• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.457-466
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• 2005

In this paper, we propose a wavelet neural network(WNN) based predictive control method for path tracking of mobile robots with multi-input and multi-output. In our control method, we use a WNN as a state predictor which combines the capability of artificial neural networks in learning processes and the capability of wavelet decomposition. A WNN predictor is tuned to minimize errors between the WNN outputs and the states of mobile robot using the gradient descent rule. And control signals, linear velocity and angular velocity, are calculated to minimize the predefined cost function using errors between the reference states and the predicted states. Through a computer simulation for the tracking performance according to varied track, we demonstrate the efficiency and the feasibility of our predictive control system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.5C
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• pp.302-308
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• 2011

In this paper we propose a coordinated beamforming(CBF) scheme considering the effects of feedback quantization and delay in time-varying multiple-input multiple-output(MIMO) broadcast channels. By equal power allocation per data stream, the proposed CBF scheme transmits multiple data streams per user terminals without additional feedback overhead when quantized feedback information is used. The proposed CBF scheme also applies a linear channel predictor to each user terminals to prevent errors due to feedback delays that are not evitable in practical wireless systems. Each user terminal utilizes Wiener filter to predict future channel responses and generates feedback information based on the predicted channels. Consequently the proposed CBF scheme adapting Wiener filter improves system performances compared with the conventional scheme using delayed feedback.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.3A
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• pp.256-268
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• 2008

This paper proposes an efficient scheme to track the time variant channel induced by multi-path Rayleigh fading in mobile wireless Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems with null sub-carriers. In the proposed method, a blind channel response predictor is designed to cope with the time variant channel. The proposed channel tracking scheme consists of a frequency domain estimation approach that is coupled with a Minimum Mean Square Error (MMSE) time domain estimation method, and does not require any matrix inverse calculation during each OFDM symbol. The main attributes of the proposed scheme are its reduced computational complexity and good tracking performance of channel variations. The simulation results show that the proposed method exhibits superior performance than the conventional channel tracking method [4] in time varying channel environments. At a Doppler frequency of 100Hz and bit error rates (BER) of 10-4, signal-to-noise power ratio (Eb/N0) gains of about 2.5dB are achieved relative to the conventional channel tracking method [4]. At a Doppler frequency of 200Hz, the performance difference between the proposed method and conventional one becomes much larger.

• Journal of Korean Academy of Nursing
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• v.30 no.6
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• pp.1543-1555
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• 2000

Home health care is moving into a set of new realities. An era of competition and cost containment has arrived. Before nurses are able to contain costs or describe the relationship between nursing activities, cost must be accurately measured based on the nurse's workload. Nurses in home health care usually desire to measure expenses for one of three reasons : reimbursement, management, or research. The purpose of the study was to investigate the work input by Registered Nurse in each of the home health care activities by relative value units and identify the factors affecting the nurses' total work input in health care services. To measure the work input by nurses, work was defined by four dimensions: time, physical effort, mental effort, and stress. This study used a descriptive-correlational design. Data collection consisted of two phases. In phase I, data on home health activities performed by nurses were collected. In phase II, data on nurses' time, physical effort, mental effort, and stress in each of home health care activities discovered phase I were collected. In this method, the respondent was asked to rate a service in relation to a reference service using a ratio scale. The sample included 39 home health care nurses. The results of the study indicated that home health care activities performed by the nurses were in 10 categories and 69 items. Measuring the relative work inputs in each of home health care activities, and foley catheterization was selected as the reference to service. In terms of time and physical effort dimensions, full bath service was rated as the most strenuous among 69 activities by the respondents, and intramuscular injection was rated as least. It was found that emergency treatment required the highest mental effort and the highest stress, while blood sugar tests required the lowest mental effort. Approximately 91.3% of the variance in total work input was accounted for by the linear combination of time, physical effort, mental effort judgement, and stress. Examining the regression coefficients of those variables, physical effort, time, and stress were found as the predictors which were significantly associated with the total work of nurses in home health care. Professional nursing's next step in the conundrum of economic volatility is to develop a tool to reflect the interaction of functional deficiency and direct professional nursing care. And this will be a more accurate predictor of nursing resource use and ultimately a great forcaeter cost.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.12
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• pp.12-20
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• 1993

In a conventional adaptive echo canceller, an ADF(Adaptive Digital Filter) with TDL(Tapped-Delay Line) structure modelling the echo path uses the LMS(Least Mean Square) algorithm to compute the coefficients, and NET detector using energy comparison method prevents the ADF to update the coefficients during the periods of the NET signal presence. The convergence speed of the LMS algorithm depends on the eigenvalue spread ratio of the reference signal and NET detector using the energy comparison method yields poor detection performance if the magnitude of the NET signal is small. This paper presents a new adaptive echo canceller which uses the pre-whitening filter to improve the convergence speed of the LMS algorithm. The pre-whitening filter is realized by using a low-order lattice predictor. Also, a new NET signal detection algorithm is presented, where the start point of the NET signal is detected by computing the cross-correlation coefficient between the primary input and the ADF output while the end point is detected by using the energy comparison method. The simulation results show that the convergence speed of the proposed adaptive echo canceller is faster than that of the conventional echo canceller and the cross-correlation coefficient yields more accurate detection of the start point of the NET signal.

• Proceedings of the Acoustical Society of Korea Conference
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• 1992.06a
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• pp.23-28
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• 1992

The purpose of this paper is to develop a new adaptive echo canceller improving convergence speed and near-end-talker detection performance of the conventional echo canceller. In a conventional adaptive echo canceller, an adaptive digital filter with TDL(Tapped-Delay Line) structure modelling the echo path uses the LMS(Least Mean Square) algorithm to cote the coefficients, and NET detector using energy comparison method prevents the adaptive digital filter to update the coefficients during the periods of the NET signal presence. The convergence speed of the LMS algorithm depends on the eigenvalue spread ratio of the reference signal and NET detector using the energy comparison method yields poor detection performance if the magnitude of the NET signal is small. This paper presents a new adaptive echo canceller which uses the pre-whitening filter to improve the convergence speed of the LMS algorithm. The pre-whitening filter is realized by using a low-order lattice predictor. Also, a new NET signal detection algorithm is presented, where the start point of the NET signal is detected by computing the cross-correlation coefficient between the primary input and the ADF(Adaptive Digital Filter) output while the end point is detected by using the energy comparison method. The simulation results show that the convergence speed of the proposed adaptive echo canceller is faster than that of the conventional echo canceller and the cross-correlation coefficient yield more accurate detection of the start point of the NET signal.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.2
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• pp.13-21
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• 1996

The necessity and usefulness of higher-order neural networks have been well-known since early days of neurocomputing. However the explosive number of terms has hampered the design and training of such networks. In this paper we present an evolutionary learning method for efficiently constructing problem-specific higher-order neural models. The crux of the method is the neural tree representation employing both sigma and pi units, in combination with the use of an MDL-based fitness function for learning minimal models. We provide experimental results in classification and prediction problems which demonstrate the effectiveness of the method. I. Introduction topology employs one hidden layer with full connectivity between neighboring layers. This structure has One of the most popular neural network models been very successful for many applications. However, used for supervised learning applications has been the they have some weaknesses. For instance, the fully mutilayer feedforward network. A commonly adopted connected structure is not necessarily a good topology unless the task contains a good predictor for the full *d*dWs %BH%W* input space.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.8
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• pp.1085-1091
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• 2014

A model based SOC estimation scheme using parameter identification is described and applied to a Lithium-ion battery module that can be installed in electric vehicles. Simulation studies are performed to verify the effect of sensor faults on the SOC estimation results for terminal voltage sensor and load current sensor. The sensor faults should be detected and isolated as soon as possible because the SOC estimation error due to any sensor fault seriously affects the overall performance of the BMS. A new fault detection and isolation(FDI) scheme by which the fault of terminal voltage sensor and load current sensor can be detected and isolated is proposed to improve the reliability of the BMS. The proposed FDI scheme utilizes the parameter estimation of an input-output model and two fuzzy predictors for residual generation; one for terminal voltage and the other for load current. Recently developed dual polarization(DP) model is taken to develope and evaluate the performance of the proposed FDI scheme. Simulation results show the practical feasibility of the proposed FDI scheme.

• The Journal of the Korea Contents Association
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• v.2 no.4
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• pp.23-29
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• 2002

This paper suggest the image compression that is needed to process PACS in medical information system. The image decoding method is used Linear-predictor and Lloyd-Max quantizer(quantization) in the Wavelet transform domain. Wavelet Transform Method is processed the multi-resolution by dividing image into 10 sub-bands of 3 levels. Low frequency domain that is sensitive to human visual characteristic is encoded by DPCM which is lossless encoding methods, and Lloyed-Max quantizer, the optimal quantizer for reducing ringing and aliasing in the image of inter sub-band, is used in the remaining high frequency domain of sub-band. The examination verifies that decompressed images are superior by the result that PSNR is 28.53dB on the input image, 512$\times$152 abdominal CT image and Chest image.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.5
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• pp.297-305
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• 2006

Trace-driven simulation is widely used for measuring the performance of a microprocessor in its initial design phase. However, since it requires much time and disk space, the statistical simulation has been studied as an alternative method. In this paper, statistical simulations are performed for a high performance superscalar microprocessor with a perceptron-based multiple branch predictor. For the verification, various hardware configurations are simulated using SPEC2000 benchmarks programs as input. As a result, we show that the statistical simulation is quite accurate and time saving for the evaluation of microprocessor architectures with multiple branch prediction.