• Korean Journal of Materials Research
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• v.29 no.6
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• pp.363-370
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• 2019

The recent development of information and communication technologies brings new changes to automobile traffic systems. The most typical example is the advancement of dedicated short range communication(DSRC). DSRC mainly consists of an intelligent transportation system(ITS), an electronic toll collection system(ETCS) and an advanced traveler information system(ATIS). These wireless communications often cause unnecessary electromagnetic waves, and these electromagnetic waves, in turn, cause frequent system malfunction. To solve this problem, an absorber of electromagnetic waves is suggested. In this research, various materials, such as powdered metal and iron oxides, are used to test the possibility for an effective absorption of the unnecessary electromagnetic waves. The various metal powders are made into a thin sheet form by compositing through processing. The electromagnetic characteristics(complex permittivity, complex permeability) of the fabricated sheet are measured. As a result, we achieve -6.5 dB at 940 MHz(77.6 % absorption rate) with a 1.0 mm-thickness electromagnet wave absorber, and -9.5 dB at 940 MHz(88.8 % absorption rate) with a 2.0 mm-thickness absorber.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.217-220
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• 2014

Wireless sensor network（WSN） consists by a large number of low-cost micro-sensor nodes, collaborate to achieve the perception of information collection, processing and transmission tasks in deployment area. It can be widely used in national defense, intelligent transportation, medical care, environmental monitoring, precision agriculture, and industrial automation and many other areas. One of the key technologies of sensor networks is the data maintenance management technology. In this paper we analyze the data management technology of wireless sensor network and pointed their problems.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.219-222
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• 2009

Recently, as the techniques of vehicle and communication have improve, the techniques of in- vehicle network that is a essential part of ITS have been focused. In-vehicle networks, however, are not unified to single network. The networks are composed of several local networks because of communication speed, cost and efficiency. It is important to communicate information between the networks. Therefore, the complexity of network design for communication increases. To solve this problem, local networks need a framework for interworking between heterogeneous networks. In this paper, a framework interworking between in-vehicle networks is proposed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2399-2413
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• 2021

A recommendation system is an information filter tool, which uses the ratings and reviews of users to generate a personalized recommendation service for users. However, the cold-start problem of users and items is still a major research hotspot on service recommendations. To address this challenge, this paper proposes a high-efficient hybrid recommendation system based on Fuzzy C-Means (FCM) clustering and supervised learning models. The proposed recommendation method includes two aspects: on the one hand, FCM clustering technique has been applied to the item-based collaborative filtering framework to solve the cold start problem; on the other hand, the content information is integrated into the collaborative filtering. The algorithm constructs the user and item membership degree feature vector, and adopts the data representation form of the scoring matrix to the supervised learning algorithm, as well as by combining the subjective membership degree feature vector and the objective membership degree feature vector in a linear combination, the prediction accuracy is significantly improved on the public datasets with different sparsity. The efficiency of the proposed system is illustrated by conducting several experiments on MovieLens dataset.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.5
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• pp.1027-1037
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• 2012

With response to the critical issue of global warming, Smart Grid system has been extensively investigated as next efficient power grid system. Domestically, Korean is trying to expand the usage of Electric Vehicles (EVs) and the charging infrastructure in order to replace the current transportation using fossil fuels holding 20% of overall CO2 emission. The EVs charging infrastructures are combined with IT technologies to build intelligent environments but have considerable number of cyber security issues because of its inherent nature of the technologies. This work not only provides logical architecture of EV charging infrastructures with security threats based on them but also analyses security requirements against security threats in order to overcome the adversarial activities to Smart Grid.

• Korean Journal of Remote Sensing
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• v.37 no.4
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• pp.747-761
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• 2021

With the increasing application of UAVs in intelligent transportation systems, vehicle detection for aerial images has become an essential engineering technology and has academic research significance. In this paper, a vehicle detection method for aerial images based on the YOLOv4 deep learning algorithm is presented. At present, the most known datasets are VOC (The PASCAL Visual Object Classes Challenge), ImageNet, and COCO (Microsoft Common Objects in Context), which comply with the vehicle detection from UAV. An integrated dataset not only reflects its quantity and photo quality but also its diversity which affects the detection accuracy. The method integrates three public aerial image datasets VAID, UAVD, DOTA suitable for YOLOv4. The training model presents good test results especially for small objects, rotating objects, as well as compact and dense objects, and meets the real-time detection requirements. For future work, we will integrate one more aerial image dataset acquired by our lab to increase the number and diversity of training samples, at the same time, while meeting the real-time requirements.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.167-174
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• 2022

Vehicular Ad hoc Networks are considered as special kind of Mobile Ad Hoc Networks. VANETs are a new emerging recently developed, advanced technology that allows a wide set of applications related to providing more safety on roads, more convenience for passengers, self-driven vehicles, and intelligent transportation systems (ITS). Delay Tolerant Networks (DTN) are networks that allow communication in the event of connection problems, such as delays, intermittent connections, high error rates, and so on. Moreover, these are used in areas that may not have end-to-end connectivity. The expansion from DTN to VANET resulted in Vehicle Delay Tolerant Networks (VDTN). In this approach, a vehicle stores and carries a message in its buffer, and when the opportunity arises, it forwards the message to another node. Carry-store-forward mechanisms, packets in VDTNs can be delivered to the destination without clear connection between the transmitter and the receiver. The primary goals of routing protocols in VDTNs is to maximize the probability of delivery ratio to the destination node, while minimizing the total end-to-end delay. DTNs are used in a variety of operating environments, including those that are subject to failures and interruptions, and those with high delay, such as vehicle ad hoc networks (VANETs). This paper discusses DTN routing protocols belonging to unicast delay tolerant position based. The comparison was implemented using the NS2 simulator. Simulation of the three DTN routing protocols GeOpps, GeoSpray, and MaxProp is recorded, and the results are presented.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.189-197
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• 2022

Today, we are exposed to various text-based media such as newspapers, Internet articles, and SNS, and the amount of text data we encounter has increased exponentially due to the recent availability of Internet access using mobile devices such as smartphones. Collecting useful information from a lot of text information is called text analysis, and in order to extract information, it is performed using technologies such as Natural Language Processing (NLP) for processing natural language with the recent development of artificial intelligence. For this purpose, a morpheme analyzer based on everyday language has been disclosed and is being used. Pre-learning language models, which can acquire natural language knowledge through unsupervised learning based on large numbers of corpus, are a very common factor in natural language processing recently, but conventional morpheme analysts are limited in their use in specialized fields. In this paper, as a preliminary work to develop a natural language analysis language model specialized in the railway field, the procedure for construction a corpus specialized in the railway field is presented.

• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.4
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• pp.23-30
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• 2022

Recently, traffic congestion and environmental pollution have occurred due to population concentration and vehicle increase in large cities. Various studies are being conducted to solve these problems. Most of the traffic congestion in cities is caused by traffic signals at intersections. This paper proposes a dynamic traffic light control (DTLC) scheme to support safe vehicle operation and smooth traffic flow using real-time traffic information based on VANET. DTLC receives instantaneous speed and directional information of each vehicle through road side units (RSUs) to obtain the density and average speed of vehicles for each direction. RSUs deliver this information to traffic light controllers (TLCs), which utilize it to dynamically control traffic lights at intersections. To demonstrate the validity of DTLC, simulations were performed on average driving speed and average waiting time using the ns-2 simulator. Simulation results show that DTLC can provide smooth traffic flow by increasing average driving speed at dense intersections and reducing average waiting time.

• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.4
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• pp.1-11
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• 2022

Recently, research on license plate recognition, which is a core technology of an intelligent transportation system(ITS), is being actively conducted. In this paper, we propose a method to extract numbers and characters from low-quality license plate images by applying the YOLOv4 algorithm. YOLOv4 is a one-stage object detection method using convolution neural network including BACKBONE, NECK, and HEAD parts. It is a method of detecting objects in real time rather than the previous two-stage object detection method such as the faster R-CNN. In this paper, we studied a method to directly extract number and character regions from low-quality license plate images without additional edge detection and image segmentation processes. In order to evaluate the performance of the proposed method we experimented with 500 license plate images. In this experiment, 350 images were used for training and the remaining 150 images were used for the testing process. Computer simulations show that the mean average precision of detecting number and character regions on vehicle license plates was about 93.8%.