• 한국지반공학회지:지반
• /
• 제14권6호
• /
• pp.127-138
• /
• 1998

신선한 쓰레기 매립지에서는 쓰레기에 포함되어 있는 유기물의 분해로 인하여 장기간에 걸쳐 상당한 양의 침하가 유발되는 것으로 알려져 있다. 본 연구에서는 여러 신선한 쓰레기 매립지들의 침하자료에 대하여 기존에 제안된 몇몇 침하모델들을 적용하였으며. 얻어진 침하예측곡선과 장기침하량을 분석함으로써 분해로 의한 침하양상이 장기침하량 예측에 미치는 영향을 살펴보았다. 사용된 모델과는 상관없이 선정된 모델변수 값들이 분해효과를 포함하지 않는 한 장기침하를 적절히 평가할 수 없었다. 몇몇 예측방법 가운데 Gibson & Lo 모델과 쌍곡선 모델은 쓰레기 매립지의 장기침하 거동특성을 비교적 타당성 있게 예측한 반면에 power creep law는 상당히 과다예측하는 것으로 나타났다.

• 한국지반공학회지:지반
• /
• 제14권1호
• /
• pp.5-14
• /
• 1998

쓰레기 매립지의 장기 침하 메카니즘은 일반적인 홀의 거동과는 달리 생물학적인 분해에 의해서 크게 영향을 받는다. 본 논문에서는 쓰레기 매립지의 장기 침하량(역학적 이차침하량과 분해에 의한 이차침하량)을 예측하기 위하여 두 식이 사용되었다. 역학적 이차침하는 변형률-대수 시간에서 선형적인 관계를 갖는다고 가정하였다. 분해에 의한 침하를 평가하기 위하여, 분해 가능한 쓰레기 고형물의 용액화에 관련된 일차반응기작으로 모사되는 가수분해 과정을 고려한 모델을 숙성된 (aged) 쓰레기로 충진된 Lysimeter 침하자료에 적용하였다. 본 연구에서는 분해과정 가운데 쓰레기 고형물의 용액화로 말미암아 유발되는 침하를 효과적으로 모사할 수 있는 간단한 수학적 모델을 제안하고자 하였다.

• 노동경제논집
• /
• 제43권2호
• /
• pp.75-107
• /
• 2020

한국의 1980~2017년까지의 데이터를 사용하여 성별 임금격차의 장기 추세를 확인하고, 요인 분해기법을 사용하여 발생 요인을 살펴보았다. 1990년대까지는 성별 임금격차 감소 추세가 뚜렷하였으나, 2000년대 이후 감소 추세가 전반적으로 둔화한 것이 특징적이다. 요인 분해기법으로는 Gelbach의 요인 분해기법을 사용하였다. 분석 결과 연령, 학력, 사업체 규모, 산업 및 직업 등 전통적으로 임금을 결정하는 요인들이 성별 임금격차를 설명하는 비중이 지속적으로 감소하고 있음을 알 수 있었다. 특히 연령과 학력이 성별 임금격차를 설명하는 비중이 줄어들고, 근속연수가 설명하는 비중이 늘어나는 것으로 나타났다. 또한, 산업이 성별 임금격차를 설명하는 비중이 1990년대 감소하다가 2000년대 이후 다시 증가하는 경향이 있음을 알 수 있었다.

• Wind and Structures
• /
• 제27권6호
• /
• pp.357-368
• /
• 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
• /
• 제38권1호
• /
• pp.75-91
• /
• 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
• /
• 제54권12호
• /
• pp.4441-4448
• /
• 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
• /
• 제8권1호
• /
• pp.23-31
• /
• 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 .

• 한국지반공학회지:지반
• /
• 제14권2호
• /
• pp.21-30
• /
• 1998

쓰레기 매립지는 쓰레기 고형물이 장기간에 걸쳐 생물학적으로 분해됨으로 말미암아 상당한 양의 침하가 유발되는 독특한 침하특성을 갖고 있다. 분해에 의한 총 압축량은 분해가능한 쓰레기의 고형물 함량 및 매립연한에 크게 의존하며. 매립지 침하의 안정화 속도는 분해조건에 의존한다. 쓰레기 매립지의 이러한 독특한 침하거동을 규명하기 위하여 제안되었던 침하모델을 매립 연한이 서로 다른 쓰레기 매립지 침하자료들에 대하여 적용하였다. 모델 변수 값들이 각각 구해 졌으며, 그 경향들이 분석되었다. 쓰레기 매립지의 장기 침하 양상이 제안된 모델에 의해 잘 예측될 수 있으며, 매립연한 및 두개의 적합한 설계변수에 근거하여 잔존 침하량을 예측할 수 있다.

• Smart Structures and Systems
• /
• 제19권2호
• /
• pp.137-149
• /
• 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.

• 전력전자학회논문지
• /
• 제27권1호
• /
• pp.48-55
• /
• 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.