Increasing consumption of energy calls for proper approximation demand towards a sustainable and cost-effective development. In this work, novel hybrid methodologies aim to predict the annual thermal (ATED) by analyzing characteristics building, such as transmission coefficients elements, glazing, air-change conditions. For objective, an adaptive neuro-fuzzy-inference system (ANFIS) was optimiz...

Abstract To address the deficiency and predict adsorption performance in different adsorbents, this study proposes a new optimizer linked to machine learning (ML) model considering of process. The main goal is under process conditions with adsorbents provide unified framework, leading prediction phenomena instead traditional isotherm models. This research focuses on predicting adsorbed amount O...

The internet of things (IoT) and industrial IoT (IIoT) play a major role in today’s world intelligent networks, they essentially use wireless sensor network (WSN) as perception layer to collect the intended data. This data is processed information send cloud servers through base station, challenge here consumption minimum energy for processing communication. dynamic formation cluster heads awar...

Electrochemical machining (ECM) is a preferred advanced process for Monel 400 alloys. During the machining, toxic nickel hydroxides in sludge are formed. Therefore, it becomes necessary to determine optimum ECM parameters that minimize presence (NP) emission while maximizing material removal rate (MRR). In this investigation, predominant parameters, such as applied voltage, flow rate, and elect...

This article presents a classification method that utilizes impedance spectrum features and an enhanced K-means algorithm for Lithium-ion batteries. Additionally, parameter identification the fractional order model is proposed, which based on flow direction (FDA). In to reduce dimensionality of battery features, Pearson correlation coefficient employed analyze between features. The carried out ...

Digital Learning Playground: supporting authentic learning experiences in the classroom Gwo-Dong Chen a , Nurkhamid a b , Chin-Yeh Wang c , Su-Hang Yang d , Wei-Yuan Lu e & Chih-Kai Chang f a Department of Computer Science and Information Engineering, National Central University, Jhongli City, Taiwan b Faculty of Engineering, Yogyakarta State University, Yogyakarta, Indonesia c Research Center ...

To solve the problem of fault signals wind turbine bearings being weak, not easy to extract, and difficult identify, this paper proposes a diagnosis method for fan based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) Grey Wolf Algorithm Optimization Kernel Extreme Learning Machine (GWO-KELM). First, eliminating interference noise collected vibration signal shoul...

In order to ensure safe and sustainable design of geosynthetic-reinforced soil foundation (GRSF), settlement prediction is a challenging task for practising civil/geotechnical engineers. this paper, new hybrid technique predicting the GRSF has been proposed based on combination evolutionary algorithm, that is, grey-wolf optimisation (GWO) artificial neural network (ANN), abbreviated as ANN-GWO ...

The performance of photovoltaic (PV) systems must be predicted through accurate simulation designs before proceeding to a real-time application avoid errors. However, predicting the cohesive relationship between current and voltage estimating parameters single diode model become perplexing task due insufficient data in datasheet PV panels. This research work presents single-diode solar system a...

Metaheuristic optimization algorithms play a crucial role in problems. However, the traditional identification methods have following problems: (1) difficulties nonlinear data processing; (2) high error rates caused by local stagnation; and (3) low classification resulting from premature convergence. This paper proposed variant based on gray wolf algorithm (GWO) with chaotic disturbance, candid...