• Transactions of the Korean Society of Automotive Engineers
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• v.23 no.3
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• pp.271-278
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• 2015

This paper develops an OBD Diagnostic Program (Program) using Visual Studio (C#), which was used to diagnosis malfunction information from OBD-II system vehicles. We accomplished this using the Program, Diagnostic tests, Board (STN1110), FTDI Basic Cable, Mini USB Cable, OBD Data Cable, and both hybrid and regular vehicles. The Program tests real-time data output, DTC output, sensor value output, engine RPM, waveform data, OBD type check, PID inspection, and whole monitoring. We found vehicles used in this research had 19 PIDs, which was within OBD-II regulations. We also gathered data on control and diagnostic code regulated by OBD-II system, such as, sensor output value, engine RPM, DTC output, each PID analytic value, OBD type, fuel mode, and whole monitoring result value. Using the data collected through the Program appropriately can lead to more effective diagnostic practices and contribute to education.

• Journal of the Korean Data and Information Science Society
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• v.18 no.2
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• pp.301-314
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• 2007

The aim of this paper is to deal with a method to construct a separate fuzzy regression model with crisp input and fuzzy output data using a best response function for the center and the width of the predicted output. Also we introduce the crisp mean and variance of the predicted fuzzy value and also give some examples to compare a performance of the proposed fuzzy model with various other fuzzy regression model.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.43-46
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• 2001

We, in this paper, design the Sugeno-models fuzzy controller by using the membership function modification algorithm and ANFIS, which are clustering and learning the input-output data. The membership function modification algorithm constructs the more concrete fuzzy controller by clustering the input-output data from the fuzzy inference system. ANFIS construct the Sugeno-models fuzzy controller by learning the input-output data from the above controller. We showed that the fuzzy controller designed by our method could have the stable learning and the enhanced performance.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.101.5-101
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• 2001

A linear input/output data-based predictive control with integral action is developed. The control input is obtained directly from the input/output data in a single step. However, the state estimation in subspace identification gives a biased estimate and there is model mismatch when the controller is applied to a nonlinear process. To overcome such difficulties, we add integral action to a linear input/output data-based predictive controller by augmenting the integrated white noise disturbance model and use each of best linear unbiased estimation(BLUE) filter and Kalman filter as a stochastic observer for the unmeasured disturbance. When applied to a continuous styrene polymerization reactor the proposed controller demonstrates.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.12
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• pp.8753-8759
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• 2015

Establishment of analysis procedures and validated performance measurements for generator output is required to maintain stable management of generator output in turbine power generation cycle. We developed turbine expansion model and measurement validation model for the performance calculation of generator using turbine output based on ASME (American Society of Mechanical Engineers) PTC (Performance Test Code). We also developed verification model for uncertain measurement data related to the turbine and generator output. Although the model in previous researches was developed using artificial neural network and kernel regression, the verification model in this paper was based on algorithms through Support Vector Machine (SVM) model to overcome the problems of unmeasured data. The selection procedures of related variables and data window for verification learning was also developed. The model reveals suitability in the estimation procss as the learning error was in the range of about 1%. The learning model can provide validated estimations for corrective performance analysis of turbine cycle output using the predictions of measurement data loss.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.3
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• pp.1147-1152
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• 2012

In this paper, it is key issue about analysis of characteristics processing of digital random output stream of hardware random number generator, which is applied in medical area. The output stream of random number generator based on hardware binary random number is effected from factors such as delay, jitter, temperature, and so on. In this paper, it presents about major factor, which effects hardware output random number stream, and the randomness of output stream data, which are combined output stream and postprocessing data such as encryption algorithm, encoding algorithm, is analyzed. the analyzed results are evaluated by major test items of randomness.

• Proceedings of the KIEE Conference
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• 2009.04a
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• pp.186-188
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• 2009

In this thesis, output voltage, current and power of solar module were classified by irradiation and module temperature from data of overall operating characteristics collected for one year in order to manage efficient photovoltaic generation system and deliver maximum power. In addition, from these data, correlations between irradiation, module temperature of photovoltaic cell and amount of power given by photovoltaic cell was quantitatively examined to deduce optimization of the design and construction of photovoltaic generation system. The results of this thesis can be summarized as follows. As output power characteristics according to a temperature range of 10$\sim$50[], output power was increased with an increase in temperature. Since output power increases with temperature increase, the result corresponds well to the related equation on temperature and output power.

• Journal of KIISE:Information Networking
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• v.28 no.4
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• pp.621-630
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• 2001

Recently, users' demands for multimedia service are increasing. But, server systems offer inefficient multimedia data service to users. In this paper, to transport multimedia data in the server system more efficiently, we propose the SRTPIO(Special RTP Input/Output) module that process the RTP(Real-time Transport Protocol) data in the kernel with the SIO(Special Input/Output) Mechanism. The SIO mechanism improve a transfer speed because it reduces overheads associated with data copying and context-switching between the user mode and the kernel mode occured in general server system in the kernel-level. The SRTPIO module, integrating the SIO mechanism and the RTP data processing in the kernel, support efficient multimedia data transfer architecture.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.11
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• pp.84-91
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• 2009

A high speed serial link transmitter using multi-level signaling is proposed. To achieve high data rate m high speed serial link, 4-pulse amplitude modulation (PAM) is used. By transmitting 2 bit data in each symbol time, high speed data transmission, two times than binary signaling, is achieved. The transmitter transmits current-mode output instead of voltage-mode output Current-mode output is much faster than voltage-mode output, so higher data transmission is available by increasing switching speed of driver. $2^5-1$ pseudo-random bit sequence (PRBS) generator is contained to perform built-in self test (BIST). The 4-PAM transmitter is designed in Dongbu HiTek $0.18{\mu}m$ CMOS technology and achieves 8 Gb/s, 160 mV of eye height with 1.8 V supply voltage. The transmitter consumes only 98 mW for 8 Gb/s transmission.

• IE interfaces
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• v.12 no.4
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• pp.551-556
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• 1999

In this paper, we compare the performance of three data mining classification algorithms(neural network, decision tree, logistic regression) in consideration of various characteristics of categorical input and output data. $2^{4-1}$. 3 fractional factorial design is used to simulate the comparison situation where factors used are (1) the categorical ratio of input variables, (2) the complexity of functional relationship between the output and input variables, (3) the size of randomness in the relationship, (4) the categorical ratio of an output variable, and (5) the classification algorithm. Experimental study results indicate the following: decision tree performs better than the others when the relationship between output and input variables is simple while logistic regression is better when the other way is around; and neural network appears a better choice than the others when the randomness in the relationship is relatively large. We also use Taguchi design to improve the practicality of our study results by letting the relationship between the output and input variables as a noise factor. As a result, the classification accuracy of neural network and decision tree turns out to be higher than that of logistic regression, when the categorical proportion of the output variable is even.