• Fire Science and Engineering
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• v.18 no.2
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• pp.27-33
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• 2004

An accurate knowledge of the AIT(Autoignition temperatures) of chemicals is important in developing appropriate prevention and control measures in industrial fire protection. The AITs describe the minimum temperature to which a substance must be heated, without the application of a flame or spark, which will cause that substance to ignite. The measurement AITs are dependent upon many factors. namely initial temperature. pressure, volume, fuel/air stoichiometry. catalyst material, concentration of vapor, ignition delay time. This study measured the AITs of acids from ignition delay time by using ASTM E659-78 apparatus which was produced in the year 1994. The experiment AITs were a good agreement with the calculated AITs by the proposed equations with a few A.A.P.E.(average absolute percent error) and A.A.D.(average absolute deviation).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.2
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• pp.534-540
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• 1996

This paper presents an Adaptive Time-Delay Recurrent Neural Network (ATRN) for learning and recognition of temporal correlations of temporal patterns. The ATRN employs adaptive time-delays and recurrent connections, which are inspired from neurobiology. In the ATRN, the adaptive time-delays make the ATRN choose the optimal values of time-delays for the temporal location of the important information in the input parrerns, and the recurrent connections enable the network to encode and integrate temporal information of sequences which have arbitrary interval time and arbitrary length of temporal context. The ATRN described in this paper, ATNN proposed by Lin, and TDNN introduced by Waibel were simulated and applied to the chaotic time series preditcion of Mackey-Glass delay-differential equation. The simulation results show that the normalized mean square error (NMSE) of ATRN is 0.0026, while the NMSE values of ATNN and TDNN are 0.014, 0.0117, respectively, and in temporal learning, employing recurrent links in the network is more effective than putting multiple time-delays into the neurons. The best performance is attained bythe ATRN. This ATRN will be sell applicable for temporally continuous domains, such as speech recognition, moving object recognition, motor control, and time-series prediction.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.15 no.4
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• pp.44-48
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• 2006

Recently, demand the ultra precision product which is increasing rapidly is used extensively frontier industry field such as semi-conductor, computer, aerospace, precision machine. Ultra precision processing is the portion that is very needed to NT in the field of mechanical engineering. The latest date, together with radical advancement of electronic and photonics industry, necessity of ultra precision processing is on the increase for the manufacture of various kernel parts those are connected with these industrial fields. Specially, require motion accuracy of high resolution of nm order in stroke of hundreds millimeters according as diameter of processing object great and processing accuracy rises. In this case ,the response speed absolute delay because inertial mass of moving part is very large. Therefore, real time motion error compensation becomes very hardly. In this paper, we used ultra precision cutting unit(UPCU) to cope such problem. a UPCU is designed and tested to obtain sub-micrometer from accuracy in diamond turning of flat surfaces. The thermal growth spindle error is compensated for real time using a UPCU driven by piezoelectric actuator along with a laser encoder displacement sensor.

• Journal of Communications and Networks
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• v.15 no.5
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• pp.496-503
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• 2013

In an orthogonal frequency division multiplexing system, conventional interpolation techniques cannot correctly balance performance and overhead when estimating dynamic long-delay channels in single frequency networks (SFNs). In this study, classical filter analysis and design methods are employed to derive a complex interpolator for maximizing the resistible echo delay in a channel estimator on the basis of the correlation between frequency domain interpolating and time domain windowing. The coefficient computation of the complex interpolator requires a key parameter, i.e., channel length, which is obtained in the frequency domain with a tentative estimation scheme having low implementation complexity. The proposed complex adaptive interpolator is verified in a simulated digital video broadcasting for terrestrial/handheld receiver. The simulation results indicate that the designed channel estimator can not only handle SFN echoes with more than $200{\mu}s$ delay but also achieve a bit-error rate performance close to the optimum minimum mean square error method, which significantly outperforms conventional channel estimation methods, while preserving a low implementation cost in a short-delay channel.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.6
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• pp.390-395
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• 2005

Sunspots are dark areas that grow and decay on the lowest level of the sun that is visible from the Earth. Shot-term predictions of solar activity are essential to help plan missions and to design satellites that will survive for their useful lifetimes. This paper presents a parallel-structure fuzzy system(PSFS) for prediction of sunspot number time series. The PSFS consists of a multiple number of component fuzzy systems connected in parallel. Each component fuzzy system in the PSFS predicts future data independently based on its past time series data with different embedding dimension and time delay. An embedding dimension determines the number of inputs of each component fuzzy system and a time delay decides the interval of inputs of the time series. According to the embedding dimension and the time delay, the component fuzzy system takes various input-output pairs. The PSFS determines the final predicted value as an average of all the outputs of the component fuzzy systems in order to reduce error accumulation effect.

• Journal of Digital Convergence
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• v.13 no.10
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• pp.319-325
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• 2015

We proposed the blocking channel to reduce the performance decrease using the low correlation with cyclic delay scheme in LED-ID system. LED-ID is based on the visible light to transmit the data. However, It is occurred the block channel by structure or environment of indoor for light of straightness. LED-ID system is degraded the performance by the block channel as loss of data, and burst error. To solve the block channel, the proposed system is overcome the burst error by low correlation among data, which is able to obtain the maximize time diversity gain to improve the performance of BER by cyclic delay scheme. The BER performance is evaluated by computer simulation according to channel parameter. The simulation results shows that proposed system gives much better performance than conventional system and constant cyclic delay scheme system.

• The Journal of the Acoustical Society of Korea
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• v.37 no.5
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• pp.271-275
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• 2018

There are several methods for estimating the time delay between incoming signals to two sensors. Among them, the GCC-PHAT (Generalized Cross Correlation-Phase Transform) method, which estimates the relative delay from the signal whitening and the cross-correlation between the different signal inputs to the two sensors, is a traditionally well known method for achieving stable performance. In this paper, we have identified a part of GCC-PHAT that can improve the periodicity. Also, we apply the auto-correlation method that is widely used as a method to improve the periodicity. Comparing the proposed method with the GCC-PHAT method, we show that the proposed method improves the mean square error performance by 5 dB ~ 15 dB at the SNR above 0 dB for white Gaussian signal source and also show that the method improves the mean square error performance up to 15 dB at the SNR above 2 dB for the color signal source.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.8
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• pp.752-759
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• 2008

In this paper, we propose a high accurate geo-location system based on a single base station, where its location is obtained by Time-of-Arrival(ToA) and Direction-of-Arrival(DoA) of the radio signal. For estimating accurate ToA and DoA information, a MUltiple SIgnal Classification(MUSIC) is adopted. However, the estimation of ToA and DoA using MUSIC algorithm is a time-consuming process. The position tracking bias is occurred by the time delay caused by the estimation process. In order to mitigate the bias error, we propose the estimation method of the position tracking bias and compensate the location error produced by the time delay using the position tracking bias mitigation. For accurate self-localization of mobile robot, the Unscented Kalman Filter(UKF) with position tracking bias is applied. The simulation results show the efficiency and accuracy of the proposed geo-location system and the enhanced performance when the Unscented Kalman Filter is adopted for mobile robot application.

• Journal of the Korean Society of Safety
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• v.19 no.2
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• pp.54-60
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• 2004

Flammable substances are frequently used chemical industry processes. An accurate knowledge of the ALTs(Autoignition Temperatures) is important in developing appropriate prevention and control measures in industrial fire protection. The AITs describe the minimum temperature to which a substance must be heated, without the application of a flame or spark, which will cause that substance to ignite. The AITs are dependent upon many factors, namely initial temperature, pressure, volume, fuel/air stoichiometry, catalyst material, concentration of vapor, ignition delay. This study measured relationship between the AITs and the ignition delay times by using ASTM E659-78 apparatus for methanol and ethanol. The A.A.P.E.(Average Absolute Percent Error) and the A.A.D.(Average Absolute Deviation) of the experimental and the calculated delay times by the AITs for methanol were 14.59 and 1.76 respectively. Also the A.A.P.E. and the A.A.D. of the experimental and the calculated delay times by the ATIs for ethanol were 8.33 and 0.88.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.3057-3075
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• 2020

End-to-end network delay plays an vital role in distributed services. This delay is used to measure QoS (Quality-of-Service). It would be beneficial to know all node-pair delay information, but unfortunately it is not feasible in practice because the use of active probing will cause a quadratic growth in overhead. Alternatively, using the measured network delay to estimate the unknown network delay is an economical method. In this paper, we adopt the state-of-the-art matrix completion technology to better estimate the network delay from limited measurements. Although the number of measurements required for an exact matrix completion is theoretically bounded, it is practically less helpful. Therefore, we propose an online adaptive sampling algorithm to measure network delay in which statistical leverage scores are used to select potential matrix elements. The basic principle behind is to sample the elements with larger leverage scores to keep the traits of important rows or columns in the matrix. The amount of samples is adaptively decided by a proposed stopping condition. Simulation results based on real delay matrix show that compared with the traditional sampling algorithm, our proposed sampling algorithm can provide better performance (smaller estimation error and less convergence pressure) at a lower cost (fewer samples and shorter processing time).