Browsing by Subject "artificial neural network"

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    Air Compressor Load Forecasting using Artificial Neural Network
    (Elsevier, 2021-04) Wu, Da-Chun; Bahrami Asl, Babak; Razban, Ali; Chen, Jie; Mechanical and Energy Engineering, School of Engineering and Technology
    Air compressor systems are responsible for approximately 10% of the electricity consumed in United States and European Union industry. As many researches have proven the effectiveness of using Artificial Neural Network in air compressor performance prediction, there is still a need to forecast the air compressor electrical load profile. The objective of this study is to predict compressed air systems' electrical load profile, which is valuable to industry practitioners as well as software providers in developing better practice and tools for load management and look-ahead scheduling programs. Two artificial neural networks, Two-Layer Feed-Forward Neural Network and Long Short-Term Memory were used to predict an air compressors electrical load. Compressors with three different control mechanisms are evaluated with a total number of 11,874 observations. The forecasts were validated using out-of-sample datasets with 5-fold cross-validation. Models produced average coefficient of determination values from 0.24 to 0.94, average root-mean-square errors from 0.05 kW - 5.83 kW, and mean absolute scaled errors from 0.20 to 1.33. The results indicate that both artificial neural networks yield good results for compressors using variable speed drive (average R2 = 0.8 and no naïve forecasting), only the long short-term memory model gives acceptable results for compressors using on/off control (average R2 = 0.82 and no naïve forecasting), and no satisfactory results are obtained for load/unload type air compressors (models constituting naïve forecasting).
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    A Framework for Optimizing Process Parameters in Powder Bed Fusion (PBF) Process Using Artificial Neural Network (ANN)
    (2019) Marrey, Mallikharjun; Malekipour, Ehsan; El-Mounayri, Hazim; Faierson, Eric J.; Mechanical and Energy Engineering, School of Engineering and Technology
    Powder bed fusion (PBF) process is a metal additive manufacturing process, which can build parts with any complexity from a wide range of metallic materials. Research in the PBF process predominantly focuses on the impact of few parameters on the ultimate properties of the printed part. The lack of a systematic approach to optimizing the process parameters for a better performance of given material results in a sub-optimal process. This process needs a comprehensive study of all the influential parameters and their impact on the mechanical and microstructural properties of a fabricated part. Furthermore, there is a need to develop a quantitative system for mapping the material properties and process parameters with the ultimate quality of the fabricated part to achieve improvement in the manufacturing cycle as well as the quality of the final part produced by the PBF process. To address the aforementioned challenges, this research proposes a framework to optimize the process for 316L stainless steel material. This framework characterizes the influence of process parameters on the microstructure and mechanical properties of the fabricated part using a series of experiments. These experiments study the significance of process parameters and their variance as well as study the microstructure and mechanical properties of fabricated parts by conducting tensile, impact, hardness, surface roughness, and densification tests, and ultimately obtain the optimum range of parameters. This would result in a more complete understanding of the correlation between process parameters and part quality. Furthermore, these experiments provide the required data needed to develop an Artificial Neural Network (ANN) model to optimize process parameters (for achieving the desired properties) and estimate fabrication time.
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    Meth math: modeling temperature responses to methamphetamine
    (American Physiological Society (APS), 2014-04-15) Molkov, Yaroslav I.; Zaretskaia, Maria V.; Zaretsky, Dmitry V.; Department of Emergency Medicine, IU School of Medicine
    Methamphetamine (Meth) can evoke extreme hyperthermia, which correlates with neurotoxicity and death in laboratory animals and humans. The objective of this study was to uncover the mechanisms of a complex dose dependence of temperature responses to Meth by mathematical modeling of the neuronal circuitry. On the basis of previous studies, we composed an artificial neural network with the core comprising three sequentially connected nodes: excitatory, medullary, and sympathetic preganglionic neuronal (SPN). Meth directly stimulated the excitatory node, an inhibitory drive targeted the medullary node, and, in high doses, an additional excitatory drive affected the SPN node. All model parameters (weights of connections, sensitivities, and time constants) were subject to fitting experimental time series of temperature responses to 1, 3, 5, and 10 mg/kg Meth. Modeling suggested that the temperature response to the lowest dose of Meth, which caused an immediate and short hyperthermia, involves neuronal excitation at a supramedullary level. The delay in response after the intermediate doses of Meth is a result of neuronal inhibition at the medullary level. Finally, the rapid and robust increase in body temperature induced by the highest dose of Meth involves activation of high-dose excitatory drive. The impairment in the inhibitory mechanism can provoke a life-threatening temperature rise and makes it a plausible cause of fatal hyperthermia in Meth users. We expect that studying putative neuronal sites of Meth action and the neuromediators involved in a detailed model of this system may lead to more effective strategies for prevention and treatment of hyperthermia induced by amphetamine-like stimulants.