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  • Open Access

    ARTICLE

    Earthquake Risk Assessment Approach Using Multiple Spatial Parameters for Shelter Demands

    Wenquan Jin1, Naeem Iqbal2, Hee-Cheal Kang3, Dohyeun Kim2,*

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3763-3780, 2022, DOI:10.32604/cmc.2022.020336 - 27 September 2021

    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… More >

  • Open Access

    ARTICLE

    Two-Stage Production Planning Under Stochastic Demand: Case Study of Fertilizer Manufacturing

    Chia-Nan Wang1, Shao-Dong Syu1,2,*, 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 - 07 September 2021

    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… More >

  • Open Access

    ARTICLE

    Energy Demand Forecasting Using Fused Machine Learning Approaches

    Taher M. Ghazal1,2, Sajida Noreen3, Raed A. Said4, Muhammad Adnan Khan5,*, Shahan Yamin Siddiqui3,6, Sagheer Abbas3, Shabib Aftab3, Munir Ahmad3

    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 539-553, 2022, DOI:10.32604/iasc.2022.019658 - 03 September 2021

    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 More >

  • Open Access

    ARTICLE

    Electricity Demand Time Series Forecasting Based on Empirical Mode Decomposition and Long Short-Term Memory

    Saman Taheri1, Behnam Talebjedi2,*, Timo Laukkanen2

    Energy Engineering, Vol.118, No.6, pp. 1577-1594, 2021, DOI:10.32604/EE.2021.017795 - 10 September 2021

    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… More >

  • Open Access

    ARTICLE

    Technical System Construction in the Market Trading System for Demand Response Based on the Energy Internet

    Yinhe Bu1, Xingping Zhang1,2,3,*

    Energy Engineering, Vol.118, No.4, pp. 1095-1109, 2021, DOI:10.32604/EE.2021.015893 - 31 May 2021

    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 More >

  • Open Access

    ARTICLE

    Experimental Analysis of a Pneumatic Drop-on-Demand (DOD) Injection Technology for 3D Printing Using a Gallium-Indium Alloy

    Yanpu Chao1, Hao Yi2,3,*

    FDMP-Fluid Dynamics & Materials Processing, Vol.17, No.3, pp. 587-595, 2021, DOI:10.32604/fdmp.2021.015478 - 29 April 2021

    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 (GaIn24.5) has been used in combination with a pneumatic drop-on-demand (DOD) injection technology to carry out a series of More >

  • Open Access

    ARTICLE

    Long-Term Electricity Demand Forecasting for Malaysia Using Artificial Neural Networks in the Presence of Input and Model Uncertainties

    Vin Cent Tai1,*, 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 - 22 March 2021

    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… More >

  • Open Access

    ARTICLE

    Hybridized Model with Activity Load Adjusting and QOS for Wi-Fi Network System

    S. Praveen Kumar1,*, S. Appavu alias Balamurugan2, S. Kannan3

    Intelligent Automation & Soft Computing, Vol.27, No.2, pp. 333-346, 2021, DOI:10.32604/iasc.2021.013310 - 18 January 2021

    Abstract The establishment of multi-channel routing systems in WSNs has shown the reliability of the transmission across several channels. Meeting the service quality needs of all the applications is still a challenging task. Various surveys carried out, have suggested for the development of user-oriented Wireless Sensor Network (WSN) architectures. Throughout WSNs, the Optimized Link State Routing (OLSR) and the Adhoc On Demand Vector (AODV) are the multi-way routing systems that exhibit the reliability of the multipath delivery to fulfill the customer service quality requirements. The topology is modified only on requests, by selecting the best route… More >

  • Open Access

    ARTICLE

    Adaptive Expanding Ring Search Based Per Hop Behavior Rendition of Routing in MANETs

    Durr-e-Nayab1,*, Mohammad Haseeb Zafar1,2, Mohammed Basheri2

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 1137-1152, 2021, DOI:10.32604/cmc.2021.014687 - 12 January 2021

    Abstract Routing protocols in Mobile Ad Hoc Networks (MANETs) operate with Expanding Ring Search (ERS) mechanism to avoid flooding in the network while tracing step. ERS mechanism searches the network with discerning Time to Live (TTL) values described by respective routing protocol that save both energy and time. This work exploits the relation between the TTL value of a packet, traffic on a node and ERS mechanism for routing in MANETs and achieves an Adaptive ERS based Per Hop Behavior (AERSPHB) rendition of requests handling. Each search request is classified based on ERS attributes and then… More >

  • Open Access

    ARTICLE

    Demand Responsive Market Decision-Makings and Electricity Pricing Scheme Design in Low-Carbon Energy System Environment

    Hongming Yang1,*, Qian Yu1, Xiao Huang1, Ben Niu2, Min Qi3

    Energy Engineering, Vol.118, No.2, pp. 285-301, 2021, DOI:10.32604/EE.2021.013734 - 23 December 2020

    Abstract The two-way interaction between smart grid and customers will continuously play an important role in enhancing the overall efficiency of the green and low-carbon electric power industry and properly accommodating intermittent renewable energy resources. Thus far, the existing electricity pricing mechanisms hardly match the technical properties of smart grid; neither can they facilitate increasing end users participating in the electricity market. In this paper, several relevant models and novel methods are proposed for pricing scheme design as well as to achieve optimal decision-makings for market participants, in which the mechanisms behind are compatible with demand… More >

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