Search results for: modeling and simulation

Authors: Maumita Bhattacharya

Abstract:

Evolutionary Algorithms are population-based, stochastic search techniques, widely used as efficient global optimizers. However, many real life optimization problems often require finding optimal solution to complex high dimensional, multimodal problems involving computationally very expensive fitness function evaluations. Use of evolutionary algorithms in such problem domains is thus practically prohibitive. An attractive alternative is to build meta models or use an approximation of the actual fitness functions to be evaluated. These meta models are order of magnitude cheaper to evaluate compared to the actual function evaluation. Many regression and interpolation tools are available to build such meta models. This paper briefly discusses the architectures and use of such meta-modeling tools in an evolutionary optimization context. We further present two evolutionary algorithm frameworks which involve use of meta models for fitness function evaluation. The first framework, namely the Dynamic Approximate Fitness based Hybrid EA (DAFHEA) model [14] reduces computation time by controlled use of meta-models (in this case approximate model generated by Support Vector Machine regression) to partially replace the actual function evaluation by approximate function evaluation. However, the underlying assumption in DAFHEA is that the training samples for the metamodel are generated from a single uniform model. This does not take into account uncertain scenarios involving noisy fitness functions. The second model, DAFHEA-II, an enhanced version of the original DAFHEA framework, incorporates a multiple-model based learning approach for the support vector machine approximator to handle noisy functions [15]. Empirical results obtained by evaluating the frameworks using several benchmark functions demonstrate their efficiency

Keywords: Meta model, Evolutionary algorithm, Stochastictechnique, Fitness function, Optimization, Support vector machine.

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Authors: D. Sun, T. F. Lu, A. Zander, M. Trinkle

Abstract:

This paper investigates the potential use of airborne ultrasonic phased arrays for imaging in outdoor environments as a means of overcoming the limitations experienced by kinect sensors, which may fail to work in the outdoor environments due to the oversaturation of the infrared photo diodes. Ultrasonic phased arrays have been well studied for static media, yet there appears to be no comparable examination in the literature of the impact of a flowing medium on the focusing behaviour of near field focused ultrasonic arrays. This paper presents a method for predicting the sound pressure fields produced by a single ultrasound element or an ultrasonic phased array influenced by airflows. The approach can be used to determine the actual focal point location of an array exposed in a known flow field. From the presented simulation results based upon this model, it can be concluded that uniform flows in the direction orthogonal to the acoustic propagation have a noticeable influence on the sound pressure field, which is reflected in the twisting of the steering angle of the array. Uniform flows in the same direction as the acoustic propagation have negligible influence on the array. For an array impacted by a turbulent flow, determining the location of the focused sound field becomes difficult due to the irregularity and continuously changing direction and the speed of the turbulent flow. In some circumstances, ultrasonic phased arrays impacted by turbulent flows may not be capable of producing a focused sound field.

Keywords: Airborne, airflow, focused sound field, ultrasonic phased array.

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Authors: Satyasen Panda, Urmila Bhanja

Abstract:

This paper presents a SAC-OCDMA code with zero cross correlation property to minimize the Multiple Access Interface (MAI) as New Zero Cross Correlation code (NZCC), which is found to be more scalable compared to the other existing SAC-OCDMA codes. This NZCC code is constructed using address segment and data segment. In this work, the proposed NZCC code is implemented in an optical system using the Opti-System software for the spectral amplitude coded optical code-division multiple-access (SAC-OCDMA) scheme. The main contribution of the proposed NZCC code is the zero cross correlation, which reduces both the MAI and PIIN noises. The proposed NZCC code reveals properties of minimum cross-correlation, flexibility in selecting the code parameters and supports a large number of users, combined with high data rate and longer fiber length. Simulation results reveal that the optical code division multiple access system based on the proposed NZCC code accommodates maximum number of simultaneous users with higher data rate transmission, lower Bit Error Rates (BER) and longer travelling distance without any signal quality degradation, as compared to the former existing SAC-OCDMA codes.

Keywords: Cross Correlation, Optical Code Division Multiple Access, Spectral Amplitude Coding Optical Code Division Multiple Access, Multiple Access Interference, Phase Induced Intensity Noise, New Zero Cross Correlation code.

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Authors: M. Ashaque Meah, M. Shah Noor, M Asif Arefin, Md. Fazlul Karim

Abstract:

A numerical technique in a boundary-fitted curvilinear grid model is developed to simulate the extent of inland inundation along the coastal belts of Peninsular Malaysia and Southern Thailand due to 2004 Indian ocean tsunami. Tsunami propagation and run-up are also studied in this paper. The vertically integrated shallow water equations are solved by using the method of lines (MOL). For this purpose the boundary-fitted grids are generated along the coastal and island boundaries and the other open boundaries of the model domain. A transformation is used to the governing equations so that the transformed physical domain is converted into a rectangular one. The MOL technique is applied to the transformed shallow water equations and the boundary conditions so that the equations are converted into ordinary differential equations initial value problem. Finally the 4^th order Runge-Kutta method is used to solve these ordinary differential equations. The moving boundary technique is applied instead of fixed sea side wall or fixed coastal boundary to ensure the movement of the coastal boundary. The extent of intrusion of water and associated tsunami propagation are simulated for the 2004 Indian Ocean tsunami along the west coast of Peninsular Malaysia and southern Thailand. The simulated results are compared with the results obtained from a finite difference model and the data available in the USGS website. All simulations show better approximation than earlier research and also show excellent agreement with the observed data.

Keywords: Open boundary condition, moving boundary condition, boundary-fitted curvilinear grids, far field tsunami, Shallow Water Equations, tsunami source, Indonesian tsunami of 2004.

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Authors: Cheng-Chi Yu, Chi-Shiun Chiou

Abstract:

This study developed a plug-in hybrid powertrain system which consisted of two continuous variable transmissions. By matching between the engine, motor, generator, and dual continuous variable transmissions, this integrated power system can take advantages of the components. The hybrid vehicle can be driven by the internal combustion engine, or electric motor alone, or by these two power sources together when the vehicle is driven in hard acceleration or high load. The energy management of this integrated hybrid system controls the power systems based on rule-based control strategy to achieve better fuel economy. When the vehicle driving power demand is low, the internal combustion engine is operating in the low efficiency region, so the internal combustion engine is shut down, and the vehicle is driven by motor only. When the vehicle driving power demand is high, internal combustion engine would operate in the high efficiency region; then the vehicle could be driven by internal combustion engine. This strategy would operate internal combustion engine only in optimal efficiency region to improve the fuel economy. In this research, the vehicle simulation model was built in MATLAB/ Simulink environment. The analysis results showed that the power coupled efficiency of the hybrid powertrain system with dual continuous variable transmissions was better than that of the Honda hybrid system on the market.

Keywords: Plug-in hybrid power system, fuel economy, performance, continuous variable transmission.

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Authors: Lakshmi Pujitha Dachuri, Nalini Uppala

Abstract:

In many applications retransmissions of lost packets are not permitted. OFDM is a multi-carrier modulation scheme having excellent performance which allows overlapping in frequency domain. With OFDM there is a simple way of dealing with multipath relatively simple DSP algorithms.

 In this paper, an image frame is compressed using DWT, and the compressed data is arranged in data vectors, each with equal number of coefficients. These vectors are quantized and binary coded to get the bit steams, which are then packetized and intelligently mapped to the OFDM system. Based on one-bit channel state information at the transmitter, the descriptions in order of descending priority are assigned to the currently good channels such that poorer sub-channels can only affect the lesser important data vectors. We consider only one-bit channel state information available at the transmitter, informing only about the sub-channels to be good or bad. For a good sub-channel, instantaneous received power should be greater than a threshold P_th. Otherwise, the sub-channel is in fading state and considered bad for that batch of coefficients. In order to reduce the system power consumption, the mapped descriptions onto the bad sub channels are dropped at the transmitter. The binary channel state information gives an opportunity to map the bit streams intelligently and to save a reasonable amount of power. By using MAT LAB simulation we can analysis the performance of our proposed scheme, in terms of system energy saving without compromising the received quality in terms of peak signal-noise ratio.

Keywords: Binary channel state, Channel state feedback, DWT-OFDM system, Energy saving, Fading broadcast channel.

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Authors: Hazem M. El-Bakry

Abstract:

In this paper, an automatic determination algorithm for nuclear magnetic resonance (NMR) spectra of the metabolites in the living body by magnetic resonance spectroscopy (MRS) without human intervention or complicated calculations is presented. In such method, the problem of NMR spectrum determination is transformed into the determination of the parameters of a mathematical model of the NMR signal. To calculate these parameters efficiently, a new model called modified Hopfield neural network is designed. The main achievement of this paper over the work in literature [30] is that the speed of the modified Hopfield neural network is accelerated. This is done by applying cross correlation in the frequency domain between the input values and the input weights. The modified Hopfield neural network can accomplish complex dignals perfectly with out any additinal computation steps. This is a valuable advantage as NMR signals are complex-valued. In addition, a technique called “modified sequential extension of section (MSES)" that takes into account the damping rate of the NMR signal is developed to be faster than that presented in [30]. Simulation results show that the calculation precision of the spectrum improves when MSES is used along with the neural network. Furthermore, MSES is found to reduce the local minimum problem in Hopfield neural networks. Moreover, the performance of the proposed method is evaluated and there is no effect on the performance of calculations when using the modified Hopfield neural networks.

Keywords: Hopfield Neural Networks, Cross Correlation, Nuclear Magnetic Resonance, Magnetic Resonance Spectroscopy, Fast Fourier Transform.

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Authors: Waqed H. Almohammed, Mohammed Y. Fattah, Sajjad E. Rasheed

Abstract:

Dynamic traffic loads cause deformation of underground pipes, resulting in vehicle discomfort. This makes it necessary to reinforce the layers of soil above underground pipes. In this study, the subbase layer was reinforced. Finite element software (PLAXIS 3D) was used to in the simulation, which includes geocell reinforcement, vehicle loading, soil layers and Glass Fiber Reinforced Plastic (GRP) pipe. Geocell reinforcement was modeled using a geogrid element, which was defined as a slender structure element that has the ability to withstand axial stresses but not to resist bending. Geogrids cannot withstand compression but they can withstand tensile forces. Comparisons have been made between the numerical models and experimental works, and a good agreement was obtained. Using the mathematical model, the performance of three different pipes of diameter 600 mm, 800 mm, and 1000 mm, and three different vehicular speeds of 20 km/h, 40 km/h, and 60 km/h, was examined to determine their impact on surface settlement and vertical pressure at the pipe crown for two cases: with and without geocell reinforcement. The results showed that, for a pipe diameter of 600 mm under geocell reinforcement, surface settlement decreases by 94 % when the speed of the vehicle is 20 km/h and by 98% when the speed of the vehicle is 60 km/h. Vertical pressure decreases by 81 % when the diameter of the pipe is 600 mm, while the value decreases to 58 % for a pipe with diameter 1000 mm. The results show that geocell reinforcement causes a significant and positive reduction in surface settlement and vertical stress above the pipe crown, leading to an increase in pipe safety.

Keywords: Dynamic loading, geocell reinforcement, GRP pipe, PLAXIS 3D, surface settlement.

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Authors: C. Ardil

Abstract:

In this paper, the comparison of composite programming and compromise programming for the aircraft selection problem is discussed using the multiple criteria decision analysis method. The decision making process requires the prior definition and fulfillment of certain factors, especially when it comes to complex areas such as aircraft selection problems. The proposed technique gives more efficient results by extending the composite programming and compromise programming, which are widely used in modeling multiple criteria decisions. The proposed model is applied to a practical decision problem for evaluating and selecting aircraft problems.A selection of aircraft was made based on the proposed approach developed in the field of multiple criteria decision making. The model presented is solved by using the following methods: composite programming, and compromise programming. The importance values of the weight coefficients of the criteria are calculated using the mean weight method. The evaluation and ranking of aircraft are carried out using the composite programming and compromise programming methods. In order to determine the stability of the model and the ability to apply the developed composite programming and compromise programming approach, the paper analyzes its sensitivity, which involves changing the value of the coefficient λ and q in the first part. The second part of the sensitivity analysis relates to the application of different multiple criteria decision making methods, composite programming and compromise programming. In addition, in the third part of the sensitivity analysis, the Spearman correlation coefficient of the ranks obtained was calculated which confirms the applicability of all the proposed approaches.

Keywords: composite programming, compromise programming, additive weighted model, multiplicative weighted model, multiple criteria decision making analysis, MCDMA, aircraft selection

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Authors: Robert O. Abolade, Olumide O. Ajayi, Zacheaus K. Adeyemo, Solomon A. Adeniran

Abstract:

Link adaptation is an important strategy for achieving robust wireless multimedia communications based on quality of service (QoS) demand. Scheme switching in multiple-input multiple-output (MIMO) systems is an aspect of link adaptation, and it involves selecting among different MIMO transmission schemes or modes so as to adapt to the varying radio channel conditions for the purpose of achieving QoS delivery. However, finding the most appropriate switching method in MIMO links is still a challenge as existing methods are either computationally complex or not always accurate. This paper presents an intelligent switching method for the MIMO system consisting of two schemes - transmit diversity (TD) and spatial multiplexing (SM) - using fuzzy logic technique. In this method, two channel quality indicators (CQI) namely average received signal-to-noise ratio (RSNR) and received signal strength indicator (RSSI) are measured and are passed as inputs to the fuzzy logic system which then gives a decision – an inference. The switching decision of the fuzzy logic system is fed back to the transmitter to switch between the TD and SM schemes. Simulation results show that the proposed fuzzy logic – based switching technique outperforms conventional static switching technique in terms of bit error rate and spectral efficiency.

Keywords: Channel quality indicator, fuzzy logic, link adaptation, MIMO, spatial multiplexing, transmit diversity.

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Authors: Ganesh D. Kale, Sheela N. Vadsola

Abstract:

Soil erosion is the most serious problem faced at global and local level. So planning of soil conservation measures has become prominent agenda in the view of water basin managers. To plan for the soil conservation measures, the information on soil erosion is essential. Universal Soil Loss Equation (USLE), Revised Universal Soil Loss Equation 1 (RUSLE1or RUSLE) and Modified Universal Soil Loss Equation (MUSLE), RUSLE 1.06, RUSLE1.06c, RUSLE2 are most widely used conventional erosion estimation methods. The essential drawbacks of USLE, RUSLE1 equations are that they are based on average annual values of its parameters and so their applicability to small temporal scale is questionable. Also these equations do not estimate runoff generated soil erosion. So applicability of these equations to estimate runoff generated soil erosion is questionable. Data used in formation of USLE, RUSLE1 equations was plot data so its applicability at greater spatial scale needs some scale correction factors to be induced. On the other hand MUSLE is unsuitable for predicting sediment yield of small and large events. Although the new revised forms of USLE like RUSLE 1.06, RUSLE1.06c and RUSLE2 were land use independent and they have almost cleared all the drawbacks in earlier versions like USLE and RUSLE1, they are based on the regional data of specific area and their applicability to other areas having different climate, soil, land use is questionable. These conventional equations are applicable for sheet and rill erosion and unable to predict gully erosion and spatial pattern of rills. So the research was focused on development of nonconventional (other than conventional) methods of soil erosion estimation. When these non-conventional methods are combined with GIS and RS, gives spatial distribution of soil erosion. In the present paper the review of literature on non- conventional methods of soil erosion estimation supported by GIS and RS is presented.

Keywords: Conventional methods, GIS, non-conventionalmethods, remote sensing, soil erosion modeling

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Authors: Sambit Prasad Kar, P.Palanisamy

Abstract:

In this paper a novel method for multiple one dimensional real valued sinusoidal signal frequency estimation in the presence of additive Gaussian noise is postulated. A computationally simple frequency estimation method with efficient statistical performance is attractive in many array signal processing applications. The prime focus of this paper is to combine the subspace-based technique and a simple peak search approach. This paper presents a variant of the Propagator Method (PM), where a collaborative approach of SUMWE and Propagator method is applied in order to estimate the multiple real valued sine wave frequencies. A new data model is proposed, which gives the dimension of the signal subspace is equal to the number of frequencies present in the observation. But, the signal subspace dimension is twice the number of frequencies in the conventional MUSIC method for estimating frequencies of real-valued sinusoidal signal. The statistical analysis of the proposed method is studied, and the explicit expression of asymptotic (large-sample) mean-squared-error (MSE) or variance of the estimation error is derived. The performance of the method is demonstrated, and the theoretical analysis is substantiated through numerical examples. The proposed method can achieve sustainable high estimation accuracy and frequency resolution at a lower SNR, which is verified by simulation by comparing with conventional MUSIC, ESPRIT and Propagator Method.

Keywords: Frequency estimation, peak search, subspace-based method without eigen decomposition, quadratic convex function.

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Authors: Hazem M. El-Bakry

Abstract:

Neural processors have shown good results for detecting a certain character in a given input matrix. In this paper, a new idead to speed up the operation of neural processors for character detection is presented. Such processors are designed based on cross correlation in the frequency domain between the input matrix and the weights of neural networks. This approach is developed to reduce the computation steps required by these faster neural networks for the searching process. The principle of divide and conquer strategy is applied through image decomposition. Each image is divided into small in size sub-images and then each one is tested separately by using a single faster neural processor. Furthermore, faster character detection is obtained by using parallel processing techniques to test the resulting sub-images at the same time using the same number of faster neural networks. In contrast to using only faster neural processors, the speed up ratio is increased with the size of the input image when using faster neural processors and image decomposition. Moreover, the problem of local subimage normalization in the frequency domain is solved. The effect of image normalization on the speed up ratio of character detection is discussed. Simulation results show that local subimage normalization through weight normalization is faster than subimage normalization in the spatial domain. The overall speed up ratio of the detection process is increased as the normalization of weights is done off line.

Keywords: Fast Character Detection, Neural Processors, Cross Correlation, Image Normalization, Parallel Processing.

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Authors: Ashok Kumar, Vinod Kumar

Abstract:

Wireless Sensor Networks (WSNs) are used to monitor/observe vast inaccessible regions through deployment of large number of sensor nodes in the sensing area. For majority of WSN applications, the collected data needs to be combined with geographic information of its origin to make it useful for the user; information received from remote Sensor Nodes (SNs) that are several hops away from base station/sink is meaningless without knowledge of its source. In addition to this, location information of SNs can also be used to propose/develop new network protocols for WSNs to improve their energy efficiency and lifetime. In this paper, range free localization protocols for WSNs have been proposed. The proposed protocols are based on weighted centroid localization technique, where the edge weights of SNs are decided by utilizing fuzzy logic inference for received signal strength and link quality between the nodes. The fuzzification is carried out using (i) Mamdani, (ii) Sugeno, and (iii) Combined Mamdani Sugeno fuzzy logic inference. Simulation results demonstrate that proposed protocols provide better accuracy in node localization compared to conventional centroid based localization protocols despite presence of unintentional radio frequency interference from radio frequency (RF) sources operating in same frequency band.

Keywords: localization, range free, received signal strength, link quality indicator, Mamdani fuzzy logic inference, Sugeno fuzzy logic inference.

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Authors: Mohamad Reza. Mohaghegh, Majid. Malek Jafarian

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This research proposes an algorithm for the simulation of time-periodic unsteady problems via the solution unsteady Euler and Navier-Stokes equations. This algorithm which is called Time Spectral method uses a Fourier representation in time and hence solve for the periodic state directly without resolving transients (which consume most of the resources in a time-accurate scheme). Mathematical tools used here are discrete Fourier transformations. It has shown tremendous potential for reducing the computational cost compared to conventional time-accurate methods, by enforcing periodicity and using Fourier representation in time, leading to spectral accuracy. The accuracy and efficiency of this technique is verified by Euler and Navier-Stokes calculations for pitching airfoils. Because of flow turbulence nature, Baldwin-Lomax turbulence model has been used at viscous flow analysis. The results presented by the Time Spectral method are compared with experimental data. It has shown tremendous potential for reducing the computational cost compared to the conventional time-accurate methods, by enforcing periodicity and using Fourier representation in time, leading to spectral accuracy, because results verify the small number of time intervals per pitching cycle required to capture the flow physics.

Keywords: Time Spectral Method, Time-periodic unsteadyflow, Discrete Fourier transform, Pitching airfoil, Turbulence flow

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Authors: M. Jayekumar, V. Nagarajan

Abstract:

Wireless sensor network finds role in environmental monitoring, industrial applications, surveillance applications, health monitoring and other supervisory applications. Sensing devices form the basic operational unit of the network that is self-battery powered with limited life time. Sensor node spends its limited energy for transmission, reception, routing and sensing information. Frequent energy utilization for the above mentioned process leads to network lifetime degradation. To enhance energy efficiency and network lifetime, we propose a modified energy optimization and node recovery post failure method, Energy-Link Failure Recovery Routing (E-LFRR) algorithm. In our E-LFRR algorithm, two phases namely, Monitored Transmission phase and Replaced Transmission phase are devised to combat worst case link failure conditions. In Monitored Transmission phase, the Actuator Node monitors and identifies suitable nodes for shortest path transmission. The Replaced Transmission phase dispatches the energy draining node at early stage from the active link and replaces it with the new node that has sufficient energy. Simulation results illustrate that this combined methodology reduces overhead, energy consumption, delay and maintains considerable amount of alive nodes thereby enhancing the network performance.

Keywords: Actuator node, energy efficient routing, energy hole, link failure recovery, link utilization, wireless sensor network.

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Authors: Paua Ketan, Bhagate Rajkumar, Adiga Ganesh, M. Kiran

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Low gear noise is an important sound quality feature in modern passenger cars. Annoying gear noise from the gearbox is influenced by the gear design, gearbox shaft layout, manufacturing deviations in the components, assembly errors and the mounting arrangement of the complete gearbox. Geometrical deviations in the form of profile and lead errors are often present on the flanks of the inspected gears. Ghost frequencies of a gear are very challenging to identify in standard gear measurement and analysis process due to small wavelengths involved. In this paper, gear whine noise occurring at non-integral multiples of gear mesh frequency of passenger car gearbox is investigated and the root cause is identified using the displacement deviation analysis (DDA) method. DDA method is applied to identify ghost frequency excitations on the flanks of gears arising out of generation grinding. Frequency identified through DDA correlated with the frequency of vibration and noise on the end-of-line machine as well as vehicle level measurements. With the application of DDA method along with standard lead profile measurement, gears with ghost frequency geometry deviations were identified on the production line to eliminate defective parts and thereby eliminate ghost frequency noise from a vehicle. Further, displacement deviation analysis can be used in conjunction with the manufacturing process simulation to arrive at suitable countermeasures for arresting the ghost frequency.

Keywords: Displacement deviation analysis, gear whine, ghost frequency, sound quality.

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Authors: P. Naderi, S.M.T. Bathaee, R. Hoseinnezhad, R. Chini

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Using electrical machine in conventional vehicles, also called hybrid vehicles, has become a promising control scheme that enables some manners for fuel economy and driver assist for better stability. In this paper, vehicle stability control, fuel economy and Driving/Regeneration braking for a 4WD hybrid vehicle is investigated by using an electrical machine on each non-driven wheels. In front wheels driven vehicles, fuel economy and regenerative braking can be obtained by summing torques applied on rear wheels. On the other hand, unequal torques applied to rear wheels provides enhanced safety and path correction in steering. In this paper, a model with fourteen degrees of freedom is considered for vehicle body, tires and, suspension systems. Thereafter, powertrain subsystems are modeled. Considering an electrical machine on each rear wheel, a fuzzy controller is designed for each driving, braking, and stability conditions. Another fuzzy controller recognizes the vehicle requirements between the driving/regeneration and stability modes. Intelligent vehicle control to multi objective operation and forward simulation are the paper advantages. For reaching to these aims, power management control and yaw moment control will be done by three fuzzy controllers. Also, the above mentioned goals are weighted by another fuzzy sub-controller base on vehicle dynamic. Finally, Simulations performed in MATLAB/SIMULINK environment show that the proposed structure can enhance the vehicle performance in different modes effectively.

Keywords: Hybrid, pitch, roll, regeneration, yaw.

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Authors: Vijay Shankar, C.S.P. Ojha, K.S. Hari Prasad

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Maize and Indian mustard are significant crops in semi-arid climate zones of India. Improved water management requires precise scheduling of irrigation, which in turn requires an accurate computation of daily crop evapotranspiration (ETc). Daily crop evapotranspiration comes as a product of reference evapotranspiration (ET0) and the growth stage specific crop coefficients modified for daily variation. The first objective of present study is to develop crop coefficients Kc for Maize and Indian mustard. The estimated values of Kc for maize at the four crop growth stages (initial, development, mid-season, and late season) are 0.55, 1.08, 1.25, and 0.75, respectively, and for Indian mustard the Kc values at the four growth stages are 0.3, 0.6, 1.12, and 0.35, respectively. The second objective of the study is to compute daily crop evapotranspiration from ET0 and crop coefficients. Average daily ETc of maize varied from about 2.5 mm/d in the early growing period to > 6.5 mm/d at mid season. The peak ETc of maize is 8.3 mm/d and it occurred 64 days after sowing at the reproductive growth stage when leaf area index was 4.54. In the case of Indian mustard, average ETc is 1 mm/d at the initial stage, >1.8 mm/d at mid season and achieves a peak value of 2.12 mm/d on 56 days after sowing. Improved schedules of irrigation have been simulated based on daily crop evapo-transpiration and field measured data. Simulation shows a close match between modeled and field moisture status prevalent during crop season.

Keywords: Crop coefficient, Crop evapotranspiration, Field moisture, Irrigation Scheduling

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Authors: Mohamed Ouzzane, Mahmoud Bady

Abstract:

Due to energy and environment context, research is looking for the use of clean and energy efficient system in cooling industry. In this regard, the ejector represents one of the promising solutions. The thermal ejector is a passive component used for thermal compression in refrigeration and cooling systems, usually activated by heat either waste or solar. The present study introduces a theoretical analysis of the cooling system which uses a gas ejector thermal compression. A theoretical model is developed and applied for the design and simulation of the ejector, as well as the whole cooling system. Besides the conservation equations of mass, energy and momentum, the gas dynamic equations, state equations, isentropic relations as well as some appropriate assumptions are applied to simulate the flow and mixing in the ejector. This model coupled with the equations of the other components (condenser, evaporator, pump, and generator) is used to analyze profiles of pressure and velocity (Mach number), as well as evaluation of the cycle cooling capacity. A FORTRAN program is developed to carry out the investigation. Properties of refrigerant R134a are calculated using real gas equations. Among many parameters, it is thought that the generator pressure is the cornerstone in the cycle, and hence considered as the key parameter in this investigation. Results show that the generator pressure has a great effect on the ejector and on the whole cooling system. At high generator pressures, strong shock waves inside the ejector are created, which lead to significant condenser pressure at the ejector exit. Additionally, at higher generator pressures, the designed system can deliver cooling capacity for high condensing pressure (hot season).

Keywords: Air cooling system, refrigeration, thermal ejector, thermal compression.

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Authors: A. Fayad, Q. Alqhazaly, T. Cinkler

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In light of the substantial increase in end-user requirements and the incessant need of network operators to upgrade the capabilities of access networks, in this paper, the performance of the different modulation formats on eight-channels Time and Wavelength Division Multiplexing Passive Optical Network (TWDM-PON) transmission system has been examined and compared. Limitations and features of modulation formats have been determined to outline the most suitable design to enhance the data rate and transmission reach to obtain the best performance of the network. The considered modulation formats are On-Off Keying Non-Return-to-Zero (NRZ-OOK), Carrier Suppressed Return to Zero (CSRZ), Duo Binary (DB), Modified Duo Binary (MODB), Quadrature Phase Shift Keying (QPSK), and Differential Quadrature Phase Shift Keying (DQPSK). The performance has been analyzed by varying transmission distances and bit rates under different channel spacing. Furthermore, the system is evaluated in terms of minimum Bit Error Rate (BER) and Quality factor (Qf) without applying any dispersion compensation technique, or any optical amplifier. Optisystem software was used for simulation purposes.

Keywords: Bit Error Rate, BER, Carrier Suppressed Return to Zero, CSRZ, Duo Binary, DB, Differential Quadrature Phase Shift Keying, DQPSK, Modified Duo Binary, MODB, On-Off Keying Non-Return-to-Zero, NRZ-OOK, Quality factor, Qf, Time and Wavelength Division Multiplexing Passive Optical Network, TWDM-PON.

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Authors: Jiakai Li, Gursel Serpen, Steven Selman, Matt Franchetti, Mike Riesen, Cynthia Schneider

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This paper presents the development of a Bayesian belief network classifier for prediction of graft status and survival period in renal transplantation using the patient profile information prior to the transplantation. The objective was to explore feasibility of developing a decision making tool for identifying the most suitable recipient among the candidate pool members. The dataset was compiled from the University of Toledo Medical Center Hospital patients as reported to the United Network Organ Sharing, and had 1228 patient records for the period covering 1987 through 2009. The Bayes net classifiers were developed using the Weka machine learning software workbench. Two separate classifiers were induced from the data set, one to predict the status of the graft as either failed or living, and a second classifier to predict the graft survival period. The classifier for graft status prediction performed very well with a prediction accuracy of 97.8% and true positive values of 0.967 and 0.988 for the living and failed classes, respectively. The second classifier to predict the graft survival period yielded a prediction accuracy of 68.2% and a true positive rate of 0.85 for the class representing those instances with kidneys failing during the first year following transplantation. Simulation results indicated that it is feasible to develop a successful Bayesian belief network classifier for prediction of graft status, but not the graft survival period, using the information in UNOS database.

Keywords: Bayesian network classifier, renal transplantation, graft survival period, United Network for Organ Sharing

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Authors: Jalal Khan, Daniyal Ali Sehrai, Shakeel Ahmad

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This paper presents the design and performance evaluation of multiband metamaterial based antenna operating in the 3.6 GHz (4G), 14.33 GHz, and 28.86 GHz (5G) frequency bands, for future mobile and handheld devices. The radiating element of the proposed design is made up of a conductive material supported by a 1.524 mm thicker Rogers-4003 substrate, having a relative dielectric constant and loss tangent of 3.55 and 0.0027, respectively. The substrate is backed by truncated ground plane. The future mobile communication system is based on higher frequencies, which are highly affected by the atmospheric conditions. Therefore, to overcome the path loss problem, essential enhancements and improvements must be made in the overall performance of the antenna. The traditional ground plane does not provide the in-phase reflection and surface wave suppression due to which side and back lobes are produced. This will affect the antenna performance in terms of gain and efficiency. To enhance the overall performance of the antenna, a metamaterial acting as a high impedance surface (HIS) is used as a reflector in the proposed design. The simulated gain of the metamaterial based antenna is enhanced from {2.76-6.47, 4.83-6.71 and 7.52-7.73} dB at 3.6, 14.33 and 28.89 GHz, respectively relative to the gain of the antenna backed by a traditional ground plane. The proposed antenna radiated efficiently with a radiated efficiency (>85 %) in all the three frequency bands with and without metamaterial surface. The total volume of the antenna is (L x W x h=45 x 40 x 1.524) mm³. The antenna can be potentially used for wireless handheld devices and mobile terminal. All the simulations have been performed using the Computer Simulation Technology (CST) software.

Keywords: Multiband, fourth generation (4G), fifth generation (5G), metamaterial, CST MWS.

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Authors: M.karavanmasjedi, N.Hedayat , A.Rohani, H.Shirin

Abstract:

Sedimentation process resulting from soil erosion in the water basin especially in arid and semi-arid where poor vegetation cover in the slope of the mountains upstream could contribute to sediment formation. The consequence of sedimentation not only makes considerable change in the morphology of the river and the hydraulic characteristics but would also have a major challenge for the operation and maintenance of the canal network which depend on water flow to meet the stakeholder-s requirements. For this reason mathematical modeling can be used to simulate the effective factors on scouring, sediment transport and their settling along the waterways. This is particularly important behind the reservoirs which enable the operators to estimate the useful life of these hydraulic structures. The aim of this paper is to simulate the sedimentation and erosion in the eastern and western water intake structures of the Dez Diversion weir using GSTARS-3 software. This is done to estimate the sedimentation and investigate the ways in which to optimize the process and minimize the operational problems. Results indicated that the at the furthest point upstream of the diversion weir, the coarser sediment grains tended to settle. The reason for this is the construction of the phantom bridge and the outstanding rocks just upstream of the structure. The construction of these along the river course has reduced the momentum energy require to push the sediment loads and make it possible for them to settle wherever the river regime allows it. Results further indicated a trend for the sediment size in such a way that as the focus of study shifts downstream the size of grains get smaller and vice versa. It was also found that the finding of the GSTARS-3 had a close proximity with the sets of the observed data. This suggests that the software is a powerful analytical tool which can be applied in the river engineering project with a minimum of costs and relatively accurate results.

Keywords: Erosion, sedimentation, Dez Diversion weir, GSTARS-3

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Authors: M. R. Ghasemi, R. Ghiasi, H. Varaee

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Model updating method has received increasing attention in damage detection structures based on measured modal parameters. Therefore, a probability-based damage detection (PBDD) procedure based on a model updating procedure is presented in this paper, in which a one-stage model-based damage identification technique based on the dynamic features of a structure is investigated. The presented framework uses a finite element updating method with a Monte Carlo simulation that considers the uncertainty caused by measurement noise. Enhanced ideal gas molecular movement (EIGMM) is used as the main algorithm for model updating. Ideal gas molecular movement (IGMM) is a multiagent algorithm based on the ideal gas molecular movement. Ideal gas molecules disperse rapidly in different directions and cover all the space inside. This is embedded in the high speed of molecules, collisions between them and with the surrounding barriers. In IGMM algorithm to accomplish the optimal solutions, the initial population of gas molecules is randomly generated and the governing equations related to the velocity of gas molecules and collisions between those are utilized. In this paper, an enhanced version of IGMM, which removes unchanged variables after specified iterations, is developed. The proposed method is implemented on two numerical examples in the field of structural damage detection. The results show that the proposed method can perform well and competitive in PBDD of structures.

Keywords: Enhanced ideal gas molecular movement, ideal gas molecular movement, model updating method, probability-based damage detection, uncertainty quantification.

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Authors: Rukayya Ibrahim Muazu, Aiduan Li Borrion, Julia A. Stegemann

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Biomass briquette gasification is regarded as a promising route for efficient briquette use in energy generation, fuels and other useful chemicals. However, previous research has been focused on briquette gasification in fixed bed gasifiers such as updraft and downdraft gasifiers. Fluidised bed gasifier has the potential to be effectively sized to medium or large scale. This study investigated the use of fuel briquettes produced from blends of rice husks and corn cobs biomass, in a bubbling fluidised bed gasifier. The study adopted a combination of numerical equations and Aspen Plus simulation software, to predict the product gas (syngas) composition base on briquette density and biomass composition (blend ratio of rice husks to corn cobs). The Aspen Plus model was based on an experimentally validated model from the literature. The results based on a briquette size 32 mm diameter and relaxed density range of 500 to 650kg/m3, indicated that fluidisation air required in the gasifier increased with increase in briquette density, and the fluidisation air showed to be the controlling factor compared with the actual air required for gasification of the biomass briquettes. The mass flowrate of CO2 in the predicted syngas composition increased with an increase in air flow, in the gasifier, while CO decreased and H2 was almost constant. The ratio of H2 to CO for various blends of rice husks and corn cobs did not significantly change at the designed process air, but a significant difference of 1.0 was observed between 10/90 and 90/10 % blend of rice husks and corn cobs.

Keywords: Briquettes, fluidised bed, gasification, Aspen Plus, syngas.

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Authors: J-B. Clavel, N. Thiollière, B. Mouginot

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The presented work is motivated by a French law regarding nuclear waste management. A new conceptual Accelerator Driven System (ADS) designed for the Minor Actinides (MA) transmutation has been assessed by numerical simulation. The MUltiple Spallation Target (MUST) ADS combines high thermal power (up to 1.4 GWth) and high specific power. A 30 mA and 1 GeV proton beam is divided into three secondary beams transmitted on three liquid lead-bismuth spallation targets. Neutron and thermalhydraulic simulations have been performed with the code MURE, based on the Monte-Carlo transport code MCNPX. A methodology has been developed to define characteristic of the MUST ADS concept according to a specific transmutation scenario. The reference scenario is based on a MA flux (neptunium, americium and curium) providing from European Fast Reactor (EPR) and a plutonium multireprocessing strategy is accounted for. The MUST ADS reference concept is a sodium cooled fast reactor. The MA fuel at equilibrium is mixed with MgO inert matrix to limit the core reactivity and improve the fuel thermal conductivity. The fuel is irradiated over five years. Five years of cooling and two years for the fuel fabrication are taken into account. The MUST ADS reference concept burns about 50% of the initial MA inventory during a complete cycle. In term of mass, up to 570 kg/year are transmuted in one concept. The methodology to design the MUST ADS and to calculate fuel composition at equilibrium is precisely described in the paper. A detailed fuel evolution analysis is performed and the reference scenario is compared to a scenario where only americium transmutation is performed.

Keywords: Accelerator Driven System, double strata scenario, minor actinides, MUST, transmutation.

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Authors: Derjew Ayele Ejigu, Houde Song, Xiaojing Liu

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This work presents the particle swarm optimization trained neural network (PSO-NN) supervisory proportional integral derivative (PID) control method to monitor the pressurized water reactor (PWR) core power for safe operation. The proposed control approach is implemented on the transfer function of the PWR core, which is computed from the state-space model. The PWR core state-space model is designed from the neutronics, thermal-hydraulics, and reactivity models using perturbation around the equilibrium value. The proposed control approach computes the control rod speed to maneuver the core power to track the reference in a closed-loop scheme. The particle swarm optimization (PSO) algorithm is used to train the neural network (NN) and to tune the PID simultaneously. The controller performance is examined using integral absolute error, integral time absolute error, integral square error, and integral time square error functions, and the stability of the system is analyzed by using the Bode diagram. The simulation results indicated that the controller shows satisfactory performance to control and track the load power effectively and smoothly as compared to the PSO-PID control technique. This study will give benefit to design a supervisory controller for nuclear engineering research fields for control application.

Keywords: machine learning, neural network, pressurized water reactor, supervisory controller

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Authors: Mutuku Muvengei, John Kihiu

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This paper focuses on the development of bond graph dynamic model of the mechanical dynamics of an excavating mechanism previously designed to be used with small tractors, which are fabricated in the Engineering Workshops of Jomo Kenyatta University of Agriculture and Technology. To develop a mechanical dynamics model of the manipulator, forward recursive equations similar to those applied in iterative Newton-Euler method were used to obtain kinematic relationships between the time rates of joint variables and the generalized cartesian velocities for the centroids of the links. Representing the obtained kinematic relationships in bondgraphic form, while considering the link weights and momenta as the elements led to a detailed bond graph model of the manipulator. The bond graph method was found to reduce significantly the number of recursive computations performed on a 3 DOF manipulator for a mechanical dynamic model to result, hence indicating that bond graph method is more computationally efficient than the Newton-Euler method in developing dynamic models of 3 DOF planar manipulators. The model was verified by comparing the joint torque expressions of a two link planar manipulator to those obtained using Newton- Euler and Lagrangian methods as analyzed in robotic textbooks. The expressions were found to agree indicating that the model captures the aspects of rigid body dynamics of the manipulator. Based on the model developed, actuator sizing and valve sizing methodologies were developed and used to obtain the optimal sizes of the pistons and spool valve ports respectively. It was found that using the pump with the sized flow rate capacity, the engine of the tractor is able to power the excavating mechanism in digging a sandy-loom soil.

Keywords: Actuators, bond graphs, inverse dynamics, recursive equations, quintic polynomial trajectory.

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Authors: Abobaker Mohammed Qasem Farhan, Shangming Yang, Mohammed Al-Nehari

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Pneumonia is a kind of lung disease that creates congestion in the chest. Such pneumonic conditions lead to loss of life due to the severity of high congestion. Pneumonic lung disease is caused by viral pneumonia, bacterial pneumonia, or COVID-19 induced pneumonia. The early prediction and classification of such lung diseases help reduce the mortality rate. We propose the automatic Computer-Aided Diagnosis (CAD) system in this paper using the deep learning approach. The proposed CAD system takes input from raw computerized tomography (CT) scans of the patient's chest and automatically predicts disease classification. We designed the Hybrid Deep Learning Algorithm (HDLA) to improve accuracy and reduce processing requirements. The raw CT scans are pre-processed first to enhance their quality for further analysis. We then applied a hybrid model that consists of automatic feature extraction and classification. We propose the robust 2D Convolutional Neural Network (CNN) model to extract the automatic features from the pre-processed CT image. This CNN model assures feature learning with extremely effective 1D feature extraction for each input CT image. The outcome of the 2D CNN model is then normalized using the Min-Max technique. The second step of the proposed hybrid model is related to training and classification using different classifiers. The simulation outcomes using the publicly available dataset prove the robustness and efficiency of the proposed model compared to state-of-art algorithms.

Keywords: CT scans, COVID-19, deep learning, image processing, pneumonia, lung disease.

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