• 디지털융복합연구
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• 제15권4호
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• pp.259-266
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• 2017

무선 홈 IoT (Internet of Things)상에서 대용량 트래픽 간섭은 패킷 손실의 원인이 되며, 패킷 손실은 무선 홈 네트워크의 QoS와 처리율을 떨어뜨린다. 본 논문에서는 실시간 트래픽과 비실시간 트래픽을 탐지하여 무선 홈 IoT 서비스의 QoS 및 처리율을 향상시키기 위한 새로운 적응형 트래픽 간섭 제어 시스템, ATICS(Adaptive Traffic Interference Control System)을 제안한다. 제안된 시스템은 트래픽 특성에 따라 단기(short term) 트래픽 혼잡 프로세스와 장기(long-term) 트래픽 혼잡 프로세스로 구분하여 트래픽 간섭을 제어한다. 시뮬레이션 결과 제안된 기법은 다른 비교 기법들에 비해서 트래픽 간섭 제어 성능 척도가 더 효율적임을 보인다.

• Smart Structures and Systems
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• 제17권6호
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• pp.995-1015
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• 2016

In the design and condition assessment of bridges, it is usually necessary to take into consideration the extreme conditions which are not expected to occur within a short time period and thus require an extrapolation from observations of limited duration. Long-term structural health monitoring (SHM) provides a rich database to evaluate the extreme conditions. This paper focuses on the extrapolation of extreme traffic load effects on bridges using long-term monitoring data of structural strain. The suspension Tsing Ma Bridge (TMB), which carries both highway and railway traffic and is instrumented with a long-term SHM system, is taken as a testbed for the present study. Two popular extreme value extrapolation methods: the block maxima approach and the peaks-over-threshold approach, are employed to extrapolate the extreme stresses induced by highway traffic and railway traffic, respectively. Characteristic values of the extreme stresses with a return period of 120 years (the design life of the bridge) obtained by the two methods are compared. It is found that the extrapolated extreme stresses are robust to the extrapolation technique. It may owe to the richness and good quality of the long-term strain data acquired. These characteristic extremes are also compared with the design values and found to be much smaller than the design values, indicating conservative design values of traffic loading and a safe traffic-loading condition of the bridge. The results of this study can be used as a reference for the design and condition assessment of similar bridges carrying heavy traffic, analogous to the TMB.

• 한국통신학회논문지
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• 제40권3호
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• pp.541-550
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• 2015

급하게 일어나는 인터넷의 활성화는 그 어느 때보다 효율적인 엔터프라이즈 망 운영 방안을 필요로 하고 있다. 효율적인 망 운영을 위해서는 장기간의 트래픽 분석을 통해 망의 특성을 정확히 반영한 운영 정책 적용이 필요하다. 하지만 기존에는 급격하게 증가하는 장기간 트래픽 데이터의 처리가 불가능했고, 다양한 분석 결과를 낼 수 없는 단기간 분석만 이루어졌다. 최근 빅 데이터 분석 플랫폼과 도구의 개발로 인해 장기간 트래픽 분석이 가능하게 되었고, 이를 이용해 망의 특성을 정확히 반영할 수 있는 장기간 트래픽 분석을 통한 엔터프라이즈 망 자원효율화 방안이 요구되고 있다. 본 논문에서는 엔터프라이즈 망에서 발생한 장기간의 트래픽을 수집하고 저장 및 관리하는 방안에 대해 제안한다. 또한 분류기준을 정의하였으며, 수집된 빅 데이터 트래픽을 각 분류 기준으로 분류한 뒤 다각적인 통계 분석을 통해 망 자원을 효율화 하는 방안을 제안한다. 제안하는 방법을 학내 망에 적용하여 실험하였으며, 통계 분석 결과 시간과 공간, 그리고 사용목적에 따라 Quality of Service(QoS)정책을 달리 적용해야 함을 확인하였다.

• Journal of information and communication convergence engineering
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• 제5권2호
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• pp.171-176
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• 2007

To give a guarantee a consistently high level of quality and reliability of Telematics traffic service, traffic flow forecasting is very important issue. In this paper, we proposed an adaptable integrated prediction model to predict the traffic flow in the future. Our model combines two methods, short-term prediction model and long-term prediction model with different combining coefficients to reflect current traffic condition. Short-term model uses the Kalman filtering technique to predict the future traffic conditions. And long-term model processes accumulated speed patterns which means the analysis results for all past speeds of each road by classifying the same day and the same time interval. Combining two models makes it possible to predict future traffic flow with higher accuracy over a longer time range. Many experiments showed our algorithm gives a better precise prediction than only an accumulated speed pattern that is used commonly. The result can be applied to the car navigation to support a dynamic shortest path. In addition, it can give users the travel information to avoid the traffic congestion areas.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권1호
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• pp.216-238
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• 2023

In intelligent transportation systems, traffic management is an important task. The accurate forecasting of traffic characteristics like flow, congestion, and density is still active research because of the non-linear nature and uncertainty of the spatiotemporal data. Inclement weather, such as rain and snow, and other special events such as holidays, accidents, and road closures have a significant impact on driving and the average speed of vehicles on the road, which lowers traffic capacity and causes congestion in a widespread manner. This work designs a model for multivariate short-term traffic congestion prediction using SLSTM_AE-BiLSTM. The proposed design consists of a Bidirectional Long Short Term Memory(BiLSTM) network to predict traffic flow value and a Convolutional Neural network (CNN) model for detecting the congestion status. This model uses spatial static temporal dynamic data. The stacked Long Short Term Memory Autoencoder (SLSTM AE) is used to encode the weather features into a reduced and more informative feature space. BiLSTM model is used to capture the features from the past and present traffic data simultaneously and also to identify the long-term dependencies. It uses the traffic data and encoded weather data to perform the traffic flow prediction. The CNN model is used to predict the recurring congestion status based on the predicted traffic flow value at a particular urban traffic network. In this work, a publicly available Caltrans PEMS dataset with traffic parameters is used. The proposed model generates the congestion prediction with an accuracy rate of 92.74% which is slightly better when compared with other deep learning models for congestion prediction.

• International journal of advanced smart convergence
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• 제12권4호
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• pp.88-97
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• 2023

Traffic flow prediction is of great significance in urban planning and traffic management. As the complexity of urban traffic increases, existing prediction methods still face challenges, especially for the fusion of spatiotemporal information and the capture of long-term dependencies. This study aims to use the fusion model of graph neural network to solve the spatio-temporal information fusion problem in traffic flow prediction. We propose a new deep learning model Spatio-Temporal Information Fusion using Graph Neural Networks (STFGNN). We use GCN module, TCN module and LSTM module alternately to carry out spatiotemporal information fusion. GCN and multi-core TCN capture the temporal and spatial dependencies of traffic flow respectively, and LSTM connects multiple fusion modules to carry out spatiotemporal information fusion. In the experimental evaluation of real traffic flow data, STFGNN showed better performance than other models.

• 대한토목학회논문집
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• 제30권5D호
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• pp.505-512
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• 2010

교통하중이 작용하는 기초지반의 성능 및 도로하부 지반에서의 변형예측을 위해서는 반복적인 교통하중하에서의 장기변형 예측이 필요하다. 그러나 도로와 철도와 같은 다층시스템에서의 장기변형을 예측하는 것은 쉽지 않은 일이다. 따라서 보다 정량적인 해석을 위해서는 적절한 해석방식, 재료모형, 그리고 재료의 상수들을 통한 역학-경험적인 방식이 필요하다. 따라서 본 연구에서는 반복 교통하중에 의한 응력의존적인 기초 지반재료의 장기변형 거동 파악을 위해 반복 하중의 응력수준과 함수비 조건이 고려된 반복재하 장기변형실험을 실시한 결과를 분석하고 해석에 활용하였다. 여러 응력상태조건에서 기초 지반재료의 장기변형 특성이 반영된 유한요소해석을 실시하였고 장기변형 예측모델의 실내시험규모에서의 적용성을 평가하였다.

• 한국음향학회지
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• 제18권1호
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• pp.72-77
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• 1999

본 논문에서는 Wavelet 변환과 Vector Quantization(VQ)을 이용한 VBR (variable-bit-rate) 비디오 트래픽 모델을 제안하고 있다. 여기에서 제안된 방법은 영상 트래픽을 Wavelet 변환한 후 두 개의 요소로 분해하여 각각을 분리하여 모델링한다. 첫 번째 요소는 AR(1) 프로세스 모델로 이것은 트래픽의 비교적 장시간에 걸친 변화 특성을 표현한다. 두 번째 요소는 벡터 양자화(VQ)를 사용하여 비교적 짧은 시간의 트래픽 특성을 표현한다. 다른 VBR 트래픽의 모델 방법과 비교해서 본 논문에서 제안하는 모델은 세 가지 장점을 가지고 있다. 첫째로 영상 트래픽의 특성을 장시간과 단시간의 형태로 나누어 모델링을 할 수 있다. 둘째로 트래픽 데이터의 주기적 코딩 구조를 보존한다. 마지막으로 프레임 레벨과 슬라이스 레벨의 트래픽 모델링을 통합할 수 있다. 통계적 측정과 네트워크 성능 실험을 통하여 제안된 모델의 타당성을 검증하였다.

• ETRI Journal
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• 제41권5호
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• pp.560-573
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• 2019

Two supervised learning algorithms, a basic neural network and a long short-term memory recurrent neural network, are applied to traffic including DDoS attacks. The joint effects of preprocessing methods and hyperparameters for machine learning on performance are investigated. Values representing attack characteristics are extracted from datasets and preprocessed by two methods. Binary classification and two optimizers are used. Some hyperparameters are obtained exhaustively for fast and accurate detection, while others are fixed with constants to account for performance and data characteristics. An experiment is performed via TensorFlow on three traffic datasets. Three scenarios are considered to investigate the effects of learning former traffic on sequential traffic analysis and the effects of learning one dataset on application to another dataset, and determine whether the algorithms can be used for recent attack traffic. Experimental results show that the used preprocessing methods, neural network architectures and hyperparameters, and the optimizers are appropriate for DDoS attack detection. The obtained results provide a criterion for the detection accuracy of attacks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권11호
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• pp.4246-4267
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• 2020

As the network goes deep into all aspects of people's lives, the number and the complexity of network traffic is increasing, and traffic classification becomes more and more important. How to classify them effectively is an important prerequisite for network management and planning, and ensuring network security. With the continuous development of deep learning, more and more traffic classification begins to use it as the main method, which achieves better results than traditional classification methods. In this paper, we provide a comprehensive review of network traffic classification based on deep learning. Firstly, we introduce the research background and progress of network traffic classification. Then, we summarize and compare traffic classification based on deep learning such as stack autoencoder, one-dimensional convolution neural network, two-dimensional convolution neural network, three-dimensional convolution neural network, long short-term memory network and Deep Belief Networks. In addition, we compare traffic classification based on deep learning with other methods such as based on port number, deep packets detection and machine learning. Finally, the future research directions of network traffic classification based on deep learning are prospected.