The significance of precise energy usage forecasts has been highlighted by the increasing need for sustainability and energy efficiency across a range of *** order to improve the precision and openness of energy consu...
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The significance of precise energy usage forecasts has been highlighted by the increasing need for sustainability and energy efficiency across a range of *** order to improve the precision and openness of energy consumption projections,this study investigates the combination of machine learning(ML)methods with Shapley additive explanations(SHAP)*** study evaluates three distinct models:the first is a Linear Regressor,the second is a Support Vector Regressor,and the third is a Decision Tree Regressor,which was scaled up to a Random Forest Regressor/Additions made were the third one which was Regressor which was extended to a Random Forest *** models were deployed with the use of Shareable,Plot-interpretable Explainable Artificial Intelligence techniques,to improve trust in the *** findings suggest that our developedmodels are superior to the conventional models discussed in prior studies;with high Mean Absolute Error(MAE)and Root Mean Squared Error(RMSE)values being close to *** detail,the Random Forest Regressor shows the MAE of 0.001 for predicting the house prices whereas the SVR gives 0.21 of MAE and 0.24 *** outcomes reflect the possibility of optimizing the use of the promoted advanced AI models with the use of Explainable AI for more accurate prediction of energy consumption and at the same time for the models’decision-making procedures’*** addition to increasing prediction accuracy,this strategy gives stakeholders comprehensible insights,which facilitates improved decision-making and fosters confidence in AI-powered energy *** outcomes show how well ML and SHAP work together to enhance prediction performance and guarantee transparency in energy usage projections.
We investigate feature selection problem for generic machine learning models. We introduce a novel framework that selects features considering the outcomes of the model. Our framework introduces a novel feature maskin...
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We investigate feature selection problem for generic machine learning models. We introduce a novel framework that selects features considering the outcomes of the model. Our framework introduces a novel feature masking approach to eliminate the features during the selection process, instead of completely removing them from the dataset. This allows us to use the same machine learning model during feature selection, unlike other feature selection methods where we need to train the machine learning model again as the dataset has different dimensions on each iteration. We obtain the mask operator using the predictions of the machine learning model, which offers a comprehensive view on the subsets of the features essential for the predictive performance of the model. A variety of approaches exist in the feature selection literature. However, to our knowledge, no study has introduced a training-free framework for a generic machine learning model to select features while considering the importance of the feature subsets as a whole, instead of focusing on the individual features. We demonstrate significant performance improvements on the real-life datasets under different settings using LightGBM and multilayer perceptron as our machine learning models. Our results show that our methods outperform traditional feature selection techniques. Specifically, in experiments with the residential building dataset, our general binary mask optimization algorithm has reduced the mean squared error by up to 49% compared to conventional methods, achieving a mean squared error of 0.0044. The high performance of our general binary mask optimization algorithm stems from its feature masking approach to select features and its flexibility in the number of selected features. The algorithm selects features based on the validation performance of the machine learning model. Hence, the number of selected features is not predetermined and adjusts dynamically to the dataset. Additionally, we openly s
The component aging has become a significant concern worldwide,and the frequent failures pose a serious threat to the reliability of modern power *** light of this issue,this paper presents a power system reliability ...
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The component aging has become a significant concern worldwide,and the frequent failures pose a serious threat to the reliability of modern power *** light of this issue,this paper presents a power system reliability evaluation method based on sequential Monte Carlo simulation(SMCS)to quantify system reliability considering multiple failure modes of ***,a three-state component reliability model is established to explicitly describe the state transition process of the component subject to both aging failure and random failure *** this model,the impact of each failure mode is decoupled and characterized as the combination of two state duration variables,which are separately modeled using specific probability ***,SMCS is used to integrate the three-state component reliability model for state transition sequence generation and system reliability ***,various reliability metrics,including the probability of load curtailment(PLC),expected frequency of load curtailment(EFLC),and expected energy not supplied(EENS),can be *** ensure the applicability of the proposed method,Hash table grouping and the maximum feasible load level judgment techniques are jointly adopted to enhance its computational *** studies are conducted on different aging scenarios to illustrate and validate the effectiveness and practicality of the proposed method.
This article presents an in-depth exploration of the acoustofluidic capabilities of guided flexural waves(GFWs)generated by a membrane acoustic waveguide actuator(MAWA).By harnessing the potential of GFWs,cavity-agnos...
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This article presents an in-depth exploration of the acoustofluidic capabilities of guided flexural waves(GFWs)generated by a membrane acoustic waveguide actuator(MAWA).By harnessing the potential of GFWs,cavity-agnostic advanced particle manipulation functions are achieved,unlocking new avenues for microfluidic systems and lab-on-a-chip *** localized acoustofluidic effects of GFWs arising from the evanescent nature of the acoustic fields they induce inside a liquid medium are numerically investigated to highlight their unique and promising *** traditional acoustofluidic technologies,the GFWs propagating on the MAWA’s membrane waveguide allow for cavity-agnostic particle manipulation,irrespective of the resonant properties of the fluidic ***,the acoustofluidic functions enabled by the device depend on the flexural mode populating the active region of the membrane *** demonstrations using two types of particles include in-sessile-droplet particle transport,mixing,and spatial separation based on particle diameter,along with streaming-induced counter-flow virtual channel generation in microfluidic PDMS *** experiments emphasize the versatility and potential applications of the MAWA as a microfluidic platform targeted at lab-on-a-chip development and showcase the MAWA’s compatibility with existing microfluidic systems.
In recent years, copper oxide (CuxO) has emerged as a promising p-type oxide semiconductor owing to its high Hall mobility. However, its inherent drawbacks, such as the substantial native defects and uncontrolled stoi...
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Recent advancements in deep neural networks (DNNs) have made them indispensable for numerous commercial applications. These include healthcare systems and self-driving cars. Training DNN models typically demands subst...
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The global health crisis caused by the COVID-19 pandemic has brought new challenges to speaker identification systems, particularly due to the acoustic alterations caused by the widespread use of face masks. Aiming to...
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The Sultanate of Oman has been dealing with a severe renewable energy issue for the past few decades,and the government has struggled to find a *** addition,Oman’s strategy for converting power generation to sources ...
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The Sultanate of Oman has been dealing with a severe renewable energy issue for the past few decades,and the government has struggled to find a *** addition,Oman’s strategy for converting power generation to sources of renewable energy includes a goal of 60 percent of national energy demands being met by renewables by 2040,including solar and wind ***,the use of small-scale energy from wind devices has been on the rise in recent *** upward trend is attributed to advancements in wind turbine technology,which have lowered the cost of energy from *** calculate the internal and external factors that affect the small-scale energy of wind technologies,the study used a fuzzy analytical hierarchy process technique for order of preference by similarity to an ideal *** a result,in the decision model,four criteria,seventeen sub-criteria,and three resources of renewable energy were calculated as options from the viewpoint of the Sultanate of *** research is based on an examination of statistics on energy produced by wind turbines at various locations in the Sultanate of ***,six distinct miniature wind turbines were investigated for four different *** outcomes of this study indicate that the tiny wind turbine has a lot of potential in the Sultanate of Oman for applications such as homes,schools,college campuses,irrigation,greenhouses,communities,and small *** government should also use renewable energy resources to help with the renewable energy issue and make sure that the country has enough renewable energy for its long-term growth.
In federated learning (FL), the communication constraint between the remote clients and the Parameter Server (PS) is a crucial bottleneck. For this reason, model updates must be compressed so as to minimize the loss i...
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In this paper, a novel on–off linear quadratic regulator (LQR) control for satellite rendezvous as an example of linear systems with on–off inputs has been proposed for the first time. It simultaneously benefits fro...
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