• 한국콘텐츠학회논문지
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• 제8권10호
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• pp.76-87
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• 2008

동적 경로 안내 시스템과 같은 첨단 여행 정보 시스템(ATIS)의 발전에 따라 도로 네트워크 상에서 보다 정확한 주행 시간 예측 기법에 대한 연구가 활발히 진행되고 있다. 그러나 기존 대부분의 연구들은 주어진 경로 상의 평균 주행 속도만을 기반으로 주행 시간을 예측한다. 이는 러시아워 시간대의 혼잡한 도로, 주말에 교외로 나가는 대규모의 차량 등과 같은 일별 혹은 주별 도로 교통 상황을 반영하지 못하기 때문에, 주행 시간 예측의 정확도가 저하된다. 이를 해결하기 위해 본 연구에서는 규칙-기반 분류화 기법을 이용한 주행 시간 예측 알고리즘을 제안한다. 제안된 알고리즘은 데이터마이닝 기법인 규칙-기반 분류화 기법을 사용하여, 과거 차량의 궤적 데이터로부터 하루의 시간대별 교통량과 주별 차량의 운행 양식 등 도로 교통 상황을 추출하고, 이를 통해 차량의 주행 시간을 보다 정확하게 예측한다. 제안된 알고리즘 기존의 링크-기반 예측(link-based prediction) 알고리즘, Micro T* 알고리즘[3], 그리고 스위칭 (switching) 알고리즘[10]과 예측 정확도 측면에서 성능 비교를 수행한다. 예측 정확도 성능 비교 결과, 제안된 기법이 타 예측 기법에 비해 MARE (mean absolute relative error) 가 크게 감소하여 성능이 향상됨을 보인다. 그 밖에 다른 기법들과 장단점을 비교하여, 제안된 기법의 유용성을 나타낸다.

• 한국IT서비스학회지
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• 제22권5호
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• pp.99-108
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• 2023

As the computerization of hospitals becomes more advanced, security issues regarding data generated from various medical devices within hospitals are gradually increasing. For example, because hospital data contains a variety of personal information, attempts to attack it have been continuously made. In order to safely protect data from external attacks, each hospital has formed an internal team to continuously monitor whether the computer network is safely protected. However, there are limits to how humans can monitor attacks that occur on networks within hospitals in real time. Recently, artificial intelligence models have shown excellent performance in detecting outliers. In this paper, an experiment was conducted to verify how well an artificial intelligence model classifies normal and abnormal data in network traffic data generated from medical devices. There are several models used for outlier detection, but among them, Random Forest and Tabnet were used. Tabnet is a deep learning algorithm related to receive and classify structured data. Two algorithms were trained using open traffic network data, and the classification accuracy of the model was measured using test data. As a result, the random forest algorithm showed a classification accuracy of 93%, and Tapnet showed a classification accuracy of 99%. Therefore, it is expected that most outliers that may occur in a hospital network can be detected using an excellent algorithm such as Tabnet.

• Journal of Communications and Networks
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• 제3권4호
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• pp.324-333
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• 2001

The Differentiated Services (Diffserv) framework has been proposed by the IETF as a simple service structure that can provide different Quality of Service (QoS) to different classes of packets in IP networks. IP packets are classified into one of a limited number of service classes, and are marked in the packet header for easy classification and differentiated treatments when transferred within a Diffserv domain. The Diffserv framework defines simple and efficient QoS differentiation mechanisms for the Internet. However, the original Diffserv concept does not provide a complete QoS management framework. Since traffic flows in IP networks are unidirectional from one network point to the other and routing paths and traffic demand get dynamically altered, it is important to monitor end-to-end traffic status, as well as traffic status in a single node. This paper suggests a distributed QoS monitoring method that collects the statistical data of each service class in every Diffserv router and calculates edge-to-edge QoS of the aggregated IP flows by combining routing topology and traffic status. A format modeling of edge-to-edge Diffserv flows and algorithms for aggregating edge-to-edge QoS is presented. Also an SNMP-based QoS management prototype system for Diffserv networks is presented, which validates our QoS management framework and demonstrates useful service management functionality.

• 한국통신학회논문지
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• 제38B권5호
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• pp.368-376
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• 2013

최근 급격한 인터넷의 발전으로 효율적인 네트워크관리를 위해 응용 트래픽 분석의 중요성이 강조되고 있다. 본 논문에서는 기존 분석 방법의 한계점을 보완하기 위하여 행위기반 시그니쳐를 이용한 응용 트래픽 분석 방법을 제안한다. 행위기반 시그니쳐는 기존에 제안된 다양한 트래픽 특징을 조합하여 사용할 뿐만 아니라, 복수 개 플로우들의 첫 질의 패킷을 분석 단위로 사용한다. 제안한 행위기반 시그니쳐의 타당성을 검증하기 위해 국내외 응용 5종을 대상으로 정확도를 측정결과, 모든 응용에서 100% Precision을 나타내었다.

• 한국구조물진단유지관리공학회 논문집
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• 제10권2호
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• pp.111-123
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• 2006

교량의 설계에 있어서 정확한 하중의 산정은 교량의 안전성 확보에 가장 핵심적인 사항이며 향후 유지관리 측면에서도 매우 중요하다. 교량구조물에서 차량에 의한 하중효과는 주로 활하중(충격하중 포함) 및 피로하중으로 나타난다. 이들 하중의 정형화를 위해서는 실제 교량상을 주행하는 중차량의 중량 및 통행특성을 정확히 파악하는 것이 중요하다. 이를 위해서 주행중인 차량을 정지시키지 않고 중량을 계측할 수 있는 시스템(Bridge Weigh-In-Motion, BWIM)의 개발이 필요하다. 본 연구에서는 다양한 기능을 갖는 BWIM시스템을 국내실정에 맞게 개발하고 이를 고속도로상의 교량에서 검증하였다.

• 한국농공학회:학술대회논문집
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• 한국농공학회 2005년도 학술발표논문집
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• pp.461-466
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• 2005

The purpose of this study is to classify rural villages through the amenity investigation by a village unit. PCA(Principal component analysis) is used for the classification of rural villages. The principal components of rural villages are deduced scale, population, infrastructure, traffic, education welfare and sightseeing by PCA.

• 대한교통학회지
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• 제26권2호
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• pp.157-166
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• 2008

본 연구에서는 실시간으로 수집되는 고속도로의 검지기 자료를 이용하여 교통사고 발생 가능성을 확률적 관측값으로 나타낼 수 있는 모형을 개발하였다. 사고발생 지점을 기준으로 상류부 및 하류부에서 수집된 사고발생 이전의 교통자료를 모형의 독립변수로 설정하였다. 이항 로지스틱 회귀분석 기법을 적용하여 교통사고 발생을 유발할 잠재력이 높은 교통상황을 교통사고와는 무관한 교통상황으로부터 추출하는 분류문제(classification problem)로 설정하고 모형을 개발하였다. 최근 3년간 서해안 고속도로에서 발생한 사고자료와 검지기 자료를 맵핑하였으며, 유효한 검지기 자료를 모형에 적용하기 위하여 이상치 제거 및 결측치 보정을 위한 자료처리 과정을 별도로 수행하였다. 본 연구에서 개발한 모형에서 산출되는 계량화된 교통사고 발생가능성은 고속도로상에서 실시간 경고정보 제공 및 다양한 교통운영관리 전략의 교통안전 측면에서의 효과를 평가하는데 유용하게 적용될 수 있을 것으로 기대된다.

• International Journal of Computer Science & Network Security
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• 제23권8호
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• pp.95-100
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• 2023

Due to Traffic accidents people faces health and economical casualties around the world. As the population increases vehicles on road increase which leads to congestion in cities. Congestion can lead to increasing accident risks due to the expansion in transportation systems. Modern cities are adopting various technologies to minimize traffic accidents by predicting mathematically. Traffic accidents cause economical casualties and potential death. Therefore, to ensure people's safety, the concept of the smart city makes sense. In a smart city, traffic accident factors like road condition, light condition, weather condition etcetera are important to consider to predict traffic accident severity. Several machine learning models can significantly be employed to determine and predict traffic accident severity. This research paper illustrated the performance of a hybridized neural network and compared it with other machine learning models in order to measure the accuracy of predicting traffic accident severity. Dataset of city Leeds, UK is being used to train and test the model. Then the results are being compared with each other. Particle Swarm optimization with artificial neural network (PSO-ANN) gave promising results compared to other machine learning models like Random Forest, Naïve Bayes, Nearest Centroid, K Nearest Neighbor Classification. PSO- ANN model can be adopted in the transportation system to counter traffic accident issues. The nearest centroid model gave the lowest accuracy score whereas PSO-ANN gave the highest accuracy score. All the test results and findings obtained in our study can provide valuable information on reducing traffic accidents.

• International Journal of Computer Science & Network Security
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• 제23권11호
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• pp.99-109
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• 2023

The darknet is frequently referred to as the hub of illicit online activity. In order to keep track of real-time applications and activities taking place on Darknet, traffic on that network must be analysed. It is without a doubt important to recognise network traffic tied to an unused Internet address in order to spot and investigate malicious online activity. Any observed network traffic is the result of mis-configuration from faked source addresses and another methods that monitor the unused space address because there are no genuine devices or hosts in an unused address block. Digital systems can now detect and identify darknet activity on their own thanks to recent advances in artificial intelligence. In this paper, offer a generalised method for deep learning-based detection and classification of darknet traffic. Furthermore, analyse a cutting-edge complicated dataset that contains a lot of information about darknet traffic. Next, examine various feature selection strategies to choose a best attribute for detecting and classifying darknet traffic. For the purpose of identifying threats using network properties acquired from darknet traffic, devised a hybrid deep learning (DL) approach that combines Recurrent Neural Network (RNN) and Bidirectional LSTM (BiLSTM). This probing technique can tell malicious traffic from legitimate traffic. The results show that the suggested strategy works better than the existing ways by producing the highest level of accuracy for categorising darknet traffic using the Black widow optimization algorithm as a feature selection approach and RNN-BiLSTM as a recognition model.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2012년도 추계학술대회 논문집
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• pp.153-158
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• 2012

Noise maps are produced according to 'The Method of making a Noise Map' in order to noise control efficiently, and prediction model to predict road traffic noise which may apply to Korean situation, include CRTN, RLS 90, NMPB, Nord 2000 and ASJ 2003. Of them, ASJ 2003, Japan's prediction model has not been verified for the application to Korean situation according to the classification of vehicle. In addition, ASJ 2003 was revised to ASJ 2008 recently, a classification for motorcycle was added. This study attempts to check the classification of vehicle in ASJ 2008 and 'The Method of making a Noise Map' to confirm the suitability of the application of them to Korean situation.