• Biotechnology and Bioprocess Engineering:BBE
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• v.11 no.1
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• pp.67-71
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• 2006

This paper compares regression and neural network modeling approaches to predict competitive biosorption equilibrium data. The regression approach is based on the fitting of modified Langmuir-type isotherm models to experimental data. Neural networks, on the other hand, are non-parametric statistical estimators capable of identifying patterns in data and correlations between input and output. Our results show that the neural network approach outperforms traditional regression-based modeling in correlating and predicting the simultaneous uptake of copper and cadmium by a microbial biosorbent. The neural network is capable of accurately predicting unseen data when provided with limited amounts of data for training. Because neural networks are purely data-driven models, they are more suitable for obtaining accurate predictions than for probing the physical nature of the biosorption process.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.13 no.2
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• pp.177-188
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• 2001

The paper presents the method to estimate the bottom shear stress driven by waves and current on rough turbulent flow. Parameter adjusting technique is suggested for the computation of bed shear stress driven by uni-directional flow, and the value ofpararneter is determined by comparing the computational results against Bijker's laboratory data. For the computation of combined flow bottom shear stress, two methods are presented; one is the modified Bijker approach (BYO Model) and the other is the modified Fredsoe approach (FY Model), both of which are refined by the present writers. BYO model is again refined in the computation of maximum shear stress, and the final version is tested against Bijkcr's laboratory data.

• Smart Structures and Systems
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• v.22 no.2
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• pp.185-192
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• 2018

Participants in the Asia Pacific Conference of the Prognostics and Health Management Society 2017 (PHMAP 2017) Data Challenge were given measured vibration signals from motor-driven gearboxes used in pulverizers. Using this information, participants were requested to predict failure dates and the faulty components. The measured signals were affected by significant noise and disturbance, as the pulverizers in the provided data worked under actual operating conditions. This paper thus presents a fault prediction method for a motor-driven gearbox in a pulverizer system that can perform under external noise and disturbance conditions. First, two fault features, an RMS value in the higher frequency zones (HRMS) and an amplitude of a period for high-speed shaft in the quefrency domain ($QA_{HSS}$), were extracted based on frequency analysis using the higher and lower sampling rate data. The two features were then applied to each pulverizer based on results of frequency responses to impact loadings. Then, a regression analysis was used to predict the failure date using the two extracted features. A weighted regression analysis was used to compensate for the imbalance of the features in the given period. In addition, the faulty components in the motor-driven gearboxes were predicted based on the modulated frequency components. The score predicted by the proposed approach was ranked first in the PHMAP 2017 Data Challenge.

• Speech Sciences
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• v.11 no.2
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• pp.247-257
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• 2004

There has been many research efforts to overcome the problems of speech recognition in the noisy conditions. Among them, the model-based compensation methods such as the parallel model combination (PMC) and vector Taylor series (VTS) have been found to perform efficiently compared with the previous speech enhancement methods or the feature-based approaches. In this paper, a data-driven model compensation approach that adapts the HMM(hidden Markv model) parameters for the noisy speech recognition is proposed. Instead of assuming some statistical approximations as in the conventional model-based methods such as the PMC, the statistics necessary for the HMM parameter adaptation is directly estimated by using the Baum-Welch algorithm. The proposed method has shown improved results compared with the PMC for the noisy speech recognition.

• Journal of Korea Water Resources Association
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• v.43 no.3
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• pp.257-264
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• 2010

The amount and continuity of precipitation data used in a hydrological analysis may exert a big influence on the reliability of the analysis. It is a fundamental process to estimate the missing data caused by such as a breakdown of the rainfall recording machine or to expand a short period of rainfall data. In this study a linear programming method treated as a data-driven approach for estimating the missing rainfall data is compared with seven other methods widely used and its superiority is certified. The data used in this research are annual precipitation ones during 17 years at the Cheolwon station including an ungauged period of 15 years and its five surrounding stations. By use of this certified method the ungauged precipitation values at the Cheolweon station are estimated and the areal averages of annual precipitation data for 32 years at the Han River basin are calculated.

• Safety and Health at Work
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• v.12 no.4
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• pp.462-470
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• 2021

Background: Fatigue is pervasive, under-reported, and potentially deadly where flight operations are concerned. The aviation industry appears to lack a standardized, practical, and easily replicable protocol for fatigue risk assessment which can be consistently applied across operators. Aim: Our paper sought to present a framework, supported by real-world data with subjective and objective parameters, to monitor aircrew fatigue and performance, and to determine the safe crew configuration for commercial airline operations. Methods: Our protocol identified risk factors for fatigue-induced performance degradation as triggers for fatigue risk and performance assessment. Using both subjective and objective measurements of sleep, fatigue, and performance in the form of instruments such as the Karolinska Sleepiness Scale, Samn-Perelli Crew Status Check, Psychomotor Vigilance Task, sleep logs, and a wearable actigraph for sleep log correlation and sleep duration and quality charting, a workflow flagging fatigue-prone flight operations for risk mitigation was developed and trialed. Results: In an operational study aimed at occupational assessment of fatigue and performance in airline pilots on a three-men crew versus a four-men crew for a long-haul flight, we affirmed the technical feasibility of our proposed framework and approach, the validity of the battery of assessment instruments, and the meaningful interpretation of fatigue and work performance indicators to enable the formulation of safe work recommendations. Conclusion: A standardized occupational assessment protocol like ours is useful to achieve consistency and objectivity in the occupational assessment of fatigue and work performance.

• Computers and Concrete
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• v.32 no.6
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• pp.625-637
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• 2023

The application of ML approaches in determining the resisting capacity of fire damaged RC columns is introduced in this paper, on the basis of analysis data driven ML modeling. Considering the characteristics of the structural behavior of fire damaged RC columns, the representative five approaches of Kernel SVM, ANN, RF, XGB and LGBM are adopted and applied. Additional partial monotonic constraints are adopted in modelling, to ensure the monotone decrease of resisting capacity in RC column with fire exposure time. Furthermore, additional suggestions are also added to mitigate the heterogeneous composition of the training data. Since the use of ML approaches will significantly reduce the computation time in determining the resisting capacity of fire damaged RC columns, which requires many complex solution procedures from the heat transfer analysis to the rigorous nonlinear analyses and their repetition with time, the introduced ML approach can more effectively be used in large complex structures with many RC members. Because of the very small amount of experimental data, the training data are analytically determined from a heat transfer analysis and a subsequent nonlinear finite element (FE) analysis, and their accuracy was previously verified through a correlation study between the numerical results and experimental data. The results obtained from the application of ML approaches show that the resisting capacity of fire damaged RC columns can effectively be predicted by ML approaches.

• Molecules and Cells
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• v.46 no.1
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• pp.65-67
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• 2023

SMILES (simplified molecular-input line-entry system) information of small molecules parsed by one-hot array is passed to a convolutional neural network called black box. Outputs data representing a gene signature is then matched to the genetic signature of a disease to predict the appropriate small molecule. Efficacy of the predicted small molecules is examined by in vivo animal models. GSEA, gene set enrichment analysis.

• Smart Structures and Systems
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• v.14 no.5
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• pp.917-942
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• 2014

Multivariate statistics based damage detection algorithms employed in conjunction with novel sensing technologies are attracting more attention for long term Structural Health Monitoring of civil infrastructure. In this study, two practical data driven methods are investigated utilizing strain data captured from a 4-span bridge model by Fiber Bragg Grating (FBG) sensors as part of a bridge health monitoring study. The most common and critical bridge damage scenarios were simulated on the representative bridge model equipped with FBG sensors. A high speed FBG interrogator system is developed by the authors to collect the strain responses under moving vehicle loads using FBG sensors. Two data driven methods, Moving Principal Component Analysis (MPCA) and Moving Cross Correlation Analysis (MCCA), are coded and implemented to handle and process the large amount of data. The efficiency of the SHM system with FBG sensors, MPCA and MCCA methods for detecting and localizing damage is explored with several experiments. Based on the findings presented in this paper, the MPCA and MCCA coupled with FBG sensors can be deemed to deliver promising results to detect both local and global damage implemented on the bridge structure.

• Smart Structures and Systems
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• v.24 no.4
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• pp.507-524
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• 2019

A Bayesian dynamic linear model (BDLM) is presented for a data-driven analysis for response prediction and load effect separation of a revolving auditorium structure, where the main loads are self-weight and dead loads, temperature load, and audience load. Analyses are carried out based on the long-term monitoring data for static strains on several key members of the structure. Three improvements are introduced to the ordinary regression BDLM, which are a classificatory regression term to address the temporary audience load effect, improved inference for the variance of observation noise to be updated continuously, and component discount factors for effective load effect separation. The effects of those improvements are evaluated regarding the root mean square errors, standard deviations, and 95% confidence intervals of the predictions. Bayes factors are used for evaluating the probability distributions of the predictions, which are essential to structural condition assessments, such as outlier identification and reliability analysis. The performance of the present BDLM has been successfully verified based on the simulated data and the real data obtained from the structural health monitoring system installed on the revolving structure.