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Due to increasing complexity of mobile applications, and limited computation resources of smart mobile devices, the quality of service requirements of mobile application can be enhanced by offloading the computation tasks of the mobile applications to edge servers, such as cloudlets, which exist at the edge of wireless networks. However, improper placement of mobile tasks on the edge servers may increase the waiting time and the transmission time. This, in turn, will increase the response time, and eventually violates the quality of service. This paper proposes an effective offloading strategy in a mobile edge environment using the queuing networks and an evolutionary algorithm, namely the genetic algorithm (GA). The queuing network is used to model the waiting time and the service time of the mobile tasks on the edge servers. The genetic algorithm finds the best allocation of mobile tasks on the edge servers to minimize tasks response time considering the transmission times and the load conditions on edge servers represented by the waiting times and the service times which are calculated using the queuing network. The proposed GA-based offloading algorithm is compared with another evolutionary algorithm, namely particle swarm optimization (PSO). Experimental evaluations show that the GA-based offloading algorithm outperforms both of round robin offloading and the PSO-based offloading algorithms, and effectively improves mobile applications response time.

The focus of this research is to develop the optimum design of torsional vibration damper using hybrid damping approach to decrease the torsional vibrations in the cranktrain system of internal combustion engines (ICE). For this purpose, a double mass rubber and viscous torsional vibration damper (DMRV-TVD) are combined. The optimization procedure is carried out using genetic algorithm (GA) to determine the best hybrid damping performance on cranktrain system of a four stroke and four cylinder diesel engine. Accordingly, twelve degrees of freedom lumped mass mathematical model of the proposed cranktrain system is created. The stiffness and damping coefficients of viscous and rubber materials used in DMRV-TVD model are verified by modal test and finite element natural frequency analysis. Then, the excitation torque is calculated considering the inertia forces and gas force, and Fourier series expansion is performed to obtain the harmonics of driven torque as the input load on the relevant masses. The relative angular deflection of the front end point of the crankshaft is determined. Additionally, in order to decrease the torsional vibrations of the crankshaft, DMRV-TVD model is optimized depending on the viscous material parameters by defining the boundary conditions and objective function of the genetic algorithm. The comparative results show that the developed hybrid design of optimized DMRV-TVD reduced the torsional vibrations by 50.17% when compared to the non-optimized DMRV-TVD. This achieved reduction in the torsional vibrations is expected to increase the engine performance and its durability as well as providing a better driving comfort and fuel efficiency.

Optimizing the design of the preform shapes used in warm forging processes is important for improving quality and reducing cost. The main objective of this study was to optimize the design of a preform shape for a bevel gear to obtain a good product and to improve the efficiency of the warm forging process. Quality parameters included in the fitness function were form-filling, preform volume, and complexity. First, basic principles pertaining to the cross sections, thickness, and concave radius of a bevel gear preform were applied in conjunction with certain theoretical expressions and empirical methods to produce an initial preform shape and initial geometry. Subsequently, mathematical evaluation models of the fitness function were generated to optimize the preform shape. Finally, a genetic algorithm was applied to solve the multi-objective optimization problem, and the best solution was determined considering the accuracy of the results, within the close proximity set generated by the multi-objective genetic algorithm optimization. The optimal preform shape was decreased in weight by approximately 8% and its flash dimensions were decreased by about 5% compared to the initial preform shape. This research is expected to improve the efficiency of fabricating bevel gears by means of warm forging processes.

This paper introduces a methodology utilising a ply-ply damage Finite Element models with Genetic algorithm optimisation procedure to investigate the effect of lay-up configuration on the impact absorption properties of fibre metal laminates (FMLs). The methodology was carried out in two steps. In the first step, a pseudo-2D model was used to explore the vast design space to identify potential optimised layup-configurations. In the second step, the optimised configurations were studied in full 3 D, with high fidelity simulations, verifying the results obtained from the optimisation process. The design variables used include thickness and material (including fibre orientation) of each ply. The results produced an optimised configuration consisting of a metallic ply on the impacted side followed by a cross-ply composite lay-up. The results also suggest that the first composite ply (second ply of the FML) should be about 3 times thicker than the other plies.

Condensate stabilization is a process where hydrocarbon condensate recovered from natural gas reservoirs is processed to meet the required storage, transportation, and export specifications. The pr...

Wave energy has gained its popularity in recent decades due to the vast amount of untapped wave energy resources. There are numerous types of wave energy convertor (WEC) being proposed and to be economically viable, various means to enhance the power generation from WECs have been studied and investigated. In this paper, a novel pontoon-type WEC, which is formed by multiple plate-like modules connected by hinges, are considered. The power enhancement of this pontoon-type WEC is achieved by allowing certain level of structural deformation and by utilizing a series of optimal variable power take-off (PTO) system. The wave energy is converted into useful electricity by attaching the PTO systems on the hinge connectors such that the mechanical movements of the hinges could produce electricity. In this paper, various structural rigidity of the interconnected modules are considered by changing the material Young's modulus in order to investigate its impact on the power enhancement. In addition, the genetic algorithm optimization scheme is utilized to seek for the optimal PTO damping in the variable PTO system. It is observed that under certain condition, the flexible pontoon-type WEC with lesser connection joints is more effective in generating energy as compared to its rigid counterpart with higher connection joints. It is also found that the variable PTO system is able to generate greater energy as compared to the PTO system with constant/uniform PTO damping. Keywords: Power generation enhancement, Pontoon-type wave energy convertor, Very large floating structure (VLFS), Hydroelastic response, Variable power take-off system, Genetic algorithm optimization

International audience; To take advantage of the data generated in buildings, this document proposes a methodology based on a machine learning model to improve thermal comfort and energy efficiency. This methodology uses measured data (e.g., indoor/outdoor temperature, relative humidity, etc.) and forecast data (e.g., meteorological data) to train a multiple linear regression model to forecast the indoor temperature of the space under study. Using the genetic algorithm optimization method, this model is then used to evaluate the different heating strategies generated. For each strategy, a score is assigned according to user-defined criteria in order to prioritize them and select the best one. By studying an office building simulated under the TRNSYS software, a multiple linear regression model was implemented with errors less than 1% and an adjusted R2 coefficient close to 0.9. Compared to a conventional heating strategy, this methodology can improve thermal comfort by up to 43%.

A prosthetic finger based on a one degree of freedom mechanism is synthesized in this paper using a genetic algorithm optimization method. The features of this method are defined so that the mechanism performs an anatomical movement of a hand finger. Then, a sensitivity analysis is proposed to identify which links most affect the end-effector position. Some simulation results are presented to analyze this proposal.

Interweave Dynamic spectrum sharing is adistributive opportunistic spectrum access for wireless users to access the spectrum any time dynamically. This proposed approach provides a spectrum allocation technique to the users based on a multi-objective genetic optimization algorithm in interweave mode of spectrum sharing. The multiple objectives targeted are minimizing BER, minimizing Power consumption, and maximizing the Throughput. The proposed algorithm is compared with the existing Brute force algorithm and provides a gradual increase in the fitness value for an increase in the transmission BW.

Multi-objective optimization of the hydrofoil shaped pin fins for micro heat sinks was studied. Optimization tool utilized a multi objective genetic algorithm (MOGA) and a numerical model that was validated against previously obtained experimental single-phase heat transfer results on a hydrofoil shape micro scale pin fin. The numerical model was built using StarCCM + software. Several turbulence models were examined against single-phase experimental data and the Lag-EB k-epsilon turbulence model was chosen for validation. Optimization tool varied the shape of the hydrofoil pin fin through flexible parametrization utilizing an ellipse and polynomials. An algorithm coupling pin fin parametrization code, validated the 3D CFD solutions and a MOGA interface was implemented. Subsequently, multi-objective optimization was performed to maximize the heat transfer coefficient while minimizing pressure drop. A broad range of novel geometries were obtained with a tradeoff between thermal and hydraulic performance for low Reynolds number. Novel pin fin shape, bird design, is proposed as a result of the study. Additional 3D steady state conjugate CFD simulations including the bird design pin fin were performed using already verified setup, and it was shown that the enhancement in the average heat transfer coefficient dominates over the pressure drop increase at higher Reynolds numbers.

Modeling of material behaviour under strain-controlled and stress-controlled loading for cyclically stable materials (CSMs) is generally done using kinematic hardening, neglecting the isotropic hardening component. Although this approach is able to simulate the hysteresis loops, the quantitative ratcheting characteristics are often not of the desired accuracy; thus earlier investigators have introduced different modifications in the kinematic hardening component for case to case basis to obtain better agreement to the experimentally observed cyclic-plastic behaviour. Combined hardening models, though used for cyclic softening/hardening materials, have not been used for cyclically stable materials to the best knowledge of the authors. The primary aim of this study is to improve the ratcheting predictions for CSMs considering the well-known Chaboche's combined hardening model using a new generalized methodology. The initial estimates of the hardening components are obtained from symmetric strain-controlled tests subjected to their refinement by genetic algorithm based optimization technique. The Chaboche's parameters have been evaluated using the proposed methodology for available reported experimental data on cyclically stable steels to demonstrate the response of materials under stress-controlled and strain-controlled loading conditions.

Biogas power generation is renewable energy made from biological materials. Biogas power production is technology which helps in development of sustainable energy supply systems. This paper develops Genetic Algorithm optimization model for Biogas electrical power generation of Ilora in Oyo, Oyo state. The production is done using co-digestion system of pig dung and Poultry dung under the process of anaerobic digestion. The pig dung and poultry dung were mixed 50:50%. MATLAB and VISUAL BASIC Software was used to carry out simulations to develop optimized Genetic Algorithm model for Biogas power production with aims to improving electricity accessibility and durability of the community. The results of the research reveal the Empirical Biogas power production without and with Genetic Algorithm optimization. The Result showed that biogas electrical power generated without and with Genetic Algorithm Optimization were 5KW and 11.18KW respectively. The biogas power generation was increased by 6.18KW, which is 38.2% increase after Genetic Algorithm optimization. The results show the application of the Genetic Algorithm optimization model which can be used to improving Biogas power generation when amount of methane gas produced from the animal dung varies with speed of thermal rotating shaft.

This study aims to enhance the transesterification process to convert supercritical methanol (SCM) to biodiesel by applying artificial neural network-coupled genetic algorithm (ANN-GA) optimization...

The present paper deals with the hydrogen liquefaction system with absorption precooling cycle assisted by geothermal energy is modeled and analyzed as an exergoeconomic. Uses part of the geothermal water heat for absorption refrigeration to precool the hydrogen gas and part of the geothermal water heat to produce work with a binary geothermal cycle and use it in a liquefaction cycle. Exergoeconomic optimization procedure is applied using genetic algorithm method to the integrated system. The objective is to minimize the unit cost of hydrogen liquefaction of the composed system. Based on optimization calculations, hydrogen gas can be cooled down to −30 °C in the precooling cycle. The actual work consumption in the hydrogen liquefaction is calculated to be 10.06 kWh/kg LH2. The unit exergetic liquefaction cost of hydrogen is calculated to be 1.114 $/kg LH2 or 9.27 $/GJ, respectively in the optimum case.

Quasi-cyclic low-density parity-check (QC–LDPC) codes are introduced as a physical channel coding solution for data channels in 5G new radio (5G NR). Depending on the use case scenario, this standard proposes the usage of a wide variety of codes, which imposes the need for high encoder flexibility. LDPC codes from 5G NR have a convenient structure and can be efficiently encoded using forward substitution and without computationally intensive multiplications with dense matrices. However, the state-of-the-art solutions for encoder hardware implementation can be inefficient since many hardware processing units stay idle during the encoding process. This paper proposes a novel partially parallel architecture that can provide high hardware usage efficiency (HUE) while achieving encoder flexibility and support for all 5G NR codes. The proposed architecture includes a flexible circular shifting network, which is capable of shifting a single large bit vector or multiple smaller bit vectors depending on the code. The encoder architecture was built around the shifter in a way that multiple parity check matrix elements can be processed in parallel for short codes, thus providing almost the same level of parallelism as for long codes. The processing schedule was optimized for minimal encoding time using the genetic algorithm. The optimized encoder provided high throughputs, low latency, and up-to-date the best HUE.

The optimal transmitter position for maximum WIFI coverage in a residential home is investigated. A 60-GHz, 32 by 32 patch array antenna is implemented as the transmitter and the receiver antennas. The receivers are placed in each room of the home at equally spaced distances. The power received by each receiver is taken into account. By using genetic algorithm optimization in parallel, the optimal transmitter position is chosen as the one with the minimal number of receivers having received power less than −87.5 dBm. The GA chosen position results in acceptable received power at all but six receiver positions.

One-dimensional polarization-independent grating couplers are demonstrated with the aid of the adaptive genetic algorithm optimization. By adjusting the relative weight between the coupling efficiency and the bandwidth of the polarization-dependent loss (PDL), we control the evolution direction and customize the final performance of the device. Two specific designs are generated by giving more weight to the coupling efficiency and the PDL bandwidth, respectively. Coupling efficiencies of the first design are measured to be − 7.6 d B and − 7.9 d B at 1550 nm for TE and TM polarizations, respectively, while its 1.0 dB PDL bandwidth is 25.0 nm. In contrast, the second design presents higher coupling efficiencies of − 7.6 d B and − 7.2 d B at 1550 nm for TE and TM polarizations, respectively. However, its 1.0 dB PDL bandwidth is 22.0 nm.

Truss optimization has the goal of achieving savings in costs and material while maintaining structural characteristics. In this research a 10 bar truss was structurally optimized in Rhino 6 using genetic algorithm optimization method. Results from previous research where sizing optimization was limited to using only three different cross-sections were compared to a sizing and shape optimization model which uses only those three cross-sections. Significant savings in mass have been found when using this approach. An analysis was conducted of the necessary bill of materials for these solutions. This research indicates practical effects which optimization can achieve in truss design.

We demonstrate the production of broadband long-wave infrared pulses cen-tered near 10.3 μm using difference-frequency generation. An accompanying simulation shows that genetic algorithm optimization of chirp can significantly increase the spectral bandwidth.

Considering the air-condition system is difficult to realize the dynamic control of indoor thermal comfort, this paper put forward a new type of fuzzy controller design based on genetic algorithm optimization. At first, a genetic algorithm is used to optimize the fuzzy rules of the traditional fuzzy control, realize the design of the air conditioning fuzzy controller. Then the optimized fuzzy air conditioning controller is acted on the indoor environment. Finally, a simulation and experimental test is used to verify the validity of the method. The results show that the fuzzy controller for real-time control is effective on the indoor thermal environment and is suitable for the non-linear indoor environment system.

A general approach is proposed to design ultra-wideband radar cross section reduction (RCSR) metasurface by using non-resonant unit cells in chessboard arrangement. The proposed miniaturized artificial magnetic conductor unit cell is composed of two stacked non-resonant patches separated from one another by thin dielectric substrates. The genetic optimization algorithm is used to optimize the unit cell design parameters and obtain wide 10-dB RCSR bandwidth. The proposed approach is performed to design three different RCSR metasurfaces, ideal, ROGERS and low cost. The low cost RCSR metasurface composed of low cost commercially available FR-4 substrate is fabricated and tested which reduces RCS more than 10-dB from 5.22 GHz to 30.85 GHz, 142% fractional bandwidth. This metasurface has significantly wider RCSR bandwidth rather than the state of the art references as well as low cost, simple and light weight structure which facilities its practical applications.

Distribution networks (DN) have transformed more than ever before due to the penetration level of distributed energy resources. One of the promising technologies to generate power at the consumer level is the photovoltaic system (PV). To facilitate a higher level of PV penetration, DN planning and system operators encounter several challenges related to power reliability, stability, and quality that are affected by the intermittent nature of PV. A practical solution to overcome the uncertainty behavior of PV s is to use a Battery Energy Storage System (BESS). However, the appropriate placement of the BESS in the DN plays a significant role to mitigate the higher level of power losses. Hence, optimal allocation is extremely desirable to maximize the benefits of BESS. In this paper, the optimal allocation of BESS in a DN with a high penetration level of the PV system is examined towards power losses reduction. The optimal allocation of BESS in DN performed using a genetic algorithm optimization technique. The optimal placement of BESS in DN was compared between an aggregated BESS and distributed BESS. The outcomes of both of them showed a reduction in power losses, where the optimal allocation in distributed BESS has the highest power losses reduction.

Fabric defect detection plays a crucial role in fabric inspection and quality control. Convolutional neural networks (CNNs)-based model has been proved successful in various defect inspection applications. However, the sophisticated background texture is still a challenging task for fabric defect detection. To address the texture interference problem, taking advantage of Gabor filter in frequency analysis, we improved the Faster Region-based Convolutional Neural Network (Faster R-CNN) model by embedding Gabor kernels into Faster R-CNN, termed the Genetic Algorithm Gabor Faster R-CNN (Faster GG R-CNN); in addition, a two-stage training method based on Genetic Algorithm (GA) and back-propagation was designed to train the new Faster GG R-CNN model; finally, extensive experimental validations were conducted to evaluate the proposed model. The experimental results show that the proposed Faster GG R-CNN model outperforms the typical Faster R-CNN model in terms of accuracy. The proposed method’ mean average precision (mAP) is 94.57%, compared to 78.98% with the Faster R-CNN.

The static aeroelastic torsion divergence problem is the main obstacle to bring forward swept wing into massive applications. The aeroelastic tailoring technique-based radial basis function neural networks (RBFNNs) and genetic algorithm (GA) optimization in MATLAB considering the material orientation, thickness, and lay-up are elucidated in the present work. RBFNNs are used to build a surrogate model between the composite parameters and structure displacement, which is proved robust and accurate. Then an optimal structure is obtained by GA global search based on RBFNNs model with the weight constrain. The displacements of the forward swept wing caused by an approximate aerodynamic load are decreased 32.5% through finite element method (FEM) static structural analysis. The modal analysis illustrates that the first mode frequency increases by 33.0% and the second mode increases by 37.9%. A computational aeroelasticity approach is developed by in-house Hybrid Unstructured Reynolds-Averaged Navier-Stokes solver associating an open source FEM code – Calculix. The results of coupling calculations show effectiveness of aeroelastic tailoring optimization of composite forward swept wing without weight penalty. The results obtained demonstrate that for the forward swept wing, the most violent situation appears around Mach Number 1.0 where the aeroelastic tailoring optimization could decrease the torsion angle by nearly 70.0%. The torsion of forward swept wing will increase at subsonic and decrease at supersonic with the increase of velocity.

A methodology for structural analysis and optimal design of conical anisogrid composite lattice shell structures subject to different external loads concurrently applied and multiple stiffness constraints is presented. The critical buckling load of the anisogrid lattice conical structure is exactly assessed, independently of the buckling failure mode, by means of a discrete approach. The method makes use of a full FE parametric modeling technique able to manage all the geometrical parameters of the anisogrid composite lattice structure. Additionally, the genetic algorithm NSGA-II is employed to set up an optimization procedure which allows to analyze different sets of geometrical variables, both continuous and discrete, to reach the optimal solution in terms of mass amount and fulfilling of structural and stiffness requirements, aiming at the preliminary design of an actual structure. Numerical case-studies are outlined in order to demonstrate the practical usefulness and versatility of the proposed procedure to industrial cases where the anisogrid lattice conical structure undergoes multiple external loads and various stiffness constraints must be satisfied.

We theoretically present a method for equalizing modal gain in a six-LP-mode erbium-doped fiber amplifier by combing two sections of erbium-doped fibers (EDFs) with different doping profiles. In the two EDFs, the dopant distributions are designed to be spatially mismatched to mainly support the optical amplification of different spatial modes. The optimum fiber designs have been found by employing genetic algorithm optimization when pumped by LP11 mode, resulting in less than 1.0 dB DMG over 25 nm bandwidth in the C-band for the six-LP signal modes. In addition, tolerance study of various fiber parameters has been carried out theoretically to assess fabrication challenge in realizing a desirable amplification performance.

A structural analysis and an optimization method for anisogrid composite lattice shell structures is proposed, considering cylindrical structures simultaneously subjected to different external loads and multiple stiffness constraints. A discrete approach is used to exactly estimate the critical buckling load of the anisogrid lattice structure, independently of the buckling failure mode. The method makes use of a full FE parametric modeling technique able to manage all the geometrical parameters of the anisogrid composite lattice structure. Then an optimization procedure based on the genetic algorithm NSGA-II has been performed; it allows to analyze different alternatives in terms of geometrical variables, both continuous and discrete, driving the search towards the optimal solution in term of mass and conformity with all structural and stiffness constraints, aiming at the preliminary design of an actual structure. The practical usefulness and applicability of the proposed procedure to industrial cases was demonstrated through numerical examples where the anisogrid lattice structure was subjected to multiple external loads and stiffness constraints simultaneously applied.

Purpose: To investigate a genetic algorithm approach to automatic treatment planning. Methods: A Python script based on genetic algorithm (GA) was implemented for VMAT treatment planning of prostate tumor. The script was implemented in RayStation treatment planning system using Python code. Two different clinical prescriptions were considered: 78 Gy prescribed to planning target volume in 39 fractions (GROUP 1) and simultaneous integrated boost (70.2 Gy to prostate bed and 61.1 Gy to seminal vesicles) in 26 fractions (GROUP 2). The script automatically optimizes doses to PTV and OARs according to GA. A comparison with corresponding plans created with Monaco TPS (M) and Auto-Planning module of Pinnacle3 (AP) was carried out. The plans were evaluated with a total score (TS) of PlanIQ software in terms of target coverage and sparing of OARs as well as clinical score (CS) performed by a Radiation Oncologist. Results: In GROUP 1, mean value of TS were 150.6 ± 30.7, 146.3 ± 36.1 and 137.4 ± 35.7 for AP, GA and M respectively. For GROUP 2, mean value for TS were 163.5 ± 16.8, 163.4 ± 24.7 and 162.9 ± 16.6 for AP, GA and M respectively with no significance differences. In terms of CS, the highest value has been attributed to GA in four patients out of five for both GROUP 1 and 2. Conclusions: Genetic approach is practicable for prostate VMAT plan generation and studies are underway in other anatomical sites such as Head and Neck and Rectum.

According to the ideal vehicle model with two degrees of freedom of the transverse effect of yaw rate and sideslip angle of the vehicle stability, the yaw rate and side slip angle as the control parameter of vehicle stability control system, through the PID control algorithm and the Genetic Optimization PID control algorithm is studied on the simulation diagram, and building in matlab/simulink, the author makes a comparison and simulation analysis results of the two algorithms. Through the combination of simulation results, Genetic Optimization PID control algorithm has better control effect than PID control algorithm. The amplitude of the yaw rate and sideslip angle and the overshoot is significantly reduced, significantly improving the stability of automobile when steering.

Software for optimizing the configuration of manufacturing systems so as to reduce expenditures in all stages of the product life cycle is considered. Mathematical expressions are given for the three crossover operators used in the genetic optimization algorithm. A computational experiment indicates the most effective crossover operator.

In the present work, the Adaptive-Weight Genetic Algorithm was employed in order to determine the gear shifting strategies that allow an automobile to work in the best compromise among fuel consumption, engine emissions, and vehicle performance. For the assessment of each of the three objective functions, a simulation model based on engine data and on the well-established equations of the longitudinal dynamics was developed. The driving cycle chosen for the calculations was the FTP-75, which takes into account both cold and hot starts, meaning that the transient operation during the warm-up of the catalyst is also considered.

The study deals with the design and optimization of external and internal geometry of micro-wind turbines blades. A specified objective function which consists of the power coefficient and the starting time was defined and the genetic algorithm optimization technique in conjunction with the blade-element momentum theory was adopted to find the geometry of the blades including the distributions of the chord, the twist angle and also the shell thickness. Moreover, the allowable stress of the blades was considered as a constraint to the objective function. Results show that a reasonable compromise is achievable such that the starting time of the blades reduces noticeably in return for a small drop in the power coefficient. The significant improvement of the hollow blades over the solid ones indicates that the power coefficient and the starting performance could be improved through the appropriate distributions of the considered decision variables, i.e. the chord, the twist angle and also the shell th...

Design of a Small Diesel-Battery Submarine (SDBS) has been carried out using Multi-Objectives Genetic Algorithm optimization combined with Analytical Hierarchy Process to meet the operation requirements. The objective attributes for this optimization are cost and effectiveness. The product of this optimization is a series of cost-effectiveness frontiers in the scattered graph which are used to select alternative designs based on the Indonesian Navy's preference. It is found that the Length of the submarine is 28 m with Diameter 4 m. The sprint power of 20 knots determines the motor power while the endurance power of 5 knots determines the battery capacity.

This research work proposes unimodal image registration based on genetic algorithm. The intensity-based image registration is employed here, and normalized cross-correlation is used as the similarity index, and for choosing the optimal values of image registration parameters, genetic algorithm was employed. The performance of the image registration was validated by the performance metrics and tested on MR brain images of BrainWeb database. The performance metrics peak-to-signal noise ratio (PSNR), mean squared error (MSE), normalized cross-correlation (NCC), and mutual information (MI) reveals the superiority of the image registration algorithm.

In order to improve the accuracy of the BP neural network prediction model to predict the transmission synchronizer shift fault, a BP neural network prediction method based on genetic algorithm optimization is proposed. The characteristics and defects of BP neural network and genetic algorithm are introduced. Further study the relevant technology combining BP neural net-work and genetic algorithm. The genetic algorithm is used to optimize the weight and threshold of BP neural network, and train the BP neural network prediction model to obtain the optimal solution. The advantages of the local search ability of BP-neural network and global search ability of genetic algorithm are fully displayed. The simulation results show that the method has higher accuracy and better nonlinear fitting ability for transmission synchronizer shift fault.

Direct alcohol fuel cells are one of the suitable candidates for sustainable power generation in portable applications. Among the different alcohol types that can be used in these fuel cells, DME, which is almost non-toxic at room temperature and easy to liquefy, and has a molecular structure similar to methanol, is one of the suitable options. For this reason, many studies have been carried out to develop direct dimethyl ether fuel cell (DDMEFC). Mathematical modeling studies also play an important role in the development of DDMEFC since they enable the understanding of the performance of the fuel cells more thoroughly. In this study, a model has been developed by using the principles of conservation chemical species and electrochemistry. A modeling study was performed using MATLAB. The values of some modeling parameters were estimated using the genetic algorithm optimization technique.

In this paper, the optimal design of a hybrid energy plant composed of a solar thermal collector, a photovoltaic panel, a combined heat and power system, an absorption chiller, an air source heat pump, a ground source heat pump and a thermal energy storage is studied. The size of each technology is optimized by applying a model implemented in Matlab® environment. The optimization goal is the minimization of the primary energy consumed throughout the life cycle of the hybrid energy plant by using a genetic algorithm. The primary energy consumed during the manufacturing phase of the hybrid energy plant is represented by the cumulative energy demand and is calculated by carrying out a cradle to gate life cycle assessment. The primary energy consumed during the operation phase is evaluated by simulating the system throughout one year. The cumulative energy demand of each system composing the hybrid energy plant is calculated as a function of the technology size. Therefore, the problem of life cycle assessment scaling of renewable and non-renewable energy systems is also taken into account in this paper. A tower located in the north of Italy is selected as a case study and two different approaches are evaluated. The first approach consists of solving the sizing optimization problem by minimizing the primary energy consumption only during the operation phase, while in the second approach the primary energy consumption is minimized throughout the life cycle of the plant by integrating the life cycle assessment into the optimization process. The results show that, if life cycle assessment is accounted for, the optimal hybrid energy plant configuration is different and a higher primary energy saving (approximately 12%) is achieved.

This paper addresses the numeric optimization for NaBH4 powder flow which is commonly used for hydrogen gas production. During the motion process of the powder, a high number of collisions occur between particles constituting the powder. This paper focuses on modeling and finding the parameters that govern these collisions. We use a discrete element method to model the powder and assume that the powder is composed of tiny spheres interacting according to a specific spring damping model. In a series of appropriate physical wedge penetration experiments, force-displacement graphs were measured. In addition, a set of shear tests were conducted from which normal-shear force graphs were extracted. Analytical estimations were formulated for each of the experiments. These graphs were then compared with graphs generated by corresponding simulation tests. Using Genetic Algorithm optimization we obtained a set of governing parameters that best fits the powder behavior. In order to refine our results we have used our analytical formulations to manually search the parameter space for a better fit. Lastly, an angle of repose test validated our model. Keywords: Powder simulated behavior, Discrete element simulation, NaBH4 mechanical parameters, Genetic algorithm as a search method

In order to improve the prediction accuracy of compressive strength of concrete,103 groups of concrete data were collected as the samples.We selected seven kinds of ingredients from the concrete samples, using Grid-SVM, PSO-SVM, and GA-SVM models to establish the prediction model of cubic meter compressive strength of concrete.The experimental results show that SVM model based on Grid optimization algorithm,SVM model based on Particle swarm optimization algorithm,SVM model based on Genetic optimization algorithm mean square error respectively are 0.001, 0.489 8, and 0.304 2, correlation coefficients are 0.994 8, 0.994 6, and 0.993 0. It is shown that cubic meter compressive strength prediction method based on Grid-SVM model is the best optimization algorithm.

The present study investigates the use of HVAC and solar heating systems inside residential zones using a combination of direct numerical modeling and genetic optimization method in different climates of Iran. The direct numerical modeling is capable of calculating temperature field inside buildings. Then, direct modelling algorithm is used to determine energy consumption rate of the building in the different climates of Iran. The results of direct numerical modelling are checked against those of carrier and energy-plus software. The results show good accordance with those of numerical simulations presented in the open literature.

Radially self-accelerating beams are rotating shape-invariant solutions of the Helmholtz equation. As such, they exhibit continuous indefinitely spiraling trajectories. Here we present the possibility to tailor those beams to achieve almost any desired beam cross section, without sacrificing the unique propagation characteristics. We discuss analytical tailoring opportunities and highlight their inherent limitations. Following that, by way of example, a genetic optimization algorithm is applied to demonstrate the feasibility of heuristic computer optimization methods in general. The convergence towards several test-targets is demonstrated and the influence of the chosen fitness function is discussed.