• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05b
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• pp.386-390
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• 2004

유무선 혼합 망에서 GFR서비스의 최소대역, 효율 및 트래픽 제어를 위한 버퍼관리 기법 등을 유무선 혼합망의 셀기반 네트워크에서 가중치를 높이는 시뮬레이션을 통해 공평성을 입증하는 연구를 한다. GFR서비스는 TCP/IP 패킷의 품질보장과 베스트 에포트 트래픽에 대한 최소의 대역폭 보장을 하며, 가용 대역폭에 대한 공평성 있는 대역할당을 지원해야 하기 때문에 버퍼관리를 통한 셀 폐기와 셀 스케줄링 둥이 중요한 요소이다. 본 논문에서는 유무선 혼합 네트워크에서 MCR을 보장하기 위하여 버퍼의 사용 가능한 영역의 활성 VC에 각 VC가중치를 비례해 할당하는 per-VC 어카운트에 기초한 트래픽 제어의 버퍼관리에 대한 유동적 WBA알고리즘을 제안하고 GFR 서비스의 효율 및 공평성을 유무선 혼합 망 셀 기반의 스위치를 이용하여 시뮬레이션으로 결과를 보인다.

• Journal of information and communication convergence engineering
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• v.2 no.2
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• pp.106-109
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• 2004

This study designs a load balancer via direct routing that share a virtual service on a single IP address in the Linux system and suggests an efficient load balancing method to improve transmission speed in the web server cluster environment. It will increase performance and scalability with fast packet transfer and removing bottleneck problem by using TCP Splicing and Content-Aware Distributor method. This method is expected to be the noticeable technology that provides an important interface, which make application services for e-commerce effectively be applied to high-speed network infrastructure. At this time, it is required to study further on the optimum balancing method in the web server cluster environment so as to apply the hybrid (optimum load balancing method by software and hardware) method and improve the reuse of security cession based on high-speed TCP connections.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06b
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• pp.502-507
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• 2007

그리드 컴퓨팅 기술을 기반으로 한 원격 실험 환경 구축에 있어서 원격 실험만큼이나 실험결과 데이터를 저장하고 재연하는 것이 중요하게 대두되고 있다. 본 논문에서는 KOCED 프로젝트의 건설 연구 실험시설 중 하나인 실시간 하이브리드 다자유도 실험시설에 대한 프로토타입인 원격 하이브리드 실험에 대하여 나라다 브로커링 이라는 발간 및 구독 패러다임의 스트리밍 서버와 글로버스 툴킷에 기반 한 리코딩 및 리플레이 서비스를 통하여 실험결과 데이터를 저장하고 재연하는 시스템을 구축 하였다. 기존에 진행된 실험결과를 검색하여 볼 수 있게 함으로써 중복된 실험으로 인한 비용을 줄이고, 사용자가 원하는 데이터에 대한 토픽정보를 통하여 재연함으로써 실험결과 데이터의 효용성을 높일 수 있을 것으로 판단된다.

• 한국IT서비스학회:학술대회논문집
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• 2006.11a
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• pp.360-365
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• 2006

VoD (Video-on-Demand) 서비스는 가입자가 원하는 컨텐츠를 실시간으로 전송하는 서비스의 형태이다. 오늘날은 방대한 양의 멀티미디어 데이터를 효율적으로 압축하고 보다 빠르게 전송할 수 있는 시스템의 발달이 가속화되고 있고 이는 VoD 서비스의 증가로도 이어지고 있다. 그러므로 가입자가 원하는 컨텐츠를 보다 효과적으로 제공할 수 있는 VoD 서비스의 방법을 찾는 것은 중요한 일이다. 본 논문에서는 기존의 NVoD (Near-VoD) 서비스 또는 TVoD (True-VoD) 서비스 만을 제공함으로서 생길 수 있는 단점을 없애고 장점만 살릴 수 있는 방법을 제안하였다. 이는 기존의 가입자단에 Buffer를 제공하여 NVoD를 TVoD화 함으로서 가능하게 하였다. 또한, 본 논문에서 제안한 방법이 필요한 버퍼의 크기를 제시하고 전체적인 알고리즘을 제시하여 이를 구체화 하였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.408-411
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• 2006

Currently, the nano economy threatens the mass economy. This is based on the internet business models. In the nano business models based on internet, the diversely personalized services are needed. Many researches of the personalization on the web have been studied. The web usage mining using click stream data is a tool for personalization model. In this paper, we propose an internet business model using evolutionary support vector machine and web response survey as a web usage mining. After analyzing click stream data for web usage mining, a personalized service model is constructed in our work. Also, using an approach of web response survey, we improve the performance of the customers' satisfaction. From the experimental results, we verify the performance of proposed model using two data sets from KDD Cup 2000 and our web server.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.374-386
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• 2022

Nowadays, research in deep learning leveraged automated computing and networking paradigm evidenced rapid contributions in terms of Software Defined Networking (SDN) and its diverse security applications while handling cybercrimes. SDN plays a vital role in sniffing information related to network usage in large-scale data centers that simultaneously support an improved algorithm design for automated detection of network intrusions. Despite its security protocols, SDN is considered contradictory towards DDoS attacks (Distributed Denial of Service). Several research studies developed machine learning-based network intrusion detection systems addressing detection and mitigation of DDoS attacks in SDN-based networks due to dynamic changes in various features and behavioral patterns. Addressing this problem, this research study focuses on effectively designing a multistage hybrid and intelligent deep learning classifier based on modified deep forest classification to detect DDoS attacks in SDN networks. Experimental results depict that the performance accuracy of the proposed classifier is improved when evaluated with standard parameters.

• Journal of Sport and Applied Science
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• v.6 no.2
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• pp.1-7
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• 2022

Purpose: This study began with an interest in the meaning and importance of Olympic mascots, and this paper aimed to analyze the types and features of mascots by comparing the Summer Olympics mascots from Munich 1972 to Tokyo 2020 and ultimately to understand the attributes of the Summer Olympic mascots. Research design, data, and methodology: The approach of this study is the exploratory approach by literature reviews. This study carried out exploratory research on thinking about understanding the characteristics of the Summer Olympic Games Mascots. Results: First, the Munich 1972 mascot, Waldi is known as the first official mascot. Second, many mascots are designed the most in animal form. But the process of change according to the material of the Olympic mascot changed from a simple animal form to a finely expressed hybrid mascot, and multiple mascots appeared in the 2000s. Conclusions: The Olympic mascot is a representative symbol of the Olympic Games, representing the identity of the host country. Five attributes of the Olympic mascots were identified: friendliness, a symbolic meaning, originality, diversity, and value. Further implications were discussed.

• Advances in nano research
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• v.15 no.6
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• pp.513-520
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• 2023

The exponential proliferation of cutting-edge computing technologies has spurred organizations to outsource their data and computational needs. In the realm of cloud-based computing environments, ensuring robust security, encompassing principles such as confidentiality, availability, and integrity, stands as an overarching imperative. Elevating security measures beyond conventional strategies hinges on a profound comprehension of malware's multifaceted behavioral landscape. This paper presents an innovative paradigm aimed at empowering cloud service providers to adeptly model user behaviors. Our approach harnesses the power of a Particle Swarm Optimization-based Probabilistic Neural Network (PSO-PNN) for detection and recognition processes. Within the initial recognition module, user behaviors are translated into a comprehensible format, and the identification of malicious nano-structures behaviors is orchestrated through a multi-layer neural network. Leveraging the UNSW-NB15 dataset, we meticulously validate our approach, effectively characterizing diverse manifestations of malicious nano-structures behaviors exhibited by users. The experimental results unequivocally underscore the promise of our method in fortifying security monitoring and the discernment of malicious nano-structures behaviors.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.

• Advances in Energy Research
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• v.5 no.2
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• pp.107-128
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• 2017

Energy is a major component of almost all economic, production, and service activities, and rapid population growth, urbanization and industrialization have led to ever growing demand for energy. Limited energy resources and increasingly evident environmental effects of fossil fuel consumption has led to a growing awareness about the importance of further use of renewable energy sources in the countries energy portfolio. Renewable hydrogen production is a convenient method for storage of unstable renewable energy sources such as wind and solar energy for use in other place or time. In this study, suitability of 25 cities located in Iran's western region for renewable hydrogen production are evaluated by multi-criteria decision making techniques including TOPSIS, VIKOR, ELECTRE, SAW, Fuzzy TOPSIS, and also hybrid ranking techniques. The choice of suitable location for the centralized renewable hydrogen production is associated with various technical, economic, social, geographic, and political criteria. This paper describes the criteria affecting the hydrogen production potential in the study region. Determined criteria are weighted with Shannon entropy method, and Angstrom model and wind power model are used to estimate respectively the solar and wind energy production potential in each city and each month. Assuming the use of proton exchange membrane electrolyzer for hydrogen production, the renewable hydrogen production potential of each city is then estimated based on the obtained wind and solar energy generation potentials. The rankings obtained with MCDMs show that Kermanshah is the best option for renewable hydrogen production, and evaluation of renewable hydrogen production capacities show that Gilangharb has the highest capacity among the studied cities.