Much has been written about levels of automation (LOA), but comparatively little has been written about levels of digitization (LODi) and levels of digitalization (LODa). Digitization is a digital representation of an...
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automation technologies such as robotic processautomation (RPA) and intelligent automation (IA) are essential for managing rising healthcare costs and ensuring sustainable health services. Although these solutions ha...
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Outdated testing methods hinder the success rate of carbonized cable preparation in low-voltage arc fault tests,leading to incomplete tests and high failure *** address this issue,we finely categorized the preparation...
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Outdated testing methods hinder the success rate of carbonized cable preparation in low-voltage arc fault tests,leading to incomplete tests and high failure *** address this issue,we finely categorized the preparation results of carbonized cable specimens by analyzing the experimental phenomena during the carbonization process and assessing the impact of high-voltage energization time on the outcomes,presenting a processcontrol strategy aimed at optimizing the preparation results of carbonized cable *** method utilizes three periodic moving algorithms(root-mean-square,average,and shoulder percentage)to classify the cable specimens into four preparation categories:open-circuit carbonization,under-carbonization,short-circuit carbonization,and successful *** high-voltage energization time during carbonization or secondary carbonization was adjusted to optimize the preparation of the carbonized cables by considering different discrimination ***,the proposed method was tested on a purpose-built carbonized cable experimental platform,which confirmed its effectiveness in differentiating the preparation outcomes of the carbonized cable specimens and improving the success rate of the carbonized cable *** proposed method has significant potential for application in low-voltage arc fault test systems.
Accurate prediction of strip width is a key factor related to the quality of hot rolling ***,based on strip width formation mechanism model within strip rolling process,an improved width mechanism calculation model is...
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Accurate prediction of strip width is a key factor related to the quality of hot rolling ***,based on strip width formation mechanism model within strip rolling process,an improved width mechanism calculation model is delineated for the optimization of process parameters via the particle swarm optimization ***,a hybrid strip width prediction model is proposed by effectively combining the respective advantages of the improved mechanism model and the data-driven *** acknowledgment of prerequisite for positive error in strip width prediction,an adaptive width error compensation algorithm is ***,comparative simulation experiments are designed on the actual rolling dataset after completing data cleaning and feature *** experimental results show that the hybrid prediction model proposed has superior precision and robustness compared with the improved mechanism model and the other eight common data-driven models and satisfies the needs of practical ***,the hybrid model can realize the complementary advantages of the mechanism model and the data-driven model,effectively alleviating the problems of difficult to improve the accuracy of the mechanism model and poor interpretability of the data-driven model,which bears significant practical implications for the research of strip width control.
Deep learning is the subset of artificial intelligence and it is used for effective decision *** Sensor based automated irrigation system is proposed to monitor and cultivate *** system consists of Distributed wire-les...
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Deep learning is the subset of artificial intelligence and it is used for effective decision *** Sensor based automated irrigation system is proposed to monitor and cultivate *** system consists of Distributed wire-less sensor environment to handle the moisture of the soil and temperature *** is automated process and useful for minimizing the usage of resources such as water level,quality of the soil,fertilizer values and controlling the whole *** mobile app based smart control system is designed using deep belief *** system has multiple sensors placed in agriculturalfield and collect the *** collected transmitted to cloud server and deep learning process is applied for making *** residue analysis method is proposed for analyzing auto-mated and sensor captured ***,we used 512×512×3 layers deep belief network and 10000 trained data and 2500 test data are taken for *** is automated process once data is collected deep belief network is *** performance is compared with existing results and our process method has 94%of accuracy ***,our system has low cost and energy consumption also suitable for all kind of agriculturalfields.
The idea of active sensing is to embed sensor systems with intelligence to require less human interaction. Accurate but limited main measurement systems are complemented with broadband auxiliary measurements that gath...
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This study investigates the dry reformation of methane(DRM)over Ni/Al_(2)O_(3)catalysts in a dielectric barrier discharge(DBD)non-thermal plasma reactor.A novel hybrid machine learning(ML)model is developed to optimiz...
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This study investigates the dry reformation of methane(DRM)over Ni/Al_(2)O_(3)catalysts in a dielectric barrier discharge(DBD)non-thermal plasma reactor.A novel hybrid machine learning(ML)model is developed to optimize the plasma-catalytic DRM reaction with limited experimental *** address the non-linear and complex nature of the plasma-catalytic DRM process,the hybrid ML model integrates three well-established algorithms:regression trees,support vector regression,and artificial neural networks.A genetic algorithm(GA)is then used to optimize the hyperparameters of each algorithm within the hybrid ML *** ML model achieved excellent agreement with the experimental data,demonstrating its efficacy in accurately predicting and optimizing the DRM *** model was subsequently used to investigate the impact of various operating parameters on the plasma-catalytic DRM *** found that the optimal discharge power(20 W),CO_(2)/CH_(4)molar ratio(1.5),and Ni loading(7.8 wt%)resulted in the maximum energy yield at a total flow rate of∼51 mL/***,we investigated the relative significance of each operating parameter on the performance of the plasma-catalytic DRM *** results show that the total flow rate had the greatest influence on the conversion,with a significance exceeding 35%for each output,while the Ni loading had the least impact on the overall reaction *** hybrid model demonstrates a remarkable ability to extract valuable insights from limited datasets,enabling the development and optimization of more efficient and selective plasma-catalytic chemical processes.
Developing an accurate and reliable anomaly detection model is of great significance for safe operation in the process industry. To minimize false positives, it is crucial to accurately model the intricate topological...
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Although construction is one of the oldest sectors of the global economy, the digital innovation and application of artificial intelligence (AI) in the industry are still insignificant. For the past several years, how...
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Surface chokes are widely utilized equipment installed on wellheads to control hydrocarbon flow *** correlations have been suggested to model the multiphase flow of oil and gas via surface ***,substantial errors have ...
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Surface chokes are widely utilized equipment installed on wellheads to control hydrocarbon flow *** correlations have been suggested to model the multiphase flow of oil and gas via surface ***,substantial errors have been reported in empirical fitting models and correlations to estimate hydrocarbon flow because of the reservoir's heterogeneity,anisotropism,variance in reservoir fluid characteristics at diverse subsurface depths,which introduces complexity in production ***,the estimation of daily oil and gas production rates is still challenging for the petroleum ***,hybrid data-driven techniques have been reported to be effective for estimation problems in various aspects of the petroleum *** paper investigates hybrid ensemble data-driven approaches to forecast multiphase flow rates through the surface choke(*** generalization and voting architectures),followed by an assessment of the impact of input productioncontrol ***,machine learning models are also trained and tested individually on the production data of hydrocarbon wells located in North *** engineering has been properly applied to select the most suitable contributing control variables for daily production rate *** study provides a chronological explanation of the data analytics required for the interpretation of production *** test results reveal the estimation performance of the stacked generalization architecture has outperformed other significant paradigms considered for production forecasting.
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