• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.4
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• pp.42-50
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• 2009

Ubiquitous computing technology is widely used in not only our everyday lives but also in education, medical care, military, environment and administration. RFID system, the basis of ubiquitous, is in the spotlight which can be an alternative solution of a bar code recognition system and magnetic system as they basically have practicality and security issues. An electronic authentication named smart-key system is recently concerned by an alternative solution of the security unit for an automobile. RFID system which has a general purpose is also in the limelight by an application technology. In this paper we designed vehicle smart key system with general-propose RFID system that is already in use. First, we designed control unit and RFID card reader for vehicle smart key system. Then we propose an algorithm and prove that the vehicle key system is controllable by showing the result of implementing and testing, after installing. Also security level is enlarged by proposing a authentication protocol between RFID reader and control unit.

• Journal of the Korea Convergence Society
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• v.10 no.11
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• pp.327-333
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• 2019

The Fuel-cut driving is started when the acceleration pedal released with transmission gear engaged. Fuel economy of the vehicle improves by active fuel-cut driving. A deep-learning technique is proposed to predict fuel-cut driving with vehicle speed, acceleration and road gradient data in the study. It's 3~10 of hidden layers and 10~20 of variables and is applied to the 9600 data obtained in the test driving of a vehicle in the road of 12km. Its accuracy is about 84.5% with 10 variables, 7 hidden layers and Relu as activation function. Its error is regarded from the fact that the change rate of input data is higher than the rate of fuel consumption data. Therefore the accuracy can be better by the normalizing process of input data. It's unnecessary to get the signal of vehicle injector or OBD, and a deep-learning technique applied to the data to be got easily, like GPS. It can contribute to eco-drive for the computing time small.

• Smart Structures and Systems
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• v.21 no.5
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• pp.611-621
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• 2018

Compared with the highway bridges, the relatively higher requirement on the safety and comfort of vehicle makes the high-speed railway (HSR) bridges need to present enhanced dynamic performance. To this end, installing a health monitor system (HMS) on selected key HSR bridges has been widely applied. Typically, the HSR takes fully enclosed operation model and its skylight time is very short, which means that it is not easy to operate the acquisition devices and download data on site. However, current HMS usually involves manual operations, which makes it inconvenient to be used for the HSR. Hence, a HMS named DASP-MTS (Data Acquisition and Signal Processing - Monitoring Test System) that integrates the internet, cloud computing (CC) and virtual instrument (VI) techniques, is developed in this study. DASP-MTS can realize data acquisition and transmission automatically. Furthermore, the acquired data can be timely shared with experts from various locations to deal with the unexpected events. The system works in a Browser/Server frame so that users at any places can obtain real-time data and assess the health situation without installing any software. The developed integrated HMS has been applied to the Xijiang high-speed railway arch bridge. Preliminary analysis results are presented to demonstrate the efficacy of the DASP-MTS as applied to the HSR bridges. This study will provide a reference to design the HMS for other similar bridges.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.4988-5012
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• 2019

As a subset of the Internet of Things, the Internet of Energy (IoE) is expected to tackle the problems faced by the current smart grid framework. Notably, the conventional day-ahead power scheduling of the smart grid should be redesigned in the IoE architecture to take into consideration the intermittence of scattered renewable generations, large amounts of power consumption data, and the uncertainty of the arrival time of electric vehicles (EVs). Accordingly, a day-ahead power scheduling system for the future IoE is proposed in this research to maximize the usage of distributed renewables and reduce carbon emission caused by the traditional power generation. Meanwhile, flexible charging mechanism of EVs is employed to provide preferred charging options for moving EVs and flatten the load profile simultaneously. The simulation results revealed that the proposed power scheduling mechanism not only achieves emission reduction and balances power load and supply effectively, but also fits each individual EV user's preference.

• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.119-127
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• 2020

As the electric vehicle (EV) market in South Korea grows, it is required to expand charging facilities to respond to rapidly increasing EV charging demand. In order to conduct a comprehensive facility planning, it is necessary to forecast future demand for electricity and systematically analyze the impact on the load capacity of facilities based on this. In this paper, we design and develop a Long Short-Term Memory (LSTM) neural network model that predicts the daily peak electric load at each charging station using the EV charging data of KEPCO. First, we obtain refined data through data preprocessing and outlier removal. Next, our model is trained by extracting daily features per charging station and constructing a training set. Finally, our model is verified through performance analysis using a test set for each charging station type, and the limitations of our model are discussed.

• IEIE Transactions on Smart Processing and Computing
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• v.2 no.4
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• pp.189-196
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• 2013

This paper presents a two-stage approach to detect pedestrians in video sequences taken from a moving vehicle. The first stage is a preprocessing step, in which potential pedestrians are hypothesized. During the preprocessing step, a difference image is constructed using a global motion estimation, vertical and horizontal edge maps are extracted, and the color difference between the road and pedestrians are determined to create candidate regions where pedestrians may be present. The candidate regions are refined further using the vertical edge symmetry features of the pedestrians' legs. In the next stage, each hypothesis is verified using the integral channel features and an AdaBoost classifier. In this stage, a decision is made as to whether or not each candidate region contains a pedestrian. The proposed algorithm was tested on a range of dataset images and showed good performance.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.4
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• pp.216-223
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• 2015

The autonomous underwater vehicle (AUV) is being widely researched in order to achieve superior performance when working in hazardous environments. This research focuses on using image processing techniques to estimate the AUV's egomotion and the changes in orientation, based on image frames from different time frames captured from a single high-definition web camera attached to the bottom of the AUV. A visual odometry application is integrated with other sensors. An internal measurement unit (IMU) sensor is used to determine a correct set of answers corresponding to a homography motion equation. A pressure sensor is used to resolve image scale ambiguity. Uncertainty estimation is computed to correct drift that occurs in the system by using a Jacobian method, singular value decomposition, and backward and forward error propagation.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.1
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• pp.7-18
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• 2021

Recently, various connected industrial parks (CIPs) architectures using new technologies such as cloud computing, CPS, big data, fifth-generation mobile communication 5G, IIoT, VR-AR, and ventilation transportation AI algorithms have been proposed in Korea. Korea's small and medium-sized enterprises do not have the upper hand in technological competitiveness than overseas advanced countries such as the United States, Europe and Japan. For this reason, Korea's small and medium-sized enterprises have to invest a lot of money in technology research and development. As a latecomer, Korean SMEs need to improve their profitability in order to find sustainable growth potential. Financially, it is most efficient for small and medium-sized Korean companies to cut costs to increase their profitability. This paper made profitability improvement by reducing costs for small and medium-sized enterprises located in CIPs in Korea a major task. VJP (Vehicle Action Program) was noted as a way to reduce costs for small and medium-sized enterprises located in CIPs in Korea. The method of achieving minimum logistics costs for small businesses through the Korean CIPs payment system was analyzed. The details of the new Korean CIPs payment system were largely divided into four types: "Business", "Data", "Technique", and "Finance". Cost Benefit Analysis (CBA) was used as a performance analysis method for CIPs payment systems.

• Journal of Computing Science and Engineering
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• v.6 no.3
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• pp.227-242
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• 2012

Vehicular Ad Hoc Networks (VANETs) are highly dynamic and unstable due to the heterogeneous nature of the communications, intermittent links, high mobility and constant changes in network topology. Currently, some of the most important challenges of VANETs are the scalability problem, congestion, unnecessary duplication of data, low delivery rate, communication delay and temporary fragmentation. Many recent studies have focused on a hybrid mechanism to disseminate information implementing the store and forward technique in sparse vehicular networks, as well as clustering techniques to avoid the scalability problem in dense vehicular networks. However, the selection of intermediate nodes in the store and forward technique, the stability of the clusters and the unnecessary duplication of data remain as central challenges. Therefore, we propose an adaptable destination-based dissemination algorithm (DBDA) using the publish/subscribe model. DBDA considers the destination of the vehicles as an important parameter to form the clusters and select the intermediate nodes, contrary to other proposed solutions. Additionally, DBDA implements a publish/subscribe model. This model provides a context-aware service to select the intermediate nodes according to the importance of the message, destination, current location and speed of the vehicles; as a result, it avoids delay, congestion, unnecessary duplications and low delivery rate.

• IEIE Transactions on Smart Processing and Computing
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• v.1 no.1
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• pp.17-26
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• 2012

This paper presents an image processing-based validation method for unrecognizable numbers in severely distorted license plate images which have been degraded by various factors including low-resolution, low light-level, geometric distortion, and periodic noise. Existing vehicle license plate recognition (LPR) methods assume that most of the image degradation factors have been removed before performing the recognition of printed numbers and letters. If this is not the case, conventional LPR becomes impossible. The proposed method adopts a novel approach where a set of reference number images are intentionally degraded using the same factors estimated from the input image. After a series of image processing steps, including geometric transformation, super-resolution, and filtering, a comparison using cross-correlation between the intentionally degraded reference and the input images can provide a successful identification of the visually unrecognizable numbers. The proposed method makes it possible to validate numbers in a license plate image taken under low light-level conditions. In the experiment, using an extended set of test images that are unrecognizable to human vision, the proposed method provides a successful recognition rate of over 95%, whereas most existing LPR methods fail due to the severe distortion.