• IEIE Transactions on Smart Processing and Computing
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• v.5 no.6
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• pp.430-439
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• 2016

Estimation and analysis of traffic jams plays a vital role in an intelligent transportation system and advances safety in the transportation system as well as mobility and optimization of environmental impact. For these reasons, many researchers currently mainly focus on the brilliant machine learning-based prediction approaches for traffic prediction systems. This paper primarily addresses the analysis and comparison of prediction accuracy between two machine learning algorithms: Naïve Bayes and K-Nearest Neighbor (K-NN). Based on the fact that optimized estimation accuracy of these methods mainly depends on a large amount of recounted data and that they require much time to compute the same function heuristically for each action, we propose an approach that applies multi-threading to these heuristic methods. It is obvious that the greater the amount of historical data, the more processing time is necessary. For a real-time system, operational response time is vital, and the proposed system also focuses on the time complexity cost as well as computational complexity. It is experimentally confirmed that K-NN does much better than Naïve Bayes, not only in prediction accuracy but also in processing time. Multi-threading-based K-NN could compute four times faster than classical K-NN, whereas multi-threading-based Naïve Bayes could process only twice as fast as classical Bayes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.4
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• pp.1931-1953
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• 2017

The reverse skyline query plays an important role in information searching applications. This paper deals with continuous reverse skyline queries in sensor networks, which retrieves reverse skylines as well as the set of nodes that reported them for continuous sampling epochs. Designing an energy-efficient approach to answer continuous reverse skyline queries is non-trivial because the reverse skyline query is not decomposable and a huge number of unqualified nodes need to report their sensor readings. In this paper, we develop a new algorithm that avoids transmission of updates from nodes that cannot influence the reverse skyline. We propose a data mapping scheme to estimate sensor readings and determine their dominance relationships without having to know the true values. We also theoretically analyze the properties for reverse skyline computation, and propose efficient pruning techniques while guaranteeing the correctness of the answer. An extensive experimental evaluation demonstrates the efficiency of our approach.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.814-836
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• 2021

Wildlife ecologists and biologists recapture deer to collect tracking data from deer collars or wait for a drop-off of a deer collar construction that is automatically detached and disconnected. The research teams need to manage a base camp with medical trailers, helicopters, and airplanes to capture deer or wait for several months until the deer collar drops off of the deer's neck. We propose an intelligent robust base-station research with a low-cost and time saving method to obtain recording sensor data from their collars to a listener node, and readings are obtained without opening the weatherproof deer collar. We successfully designed the and implemented a robust base station system for automatically collecting data of the collars and listener motes in harsh wilderness environments. Intelligent solutions were also analyzed for improved data collections and pattern predictions with drone-based detection and tracking algorithms.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.1
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• pp.28-38
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• 2016

Cooperative-Intelligent Transport Systems (c-ITS) has emphasized a real-time traffic safety service in urgent situations among highway infrastructure and four-wheeled vehicles, while two-wheeled vehicles, e.g. bicycles and motorcycle, sharing highway space and endangering highway safety, have yet been out of its interest. This paper delivers the results of a study conducted to analyze the patterns of two-wheeled-vehicle traffic accidents experienced in the past, the last three years (2011~2013), and to propose the types of service enhancing the safety of the riders of those. It was found from the analysis of historical accident data that the side collision on a link section should be taken care of for further safety treatment, while the old female drivers need additional care to decrease their fatality rate. By combining the services proposed for bicycles and motorcycles, this paper proposes (1) eight different bicycle-to-everything (B2X) services which can be eventually provided in c-ITS and (2) three of those that would be available in the near future with the current communication technologies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1510-1532
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• 2017

In smart grid, privacy implications to individuals and their families are an important issue because of the fine-grained usage data collection. Wireless communications are utilized by many utility companies to obtain information. Network coding is exploited in smart grids, to enhance network performance in terms of throughput, delay, robustness, and energy consumption. However, random linear network coding introduces a new challenge for privacy preserving due to the encoding of data and updating of coefficients in forwarder nodes. We propose a distributed privacy preserving scheme for random linear network coding in smart grid that considers the converged flows character of the smart grid and exploits a homomorphic encryption function to decrease the complexities in the forwarder node. It offers a data confidentiality privacy preserving feature, which can efficiently thwart traffic analysis. The data of the packet is encrypted and the tag of the packet is encrypted by a homomorphic encryption function. The forwarder node random linearly codes the encrypted data and directly processes the cryptotext tags based on the homomorphism feature. Extensive security analysis and performance evaluations demonstrate the validity and efficiency of the proposed scheme.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.6
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• pp.98-111
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• 2009

The objective of this study is to revise the related regulations and laws of intelligent transport systems performance tests and methods. This study improves the reliability of present traffic information, reduce public discontent by systematic reforms and reduce national and social costs through the analysis of intelligent transport system performance tests'current status and problems. This study will maximize the investment profits and efficiency of ITS system as the reliability of transportation information which is created through the maintenance of ITS equipment and center, systemization of work responsibility through close analysis of ITS performance test and overall evaluation of role of ITS performance assessment organization will improve and new level of service will be born.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1464-1479
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• 2018

In face recognition, sometimes the number of available training samples for single category is insufficient. Therefore, the performances of models trained by convolutional neural network are not ideal. The small sample face recognition algorithm based on novel Siamese network is proposed in this paper, which doesn't need rich samples for training. The algorithm designs and realizes a new Siamese network model, SiameseFacel, which uses pairs of face images as inputs and maps them to target space so that the $L_2$ norm distance in target space can represent the semantic distance in input space. The mapping is represented by the neural network in supervised learning. Moreover, a more lightweight Siamese network model, SiameseFace2, is designed to reduce the network parameters without losing accuracy. We also present a new method to generate training data and expand the number of training samples for single category in AR and labeled faces in the wild (LFW) datasets, which improves the recognition accuracy of the models. Four loss functions are adopted to carry out experiments on AR and LFW datasets. The results show that the contrastive loss function combined with new Siamese network model in this paper can effectively improve the accuracy of face recognition.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3280-3298
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• 2019

Vehicular ad-hoc networks (VANETs) have become increasingly significant in intelligent transportation systems, they play a great role in improving traffic safety and efficiency. In the deployment of intelligent VANETs, intelligent vehicles can efficiently exchange important or urgent traffic information and make driving decisions. Meanwhile, secure data communication and vehicle's identity privacy have been highlighted. To cope with these security issues, in this paper, we construct an efficient anonymous authentication scheme with secure communication in intelligent VANETs. Combing the ElGamal encryption technique with a modified Schnorr signature technique, the proposed scheme provides secure anonymous authentication process for encrypted message in the vehicle-to-infrastructure communication model, and achieves identity privacy, forward security, and reply attack resistance simultaneously. Moreover, except the trusted authority (TA), any outside entity cannot trace the real identity of an intelligent vehicle. The proposed scheme is designed on an identity-based system, which can remove the costs of establishing public key infrastructure (PKI) and certificates management. Compared with existing authentication schemes, the proposed scheme is much more practical in intelligent VANETs.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.1
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• pp.157-165
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• 2017

Intelligent transportation systems are currently being developed for elemental technology development of cooperative intelligent transport systems, which enable vehicles to communicate with each other or reduce the risk of traffic accidents, We have been defining and standardizing services according to the purpose of solving traffic safety problems depending on countries. Therefore, in this study, the developed countries of V2X(vehicle-to-everything) based on USA, Europe, Japan, etc., analyzed the service cases selected in the field demonstration stage after completion of the element technology devanalyzed the service cases selected in the field demonstration stage after completion of the element technology development, and to suggest the direction of futureelopment, and to suggest the direction of future policy direction.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.12
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• pp.117-124
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• 2009

In this paper, we have developed communication modules for ubiquitous transportation sensor network (u-TSN). The developed module can be used for intelligent transportation services. The developed systems are based on IEEE 802.11a and IEEE 802.11g technologies for vehicle and infrastructure systems, respectively. We have found that the throughput for the developed systems is at maximum around 15 Mbps. It is reduced to 10 Mbps at a long distance and high speed condition. The performance is enough to support traffic control services in dense traffic condition.