• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.8C
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• pp.758-766
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• 2007

High peak-to-average power ratio(PAPR) is one of serious problems in the orthogonal frequency division multiplexing(OFDM) systems. This paper proposes a PAPR reduction technique for limited feedback multiple input multiple output(MIMO) OFDM systems. The proposed method is based on the null space of the MIMO channel where a dummy signal is made in the channel's null space and then, subtracted from the original signal to reduce the PAPR. First, we show that a problem occurs when the existing method is directly applied to limited feedback MIMO case. Then, a weight function for the dummy signal is proposed to mitigate the degradation of the receiver performance while still reducing PAPR significantly. The weight function is derived from a constrained nonlinear optimization problem to minimize the mean square error between the received signal and its ideal signal. Simulation results shows that the proposed technique provides about 2.5dB PAPR reduction with 0.2dB bit-error probability loss.

• Journal of Sensor Science and Technology
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• v.9 no.4
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• pp.288-296
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• 2000

Thin-film chromel-alumel multijunction thermal converters with a low output resistance of $64{\sim}85\;{\Omega}$ showed approximately the square law-dependent input-output relation. The voltage responsivities were very low with $0.34{\sim}0.67\;V/W$ in air and $1.15{\sim}1.48\;V/W$ in vacuum, respectively, and the ac-dc voltage transfer error was very large with about +340 ppm in the frequency range of $40\;Hz{\sim}10\;kHz$ in the case of 1 V-input sinewave rms voltage. It can be concluded that the large transfer error of the thermal converter was mainly caused by the low voltage responsivity and the large heat loss due to low output resistance, which implies that the optimization for small ac-dc transfer error is required.

• Journal of Navigation and Port Research
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• v.36 no.8
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• pp.637-641
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• 2012

In this paper, the method that reduce a computation time by optimizing computation process is proposed to realize low-power underwater acoustic communication system. At first, dependency of decision delay on tap length of linear equalizer was investigated. Variance is calculated based on this result, and the optimal decision delay bound is estimated. In addition to decide optimal tap length with decision delay, we extracted the MSE(Mean Square Error) graph. From the graph, we obtained variance value of the MSE-decision delay, and estimated the optimum decision delay range from the variance value. Also, using the extracted optimal parameters, we performed a simulation. According to the result, the simulation employing optimal tap length, which is only 40% of maximum tap length, showed a satisfactory performance comparable to simulation employing maximum tap length. We verified that the proposed method has 33% lower tap length than maximal tap length via sea trial.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.9
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• pp.4462-4466
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• 2013

In optical system, the signal and additive noise for statistical properties of a variety of ways to evaluate the performance of the system is essential for the optimization. In this paper, performance analysis of spectrum-sliced optical system in the optical pre-amplifier in the receiver the received signal by including the error limits for the bit that is, the bit error rate (BER: Bit Error Rate) required to maintain the average optical power represents the number of photons per bit is included in this paper to digital form, noticeable signal the receiver to calculate the sensitivity of the method for the calculation was performed. The general strength of the transmission of the modulated signal and digital signal transmission was required for the comparison of optical power. As shown in Figure 3, the general strength of the digital signal transmission system for transmitting a modulated signal compared with the case is improved by at least 10dB.

• Journal of the Korea Computer Graphics Society
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• v.25 no.2
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• pp.19-29
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• 2019

We develop a complete system that includes data collection, signal processing, and real-time interaction for effective neurofeedback training. Our system supports a sophisticated technique to find threshold values which are quite important for effective neurofeedback system. A therapist specifies a target success rate of positive feedback, allowable error and time. The system computes a current success rate and compare it with the target one. If the difference between two rates exceeds the allowable error for allowable time, we find an optimum threshold value to obtain the target success rate by using numerical optimization technique. We conduct several experiments by varying input parameters: target success rate, allowable error and time, and demonstrate the effectiveness of our technique by showing the desired target success rate is stably obtained and systematically controlled by input parameters.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.987-994
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• 2020

In this paper, we studied the effect of kernel size of CNN layer on performance in acoustic noise attenuators. This system uses a deep learning algorithm using a neural network adaptive prediction filter instead of using the existing adaptive filter. Speech is estimated from a single input speech signal containing noise using a 100-neuron, 16-filter CNN filter and an error back propagation algorithm. This is to use the quasi-periodic property in the voiced sound section of the voice signal. In this study, a simulation program using Tensorflow and Keras libraries was written and a simulation was performed to verify the performance of the noise attenuator for the kernel size. As a result of the simulation, when the kernel size is about 16, the MSE and MAE values are the smallest, and when the size is smaller or larger than 16, the MSE and MAE values increase. It can be seen that in the case of an speech signal, the features can be best captured when the kernel size is about 16.

• Advances in nano research
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• v.12 no.5
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• pp.515-527
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• 2022

The application of fibers in concrete obviously enhances the properties of concrete, also the application of natural fibers in concrete is raising due to the availability, low cost and environmentally friendly. Besides, predicting the mechanical properties of concrete in general and shear strength in particular is highly significant in concrete mixture with fiber nanocomposite reinforced concrete (FRC) in construction projects. Despite numerous studies in shear strength, determining this strength still needs more investigations. In this research, Adaptive Neuro-Fuzzy Inference System (ANFIS) have been employed to determine the strength of reinforced concrete with fiber. 180 empirical data were gathered from reliable literature to develop the methods. Models were developed, validated and their statistical results were compared through the root mean squared error (RMSE), determination coefficient (R²), mean absolute error (MAE) and Pearson correlation coefficient (r). Comparing the RMSE of PSO (0.8859) and ANFIS (0.6047) have emphasized the significant role of structural parameters on the shear strength of concrete, also effective depth, web width, and a clear depth rate are essential parameters in modeling the shear capacity of FRC. Considering the accuracy of our models in determining the shear strength of FRC, the outcomes have shown that the R² values of PSO (0.7487) was better than ANFIS (2.4048). Thus, in this research, PSO has demonstrated better performance than ANFIS in predicting the shear strength of FRC in case of accuracy and the least error ratio. Thus, PSO could be applied as a proper tool to maximum accuracy predict the shear strength of FRC.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2529-2551
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• 2022

Visual servoing (VS) based on the Kalman filter (KF) algorithm, as in the case of KF-based image-based visual servoing (IBVS) systems, suffers from three problems in uncalibrated environments: the perturbation noises of the robot system, error of noise statistics, and slow convergence. To solve these three problems, we use an IBVS based on KF, African vultures optimization algorithm enhanced extreme learning machine (AVOA-ELM), and fuzzy logic (FL) in this paper. Firstly, KF online estimation of the Jacobian matrix. We propose an AVOA-ELM error compensation model to compensate for the sub-optimal estimation of the KF to solve the problems of disturbance noises and noise statistics error. Next, an FL controller is designed for gain adaptation. This approach addresses the problem of the slow convergence of the IBVS system with the KF. Then, we propose a visual servoing scheme combining FL and KF-AVOA-ELM (FL-KF-AVOA-ELM). Finally, we verify the algorithm on the 6-DOF robotic manipulator PUMA 560. Compared with the existing methods, our algorithm can solve the three problems mentioned above without camera parameters, robot kinematics model, and target depth information. We also compared the proposed method with other KF-based IBVS methods under different disturbance noise environments. And the proposed method achieves the best results under the three evaluation metrics.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.6
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• pp.251-258
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• 2023

It is necessary to predict peak load accurately in order to supply electric power and operate the power system stably. Especially, it is more important to predict peak load accurately in winter and summer because peak load is higher than other seasons. If peak load is predicted to be higher than actual peak load, the start-up costs of power plants would increase. It causes economic loss to the company. On the other hand, if the peak load is predicted to be lower than the actual peak load, blackout may occur due to a lack of power plants capable of generating electricity. Economic losses and blackouts can be prevented by minimizing the prediction error of the peak load. In this paper, the latest deep learning model such as TCN is used to minimize the prediction error of peak load. Even if the same deep learning model is used, there is a difference in performance depending on the hyper-parameters. So, I propose methods for optimizing hyper-parameters of TCN for predicting the peak load. Data from 2006 to 2021 were input into the model and trained, and prediction error was tested using data in 2022. It was confirmed that the performance of the deep learning model optimized by the methods proposed in this study is superior to other deep learning models.

• Geomechanics and Engineering
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• v.33 no.3
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• pp.279-289
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• 2023

Brittleness index (BI) is an important property of rocks because it is a good index to predict rockburst. Due to its importance, several empirical and soft computing (SC) models have been proposed in the literature based on the punch penetration test (PPT) results. These models are very important as there is no clear-cut experimental means for measuring BI asides the PPT which is very costly and time consuming to perform. This study used a novel Multivariate Adaptive regression spline (MARS), M5P, and white-box ANN to predict the BI of rocks using the available data in the literature for an improved BI prediction. The rock density, uniaxial compressive strength (σ_c) and tensile strength (σ_t) were used as the input parameters into the models while the BI was the targeted output. The models were implemented in the MATLAB software. The results of the proposed models were compared with those from existing multilinear regression, linear and nonlinear particle swarm optimization (PSO) and genetic algorithm (GA) based models using similar datasets. The coefficient of determination (R²), adjusted R² (Adj R²), root-mean squared error (RMSE) and mean absolute percentage error (MAPE) were the indices used for the comparison. The outcomes of the comparison revealed that the proposed ANN and MARS models performed better than the other models with R² and Adj R² values above 0.9 and least error values while the M5P gave similar performance to those of the existing models. Weight partitioning method was also used to examine the percentage contribution of model predictors to the predicted BI and tensile strength was found to have the highest influence on the predicted BI.