• Environmental Engineering Research
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• 제22권3호
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• pp.302-311
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• 2017

Nowadays, with the burgeoning development of economy, $CO_2$ emissions increase rapidly in China. It has become a common concern to seek effective methods to forecast $CO_2$ emissions and put forward the targeted reduction measures. This paper proposes a novel hybrid model combined principal component analysis (PCA) with regularized extreme learning machine (RELM) to make $CO_2$ emissions prediction based on the data from 1978 to 2014 in China. First eleven variables are selected on the basis of Pearson coefficient test. Partial autocorrelation function (PACF) is utilized to determine the lag phases of historical $CO_2$ emissions so as to improve the rationality of input selection. Then PCA is employed to reduce the dimensionality of the influential factors. Finally RELM is applied to forecast $CO_2$ emissions. According to the modeling results, the proposed model outperforms a single RELM model, extreme learning machine (ELM), back propagation neural network (BPNN), GM(1,1) and Logistic model in terms of errors. Moreover, it can be clearly seen that ELM-based approaches save more computing time than BPNN. Therefore the developed model is a promising technique in terms of forecasting accuracy and computing efficiency for $CO_2$ emission prediction.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2007년도 추계 종합학술대회 논문집
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• pp.539-542
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• 2007

분자상호작용에 대한 실험방법이 소개되면서 이 분야의 자료가 빠르게 증가되고 있으며, 이와 관련된 데이터베이스는 급격히 증가하고 있다. 하지만 이와 같은 정보를 기반으로 질병의 이해와 같은 분자수준의 반응 기작 연구의 활용에는 어려움이 있었다. 본 연구 우리는 다양한 형태의 분자상호작용 정보를 불러들이고 이를 가시화 할 수 있는 브라우저를 개발하였다. 이 프로그램은 복잡한 구조의 상호작용을 좀더 쉽게 이해시킬 수 있도록 3차원 형태의 네비게이션 기능을 제공하며, 이를 통해 연구자가 자신의 원하는 상호작용 모델을 생성할 수 있도록 해준다. 또한 얻어진 모델을 이용하여 손쉽게 분자 시뮬레이션을 수행할 수 있도록 시뮬레이션 기능을 제공한다. 대장균의 주화성에 대한 신호전달기작을 대상으로 시뮬레이션 분석과정을 테스트해 보았다.

• Fibers and Polymers
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• 제5권3호
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• pp.198-203
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• 2004

Composite films were prepared by casting the solution of polyacrylonitrile (PAN) and single wall nanotube (SWNT) in DMF subsequent to sonication. The SWNTs in the films are well dispersed as ropes with 20-30 nm thickness. Moreover, AFM surface image of the composite film displays an interwoven fibrous structure of nanotubes which may give rise to conductive passways and lead to high conductivity. The polarized Raman spectroscopy is an ideal characterization technique for identification and the orientation study of SWNT. The well-defined G-peak intensity at 1580 $cm^{-1}$shows a dependency on the draw ratio under cross-Nicol. The degree of nanotube orientation in the drawn film was measurable from the sine curve obtained by rotating the drawn film on the plane of cross-Nicol of polarized Raman microscope. The threshold loading of SWNT for electrical conductivity in PAN is found to be lower than 1 wt% in the composite film. The electrical conductivity of the SWNT/PAN composite film decreased with increasing of draw ratio due to the collapse of the interwoven fibrous network of the nanotubes with uniaxial orientation.

• KIEE International Transactions on Electrophysics and Applications
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• 제5C권4호
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• pp.165-170
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• 2005

A series of inorganic-organic hybrid membranes were prepared with a systematic variation of titanium dioxide nanoparticle content. Their water uptake, methanol permeability and proton conductivity as a function of temperature were investigated. The results obtained show that the inorganic oxide network decreases the proton conductivity and water swelling. It is also found that increase in inorganic oxide content leads to decrease of methanol permeability. In terms of the morphology, membranes are homogeneous and exhibit good adhesion between inorganic domains and the polymer matrix. The properties of the composite membranes are compared with the standard nafion membrane.

• 대한전기학회논문지:전기물성ㆍ응용부문C
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• 제54권3호
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• pp.120-126
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• 2005

The Korean Superconducting Tokamak Advanced Research (KSTAR) tokamak device is being constructed to perform long-pulse, high-beta, advanced tokamak fusion physics experiments. The long-pulse operation requires the non-inductive current drive system such as the Lower-Hybrid Current Drive (LHCD) system. The LHCD system drives the non-inductive plasma current by means of C-band RF with 2-MW CW power and 5-GHz frequency. For the LHCD test experiments, an RF test system is developed. It is composed of a 5-GHz, 1.5-MW pulsed magnetron and a compact pulse modulator with $4\;{\mu}s$ of pulse width. The pulse modulator provides the maximum output voltage of 45 kV and the maximum current of 90 A. It is composed of 7 stages of Pulse Forming Network (PFN), a thyratron tube (E2V, CX1191D), and a pulse transformer with 1:4 step-up ratio. In this paper, the detailed design and the performance test of the pulse modulator are presented.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.1964-1966
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• 2001

Active Noise control(ANC) is rapidly becoming the most effective way to reduce noises that can otherwise be very difficult and expensive to control. This research presents ANFIS (Adaptive Network Fuzzy Inference System) controller for adaptively noise cancelling in a duct. ANC system generates secondary control sound pressure with same amplitude and with opposite phase as noise to be eliminated. ANFIS controller is trained to optimize its parameters for adaptively cancelling noise. That is ANFIS train its parameters by gradient descent and LSE method so called hybrid method. This paper present ANFIS in active noise control which provides an improvement convergence speed and limitation of linearity condition. It can model nonlinear functions of arbitrary complexity and ANFIS can construct an input-ouput mapping based on both human knowledge in the form of Takagi and Sugeno's fuzzy if-then rules and stipulated input-output data pairs. This paper also shows that the proposed ANFIS active noise control system successfully cancelled noise.

• 대한원격탐사학회지
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• 제35권1호
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• pp.93-115
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• 2019

The availability of high-resolution laser scanning data and advanced machine learning algorithms has enabled an accurate potential rockfall source identification. However, the presence of other mass movements, such as landslides within the same region of interest, poses additional challenges to this task. Thus, this research presents a method based on an integration of Gaussian mixture model (GMM) and ensemble artificial neural network (bagging ANN [BANN]) for automatic detection of potential rockfall sources at Kinta Valley area, Malaysia. The GMM was utilised to determine slope angle thresholds of various geomorphological units. Different algorithms(ANN, support vector machine [SVM] and k nearest neighbour [kNN]) were individually tested with various ensemble models (bagging, voting and boosting). Grid search method was adopted to optimise the hyperparameters of the investigated base models. The proposed model achieves excellent results with success and prediction accuracies at 95% and 94%, respectively. In addition, this technique has achieved excellent accuracies (ROC = 95%) over other methods used. Moreover, the proposed model has achieved the optimal prediction accuracies (92%) on the basis of testing data, thereby indicating that the model can be generalised and replicated in different regions, and the proposed method can be applied to various landslide studies.

• The Journal of Asian Finance, Economics and Business
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• 제8권3호
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• pp.717-729
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• 2021

This paper examines the efficacy of the default risk factor in an emerging market context using the Fama-French five-factor model. Our aim is to test whether the Fama-French five-factor model augmented with a default risk factor improves the predictability of returns of portfolios sorted on the firm's characteristics as well as on industry. The default risk factor is constructed by estimating the probability of default using a hybrid version of dynamic panel probit and artificial neural network (ANN) to proxy default risk. This study also provides evidence on the temporal stability of risk premiums obtained using the Fama-MacBeth approach. Using a sample of 3,806 firm-year observations on non-financial listed companies of Pakistan over 2006-2015 we found that the augmented model performed better when tested across size-investment-default sorted portfolios. The investment factor contains some default-related information, but default risk is independently priced and bears a significantly positive risk premium. The risk premiums are also found temporally stable over the full sample and more recent sample period 2010-2015 as evidence by the Fama-MacBeth regressions. The finding suggests that the default risk factor is not a useless factor and due to mispricing, default risk anomaly prevails in the Pakistani equity market.

• Advances in nano research
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• 제14권2호
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• pp.141-154
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• 2023

Effects of viscoelastic foundation on vibration of curved-beam structure with clamped and simply-supported boundary conditions is investigated in this study. In doing so, a micro-scale laminate composite beam with two piezoelectric face layer with a carbon nanotube reinforces composite core is considered. The whole beam structure is laid on a viscoelastic substrate which normally occurred in actual conditions. Due to small scale of the structure non-classical elasticity theory provided more accurate results. Therefore, nonlocal strain gradient theory is employed here to capture both nano-scale effects on carbon nanotubes and microscale effects because of overall scale of the structure. Equivalent homogenous properties of the composite core is obtained using Halpin-Tsai equation. The equations of motion is derived considering energy terms of the beam and variational principle in minimizing total energy. The boundary condition is assumed to be clamped at one end and simply supported at the other end. Due to nonlinear terms in the equations of motion, semi-analytical method of general differential quadrature method is engaged to solve the equations. In addition, due to complexity in developing and solving equations of motion of arches, an artificial neural network is design and implemented to capture effects of different parameters on the inplane vibration of sandwich arches. At the end, effects of several parameters including nonlocal and gradient parameters, geometrical aspect ratios and substrate constants of the structure on the natural frequency and amplitude is derived. It is observed that increasing nonlocal and gradient parameters have contradictory effects of the amplitude and frequency of vibration of the laminate beam.

• International Journal of Advanced Culture Technology
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• 제11권4호
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• pp.393-405
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• 2023

Accurate prediction of wind speed and power is vital for enhancing the efficiency of wind energy systems. Numerous solutions have been implemented to date, demonstrating their potential to improve forecasting. Among these, deep learning is perceived as a revolutionary approach in the field. However, despite their effectiveness, the noise present in the collected data remains a significant challenge. This noise has the potential to diminish the performance of these algorithms, leading to inaccurate predictions. In response to this, this study explores a novel feature engineering approach. This approach involves altering the data input shape in both Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) and Autoregressive models for various forecasting horizons. The results reveal substantial enhancements in model resilience against noise resulting from step increases in data. The approach could achieve an impressive 83% accuracy in predicting unseen data up to the 24th steps. Furthermore, this method consistently provides high accuracy for short, mid, and long-term forecasts, outperforming the performance of individual models. These findings pave the way for further research on noise reduction strategies at different forecasting horizons through shape-wise feature engineering.