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Dementia diagnosis often relies on expensive and invasive neuroimaging techniques that limit access to early screening. This study proposes an innovative approach for facilitating early dementia screening by estimating diffusion tensor imaging (DTI) measures using accessible lifestyle and brain imaging factors. Conventional DTI analysis, though effective, is often hindered by high costs and limited accessibility. To address this challenge, fuzzy subtractive clustering identified 14 influential variables from the Lifestyle for Brain Health and Brain Atrophy and Lesion Index frameworks, encompassing demographics, medical conditions, lifestyle factors, and structural brain markers. A multilayer perceptron (MLP) neural network was developed using these selected variables to predict fractional anisotropy (FA), a DTI metric reflecting white matter integrity and cognitive function. The MLP model achieved promising results, with a mean squared error of 0.000 878 on the test set for FA prediction, demonstrating its potential for accurate DTI estimation without costly neuroimaging techniques. The FA values in the dataset ranged from 0 to 1, with higher values indicating greater white matter integrity. Thus, a mean squared error of 0.000 878 suggests that the model's predictions were highly accurate compared to the observed FA values. This multifactorial approach aligns with the current understanding of dementia's complex etiology influenced by various biological, environmental, and lifestyle factors. By integrating readily available data into a predictive model, this method enables widespread, cost-effective screening for early dementia risk assessment. The proposed accessible screening tool could facilitate timely interventions, preventive strategies, and efficient resource allocation in public health programs, ultimately improving patient outcomes and caregiver burden. [ABSTRACT FROM AUTHOR]

This article first reviews the position control of piezoelectric actuators, particularly charge-based sensorless control systems, which often include a charge estimator as a key component. The rest of the paper is about charge estimators for piezoelectric actuators. Two of the most recent/effective types of these estimators utilise either a sensing capacitor (type I in this paper) or a sensing resistor (type II); the latter (and the newer) type is broadly known as a digital charge estimator. Some experimental results in the literature show that, with the same loss in excitation voltage, a considerably higher amount of charge can be estimated with a type II estimator in comparison with a type I estimator; therefore, the superiority of type II estimators was acknowledged. In order to re-assess this conclusion, this paper equitably compares type I and II estimators through analytical modelling and experimentation. The results indicate that type II estimators have only a slight advantage in estimating higher amounts of charge, if both type I and II estimators are designed appropriately. At the same time, type II estimators have disadvantages; e.g., the resistance of type II estimators has to be tuned to suit different excitation frequencies. This research concludes that capacitor-based (type I) charge estimators for piezoelectric actuators, with pertinent design and implementation, can be still the prime solution for many charge estimation problems despite claims in the literature in the last decade. [ABSTRACT FROM AUTHOR]

In this research, a design guideline for a kinetic energy converter using a cantilever triple-layer piezoelectric harvester (CTLPH) for low-frequency applications is presented. By combining the constitutive and internal energy equations, the analytical equations for harvested voltage and power were developed. It was also found that frequency of motion, applied tip force, piezoelectric coefficients, geometrical dimensions, and mechanical properties of layers play significant roles in the performance of the harvester. Having characterised the voltage regulator module, LTC3588, the dependency of output voltage on both the storage and output capacitors of the LTC3588 was investigated. An experimental measurement using the optical method was carried out to determine the applied tip force. Furthermore, the performance of the CTLPH in low frequencies (<3.3 Hz) for various resistive loads was investigated. It was found that both excitation frequency and external resistance load are effective on the maximum generated power. The developed CTLPH shows the optimum power of 17.31 μ W at the external resistance of 20 kΩ, which is highly appropriate for micropower devices with at least 3.2 Hz of kinetic vibration in their environment. [ABSTRACT FROM AUTHOR]

Ionic liquids have many interesting properties as they share the properties of molten salts as well as organic liquids, such as low volatility, thermal stability, electrical conductivity, non-flammability, and much more. Ionic liquids are known to be good solvents for many polar and nonpolar solutes. Combined with their special properties, ionic liquids are good replacements for the conventional toxic and volatile organic solvents. Each ionic liquid has different properties than others. In order to alter, tune, and enhance the properties of ionic liquids, sometimes, it is necessary to mix different ionic liquids to achieve the desired properties. However, using mixtures of ionic liquids in chemical processes requires reliable estimations of the mixtures' physical properties such as refractive index and density. The ionic liquids used in this work are 1-butyl-3-methylimidazolium thiocyanate ([BMIM][SCN]), 1-butyl-3-methylimidazolium tetrafluoroborate ([BMIM][BF4]), 1-hexyl-3-methylimidazolium tetrafluoroborate ([HMIM][BF4]), and 1-hexyl-3-methylimidazolium hexafluorophosphate ([HMIM][PF6]). These ionic liquids were supplied by Io-li-tec and used as received. However, new measurements for the density and refractive index were taken for the pure ionic liquids to be used as reference. In the present work, the densities and refractive indices of four different binary mixtures of ionic liquids with common cations and/or anions have been measured at various compositions and room conditions. The accuracy of different empirical mixing rules for calculation of the mixtures refractive indices was also studied. It was found that the overall absolute average percentage deviation from the ideal solution in the calculation of the molar volume of the examined binary mixtures was 0.78%. Furthermore, all of the examined mixing rules for the calculation of the refractive indices of the mixtures were found to be accurate. However, the most accurate empirical formula was found to be Heller's relation, with an average percentage error of 0.24%. Furthermore, an artificial intelligence model, an adaptive neuro-fuzzy inference system (ANFIS), was developed to predict the density and refractive index of the different mixtures studied in this work as well as the published literature data. The predictions of the developed model were analyzed by various methods including both statistical and graphical approaches. The obtained results show that the developed model accurately predicts the density and refractive index with overall R2, RMSE, and AARD% values of 0.968, 7.274, 0.368% and 0.948, 7.32 × 10−3 and 0.319%, respectively, for the external validation dataset. Finally, a variance-based global sensitivity analysis was formed using extended the Fourier amplitude sensitivity test (EFAST). Our modelling showed that the ANFIS model outperforms the best available empirical models in the literature for predicting the refractive index of the different mixtures of ionic liquids. [ABSTRACT FROM AUTHOR]

Experiments have shown that, for an extensive area of operating, charge of a piezoelectric actuator is proportional to its displacement from relaxing state. Consequently, accurate estimation of charge can lead to position/displacement estimation for piezoelectric actuators, a prominent progress towards precise sensorless micro/nanopositioning. However, disadvantageously, all known charge estimators of piezoelectric actuators have electrical element(s), e.g. (a) resistor(s) or (a) capacitor(s), in series with the actuator. Such elements, known as sensing elements, take a considerable share of the excitation voltage. Voltage taken by the sensing elements is called voltage drop. Charge estimators with a resistor in series with the actuator (also known as digital charge estimators) have been reported to witness the smallest voltage drop. The aim of this paper is to design such charge estimators so as to achieve maximum precision at minimum possible voltage drop. The aforementioned aim is shown to be obtained when the range of the voltage across the resistor equals the narrowest input range of the analogue to digital convertor of the charge estimator. This, however, is impossible to happen for wide operating areas with a sensing resistor with unchangeable resistance, according to experimental results. The alternative is an adaptive charge estimator with a resistor, in which its resistance varies with operating conditions. This paper presents two methods to estimate such a varying sensing resistor: approximate analytical formulation and artificial intelligence, in which, the latter shows evident superiority. [ABSTRACT FROM AUTHOR]

Summary: A new inspection technique for complex mechanical structures is proposed in this paper, where a fuzzy inference system carries out structural inspection. The inputs to the fuzzy inference system are the elements of a fault signature, an array of numbers prepared with use of below 5 kHz resonance frequencies of faultless and a number of faulty specimens. Advantage: Below 5 kHz resonance frequencies are easier and less expensive to obtain compared to higher frequency ones. Limit: Due to high expenses of experiments, reliable finite element models were alternatively used to obtain resonance frequencies of the faulty specimens. Results: The developed fuzzy inference system in this research accurately located an under-surface fault in an engine cylinder block. [ABSTRACT FROM AUTHOR]

This paper first investigates existing empirical models which predict head or pressure increase of two-phase petroleum fluids in electrical submersible pumps (ESPs); then, proposes an alternative model, a shallow artificial neural network (ANN) for the same purpose. Empirical models of ESP are widely used; whereas, analytical models are still unappealing due to their reliance on over-simplified assumptions, need to excessive extent of information or lack of accuracy. The proposed shallow ANN is trained and cross-validated with the same data used in developing a number of empirical models; however, the ANN evidently outperforms those empirical models in terms of accuracy in the entire operating area. Mean of absolute prediction error of the ANN, for the experimental data not used in its training, is 69% less than the most accurate existing empirical model. [ABSTRACT FROM AUTHOR]

This paper aims at introduction, design, and validation of a temperature estimation algorithm for an infrared dryer. The proposed algorithm estimates the temperature at one or some points in a thermal system (e.g., an infrared dryer) based on the measured temperature at a number of other points. In this research, the designed algorithm estimates the temperature of a single point; however, the methodology can be evidently extended to multiple points. Inspired by direct and inverse heat transfer models, a mathematical model is presented for this purpose. This model is developed and identified using artificial neural network (ANN) technique and laboratory experimental data. The proposed method exhibits excellent accuracy with no need to thermo-physical properties of the system. [ABSTRACT FROM AUTHOR]

Byline: Morteza Mohammadzaheri, Ali Mirsepahi In this research, a fuzzy knowledge-base controller is designed for yaw control of model helicopter. At the next stage, an adjusting algorithm is presented to reduce the influence of high inertia on fuzzy controlled systems. Inertia may cause significant overshoot, which is undesirable and difficult to eliminate. In order to solve this problem, a simple algorithm is presented to reduce the control input by adjusting the fuzzy controller parameters while the system is getting close to the desired condition. Implementing this approach (including a lateral algorithm to reset the parameters in special conditions) for yaw angle control of a model helicopter reduces the overshoot and energy consumption considerably without significant decrease of the settling time.

This article (1) proposes the use of a varying sensing resistor for digital charge estimators of piezoelectric actuators, (2) shows the usefulness of this proposal and (3) derives formulae to estimate the proposed varying sensing resistor. Digital charge estimators are used for charge-based precise position control of piezoelectric actuators. In existing digital charge estimators, an uncalculated resistor is used for a wide range of operating areas. Improper choice of the sensing resistor causes excessive drop in accuracy of charge estimation and/or in the driver voltage across the actuator. These issues are shown to nearly vanish with the use of proposed varying resistors. [ABSTRACT FROM AUTHOR]

Piezoelectric actuators are the most commonly used actuators in nanopositioning. They provide excellent operating bandwidth and precision. These actuators are normally driven by voltage amplifiers; however, the relationship of the applied voltage on the actuator and its position is nonlinear and complex due to phenomena such as hysteresis. This adversely influences actuator’s position control which is the core component of nanopositioning. However, the electrical charge applied on a piezoelectric actuator has a significantly less complex relationship with its position. As a result, driving piezoelectric actuators with charge amplifiers has attracted significant attention. In this article, charge driving methods are classified, and their advantages, major issues and the solutions to these issues are critically reviewed. [ABSTRACT FROM AUTHOR]

This paper presents the idea of virtual temperature sensors for irradiative enclosures. Such a virtual sensor is an algorithm which receives the temperature of a number of points on surfaces of an enclosure and estimates the temperature of another point (or a number of other points) within enclosure. This paper proposes a data-driven method based on fuzzy inference systems to develop temperature virtual sensing algorithms. The proposed method is validated on an experimental setup exhibiting excellent estimation accuracy with no need to thermo-physical properties of the enclosure. In this research, the designed and validated algorithm estimates the temperature of a single point; however, the methodology can be evidently extended to multiple points. [ABSTRACT FROM AUTHOR]

In this research, a hybrid control system is proposed to address the temperature control of an infrared dryer. The control system includes a feedback-predictive controller and a neural network steady state control law. The feedback-predictive controller outputs the amplified value of the predicted error as the transient control command. The predictive model was employed to suppress the undesirable effect of the dead-time of the system. A multilayer perceptron was designed and tested based on a control equilibrium point and steady state control to be used as a feedforward controller. The stability of the control system in a continuous domain was proved with no limit on the amplification gain of the predictive-feedback controller. In other words, there is no concern about losing stability with accelerating convergence towards the reference. The entire control system was constructed in Simulink and compiled to a C code and applied on the experimental setup. Experimental results are outstanding in comparison with the results of an interactively tuned IMC-based PID controller. [ABSTRACT FROM AUTHOR]

Piezoelectric actuators are the foremost actuators in the area of nanopositioning. However, the sensors employed to measure the actuator displacement are expensive and difficult, if not impossible, to use. Mathematical models can map the easy-to-measure electrical signals to the displacements of the actuators as the displacement sensors are replaced with the models. In addition, these models can be used in model-based control system design. Two main groups of mathematical models are used for this purpose: black box and physics-based models. As an advantage, the latter has a much smaller number of parameters reducing computational demand in real-time applications. However, physics-based models suffer from (1) the relatively low accuracy of the models and (2) non-standard and ad-hoc parameter identification methods. In this research, to improve the model accuracy, mathematical structure of a well-known physics-based model, the Voigt model, is enhanced by adding two complementary terms inspired by another model, the Preisach model. Then, a standard method based on the evolutionary algorithms is proposed to identify the model's parameters. The proposed ideas are substantiated to increase the applicability and accuracy of the model, and they are easily extendable to other physics-based models of piezoelectric actuators. The newly proposed enhanced structure of the Voigt model doubles the estimation accuracy of the original model and results in accuracies comparable with black box models. [ABSTRACT FROM AUTHOR]

If light is trapped inside a microsphere and resonance occurs, the resonance modes known as whispering gallery modes could be employed for sensing the environment around the microsphere. The discrepancy of the resonance wavelengths for the microsphere surrounded by different media quantifies the sensing ability of the microsphere. However, the microsphere size and material are crucial factors on determining the minimum detection limit (DL) of the microsphere as a sensor. Therefore, through investigating an appropriate size and material for the microsphere, the sensing performance and efficiency of the microresonator increase. In this paper, through a comprehensive experimental study, different refractometric microspheres are presented and their optical properties are measured and analyzed. The microspheres, five different size polystyrene and one size silica microspheres, are coated with quantum dots (QDs) and the QDs are excited by an Nd:YAG laser. Then, the microspheres sensing ability is quantified when their surrounding environment is modified. According to the presented results, the microspheres' DL, in direct proportion to the microsphere size, corresponds well to the theory. In addition, comparing the optical properties of the microspheres indicates the optimum size for the polymer microspheres to detect the environment. Furthermore, the optical properties of the silica microsphere illustrate a better performance of glass microspheres over polymer microspheres. This paper moves forward actual knowledge and evidence of extant modeling theory. It investigates a more efficient physical feature for a microsphere as a sensor. This is a key interim stage in developing more sensitive and effective sensors. [ABSTRACT FROM PUBLISHER]

This paper addresses the sensorless control of piezoelectric tube actuators to avoid the expense and practical limits of displacement sensors in nanopositioning applications. Three electrical signals have traditionally been used to estimate displacement: the piezoelectric voltage, the voltage induced in sensing electrodes and the voltage across a sensing resistor. In this work, the piezoelectric voltage was employed to estimate displacement; the use of this signal does not necessitate drift removal like the sensing voltage, and its superiority over the induced voltage is shown in this paper. The piezoelectric voltage is the actuating signal, so a feedforward architecture based on an inverse model is used for sensorless control. Inspired by internal model control (IMC), a filter together with the inverted model of the system, derived using system identification techniques, was used as the feedforward controller. The fixed-slope-input effect is illustrated as a prominent source of control error in tracking triangular references, then an additional nonlinear control command is proposed to address this effect and improve the control performance. [ABSTRACT FROM AUTHOR]

Abstract: Currently in most buildings, the heating, ventilation and air conditioning (HVAC) systems are controlled by the present temperature in the building. If the predictions for future temperature in the building or a zone were available, the building management system (BMS) could use both present and future temperatures to control HVAC systems, the energy consumed by HAVC systems could then be minimised. Therefore, a lot of research effort has been devoted to develop accurate temperature prediction models using various approaches, e.g. traditional thermodynamic, artificial neural networks (ANN), generic algorithms (GA) and fuzzy logic approaches. When the historical data of the building is available, the ANN approach is thought to be the most cost-effective method. Most of previous studies of ANN modelling of building temperature, have either focused on single-zone examination or assumed that zones’ temperatures were the same throughout the building. In this study, a more realistic multi-zone scenario in a large building is proposed in the developing of the ANN temperature predictive model. The coupled effects between zones caused by the temperature difference are considered in the model. The results of a case study show that the new ANN model that considers the temperatures of the neighbouring zones, achieves more accurate results. The proposed modelling methodology can be extended to include other inputs, besides neighboring zones’ temperatures, usage pattern of the building, so that the better intelligent control strategies can be developed for energy saving purposes, based on the more accurate predicted temperatures form the new model. [Copyright &y& Elsevier]

In this review article, the most popular types of neural network control systems are briefly introduced and their main features are reviewed. Neuro control systems are defined as control systems in which at least one artificial neural network (ANN) is directly involved in generating the control command. Initially, neural networks were mostly used to model system dynamics inversely to produce a control command which pushes the system towards a desired or reference value of the output (1989). At the next stage, neural networks were trained to track a reference model, and ANN model reference control appeared (1990). In that method, ANNs were used to extend the application of adaptive reference model control, which was a well-known control technique. This attitude towards the extension of the application of well-known control methods using ANNs was followed by the development of ANN model-predictive (1991), ANN sliding mode (1994) and ANN feedback linearization (1995) techniques. As the first category of neuro controllers, inverse dynamics ANN controllers were frequently used to form a control system together with other controllers, but this attitude faded as other types of ANN control systems were developed. However, recently, this approach has been revived. In the last decade, control system designers started to use ANNs to compensate/cancel undesired or uncertain parts of systems' dynamics to facilitate the use of well-known conventional control systems. The resultant control system usually includes two or three controllers. In this paper, applications of different ANN control systems are also addressed. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [ABSTRACT FROM AUTHOR]