Fuzzy logic controller and cascade inverter for direct torque control of IM

In this paper, a five-level cascaded H-bridge multilevel inverters topology is applied on induction motor control known as direct torque control (DTC) strategy. More inverter states can be generated by a five-level inverter which improves voltage selection capability. This paper also introduces two different control methods to select the appropriate output voltage vector for reducing the torque and flux error to zero. The first is based on the conventional DTC scheme using a pair of hysteresis comparators and look up table to select the output voltage vector for controlling the torque and flux. The second is based on a new fuzzy logic controller using Sugeno as the inference method to select the output voltage vector by replacing the hysteresis comparators and lookup table in the conventional DTC, to which the results show more reduction in torque ripple and feasibility of smooth stator current. By using Matlab/Simulink, it is verified that using five-level inverter in DTC drive can reduce the torque ripple in comparison with conventional DTC, and further torque ripple reduction is obtained by applying fuzzy logic controller. The simulation results have also verified that using a fuzzy controller instead of a hysteresis controller has resulted in reduction in the flux ripples significantly as well as reduces the total harmonic distortion of the stator current to below 4 %.     

Preparation and characterization of poly(vinyl alcohol)/gum tragacanth/cellulose nanocomposite film

The effects of gum tragacanth obtained from two species of Astragalus Gossypinus (GT-G) and A. Parrowianus (GT-P) at two levels of 10% and 30% combined with cellulose nanofibers (CNF; 5%) on the physico-mechanical and structural properties of polyvinyl alcohol (PVA) nanocomposite film were investigated in this study. The water solubility and water vapor permeability of the films decreased with increasing the content of both gums, especially in the film containing 30% GT-P. The highest values of the tensile strength (39.3 MPa) and elongation at break (445%) belonged to the treatment containing 10% GT-P (90/10P/0). The FTIR and DSC analyses confirmed good interactions between GT and PVA in the 90/10P/0 treatment. SEM images indicated the dense structure of this film as the optimum treatment. Although the presence of CNF in the films containing GT-G improved some properties, especially the Young modulus, it impaired all the functional properties of nanocomposite GT-P film.     

Analysis and forecasting of IPO underpricing

Probability of withdrawal is a feature of initial public offering (IPOs), which can be an important parameter in decisions of investors and issuers. Considering the probability of offering withdrawal facilitates more precise estimation of underpricing. In this paper, the effective factors on probability of IPO withdrawal and underpricing in Tehran Stock Exchange have been characterized using regression, and then neural network is applied to estimate the probability of IPO withdrawal and underpricing. To evaluate the performance of our applied method, fuzzy regression is employed and compared with neural network. According to the obtained empirical results, neural network demonstrates better accuracy than fuzzy regression. The results indicate that there is a meaningful relationship between underpricing and probability of withdrawal, and the probability of IPO withdrawal plays an important role in precise evaluation of underpricing.     

Integrated Inspection Scheduling and Maintenance Planning for Infrastructure Systems

Operation and maintenance of an infrastructure system rely on information collected on its components, which can provide the decision maker with an accurate assessment of their condition states. However, resources to be invested in data gathering are usually limited and observations should be collected based on their Value of Information (VoI). Assessing the VoI is computationally intractable for most applications involving sequential decisions, such as long‐term infrastructure maintenance. In this article, we propose an approach for integrating adaptive maintenance planning based on Partially Observable Markov Decision Process (POMDP) and inspection scheduling based on a tractable approximation of VoI. Two alternative myopic approaches, namely pessimistic and optimistic, are introduced, and compared theoretically and by numerical examples.     

Development of Novel Pre-alloyed PM Steels for Optimization of Machinability and Fatigue Resistance of PM Components

It is well known that a large proportion of ferrous PM components require secondary machining operations for dimensional conformance or for producing geometrical features that cannot be generated during die compaction. Nevertheless, the machining behavior of PM parts is generally characterized as being “difficult” due to the presence of residual porosity that lowers thermal conductivity and induces interrupted cutting. Several admixed additives such as MnS and BN-h can be used to improve the machining behavior of PM steels. Nevertheless, their negative effect on mechanical properties, especially fatigue resistance, makes their utilization uninteresting for the fabrication of high-performance PM steel components. This article summarizes the work carried out to develop a novel PM steel that was especially engineered to form machinability enhancing precipitates. This new material is pre-alloyed with tin (Sn) in order to form Cu-Sn (Cu(α)) precipitates during transient liquid phase sintering. The newly developed material presents machinability improvement of 165% compared to reference material used in the PM industry as well as increases in toughness and fatigue resistance of 100% and 13%, respectively.     

Chip geometry and cutting force prediction in gear hobbing

This paper presents a tri-dexel geometric engine integrated simulation model for the gear hobbing operation. The process kinematics are modeled and validated using CNC signals from a Liebherr LC500 hobbing machine. Workpiece geometry updating and cutter-workpiece engagement (CWE) calculations are efficiently realized in the tri-dexel engine. 3D force contributions at discretized nodes along the hob's cutting edges are computed considering the localized principal cutting directions, and rake and inclination angles. To measure cutting forces, a rotary dynamometer is successfully adapted and used alongside a Kalman filter to compensate for structural dynamics. The predicted forces agree well with their experimental counterparts.     

Synthesis and Characterization of Superparamagnetic NiBaO<Subscript>2</Subscript> Nano-Oxide Using Novel Precursor Complex [Ba(H<Subscript>2</Subscript>O)<Subscript>8</Subscript>][Ni(dipic)<Subscript>2</Subscript>]

In this paper, for the first time, synthesis of [Ba(H₂O)₈][Ni(dipic)₂] complex and preparation of NiBaO₂ nano-oxide are reported through thermal decomposition under surfactant free condition. This novel complex was characterized by Fourier transform infrared spectroscopy (FT-IR), ultra violet–visible spectroscopy, conductivity measurement and elemental analysis. Formation of novel nanoparticles was supported by FT-IR and energy-dispersive X-ray spectroscopy and the orthorhombic structure of nanocrystals was confirmed by powder X-ray diffraction analysis. In addition, size distribution as well as uniform morphology of prepared nano-oxide were recorded by dynamic light scattering analysis and field-emission scanning electron microscopy, respectively. Magnetic features measured by vibrating sample magnetometer, illustrate superparamagnetic behavior of the oxide.     

Development of artificial neural networks for real time, in situ ellipsometry data reduction

Development of real time in situ monitoring and control of thin film depositions using ellipsometry requires both data acquisition and processing to be rapid. Present speeds of measurement and computation of basic parameters, Ψ and Δ, are sufficient for data acquisition which is essentially real time. However, computation of film parameters, such as thickness and optical properties, generally cannot keep up with the incoming data and must be performed in a batch mode after the deposition.

This work describes the development of enhanced, high speed data reduction algorithms using artificial neural networks (ANN). The networks are trained using computed data and subsequently give values of film parameters in the millisecond time regime. The ANN outputs are used as initial estimates in a variably damped least squares algorithm for accuracy improvement. The combination of these two algorithms provides very accurate solutions in 75 ms per point on a DEC VAX 8800 multiprocessor system running at a combined 12 Mips. This speed is suitable for real time film monitoring and control for growth rates up to 10 nm per second. Results for fixed angle of incidence, single wavelength, in situ data for Ni deposited on BK7 substrates are presented.     

Advances in the Application of Magnetic Nanoparticles for Sensing

Magnetic nanoparticles (MNPs) are of high significance in sensing as they provide viable solutions to the enduring challenges related to lower detection limits and nonspecific effects. The rapid expansion in the applications of MNPs creates a need to overview the current state of the field of MNPs for sensing applications. In this review, the trends and concepts in the literature are critically appraised in terms of the opportunities and limitations of MNPs used for the most advanced sensing applications. The latest progress in MNP sensor technologies is overviewed with a focus on MNP structures and properties, as well as the strategies of incorporating these MNPs into devices. By looking at recent synthetic advancements, and the key challenges that face nanoparticle‐based sensors, this review aims to outline how to design, synthesize, and use MNPs to make the most effective and sensitive sensors.