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This paper mainly studies the hardware implementation of a fully connected neural network based on the 1T1R (one-transistor-one-resistor) array and its application in handwritten digital image recognition. The 1T1R arrays are prep...
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This paper mainly studies the hardware implementation of a fully connected neural network based on the 1T1R (one-transistor-one-resistor) array and its application in handwritten digital image recognition. The 1T1R arrays are prepared by connecting the memristor and nMOSFET in series, and a single-layer and a double-layer fully connected neural network are established. The recognition accuracy of 8 × 8 handwritten digital images reaches 95.19%. By randomly replacing the devices with failed devices, it is found that the stuck-off devices have little effect on the accuracy of the network, but the stuck-on devices will cause a sharp reduction of accuracy. By using the measured conductivity adjustment range and precision data of the memristor, the relationship between the recognition accuracy of the network and the number of hidden neurons is simulated. The simulation results match the experimental results. Compared with the neural network based on the precision of 32-bit floating point, the difference is lower than 1%.
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Computational models are based on symbolic architecture. For this reason, computational models function problematically in dynamic, noisy, and continuous environments. The ACT/R (Adaptive Control of Thought-Rational) model is also...
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Computational models are based on symbolic architecture. For this reason, computational models function problematically in dynamic, noisy, and continuous environments. The ACT/R (Adaptive Control of Thought-Rational) model is also problematic, as it is purely based on symbolic architecture like other computational models. The ACT/R decision-making process is based on the production operator on the input subject set. This approach firstly does not make a non-linear mapping between input and the decision-making result in ACT/R. Secondly, it is not possible to decide on the input subjects with a continuous input range because of the need to introduce numerous rules. The objective of presenting the ACT/R-radial basis function (RBF) hybrid architecture method was to create a communication network between input concepts in which the reception of and decision making on a combination of subjects and symbols are possible. Moreover, a non-linear mapping between input and the decision-making result can be created. The said capabilities have been obtained by the combination of ACT/R with an RBF neural network and calculation of the decision-making centers in the said network using clustering. The empirical experiments indicate desirable results in this regard.
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This study, deals with the potential application of the artificial neural networks (ANNs) to represent PVTx (pressure-specific volume-temperature-vapor quality) data in the range of temperature of 173-498 K and pressure of 10-3600...
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This study, deals with the potential application of the artificial neural networks (ANNs) to represent PVTx (pressure-specific volume-temperature-vapor quality) data in the range of temperature of 173-498 K and pressure of 10-3600 kPa. Generally, numerical equations of thermodynamic properties are used in the computer simulation analysis instead of analytical differential equations. And also analytical computer codes usually require a large amount of computer power and need a considerable amount of time to give accurate predictions. Instead of complex rules and mathematical routines, this study proposes an alternative approach based on ANN to determine the thermodynamic properties of an environmentally friendly refrigerant (R404a) for both saturated liquid-vapor region (wet vapor) and superheated vapor region as numerical equations. Therefore, reducing the risk of experimental uncertainties and also removing the need for complex analytic equations requiring long computational time and effort. R~2 values -which are errors known as absolute fraction of variance - in wet vapor region are 0.999401, 0.999982 and 0.999993 for specific volume, enthalpy and entropy for training data, respectively. For testing data, these values are 0.998808, 0.999988, and 0.999993. Similarly, for superheated vapor region, they are: 0.999967, 0.999999 and 0.999999 for training data, 0.999978, 0.999997 and 0.999999 for testing data. As seen from the results of mathematical modeling, the calculated thermodynamic properties are obvi ously within acceptable uncertainties.
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The aim of this study was the quantification of vapors of the ozone-depleting refrigerant R22 in the presence of its most important substitute R134a, by the use of the reflectometric interference spectroscopy and polymers as sensi...
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The aim of this study was the quantification of vapors of the ozone-depleting refrigerant R22 in the presence of its most important substitute R134a, by the use of the reflectometric interference spectroscopy and polymers as sensitive layers. First, the sorption characteristic of different types of polymers exposed to the vapors of the two analytes was investigated. Then, binary mixtures of the two refrigerants were measured with an array set-up on the basis of six polymer sensors. The measurements were evaluated by the use of neural networks, whereby low limits of detection of 0.45 percentage volume (vol.%) for R22 and 1.45 vol. % for R134a could be established. Additionally, one polar polymer and one microporous polymer were selected for the measurements with a low-cost set-up. The quantification of R22 in the presence of R134a with this low-cost set-up was possible with a limit of detection of 0.44 vol. %, which would enable a fast and economical monitoring at recycling stations.
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A capillary tube is a common expansion device widely used in small-scale refrigeration and air-conditioning systems Generalized correlation method for refrigerant flow rate through adiabatic capillary tubes is developed by combini...
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A capillary tube is a common expansion device widely used in small-scale refrigeration and air-conditioning systems Generalized correlation method for refrigerant flow rate through adiabatic capillary tubes is developed by combining dimensional analysis and artificial neural network (ANN). Dimensional analysis is utilized to provide the generalized dimensionless parameters and reduce the number of input parameters, while a three-layer feedforward ANN is served as a universal approximator of the nonlinear multi-input and single-output function. For ANN training and test, measured data for R12, R134a, R22, R290, R407C, R410A, and R600a in the open literature are employed. The trained ANN with just one hidden neuron is good enough for the training data with average and standard deviations of 0.4 and 6.6%, respectively. By comparison, for two test data sets, the trained ANN gives two different results. It is well interpreted by evaluating the outlier with a homogeneous equilibrium model.
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Diabetes is a chronic, metabolic disease characterized by high blood sugar levels. Among the main types of diabetes, type 2 is the most common. Early diagnosis and treatment can prevent or delay the onset of complications. Previou...
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Diabetes is a chronic, metabolic disease characterized by high blood sugar levels. Among the main types of diabetes, type 2 is the most common. Early diagnosis and treatment can prevent or delay the onset of complications. Previous studies examined the application of machine learning techniques for prediction of the pathology, and here an artificial neural network shows very promising results as a possible valuable aid in the management and prevention of diabetes. Additionally, its superior ability for long-term predictions makes it an ideal choice for this field of study. We utilized machine learning methods to uncover previously undiscovered associations between an individual’s health status and the development of type 2 diabetes, with the goal of accurately predicting its onset or determining the individual’s risk level. Our study employed a binary classifier, trained on scratch, to identify potential nonlinear relationships between the onset of type 2 diabetes and a set of parameters obtained from patient measurements. Three datasets were utilized, i.e., the National Center for Health Statistics’(NHANES) biennial survey, MIMIC-III and MIMIC-IV. These datasets were then combined to create a single dataset with the same number of individuals with and without type 2 diabetes. Since the dataset was balanced, the primary evaluation metric for the model was accuracy. The outcomes of this study were encouraging, with the model achieving accuracy levels of up to 86% and a ROC AUC value of 0.934. Further investigation is needed to improve the reliability of the model by considering multiple measurements from the same patient over time.
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The utilization of electronic expansion valves (EEVs) in refrigeration and air conditioning systems is increased for energy saving and comfort environmental. However, experimental data and refrigerant mass flow models through EEVs...
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The utilization of electronic expansion valves (EEVs) in refrigeration and air conditioning systems is increased for energy saving and comfort environmental. However, experimental data and refrigerant mass flow models through EEVs are very limited in open literature. In this study, a new technique using artificial neural network (ANN) is applied to depict the mass flow rates of R22 and its alternatives R407C and R410A flowing through EEVs based on the error back propagation learning algorithm. Two strategies are followed; the first is to construct individual ANN models for each refrigerant, and the second is to construct a generalized ANN model for the three investigated refrigerants. The experimental results from open literature are used to construct the ANN models. The ANN models results showed a good agreement with the corresponding experimental data. For individual models, the relative deviations for R22, R407C, and R410A are within +/- 0.7%, +/- 1.1%, and +/- 0.006%, respectively. While for generalized model, the relative deviations are within +/- 2.5%. Also the generalized model was tested out of its construction range in a predictive mode and it was found to be a reliable tool to estimate the refrigerants mass flow rates. (C) 2016 Elsevier Ltd and International Institute of Refrigeration. All rights reserved.
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This article presents the development, validation, and comparison of two methods for modeling a reciprocating compressor. Initially, the physical mode is based on eight internal sub-processes that incorporate infinitesimal displac...
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This article presents the development, validation, and comparison of two methods for modeling a reciprocating compressor. Initially, the physical mode is based on eight internal sub-processes that incorporate infinitesimal displacements according to the piston movement. Next, the analysis and modeling of the compressor through the application of artificial neural networks are presented. The input variables are: suction pressure, suction temperature, discharge pressure, and compressor rotation speed. The output parameters are: refrigerant mass flow rate, discharge temperature, and energy consumption. Both models are validated with experimental data for the refrigerants R1234yf and R134a; computer simulations show that mean relative errors are below +/- 10% with the physical model, and below +/- 1% when artificial neural networks are used. Additionally, the performance of the models is evaluated through the computation of the squared absolute error. Finally, these models are used to compute an energy comparison between both refrigerants. (C) 2015 Elsevier Ltd and International Institute of Refrigeration. All rights reserved.
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This paper proposes an original architecture for a fraud management system (FMS) for convergent. Next-generation networks (NGNs), which are based on the Internet protocol (IP). The architecture has the potential to satisfy the req...
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This paper proposes an original architecture for a fraud management system (FMS) for convergent. Next-generation networks (NGNs), which are based on the Internet protocol (IP). The architecture has the potential to satisfy the requirements of flexibility and application-independency for effective fraud detection in NGNs that cannot be met by traditional FMSs. The proposed architecture has a thorough four-stage detection process that analyses billing records in IP detail record (IPDR) format - an emerging IP-based billing standard - for signs of fraud. Its key feature is its usage of neural networks in the form of self-organising maps (SOMs) to help uncover unknown NGN fraud scenarios. A prototype was implemented to test the effectiveness of using a SOM for fraud detection and is also described in the paper.
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Taste perception plays an important role in the mediation of food choices in mammals. The first porcine taste receptor genes identified, sequenced and characterized, TAS1R1 and TAS1R3, were related to the dimeric receptor for umam...
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Taste perception plays an important role in the mediation of food choices in mammals. The first porcine taste receptor genes identified, sequenced and characterized, TAS1R1 and TAS1R3, were related to the dimeric receptor for umami taste. However, little is known about their regulatory network. The objective of this study was to unfold the genetic network involved in porcine umami taste perception. We performed a meta-analysis of 20 gene expression studies spanning 480 porcine microarray chips and screened 328 taste-related genes by selective mining steps among the available 12320 genes. A porcine umami taste-specific regulatory network was constructed based on the normalized coexpression data of the 328 genes across 27 tissues. From the network, we revealed the taste module' and identified a coexpression cluster for the umami taste according to the first connector with the TAS1R1/TAS1R3 genes. Our findings identify several taste-related regulatory genes and extend previous genetic background of porcine umami taste.
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