• Genomics & Informatics
• /
• v.1 no.1
• /
• pp.7-19
• /
• 2003

The latest measure of the relative evolutionary age of protein structure families was applied (based on taxonomic diversity) using the protein structural interactome map (PSIMAP). It confirms that, in general, protein domains, which are hubs in this interaction network, are older than protein domains with fewer interaction partners. We apply a hypothesis of 'biological network evolution' to explain the positive correlation between interaction and age. It agrees to the previous suggestions that proteins have acquired an increasing number of interaction partners over time via the stepwise addition of new interactions. This hypothesis is shown to be consistent with the scale-free interaction network topologies proposed by other groups. Closely co-evolved structural interaction and the dynamics of network evolution are used to explain the highly conserved core of protein interaction pathways, which exist across all divisions of life.

• International Journal of Internet, Broadcasting and Communication
• /
• v.13 no.1
• /
• pp.210-218
• /
• 2021

CNN (Convolutional Neural Network), a type of deep learning algorithm, is a type of artificial neural network used to analyze visual images. In deep learning, it is classified as a deep neural network and is most commonly used for visual image analysis. Accordingly, an AI autonomous driving model was constructed through real-time image processing, and a crosswalk image of a road was used as an obstacle. In this paper, we proposed a low-cost model that can actually implement autonomous driving based on the CNN model. The most well-known deep neural network technique for autonomous driving is investigated and an end-to-end model is applied. In particular, it was shown that training and self-driving on a simulated road is possible through a practical approach to realizing lane detection and keeping.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.11
• /
• pp.4483-4501
• /
• 2020

Recent findings identified the scatter and shape of MFD (macroscopic fundamental diagram) is heavily influenced by the spatial distribution of link density in a road network. This implies that the concept of MFD can be utilized to divide a heterogeneous road network with different degrees of congestion into multiple homogeneous subnetworks. Considering the actual traffic data is usually incomplete and inaccurate while most traffic partition algorithms rely on the completeness of the data, we proposed a three-step partitioned algorithm called Iso-MB (Isoperimetric algorithm - Merging - Boundary adjustment) permitting of incompletely input data in this paper. The proposed algorithm was implemented and verified in a simulated urban transportation network. The existence of well-defined MFD in each subnetwork was revealed and discussed and the selection of stop parameter in the isoperimetric algorithm was explained and dissected. The effectiveness of the approach to the missing input data was also demonstrated and elaborated.

• Journal of Korea Multimedia Society
• /
• v.16 no.2
• /
• pp.180-189
• /
• 2013

The SMART Highway project combines road construction with advanced technology and vehicle telecommunications. Its expected outcome is a world-leading intelligent road that is green, fast, and comfortable. A vehicular ad-hoc network(VANET) is the core technology of the SMART Highway, whose transport operation is based on road vehicles. The VANET is a next-generation networking technology that enables wireless communication between vehicles or between vehicles and a road side unit(RSU). In the VANET system, a vehicle accident is likely to cause a serious disaster. Therefore, some information on safety is essential to serve as the key exchange protocol for communication between vehicles. However, the key exchange scheme of the general network proposed for a fast-moving communication environment is unsuitable for vehicles. In this paper, communication between multiple vehicles more efficient and secure key exchange at the vehicle certification by signcryption is proposed.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.12 no.1
• /
• pp.396-404
• /
• 2011

Recently, the wide spread popularity of smart phones causes the advent of various mobile applications base on the location information. Since previous pedestrian navigations are applied by extending car navigations, these are not only difficult to provide the appropriate route information, but also raise limitations in the efficient query processing by data structures of car road networks. In addition, these increase the power consumption caused by the growth of I/O frequency. In this paper, we propose a pedestrian road network model for the accurate route information and a storage structure for the pedestrian road network based on POI to reduce the I/O frequency. The proposed method enables efficient route searches over POI reflecting the characteristics and requirements of pedestrian roads. Also, a reduction of query processing costs for the route searching by a data structure considered with POI can save the power consumption more than previous approaches.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2014.10a
• /
• pp.620-623
• /
• 2014

Network topology for communication between vehicles are quickly changing because vehicles have a special movement pattern, especially character which is quickly changed by velocity and situation of road. Because of these feature, it is not easy to apply reliable routing on VANET(Vehicular Ad-hoc Network). Clustering method is one of the alternatives which are suggested for overcoming weakness of routing algorithm. Clustering is the way to communicate and manage vehicles by binding them around cluster head. Therefore choosing certain cluster head among vehicles has a decisive effect on decreasing overhead in relevant clustering and determining stability and efficiency of the network. This paper introduces new cluster head selection algorithm using RSU(Road Side Unit) different from existing algorithms. We suggest a more stable and efficient algorithm which decides a priority of cluster head by calculating vehicles' velocity and distance through RSU than existing algorithms.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.20 no.1
• /
• pp.193-202
• /
• 2021

The update of a high-precision map was carried out by modifying the geometric information using ortho-images or point-cloud data as the source data and then reconstructing the relationship between the spatial objects. These series of processes take considerable time to process the geometric information, making it difficult to apply real-time route planning to a vehicle quickly. Therefore, this study proposed a method to update the road network for route planning using a graph data structure and storage type of graph data structure considering the characteristics of the road network. The proposed method was also reviewed to assess the feasibility of real-time route information transmission by applying it to actual road data.

• International Journal of Automotive Technology
• /
• v.6 no.1
• /
• pp.53-63
• /
• 2005

We propose a fuzzy neural network (FNN) theory capable of deciding the quality of a road image prior to extracting lane-related information. The accuracy of lane-related information obtained by image processing depends on the quality of the raw images, which can be classified as good or bad according to how visible the lane marks on the images are. Enhancing the accuracy of the information by an image-processing algorithm is limited due to noise corruption which makes image processing difficult. The FNN, on the other hand, decides whether road images are good or bad with respect to the degree of noise corruption. A cumulative distribution function (CDF), a function of edge histogram, is utilized to extract input parameters from the FNN according to the fact that the shape of the CDF is deeply correlated to the road image quality. A suitability analysis shows that this deep correlation exists between the parameters and the image quality. The input pattern vector of the FNN consists of nine parameters in which eight parameters are from the CDF and one is from the intensity distribution of raw images. Experimental results showed that the proposed FNN system was quite successful. We carried out simulations with real images taken in various lighting and weather conditions, and obtained successful decision-making about $99\%$ of the time.

• International Journal of Highway Engineering
• /
• v.14 no.4
• /
• pp.163-173
• /
• 2012

PURPOSES : The purpose of this study is to check the possibilities of traffic pattern analysis using MatSIM for urban road network operation in incident case. METHODS : One of the stochastic dynamic models is MatSIM. MatSIM is a transportation simulation tool based on stochastic dynamic model and activity based model. It is an open source software developed by IVT, ETH zurich, Switzerland. In MatSIM, various scenario comparison analyses are possible and analyses results are expressed using the visualizer which shows individual vehicle movements and traffic patterns. In this study, trip distribution in 24-hour, traffic volume, and travel speed using MatSIM are similar to those of measured values. Therefore, results of MatSIM are reasonable comparing with measured values. Traffic patterns are changed according to incident from change of individual behavior. RESULTS : The simulation results and the actual measured values are similar. The simulation results show reasonable ranges which can be used for traffic pattern analysis. CONCLUSIONS : The change of traffic pattern including trip distribution, traffic volumes and speeds according to various incident scenarios can be used for traffic control policy decision to provide effective operation of urban road network.

• Journal of Institute of Control, Robotics and Systems
• /
• v.8 no.6
• /
• pp.468-476
• /
• 2002

The confidence of information from image processing depends on the original image quality. Enhancing the confidence by an algorithm has an essential limitation. Especially, road images are exposed to lots of noisy sources, which makes image processing difficult. We, in this paper, propose a FNN (fuzzy-neural network) capable oi deciding the quality of a road image prior to extracting lane-related information. According to the decision by the FNN, road images are classified into good or bad to extract lane-related information. A CDF (cumulative distribution function), a function of edge histogram, is utilized to construct input parameters of the FNN, it is based on the fact that the shape of the CDF and the image quality has large correlation. Input pattern vector to the FNN consists of ten parameters in which nine parameters are from the CDF and the other one is from intensity distribution of raw image. Correlation analysis shows that each parameter represents the image quality well. According to the experimental results, the proposed FNN system was quite successful. We carried out simulations with real images taken by various lighting and weather conditions and achieved about 99% successful decision-making.