• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.224-227
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• 2015

Data transmission via the car driver's tethered smart phone may have a volume-dependent billing in case car driver' phone transmits data in real-time to the remote data center. The on-board diagnosis data generated are temporary stored locally to mobile remote diagnosis application on the car driver's phone, and then transmit to the data center later when car driver connects to the Internet. To increase the easiest of using the remote vehicle application without blocking other tasks to be executing on the cloud, node.js stands as a suitable candidate for handling tasks of data storage on the cloud via mobile network. We demonstrate the effectiveness of the proposed architecture by simulating a preliminary case study of an android application responsible of real time analysis by using a vehicle-to- smart phones applications interface approach that considers the smart phones to act as a remote user which passes driver inputs and delivers output from external applications. In this paper, we propose a study on development of Remote Vehicle fault diagnostic system features web server architecture based event loop approach using node.js platform, and wireless communication to handle vehicle diagnostics data to a data center.

• Journal of Auto-vehicle Safety Association
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• v.13 no.1
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• pp.38-44
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• 2021

This study aimed to verify the criteria of the driver monitoring systems proposed by UNECE ACSF informal working group and the ministry of land, infrastructure, and transport of South Korea using driving behavior data. In order to verify the criteria, we investigated the safety regulations of driver monitoring systems in a conditional autonomous vehicle and found that the driver monitoring measures were related to eye blinks times, head movements, and eye closed duration. Thus, we took two different experimental data including real-world driving and simulator-based drowsy driving behaviors in previous studies. The real-world driving data were used for analyzing blink times and head movement intervals, and the drowsiness data were used for eye closed duration. In the real-world driving study, 52 drivers drove approximately 11.0 km of rural road (about 20 min), 7.9 km of urban road (about 25 min), and 20.8 km of highway (about 20 min). The results suggested that the appropriate number of blinks during the last 60 seconds was 4 times, and the head movement interval was 35 seconds. The results from drowsy driving data will be presented in another paper - part 2.

• Journal of Auto-vehicle Safety Association
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• v.13 no.1
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• pp.45-50
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• 2021

This study aimed to verify the criteria of the driver monitoring systems proposed by UNECE ACSF informal working group and the ministry of land, infrastructure, and transport of South Korea using driving behavior data. In order to verify the criteria, we investigated the safety regulations of driver monitoring systems in a conditional autonomous vehicle and found that the driver monitoring measures were related to eye blinks times, head movements, and eye closed duration. Thus, we took two different experimental data including real-world driving and simulator-based drowsy driving behaviors in previous studies. The real-world driving data were used for analyzing blink times and head movement intervals, and the drowsiness data were used for eye closed duration. In the drowsy driving study, 10 drivers drove approximately 37 km of a monotonous highway (about 22 min) twice. The results suggested that the appropriate duration of eyes continuously closed was 4 seconds. The results from real-world driving data were presented in the other paper - part 1.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.9
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• pp.1985-1992
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• 2011

PE(Passive Entry) is an automotive technology which allows a driver to lock and unlock door of vehicle without using smart key buttons personally. PG(Pssive Go) is an automotive technology which offers the ability to start and stop the engine when there is a driver in vehicle with smart key. When these two functions are unified, we call it PEG(Passive Entry/Go). LF(Low Frequency) antenna driver which is one of core technologies in PEG is composed of a digital part which processes commands and an analog part which generates sine waveform. The digital part of antenna driver receives commands from MCU(or ECU), and processes requested commands by MCU, and stores antenna-related driver commands and data on an internal FIFO block. The digital part takes corresponding actions for commands read from FIFO and then transfers modulated LF data to analog part. The analog part generates sine waveform and transmits outside through antenna. The designed digital part for LF antenna driver can acomplish faster LF data transmission than that of conventional product. LF antenna driver can be applicable to the areas such as PEG for automotive and gate opening and closing of building.

• Journal of the Korea Society of Computer and Information
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• v.3 no.3
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• pp.23-30
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• 1998

The ODBC(Open Database Connectivity) is particularly efficient in the dynamic client/server environment. Besides, the ODBC provides the functions that can handle multimedia data. Due to this feature, we are able to use the DBMS that supports the ODBC as a multimedia server. In this thesis, we describe the development of the ODBC driver for Relational DBMS. The Relational DBMS ODBC Driver consists of the client module and the server module. The client module is called the SRM(Server Request Module) and the server module is CSM(Client Service Module). These two modules are connected through the network module called the NSM(Network Service Module). We have conducted both the functional and the interoperability test of our ODBC Driver It turned out that the ODBC driver operated with these client DBMS tools successfully. In all, due to our development of the Relational DBMS ODBC Driver, DBMS is now capable of processing multimedia data and supporting the client applications including the DBMS tools.

• 한국정보디스플레이학회:학술대회논문집
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• 2005.07b
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• pp.1398-1400
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• 2005

A novel data driver for passive matrix organic lightemitting devices (PM-OLEDs) with high gray scale images was designed. The proposed circuit consisted of a main current bias circuit as well as sample & hold circuits in each channel of the data driver to compensate a current offset. These results indicate that a data driver designed by using the current offset compensation technique holds promise for poten tial applications in PM-OLED displays with high gray scale images.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.2
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• pp.216-222
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• 2002

The ergonomic human model can be considered as a tool for the evaluation of ergonomic factors in vehicle design process. The proper anthropometric data on driver's postures are needed in order to apply a human model to vehicle design. Although studies on driver's posture have been carried out for the last few decades, there are still some problems for the posture data to be applied directly to the human model due to the lack of fitness because such studies were not carried out under the conditions for the human model application. In the traditional researches, the joint angles were evaluated by the categorized data, which are not appropriate for the human model application because it is so extensive that it can not explain the posture evaluation data in detail. And the human models require whole-body posture evaluation data rather than joint evaluation data. In this study a postural evaluation function was developed not by category data but by the concept of the loss function in quality engineering. The loss was defined as the discomfort in driver's posture and measured by the magnitude estimation technique in the experiment using a seating buck. Four loss functions for the each joint - knee, hip, shoulder, and elbow were developed and a whole-body postural evaluation function was constructed by the regression analysis using these loss functions as independent factors. The developed postural evaluation function shows a good prediction power for the driver's posture discomfort in validation test. It is expected that the driver's postural evaluation function based on the loss function can be used in the human model application to the vehicle design process.

• Journal of the Korean Society of Safety
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• v.27 no.5
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• pp.203-210
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• 2012

Traffic accident risk of young drivers(less than 25) is reported to have 8 times as high as that of middle aged drivers(between 30 and 49). Despite the rise of traffic accident risk, few have been attempted to take a look into driving characteristics of young drivers. The purpose of this paper is to analyze age-specific risks of young driver by means of database of insurance and vehicle inspection, thereby collecting data such as age, vehicle mileage, injuries and so on. We conducted Data-Mining(CART) and Portfolio analysis according to age groups(every 10 years). The conclusions which can be drawn from this empirical study are as follows: (1) Despite the fact that young drivers have low vehicle mileage, the rate of fatality is relatively high. (2) Being concerned of vehicle mileage, 24,000km of driving experience is thought to be critical in differing in fatality rate. Having annual average mileage fewer than 24,169 km, accident frequency is relatively lower than that exceeding 24,169 km(1,571 cases). Backed upon these, some recommendations about driver's license system for young driver to improve are given.

• Journal of the Ergonomics Society of Korea
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• v.22 no.3
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• pp.57-73
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• 2003

Although body pressure distribution is sensitive to movements and is relatively simple to measure even in small space, there are few researches involving applications to driver's posture and its change. The main objective in this study is the application of body pressure distribution measurements for the prediction of the driver's posture and its change. This requires quantitative analyses of the dynamic body pressure distribution, which is the change of body pressure distribution with time. The experiment involved 16 male subjects who drove for 45 minutes in a seating buck. Measurement time, stature group, and lumbar support prominence were selected as independent variables, with subjective ratings of driver's discomfort, body posture data of hip, torso. knee angle, and body pressure data variables as dependent variables. The body pressure change variables and subjective ratings were found to increase as the measurement time increased and body pressure ratio variables reflected the torso angle. From the results and analysis of the body posture data and subjective rating results, it was predicted that the seats and the design of the lumbar supports used in the experiment was not fit for tall subjects, which could also be confirmed through the body pressure distribution data.

• JSTS:Journal of Semiconductor Technology and Science
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• v.15 no.2
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• pp.184-193
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• 2015

This paper proposes a cost-efficient and automatic method for large data acquisition from a test chip without expensive equipment to characterize random process variation in an integrated circuit. Our method requires only a test chip, a personal computer, a cheap digital-to-analog converter, a controller and multimeters, and thus large volume measurement can be performed on an office desk at low cost. To demonstrate the proposed method, we designed a test chip with a current model logic driver and an array of 128 current mirrors that mimic the random process variation of the driver's tail current mirror. Using our method, we characterized the random process variation of the driver's voltage due to the random process variation on the driver's tail current mirror from large volume measurement data. The statistical characteristics of the driver's output voltage calculated from the measured data are compared with Monte Carlo simulation. The difference between the measured and the simulated averages and standard deviations are less than 20% showing that we can easily characterize the random process variation at low cost by using our cost-efficient automatic large data acquisition method.