• Steel and Composite Structures
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• v.42 no.6
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• pp.733-745
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• 2022

This study proposed a robust artificial intelligence (AI) model based on the social behaviour of the imperialist competitive algorithm (ICA) and artificial neural network (ANN) for modelling the deflection of reinforced concrete beams, abbreviated as ICA-ANN model. Accordingly, the ICA was used to adjust and optimize the parameters of an ANN model (i.e., weights and biases) aiming to improve the accuracy of the ANN model in modelling the deflection reinforced concrete beams. A total of 120 experimental datasets of reinforced concrete beams were employed for this aim. Therein, applied load, tensile reinforcement strength and the reinforcement percentage were used to simulate the deflection of reinforced concrete beams. Besides, five other AI models, such as ANN, SVM (support vector machine), GLMNET (lasso and elastic-net regularized generalized linear models), CART (classification and regression tree) and KNN (k-nearest neighbours), were also used for the comprehensive assessment of the proposed model (i.e., ICA-ANN). The comparison of the derived results with the experimental findings demonstrates that among the developed models the ICA-ANN model is that can approximate the reinforced concrete beams deflection in a more reliable and robust manner.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.9
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• pp.105-114
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• 2022

Financial markets have long been known to be prone to behavioral biases. One such behavioural bias that is consequential yet pervasive in financial markets is the herd effect. The objective of this study is to determine whether or not there exist herd behaviour in the new and bourgeoning Decentralized Finance (DeFi) Tokens market. This is accomplished by using daily returns of 22 DeFi tokens from January 29, 2017 to August 19, 2021, and the Cross-sectional Absolute Deviation (CSAD) of market returns to capture herd behavior. The results fail to provide any evidence of herding in the DeFi token market on bullish days, that is days for which the average market returns is positive. For bearish days however, that is days for which the market returns is negative, our empirical findings point to the presence of adverse herding in the DeFi token market. This phenomenon can be explained to some extent by the investor composition of the DeFi market. The DeFi token space is a growth market dominated by experts and/or enthusiasts who are insulated against the temptation and panic of negative market swings by the level of market and technical information they possess on the assets they invest.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.9
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• pp.617-625
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• 2022

This study investigates the use of movable calibration target for gyroscopic and accelerometer bias compensation of inertial measurement units for firing lane alignment. Calibration source is detected with the help of vision sensor and its information in fused with other sensors on launcher for error correction. An algorithm is proposed and tested in simulation. It has been shown that it is possible to compensate sensor biases in firing launcher in few seconds by accurately estimating the location of calibration target in inertial frame of reference.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.363-363
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• 2023

Artificial Neural Networks (ANN) is a powerful tool for addressing various practical problems and it has been extensively applied in areas of water resources. In this study, Artificial Neural Networks (ANNs) were developed for flood forecasting at specific locations on the Han River. The Partial Mutual Information (PMI) technique was used to select input variables for ANNs that are neither over-specified nor under-specified while adequately describing the underlying input-output relationships. Historical observations including discharges at the Paldang Dam, flows from tributaries, water levels at the Paldang Bridge, Banpo Bridge, Hangang Bridge, and Junryu gauge station, and time derivatives of the observed water levels were considered as input candidates. Lagged variables from current time t to the previous five hours were assumed to be sufficient in this study. A three-layer neural network with one hidden layer was used and the neural network was optimized by selecting the optimal number of hidden neurons given the selected inputs. Given an ANN architecture, the weights and biases of the network were determined in the model training. The use of PMI-based input variable selection and optimized ANNs for different sites were proven to successfully predict water levels during flood periods.

• Nuclear Engineering and Technology
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• v.55 no.1
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• pp.270-277
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• 2023

Exposure to ionizing radiation induces free radicals in human nails. These free radicals generate a radiation-induced signal (RIS) in electron paramagnetic resonance (EPR) spectroscopy. Compared with the RIS of tooth enamel samples, that in human nails is more affected by moisture and heat, but has the advantages of being sensitive to radiation and easy to collect. The fingernail as a biological sample is applicable in retrospective dosimetry in cases of localized hand exposure accidents. In this study, the dosimetric characteristics of fingernails were analyzed in fingernail clippings collected from Korean donors. The dose response, fading of radiation-induced and mechanically induced signals, treatment method for evaluation of background signal, minimum detectable dose, and minimum detectable mass were investigated to propose a fingernail-EPR dosimetry protocol. In addition, to validate the practicality of the protocol, blind and field experiments were performed in the laboratory and a non-destructive testing facility. The relative biases in the dose assessment result of the blind and field experiments were 8.43% and 21.68% on average between the reference and reconstructed doses. The results of this study suggest that fingernail-EPR dosimetry can be a useful method for the application of retrospective dosimetry in cases of radiological accidents.

• Journal of Research and Publication Ethics
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• v.3 no.1
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• pp.1-5
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• 2022

Purpose: A conflict of interest is defined broadly as a scenario in which one's responsibilities and self-interest collide in a manner that has a significant probability of corrupting one's discernments, motivations, actions, desires, values, and judgments. This study aims to investigate various cases of conflict of interest. Research design, data and methodology: Our study used the preferred reporting items for systematic reviews and meta-analyses (PRISMA) to identify resources. The eligibility of selected prior studies thoroughly was investigated whether they are suitable for the topic of present study. Finally, we collected total 15 previous studies published between 2000 and 2021. Results: Research findings indicate that there are three main cases that might cause a conflict of interest and mandated research ethics education might provide researchers with the tools to identify and battle the temptations and biases provided by conflicts of interest. Researchers could likely be better prepared for conflicts of interest if they investigated the moral difficulties associated with them in advance. Conclusions: Researchers might evade deliberate or unconscious detriment of duties, and also objectivity loss because of the siren song of self-interest by escaping situations whereby they may be tempted to shirk their responsibilities, not to remark the hassles of unveiling conflicts.

• Genomics & Informatics
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• v.21 no.1
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• pp.10.1-10.6
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• 2023

The fishes of the Chiasmodontidae family, known as swallower fishes, are species adapted to live in deep seas. Several studies have shown the proximity of this family to Tetragonuridae and Amarsipidae. However, the phylogenetic position of this clade related to other Pelagiaria groups remains uncertain even when phylogenomic studies are employed. Since the low number of published mitogenomes, our study aimed to assemble six new mitochondrial genomes of Chiasmodontidae from database libraries to expand the discussion regarding the phylogeny of this group within Scombriformes. As expected, the composition and organization of mitogenomes were stable among the analyzed species, although we detected repetitive sequences in the D-loop of species of the genus Kali not seen in Chiasmodon, Dysalotus, and Pseudoscopelus. Our phylogeny incorporating 51 mitogenomes from several families of Scombriformes, including nine chiasmodontids, recovered interfamilial relationships well established in previous studies, including a clade containing Chiasmodontidae, Amarsipidae, and Tetragonuridae. However, phylogenetic relationships between larger clades remain unclear, with disagreements between different phylogenomic studies. We argue that such inconsistencies are not only due to biases and limitations in the data but mainly to complex biological events in the adaptive irradiation of Scombriformes after the Cretaceous-Paleogene extinction event.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.3
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• pp.214-226
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• 2023

A transfer alignment is used to initialize, align, and calibrate a SINS(Slave INS) using a MINS(Master INS) in motion. This paper presents an airborne transfer alignment with velocity and azimuth matching to estimate inertial sensor biases under the wing flexure influence. This study also considers the lever arm, time delay and relative orientation between MINS and SINS. The traditional transfer alignment only uses velocity matching. In contrast, this paper utilizes the azimuth matching to prevent divergence of the azimuth when the aircraft is stationary or quasi-stationary since the azimuth is less affected by the wing flexibility. The performance of the proposed Kalman filter is analyzed using two factors; one is the estimation performance of gyroscope and accelerometer bias and the other is comparing aircraft dynamics and attitude covariance. The performance of the proposed filter is verified using a long term flight test. The test results show that the proposed scheme can be effectively applied to various platforms that require airborne transfer alignment.

• Steel and Composite Structures
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• v.47 no.6
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• pp.759-779
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• 2023

The paper proposes two hybrid metaheuristic optimization and artificial neural network (ANN) methods for the close prediction of the ultimate axial compressive capacity of concentrically loaded concrete filled double skin steel tube (CFDST) columns. Two metaheuristic optimization, namely genetic algorithm (GA) and particle swarm optimization (PSO), approaches enable the dynamic training architecture underlying an ANN model by optimizing the number and sizes of hidden layers as well as the weights and biases of the neurons, simultaneously. The former is termed as GA-ANN, and the latter as PSO-ANN. These techniques utilize the gradient-based optimization with Bayesian regularization that enhances the optimization process. The proposed GA-ANN and PSO-ANN methods construct the predictive ANNs from 125 available experimental datasets and present the superior performance over standard ANNs. Both the hybrid GA-ANN and PSO-ANN methods are encoded within a user-friendly graphical interface that can reliably map out the accurate ultimate axial compressive capacity of CFDST columns with various geometry and material parameters.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.204-204
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• 2021

Terrestrial water storage (TWS) includes all components of water (e.g., surface water, groundwater, snow and ice) over the land. So accurately predicting and estimating TWS is important in water resource management. Although many land surface models are used to predict the TWS, model output has errors and biases in comparison to the observation data due to the model deficiencies in the model structure, atmospheric forcing datasets, and parameters. In this study, Gravity Recovery And Climate Experiment (GRACE) satelite TWS data is assimilated in the Community Land Model version 5 with a biogeochemistry module (CLM5.0-BGC) over East Asia from 2003 to 2010 by employing the Ensemble Adjustment Kalman Filter (EAKF). Results showed that TWS over East Asia continued to decrease during the study period, and the ability to simulate the surface water storage, which is the component of the CLM derived TWS, was greatly improved. We further investigated the impact of assimilated TWS on the vegetated and carbon related variables, including the leaf area index and primary products of ecosystem. We also evaluated the simulated total ecosystem carbon and calculated its correlation with TWS. This study shows that how the better simulated TWS plays a role in capturing not only water but also carbon fluxes and states.