

Recent years with the development of artificial intelligence and the success of the deep model, they have been deployed in all fields of computer vision. Action recognition, as an important branch of human perception and computer vision system research, has attracted more and more attention. Action recognition is a challenging task due to the special complexity of human movement, the same movement may exist between multiple individuals. The human action exists as a continuous image frame in the video, so action recognition requires more computational power than processing static images. And the simple use of the CNN network cannot achieve the desired results. Recently, the attention model has achieved good results in computer vision and natural language processing. In particular, for video action classification, after adding the attention model, it is more effective to focus on motion features and improve performance. It intuitively explains which part the model attends to when making a particular decision, which is very helpful in real applications. In this paper, we proposed a 3D dense convolutional network based on attention mechanism(ADD-Net), recognition of human motion behavior in the video.