• Geotechnical Engineering
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• v.14 no.6
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• pp.127-138
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• 1998

In refuse landfills, a considerable amount of settlement occurs due to the decomposition of refuse over several years. In this paper, several prediction methods are applied to the measured settlement data of fresh refuse sites. The effect of biological decomposition on the settlement characteristics is investigated in predicting the long-term settlement of refuse landfill sites in view of the predicted settlement curves and the amount of long-term settlement. Irrespective of the applied models, the long term settlement may not be correctly estimated if the model parameters do not contain the decomposition effects. Among the proposed several prediction methods, Gibson & Lo model and hyperbolic model seem to represent the long-term settlement characteristics, but the power creep law seems to considerably overestimate the long-term settlement.

• Geotechnical Engineering
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• v.14 no.1
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• pp.5-14
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• 1998

In refuse landfill, long-term settlement is considerably dependent upon the biological decomposition of refuse which is distinguished from typical soil behavior. Two equations are combined in order to model long-term settlement behavior of refuse landfill caused by mechanical secondary compression and secondary compression caused by the decomposition of biolegradable refuse. It is suggested that mechanical secondary compression is linear with respcet to the logarithm of time. In order to estimate the settlement that occurs due. to the decomposition of biodegradable refuse, a mathematical model is used which theoretically conoiders the decomposition process related to the solubilization stage of biodegradable refuse solid. This model is based on hydrolysis process and expressed as first order kinetics. The proposed model is applied to Lysimeter compression data of an old refuse. This paper intends to propose the simplest mathematical model which effectively represents settlement caused by the solubilization stage of biodegradable refuse solid on decomposition process.

• Journal of Labour Economics
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• v.43 no.2
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• pp.75-107
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• 2020

Using the data from 1980 to 2017, I show the long-term trends in the gender wage gap in Korea and analyze factors using decomposition method. It tended to decline until the 1990s, but gradually slowed after the 2000s. Gelbach(2016)'s decomposition method is used as an alternative rather than Blinder-Oaxaca decomposition. The results show that the proportion of explanation of traditional factors, such as age, education, firm size, industry and occupation, are continuously decreasing in explaining the gender wage gap. Expecially, the proportion of explanation of age and that of education have decreased, and that of industry tended to decrease in the 1990s but to increase after the 2000s.

• Wind and Structures
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• v.27 no.6
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• pp.357-368
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• 2018

For a long cable-stayed bridge, stay cables are its most important load-carrying components. In this paper, long-term monitoring of super-long stay cables of Sutong Bridge is introduced. A comprehensive data analysis procedure is presented, in which time domain and frequency domain based analyses are carried out. In time domain, the vibration data of several long stay cables are firstly analyzed and the standard deviation of the acceleration of stay cables, and its variation with time are obtained, as well as the relationship between in-plane vibration and out-plane vibration. Meanwhile, some vibrations such as wind and rain induced vibration are detected. Through frequency domain analysis, the basic frequencies of the stay cables are identified. Furthermore, the axial forces and their statistical parameters are acquired. To investigate the vibration deflection, an FFT-based decomposition method is used to get the modal deflection. In the end, the relationship between the vibration amplitude of stay cables and the wind speed is investigated based on correlation analysis. Through the adopted procedure, some structural parameters of the stay cables have been derived, which can be used for evaluating the component performance and corresponding management of stay cables.

• Wind and Structures
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• v.38 no.1
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• pp.75-91
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• 2024

Reliable wind signal reconstruction can be beneficial to the operational safety of long-span bridges. Non-Gaussian characteristics of wind signals make the reconstruction process challenging. In this paper, non-Gaussian wind signals are converted into a combined prediction of two kinds of features, actual wind speeds and wind angles of attack. First, two decomposition techniques, empirical mode decomposition (EMD) and variational mode decomposition (VMD), are introduced to decompose wind signals into intrinsic mode functions (IMFs) to reduce the randomness of wind signals. Their principles and applicability are also discussed. Then, four artificial intelligence (AI) algorithms are utilized for wind signal reconstruction by combining the particle swarm optimization (PSO) algorithm with back propagation neural network (BPNN), support vector regression (SVR), long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM), respectively. Measured wind signals from a bridge site in a deep-cutting gorge are taken as experimental subjects. The results showed that the reconstruction error of high-frequency components of EMD is too large. On the contrary, VMD fully extracts the multiscale rules of the signal, reduces the component complexity. The combination of VMD-PSO-Bi-LSTM is demonstrated to be the most effective among all hybrid models.

• Nuclear Engineering and Technology
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• v.54 no.12
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• pp.4441-4448
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• 2022

The electrochemical oxidation process has been widely studied in the field of wastewater treatment for the decomposition of organic materials through oxidation using ·OH generated on the anode. Pt anode electrodes with high durability and long-term operability have a low oxygen evolution potential, making them unsuitable for electrochemical oxidation processes. Therefore, to apply Pt electrodes that are suitable for long-term operation and large-scale processes, it is necessary to develop a new method for improving the decomposition rate of organic materials. This study introduces a method to improve the decomposition rate of organic materials when using a Pt anode electrode in the electrochemical oxidation process for the treatment of organic decontamination liquid waste. Electrochemical decomposition tests were performed using sodium dodecylbenzenesulfonate (SDBS) as a representative organic material and a Pt mesh as the anode electrode. Y₂O₃ particles were introduced into the electrolytic cell to improve the decomposition rate. The decomposition rate significantly improved from 21% to 99%, and the current efficiency also improved. These results can be applied to the electrochemical oxidation process without additional system modification to enhance the decomposition rate and current efficiency.

• Biotechnology and Bioprocess Engineering:BBE
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• v.8 no.1
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• pp.23-31
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• 2003

A mathematical model was formulated to simulate the long-term performance of an anaerobic bioreactor designed to digest Korean food wastes. The system variables of various decomposition steps were built into the model, which predicts the temporal characters of Solid waste, and volatile fatty acid (VFA) in the reactor, and gas production in response to various input loadings and temperatures. The predicted values of VFA and gas production were found to be in good agreement with experimental observations in batch and repeated-input systems. Finally, long-term reactor performance was simulated with respect to the seasonal temperature changes from 5C in winter to 25C in Summer at different food waste input loadings. The simulation results provided us with information concerning the success or failure of a process during long-term operation .

• Geotechnical Engineering
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• v.14 no.2
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• pp.21-30
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• 1998

The settlement characteristics of refuse landfills are peculiar because considerable amount of settlement occurs due to the decomposition of refuse organic solids for very long period. The total amount of compression that occurs due to the decomposition in refuse landfill is mainly dependent on the amount of biodegradable refuse solids and fill Ige of the refuse landfill, and the settlement stabilization speed is dependent on the decomposition condition. In order to figure out the settlement characteristics of refuse landfills. a proposed mathematical model is applied to settlement data of refuse landfills with different fill ages. A data bank of model parameters was obtained and the trends were analyzed. The long-term settlement behavior of refuse landfills can be estimated fairly well by the proposed model. The total remaining amount of settlement may be predicted on the basis of the fill age and appropriate two design parameters.

• Smart Structures and Systems
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• v.19 no.2
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• pp.137-149
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• 2017

Under the interference of the temperature effect, the alternation of cable force due to damages of a cable-stayed bridge could be difficult to distinguish. Considering the convenience and applicability in engineering practice, simple air or cable temperature measurements are adopted in the current study for the exclusion of temperature effect from the variation of cable force. Using the data collected from Ai-Lan Bridge located in central Taiwan, this work applies the ensemble empirical mode decomposition to process the time histories of cable force, air temperature, and cable temperature. It is evidently observed that the cable force and both types of temperature can all be categorized as the daily variation, long-term variation, and high-frequency noise in the order of decreasing weight. Moreover, the correlation analysis conducted for the decomposed variations of all these three quantities undoubtedly indicates that the daily and long-term variations with different time shifts have to be distinguished for accurately evaluating the temperature effect on the variation of cable force. Finally, consistent results in reducing the range of cable force variation after the elimination of temperature effect confirm the validity and stability of the developed method.

• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.1
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• pp.48-55
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• 2022

This paper proposes a battery remaining useful life (RUL) prediction method using a deep learning-based EMD-CNN-LSTM hybrid method. The proposed method pre-processes capacity data by applying empirical mode decomposition (EMD) and predicts the remaining useful life using CNN-LSTM. CNN-LSTM is a hybrid method that combines convolution neural network (CNN), which analyzes spatial features, and long short term memory (LSTM), which is a deep learning technique that processes time series data analysis. The performance of the proposed remaining useful life prediction method is verified using the battery aging experiment data provided by the NASA Ames Prognostics Center of Excellence and shows higher accuracy than does the conventional method.