• The Journal of Korean Institute of Communications and Information Sciences
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• v.37B no.12
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• pp.1113-1118
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• 2012

Smart grid and intelligent transportation system draw significant interest since they are considered as one of the green technologies. These systems require a large number of sensors, actuators, and controllers. Also, machine-to-machine (M2M) communications is important because of the automatic control. The LTE-Advanced networks is preparing a set of functions that facilitate the M2M communications, and particularly the development of an efficient call admission control mechanism is critical. A method that groups MTC devices according to QoS constraints and determines the admission depending on the QoS satisfaction is limitedly applied only if the data transmission period and the maximum delay are identical. This paper proposed a call admission control that is free from such limitation and also optimizes the admission process under the certain condition of the transmission period and maximum delay. The theorems regarding the proposed method are presented with the proofs. The simulations confirms its validity and shows it is better in call admission probability than existing works.

• Journal of Korean Society for Geospatial Information Science
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• v.14 no.3 s.37
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• pp.3-11
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• 2006

The need for edge detection about topography data from the high resolution satellite imagery is happening with increasing frequency according to many people utilize the its imagery as various fields recently. Many experts is recognizing of other GIS will make use of the road detection from the high resolution satellite imagery, including ITS (Intelligent Transportation Systems) and urban planning. This paper is comparative analysis of LPF (Low Pass Filtering) and HPF (High Pass Filtering) for roads edge detection from high resolution satellite imagery. As a result, LPF and HPF can be highlight selective pixels at edge area about input data. In case or applying to other techniques such as LPF for the same purpose, they aye more effective for wide road width which often cause the slight distortion of boundary or overall change of brightness values on the whole Image. Whereas, HPF has ability to enhance selectively detailed components in a target image.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.3
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• pp.271-278
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• 2014

In spite of that stop line and crosswalk should be aware of the most basic objects in transportation system, its features extracted are very limited. In addition to image-based recognition technology, laser and RF, GPS/INS recognition technology, it is difficult to recognize. For this reason, the limited research in this area has been done. In this paper, the algorithm to recognize the stop line and crosswalk is designed and implemented using image-based recognition technology with the images input through a vision sensor. This algorithm consists of three functions.; One is to select the area, in advance, needed for feature extraction in order to speed up the data processing, 'Region of Interest', another is to process the images only that white color is detected more than a certain proportion in order to remove the unnecessary operation, 'Color Pattern Inspection', the other is 'Feature Extraction and Recognition', which is to extract the edge features and compare this to the previously-modeled one to identify the stop line and crosswalk. For this, especially by using case based feature comparison algorithm, it can identify either both stop line and crosswalk exist or just one exists. Also the proposed algorithm is to develop existing researches by comparing and analysing effect of in-vehicle camera installation and changes in recognition rate of distance estimation and various constraints such as backlight and shadow.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.11B
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• pp.1346-1356
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• 2011

Transportation has been playing important role in our society by providing for people, freight, and information. However, it cuts its own throat by causing car accidents, traffic congestion, and air pollution. The main cause of these problems is a noticeable growth in the number of vehicles. The easiest way to mitigate these problems is to build new road infrastructures unless resources such as time, money, and space are limited. Therefore, there is a need to manage the existing road infrastructures effectively and safely. In this paper, we propose a road reservation scheme that provides fast and safe response for emergency vehicles using ubiquitous sensor network. Our idea is to allow emergency vehicle to reserve a road on a freeway for arriving to the scene of the accident quickly and safely. We evaluate the performance by three reservation method (No, Hop, and Full) to show that emergency vehicles such as ambulances, fire trucks, or police cars can rapidly and safely reach their destination. Simulation results show that the average speed of road reservation is about 1.09 ~ 1.20 times faster than that of non-reservation at various flow rates. However, road reservation should consider the speed of the emergency vehicle and the road density of the emergency vehicle processing direction, as a result of Hop Reservation and Full Reservation performance comparison analysis. We confirm that road reservation can guarantee safe driving of emergency vehicles without reducing their speed and help to mitigate traffic congestion.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.4
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• pp.133-142
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• 2021

With the recent advancements of deep learning, companies such as smart home, healthcare, and intelligent transportation systems are utilizing its functionality to provide high-quality services for vehicle detection, emergency situation detection, and controlling energy consumption. To provide reliable services in such sensitive systems, deep learning models are required to have high accuracy. In order to develop a deep learning model for analyzing previously mentioned services, developers should utilize the state of the art deep learning models that have already been verified for higher accuracy. The developers can verify the accuracy of the referenced model by validating the model on the dataset. For this validation, the developer needs structural information to document and apply deep learning models, including metadata such as learning dataset, network architecture, and development environments. In this paper, we propose a description language that represents the network architecture of the deep learning model along with its metadata that are necessary to develop a deep learning model. Through the proposed description language, developers can easily verify the accuracy of the referenced deep learning model. Our experiments demonstrate the application scenario of a deep learning description document that focuses on the license plate recognition for the detection of illegally parked vehicles.

• Journal of Korean Society for Atmospheric Environment
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• v.26 no.4
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• pp.367-379
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• 2010

Future climate changes over the Seoul metropolitan area (SMA) were predicted by the Weather Research and Forecasting (WRF) model using future land-use data from the urban growth model (SLEUTH) and forecast fields from ECHAM5/MPI-OM1 GCM (IPCC scenario A1B). Simulations from the SLEUTH model with GIS information (slope, urban, hill-shade, etc.) derived from the water management information system (WAMIS) and the intelligent transportation systems-standard nodes link (ITS-SNL) showed that considerable increase by 17.1% in the fraction of urban areas (FUA) was found within the SMA in 2020. To identify the effects of the urban growth on the temperature and wind variations in the future, WRF simulations by considering urban growth were performed for two seasons (summer and winter) in 2020s (2018~2022) and they were compared with those in the present (2003~2007). Comparisons of model results showed that significant changes in surface temperature (2-meter) were found in an area with high urban growth. On average in model domain, positive increases of $0.31^{\circ}C$ and $0.10^{\circ}C$ were predicted during summer and winter, respectively. These were higher than contributions forced by climate changes. The changes in surface temperature, however, were very small expect for some areas. This results suggested that surface temperature in metropolitan areas like the SMA can be significantly increased only by the urban growth during several decades.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.8
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• pp.1933-1954
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• 2013

Multiple wireless devices are being widely deployed in Intelligent Transportation System (ITS) services on the road to establish end-to-end connection between vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) networks. Vehicular ad hoc networks (VANETs) play an important role in supporting V2V and V2I communications (also called V2X communications) in a variety of urban environments with distinct topological characteristics. In fact, obstacles such as big buildings, moving vehicles, trees, advertisement boards, traffic lights, etc. may block the radio signals in V2X communications. Their impact has been neglected in VANET research. In this paper, we present a realistic and efficient radio propagation model to handle different sizes of static and moving obstacles for V2X communications. In the proposed model, buildings and large moving vehicles are modeled as static and moving obstacles, and taken into account their impact on the packet reception rate, Line-of-sight (LOS) obstruction, and received signal power. We use unsymmetrical city map which has many dead-end roads and open faces. Each dead-end road and open faces are joined to the nearest edge making a polygon to model realistic obstacles. The simulation results of proposed model demonstrates better performance compared to some existing models, that shows proposed model can reflect more realistic simulation environments.

• Journal of Communications and Networks
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• v.14 no.6
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• pp.662-671
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• 2012

Plug-in hybrid electric vehicles (PHEVs) will be widely used in future transportation systems to reduce oil fuel consumption. Therefore, the electrical energy demand will be increased due to the charging of a large number of vehicles. Without intelligent control strategies, the charging process can easily overload the electricity grid at peak hours. In this paper, we consider a smart charging and discharging process for multiple PHEVs in a building's garage to optimize the energy consumption profile of the building. We formulate a centralized optimization problem in which the building controller or planner aims to minimize the square Euclidean distance between the instantaneous energy demand and the average demand of the building by controlling the charging and discharging schedules of PHEVs (or 'users'). The PHEVs' batteries will be charged during low-demand periods and discharged during high-demand periods in order to reduce the peak load of the building. In a decentralized system, we design an energy cost-sharing model and apply a non-cooperative approach to formulate an energy charging and discharging scheduling game, in which the players are the users, their strategies are the battery charging and discharging schedules, and the utility function of each user is defined as the negative total energy payment to the building. Based on the game theory setup, we also propose a distributed algorithm in which each PHEV independently selects its best strategy to maximize the utility function. The PHEVs update the building planner with their energy charging and discharging schedules. We also show that the PHEV owners will have an incentive to participate in the energy charging and discharging game. Simulation results verify that the proposed distributed algorithm will minimize the peak load and the total energy cost simultaneously.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.3
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• pp.27-37
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• 2017

As the global uncertainty of manufacturing has increased and the quality problem has become global, the recall has become a fatal risk that determines the durability of the company. In addition, as the convergence of PSS (product-service system) product becomes common due to the development of IT convergence technology, if the function of any part of hardware or software does not operate normally, there will be a problem in the entire function of PSS product. In order to manage the quality of such PSS products in a stable manner, a new approaches is needed to analyze and manage the hardware and software parts at the same time. However, the Fishbone diagram, FTA, and FMEA, which are widely used to interpret the current quality problem, are not suitable for analyzing the quality problem by considering the hardware and software at the same time. In this paper, a quality risk assessment model combining FTA and FMEA based on defect rate to be assessed daily on site to manage quality and fishbone diagram used in group activity to solve defective problem. The proposed FTA-FMEA based risk assessment model considers the system structure characteristics of the defect factors in terms of the relationship between hardware and software, and further recognizes and manages them as risk. In order to evaluate the proposed model, we applied the functions of ITS (intelligent transportation system). It is expected that the proposed model will be more effective in assessing quality risks of PSS products because it evaluates the structural characteristics of products and causes of defects considering hardware and software together.

• Journal of Korean Society of Transportation
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• v.18 no.4
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• pp.117-126
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• 2000

Conventional methods for collecting origin-destination trips have been mainly relied on the surveys of home or roadside interview. However, the methods tend to be costly, labor intensive and time disruptive to the trip makers, thus the methods are not considered suitable for Planning applications such as routing guidance, arterial management and information Provision, as the parts of deployments in Intelligent Transport Systems Motivated by the problems, more economic ways to estimate origin-destination trip tables have been studied since the late 1970s. Some of them, which have been estimating O-D table from link traffic counts are generally Entropy maximizing, Maximum likelihood, Generalized least squares(GLS), and Bayesian inference estimation etc. In the Paper, with user equilibrium constraint we formulate GLS problem for estimating O-D trips and develop a solution a1gorithm by using Genetic Algorithm, which has been known as a g1oba1 searching technique. For the purpose of evaluating the method, we apply it to Seoul inner ringroad and compare it with gradient method proposed by Spiess(1990). From the resu1ts we fond that the method developed in the Paper is superior to other.