Search

In this paper, we have developed an in-vehicle wireless driver breath alcohol detection (IDBAD) system based on Sn-doped CuO nanostructures. When the proposed system detects the ethanol trace in the driver's exhaled breath, it can alarm and then prevents the car to be started and also sends the location of the car to the mobile phone. The sensor used in this system is a two-sided micro-heater integrated resistive ethanol gas sensor fabricated based on Sn-doped CuO nanostructures. Pristine and Sn-doped CuO nanostructures were synthesized as the sensing materials. The micro-heater is calibrated to provide the desired temperature by applying voltage. The results showed that by Sn-doping in CuO nanostructures, the sensor performance can be significantly improved. The proposed gas sensor has a fast response, good repeatability along with good selectivity that makes it suitable for being used in practical applications such as the proposed system. [ABSTRACT FROM AUTHOR]

In this paper, a novel RF MEMS shunt capacitive switch with application in the Ka frequency band is proposed. The spring design and the step structure added to the beam succeeded in improving the performance of the switch and reducing the stress which results in extended life-time of the switch. Also, by optimally reducing the gap between the dielectric and the beam (without problems such as self-actuation), the actuation voltage of the switch is significantly reduced. Electromechanical and scattering parameters analysis have been done by using COMSOL Multiphysics and HFSS software, respectively. The actuation voltage of the proposed device is 9.2 V. Since the aluminum has a lower mass compared to gold, an aluminum beam has been used in the switch. Desirable scattering parameters at the resonance frequency of 33.5 GHz have been obtained which include insertion loss of − 0.3 dB and return loss of − 18 dB. The high isolation of − 57 dB verifies the improved performance of the switch. Finally, as another innovation in this paper, the effect of inductor and capacitor presence in the input of transmission line is investigated. This analysis has been done by using ADS. Results of the circuit analysis presented in this paper, help the MEMS switch designers to understand the realistic switch behavior before fabrication which considerably saves cost and time. [ABSTRACT FROM AUTHOR]

Isolation and actuation voltages are important parameters in micro-electro-mechanical switches. These parameters can be destroyed by the effect of creating bulge and erosion in the surface of the beam. In this paper, a fabrication method is proposed to create a smooth and flat beam. Additionally, using two short high impedance transmission lines, the destructive effects on the input/output matching have been reduced. S-parameters and mechanical parameters of the proposed switch are calculated by the network analyzer and laser Doppler vibrometer. According to the results obtained from the proposed switch in the C–K band, the return loss, which is less than − 20 dB, and the highest isolation value, which is equal to − 20 dB, occur at the frequency of 21 GHz. Furthermore, the insertion loss is better than − 1 dB in the full frequency band, which is very desirable. The actuation voltage and resonance frequency were obtained at 18 V and 164 kHz, respectively. Finally, four steps are proposed to optimize the actuation voltage, isolation, stress and switching time, which results in reducing the actuation voltage by 37% and the isolation will increase by 46%. Maximum stress in the initial state is 25 MPa, and decreases to 10 MPa after optimization, which increases the lifetime of the switch. [ABSTRACT FROM AUTHOR]

RF-MEMS switches may be divided into capacitive and metal-to-metal types in terms of type of connection. In capacitive switches in the OFF-state the beam does not attach to the transmission line due to the presence of a thin dielectric layer and it gets released easily. Capacitive switches are better compared with the metal-to-metal types due to their ability to transmit signals of higher frequency and power. In this paper, a novel RF MEMS shunt capacitive switch with a unique spring design is proposed. The proposed design benefits from high isolation, low loss, low actuation voltage, small size and weight compared to similar switches and is designed for application in the Ka-band. Step structure is used in the mechanical design of the switch to reduce the air gap between the bridge and the transmission line that has resulted in reduction of actuation voltage to 2.2 V. The proposed switch has been placed upon a coplanar waveguide (CPW) line with an impedance of 50 Ohms. SiO2 with a thickness of 1000 Å has been used as dielectric to increase isolation and down-state capacitance. The unique design of the spring results in the reduction of stress to a minimum at all points of the switch especially the springs at the time of applying the actuation voltage. This advantage would reduce switch failures as time passes and results in increased life of the switch. Up-state capacitance is 93.3 fF and down-state capacitance is 2.64 pF. Therefore, a capacitive ratio of 28 is obtained. The dynamic behavior of the switch is studied by using the COMSOL Multiphysics software package and the RF characteristics have been obtained by using the HFSS software and show good RF performance. The results show that in the up-state, the designed switch including S11 is less than − 11.5 dB and S21 is more than − 0.85 dB at 1-40 GHz. In the down-state, the switch has excellent isolation in the Ka-band. Maximum isolation is − 71 dB that occurs at the resonance frequency of 30.5 GHz. [ABSTRACT FROM AUTHOR]

Deposition of the wax is one of the thorny issues in the petroleum industry, invoking costly problems during the transportation and production of crude oil. Owing to its devastating impacts on oil companies' economy, it is essential to develop a simple and robust strategy for the quantitative estimation of wax deposition. In this paper, support vector regression (SVR) is first proposed to estimate the amount of wax deposition. Subsequently, an artificial neural network (ANN) is developed for wax deposition prediction. Eventually, a sophisticated committee machine (CM) is constructed for combining the results of the SVR and ANN models. Optimal contribution of each model in final prediction of the wax deposit is determined through genetic algorithm in CM. Statistical error analysis shows that the CM model performs better than the individual models performing alone. [ABSTRACT FROM AUTHOR]

Porosity is one of the most petrophysical parameters, which has profound impact on reservoir characterization, reserves estimation, and production forecasting. This parameter is determined from experimental implementation in laboratory core analysis, as well as interpretation of porosity log. Since there exists no reliable source of porosity data (core data and porosity log data) before of drilling a well, developing a reliable model for estimation of porosity from seismic attributes is highly valuable. In current study, a merged model is proposed for identifying formulation between porosity and seismic attributes in a field which is located in the Persian Gulf. In the first step, suitable seismic attributes which have prominent influence on porosity are extracted through forward stepwise selection variable method and considered as input parameters of the model. Secondly, input variables are transformed into higher correlated data space by virtue of nonparametric method so-called alternating conditional expectation (ACE). In third step, making quantitative correlation between ACE transformed of input parameters and porosity through improved intelligence model, including optimized neural network (ONN), optimized support vector regression (OSVR), and optimized fuzzy logic (OFL) are achieved. Optimization method which embedded in intelligence models formulation for ameliorating those performances is bat-inspired algorithm (BA). In the last step, outputs of improved models are combined through a committee machine (CM) in the sake of enhancing in the prediction accuracy. Optimal contribution of improved models in overall estimation is computed by mean of the BA method. Comparison between the individual models and the CM model shows that integrating models with the CM produce results with lowest error and highest correlation and consequently is superior. The results prove that proposed strategy in this study is reliable alternative way for mapping functional dependency between porosity and seismic attributes. [ABSTRACT FROM AUTHOR]

Precipitation of asphaltene largely affects the production rate of crude oil owing to the clogging of transportation pipeline as well as formation damage. Therefore, it is imperative to search for a robust method for the modeling of asphaltene precipitation. This paper aims to propose a new mathematical model for computing asphaltene precipitation as a function of titration data including dilution ratio, temperature, and molecular weight of solvent. This model is constructed based on integrating the support vector regression with the imperialist competitive algorithm. Optimization increases the performance of support vector regression by virtue of determining the optimal values of their parameters. The constructed model is applied to experimental data and its performance is compared with scaling equation which is traditionally employed for modeling of asphaltene precipitation. The results of this study show that the integration of imperialist competitive algorithm and support vector regression has a better performance than the scaling equation. This paper offers that the support vector regression optimized by virtue of the imperialist competitive algorithm is a reliable model for modeling asphaltene precipitation. [ABSTRACT FROM AUTHOR]

Use of intelligence based approach for modeling of crude oil saturation pressure is viable alternative since this parameter plays influential role in the reservoir calculation. The objective of current study is to develop a smart model based on fusing of support vector regression model and optimization technique for learn the relation between the saturation pressure and compositional data viz. temperature, hydrocarbon and non-hydrocarbon compositions of crudes, and heptane-plus specifications. The optimization methods improve performance of the support vector regression (SVR) model through finding the proper value of their free parameters. The optimization methods which embedded in the SVR formulation in this study are genetic algorithm (GA), imperialist competitive algorithm (ICA), particle swarm optimization algorithm (PSO), cuckoo search algorithm (CS), and bat-inspired algorithm (BA). The optimized models were applied to experimental data given in open source literatures and the performance of optimization algorithm was assessed by virtue of statistical criteria. This evaluation resulted clearly show the superiority of BA when integrated with support vector regression for determining the optimal value of its parameters. In addition, the results of aforementioned optimized models were compared with currently available predictive approaches. The comparative results revealed that hybrid of BA and SVR yield robust model which outperform other models in term of higher correlation coefficient and lower mean square error. [ABSTRACT FROM AUTHOR]

In this paper we propose a new method for predicting rock porosity based on a combination of several artificial intelligence systems. The method focuses on one of the Iranian carbonate fields in the Persian Gulf. Because there is strong heterogeneity in carbonate formations, estimation of rock properties experiences more challenge than sandstone. For this purpose, seismic colored inversion (SCI) and a new approach of committee machine are used in order to improve porosity estimation. The study comprises three major steps. First, a series of sample-based attributes is calculated from 3D seismic volume. Acoustic impedance is an important attribute that is obtained by the SCI method in this study. Second, porosity log is predicted from seismic attributes using common intelligent computation systems including: probabilistic neural network (PNN), radial basis function network (RBFN), multi-layer feed forward network (MLFN), ε-support vector regression (ε-SVR) and adaptive neuro-fuzzy inference system (ANFIS). Finally, a power law committee machine (PLCM) is constructed based on imperial competitive algorithm (ICA) to combine the results of all previous predictions in a single solution. This technique is called PLCM-ICA in this paper. The results show that PLCM-ICA model improved the results of neural networks, support vector machine and neuro-fuzzy system. [ABSTRACT FROM AUTHOR]

Abstract: Over the past decade, several new approaches have emerged to renewable energy education that seek to address the needs of the 21st century for sustainable energy supply systems. In addition, unlike conventional architectural education which does not cover renewable energy issues in its curriculum, these days, renewable energy education has an identity of its own within the discipline of architecture. This paper will clarify academic obstacles that are impeding the development of sustainable architectural education. These obstacles include: ambiguous definitions of sustainable architecture, confusion over the meaning of sustainability, and lack of experts in this field. In the last part of this paper we compare academic architectural education in the field of renewable energy in two countries, Iran as a developing country in Asia and Australia as a developed country in Oceania. Lessons can be drawn from these case studies in regard to the obstacles and progress in the development of sustainable architecture education. [Copyright &y& Elsevier]

Sustainability is a crucial issue for our times and architecture has an important role to play in sustainable development. Buildings are responsible for approximately 40% of the total world annual energy consumption. In addition, over the past decade, several new approaches have emerged for the incorporation of sustainability and renewable energy into architectural education. One way of reducing building energy consumption is to educate architects to design buildings that are powered by renewable forms of energy for heating, lighting, cooling, ventilation and hot water supply. A comparison of sustainable architectural education in economically developed and developing countries provides some insights about how to modernise the architectural curriculum to facilitate the process of sustainable development. The key findings of this research are to: (1) include principles of sustainable development in the core content of all architecture courses, including the role of renewable energy, energy efficiency and embodied energy; (2) offer design studio courses in architectural education that involve the practical use of renewable energy technology; (3) offer short courses and flexible courses, which are particularly attractive to busy professional architects who want to keep their knowledge base up to date; and (4) offer online courses to attract foreign students and that are also suitable for disabled students. [ABSTRACT FROM AUTHOR]

Digital encoders are one of the key devices required in optical communication and digital signal processing systems. In this paper, a new photonic crystal structure is used to design all-optical 4 × 2 encoder constructed from GaAs rods with square lattice in the pentane background based on plasmonic effect. Gold rods have also been used at the interface of dielectric rods and lines defect, which create plasmonic properties into the photonic crystal structure. The designed optical device is composed of four input waveguides and two output waveguides with two ring resonators at the resonant wavelength of 1.14 μm with TM polarization. The presented encoder platform has the small size of 19 μm × 33 μm that makes it to integration into compact all-optical processing systems. The encoder operation is simulated and analyzed with numerical finite-difference time-domain (FDTD) method and plane wave expansion (PWE) method. In the proposed structure, we have shown that by selecting the appropriate radius size for the resonant cavities, the desirable wavelength can be obtained. The maximum values of transmission efficiency for the first and second outputs are 82% and 96%, respectively. Resonant cavities are also located in the crystal lattice in such a way that by activating third input, 50% and 48% of the input signal will be obtained in each output ports indicating (1,1) logic state. So the new plasmonic photonic crystal encoder could be future applicable in the field of optical computing. [ABSTRACT FROM AUTHOR]

The main purpose of this study is to introduce the geological controlling factors in improving an intelligence-based model to estimate shear wave velocity from seismic attributes. The proposed method includes three main steps in the framework of geological events in a complex sedimentary succession located in the Persian Gulf. First, the best attributes were selected from extracted seismic data. Second, these attributes were transformed into shear wave velocity using fuzzy inference systems (FIS) such as Sugeno's fuzzy inference (SFIS), adaptive neuro-fuzzy inference (ANFIS) and optimized fuzzy inference (OFIS). Finally, a committee fuzzy machine (CFM) based on bat-inspired algorithm (BA) optimization was applied to combine previous predictions into an enhanced solution. In order to show the geological effect on improving the prediction, the main classes of predominate lithofacies in the reservoir of interest including shale, sand, and carbonate were selected and then the proposed algorithm was performed with and without lithofacies constraint. The results showed a good agreement between real and predicted shear wave velocity in the lithofacies-based model compared to the model without lithofacies especially in sand and carbonate. [ABSTRACT FROM AUTHOR]

Prediction of porosity from the seismic data via geophysical methods when limited number of wells are available is a challenging task that has high uncertainties. This study aims to construct a hybrid data-driven predictive model to establish a quantitative correlation between seismic pre-stack (SPS) data and the porosity. First, three intelligent models that are optimized by bat-inspired algorithm (BA): optimized neural network (ONN), optimized fuzzy inference system (OFIS), and optimized support vector regression (OSVR) are constructed for relating porosity to the SPS data. Then, to benefit from all individual optimized models, a final hybrid model was built via committee machine (CM) where single models are combined with a proper weight to predict porosity in the reservoir space. This approach is examined on the SPS data from an oil field in the Persian Gulf with a single exploratory well where input parameters (V p , V s , and ρ) to the AI models are derived from a two-parameter inversion method. We found that the coefficient of determination, root mean square error, average absolute relative error, and symmetric mean absolute percentage error for the CM are 0.923615, 0.015793, 0.132280, and 0.061310, respectively. Moreover, based on four statistical indexes that are calculated for each model, CM outperformed its individual elements followed by the OSRV. A comprehensive analysis of the results confirms that CM with the OM elements is a superior approach for computing porosity from the SPS in the well and then throughout the entire reservoir volume. This strategy can aid petroleum engineers to have a better forecast of porosity population in the reservoir static model immediately following the data that is obtained from the first exploratory well. Ultimately, successful implementation of this approach will promptly delineate sweet spots that can replace uncertain and complicated conventional geophysical methods. • A hybrid data-driven predictive model to relate SPS data and porosity is established via CM. • CM elements (ONN, OFIS, OSVR) are optimized by BA then combined with proper weight to predict porosity. • CM outperformed its induvial elements followed by OSRV in the entire reservoir volume to find sweet spots. [ABSTRACT FROM AUTHOR]

Shear wave velocity (VS) afford petroleum engineers to favorable information for implementing of geomechanical, geophysical, and reservoir characterization studies. Hence, it is all-important to present a scheme for estimation of this parameter. The primary objective of this study is introducing a novel strategy for determining shear wave velocity from seismic data. For achieving the aforementioned objective, four-steps procedure are followed in this study: (1) Suitable seismic attributes (SAs) are selected as independent variables to estimate shear wave velocity by using step-wise regression method; (2) Input variables which selected in the first step are transformed into higher correlated data space through alternation conditional expectation (ACE); (3) Quantitative formulation between shear wave velocity and ACE transformed of input variables is made through three improved models namely optimized neural network (ONN), optimized support vector regression (OSVR), and optimized fuzzy inference system (OFIS). Optimization implementation of intelligence models are achieved through cuckoo search method (CS); (4) Committee machine (CM) using cuckoo search method is employed for integrating optimized models so reaps those benefits. The efficiency of proposed models is assessed founded on statistical parameters. The obtained results corroborate the superb performance of committee machine in preference to its elements. This paper infers the proposed strategy is suitable for modeling of shear wave velocity as a function of seismic data. • In current study, seismic attributes were formulated to shear wave velocity. • Optimized SVR, ANN and FIS and were employed for modeling aforementioned formulation. • ACE was used for simplifying the problem for intelligence models. • The optimization techniques which used in this study was CS. • Committee approach of aforementioned models enhanced accuracy of the prediction compared with individual methods. [ABSTRACT FROM AUTHOR]

In this study, a new strategy based on integrating geostatistical seismic inversion and optimized support vector regression (OSVR) will be utilized to transform multi seismic attributes to sand fraction log. In first step, owing to compatibility relation between acoustic impedance (AI) and sand fraction, a high resolution value of this important attribute was extracted through a geostatistical seismic inversion (GSI). In second step, in addition to AI, several physical attributes are obtained from seismic data and then all of extracted attributes (AI and other seismic attributes) evaluated by step-wise regression for selecting best attributes that have highest effect on predicting sand fraction. In final step, selected attributes have been fed in the bat inspired optimized support vector regression as input and the sand fraction log is estimated. For the assessment of proposed strategy, the values of predicted sand fraction are compared with their real corresponding values in a blind well. It will be evident from the results that the proposed strategy is qualified for modeling the sand fraction as a function of seismic attributes. [ABSTRACT FROM AUTHOR]