• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.1765-1794
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• 2019

Genetic Programming (GP) is an intelligence technique whereby computer programs are encoded as a set of genes which are evolved utilizing a Genetic Algorithm (GA). In other words, the GP employs novel optimization techniques to modify computer programs; imitating the way humans develop programs by progressively re-writing them for solving problems automatically. Trial programs are frequently altered in the search for obtaining superior solutions due to the base is GA. These are evolutionary search techniques inspired by biological evolution such as mutation, reproduction, natural selection, recombination, and survival of the fittest. The power of GAs is being represented by an advancing range of applications; vector processing, quantum computing, VLSI circuit layout, and so on. But one of the most significant uses of GAs is the automatic generation of programs. Technically, the GP solves problems automatically without having to tell the computer specifically how to process it. To meet this requirement, the GP utilizes GAs to a "population" of trial programs, traditionally encoded in memory as tree-structures. Trial programs are estimated using a "fitness function" and the suited solutions picked for re-evaluation and modification such that this sequence is replicated until a "correct" program is generated. GP has represented its power by modifying a simple program for categorizing news stories, executing optical character recognition, medical signal filters, and for target identification, etc. This paper reviews existing literature regarding the GPs and their applications in different scientific fields and aims to provide an easy understanding of various types of GPs for beginners.

• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.731-737
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• 2019

In this study, a new measurement method based on voltage transients in CdZnTe detectors response to low energy photon irradiations is applied to measure the electron mobility (${\mu}_e$) and electron mobility-lifetime product $({\mu}{\tau})_e$ in a CdZnTe detector. In the proposed method, the pulse rise times are derived from low energy photon response to 59.5 keV($^{241}Am$), 88 keV($^{109}Cd$) and 122 keV($^{57}Co$) ${\gamma}-rays$ for the irradiation of the cathode surface at each detector for different bias voltages. The electron $({\mu}{\tau})_e$ product was then determined by measuring the variation in the photopeak amplitude as a function of bias voltage at a given photon energy using a pulse-height analyzer. The $({\mu}{\tau})_e$ values were found to be $(9.6{\pm}1.4){\times}10^{-3}cm^2V^{-1}$ for $1000mm^3$, $(8.4{\pm}1.6){\times}10^{-3}cm^2V^{-1}$ for $1687.5mm^3$ and $(7.6{\pm}1.1){\times}10^{-3}cm^2V^{-1}$ for $2250mm^3$ CdZnTe detectors. Those results were then compared with the literature $({\mu}{\tau})_e$ values for CdZnTe detectors. The present results indicate that, the electron mobility ${\mu}_e$ and electron $({\mu}{\tau})_e$ values in CdZnTe detectors can be measured easily by applying voltage transients response to low energy photons, utilizing a fast signal acquisition and data reduction and evaluation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.3
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• pp.291-298
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• 2019

In this paper, we propose a Fresnel transform method using various wavelet functions to efficiently decompose digital holograms. After implementing the proposed wavelet function-based Fresnelet transforms, we apply it to the digital hologram and analyze the energy characteristics of the coefficients. The implemented wavelet transform-based Fresnelet transform is well suited for reconstructing and processing holograms which are optically obtained or generated by computer-generated hologram technique. After analyzing the characteristics of the spline function, we discuss wavelet multiresolution analysis method based on it. Through this process, we proposed a transform tool that can effectively decompose fringe patterns generated by optical interference phenomena. We implement Fresnelet transform based on wavelet function with various decomposition properties and show the results of decomposing fringe pattern using it. The results show that the energy distribution of the coefficients is significantly different depending on whether the random phase is included or not.

• Journal of radiological science and technology
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• v.45 no.2
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• pp.145-150
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• 2022

The main purpose of this work was to restore the blurry chest CT images by applying a blind deconvolution algorithm. In general, image restoration is the procedure of improving the degraded image to get the true or original image. In this regard, we focused on a blind deblurring approach with chest CT imaging by using digital image processing in MATLAB, which the blind deconvolution technique performed without any whole knowledge or information as to the fundamental point spread function (PSF). For our approach, we acquired 30 chest CT images from the public source and applied three type's PSFs for finding the true image and the original PSF. The observed image might be convolved with an isotropic gaussian PSF or motion blurring PSF and the original image. The PSFs are assumed as a black box, hence restoring the image is called blind deconvolution. For the 30 iteration times, we analyzed diverse sizes of the PSF and tried to approximate the true PSF and the original image. For improving the ringing effect, we employed the weighted function by using the sobel filter. The results was compared with the three criteria including mean squared error (MSE), root mean squared error (RMSE) and peak signal-to-noise ratio (PSNR), which all values of the optimal-sized image outperformed those that the other reconstructed two-sized images. Therefore, we improved the blurring chest CT image by using the blind deconvolutin algorithm for optimal approach.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1490-1499
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• 2022

Arrhythmia is a condition in which the heart has an irregular rhythm or abnormal heart rate, early diagnosis and management is very important because it can cause stroke, cardiac arrest, or even death. In this paper, we propose arrhythmia classification using hybrid combination model of CNN-BLSTM. For this purpose, the QRS features are detected from noise removed signal through pre-processing and a single bit segment was extracted. In this case, the GAN oversampling technique is applied to solve the data imbalance problem. It consisted of CNN layers to extract the patterns of the arrhythmia precisely, used them as the input of the BLSTM. The weights were learned through deep learning and the learning model was evaluated by the validation data. To evaluate the performance of the proposed method, classification accuracy, precision, recall, and F1-score were compared by using the MIT-BIH arrhythmia database. The achieved scores indicate 99.30%, 98.70%, 97.50%, 98.06% in terms of the accuracy, precision, recall, F1 score, respectively.

• Smart Structures and Systems
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• v.29 no.1
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• pp.181-193
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• 2022

Data-driven structural health monitoring (SHM) of civil infrastructure can be used to continuously assess the state of a structure, allowing preemptive safety measures to be carried out. Long-term monitoring of large-scale civil infrastructure often involves data-collection using a network of numerous sensors of various types. Malfunctioning sensors in the network are common, which can disrupt the condition assessment and even lead to false-negative indications of damage. The overwhelming size of the data collected renders manual approaches to ensure data quality intractable. The task of detecting and classifying an anomaly in the raw data is non-trivial. We propose an approach to automate this task, improving upon the previously developed technique of image-based pre-processing on one-dimensional (1D) data by enriching the features of the neural network input data with multiple channels. In particular, feature engineering is employed to convert the measured time histories into a 3-channel image comprised of (i) the time history, (ii) the spectrogram, and (iii) the probability density function representation of the signal. To demonstrate this approach, a CNN model is designed and trained on a dataset consisting of acceleration records of sensors installed on a long-span bridge, with the goal of fault detection and classification. The effect of imbalance in anomaly patterns observed is studied to better account for unseen test cases. The proposed framework achieves high overall accuracy and recall even when tested on an unseen dataset that is much larger than the samples used for training, offering a viable solution for implementation on full-scale structures where limited labeled-training data is available.

• Journal of the Earthquake Engineering Society of Korea
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• v.28 no.4
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• pp.183-191
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• 2024

Accurate seismic vulnerability assessment requires high quality and large amounts of ground motion data. Ground motion data generated from time series contains not only the seismic waves but also the background noise. Therefore, it is crucial to determine the high-pass cut-off frequency to reduce the background noise. Traditional methods for determining the high-pass filter frequency are based on human inspection, such as comparing the noise and the signal Fourier Amplitude Spectrum (FAS), f² trend line fitting, and inspection of the displacement curve after filtering. However, these methods are subject to human error and unsuitable for automating the process. This study used a deep learning approach to determine the high-pass filter frequency. We used the Mel-spectrogram for feature extraction and mixup technique to overcome the lack of data. We selected convolutional neural network (CNN) models such as ResNet, DenseNet, and EfficientNet for transfer learning. Additionally, we chose ViT and DeiT for transformer-based models. The results showed that ResNet had the highest performance with R² (the coefficient of determination) at 0.977 and the lowest mean absolute error (MAE) and RMSE (root mean square error) at 0.006 and 0.074, respectively. When applied to a seismic event and compared to the traditional methods, the determination of the high-pass filter frequency through the deep learning method showed a difference of 0.1 Hz, which demonstrates that it can be used as a replacement for traditional methods. We anticipate that this study will pave the way for automating ground motion processing, which could be applied to the system to handle large amounts of data efficiently.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.5
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• pp.51-57
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• 2015

In this paper, in order to design the A / D converter with a high resolution of 14 bits or more for the biological signal processing, CMOS delta sigma modulator that is a 1.8V power supply voltage - were designed. we propose a new structure of The fourth order delta-sigma modulator that needs four op amps but we use only two op amps. By using a time -interleaving technique, we can re-construct the circuit and reuse the op amps. Also, we proposed a KT/C noise reduction circuit to reduce the thermal noise from a noisy resistor. We adjust the size of sampling capacitor between sampling time and integrating time, so we can reduce almost a half of KT/C noise. The measurement results of the chip is fabricated using a Magna 0.18um CMOS n-well1 poly 6 metal process. Power consumption is $828{\mu}W$ from a 1.8V supply voltage. The peak SNDR is measured as a 75.7dB and 81.3dB of DR at 1kHz input frequency and 256kHz sampling frequency. Measurement results show that KT/C noise reduction circuit enhance the 3dB of SNDR. FOM of the circuit is calculated to be 142dB and 41pJ / step.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.9
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• pp.705-709
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• 2016

Micro-electro-mechanical systems (MEMS) gyroscopes are widely used in various robot applications. However, these conventional gyroscopes need to vibrate the proof mass using a built-in actuator at a fixed resonance frequency to sense the Coriolis force. When a robot is not moving, the meaningless vibration of the gyroscope wastes power. In addition, this continuous vibration makes the sensor vulnerable to external sound waves with a frequency close to the proof-mass resonance frequency. In this paper, a feasibility study of a new type of gyroscope inspired by insect halteres is presented. In dipterous insects, halteres are a biological gyroscope that measures the Coriolis force. Wing muscles and halteres are mechanically linked, and the halteres oscillate simultaneously with wing beats. The vibrating haltere experiences the Coriolis force if the insect is going through a rotational motion. Inspired by this haltere structure, a gyroscope using a thin mast integrated with a robot actuation mechanism is proposed. The mast vibrates only when the robot is moving without requiring a separate actuator. The Coriolis force of the mast can be measured with an accelerometer installed at the tip of the mast. However, the signal from the accelerometer has multiple frequency components and also can be highly corrupted with noise, such that raw data are not meaningful. This paper also presents a suitable signal processing technique using the amplitude modulation method. The feasibility of the proposed haltere-inspired gyroscope is also experimentally evaluated.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.8
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• pp.857-864
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• 2010

In this paper, we analyse the effect of the path imbalances(gain and phase mismatches) in LINC(LInear amplification with Nonlinear Component) system, and propose a simple scheme using LUTs(Look Up Table) to compensate the path imbalances. The EVM(Error Vector Magnitude) and ACPR(Adjacent Channel Power Ratio) of the LINC system are degraded significantly by the path imbalances because it adopts an outphasing technique. The EVM and ACPR are theoretically extracted for two variables(gain and phase mismatch factors) and 2-D LUTs for those are generated based on the analysis. The efficient and simple compensation scheme for the path imbalances is proposed using the 2-D LUTs. A LINC system with the suggested compensation scheme is implemented, and the proposed method is verified with an experiment. A 16-QAM signal with 1.5 MHz bandwidth is used. Before the compensation, the path gain ratio was 95 % and phase error was $19.33^{\circ}$. The proposed scheme adjusts those values with 99 % and $0.5^{\circ}$, and improves ACPR about 18.1 dB.