• Structural Engineering and Mechanics
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• v.87 no.3
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• pp.221-229
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• 2023

Urbanization and industrialization have significantly increased the amount of solid waste produced in recent decades, posing considerable disposal problems and environmental burdens. The practice of waste utilization in concrete has gained popularity among construction practitioners and researchers for the efficient use of resources and the transition to the circular economy in construction. This study employed Lytag aggregate, an environmentally friendly pulverized fuel ash-based lightweight aggregate, as a substitute for natural coarse aggregate. At the same time, fly ash, an industrial by-product, was used as a partial substitute for cement. Concrete mix M20 was experimented with using fly ash and Lytag lightweight aggregate. The percentages of fly ash that make up the replacements were 5%, 10%, 15%, 20%, and 25%. The Compressive Strength (CS), Split Tensile Strength (STS), and deflection were discovered at these percentages after 56 days of testing. The concrete cube, cylinder, and beam specimens were examined in the explorations, as mentioned earlier. The results indicate that a 10% substitution of cement with fly ash and a replacement of coarse aggregate with Lytag lightweight aggregate produced concrete that performed well in terms of mechanical properties and deflection. The cementitious composites have varying characteristics as the environment changes. Therefore, understanding their mechanical properties are crucial for safety reasons. CS, STS, and deflection are the essential property of concrete. Machine learning (ML) approaches have been necessary to predict the CS of concrete. The Artificial Fish Swarm Optimization (AFSO), Particle Swarm Optimization (PSO), and Harmony Search (HS) algorithms were investigated for the prediction of outcomes. This work deftly explains the tremendous AFSO technique, which achieves the precise ideal values of the weights in the model to crown the mathematical modeling technique. This has been proved by the minimum, maximum, and sample median, and the first and third quartiles were used as the basis for a boxplot through the standardized method of showing the dataset. It graphically displays the quantitative value distribution of a field. The correlation matrix and confidence interval were represented graphically using the corrupt method.

• Korean Journal of Radiology
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• v.24 no.7
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• pp.668-680
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• 2023

Objective: To compare multiparametric magnetic resonance imaging (MRI) features of intraductal carcinoma of the prostate (IDC-P) with those of prostatic acinar adenocarcinoma (PAC) and develop prediction models to distinguish IDC-P from PAC and IDC-P with a high proportion (IDC ≥ 10%, hpIDC-P) from IDC-P with a low proportion (IDC < 10%, lpIDC-P) and PAC. Materials and Methods: One hundred and six patients with hpIDC-P, 105 with lpIDC-P and 168 with PAC, who underwent pretreatment multiparametric MRI between January 2015 and December 2020 were included in this study. Imaging parameters, including invasiveness and metastasis, were evaluated and compared between the PAC and IDC-P groups as well as between the hpIDC-P and lpIDC-P subgroups. Nomograms for distinguishing IDC-P from PAC, and hpIDC-P from lpIDC-P and PAC, were made using multivariable logistic regression analysis. The discrimination performance of the models was assessed using the receiver operating characteristic area under the curve (ROC-AUC) in the sample, where the models were derived from without an independent validation sample. Results: The tumor diameter was larger and invasive and metastatic features were more common in the IDC-P than in the PAC group (P < 0.001). The distribution of extraprostatic extension (EPE) and pelvic lymphadenopathy was even greater, and the apparent diffusion coefficient (ADC) ratio was lower in the hpIDC-P than in the lpIDC-P group (P < 0.05). The ROC-AUCs of the stepwise models based solely on imaging features for distinguishing IDC-P from PAC and hpIDC-P from lpIDC-P and PAC were 0.797 (95% confidence interval, 0.750-0.843) and 0.777 (0.727-0.827), respectively. Conclusion: IDC-P was more likely to be larger, more invasive, and more metastatic, with obviously restricted diffusion. EPE, pelvic lymphadenopathy, and a lower ADC ratio were more likely to occur in hpIDC-P, and were also the most useful variables in both nomograms for predicting IDC-P and hpIDC-P.

• Journal of Cardiovascular Imaging
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• v.31 no.4
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• pp.169-177
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• 2023

BACKGROUND: Acute worsening of cardiac function frequently leads to kidney dysfunction. This study aimed to identify clinical and imaging parameters associated with impaired kidney function in patients with acute decompensated heart failure with reduced ejection fraction (HFrEF). METHODS: Data from 131 patients hospitalized with acute decompensated HFrEF (left ventricular ejection fraction, < 40%) were analyzed. Patients were divided into two groups according to the glomerular filtration rate (GFR) at admission (those with preserved kidney function [GFR ≥ 60 mL/min/1.73 m²] and those with reduced kidney function [GFR < 60 mL/min/1.73 m²]). Various echocardiographic parameters and perirenal fat thicknesses were assessed by computed tomography. RESULTS: There were 71 patients with preserved kidney function and 60 patients with reduced kidney function. Increased age (odds ratio [OR], 1.07; 95% confidence interval [CI], 1.04-1.12; p = 0.005), increased log N-terminal pro b-type natriuretic peptide (OR, 1.74; 95% CI, 1.14-2.66; p = 0.010), and increased perirenal fat thickness (OR, 1.19; 95% CI, 1.10-1.29; p < 0.001) were independently associated with reduced kidney function, even after adjusting for variable clinical and echocardiographic parameters. The optimal average perirenal fat thickness cut-off value of > 12 mm had a sensitivity of 55% and specificity of 83% for kidney dysfunction prediction. CONCLUSIONS: Thick perirenal fat was independently associated with impaired kidney function in patients hospitalized for acute decompensated HFrEF. Measurement of perirenal fat thickness may be a promising imaging marker for the detection of HFrEF patients who are more susceptible to kidney dysfunction.

• Advanced Industrial SCIence
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• v.3 no.1
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• pp.17-22
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• 2024

History, which began with the emergence of mankind, has a means of recording. Today, we can check the past through data. Generated data may only be generated and stored at a certain moment, but it is not only continuously generated over a certain time interval from the past to the present, but also occurs in the future, so making predictions using it is an important task. In order to find out trends in the use of time series data among numerous data, this paper analyzes the concept of time series data, analyzes Recurrent Neural Network and Long-Short Term Memory, which are mainly used for time series data analysis in the machine learning field, and analyzes the use of these models. Through case studies, it was confirmed that it is being used in various fields such as medical diagnosis, stock price analysis, and climate prediction, and is showing high predictive results. Based on this, we will explore ways to utilize it in the future.

• Journal of the Society of Disaster Information
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• v.20 no.1
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• pp.82-92
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• 2024

Purpose: The purpose of this study is to find a solution to the massive data construction that occurs when fire simulation data is linked to augmented reality and the resulting data overload problem. Method: An experiment was conducted to set the interval between appropriate input data to improve the reliability and computational complexity of Linear Interpolation, a data estimation technology. In addition, a validity verification was conducted to confirm whether Linear Interpolation well reflected the dynamic changes of fire. Result: As a result of application to the underground common area, which is the study target building, it showed high satisfaction in improving the reliability of Interpolation and the operation processing speed of simulation when data was input at intervals of 10 m. In addition, it was verified through evaluation using MAE and R-Squared that the estimation method of fire simulation data using the Interpolation technique had high explanatory power and reliability. Conclusion: This study solved the data overload problem caused by applying digital twin technology to fire simulation through Interpolation techniques, and confirmed that fire information prediction and visualization were of great help in real-time fire prevention.

• Korean Circulation Journal
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• v.53 no.11
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• pp.758-771
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• 2023

Background and Objectives: Paroxysmal atrial fibrillation (AF) is a major potential cause of embolic stroke of undetermined source (ESUS). However, identifying AF remains challenging because it occurs sporadically. Deep learning could be used to identify hidden AF based on the sinus rhythm (SR) electrocardiogram (ECG). We combined known AF risk factors and developed a deep learning algorithm (DLA) for predicting AF to optimize diagnostic performance in ESUS patients. Methods: A DLA was developed to identify AF using SR 12-lead ECG with the database consisting of AF patients and non-AF patients. The accuracy of the DLA was validated in 221 ESUS patients who underwent insertable cardiac monitor (ICM) insertion to identify AF. Results: A total of 44,085 ECGs from 12,666 patient were used for developing the DLA. The internal validation of the DLA revealed 0.862 (95% confidence interval, 0.850-0.873) area under the curve (AUC) in the receiver operating curve analysis. In external validation data from 221 ESUS patients, the diagnostic accuracy of DLA and AUC were 0.811 and 0.827, respectively, and DLA outperformed conventional predictive models, including CHARGE-AF, C2HEST, and HATCH. The combined model, comprising atrial ectopic burden, left atrial diameter and the DLA, showed excellent performance in AF prediction with AUC of 0.906. Conclusions: The DLA accurately identified paroxysmal AF using 12-lead SR ECG in patients with ESUS and outperformed the conventional models. The DLA model along with the traditional AF risk factors could be a useful tool to identify paroxysmal AF in ESUS patients.

• Tuberculosis and Respiratory Diseases
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• v.87 no.3
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• pp.349-356
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• 2024

Background: Acute pulmonary embolism (APE) is a fatal disease with varying clinical characteristics and imaging. The aim of this study was to define the clinical characteristics, risk factors, and outcomes in patients with APE at a university hospital in Thailand. Methods: Patients diagnosed with APE and admitted to our institute between January 1, 2017 and December 31, 2022 were retrospectively enrolled. The clinical characteristics, investigations, and outcomes were recorded. Results: Over the 6-year study period, 369 patients were diagnosed with APE. The mean age was 65 years; 64.2% were female. The most common risk factor for APE was malignancy (46.1%). In-hospital mortality rate was 23.6%. The computed tomography pulmonary artery revealed the most proximal clots largely in segmental pulmonary artery (39.0%), followed by main pulmonary artery (36.3%). This distribution was consistent between survivors and non-survivors. Multivariate logistic regression analysis revealed that APE mortality was associated with active malignancy, higher serum creatinine, lower body mass index (BMI), and tachycardia with adjusted odds ratio (95% confidence interval [CI]) of 3.70 (1.59 to 8.58), 3.54 (1.35 to 9.25), 2.91 (1.26 to 6.75), and 2.54 (1.14 to 5.64), respectively. The prediction model was constructed with area under the curve of 0.77 (95% CI, 0.70 to 0.84). Conclusion: The overall mortality rate among APE patients was 23.6%, with APE-related death accounting for 5.1%. APE mortality was associated with active malignancy, higher serum creatinine, lower BMI, and tachycardia.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.24 no.4
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• pp.495-508
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• 2019

Tidal datums are key and basic information used in fields of navigation, coastal structures' design, maritime boundary delimitation and inundation warning. In Korea, the Approximate Lowest Low Water (ALLW) and the Approximate Highest High Water (AHHW) have been used as levels of tidal datums for depth, coastline and vertical clearances in hydrography and coastal engineering fields. However, recently the major maritime countries including USA, Australia and UK have adopted the Lowest Astronomical Tide (LAT) and the Highest Astronomical Tide (HAT) as the tidal datums. In this study, 1-hr interval 19-year sea level records (1999-2017) observed at 9 tidal observation stations along the west and south coasts of Korea were used to calculate LAT and HAT for each station using 1-minute interval 19-year tidal prediction data yielded through three tidal harmonic methods: 19 year vector average of tidal harmonic constants (Vector Average Method, VA), tidal harmonic analysis on 19 years of continuous data (19-year Method, 19Y) and tidal harmonic analysis on one year of data (1-year Method, 1Y). The calculated LAT and HAT values were quantitatively compared with the ALLW and AHHW values, respectively. The main causes of the difference between them were explored. In this study, we used the UTide, which is capable of conducting 19-year record tidal harmonic analysis and 19 year tidal prediction. Application of the three harmonic methods showed that there were relatively small differences (mostly less than ±1 cm) of the values of LAT and HAT calculated from the VA and 19Y methods, revealing that each method can be mutually and effectively used. In contrast, the standard deviations between LATs and HATs calculated from the 1Y and 19Y methods were 3~7 cm. The LAT (HAT) differences between the 1Y and 19Y methods range from -16.4 to 10.7 cm (-8.2 to 14.3 cm), which are relatively large compared to the LAT and HAT differences between the VA and 19Y methods. The LAT (HAT) values are, on average, 33.6 (46.2) cm lower (higher) than those of ALLW (AHHW) along the west and south coast of Korea. It was found that the S_a and N₂ tides significantly contribute to these differences. In the shallow water constituents dominated area, the M₄ and MS₄ tides also remarkably contribute to them. Differences between the LAT and the ALLW are larger than those between the HAT and the AHHW. The asymmetry occurs because the LAT and HAT are calculated from the amplitudes and phase-lags of 67 harmonic constituents whereas the ALLW and AHHW are based only on the amplitudes of the 4 major harmonic constituents.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.9
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• pp.351-360
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• 2018

The LWR (Locally Weighted Regression) model, which is traditionally a lazy learning model, is designed to obtain the solution of the prediction according to the input variable, the query point, and it is a kind of the regression equation in the short interval obtained as a result of the learning that gives a higher weight value closer to the query point. We study on an incremental ensemble learning approach for LWR, a form of lazy learning and memory-based learning. The proposed incremental ensemble learning method of LWR is to sequentially generate and integrate LWR models over time using a genetic algorithm to obtain a solution of a specific query point. The weaknesses of existing LWR models are that multiple LWR models can be generated based on the indicator function and data sample selection, and the quality of the predictions can also vary depending on this model. However, no research has been conducted to solve the problem of selection or combination of multiple LWR models. In this study, after generating the initial LWR model according to the indicator function and the sample data set, we iterate evolution learning process to obtain the proper indicator function and assess the LWR models applied to the other sample data sets to overcome the data set bias. We adopt Eager learning method to generate and store LWR model gradually when data is generated for all sections. In order to obtain a prediction solution at a specific point in time, an LWR model is generated based on newly generated data within a predetermined interval and then combined with existing LWR models in a section using a genetic algorithm. The proposed method shows better results than the method of selecting multiple LWR models using the simple average method. The results of this study are compared with the predicted results using multiple regression analysis by applying the real data such as the amount of traffic per hour in a specific area and hourly sales of a resting place of the highway, etc.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.374-390
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• 2021

Growth and yield of field vegetable crops would be affected by climate conditions, which cause a relatively large fluctuation in crop production and consumer price over years. The yield prediction system for these crops would support decision-making on policies to manage supply and demands. The objectives of this study were to compile literatures related to onion and garlic and to perform data-mining analysis, which would shed lights on the development of crop models for these major field vegetable crops in Korea. The literatures on crop growth and yield were collected from the databases operated by Research Information Sharing Service, National Science & Technology Information Service and SCOPUS. The keywords were chosen to retrieve research outcomes related to crop growth and yield of onion and garlic. These literatures were analyzed using text mining approaches including word cloud and semantic networks. It was found that the number of publications was considerably less for the field vegetable crops compared with rice. Still, specific patterns between previous research outcomes were identified using the text mining methods. For example, climate change and remote sensing were major topics of interest for growth and yield of onion and garlic. The impact of temperature and irrigation on crop growth was also assessed in the previous studies. It was also found that yield of onion and garlic would be affected by both environment and crop management conditions including sowing time, variety, seed treatment method, irrigation interval, fertilization amount and fertilizer composition. For meteorological conditions, temperature, precipitation, solar radiation and humidity were found to be the major factors in the literatures. These indicate that crop models need to take into account both environmental and crop management practices for reliable prediction of crop yield.