Dynamic behavioral modeling of nonlinear circuits using a novel recurrent neural network technique

In this paper, a new method called local-global feedback recurrent neural network (LGFRNN) is proposed for dynamic behavioral modeling of nonlinear circuits. The structure of the proposed method is based on recurrent neural network and constructed by time-delayed local and global feedbacks. Adding time-delayed feedbacks has a great impact on the learning capability of previous neural network-based methods. Moreover, time-delayed local feedbacks alleviate the problem of slow convergency of the conventional neural network-based methods in the training phase. The proposed LGFRNN can be trained only by having sampled input-output waveforms of the original circuit without knowing the internal details of the circuit. A training algorithm based on real-time recurrent learning (RTRL) is used to train LGFRNN. After the training phase, the proposed LGFRNN provides accurate macromodel of a nonlinear circuit. The proposed method is more accurate compared with the conventional neural-based models (which do not benefit from time-delayed local-global feedbacks) and also significantly reduces the training time of the conventional models. Moreover, proposed LGFRNN is faster than the existing models in simulation tools. The validity of the proposed method is verified by time-domain modeling of three nonlinear devices including commercial TI's SN74AHCT540 device, five-stage complementary metal-oxide-semiconductor (CMOS) receiver, and commercial TI's LM324 power amplifier.     

Clad height control in laser solid freeform fabrication using a feedforward PID controller

In this paper, a feedforward proportional-integral-derivative (PID) controller is developed to effectively control the clad height in laser solid freeform fabrication (LSFF). The scanning velocity is selected as the input control variable and the clad height is chosen as the output. A novel knowledge-based Hammerstein model, including a linear dynamic and a nonlinear memoryless block, is developed, and its parameters are identified offline using experimental data. The architecture of the controller consists of a PID and a feedforward module, which is the inverse of the identified model. The advantage of adding a feedforward path to the PID controller is evaluated experimentally, in which the results show a lower overshoot and faster response times. Also, the performance of the controller is verified in the presence of geometrical disturbances, as well as in the fabrication of a nonplanar part.     

Modeling Oil Content of Sesame (Sesamum indicum L.) Using Artificial Neural Network and Multiple Linear Regression Approaches

Sesame (Sesamum indicum L.) is an important ancient oilseed crop with high oil content (OC) and quality. The direct selection to improve OC of sesame (OCS) due to low heritability leads to a low profit. The OCS modeling and indirect selection through high‐heritable characters associated with OCS using advanced modeling techniques is a beneficial approach to overcome this limitation that allows breeder to get a better idea of the plant properties that should be monitored during breeding experiments. This study, carried out in 2013 and 2014, compared the potential of artificial neural network (ANN) and multilinear regression (MLR) to predict OCS in the Imamzadeh Jafar plain of Gachsaran, Iran. Principal component analysis (PCA) and stepwise regression (SWR) were used to evaluate 18 input variables. Based on PCA and SWR, the 6 traits of number of capsules per plant (NCP), number of days from flowering to maturity (NDFM), plant height (PH), thousand seed weight, capsule length, and seed yield were chosen as input variables. The network with the sigmoid axon transfer function and 2 hidden layers was selected as the final ANN model. Results showed that the ANN predicted the OCS with more accuracy and efficacy (R² = 0.861, root mean square error [RMSE] = 0.563, and mean absolute error [MAE] = 0.432) compared with the MLR model (R² = 0.672, RMSE = 0.742, and MAE = 0.552). These results showed the potential of the ANN as a promising tool to predict OCS with good performance. Based on sensitivity tests, NCP followed by NDFM and PH, respectively, were the most influential factors in predicting OCS in both models. It seems that a breeding program to select or create long sesame genotypes with a long period from flowering to maturity can be a good approach to address OCS in the future.     

Estimator learning automata for feature subset selection in high‐dimensional spaces,case study: Email spam detection

One of the difficult challenges facing data miners is that algorithm performance degrades if the feature space contains redundant or irrelevant features. Therefore, as a critical preprocess task, dimension reduction is used to build a smaller space containing valuable features. There are 2 different approaches for dimension reduction: feature extraction and feature selection, which itself is divided into wrapper and filter approaches. In high‐dimensional spaces, feature extraction and wrapper approaches are not applicable due to the time complexity. On the other hand, the filter approach suffers from inaccuracy. One main reason for this inaccuracy is that the subset's size is not determined considering specifications of the problem. In this paper, we propose ESS (estimator learning automaton‐based subset selection) as a new method for feature selection in high‐dimensional spaces. The innovation of ESS is that it combines wrapper and filter ideas and uses estimator learning automata to efficiently determine a feature subset that leads to a desirable tradeoff between the accuracy and efficiency of the learning algorithm. To find a qualified subset for a special processing algorithm that functions on an arbitrary dataset, ESS uses an automaton to score each candidate subset upon the scale of the subset and accuracy of the learning algorithm using it. In the end, the subset with the highest score is returned. We have used ESS for feature selection in the framework of spam detection, a text classification task for email as a pervasive communication medium. The results show achievement in reaching the goal stated above.     

Age-based anonymity: a randomized routing approach to communication unobservability

Providing anonymous communication on networks of interconnected computers is an active area of research which aims to enhance the privacy of the users of such networks. Communication unobservability, stronger property compared to anonymity, attempts to guarantee that legitimate messages are not discernible from dummy traffic. A network with an active global adversary is one which it is assumed that all nodes in the network are potentially being monitored at all times, and also that at any time any node could be an adversary. This paper introduces a set of anonymous system design requirements for providing enhanced communication unobservability. A new anonymous networking system was designed based on these requirements to provide both sender and receiver anonymity. The proposed system has a structured peer-to-peer network architecture and a randomized routing algorithm to obfuscate the detection of communication paths and the message routing patterns. An age-based method is proposed to prevent even the first node after the sender from identifying the original sender. A simulation program was designed and implemented to test the proposed system. The effect of different parameters on the proposed algorithm is demonstrated using a simulation program.     

CFIN: A community-based algorithm for finding influential nodes in complex social networks

Influence maximization (IM) problem, a fundamental algorithmic problem, is the problem of selecting a set of k users (refer as seed set) from a social network to maximize the expected number of influenced users (also known as influence spread). Due to the numerous applications of IM in marketing, IM has been studied extensively in recent years. Nevertheless, many algorithms do not take into consideration the impact of communities to influence maximization and some algorithms are non-scalable and time-consuming in practice. In this paper, we proposed a fast and scalable algorithm called community finding influential node (CFIN) that selects k users based on community structure, which maximizes the influence spread in the networks. The CFIN consists of two main parts for influence maximization: (1) seed selection and (2) local community spreading. The first part of CFIN is the extraction of seed nodes from communities which obtained the running of the community detection algorithm. In this part, to decrease computational complexity effectively and scatter seed nodes into communities, the meaningful communities are selected. The second part consists of the influence spread inside communities that are independent of each other. In this part, the final seed nodes entered to distribute the local spreading by the use of a simple path inside communities. To study the performance of the CFIN, several experiments have been conducted on some real and synthetic networks. The experimental simulations on the CFIN, in comparison with other algorithms, confirm the superiority of the CFIN in terms of influence spread, coverage ratio, running time, and Dolan-Moré performance profile.

     

Retraction Note to: ANN model to predict the effects of composition and heat treatment parameters on transformation start temperature of microalloyed steels

Neural Computing and Applications - The Editor-in-Chief has retracted this article because it significantly overlaps with a number of previously published articles.     

Application of response surface methodology and central composite rotatable design for modeling the influence of some operating variables of the lab scale thickener performance

This study discussed the application of response surface methodology (RSM) and central composite rotatable design (CCRD) for modeling and optimization of the influence of some operating variables on the performance of a lab scale thickener for dewatering of tailing in the flotation circuit. Four thickener operating variables, namely feed flowrate, solid percent, flocculant dosage and feedwell height were changed during the tests based on CCRD. The ranges of values of the thickener variables used in the design were a feed flowrate of 9–21 L/min, solid percent of 8%–20%, flocculant dosage of 1.25–4.25 g/t and feedwell height of 16–26 cm. A total of 30 thickening tests were conducted using lab scale thickener on flotation tailing obtained from the Sarcheshmeh copper mine, Iran. The underflow solid percent and bed height were expressed as functions of four operating parameters of thickener. Predicted values were found to be in good agreement with experimental values (R² values of 0.992 and 0.997 for underflow solid percent and bed height, respectively). This study has shown that the RSM and CCRD could efficiently be applied for the modeling of thickener for dewatering of flotation tailing.     

Estimation of oil and gas properties in petroleum production and processing operations using rigorous model

Wellhead chokes are widely used in the petroleum industry. Owning to the high sensitivity of oil and gas production to choke size, an accurate correlation to specify choke performance is vitally important. The aim of this contribution was to develop effective relationships among the liquid flow rate, gas liquid ratio, flowing wellhead pressure, and surface wellhead choke size using the support vector machines (SVMs). The accurate data set was gathered from the 15 different fields containing 100 production samples from the vertical wells at wide ranges of wellhead choke sizes. This computational model was compared with the previous developed correlations in order to investigate its applicability for subcritical two phase flow regimes through wellhead chokes. Results confirmed amazing capability of the SVM to predict liquid flow rates. The value of R² obtained was 0.9998 for the SVM model. This developed predictive tool can be of massive value for petroleum engineer to have accurate estimations of liquid flow rates through wellhead chocks.     

Improvement of heavy oil recovery and role of nanoparticles of clay in the surfactant flooding process

Increasing demand and dwindling supply of crude oil have spurred efforts toward enhancing heavy oil recovery. Recently, applications of nanoparticles (NPs) for heavy oil recovery have been reported. In this study, the use of clay NPs is investigated for enhanced oil recovery. Surfactant solutions and newly developed nanosurfactant solutions with 1600–2000 ppm SDS were tested. The crude oil had a viscosity of 1320 mPa.sec at test conditions. In this study, the role of NPs in the adsorption of surfactant onto solid surfaces of reservoir core is studied. The core flooding experiments showed high potential of using nanoclay for enhancing heavy oil recovery, where 52% of surfactant flooded heavy oil was recovered after injecting the NPs solvent. Moreover, nanoclay has generally better performance in enhancing the oil recovery at surfactant solution, near CMC conditions. The nanoclay surfactant solutions improved oil recovery. The nanoclay, however, showed improved performance in comparison with clay.