• Journal of Electrical Engineering and Technology
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• v.8 no.5
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• pp.975-983
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• 2013

This study proposes a low order dynamic model of a building system in order to predict thermal behavior within a building and its energy consumption. The building system includes a thermally well-insulated room and an electric heater. It is modeled by a second order lumped RC thermal network based on the thermal-electrical analogy. In order to identify unknown parameters of the model, an experimental procedure is firstly detailed. Then, the different linear parametric models (ARMA, ARX, ARMAX, BJ, and OE models) are recalled. The parameters of the parametric models are obtained by the least square approach. The obtained parameters are interpreted to the parameters of the physically based model in accordance with their relationship. Afterwards, the obtained models are implemented in Matlab/Simulink(R) and are evaluated by the mean of the sum of absolute error (MAE) and the mean of the sum of square error (MSE) with the variable of indoor temperature of the room. Quantities of electrical energy and converted thermal energy are also compared. This study will permit a further study on Model Predictive Control adapting to the proposed model in order to reduce energy consumption of the building.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.27 no.1
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• pp.87-93
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• 2017

This paper investigates active noise control of tower-type air conditioners using the filtered-x least mean square (FXLMS) algorithm to reduce fan blower noise transmission. Firstly, the main components required for the active control system including the error sensor, the control speaker and the reference sensors are selected. Since the noise could significantly reduce if the reference signal includes every frequency response information, a various reference signals from accelerometers and a microphone are used. Secondly, the controller based on the FXLMS algorithm with a single-channel reference signal is implemented. Then, the control performance is examined experimentally for the different reference signals. It is found that the accelerometer signal well possesses the motor vibration related noise and a microphone signal could includes global noise. When using the reference signal with a microphone located near the motor and the fan blower, the active control system reduces the noise globally, except for several peaks.

• Journal of Satellite, Information and Communications
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• v.10 no.4
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• pp.136-140
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• 2015

In this paper, a comparative studies on the STAP and SFAP were performed, which are known as representative anti-jamming technology for adaptive array antenna. As a method of estimating the weighting vector for simulation, MMSE(Minimum Mean Square Error) algorithm was commonly used and the analyses of the simulation performance in various jamming environments were presented. Especially, performance comparison between STAP and SFAP according to the jamming power J/S(Jamming to Signal Power Ratio), performance comparison in the ratio of jamming bandwidth to signal bandwidth, and performance comparison of BER between STAP and SFAP were presented.

• Computers and Concrete
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• v.25 no.1
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• pp.83-94
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• 2020

The use of fly ash in modern-day concrete technology aiming sustainable constructions is on rapid rise. Fly ash, a spinoff from coal calcined thermal power plants with pozzolanic properties is used for cement replacement in concrete. Fly ash concrete is cost effective, which modifies and improves the fresh and hardened properties of concrete and additionally addresses the disposal and storage issues of fly ash. Soft computing techniques have gained attention in the civil engineering field which addresses the drawbacks of classical experimental and computational methods of determining the concrete compressive strength with varying percentages of fly ash. In this study, models based on soft computing techniques employed for the prediction of the compressive strengths of fly ash concrete are collected from literature. They are classified in a categorical way of concrete strengths such as control concrete, high strength concrete, high performance concrete, self-compacting concrete, and other concretes pertaining to the soft computing techniques usage. The performance of models in terms of statistical measures such as mean square error, root mean square error, coefficient of correlation, etc. has shown that soft computing techniques have potential applications for predicting the fly ash concrete compressive strengths.

• The Journal of the Korea Contents Association
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• v.15 no.4
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• pp.1-8
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• 2015

This paper presents a Electrocardiogram(ECG) identification method using adaptive weight based on Least Mean Square Error(LMSE) optimization. With a preprocessing for noise suppression, we extracts the average ECG signal and its standard deviation at every time instant. Then the extracted information is stored in database. ECG identification is achieved by matching an input ECG signal with the information in database. In computing the matching scores, the standard deviation is used. The scores are computed by applying adaptive weights to the values of the input signal over all time instants. The adaptive weight consists of two terms. The first term is the inverse of the standard deviation of an input signal. The second term is the proportional one to the standard deviation between user SAECGs stored in the DB. Experimental results show up to 100% recognition rate for 32 registered people.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.12A
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• pp.1198-1204
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• 2006

In this paper, we extend SVR-MMSE detection scheme which is proposed in MIMO system to MIMO-OFDM system, and evaluate performance of the system in frequency selective fading channel. First of all, we explain about typical MIMO-OFDM system and detection scheme of constant modulus signals in this system. And compare proposed SVR-MMSE with Zero Forcing, Minimum Mean Square Error which is conventional detection scheme. we identify that the performance of the proposed system is shown different by varying doppler frequency in frequency selective fading channel using jakes channel model. The result of detection performance by the proposed SVR-MMSE in this simulation, it shows that proposed algorithm have a good performance in MIMO-OFDM systems.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.5 no.1
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• pp.35-40
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• 2011

In this paper, we analyze that co-channel interference mitigation algorithms MMSE (Minimum Mean Square Error), OC (Optimal Combining), ML (Maximum Likelihood) using 2.4Ghz in WBAN (Wireless Body Area Network) system. Also analyze that scenario and channel model by IEEE 802.15.6. ML gives the best performance for all simulation. ML and OC have high complexity than MMSE complexity, because these algorithms should be known channel information of interference users. So these algorithms are difficult to apply to WBAN. Therefore we will study the interference mitigation algorithm that should be accomplished trade-off of between efficiency and complexity.

• The Korean Journal of Applied Statistics
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• v.7 no.1
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• pp.149-158
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• 1994

Nonparametric regression technique using kernel estimator is an attractive alternative that has received some attention, recently. The kernel estimate depends on two quantities which have to be provided by the user : the kernel function and the bandwidth. However, the more difficult problem is how to find an appropriate bandwidth which controls the amount of smoothing (see Silverman, 1986). Thus, in practical situation, it is certainly desirable to determine an appropriate bandwidth in some automatic fashion. Thus, the problem is to find a data-driven or adaptive (i.e., depending only on the data and then directly computable in practice) bandwidth that performs reasonably well relative to the best theoretical bandwidth. In this paper, we introduce a relation between bias and variance of mean square error. Thus, we present a simple and effective algorithm for selecting local bandwidths in kernel regression.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.2
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• pp.407-416
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• 2000

Adaptive digital filtering and noise cancelling technique using a tree structured filter bank are presented to reduce a undesirable aliasing due to the decimation of filtered output and improve the performance in terms of mean-square error and the convergence speed using a aliasing canceller. A signal is split into two subband by analysis filter bank and decimated by decimator and reconstructed by interpolation technique and synthesis filter bank. A variable step-size LMS algorithm is used to improve the convergence speed in case of existing the measurement noise in desired input of filter. It is shown by computer simulation that the proposed subband structure in this paper is superior to conventional subband filter structure in terms of mean-square error and convergence speed.

• Journal of Communications and Networks
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• v.7 no.3
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• pp.243-247
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• 2005

We propose an efficient block least-mean-square (BLMS) adaptive algorithm, in conjunction with error control coding, for direct-sequence code division multiple access (DS-CDMA) systems. The proposed adaptive receiver incorporates decision feedback detection and channel encoding in order to improve the performance of the standard LMS algorithm in convolutionally coded systems. The BLMS algorithm involves two modes of operation: (i) The training mode where an uncoded training sequence is used for initial filter tap-weights adaptation, and (ii) the decision-directed where the filter weights are adapted, using the BLMS algorithm, after decoding/encoding operation. It is shown that the proposed adaptive receiver structure is able to compensate for the signal-to­noise ratio (SNR) loss incurred due to the switching from uncoded training mode to coded decision-directed mode. Our results show that by using the proposed adaptive receiver (with decision feed­back block adaptation) one can achieve a much better performance than both the coded LMS with no decision feedback employed. The convergence behavior of the proposed BLMS receiver is simulated and compared to the standard LMS with and without channel coding. We also examine the steady-state bit-error rate (BER) performance of the proposed adaptive BLMS and standard LMS, both with convolutional coding, where we show that the former is more superior than the latter especially at large SNRs ($SNR\;\geq\;9\;dB$).