Ying Su¹, Morgan C. Wang¹, Shuai Liu^2,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3529-3549, 2024, DOI:10.32604/cmc.2024.047189 - 26 March 2024

Abstract Long-term time series forecasting stands as a crucial research domain within the realm of automated machine learning (AutoML). At present, forecasting, whether rooted in machine learning or statistical learning, typically relies on expert input and necessitates substantial manual involvement. This manual effort spans model development, feature engineering, hyper-parameter tuning, and the intricate construction of time series models. The complexity of these tasks renders complete automation unfeasible, as they inherently demand human intervention at multiple junctures. To surmount these challenges, this article proposes leveraging Long Short-Term Memory, which is the variant of Recurrent Neural Networks, harnessing… More >

Jun Zhao¹, Shuguo Gao¹, Yunpeng Liu^2,3, Quan Wang^2,*, Ziqiang Xu², Yuan Tian¹, Lu Sun¹

Energy Engineering, Vol.119, No.5, pp. 1811-1828, 2022, DOI:10.32604/ee.2022.020490 - 21 July 2022

Abstract Aiming at the problem of abnormal data generated by a power transformer on-line monitoring system due to the influences of transformer operation state change, external environmental interference, communication interruption, and other factors, a method of anomaly recognition and differentiation for monitoring data was proposed. Firstly, the empirical wavelet transform (EWT) and the autoregressive integrated moving average (ARIMA) model were used for time series modelling of monitoring data to obtain the residual sequence reflecting the anomaly monitoring data value, and then the isolation forest algorithm was used to identify the abnormal information, and the monitoring sequence More >

Gurram Sunitha^1,*, M. N. Pushpalatha², A. Parkavi³, Prasanthi Boyapati⁴, Ranjan Walia⁵, Rachna Kohar⁶, Kashif Qureshi⁷

Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 423-437, 2022, DOI:10.32604/iasc.2022.024757 - 15 April 2022

Abstract Crop yield is an extremely difficult trait identified using many factors like genotype, environment and their interaction. Accurate Crop Yield Prediction (CYP) necessitates the basic understanding of the functional relativity among yields and the collaborative factor. Disclosing such connection requires both wide-ranging datasets and an efficient model. The CYP is important to accomplish irrigation scheduling and assessing labor necessities for reaping and storing. Predicting yield using various kinds of irrigation is effective for optimizing resources, but CYP is a difficult process owing to the existence of distinct factors. Recently, Deep Learning (DL) approaches offer solutions… More >

Yafei Rong¹, Xudong Sun^1,2,*

Energy Engineering, Vol.119, No.1, pp. 315-329, 2022, DOI:10.32604/EE.2022.015485 - 22 November 2021

Abstract Under the dual pressures of energy crisis and environmental pollution, China’s new energy power industry has become a focal point for environmental management and requires greater investment. In this context, as a significant input of investment projects, discount rate requires a well-calibrated evaluation because new energy power investment projects are highly capital intensive. The main objective of this paper is to evaluate the discount rate of China’s new energy power industry. First, we use Moving Average to correct the parameters of capital asset pricing model (CAPM) and weighted average cost of capital, which extends the… More >

Azam Zaka¹, Riffat Jabeen^2,*, Kanwal Iqbal Khan³

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4781-4802, 2022, DOI:10.32604/cmc.2022.020316 - 11 October 2021

Abstract The continuous monitoring of the machine is beneficial in improving its process reliability through reflected power function distribution. It is substantial for identifying and removing errors at the early stages of production that ultimately benefit the firms in cost-saving and quality improvement. The current study introduces control charts that help the manufacturing concerns to keep the production process in control. It presents an exponentially weighted moving average and extended exponentially weighted moving average and then compared their performance. The percentiles estimator and the modified maximum likelihood estimator are used to constructing the control charts. The More >

Nongnuch Saengsura, Saowanit Sukparungsee^*, Yupaporn Areepong

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 635-647, 2022, DOI:10.32604/iasc.2022.019997 - 03 September 2021

Abstract In this research, we propose the new mixed control chart called the mixed Moving Average-Cumulative Sum (MA-CUSUM) control chart used for monitoring parameter changes in asymmetrical and symmetrical processes. Its efficiency was compared with that of the Shewhart, Cumulative Sum (CUSUM), Moving Average (MA), mixed Cumulative Sum-Moving Average (CUSUM-MA) and mixed Moving Average-Cumulative Sum (MA-CUSUM) control charts by using their average run lengths (ARLs), the standard deviation of the run length (SDRL), and median run length (MRL) via the Monte Carlo simulation (MC). The simulation results show that the MA-CUSUM control chart was more efficient than More >

Ambreen Shafqat^1,2, Muhammad Aslam^3,*, Muhammad Saleem⁴, Zameer Abbas⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.1, pp. 373-391, 2021, DOI:10.32604/cmes.2021.016772 - 24 August 2021

Abstract The concept of neutrosophic statistics is applied to propose two monitoring schemes which are an improvement of the neutrosophic exponentially weighted moving average (NEWMA) chart. In this study, two control charts are designed under the uncertain environment or neutrosophic statistical interval system, when all observations are undermined, imprecise or fuzzy. These are termed neutrosophic double and triple exponentially weighted moving average (NDEWMA and NTEWMA) control charts. For the proficiency of the proposed chart, Monte Carlo simulations are used to calculate the run-length characteristics (such as average run length (ARL), standard deviation of the run length… More >

Piyatida Phanthuna, Yupaporn Areepong^*, Saowanit Sukparungsee

CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.1, pp. 23-41, 2021, DOI:10.32604/cmes.2021.013810 - 30 March 2021

Abstract A modified exponentially weighted moving average (EWMA) scheme is one of the quality control charts such that this control chart can quickly detect a small shift. The average run length (ARL) is frequently used for the performance evaluation on control charts. This paper proposes the explicit formula for evaluating the average run length on a two-sided modified exponentially weighted moving average chart under the observations of a first-order autoregressive process, referred to as AR(1) process, with an exponential white noise. The performance comparison of the explicit formula and the numerical integral technique is carried out… More >