• The Journal of Information Systems
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• v.29 no.2
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• pp.131-147
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• 2020

Purpose The purpose of this study is to develop intelligent vehicle parking distribution system based on LoRa network at the circumstance of traffic congestion during cultural festival in a local city. This paper proposes a parking dispatch and distribution system using a Q-learning algorithm to rapidly disperse traffics that increases suddenly because of in-bound traffics from the outside of a city in the real-time base as well as to increase parking probability in a parking lot which is widely located in a city. Design/methodology/approach The system get information on realtime-base from the sensor network of IoT (LoRa network). It will contribute to solve the sudden increase in traffic and parking bottlenecks during local cultural festival. We applied the simulation system with Queuing model to the Yudeung Festival in Jinju, Korea. We proposed a Q-learning algorithm that could change the learning policy by setting the acceptability value of each parking lot as a threshold from the Jinju highway IC (Interchange) to the 7 parking lots. LoRa Network platform supports to browse parking resource information to each vehicle in realtime. The system updates Q-table periodically using Q-learning algorithm as soon as get information from parking lots. The Queuing Theory with Poisson arrival distribution is used to get probability distribution function. The Dijkstra algorithm is used to find the shortest distance. Findings This paper suggest a simulation test to verify the efficiency of Q-learning algorithm at the circumstance of high traffic jam in a city during local festival. As a result of the simulation, the proposed algorithm performed well even when each parking lot was somewhat saturated. When an intelligent learning system such as an O-learning algorithm is applied, it is possible to more effectively distribute the vehicle to a lot with a high parking probability when the vehicle inflow from the outside rapidly increases at a specific time, such as a local city cultural festival.

• ETRI Journal
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• v.37 no.6
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• pp.1220-1230
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• 2015

An autonomous valet parking (AVP) system is designed to locate a vacant parking space and park the vehicle in which it resides on behalf of the driver, once the driver has left the vehicle. In addition, the AVP is able to direct the vehicle to a location desired by the driver when requested. In this paper, for an AVP system, we introduce technology to recognize a parking space using image sensors. The proposed technology is mainly divided into three parts. First, spatial analysis is carried out using a height map that is based on dense motion stereo. Second, modelling of road markings is conducted using a probability map with a new salient-line feature extractor. Finally, parking space recognition is based on a Bayesian classifier. The experimental results show an execution time of up to 10 ms and a recognition rate of over 99%. Also, the performance and properties of the proposed technology were evaluated with a variety of data. Our algorithms, which are part of the proposed technology, are expected to apply to various research areas regarding autonomous vehicles, such as map generation, road marking recognition, localization, and environment recognition.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1279-1286
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• 2021

This study suggests the user's needs for a smart parking service that enables parking lot search and advance reservation service, and is a study on the user's preference selection model related to fees (reservation fee, penalty fee), etc. Two types of user preference models in the form of logit models were constructed by composing a response questionnaire for smart parking service. The first is a model for selecting a smart parking lot, which suggests a situation in which the probability of selection is higher than that of a general parking lot in the relationship between usage fee and cost. The second is a parking ticket reservation discount selection model, and the smart parking service selection probability was analyzed through the relationship model between the reservation amount and the penalty. It can be used as a design requirement that enables sophisticated and various types of smart parking service considering users' preferences.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.5
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• pp.587-595
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• 2017

The study examinesthe hindrance factors including various laws and institutions with regard to the Unbundled Parking System (UPS) that has recently attracted the greatest attention as a way to manage traffic demand for sustainable development, efficient utilization of parking spaces, and to overcome the unstable housing market.The direction of improvement of related laws and institutions is also suggested. Its usage is proven by countries such as the United States of America and France, as they have already implemented this institution. To lay the foundation for the introduction of UPS of our own country, a survey on the preference for UPS was conducted. The survey equally divided 300 respondents into three clusters based on the sales price of apartments in Seoul. The analyses revealed that all three clusters have similar preferences (cluster 1: 68%, cluster 2: 62%, cluster 3: 65%) on UPS, and younger groups seem to answer in the affirmative more than the other age groups no matter what cluster they belong to. In conclusion, the results on the estimation of selection probability on the preference of unbundled parking system and sales discount rate are as follow. The groups of non-vehicle users have higher preference on UPS. When the discount rate is 14%, 69%, 77% and 62%ofrespondents would choose unbundled parking system for clusters 1, 2 and 3, respectively (￦6,370,000/PY,￦3,930,000/PY and ￦2,270,000/PY reduce when applying avg. sales price, respectively).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.1B
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• pp.125-135
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• 2010

Recently, the problem of global warming has issued seriously, so Green IT(Information Technology) using RFID/USN is concerned in order to solve environmental problems. In this paper, we implemented that public area like an underground parking lot could reduce unnecessary energy consumption used by lighting control system based on Ubiquitous Sensor Networks. The lighting control system for underground parking lot is comprised of intersection nodes for watching cars enter and leave, and light node for controlling light. It applies the routing protocol based on hierarchical cluster, hierarchical addressing method, and probability filtering method for the specific place like an underground parking lot. The result after experiments shows that the lighting control system could decrease electrical energy consumption to around 61.7%.

• Journal of Korean Society of Transportation
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• v.26 no.3
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• pp.97-108
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• 2008

Like always selecting anything in everyday lives, We must choose a travel mode to achieve its purposes driven by diverse factors such as travel distance and accessibility of public transit. Assuming that they are differentiated depending on whether a travel purpose is commuting, shopping or leasure, the study investigated their distinguished impacts on travel mode choice by using binary logit models by travel purpose and mode. Identifying that travel time has an important role in choosing a travel mode whether its purpose is any, the results show that longer travel time tends to increase the possibilities of taking public transit, transfer and rail transit rather than bus. In addition, the easy use of a car and its parking to travelers is more important in their choosing an automobile as a travel mode than other factors. In the models of identifying the probability of mode choice between bus and rail transit, we find that its choice tends to be decided by travelers depending on whether any public transit mode is more accessible to them. When comparing the results among travel purposes, we identify that the easy use of a car and parking in their destination is more important for commuting, while accessibility of public transit in their origination increases the probability of taking a transit mode.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.3188-3207
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• 2017

In this paper, we propose an image enhancement algorithm to improve license plate extraction rate in various environments (Day Street, Night Street, Underground parking lot, etc.). The proposed algorithm is composed of image enhancement algorithm and license plate extraction algorithm. The image enhancement method can improve an image quality of the degraded image, which utilizes a histogram information and overall gray level distribution of an image. The proposed algorithm employs an interpolated probability distribution value (PDV) in order to control a sudden change in image brightness. Probability distribution value can be calculated using cumulative distribution function (CDF) and probability density function (PDF) of the captured image, whose values are achieved by brightness distribution of the captured image. Also, by adjusting the image enhancement factor of each part region based on image pixel information, it provides a function that can adjust the gradation of the image in more details. This processed gray image is converted into a binary image, which fuses narrow breaks and long thin gulfs, eliminates small holes, and fills gaps in the contour by using morphology operations. Then license plate region is detected based on aspect ratio and license plate size of the bound box drawn on connected license plate areas. The images have been captured by using a video camera or a personal image recorder installed in front of the cars. The captured images have included several license plates on multilane roads. Simulation has been executed using OpenCV and MATLAB. The results show that the extraction success rate is more improved than the conventional algorithms.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.10
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• pp.1911-1915
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• 2009

The problem of determining the proper capacity of electric vehicle charging station is studied in the presented paper. Based on the expected arrival rate and the expected charging time, we calculate the proper capacity that guarantees electric vehicles get service better than a given lower bound which is termed the loss of charging probability. The problem is studied by using certain queueing models. We first formulate the problem as a queueless model of type M/M/n/n, known as the Erlang loss system. And then the M/M/n/K type queueing model is formulated to consider the parking space constraint. Results of the study may be used for designing the electric vehicle charging station.

• Journal of Wetlands Research
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• v.21 no.2
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• pp.173-180
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• 2019

This study was conducted to understand factors affecting TSS and heavy metals transport on the road, parking lot and roof. During storm events, heavy metals, which were mostly attached to TSS, were also transported when TSS was washed off in the road, parking lot and roof. This finding may be supported by the significant correlations between TSS load and total and soluble heavy metals load including Cr, Fe, Cu, and Pb (Pearson r value: 0.52 to 0.73; probability p value<0.01). Generation and transport of TSS and heavy metals were greater in the road and parking lot compared to the roof due to vehicular activities, slope and greater catchment areas of these sites. It was found that TSS transport during peak flows of storm events ranges from 65% to 75% implying that by controlling peak flows, TSS transportation to nearby water bodies may be decreased. Depending on the target TSS and heavy metal reduction, sizing of low impact development (LID) technologies and green infrastructures (GI) such as infiltration trench, tree box filter, and rain garden may be calculated. Future researchers were recommended to assess the limitations of the systems and determine the design considerations for these types of facilities.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.693-698
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• 2018

This paper proposes a vehicle detection method for parking areas using unmanned aerial vehicles (UAVs) and using YOLOv2, which is a recent, known, fast, object-detection real-time algorithm. The YOLOv2 convolutional network algorithm can calculate the probability of each class in an entire image with a one-pass evaluation, and can also predict the location of bounding boxes. It has the advantage of very fast, easy, and optimized-at-detection performance, because the object detection process has a single network. The sliding windows methods and region-based convolutional neural network series detection algorithms use a lot of region proposals and take too much calculation time for each class. So these algorithms have a disadvantage in real-time applications. This research uses the YOLOv2 algorithm to overcome the disadvantage that previous algorithms have in real-time processing problems. Using Darknet, OpenCV, and the Compute Unified Device Architecture as open sources for object detection. a deep learning server is used for the learning and detecting process with each car. In the experiment results, the algorithm could detect cars in a dense area using UAVs, and reduced overhead for object detection. It could be applied in real time.