• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.451-453
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• 1998

Global competition of automotive market, affordable prices of electronic components, and tougher regulations on emission, fuel economy, and safety become the major reason that automotive industries rapidly employ a large number of electric and electronic systems. Considering that the application of electronic technologies for automobile is increasing at a rapid rate, it would be worthwhile to evaluate the trend of the uses of major electric and electronic systems for the 21st century vehicle. The major technology will be leaded by 32/64-bit microcontroller, on-chip flash memory, hybrid ASICs, IGBT, and smart sensors.

• The Journal of the Acoustical Society of Korea
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• v.18 no.3E
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• pp.30-36
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• 1999

In this paper, we develop a hybrid speaker recognition system [1] enhanced by pre-recognizer and post-recognizer. The pre-recognizer consists of general speech recognition systems and the post-recognizer is a pitch detection system using adapted continuous wavelet transform (ACWT) to improve the performance of the hybrid speaker recognition system. Two schemes to design ACWT is considered. One is the scheme to search basis library covering the whole band of speech fundamental frequency (speech pitch). The other is the scheme to determine which one is the best basis. Information cost functional is used for the criterion for the latter. ACWT is robust enough to classify the pitch of speech very well, even though the speech signal is badly damaged by environmental noises.

• IEMEK Journal of Embedded Systems and Applications
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• v.8 no.1
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• pp.11-16
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• 2013

With the development and general use of a variety of Android/iOS-based smart phones and smart pads, the existing e-learning contents need to be changed in such a way that they can be carried out on different smart device platforms. This paper shows what changes are needed for that aim, and, in particular, for the compatibility of different platforms by designing and implementing Android/iOS-based smart learning contents in the form of a hybrid app. This paper will hopefully help you consider what elements are required to develop smart-learning contents on a variety of platforms for mobile devices.

• Tribology and Lubricants
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• v.29 no.5
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• pp.310-317
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• 2013

To reduce vehicle fuel consumption due to not only driving but also air conditioning, battery-operated non-starting conditioning systems with thermoelectric modules and without mechanical elements like compressors are being manufactured for use by hybrid heavy trucks in the near future. In this study, the voltage and current consumed by a thermoelectric module were measured to determine the required battery power, and the performance of the conditioning system with air temperature, and humidity of the inlet/outlet modules and inside/outside the cabin for a truck, was evaluated using experimental apparatus under actual conditions. The results showed that, the thermoelectric module can be continously operated for about 1.5 h using existing 24 V batteries. The coefficent of performance(COP) of the cooling and heating modes was calculated to be an average 0.8-1.32. As expected, the heating performance was 30% more efficient than the cooling performance, which is general characteristic of thermoelectric modules.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.253-256
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• 1997

This paper proposes a control strategy of position and force control in the same direction based on hybrid position/force control. In order to control position and force in the same direction, a weighting matrix is introduced instead of a selection matrix suggested by Raibert and Craig. The major part of the controller output comes from the position controller when a position control error is large, from the force controller when a position control error is large. The proposed algorithm is implemented by the simulation and experiment focusing on the peg-in-hole task where friction exist significantly and is not constant. It also adopts and event control scheme for more efficient performance.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1281-1285
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• 2004

There are numerous emotions in the human world. Human expresses and recognizes their emotion using various channels. The example is an eye, nose and mouse. Particularly, in the emotion recognition from facial expression they can perform the very flexible and robust emotion recognition because of utilization of various channels. Hybrid-feature extraction algorithm is based on this human process. It uses the geometrical feature extraction and the color distributed histogram. And then, through the independently parallel learning of the neural-network, input emotion is classified. Also, for the natural classification of the emotion, advancing two-dimensional emotion space is introduced and used in this paper. Advancing twodimensional emotion space performs a flexible and smooth classification of emotion.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1216-1219
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• 2003

The position control is very important in semiconductor manufacturing devices, precision machining tools, precision measuring instruments, etc. The accuracy of measurement for the distance in these devices affect on the performance of the whole devices. Therefore, in those precision instruments, a sensing device that can measure the distance of movement with high-precision resolution is required. In this paper, a novel hybrid (digital and analog) type encoder is proposed. It is shown that from several experiments, a high-resolution angular position measurement device can be designed with a low cost incremental encoder and a DSP controller.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3772-3790
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• 2016

It is well known that the constant modulus algorithm (CMA) presents a large steady-state mean-square error (MSE) for high-order quadrature amplitude modulation (QAM) signals. In this paper, we propose a low-complexity hybrid adaptive blind equalization algorithm, which augments the CMA error function with a novel constellation matched error (CME) term. The most attractive advantage of the proposed algorithm is that it is computationally simpler than concurrent CMA and soft decision-directed (SDD) scheme (CMA+SDD), and modified CMA (MCMA), while the approximation of steady-state MSE of the proposed algorithm is same with CMA+SDD, and lower than MCMA. Extensive simulations demonstrate the performance of the proposed algorithm.

• Smart Structures and Systems
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• v.14 no.6
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• pp.1197-1220
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• 2014

Accurate actuator tracking is critical to achieve reliable real-time hybrid simulation results for earthquake engineering research. The frequency-domain evaluation approach provides an innovative way for more quantitative post-simulation evaluation of actuator tracking errors compared with existing time domain based techniques. Utilizing the Fast Fourier Transform the approach analyzes the actuator error in terms of amplitude and phrase errors. Existing application of the approach requires using the complete length of the experimental data. To improve the computational efficiency, two techniques including data decimation and frequency decimation are analyzed to reduce the amount of data involved in the frequency-domain evaluation. The presented study aims to enhance the computational efficiency of the approach in order to utilize it for future on-line actuator tracking evaluation. Both computational simulation and laboratory experimental results are analyzed and recommendations on the two decimation factors are provided based on the findings from this study.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.1281-1284
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• 1998

This paper proposes an efficient time series prediction of the nonlinear dynamical discrete-time systems using multilayer neural networks of a hybrid learning algorithm. The proposed learning algorithm is a hybrid backpropagation algorithm based on the steepest descent for high-speed optimization and the dynamic tunneling for global optimization. The proposed algorithm has been applied to the y00 samples of 700 sequences to predict the next 100 samples. The simulation results shows that the proposed algorithm has better performances of the convergence and the prediction, in comparision with that using backpropagation algorithm based on the gradient descent for multilayer neural network.