International Journal of Engineering
Volume:33 Issue: 2, Feb 2020

The aim of the current research is to examine the potential of locally available natural clay mineral to act as an effective absorbent for waste engine oil treatment. The clay mineral was collected from the southern region of Saudi Arabia and was used without any kind of surface modification. The clay mineral was characterized by X-ray diffraction (XRD), X-ray fluorescence (XPS) and Braunner-Emitt-Teller (BET) surface area analysis. XRD data showed the crystalline structure of the clay while chemical composition of the clay was determined by XRF analysis in which the main constituents were SiO2 (51.77%), Al2O3 and Fe2O3 with 29.17% and 13.22% respectively. Other minor compounds were detected. BET surface area was found to be around 65 m2/g. Clay adsorbent amount (ranging from 5 – 20g) was added to 100 ml waste engine oil sample under different temperature conditions. Sample analysis obtained by UV-spectrophotometer indicates that the oil sample S10 which was conducted at 450ᵒC with 5g clay showed the best correlation (the peaks and wavelength number) very close the virgin oil. Hence, suggesting these to be the optimum operating conditions.

Despite traditional four-step model is the most prominent model in majority of travel demand analysis, it does not represent the potential correlations within different travel dimensions. As a result, some researches have suggested the use of choice modelling instead. However, most of them have represented travel dimensions individually rather than jointly. This research aims to fill this gap through employing the Generalized Nested Logit model for jointly representing three major travel dimensions; destination, departure time and travel mode. The suggested research methodology depends mainly on agglomerating alternatives that have similar error term’s variances within specific gaps under common nests without any imposed restrictions. Moreover, different variance gaps lead to overlapped nesting system which can enable analysers modelling inner and inter-correlation. The proposed approach has been examined through modelling individuals’ choices among the main shopping destinations in Eskisehir city, Turkey. In the light of estimation results, the proposed model attains a relatively good over-all goodness of fit which reflects a more prominent predictability power. Moreover, individuals in Eskisehir have been found perceiving more interest to the cost rather than time. From another hand, a behaviour of trading-off between performing such trips at peak periods by using transit or making them at off-peak by private car has been detected.

A novel element with arbitrary domain shape by using decoupled scaled boundary finite element (DSBFEM) is proposed for eigenvalue analysis of 2D vibrating rods with different boundary conditions. Within the proposed element scheme, the mode shapes of vibrating rods with variable boundary conditions are modelled and results are plotted. All possible conditions for the rods ends are incorporated in analysis. The considered element stiffness and mass matrix are developed and extracrted. This element is able to model any curved or sharp edges without any aproximation and also the element is able to model any arbitrary domain shape as a single element without any meshing. The coefficient matrices for the element such as mass and stiffness matrices are diagonal symmetric and all equations are decoupled by using Gauss-Lobatto-Legendre (G.L.L) quadrature. The element is used in order to calculate modal parameters by Finite element method for some benchmark examples and comparing the answers with Helmholtz equation solution. The most important achievment of this element is solving matrix equations instead of differential equations where cause faster calculations speed. The boundaries for this element are solved with matrix calculation and the whole interior domain with solving governing equations numerically wich leads us to an exact answer in whole domain. The introduced element is applied to calculate some benchmark example which have exact solution. The results shows accuracy and high speed of calculation for this method in comparison with other common methods.

Interaction between a structure under base excitation and heavy non-structural elements that it supports is significant in the seismic analysis and design of the structure. Heavy non-structural elements may slide/rock under base excitation, and this dynamic action affects the seismic behavior of the supporting structure. Hence, in this study, a numerical model was presented to describe the seismic behavior of a primary structure (PS) supporting non-structural elements referred to as secondary bodies (SBs). The governing equations of motion for PS and SBs were developed considering Coulomb's friction model. Seismic hazard levels corresponding to Indian seismic zone III (medium hazard level) and V (highest hazard level) were considered. A parameter called displacement ratio (DR) was defined to quantify the sliding effect of SBs on the displacement response of the PS. A parametric study has been conducted to understand the variation in the DR due to varied time period of the structure, live loads to structure mass ratios and coefficients of friction between PS and SBs. From the analysis of results, it was concluded that the DR varies significantly with the time period, mass ratios, and coefficient of friction values. It can also be found from the study that the energy dissipation due to sliding of SBs was more in the highest hazard level than medium hazard level. Finally, the conditions for which the full mass of sliding secondary bodies should be considered in the seismic design of the structure are also presented.

Feature selection can significantly be decisive when analyzing high dimensional data, especially with a small number of samples. Feature extraction methods do not have decent performance in these conditions. With small sample sets and high dimensional data, exploring a large search space and learning from insufficient samples becomes extremely hard. As a result, neural networks and clustering algorithms perform poorly on this kind of data. In this paper, a novel hybrid feature selection technique is proposed, which can reduce drastically the number of features with an acceptable loss of prediction accuracy. The proposed approach operates in multiple stages, starting by removing irrelevant features with a low discrimination power, and then eliminating the ones with low variation range. Afterward, among each set of features with high cross-correlation, a single feature that is strongly correlated with the output is kept. Finally, a Genetic Algorithm with a customized cost function is provided to select a small subset of the remainder of features. To show the effectiveness of the proposed approach, we investigated two challenging case studies with sample set sizes of about 100 and the number of features larger than 1000. The experimental results look promising as they showed a percentage decrease of more than 99% in the number of features, with a prediction accuracy of more than 92%.

In recent years, there has been considerable interest among people to use short message service (SMS) as one of the essential and straightforward communications services on mobile devices. The increased popularity of this service also increased the number of mobile devices attacks such as SMS spam messages. SMS spam messages constitute a real problem to mobile subscribers; this worries telecommunication service providers as it disturbs their customers and causes them to lose business. Therefore, in this paper, we proposed a novel machine learning method for detection of SMS spam messages. The proposed model contains two main stages: feature extraction and decision making. In the first stage, we have extracted relevant features from the dataset based on the characteristics of spam and legitimate messages to reduce the complexity and improve performance of the model. Then, an averaged neural network model was applied on extracted features to classify messages into either spam or legitimate classes. The method is evaluated in terms of accuracy and F-measure metrics on a real-world SMS dataset with over 5000 messages. Moreover, the achieved results were compared against three recently published works. Our results show that the proposed approach achieved successfully high detection rates in terms of F-measure and classification accuracy, compared with other considered researches. Moreover, the achieved results were compared against three recently published works. The results show that the proposed approach achieved high detection rate, which was successful in terms of the F-measure and classification accuracy compared with other considered researches.

Quantum cellular automata (QCA) enables performing arithmetic and logic operations at the molecular scale. This nanotechnology promises high device density, low power consumption and high computational power. Unlike the CMOS technology where the ON and OFF states of the transistors represent binary information, in QCA, data is represented by the charge configuration. The primary and basic device in this paradigm is the three-input majority gate, thus in QCA, the conventional AND-OR mapping for implementation of logic functions is not effective. We introduce four primitive admissible geometric patterns,  which aid in the identification of majority functions. For a non-majority function, a genetic algorithm (GA) is used to map the function to at most four majority gates in a wide range of implementations. We show that the emergence of specific genes will result in a further reduction in the number of majority gates in the network. The GA is intrinsically parallel and results in variety of implementations, which allows  merging the layout and logic levels of the design and provides an important approach towards designing high-performance QCA circuits.

This paper proposes a stand-alone photovoltaic (PV) system based on a DC-DC positive output super lift Luo (POSLL) converter. A conventional sliding mode control, a sliding mode controller using a simple sign function and a linear controller using proportional integrator (PI) are used for the control of the PV panel voltage and POSLL converter inductor current and these methods are compared together. The POSLL converter is a DC-DC converter with higher voltage gain compared to other conventional DC-DC converters, and has a good voltage regulation with a simple structure. The stability and accessibility of the proposed nonlinear controls are investigated widely. The internal dynamics of the proposed closed-loop control systems are also investiged. A practical implementation of the system on a hardware setup is performed using a low cost Discovery real time digital control platform. The experimental and simulation results demonstrate the validity of the proposed control schemes over a stand-alone real photovoltaic system.

The combination of neural networks and fuzzy controllers is considered as the most efficient approach for different functions approximation, and indicates their ability to control nonlinear dynamical systems. This paper presents a hybrid control strategy called Neuro-Fuzzy Sliding Mode Control (NFSMC) based on the Brushless Doubly fed Induction Generator (BDFIG). This replaces the sliding surface of the control to exclude chattering phenomenon caused by the discontinuous control action. This technique offers attractive features, such as robustness to parameter variations. Simulations results of 2.5 KW BDFIG have been presented to validate the effectiveness and robustness of the proposed approach in the presence of uncertainties with respect to vector control (VC) and sliding mode control (SMC). We compare the static and dynamic characteristics of the three control techniques under the same operating conditions and in the same simulation configuration. The proposed controller schemes (NFSMC) are effective in reducing the ripple of active and reactive powers, effectively suppress sliding-mode chattering and the effects of parametric uncertainties not affecting system performance.

Some people suffering from diabetes use insulin injection pumps to control the blood glucose level. Sometimes, the fault may occur in the sensor or actuator of these pumps. The main objective of this paper is controlling the blood glucose level at the desired level and fault-tolerant control of these injection pumps. To this end, the eligibility traces algorithm is combined with the sliding mode control. The eligibility traces algorithm is one of the newest solving methods of the Reinforcement Learning approach. The major disadvantage of the sliding mode control method is the chattering phenomenon. In this paper, the novel idea is the combination of these methods to remove the chattering phenomena in simulation results. To demonstrate the superiority of the proposed method, it is compared with another combinatory method that is the sliding mode control and Artificial Neural Networks. Simulation results reveal that the combination of the eligibility traces algorithm and the sliding mode control can control the blood glucose level and insulin with a higher speed and bring them to the desired level, even in the case the sensor and actuator faults are present in the system. When the proposed hybrid method is used, the injected dosage of the drug is lower, which will result in reduced side effects. Finally, the noise, as well as the uncertainty in system parameters and initial conditions are applied to the system to investigate the performance of the proposed controller, under the faulty condition.

The electronic industry has grown vastly in recent years, and researchers are trying to minimize circuits delay, occupied area and power consumption as much as possible. In this regard, many technologies have been introduced. Quantum Cellular Automata (QCA) is one of the schemes to design nano-scale digital electronic circuits. This technology has high speed and low power consumption, and occupies very little area. Phase-locked loops (PLLs) and delay-locked loops (DLLs) are blocks that are commonly used in telecommunication applications. One of the most important parts in DLL and PLL is the phase-frequency detector. Therefore, the design of this circuit in QCA technology is of great importance. In this paper, two new phase-frequency detectors sensitive to falling and rising edge have been introduced in QCA technology. Both of the designs are composed of 104 cells; occupy only 0.13 μm2 of an area and 1.5 QCA clock cycles latency. The designs are in one layer and all the inputs and outputs are available to be used by another circuit.

In this paper, an efficient technique is presented to identify a 2500 KW wind turbine operating in Kahak wind farm, Qazvin province, Iran. This complicated system dealing with wind behavior is identified by using a proposed fractional order dynamic neural network (FODNN) optimized with evolutionary computation. In the proposed method, some parameters of FODNN are unknown during the process of identification, so a particle swarm optimization (PSO) algorithm is employed to determine the optimal values by which a fractional order nonlinear system can be completely identified with a high degree of accuracy. These parameters are very effective to achieve high performance of FODNN identifier and they include fractional order, initial values of states and weights of FODNN, and numerical algorithm step size for solving FODNN equation. Simulation results confirm the efficiency of the proposed scheme in term of accuracy. Furthermore, comparison of the results achieved by the proposed method and those of the integer order dynamic neural network (IODNN) depicts higher accuracy of the proposed FODNN.

Speech Emotion Recognition (SER) is an important part of speech-based Human-Computer Interface (HCI) applications. Previous SER methods rely on the extraction of features and training an appropriate classifier. However, most of those features can be affected by emotionally irrelevant factors such as gender, speaking styles and environment. Here, an SER method has been proposed based on a concatenated Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN). The CNN can be used to learn local salient features from speech signals, images, and videos. Moreover, the RNNs have been used in many sequential data processing tasks in order to learn long-term dependencies between the local features. A combination of these two gives us the advantage of the strengths of both networks. In the proposed method, CNN has been applied directly to a scalogram of speech signals. Then, the attention-mechanism-based RNN model was used to learn long-term temporal relationships of the learned features. Experiments on various data such as RAVDESS, SAVEE, and Emo-DB demonstrate the effectiveness of the proposed SER method.

This paper considers a scheduling problem with uncertain processing times and machine breakdowns in industriall/office workplaces and solves it via a novel robust optimization method. In the traditional robust optimization, the solution robustness is maintained only for a specific set of scenarios, which may worsen the situation  for new scenarios. Thus, a two-stage predictive algorithm is proposed to efficiently handle the uncertainties and find robust and stable solutions. The first stage creates robust solutions and ensures their stability in the new scenarios. The second stage proposes a novel stability measure to proactively offset the effects of the machine breakdowns of the former stage. Moreover, a tri-component measure based on efficiency, robustness, and stability is proposed which aims to create a realistic schedule to satisfy the customers, manufacturers, and the staff. To meet the customer’s requirements, the robustness measure is defined based on the tardiness and the delivery dates of jobs. Finally, the proposed algorithm is applied to a case study, and the findings are compared with the empirical data. The results emphasize the superiority of the proposed technique in satisfying the customers, staff, and increasing the profitability and accountability of the company.

In this work, the Fe3O4 nanoparticles from natural iron sand were combined with active carbon (AC) and polyaniline (PANI) to obtain Fe3O4/AC/PANI nanocomposites with mass variations of the AC of 0.1, 0.2, 0.3, 0.4, and 0.5 g. The crystalline phase of Fe3O4/AC/PANI nanocomposites formed from Fe3O4 with PANI having an amorphous phase. Meanwhile, the crystalline phase of AC was unmatched because of its very small composition. The presence of AC was observed through vibrations from the C-C and COOH functional groups. The existence of PANI was indicated by the vibrations of the benzoic ring and quinonoid bonds. Besides, the presence of Fe3O4 was confirmed by the presence of Fe-O functional groups from octahedral and tetrahedral positions. The optical properties of Fe3O4/AC/PANI nanocomposites were shown by increasing the energy gap along with decreasing absorption wavelength. Interestingly, increasing AC composition made the absorption bandwidth of the Fe3O4/AC/PANI nanocomposites wider, so that the radar absorption also increased marking by the greater reflection loss that reached -15.8 dB. The increase in the radar absorption performance of Fe3O4/AC/PANI nanocomposites came from the efficient complementarity between dielectric loss and magnetic loss and interfacial polarization between Fe3O4-AC or between Fe3O4-PANI.

Recently, there has been a fast growth in research and investigation in the natural fibre composite due to the advantages of these materials, such us low environmental impact, low cost and good mechanical properties compared to synthetic fibre composites. Much effort has gone into increasing the mechanical performance and applications of natural fibes. This paper examines the mechanical properties of a novel hybrid baboo/jute/polyester composite with five different combination ratios to achieve optimum properties. Fibre mats were prepared using manual mat making machine to reduce porosity in the composite. Samples were prepared and tested as per American Society for Testing and Materials (ASTM) standard. The result indicated that the hybrid composite with 10 % jute, 20 % bamboo and 70 % polyester shows highest tensile strength (yield strength of 72.03 MPa) compared to other combinations examined, and the hybrid composite with 0 % jute, 30 % bamboo and 70% polyester shows the highest flexural strength (133.9 MPa). The elongation of jute-reinforced composite was found to be the longest in the tensile test (2.98 mm).

Ultrasound wave is a kind of waves with the frequency higher than the human audible frequency. Although ultrasound was first used for military identification purposes, it has been used for decades for various other applications, especially medical applications. Medical applications of ultrasound include diagnostic and therapeutic applications, such as for the treatment of cancer. In this paper, for the first time, electrostatically-actuated micro-speakers made of a dielectric elastomer (DE) have been used to generate and focus the ultrasonic waves using Micro-Electro-Mechanical Systems (MEMS) technology. The results have shown that a single micro-speaker could not generate an efficient focused wave, so an array of the micro-speakers has been considered. The layout of the micro-speakers is a square array of Bessel Panel. It has been shown that by adopting a suitable excitation frequency as well as number of the elements in the Bessel’s panel, the DE-based capacitive micro-structures can focus the generated sound wave in a pre-determined area.

In this article, corrosion behavior of the single-layered TiN, single-layered CrN and multi-layered TiN/CrN coatings have been fabricated on 420C stainless steel (SS) by Cathodic Arc Evaporation (CAE) method in Ringer's solution was studied. The electrochemical appraisal was done by the Potentiodynamic Polarization (PDP) and Electrochemical Impedance Spectroscopy (EIS) tests. Results of PDP test revealed that all coated specimens have an increment in the polarization resistance (Rp) values (ranges from 438.2 to 593.6Ω.cm2) compared to bare SS (268.1Ω.cm2). Corrosion rate values for bare 420C SS and CrN, TiN and multi-layered TiN/CrN coatings were measured 0.086, 0.042, 0.050 and 0.068mpy, respectively. Furthermore, EIS results were showed that the single-layer CrN coating well depicted by an equivalent circuit with three time constants and has larger impedance and maximum logarithmic variation of C2. Finally, the corrosion performance results confirmed that single-layered CrN coating is an appropriate choice for deposition on 420C SS with the aim of application in general surgical instruments.

The main purpose of this paper is to develop the method of characteristic functions for calculating the detection characteristics in the case of the object surrounded by rough surfaces. This method is to be implemented in synthetic aperture radar (SAR) systems using optimal resolution algorithms. By applying the specified technique, the expressions have been obtained for the false alarm and correct detection probabilities. In order to illustrate the effective application of the introduced approach in the case of the generic SAR system, the results are presented of the calculation of the detection characteristics of the signal from the extended object surrounded by a rough surface. It is shown that the analysis allows us to substantiate the structure of the SAR signal processing channel and to obtain the improved relations for the radio observation characteristics in this case. The efficiency of the optimal signal processing in SAR systems can also be determined without the approximate calculations involved.

Micro-stamping process is one of the most cost-effective methods to manufacture metallic bipolar plates (BPPs). This research investigates the forming of titanium thin sheet as a potential candidate for BPPs in proton exchange membrane fuel cell (PEMFC). In this regard, the process was first simulated using finite element (FE) code Abaqus. Afterward, experimental tests were implemented and the validation of the FE model was confirmed using the experimental results. In the simulations, the corner radius of the die, draft angle, and friction coefficient at die/sheet interface were selected as variable factors. Forming force and thickness reduction as response functions were evaluated. It is demonstrated that the die corner radius has more influence on the maximum punch force compared to the draft angle and friction coefficient. The maximum punch force decreases with increasing the die corner radius. On the other hand, in order to have a lower thickness reduction, a high die corner radius, higher draft angle, and low friction coefficient are required.

The portable generators are the main means of compensating the shortage of national electricity power as in Iraq. However, their noise causes a huge disturbance, uncomforting and loss of concentration of the nearby resident. This problem requires a solution because most shops and residential houses use generators. Covering method is a simple and relatively low cost one for reduction of generators noise.  In the present study, an experimental investigation is conducted for evaluating the effect of using an enclosure on noise reduction of portable generators. The enclosure is made from some available and traditional materials such as foam and plywood. The sound transmission loss due to the use of enclosure is estimated for a wide range of frequency spectrum and the results showed that used enclosure helps in reduction of noise effectively. Furthermore, it was found that the generator temperature was not affected much by the use of the present enclosure. The present study is intended to be a step stone for a development of a low cost and effective commercial enclosure of portable generator.

In this paper the numerical simulation of flow field in the new design dynamic cyclone separator is investigated. The effects of different geometrical parameters such as impeller diameter, number of vanes, length and the location of impeller is investigated. The Reynolds averaged Navier–Stokes equations with Reynolds stress turbulence model (RSM) were solved. The Eulerian-Lagrangian computational procedure is used to predict particles tracking in the cyclones. The velocity fluctuations are simulated using the Discrete Random Walk (DRW). Results show that in new design dynamic cyclone, the collection efficiency is higher and the pressure drop is lower in compare with other studied on cyclones. Installing impeller at the lower positions leads to a decrease in efficiency. With increase in impeller diameter, pressure drop in dynamic new design decreases. Finally, with increase in impeller length, efficiency increases and pressure drop decreases.