• 산업경영시스템학회지
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• 제46권2호
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• pp.57-71
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• 2023

In this study, we analyze a finite-buffer M/G/1 queueing model with randomized pushout space priority and nonpreemptive time priority. Space and time priority queueing models have been extensively studied to analyze the performance of communication systems serving different types of traffic simultaneously: one type is sensitive to packet delay, and the other is sensitive to packet loss. However, these models have limitations. Some models assume that packet transmission times follow exponential distributions, which is not always realistic. Other models use general distributions for packet transmission times, but their space priority rules are too rigid, making it difficult to fine-tune service performance for different types of traffic. Our proposed model addresses these limitations and is more suitable for analyzing communication systems that handle different types of traffic with general packet length distributions. For the proposed queueing model, we first derive the distribution of the number of packets in the system when the transmission of each packet is completed, and we then obtain packet loss probabilities and the expected number of packets for each type of traffic. We also present a numerical example to explore the effect of a system parameter, the pushout probability, on system performance for different packet transmission time distributions.

• 대한토목학회논문집
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• 제34권2호
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• pp.561-571
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• 2014

전라북도내 14개 시 군의 교통사고 자료를 활용하여 고령운전자의 교통사고 피해 심각성을 분석하였다. 교통사고는 1차적으로 개인 및 운전환경 속성과 2차적으로 도시관련 속성에 의해 영향을 받는 2단계 위계적 특성을 갖는 것으로 가정하였다. 위계적 특성을 고려한 피해 심각성에 대한 영향요인을 분석하기 위해 다수준분석모형을 활용하였다. 분석결과로서 65세 이후의 고령운전자는 연령이 증가할수록 교통사고로 인한 피해상황이 심각해짐을 보여주며 안전한 운전방법의 교육과 교통사고를 미연에 방지하기 위한 대안이 필요하다. 음주운전은 고령운전자에게 사고발생시 피해 심각성을 크게 할 경향이 높은데, 사망사고에 있어서 비고령자에 비해 발생비율이 약 3.0배 이상 높았다. 고령운전자는 야간 교통사고 발생빈도가 높은 편이나, 낮 시간대의 교통사고일수록 피해 심각성은 높아졌다. 고령운전자는 비고령자보다 흐린 날씨에서 사고 발생빈도가 높으나, 심각성에서는 맑은 날에 높아짐을 보였다. 습윤상태의 노면이 피해 심각성에 큰 영향을 주고 있는데, 비고령자에 비해서 고령운전자가 중상 및 사망비율도 높은 것으로 분석되었다.

• Journal of Information Processing Systems
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• 제20권3호
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• pp.375-390
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• 2024

Edge computing architecture has effectively alleviated the computing pressure on cloud platforms, reduced network bandwidth consumption, and improved the quality of service for user experience; however, it has also introduced new security issues. Existing anomaly detection methods in big data scenarios with cloud-edge computing collaboration face several challenges, such as sample imbalance, difficulty in dealing with complex network traffic attacks, and difficulty in effectively training large-scale data or overly complex deep-learning network models. A lightweight deep-learning model was proposed to address these challenges. First, normalization on the user side was used to preprocess the traffic data. On the edge side, a trained Wasserstein generative adversarial network (WGAN) was used to supplement the data samples, which effectively alleviates the imbalance issue of a few types of samples while occupying a small amount of edge-computing resources. Finally, a trained lightweight deep learning network model is deployed on the edge side, and the preprocessed and expanded local data are used to fine-tune the trained model. This ensures that the data of each edge node are more consistent with the local characteristics, effectively improving the system's detection ability. In the designed lightweight deep learning network model, two sets of convolutional pooling layers of convolutional neural networks (CNN) were used to extract spatial features. The bidirectional long short-term memory network (BiLSTM) was used to collect time sequence features, and the weight of traffic features was adjusted through the attention mechanism, improving the model's ability to identify abnormal traffic features. The proposed model was experimentally demonstrated using the NSL-KDD, UNSW-NB15, and CIC-ISD2018 datasets. The accuracies of the proposed model on the three datasets were as high as 0.974, 0.925, and 0.953, respectively, showing superior accuracy to other comparative models. The proposed lightweight deep learning network model has good application prospects for anomaly traffic detection in cloud-edge collaborative computing architectures.

• 한국해양공학회지
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• 제12권1호
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• pp.3-9
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• 1998

According to the developement of cities, electric wires and commuinication lines which are currently above the ground effect on the bad. Nowadays, it is necessary to build up underground facilities because the construction is increasing. Excavation work has been dominant even though the inconvenient things occurred, for example a civil appeal, a traffic obstacle, safety and spoiling the fine view because of the cost or period of the construction work. But the congestion of cities are more and more serious. Therefore shield-method is the way to escape from congestion. I considered all the construction fields which are in progress or finished.

• 제어로봇시스템학회논문지
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• 제9권12호
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• pp.1055-1064
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• 2003

In many automated systems, such as manufacturing systems and process plants, a fieldbus is a very important component for the exchange of various and sometimes crucial information. Some of the information has a tendency to rapidly lose its value as time elapses after its creation. Such information or data is called real-time data that includes sensor values and control commands. In order to deliver these data in time, the fieldbus network should be tailored to have short delay with respect to the individual time limit of various data. Fine-tuning the network for a given traffic requires the knowledge on the relationship between the protocol parameters such as timer values and the performance measure such as network delay. This paper presents a mathematical performance model to calculate communication delays of the Profibus-FMS network when the timer value and the traffic characteristics are given.

• 디지털산업정보학회논문지
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• 제13권2호
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• pp.1-11
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• 2017

In this paper, we design and implement PADIL(Prediction And Detection of Information Leakage) system that predicts and detect information leakage behavior of insider by analyzing network traffic and applying a variety of machine learning methods. we defined the five-level information leakage model(Reconnaissance, Scanning, Access and Escalation, Exfiltration, Obfuscation) by referring to the cyber kill-chain model. In order to perform the machine learning for detecting information leakage, PADIL system extracts various features by analyzing the network traffic and extracts the behavioral features by comparing it with the personal profile information and extracts information leakage level features. We tested various machine learning methods and as a result, the DecisionTree algorithm showed excellent performance in information leakage detection and we showed that performance can be further improved by fine feature selection.

• 한국대기환경학회지
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• 제31권3호
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• pp.287-301
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• 2015

This study was conducted to investigate size distribution characteristics of water-soluble ionic components in the airborne particulate matter (PM) collected from an urban area in Busan using a MOUDI cascade impactor from March to October 2010. The inorganic constituents in the fine particles (${\leq}1.8{\mu}m$) predominantly consisted of sulfate, nitrate, ammonium, and potassium. Sulfate and ammonium concentrations showed a high correlation and similar equivalent concentrations in the fine modes including $0.18{\sim}0.32{\mu}m$, $0.32{\sim}0.56{\mu}m$, and $0.56{\sim}1.0{\mu}m$. This indicates that the main chemical component in the fine particles would be forms of ammonium sulfate such as $(NH_4)_3H(SO_4)_2$, $(NH_4)_2SO_4$, and $(NH_4)HSO_4$. Back trajectory analysis showed that relatively higher concentrations of ammonium, nitrate, and sulfate in the fine mode, compared to the coarse mode, are caused both by domestic sources and long-range transports originated from China continent. High concentration episodes of PM both in the fine mode and the coarse mode were attributed both by anthropogenic sources, such as ship emissions and traffic emissions, and by natural sources such as seawater (sea salt), respectively.

• Asian Journal of Atmospheric Environment
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• 제6권2호
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• pp.96-103
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• 2012

Ambient particulate matter (PM) and particle-bound polycyclic aromatic hydrocarbon (PAH) concentrations were measured continuously for 70 days at a Korean elementary school located near a highway. The $PM_{10}$, $PM_{2.5}$, and $PM_1$ values were measured with a light-scattering, multi-channel, aerosol spectrometer (Grimm, Model 1.107). The number concentrations of the particles were measured using a scanning mobility particle sizer and counter (SMPS+C) which counted particles from 11.1 to 1083.3 nm classified in 44 channels. Particle-bound PAHs were measured with a direct reading, photoelectric aerosol sensor. The daily $NO_2$, $SO_2$, and CO concentrations were obtained from a national air-monitoring station located near the school. The average concentrations of $PM_{10}$, $PM_{2.5}$, and $PM_1$ were 75.3, 59.3, and $52.1{\mu}g/m^3$, respectively. The average number concentration of the ultrafine particles (UFPs) was $46,307/cm^3$, and the averaged particle-bound PAHs concentration was $17.9ng/cm^3$ during the study period. The ambient UFP variation was strongly associated with traffic intensity, particularly peak concentrations during the traffic rush hours. Particles <100 nm corresponded to traffic-related pollutants, including PAHs. Additional longterm monitoring of ambient UFPs and high-resolution traffic measurements should be carried out in future studies. In addition, transient variations in the ambient particle concentration should be taken into consideration in epidemiology studies in order to examine the short-term health effects of urban UFPs.

• 아시안잔디학회지
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• 제17권1호
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• pp.19-33
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• 2003

본 실험은 답압에 의한 스트레스를 경감시킬 목적으로 폐타이어 칩을 한국잔디 식재 토양내 혼합 및 표면 배토시 그 효과를 평가하고자 실시하였다. 일반적으로 잔디는 답압이 진행됨에 따라 잔디 마모와 토양 물리성이 나빠져서 생육은 감소하지만 폐타이어 칩을 토양내에 처리함으로써 토양경도, 표면 경도 등 토양 물리성을 향상시키므로 생육을 향상시킬 수 있었으며, 토양 혼합처리는 가는 입자 20% 처리구에서 좋은 토양 물리성을 보였다. 배토처리시에는 무처리구와 비교시 표면의 높은 온도와 더불어, 피복효과와 마모를 가장 많이 받는 줄기 밑부분을 보호해 줌으로써 잔디 생육을 향상시킬 수 있었으며, 굵은 입자 10cm 처리구에서 표면온도가 높았다. 무기질인 폐타이어 칩을 소량으로 토양 혼합 및 배토처리함으로써 장기적인 효과가 예상되므로 향후 잔디면 조성 및 관리시에 이용할 만한 가능성이 있다고 생각된다.

• Environmental Analysis Health and Toxicology
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• 제28권
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• pp.7.1-7.9
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• 2013

Objectives We investigated the particle mass size distribution and chemical properties of air pollution particulate matter (PM) in the urban area and its capacity to induce cytotoxicity in human bronchial epithelial (BEAS-2B) cells. Methods To characterize the mass size distributions and chemical concentrations associated with urban PM, PM samples were collected by a 10-stage Micro-Orifice Uniform Deposit Impactor close to nearby traffic in an urban area from December 2007 to December 2009. PM samples for in vitro cytotoxicity testing were collected by a mini-volume air sampler with $PM_{10}$ and $PM_{2.5}$ inlets. Results The PM size distributions were bi-modal, peaking at 0.18 to 0.32 and 1.8 to $3.2{\mu}m$. The mass concentrations of the metals in fine particles (0.1 to $1.8{\mu}m$) accounted for 45.6 to 80.4% of the mass concentrations of metals in $PM_{10}$. The mass proportions of fine particles of the pollutants related to traffic emission, lead (80.4%), cadmium (69.0%), and chromium (63.8%) were higher than those of other metals. Iron was the dominant transition metal in the particles, accounting for 64.3% of the $PM_{10}$ mass in all the samples. We observed PM concentration-dependent cytotoxic effects on BEAS-2B cells. Conclusions We found that exposure to $PM_{2.5}$ and $PM_{10}$ from a nearby traffic area induced significant increases in protein expression of inflammatory cytokines (IL-6 and IL-8). The cell death rate and release of cytokines in response to the $PM_{2.5}$ treatment were higher than those with $PM_{10}$. The combined results support the hypothesis that ultrafine particles from vehicular sources can induce inflammatory responses related to environmental respiratory injury.