C. Saravanakumar^1,*, R. Priscilla¹, B. Prabha², A. Kavitha³, M. Prakash⁴, C. Arun⁵

Computer Systems Science and Engineering, Vol.42, No.1, pp. 245-256, 2022, DOI:10.32604/csse.2022.022122

Abstract Cloud Computing provides various services to the customer in a flexible and reliable manner. Virtual Machines (VM) are created from physical resources of the data center for handling huge number of requests as a task. These tasks are executed in the VM at the data center which needs excess hosts for satisfying the customer request. The VM migration solves this problem by migrating the VM from one host to another host and makes the resources available at any time. This process is carried out based on various algorithms which follow a predefined capacity of source VM leads to the capacity… More >

Zhiqiu Yu^1,*, Shuo-Yan Chou¹, Phan Nguyen Ky Phuc², Tiffany Hui-Kuang Yu³

Computer Systems Science and Engineering, Vol.41, No.3, pp. 1191-1205, 2022, DOI:10.32604/csse.2022.021239

Abstract This research aims to study the sustainability of Taiwan power supply chain based on system dynamics forecasting. The paper tries to investigate electricity shortage effects not only on the industrial side, but also from the standpoint of society. In our model, different forecasting methods such as linear regression, time series analysis, and gray forecasting are also considered to predict the parameters. Further tests such as the structure, dimension, historical fit, and sensitivity of the model are also conducted in this paper. Through analysis forecasting result, we believe that the demand for electricity in Taiwan will continue to increase to a… More >

Dharamender Singh¹, Majed G. Alharbi², Anurag Jayswal¹, Ali Akbar Shaikh^3,*

Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 167-182, 2022, DOI:10.32604/iasc.2022.021815

Abstract The inventory framework is one of the standards of activity research fundamentals in ventures and business endeavors. Production planning includes all building production plans, including organizing and appointing exercises to every individual, gathering individuals or machines, and mastering work orders in every work environment. Production booking should take care of all issues, for example, limiting client standby time and production time; and viably utilizing the undertaking’s HR. This paper considered three degrees of a production inventory model for a consistent deterioration rate. This model assumes a significant part in the production of the board and assembling units. Request rate is… More >

Wenquan Jin¹, Naeem Iqbal², Hee-Cheal Kang³, Dohyeun Kim^2,*

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3763-3780, 2022, DOI:10.32604/cmc.2022.020336

Abstract The earthquake is considered one of the most devastating disasters in any area of the world due to its potentially destructive force. Based on the various earthquake-related parameters, the risk assessment is enabled in advance to prevent future earthquake disasters. In this paper, for providing the shelter space demands to reduce the damage level and prevention costs, an earthquake risk assessment approach is proposed for deriving the risk index based on multiple spatial parameters in the gridded map. The proposed assessment approach is comprised of pre-processing, methodology model, and data visualization. The risk index model derives the earthquake risk index… More >

Saman Taheri¹, Behnam Talebjedi^2,*, Timo Laukkanen²

Energy Engineering, Vol.118, No.6, pp. 1577-1594, 2021, DOI:10.32604/EE.2021.017795

Abstract Load forecasting is critical for a variety of applications in modern energy systems. Nonetheless, forecasting is a difficult task because electricity load profiles are tied with uncertain, non-linear, and non-stationary signals. To address these issues, long short-term memory (LSTM), a machine learning algorithm capable of learning temporal dependencies, has been extensively integrated into load forecasting in recent years. To further increase the effectiveness of using LSTM for demand forecasting, this paper proposes a hybrid prediction model that incorporates LSTM with empirical mode decomposition (EMD). EMD algorithm breaks down a load time-series data into several sub-series called intrinsic mode functions (IMFs).… More >

Chia-Nan Wang¹, Shao-Dong Syu^1,2,*, Chien-Chang Chou³, Viet Tinh Nguyen⁴, Dang Van Thuy Cuc⁵

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1195-1207, 2022, DOI:10.32604/cmc.2022.019890

Abstract Agriculture is a key facilitator of economic prosperity and nourishes the huge global population. To achieve sustainable agriculture, several factors should be considered, such as increasing nutrient and water efficiency and/or improving soil health and quality. Using fertilizer is one of the fastest and easiest ways to improve the quality of nutrients inland and increase the effectiveness of crop yields. Fertilizer supplies most of the necessary nutrients for plants, and it is estimated that at least 30%–50% of crop yields is attributable to commercial fertilizer nutrient inputs. Fertilizer is always a major concern in achieving sustainable and efficient agriculture. Applying… More >

Taher M. Ghazal^1,2, Sajida Noreen³, Raed A. Said⁴, Muhammad Adnan Khan^5,*, Shahan Yamin Siddiqui^3,6, Sagheer Abbas³, Shabib Aftab³, Munir Ahmad³

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 539-553, 2022, DOI:10.32604/iasc.2022.019658

Abstract The usage of IoT-based smart meter in electric power consumption shows a significant role in helping the users to manage and control their electric power consumption. It produces smooth communication to build equitable electric power distribution for users and improved management of the entire electric system for providers. Machine learning predicting algorithms have been worked to apply the electric efficiency and response of progressive energy creation, transmission, and consumption. In the proposed model, an IoT-based smart meter uses a support vector machine and deep extreme machine learning techniques for professional energy management. A deep extreme machine learning approach applied to… More >

Yinhe Bu¹, Xingping Zhang^1,2,3,*

Energy Engineering, Vol.118, No.4, pp. 1095-1109, 2021, DOI:10.32604/EE.2021.015893

Abstract With the explosive growth of variable renewable energy, the balance between the supply and demand of the power grid is faced with new challenges. Based on the development experience from typical countries and the state quo in China, this paper further analyzes the system architecture and development trend of demand response under the background of Energy Internet. Five dimensions are considered: Energy Internet platform, demand response application scenarios, system architecture, information technology system construction, and demand response development trend. The results show that the application of the Energy Internet platform can effectively solve the problems of data acquisition and processing,… More >

Yanpu Chao¹, Hao Yi^2,3,*

FDMP-Fluid Dynamics & Materials Processing, Vol.17, No.3, pp. 587-595, 2021, DOI:10.32604/fdmp.2021.015478

Abstract Many liquid metals have a high boiling point, strong electrical conductivity, high thermal conductivity, and non-toxic properties, which make them ideal targets for applications in different fields such as optics, microcircuits, electronic switches, micro-electromechanical System (MEMS) devices and 3D printing manufacturing. However, owing to the generally high surface tension of these liquids, achieving uniform micro-droplets is often a challenge due to the inherent difficulties in controlling their size and shape. In this study, a gallium indium alloy (GaIn_24.5) has been used in combination with a pneumatic drop-on-demand (DOD) injection technology to carry out a series of experiments. The micro-droplet forming… More >

Vin Cent Tai^1,*, Yong Chai Tan¹, Nor Faiza Abd Rahman¹, Hui Xin Che², Chee Ming Chia², Lip Huat Saw³, Mohd Fozi Ali⁴

Energy Engineering, Vol.118, No.3, pp. 715-725, 2021, DOI:10.32604/EE.2021.014865

Abstract Electricity demand is also known as load in electric power system. This article presents a Long-Term Load Forecasting (LTLF) approach for Malaysia. An Artificial Neural Network (ANN) of 5-layer Multi-Layered Perceptron (MLP) structure has been designed and tested for this purpose. Uncertainties of input variables and ANN model were introduced to obtain the prediction for years 2022 to 2030. Pearson correlation was used to examine the input variables for model construction. The analysis indicates that Primary Energy Supply (PES), population, Gross Domestic Product (GDP) and temperature are strongly correlated. The forecast results by the proposed method (henceforth referred to as… More >