• The Journal of Information Systems
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• v.33 no.2
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• pp.125-142
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• 2024

Purpose The purpose of this study is to identify and analyze the key factors influencing congestion in the in-out transportation at port container terminals, and to design of a predictive model for in-out congestion based on these analysis. This study focused on architecting a deep learning-based predictive model. Design/methodology/approach This study was conducted through the following methodology. First, hypotheses were established and data were analyzed to examine the impact of vessel schedules and external truck schedules on in-out transportation. Next, explored time series forecasting models to a design the architecture for deep learning-based predictive model. Findings According to the empirical analysis results, this study confirmed that vessel schedules significantly affect in-out transportation. Specifically, the volume of transportation increases as the vessel arrival/departure time and the cargo cutoff time approach. Additionally, significant congestion patterns in transportation volume depending on the day of the week and the time of day were observed.

• Wind and Structures
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• v.38 no.2
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• pp.147-160
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• 2024

The aerodynamic force is a significant component that influences the stability and safety of structures. It has unstable properties and depends on computer precision, making its long-term prediction challenging. Accurately estimating the aerodynamic traits of structures is critical for structural design and vibration control. This paper establishes an unsteady aerodynamic time series prediction model using Long Short-Term Memory (LSTM) network. The unsteady aerodynamic force under varied Reynolds number and angles of attack is predicted by the LSTM model. The input of the model is the aerodynamic coefficients of the 1 to n sample points and output is the aerodynamic coefficients of the n+1 sample point. The model is predicted by interpolation and extrapolation utilizing Unsteady Reynolds-average Navier-Stokes (URANS) simulation data of flow around a circular cylinder, square cylinder and airfoil. The results illustrate that the trajectories of the LSTM prediction results and URANS outcomes are largely consistent with time. The mean relative error between the forecast results and the original results is less than 6%. Therefore, our technique has a prospective application in unsteady aerodynamic force prediction of structures and can give technical assistance for engineering applications.

• Journal of Korean Society of Water and Wastewater
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• v.38 no.1
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• pp.1-15
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• 2024

Occurrence of process environment changes, such as influent load variances and process condition changes, can reduce treatment efficiency, increasing effluent water quality. In order to prevent exceeding effluent standards, it is necessary to manage effluent water quality based on process operation data including influent and process condition before exceeding occur. Accordingly, the development of the effluent water quality prediction system and the application of technology to wastewater treatment processes are getting attention. Therefore, in this study, through the multi-channel measuring instruments in the bio-reactor and smart multi-item water quality sensors (location in bio-reactor influent/effluent) were installed in The Seonam water recycling center #2 treatment plant series 3, it was collected water quality data centering around COD, T-N. Using the collected data, the artificial intelligence-based effluent quality prediction model was developed, and relative errors were compared with effluent TMS measurement data. Through relative error comparison, the applicability of the artificial intelligence-based effluent water quality prediction model in wastewater treatment process was reviewed.

• Geomechanics and Engineering
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• v.39 no.2
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• pp.143-155
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• 2024

Traffic-induced cyclic loading leads to the rotation of principal stresses within pavement foundations, challenging accurate simulation with conventional triaxial testing equipment. To investigate the deformation characteristics of fiber-reinforced soil under traffic loads and to develop a fractional-order model to describe these deformations. A series of hollow cylinder torsional shear tests were conducted using the GDS-SSHCA apparatus. The effects of fiber content, load frequency, cyclic deviatoric stress amplitude, and cyclic shear stress amplitude on soil deformation were analyzed. The results revealed that fiber content up to 3% enhances soil resistance to deformation, while higher fiber content reduces it. Axial cumulative plastic deformation decreases with higher load frequencies and increases with higher cyclic stresses. The study also found that principal stress rotation exacerbates soil deformation. A fractional integral model based on the Riemann-Liouville operator was developed to describe the axial cumulative plastic strain, with its validity confirmed by supplementary tests. This model provides a scientific basis for understanding foundation deformation under traffic loading and contributes to the development of dynamic constitutive soil models.

• ETRI Journal
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• v.46 no.5
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• pp.915-927
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• 2024

We obtain the air quality index (AQI) for a descriptive system aimed to communicate pollution risks to the population. The AQI is calculated based on major air pollutants including O₃, CO, SO₂, NO, NO₂, benzene, and particulate matter PM2.5 that should be continuously balanced in clean air. Air pollution is a major limitation for urbanization and population growth in developing countries. Hence, automated AQI prediction by a deep learning method applied to time series may be advantageous. We use a seasonal autoregressive integrated moving average (SARIMA) model for predicting values reflecting past trends considered as seasonal patterns. In addition, a transductive long short-term memory (TLSTM) model learns dependencies through recurring memory blocks, thus learning long-term dependencies for AQI prediction. Further, the TLSTM increases the accuracy close to test points, which constitute a validation group. AQI prediction results confirm that the proposed SARIMA-TLSTM model achieves a higher accuracy (93%) than an existing convolutional neural network (87.98%), least absolute shrinkage and selection operator model (78%), and generative adversarial network (89.4%).

• Structural Engineering and Mechanics
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• v.37 no.4
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• pp.385-399
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• 2011

The aim of this research is to model the behaviour of recently developed high force to volume (HF2V) passive energy dissipation devices using a simple finite element (FE) model. Thus, the end result will be suitable for use in a standard FE code to enable computationally fast and efficient analysis and design. Two models are developed. First, a detailed axial model that models an experimental setup is created to validate the approach versus experimental results. Second, a computationally and geometrically simpler equivalent rotational hinge element model is presented. Both models are created in ABAQUS, a standard nonlinear FE code. The elastic, plastic and damping properties of the elements used to model the HF2V devices are based on results from a series of quasi-static force-displacement loops and velocity based tests of these HF2V devices. Comparison of the FE model results with the experimental results from a half scale steel beam-column sub-assembly are within 10% error. The rotational model matches the output of the more complex and computationally expensive axial element model. The simpler model will allow computationally efficient non-linear analysis of large structures with many degrees of freedom, while the more complex and physically accurate axial model will allow detailed analysis of joint connection architecture. Their high correlation to experimental results helps better guarantee the fidelity of the results of such investigations.

• Smart Structures and Systems
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• v.21 no.4
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• pp.435-448
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• 2018

This paper reports the development of a theoretically rigorous method for permanent way engineers to assess the condition of railway ballast under a concrete sleeper with the potential to be extended to a smart system for long-term health monitoring of railway ballast. Owing to the uncertainties induced by the problems of modeling error and measurement noise, the Bayesian approach was followed in the development. After the selection of the most plausible model class for describing the damage status of the rail-sleeper-ballast system, Bayesian model updating is adopted to calculate the posterior PDF of the ballast stiffness at various regions under the sleeper. An obvious drop in ballast stiffness at a region under the sleeper is an evidence of ballast damage. In model updating, the model that can minimize the discrepancy between the measured and model-predicted modal parameters can be considered as the most probable model for calculating the posterior PDF under the Bayesian framework. To address the problems of non-uniqueness and local minima in the model updating process, a two-stage hybrid optimization method was developed. The modified evolutionary algorithm was developed in the first stage to identify the important regions in the parameter space and resulting in a set of initial trials for deterministic optimization to locate all most probable models in the second stage. The proposed methodology was numerically and experimentally verified. Using the identified model, a series of comprehensive numerical case studies was carried out to investigate the effects of data quantity and quality on the results of ballast damage detection. Difficulties to be overcome before the proposed method can be extended to a long-term ballast monitoring system are discussed in the conclusion.

• Journal of the Korean GEO-environmental Society
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• v.2 no.4
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• pp.51-61
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• 2001

A series of model tests and analyses by load transfer function were performed to study load-settlement behaviour with relative compaction ratio of soil and embeded depth of pile. In the model tests, embeded depth ratio(L/D) of pile were installed 15, 20, 25 and relative compaction of soil(RC) is 85%, 95% and then cement were injected at around perimeter of pile. For analysis of embedded pile, the paper were compared results of model tests with analysis results by Vijayvergiya model and Castelli model, Gwizdala model of elastic plasticity-perfect plastic model and then the fitness load transfer mechanism was proposed to predict load-settlement behaviour of embeded pile. The analysis results of predicted bearing capacity by load transfer function, ultimate bearing capacity of embeded pile were approached to measured value and behaviour of initial load-settlement curve were estimated that load transfer function by Castelli were similar to measured value. The result of axial load analysis of bored pile shows that skin friction estimated by load transfer mechanism is investigated more a little than that of measured values.

• Environmental Engineering Research
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• v.21 no.2
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• pp.152-163
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• 2016

Present study investigates the potential of cassava peel and rubber tree bark for the removal of Cr (VI) from aqueous solution. Removal efficiency of more than 99% was obtained during the kinetic adsorption experiments with dosage of 3.5 g/L for cassava peel and 8 g/L for rubber tree bark. By comparing popular isotherm models and kinetic models for evaluating the kinetics of mass transfer, it was observed that Redlich-Peterson model and Langmuir model fitted well ($R^2$ > 0.99) resulting in maximum adsorption capacity as 79.37 mg/g and 43.86 mg/g for cassava peel and rubber tree bark respectively. Validation of pseudo-second order model and Elovich model indicated the possibility of chemisorption being the rate limiting step. The multi-linearity in the diffusion model was further addressed using multi-sites models (two-site series interface (TSSI) and two-site parallel interface (TSPI) models). Considering the influence of interface properties on the kinetic nature of sorption, TSSI model resulted in low mass transfer rate (5% for cassava peel and 10% for rubber tree bark) compared to TSPI model. The study highlights the employability of two-site sorption model for simultaneous representation of different stages of kinetic sorption for finding the rate-limiting process, compared to the separate equilibrium and kinetic modeling attempts.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.12 no.5
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• pp.787-794
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• 2001

In this paper, the development of large-signal model extractor for GaAs FET device through the Monolithic Microwave integrated Circuit(MMIC) is presented. The measurement program controlled by personal computer is developed for the processing of an amount of measured data, and the de-embedding algorithm is added to the program for voltage dropping as attached series resistance on measurement system. The small-signal model parameters are typically consisted of 7 elements that are considered as complexity of large-signal model and its the accuracy of the small-signal model is verified through comparing with measured data as varied bias point. The fitting function model, one of the empirical model, is used for quick simulation. In the process of large-signal model parameter extraction, one-dimensional optimization method is proposed and optimized parameters are extracted. This study can reduce the modeling and measuring time and can secure a suitable model for circuit.