• Journal of the Korea Convergence Society
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• v.8 no.10
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• pp.209-214
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• 2017

The aim of the present study was to analyze the factors that affects the peer attachment in Korean youth. Subjects were 419 middle school students (210 male, 209 female). Dependent variable was defined as peer attachment. Explanatory variables were included as gender, academic achievement satisfaction, subjective household economy level, parent - child dialogue frequency, subjective health status, depression symptom, self - esteem, subjective life satisfaction, and mobile phone dependency. In the multi-layer perceptron artificial neural network algorithm analysis, depression symptoms, gender, parent-child dialogue level for school life, subjective household economy level, subjective health status were significantly associated with peer attachment in Korean youth. Based on this result, systematic programs are required in order to prevention of peer attachment in Korean youth.

• The KIPS Transactions:PartC
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• v.10C no.1
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• pp.51-60
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• 2003

One of the most important matters in designing network and realizing service, is to grip on the traffic characteristics. Conventional traffic prediction and analysis used the models which based on the Poisson or Markovian. Recently, experimental research on the LAN, WAN and VBR traffic properties have been pointed rut that they weren't able to display actual real traffic specificities because the models based on the Poisson assumption had been underestimated the long range dependency of network traffic and self-similar peculiarities, it has been lately presented that the new approach method using self-similarity characteristics as similar as the real traffic models. Therefore, in this paper, we generated self-similar data traffic like real traffic as background load. On the existing ABR congestion control algorithm, we analyzed by classify into ACR, buffer utilization. cell drop rate, transmission throughput with the representative EFCI, ERICA, EPRCA and NIST twitch algorithm to show the efficient reaction about the burst traffic.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.30 no.6
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• pp.21-30
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• 2002

A preconditioned Navier-Stokes code was parallelized by the domain decomposition technique, and the accuracy of the parallelized code was verified through a comparison with the result of a sequential code and experimental data. Parallel performance of the code was examined on a Myrinet based PC-cluster and a Fast-Ethernet system. Speed-up ratio was examined as a major performance parameter depending on the number of processor and the network communication topology. In this test, Myrinet system shows a superior parallel performance to the Fast-Ethernet system as was expected. A test for the dependency on problem size also shows that network communication speed in a crucial factor for parallel performance, and the Myrinet based PC-cluster is a plausible candidate for high performance parallel computing system.

• The Transactions of the Korea Information Processing Society
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• v.3 no.6
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• pp.1433-1442
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• 1996

Data distribution of SPMD(Single Program Multiple Data) pattern is one of main features of HPF (High Performance Fortran). This paper describes design is sues for such data distribution and its efficient execution model on TICOM IV computer, named SPAX(Scalable Parallel Architecture computer based on X-bar network). SPAX has a hierarchical clustering structure that uses distributed shared memory(DSM). In such memory structure, it cannot make a full system utilization to apply unanimously either SMDD(shared Memory Data Distribution) or DMDD(Distributed Memory Data Distribution). Here we propose another data distribution model, called DSMDD(Distributed Shared Memory Data Distribution), a data distribution model based on hierarchical masters-slaves scheme. In this model, a remote master and slaves are designated in each node, shared address scheme is used within a node and message passing scheme between nodes. In our simulation, assuming a node size in which system performance degradation is minimized,DSMDD is more effective than SMDD and DMDD. Especially,the larger number of logical processors and the less data dependency between distributed data,the better performace is obtained.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.107-118
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• 2021

The deployment of 5G is in full swing, with a significant yearly growth in the data traffic expected to reach 26% by the year and data consumption to reach 122 EB per month by 2022 [10]. In parallel, the idea of smart cities has been implemented by various governments and private organizations. One of the main objectives of 5G deployment is to help develop and realize smart cities. 5G can support the enhanced data delivery requirements and the mass connection requirements of a smart city environment. However, for specific high-demanding applications like tactile Internet, transportation, and augmented reality, the cloud-based 5G infrastructure cannot deliver the required quality of services. We suggest using multi-access edge computing (MEC) technology for smart cities' environments to provide the necessary support. In cloud computing, the dependency on a central server for computation and storage adds extra cost in terms of higher latency. We present a few scenarios to demonstrate how the MEC, with its distributed architecture and closer proximity to the end nodes can significantly improve the quality of services by reducing the latency. This paper has surveyed the existing work in MEC for 5G and highlights various challenges and opportunities. Moreover, we propose a unique framework based on the use of MEC for 5G in a smart city environment. This framework works at multiple levels, where each level has its own defined functionalities. The proposed framework uses the MEC and introduces edge-sub levels to keep the computing infrastructure much closer to the end nodes.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.179-187
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• 2023

The outbreak of COVID-19 had affected almost every part of the world and caused disastrous results, the number of reported COVID-19 cases in past few months have reached to more than 29 million patients in the world globally. This pandemic has adversely affected all the activities of life, ranging from personal life to overall economic development. Due to the current situation, routinely turned to online resources, and people have relied on technology more than they have been before. Since cybercriminals are an opportunist and they utilized this entirely, by targeting the online services for all sectors of life. This fortnight online dependency of the community over the internet opened several easy doors for the cybercriminals. This causes exponential attacks over internet traffic during this epidemic situation. The current Covid-19 pandemic situation appeared at once, and no one was ready to prevail this. However, there is an urgent need to address the current problem in all means. . KSA is among one of the countries most affected by these CA and is a key victim for most cyber-crimes. Therefore, this paper will review the effects of COVID-19 on the cyber-world of KSA in various sectors. We will also shed light on the Saudi efforts to confront these attacks during COVID -19. As a contribution, we have provided a comprehensive framework for mitigating cybersecurity challenges.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.31-42
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• 2024

Different industries mostly rely on quality certification for promoting their products or brands. Although getting quality certification, specifically by human experts is a tough job to do. But the field of machine learning play a vital role in every aspect of life, if we talk about quality certification, machine learning is having a lot of applications concerning, assigning and assessing quality certifications to different products on a macro level. Like other brands, wine is also having different brands. In order to ensure the quality of wine, machine learning plays an important role. In this research, we use two datasets that are publicly available on the "UC Irvine machine learning repository", for predicting the wine quality. Datasets that we have opted for our experimental research study were comprised of white wine and red wine datasets, there are 1599 records for red wine and 4898 records for white wine datasets. The research study was twofold. First, we have used a technique called backward elimination in order to find out the dependency of the dependent variable on the independent variable and predict the dependent variable, the technique is useful for predicting which independent variable has maximum probability for improving the wine quality. Second, we used a robust machine learning algorithm known as "XGBoost" for efficient prediction of wine quality. We evaluate our model on the basis of error measures, root mean square error, mean absolute error, R2 error and mean square error. We have compared the results generated by "XGBoost" with the other state-of-the-art machine learning techniques, experimental results have showed, "XGBoost" outperform as compared to other state of the art machine learning techniques.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.5
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• pp.191-198
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• 2013

It is required to transmit data through shorter path between sensor and base node for real time intrusion detection in wireless sensor networks (WSN) with a mobile base node. Because minimum Wiener index spanning tree (MWST) based routing approach guarantees lower average hop count than that of minimum spanning tree (MST) based routing method in WSN, it is known that MWST based routing is appropriate for real time intrusion detection. However, the minimum Wiener index spanning tree problem which aims to find a spanning tree which has the minimum Wiener index from a given weighted graph was proved to be a NP-hard. And owing to its high dependency on certain nodes, minimum Wiener index tree based routing method has a shorter network lifetime than that of minimum spanning tree based routing method. In this paper, we propose a multi-objective ant colony optimization algorithm to tackle these problems, so that it can be used to detect intrusion in real time in wireless sensor networks with a mobile base node. And we compare the results of our proposed method with MST based routing and MWST based routing in respect to average hop count, network energy consumption and network lifetime by simulation.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.392-399
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• 2013

Due to the development of IT technology and the information age, a dependency of the network over the most of our lives have grown to a greater extent. Although it provides us to get various useful information and service, it also has negative effectiveness that can provide network intruder with vulnerable roots. In other words, we need to urgently cope with theses serious security problem causing service disableness or system connected to network obstacle with exploiting various packet information. Many experts in a field of security are making an effort to develop the various security solutions to respond against these threats, but existing solutions have a lot of problems such as lack of storage capacity and performance degradation along with the massive increase of packet data volume. Therefore we propose the packet analysis model to apply issuing Big Data technology in the field of security. That is, we used NoSQL which is technology of massive data storage to collect the packet data growing massive and implemented the packet analysis model based on K-means clustering using MapReudce which is distributed programming framework, and then we have shown its high performance by experimenting.

• Journal of KIISE:Information Networking
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• v.36 no.5
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• pp.425-436
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• 2009

In recent years, the user demands have increased for multimedia service of high quality over the broadband convergence network. These rising demands for high quality multimedia service led the popularization of various user terminals and large scale display equipments, which needs a variety type of QoS (Quality of Service). In order to support demands for QoS, numerous research projects are in progress both from the perspective of network as well as end system; For example, at the network perspective, QoS guaranteeing by improving of internet performance such as Active Queue Management, while at the end system perspective, SVC (Scalable Video Coding) encoding scheme to guarantee media quality. However, existing AQM algorithms have problems which do not guarantee QoS, because they did not consider the essential characteristics of video encoding schemes. In this paper, it is proposed to solve this problem by deploying the TS- AQM (Temporal Scalability Active Queue Management) which employs the differentiated packet dropping for dependency of the temporal level among the frames, based on SVC encoding characteristics by exploiting the TID (Temporal ID) field of the SVC NAL unit header. The proposed TS-AQM guarantees multimedia service quality through video decoding reliability for SVC streaming service, by differentiated packet dropping when congestion exists.