• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3567-3582
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• 2020

Human activities are often affected by weather conditions. Automatic weather recognition is meaningful to traffic alerting, driving assistance, and intelligent traffic. With the boost of deep learning and AI, deep convolutional neural networks (CNN) are utilized to identify weather situations. In this paper, a three-channel convolutional neural network (3C-CNN) model is proposed on the basis of ResNet50.The model extracts global weather features from the whole image through the ResNet50 branch, and extracts the sky and ground features from the top and bottom regions by two CNN5 branches. Then the global features and the local features are merged by the Concat function. Finally, the weather image is classified by Softmax classifier and the identification result is output. In addition, a medium-scale dataset containing 6,185 outdoor weather images named WeatherDataset-6 is established. 3C-CNN is used to train and test both on the Two-class Weather Images and WeatherDataset-6. The experimental results show that 3C-CNN achieves best on both datasets, with the average recognition accuracy up to 94.35% and 95.81% respectively, which is superior to other classic convolutional neural networks such as AlexNet, VGG16, and ResNet50. It is prospected that our method can also work well for images taken at night with further improvement.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.652-657
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• 2004

As the real-time multimedia applications through Internet increase, the bandwidth available to TCP connections is oppressed by the UDP traffic, result in the performance of overall system is extremely deteriorated. Therefore, developing a new transmission protocol is necessary. The TCP-friendly algorithm is an example meeting this necessity. The TCP-friendly (TFRC) is an UDP-based protocol that controls the transmission rate based on the available round transmission time (RTT) and the packet loss rate (PLR). In the data transmission processing, transmission rate is determined based on the conditions of the previous transmission period. If the one-step ahead predicted values of the control factors are available, the performance will be improved significantly. This paper proposes a prediction model of transmission rate control factors that will be used for the transmission rate control, which improves the performance of the networks. The model developed through this research is predicting one-step ahead variables of RTT and PLR. A multiplayer perceptron neural network is used as the prediction model and Levenberg-Marquardt algorithm is used for the training. The values of RTT and PLR were collected using TFRC protocol in the real system. The obtained prediction model is validated using new data set and the results show that the obtained model predicts the factors accurately.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.2
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• pp.786-799
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• 2018

Recently, NFV has attracted attention as a next-generation network virtualization technology for hardware -independent and efficient utilization of resources. NFV is a technology that not only virtualize computing, server, storage, network resources based on cloud computing but also connect Multi-Tenant of VNFs, a software network function. Therefore, it is possible to reduce the cost for constructing a physical network and to construct a logical network quickly by using NFV. However, in NFV, when a new VNF is added to a running Tenant, authentication between VNFs is not performed. Because of this problem, it is impossible to identify the presence of Fake-VNF in the tenant. Such a problem can cause an access from malicious attacker to one of VNFs in tenant as well as other VNFs in the tenant, disabling the NFV environment. In this paper, we propose Auto-configurable Security Mechanism in NFV including authentication between tenant-internal VNFs, and enforcement mechanism of security policy for traffic control between VNFs. This proposal not only authenticate identification of VNF when the VNF is registered, but also apply the security policy automatically to prevent malicious behavior in the tenant. Therefore, we can establish an independent communication channel for VNFs and guarantee a secure NFV environment.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.449-450
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• 2009

사용자의 인터넷 소비성향을 파악하고 그에 적응적인 인프라 리소스를 제공하는 일은 네트워크 설계/관리자나 인터넷 서비스 공급자(ISP)들에게는 주요 관심사이다. 이러한 분석은 한정된 네트워크 자원을 보다 적절한 지점에 효율적인 방식으로 투자하도록 도와준다. 본 논문은 각종 인터넷 서비스를 활용하는 사용자들의 서비스(각종 인터넷 어플리케이션) 소비성향을 네트워크 트래픽 관찰만으로 파악할 수 있는 성향분류 척도를 제안한다. 아울러 베이지안 분류기를 사용하여 제안 척도를 활용한 사용자 성향 분류 방법을 함께 제시한다.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.81-91
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• 2016

VANET is one of the most developed technologies many people have considered a technology for the next generation. It basically utilizes the wireless technology and it can be used for measuring the speed of the vehicle, the location and even traffic control. With sharing those information, VANET can offer Cooperative ITS which can make a solution for a variety of traffic issues. In this way, safety for drivers, efficiency and mobility can be increased with VANET but data between vehicles or between vehicle and infrastructure are included with private information. Therefore alternatives are necessary to secure privacy. If there is no alternative for privacy, it can not only cause some problems about identification information but also it allows attackers to get location tracking and makes a target. Besides, people's lives or property can be dangerous because of sending wrong information or forgery. In addition to this, it is possible to be information stealing by attacker's impersonation or private information exposure through eavesdropping in communication environment. Therefore, in this paper we propose Privacy Assurance Architecture for VANET to ensure privacy from these threats.

• Journal of Internet Computing and Services
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• v.24 no.5
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• pp.29-35
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• 2023

CCTV is used for various purposes such as crime prevention, public safety reinforcement, and traffic management. However, as the range and resolution of the camera improve, there is a risk of exposing personal information in the video. Therefore, there is a need for new technologies that can identify individuals while protecting personal information in images. In this paper, we propose histogram-based singular value decomposition for object identification and tracking. The proposed method distinguishes different objects present in the image using color information of the object. For object recognition, YOLO and DeepSORT are used to detect and extract people present in the image. Color values are extracted with a black-and-white histogram using location information of the detected person. Singular value decomposition is used to extract and use only meaningful information among the extracted color values. When using singular value decomposition, the accuracy of object color extraction is increased by using the average of the upper singular value in the result. Color information extracted using singular value decomposition is compared with colors present in other images, and the same person present in different images is detected. Euclidean distance is used for color information comparison, and Top-N is used for accuracy evaluation. As a result of the evaluation, when detecting the same person using a black-and-white histogram and singular value decomposition, it recorded a maximum of 100% to a minimum of 74%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.716-723
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• 2016

A computerized information management system of road bridges as a national key infrastructure is needed to effectively collect data of the current status, improve the reliability of data, and use the results from the analysis of the accumulated data as fundamental resources for supporting the establishment of policies. The Internet-based Bridge Information System (BIS), including a database and geographic information systems (GIS), was designed, and the data items were comprised of essential information, such as GIS-based location coordinates, bridge condition grade information and so on. The BIS was developed to be connected with a related information system, and it is possible to make the current information of traffic volume, address and so on by adopting the GIS. To enhance the reliability of the information of current bridge status, it is also possible to improve the accuracy of data through an information verifying function to prevent entry errors. In addition, the BIS can easily support the establishment of policies offering various types of knowledge information that were available in the past based on an analysis of the accumulated data. The intuitive identification and analysis of the current status is to be feasible through a GIS screen. Improvement of the business efficiency and data accuracy and time-series information analysis are available by managing the information of current status through BIS. In the future, it is expected that BIS can be used effectively for the establishment of reasonable maintenance policies of the nation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.12
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• pp.7292-7301
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• 2014

With the development of IT Info-Communications technology, the vehicle with a combination of wireless-communication technology has resulted in significant research into the convergence of the component of existing traffic with information, electronics and communication technology. Intelligent Vehicle Communication is a Machine-to-Machine (M2M) concept of the Vehicle-to-Vehicle. The Vehicle-to-Infrastructure communication consists of safety and the ease of transportation. Security technologies must precede the effective Intelligent Vehicle Communication Structure, unlike the existing internet environment, where high-speed vehicle communication is with the security threats of a wireless communication environment and can receive unusual vehicle messages. In this paper, the Vehicle Identification number between the V2I and the secure message communication protocol was proposed using hash functions and a time stamp, and the validity of the vehicle was assessed. The proposed system was the performance evaluation section compared to the conventional technique at a rate VPKI aspect showed an approximate 44% reduction. The safety, including authentication, confidentiality, and privacy threats, were analyzed.

• 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.