• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.995-1002
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• 2020

This paper investigates the achievable data rate for non-orthogonal multiple access(NOMA) with correlated information sources(CIS), under the binary phase shift keying(BPSK) modulation, in contrast to most of the existing NOMA designs using continuous Gaussian input modulations. First, the closed-form expression for the achievable data rate of NOMA with CIS and BPSK is derived, for both users. Then it is shown by numerical results that for the stronger channel user, the achievable data rate of CIS reduces, compared with that of independent information sources( IIS). We also demonstrate that for the weaker channel user, the achievable data rate of CIS increases, compared with that of IIS. In addition, the intensive analyses of the probability density function(PDF) of the observation and the inter-user interferennce(IUI) are provided to verify our theoretical results.

• Current Optics and Photonics
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• v.5 no.3
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• pp.250-260
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• 2021

A tapered As₂S₃ photonic crystal fiber (PCF) with four layers of air holes in a hexagonal array around the core is designed in this paper. Numerical simulation shows that the dispersion D decreases and the nonlinearity coefficient γ increases from the thick to the thin end along the tapered PCF. We simulate the midinfrared pulse compression in the tapered As₂S₃ PCF using the adaptive split-step Fourier method. Initial Gaussian pulses of 4.4 ps and a central wavelength of 2.5 ㎛ propagating in the tapered PCF are located in the anomalous dispersion region. With an average power of assumed input pulses at 3 mW and a repetition frequency of 81.0 MHz, we theoretically obtain a pulse duration of 56 fs and a compression factor of 78 when the pulse propagates from the thick end to the thin end of the tapered PCF. When confinement loss in the tapered PCF is included in the simulation, the minimum pulse duration reaches 72 fs; correspondingly, the maximum compression factor reaches 61. The results show that in the anomalous-dispersion region, midinfrared pulses can be efficiently compressed in a dispersion-decreasing and nonlinearity-increasing tapered As₂S₃ PCF. Due to confinement loss in the tapered fiber, the efficiency of pulse compression is suppressed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.114-121
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• 2022

In this paper, we consider a user centric clustering in order to guarantee the performance of the users in cell free multiple-input multiple-output (MIMO) network. In the user centric clustering scheme, by using large scale fading coefficients of the connected access points (APs), each user decides own cluster with the APs having the higher the large scale fading coefficients than threshold value compared to the highest large scale fading coefficient. In the determined user centric clusters, the APs design the beamformers and power allocations in the distributed manner and the APs cooperatively transmit data to users by using beamformers and power allocations. In the simulation results, we verify the performance of user centric clustering in terms of the spectral efficiency and we also find the optimal threshold value in the given configuration.

• Journal of radiological science and technology
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• v.45 no.2
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• pp.151-158
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• 2022

The Cardiac Gated Blood Pool (GBP) scintigram, a nuclear medicine imaging, calculates the left ventricular Ejection Fraction (EF) by segmenting the left ventricle from the heart. However, in order to accurately segment the substructure of the heart, specialized knowledge of cardiac anatomy is required, and depending on the expert's processing, there may be a problem in which the left ventricular EF is calculated differently. In this study, using the DeepLabV3 architecture, GBP images were trained on 93 training data with a ResNet-50 backbone. Afterwards, the trained model was applied to 23 separate test sets of GBP to evaluate the reproducibility of the region of interest and left ventricular EF. Pixel accuracy, dice coefficient, and IoU for the region of interest were 99.32±0.20, 94.65±1.45, 89.89±2.62(%) at the diastolic phase, and 99.26±0.34, 90.16±4.19, and 82.33±6.69(%) at the systolic phase, respectively. Left ventricular EF was calculated to be an average of 60.37±7.32% in the ROI set by humans and 58.68±7.22% in the ROI set by the deep learning segmentation model. (p<0.05) The automated segmentation method using deep learning presented in this study similarly predicts the average human-set ROI and left ventricular EF when a random GBP image is an input. If the automatic segmentation method is developed and applied to the functional examination method that needs to set ROI in the field of cardiac scintigram in nuclear medicine in the future, it is expected to greatly contribute to improving the efficiency and accuracy of processing and analysis by nuclear medicine specialists.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.4
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• pp.469-476
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• 2022

This study developed a Recurrent Neural Network model implemented through Long Short-Term Memory (LSTM) that generates long-term tidal level data at Busan Port using tide observation data. The tide levels in Busan Port were predicted by the Korea Hydrographic and Oceanographic Administration (KHOA) using the tide data observed at Busan New Port and Tongyeong as model input data. The model was trained for one month in January 2019, and subsequently, the accuracy was calculated for one year from February 2019 to January 2020. The constructed model showed the highest performance with a correlation coefficient of 0.997 and a root mean squared error of 2.69 cm when the tide time series of Busan New Port and Tongyeong were inputted together. The study's finding reveal that long-term tidal level data prediction of an arbitrary port is possible using the deep learning recurrent neural network model.

• Structural Engineering and Mechanics
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• v.83 no.3
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• pp.327-340
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• 2022

The Collapse Margin Ratio (CMR) is a notable index used for seismic assessment of the structures. As proposed by FEMA P695, a set of analyses including the Nonlinear Static Analysis (NSA), Incremental Dynamic Analysis (IDA), together with Fragility Analysis, which are typically time-taking and computationally unaffordable, need to be conducted, so that the CMR could be obtained. To address this issue and to achieve a quick and efficient method to estimate the CMR, the Artificial Neural Network (ANN), Response Surface Method (RSM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) will be introduced in the current research. Accordingly, using the NSA results, an attempt was made to find a fast and efficient approach to derive the CMR. To this end, 5016 IDA analyses based on FEMA P695 methodology on 114 various Reinforced Concrete (RC) frames with 1 to 12 stories have been carried out. In this respect, five parameters have been used as the independent and desired inputs of the systems. On the other hand, the CMR is regarded as the output of the systems. Accordingly, a double hidden layer neural network with Levenberg-Marquardt training and learning algorithm was taken into account. Moreover, in the RSM approach, the quadratic system incorporating 20 parameters was implemented. Correspondingly, the Analysis of Variance (ANOVA) has been employed to discuss the results taken from the developed model. Additionally, the essential parameters and interactions are extracted, and input parameters are sorted according to their importance. Moreover, the ANFIS using Takagi-Sugeno fuzzy system was employed. Finally, all methods were compared, and the effective parameters and associated relationships were extracted. In contrast to the other approaches, the ANFIS provided the best efficiency and high accuracy with the minimum desired errors. Comparatively, it was obtained that the ANN method is more effective than the RSM and has a higher regression coefficient and lower statistical errors.

• Journal of Sasang Constitutional Medicine
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• v.33 no.4
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• pp.1-9
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• 2021

Objectives The purpose of this study was to check whether the classification of the individual's Body Mass Index (BMI) could be predicted by analyzing the voice data constructed at the Korean medicine data center (KDC) using machine learning. Methods In this study, we proposed a convolutional neural network (CNN)-based BMI classification model. The subjects of this study were Korean adults who had completed voice recording and BMI measurement in 2006-2015 among the data established at the Korean Medicine Data Center. Among them, 2,825 data were used for training to build the model, and 566 data were used to assess the performance of the model. As an input feature of CNN, Mel-frequency cepstral coefficient (MFCC) extracted from vowel utterances was used. A model was constructed to predict a total of four groups according to gender and BMI criteria: overweight male, normal male, overweight female, and normal female. Results & Conclusions Performance evaluation was conducted using F1-score and Accuracy. As a result of the prediction for four groups, The average accuracy was 0.6016, and the average F1-score was 0.5922. Although it showed good performance in gender discrimination, it is judged that performance improvement through follow-up studies is necessary for distinguishing BMI within gender. As research on deep learning is active, performance improvement is expected through future research.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1635-1641
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• 2021

Portable systems with built-in active noise control is required low power operation. Excessive anti noise search operation can lead to rapid battery consumption. A method that can adaptively cancel noise according to the operating conditions of the system is required and the methods of reducing power are becoming very important key feature in today's portable systems. In this paper, we propose the method of active noise control(ANC) using binary search algorithm in noisy systems. The implemented architecture detects a frequency component considered as noise from the input signal and by using the binary search algorithm, the system find out an appropriate amplitude value for anti-noise in a much faster time than the general linear search algorithm. Through the experimental results, it was confirmed that the proposed algorithm performs a successful functional operation.

• Composite Materials and Engineering
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• v.3 no.3
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• pp.201-220
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• 2021

In this study, potential of three machine learning techniques i.e., M5P, Support vector machines and Gaussian processes were evaluated to find the best algorithm for the prediction of flexural strength of concrete mix with steel fibre. The study comprises the comparison of results obtained from above-said techniques for given dataset. The dataset consists of 124 observations from past research studies and this dataset is randomly divided into two subsets namely training and testing datasets with (70-30)% proportion by weight. Cement, fine aggregates, coarse aggregates, water, super plasticizer/ high-range water reducer, steel fibre, fibre length and curing days were taken as input parameters whereas flexural strength of the concrete mix was taken as the output parameter. Performance of the techniques was checked by statistic evaluation parameters. Results show that the Gaussian process technique works better than other techniques with its minimum error bandwidth. Statistical analysis shows that the Gaussian process predicts better results with higher coefficient of correlation value (0.9138) and minimum mean absolute error (1.2954) and Root mean square error value (1.9672). Sensitivity analysis proves that steel fibre is the significant parameter among other parameters to predict the flexural strength of concrete mix. According to the shape of the fibre, the mixed type performs better for this data than the hooked shape of the steel fibre, which has a higher CC of 0.9649, which shows that the shape of fibers do effect the flexural strength of the concrete. However, the intricacy of the mixed fibres needs further investigations. For future mixes, the most favorable range for the increase in flexural strength of concrete mix found to be (1-3)%.

• The Korea Journal of Herbology
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• v.33 no.5
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• pp.19-37
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• 2018

Objectives : Oryeong-san was composed of Alismatis Rhizoma, Atractylodis Rhizoma Alba, Poria Sclerotium, Polyporus, Cinnamomi Cortex, and known to have hundreds of chemical compounds. The aim of this study was to screen chemical compounds constituting Oryeong-san with the drug-likeness and oral bioavailability from the analysis of their physicochemical properties. Methods : A list of chemical compounds of Oryeong-san was obtained from TM-MC(database of medicinal materials and chemical compounds in Northeast Asian traditional medicine). To remove redundant compounds, the SMILES (Simplified Molecular Input Line Entry System) strings of each compound were identified. All of the physicochemical properties for the compounds were calculated using the DruLiTo(Drug Likeness Tool). Drug-likeness was estimated by QED(Quantitative Estimate of Druglikeness) and OB(Oral bioavailability) was checked based on the Veber's rules. Results : A total of 475 compounds were obtained by eliminating duplication among 544 compounds of 5 medicinal materials. Analysis of the physicochemical properties revealed that the most common values were MW(molecular weight) 200~300 g/mol, ALOGP(octanol-water partition coefficient) 1~2, HBA(number of hydrogen bond acceptors) 0~1, HBD(number of hydrogen bond donors) 0, PSA(polar surface area) 0~50 angstrom, ROTB(number of rotatable bonds) 1, AROM(number of aromatic rings) 0, and ALERT(number of structural alerts) 1. QED had 93% of the values between 0.2 and 0.7, and OB had 90% of the value of TRUE. Conclusions : We in this paper screened the candidate active compounds of Oryeong-san using the QED and Veber's rules. In the future, we will use the screening results to analyze the mechanism of Oryeong-san based on systems pharmacology.