• The Journal of the Convergence on Culture Technology
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• v.5 no.1
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• pp.429-434
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• 2019

There are several high technologies applied to the driving cars such as self-driving car and connected car for safe and convenient driving. VANET provides useful information such as route selection and gas price by communicating nearby cars and RSUs. VANET prefers CCN rather than traditional TCP/IP stack because CCN offers inherent multicast communication for sharing traffic information as well as traditional unicast. When all participating node rebroadcasts the Interest packets in a Vehicular CCN, the network may suffer from Broadcast Storm Problem. In order to mitigate the effect of the problem and to improve the Data packet transmission, not all but some selected nodes have to rebroadcast the packet. This paper simulates car movements using SUMO and evaluates data transmission performance using ns-3. According to the simulation results, when some selected nodes rebroadcast the Interest packets, the transmission performance improves 10% to 25% depending on the number of requesting nodes.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.490-492
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• 2021

In order to realize a safety society in traffic accidents, Korea prepared comprehensive government-wide measures in 2017. Efforts are being made to minimize accidents while walking by children and the elderly by lowering the speed limit in urban areas from 60 km to 50 km and limiting the vehicle to 30 km in the case of child protection zones. In this study, after pre-processing each data with the status of vehicle registration and traffic accident spatial data (GIS) by designating a specific area, Danyang-gun, where the rate of child traffic accidents is increasing every year, it is intended to understand the structure of the data and find out the structural pattern of the data analytical studies were conducted.

• Environmental and Resource Economics Review
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• v.32 no.3
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• pp.167-190
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• 2023

This study analyzes the potential demand for automobiles based on fuel type using survey data in Korea. The dependent variable of the model is the future desired fuel type, including gasoline, diesel, hybrid, electricity, and hydrogen. The main explanatory variables are the respondent demographic characteristics, key reasons for choosing vehicle fuel type and environmental awareness extracted via principal component analysis (PCA). Using a multinomial logit (MNL) model, we find that respondents who consider fuel economy and infrastructure increase the demand for a hybrid car but decrease the demand for electric and hydrogen vehicles. The denial-types increase the demand for gasoline (petrol) and diesel (light oil), and decrease the demand for electric vehicles. The anxiety-types increase the demand of hybrid vehicles, and decrease the demand for electric vehicles. In contrast, in the case of pro-types, the demand for diesel (light oil) hydrogen vehicles decreased.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.42 no.4
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• pp.15-20
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• 2005

If we can forecast the replacement time of engine oil, we extend the life-time of our engine and increase the continued ratio. But, the replacement times of engine oil is influenced by the following elements: the distance that cars or vehicles travel, vehicles that run a short range, types of engine oil etc. that run a long distance. In this paper, We forecast engine oil replacement times by using fuzzy neural network algorithm. This algerian uses the data of distance covered, color of engine oil etc. Through a sequence of simulation, the exchange system of intelligence style engine oil decides on the replacement times of engine oil quite accurately. Therefore, We expect vehicles to become more convenient if the above algorithm is a lied to the present types of cars.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.436-441
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• 2011

A TPMS is a wireless communication system designed to monitor its condition inside the pneumatic tires on various types of vehicles. These systems report the tire pressure information to the driver of the vehicle. While wireless communications is used to transmit the measurement data from TPMS sensors to a central processing unit in the vehicle, it suffers from the various interferences such as sensors of each tire or outside electrical equipments. Based on the conventional beamformer, a switching beamforming technique is proposed to minimize the interference and efficiently receive valid data. Moreover, in order to minimize the interference and reduce power consumption for communication, a system with unique Gold Code is presented for each tire. The performance of interference suppression is illustrated by computer simulations.

• The Journal of the Convergence on Culture Technology
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• v.8 no.4
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• pp.367-373
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• 2022

VANET (Vehicular Ad hoc NETwork) is one of the special cases of the ad hoc networks in which car nodes communicate with each other and/or with RSUs (Road Side Unit) in order for the drivers to receive nearby road traffic information as well as for the passengers to retrieve nearby gas price or hotel information. In case of constructing VANET over CCN, users do not need to specify a destination server address rather to input a key word such as nearby congestion in order to gather surrounding traffic congestion information. Furthermore, each car node caches its retrieved data for forwarding other nodes when requested. In addition, the data transmission is inherently multicast, which implies fast data propagation to the participating car nodes. This paper measures and evaluates the data transmission performance of the VCCN (VANET over CCN) in which nodes are equipped with diverse wireless communication channels. The simulation result indicates that 802.11a shows the best performance of the data transmission against other wireless channels. Moreover, it indicates that VCCN improves overall data transmission and provides benefit to the nodes that request the same traffic information by exploiting inherent multicast communication.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.5
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• pp.1095-1100
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• 2013

Carbon dioxide is a major cause for which accelerates Global Warming. Therefore several countries are working on the project recommended to use a bicycle instead of the car when you move to the nearby destination in an effort to reduce the emissions of carbon dioxide. In this paper, It was developed to measure the greenhouse gas reduction using Bluetooth Sensor Node by riding a bicycle instead of a car and management system in order to authenticate the riding record. The developed application provides various information such as individual bicycle mileage, greenhouse gas reductions, bicycle riding path, the number of planted ginkgo trees. This proposed system is expected to be helpful to green house gas emission reduction because the usage rate of bicycle will increase if government combine ways to offer people rewards such as pin money or tax breaks for people who take advantage of the bicycle with the project.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.301-303
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• 2015

디지털 운행기록장치는 대한민국의 경우 2011년부터 상용차에 대해 법령으로 장착이 의무화되었다. 이 장치는 자동차의 속도, 주행거리, 브레이크 상태, GPS 위지청보 등을 수집하는 기록 장치로서 이를 통해 수집된 정보로 운전 형태를 파악하고 분석해준다. 그러나 초기 장치 구입비용과 전송 방식에 따른 월별 통신서비스 비용 때문에 일반 운전자의 사용이 제한된다. 따라서 본 연구는 일반 운전자도 위험운전행동을 분석하고 파악하여, 결과를 제공하는 스마트폰 어플리케이션의 설계 및 구현을 목적으로 한다. ECU(Electronic Control Unit)에서 얻을 수 있는 주행 데이터(가속 제동, 속도, 운전 시간 등)를 OBD-II(On-Board Diagnostic version II) Scanner를 통해 수집한다. 이 정보를 바탕으로 위험운전평가 알고리즘을 이용하여 실시간 분석한다. 상기 알고리즘은 대한민국의 교통안전공단에서 제공하는 위험운전(11종)에 대한 정의를 바탕으로 한다. 이를 통해 분석된 결과는 실시간으로 사용자에게 제공되며, SQLite를 이용하여 DB에 저장되고 통신사의 이동통신(3G,4G) 혹은 Wifi의 네트워크를 이용하여 연계 서버에 전송된다. 이 위험 운전 분석 어플리케이션을 통해 운전자들의 안전 운전을 유도하고자하며, 추후 운전습관 연계 보험의 도입에 따라 합리적이고 공정한 데이터를 제공하고자 한다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.20-29
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• 2021

In the Industry 4.0 era, artificial intelligence has attracted considerable interest for learning mass data to improve the accuracy of forecasting and classification. On the other hand, the current method of detecting anomalies relies on traditional statistical methods for a limited amount of data, making it difficult to detect accurate anomalies. Therefore, this paper proposes an artificial intelligence-based anomaly detection methodology to improve the prediction accuracy and identify new data patterns. In particular, data were collected and analyzed from the point of view that sensor data collected at vehicle idle could be used to detect abnormalities. To this end, a sensor was designed to determine the appropriate time length of the data entered into the forecast model, compare the results of idling data with the overall driving data utilization, and make optimal predictions through a combination of various sensor data. In addition, the predictive accuracy of artificial intelligence techniques was presented by comparing Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) as the predictive methodologies. According to the analysis, using idle data, using 1.5 times of the data for the idling periods, and using CNN over LSTM showed better prediction results.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.141-144
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• 2000

최근에 무선통신망을 이용한 데이터 서비스가 폭넓게 제공되면서, 이동체(MS:mobile station)에 대한 위치정보나 교통상황 둥의 부가 정보 서비스가 제공되고 있다. 이와 같이 이동체가 자동차와 같은 운행수단일 때 사용자가 디스플레이 되는 문자정보를 확인하게 되면 운전의 안정성이 저하되어 실용적이지 못하다. 이를 위해서 문자를 음성으로 전환하여 주는 문자-음성변환기(text to speech : TTS)가 필요하다. 본 논문은 car navigation용 '한국어 무제한 어휘 음성합성기' 를 저가의 DSP chip(ADSP-2185)과 저용량의 4M bits ROM을 사용하여 low-cost system으로 하드웨어를 구성하였다. 본 연구에서 개발된 실시간 한국어 음성 합성기는 저가의 통신 단말기로서 사용 될 수 있으나, 반음절 연결부분의 연결이 불완전한 경우가 많았다. 그러나 종성이 없는 음절에 대해서는 명료도가 비교적 우수하였다.