Simultaneous Perturbation Stochastic Approximation Research Articles

Maximization of Flight Performance of Eight-Rotor Multirotor with Differentiated Hub Angle

The aim of this article is to design a rotary wing aircraft autopilot system that improves flight performance by changing the body shape during flight. The method is to obtain values that stabilize the longitudinal and lateral flight of the aircraft, where the amount of metamorphosis and Proportional-Integral-Derivative (PID) coefficients are determined using the simultaneous perturbation stochastic approximation (SPSA) optimization algorithm. The rotary wing aircraft has a deformable structure with eight rotors. Shape-changing rotary-wing aircraft are aircraft that can fly with the lift generated by propellers. Aerial platform; It consists of arms and trunk. The angle between mechanism A and the arm to which the rotors are connected can be changed with the horizontal plane and different configurations are obtained. When the angle between the arms is 45°, the octo configuration turns into a stable structure, while when the angle between the arms is 0°, the X8 configuration provides high maneuverability and increased controllability. Metamorphosis, its effect on longitudinal and lateral flight stability and improvement studies were carried out in a simulation environment and the results are presented in this study. As a result of the shape change, longitudinal and lateral narrowing occurred by 26.8° percent. Simulation tests were modeled in a closed environment, free from atmospheric effects. The obtained flight performance values are presented in Tables.

Simple Inverse Kinematics Computation Considering Joint Motion Efficiency.

Inverse kinematics (IK) is an important and challenging problem in the operation of industrial manipulators. This study proposes a simple IK calculation scheme for an industrial serial manipulator. The proposed technique can calculate appropriate values of the joint variables to realize the desired end-effector position and orientation while considering the motion costs of each joint. Two scalar functions are defined for the joint variables: one is to evaluate the end-effector position and orientation, whereas the other is to evaluate the motion efficiency of the joints. By combining the two scalar functions, the IK calculation of the manipulator is formulated as a numerical optimization problem. Furthermore, a simple algorithm for solving the IK via the aforementioned optimization is constructed on the basis of the simultaneous perturbation stochastic approximation with a norm-limited update vector (NLSPSA). The proposed scheme considers not only the accuracy of the position and orientation of the end-effector but also the efficiency of the robot movement. Therefore, it yields a practical result of the inverse problem. Moreover, the proposed algorithm is simple and easy to implement owing to the high-calculation efficiency of NLSPSA. Finally, the effectiveness of the proposed method is verified through numerical examples using a redundant manipulator.

Collaborative train timetabling and passenger flow control in oversaturated metro lines considering state-dependence

Congestion downgrades the moving speed of passengers in trains and platforms. This in turn affects congestion. That is, the moving speed of passengers interacts with congestion. It is state-dependent and obeys the fundamental diagram. This interaction makes the system unstable and can result in substantial passenger delays and heightened security risks in oversaturated metro lines. This study aims to rectify this by developing a more realistic collaborative optimization approach. Firstly, we formulate a deterministic fluid queuing network model to accurately capture the dynamic propagation of congestion in oversaturated metro lines. This model incorporates the fundamental diagram, thereby effectively considering the interaction between passenger movement speeds and congestion. A recursive algorithm is devised to solve the fluid queuing network model. The complexity of the algorithm is independent of the capacities of the stations and trains. This provides the possibility of analyzing large-scale metro networks. Based on the queuing network model, a mixed-integer nonlinear programming model aimed at minimizing the total passenger waiting time is built. To solve the model effectively, we employ an alternating direction method of multipliers framework to decompose the model into nonlinear programming and integer nonlinear programming problems, which are respectively solved by improved simultaneous perturbation stochastic approximation and adaptive large neighborhood search algorithms. Finally, a small-scale case and a real-world case with data from Chengdu metro lines are implemented to demonstrate the performance and effectiveness of the proposed approach. The experimental results provide some interesting insights. (1) Without consideration of state-dependence, the queuing model underestimates the passenger waiting time and overestimates the service levels. (2) As demand increases, excluding extreme oversaturation scenarios, the optimized results of the state-independent model progressively deteriorate compared to our state-dependent model, with the largest percentage decline being 50.23%. This is especially noticeable when the train capacity is limited. (3) The contribution ratio of passenger flow control in mitigating congestion gradually surpasses that of train timetabling with increasing demand. In the small-scale case, the former contribution ratio to optimization rises from negligible to 98.19%.

Real-Time Estimation of Origin–Destination Matrices Using a Deep Neural Network for Digital Twins

The digital twin, a real-time replica of physical systems, has garnered attention as a promising tool to strategize and evaluate solutions for complex real-world issues. However, developing digital twins in the field of transportation faces significant challenges related to the real-time estimation of dynamic origin–destination (OD) matrices constrained by computation time. To bridge this gap, microscopic traffic simulations with real-time synchronization are being researched. Nonetheless, the computational issue persists, emphasizing the need for more efficient OD estimation methods. In this regard, our objective is to reduce computation time in simulation-based methods by developing a data-driven metamodel using a deep neural network. The proposed model serves to map the correlation between the OD matrix and detector data. This model simplifies the computational process using hidden layers, rather than calculating complex interactions between vehicles in the traffic simulation. Compared to conventional methods, we evaluate the capability to estimate a reasonable OD matrix within a restricted time and validate our approach using detector data from Daejeon City, South Korea. The findings indicate that by combining our data-driven metamodel with the simultaneous perturbation stochastic approximation, it becomes feasible to estimate a reasonable OD matrix within a stipulated time frame, compared to the conventional method. Given a 1-min time frame, the proposed method outperforms the conventional simulation-based method by improving the calibration performance of traffic flow by 44.5 percentage points. This paper proposes a practical and versatile approach for real-time OD estimation, laying the foundation for extending microscopic traffic simulation into the digital twin.

A new approach for fracture parameters optimization of multi-fractured horizontal wells: Virtual boundary method

Horizontal well fracturing technology stands as the cornerstone for achieving large-scale commercial production in shale reservoirs. Yet, the complexity of fracture variables within horizontal wells, comprising both discrete and continuous factors, presents a formidable challenge for optimization. Existing methods struggle to holistically optimize these mixed fracture parameters, including discrete fracture number, continuous fracture location, and half-length. This study pioneers a fracture deployment optimization approach tailored for fractured horizontal wells in shale reservoirs, leveraging the innovative virtual boundary method (VBM). Initially, a virtual boundary model is developed by extending the length of the fracturing horizontal wellbore, defining the extended area as a virtual reservoir devoid of hydrocarbon. Subsequently, the net present value (NPV) is adopted as the objective function, with continuous fracture location and half-length serving as optimization variables. The modified simultaneous perturbation stochastic approximation (mSPSA) algorithm is then employed for optimization. Following optimization, the optimized fractures within the virtual area are identified and deleted via position identification, enabling the comprehensive optimization of fracture number, location, and half-length. Notably, the computational efficiency of this method surpasses existing approaches by a factor of 5.1 compared to the bisection nested SPSA optimization method. Validation through three calculation cases, encompassing homogeneous reservoir, distinct sweet spot reservoir, and strongly heterogeneous reservoir, further underscores the method's reliability. Post-optimization, NPV increases by 1.1 times, 1.4 times, and 0.34 times for the respective calculation cases compared to the initial plan. When applied to an actual shale oil reservoir, the method yields a 1.67 times increase in NPV compared to existing deployment plans. This research lays a solid theoretical foundation for the design of horizontal fracturing well construction plans in shale reservoirs.

The influence of basis sets and ansatze building to quantum computing in chemistry.

Quantum computing is an exciting area, which has grown at an astonishing rate in the last decade. It is especially promising for the computational and theoretical chemistry area. One algorithm has received a lot of attention lately, the variational quantum eigensolver (VQE). It is used to solve electronic structure problems and it is suitable to the noisy intermediate-scale quantum (NISQ) hardware. VQE calculations require ansatze and one of the most known is the unitary coupled cluster (UCC). It uses the chosen basis set to generate a quantum computing circuit which will be iteratively minimized. The present work investigates the circuit depth and the number of gates as a function of basis sets and molecular size. It has been shown that for the current quantum devices, only the smallest molecules and basis sets are tractable. The H molecule with the cc-pVTZ and aug-cc-pVTZ basis sets have circuit depths in the order of 10 to 10 gates and the C H molecule with 3-21G basis set has a circuit depth of gates. At the same time the analysis demonstrates that the H molecule with STO-3G basis set, requires at least 500 shots to reduce the error and that, although error mitigation schemes can diminish the error, they were not able to completely negate it. The quantum computing and electronic structure calculations were performed using the Qiskit package from IBM and the PySCF package, respectively. The ansatze were generated using the UCCSD method as implemented in Qiskit, using the basis sets STO-3G, 3-21G, 6-311G(d,p), def2-TZVP, cc-pVDZ, aug-cc-pVDZ, cc-pVTZ, and aug-cc-pVTZ. The operators and the Hamiltonian were mapped using the Jordan-Wigner scheme. The classical optimizer chosen was the simultaneous perturbation stochastic approximation (SPSA). The quantum computers used were the Nairobi and Osaka, with 7 and 127 qubits respectively.

Flight control system design of UAV with wing incidence angle simultaneously and stochastically varied

PurposeThis study aims to simultaneously and stochastically maximize autonomous flight performance of a variable wing incidence angle having an unmanned aerial vehicle (UAV) and its flight control system (FCS) design.Design/methodology/approachA small UAV is produced in Iskenderun Technical University Drone Laboratory. Its wing incidence angle is able to change before UAV flight. FCS parameters and wing incidence angle are simultaneously and stochastically designed to maximize autonomous flight performance using an optimization method named simultaneous perturbation stochastic approximation. Obtained results are also benefitted during UAV flight simulations.FindingsApplying simultaneous and stochastic design approach for a UAV having passively morphing wing incidence angle and its flight control system, autonomous flight performance is maximized.Research limitations/implicationsPermission of the Directorate General of Civil Aviation in Turkish Republic is necessary for real-time flights.Practical implicationsSimultaneous stochastic variable wing incidence angle having UAV and its flight control system design approach is so useful for maximizing UAV autonomous flight performance.Social implicationsSimultaneous stochastic variable wing incidence angle having UAV and its flight control system design methodology succeeds confidence, excellent autonomous performance index and practical service interests of UAV users.Originality/valueCreating an innovative method to recover autonomous flight performance of a UAV and generating an innovative procedure carrying out simultaneous stochastic variable wing incidence angle having UAV and its flight control system design idea.

Stochastic optimal tuning for flight control system of morphing arm octorotor

PurposeThis study aims to discuss the simultaneous longitudinal and lateral flight control of the octorotor, a rotary wing unmanned aerial vehicle (UAV), for the first time under the effect of morphing and to improve autonomous flight performance.Design/methodology/approachThis study aims to design and control the octorotor flight control system with stochastic optimal tuning under morphnig effect. For this purpose, models of different arm lengths of the octorotor were drawn in the Solidworks program. The morphing was carried out by simultaneously lengthening or shortening the arm lengths of the octorotor. The morphing rate was estimated by using simultaneous perturbation stochastic approximation (SPSA). The stochastic gradient descent algorithm, which is frequently used in machine learning, was used to estimate the changing moments of inertia with the change of arm lengths. The proportional integral derivative (PID) controller has been preferred as an octorotor control algorithm because of its simplicity of structure. The PID gains required to control both longitudinal and lateral flight were also estimated with SPSA.FindingsWith SPSA, three longitudinal flight PID gains, three lateral flight PID gains and one morphing ratio were estimated. PID gains remained within the limits set for SPSA, giving satisfactory results. In addition, the cost index created was 93% successful. The gradient descent algorithm used for the moment of inertia estimation achieved the optimum result in 1,570 iterations. However, in the simulations made with the obtained data, longitudinal and lateral flight was successfully carried out.Originality/valueOctorotor longitudinal and lateral flight control was performed quickly and effectively with the proposed method. In addition, the desired parameters were obtained with the optimization methods used, and the longitudinal and lateral flight of the octorotor was successfully carried out in the desired trajectory.

Enhancing buildings' energy efficiency prediction through advanced data fusion and fuzzy classification

This study proposes a method to predict buildings' energy efficiency based on available descriptive information and without a physical visit, by merging diverse datasets and employing advanced classification techniques. By integrating geographical, structural, legal, and socio-economic data with Energy Performance Certificate (EPC) observations, our approach yields a rich learning set. Through variable selection methods like forward selection with KNN and simultaneous perturbation stochastic approximation for fuzzy KNN, we refine model variables. Comparing fuzzy and hard classification using KNN, Kriging or Random Forest approaches, we find fuzzy classification more adept at capturing nuanced energy inefficiency indicators. Our study highlights the importance of mass energy efficiency prediction for sustainable renovation efforts.

Optimization of nonlinear turbulence in stellarators

We present new stellarator equilibria that have been optimized for reduced turbulent transport using nonlinear gyrokinetic simulations within the optimization loop. The optimization routine involves coupling the pseudo-spectral GPU-native gyrokinetic code GX with the stellarator equilibrium and optimization code DESC. Since using GX allows for fast nonlinear simulations, we directly optimize for reduced nonlinear heat fluxes. To handle the noisy heat flux traces returned by these simulations, we employ the simultaneous perturbation stochastic approximation (SPSA) method that only uses two objective function evaluations for a simple estimate of the gradient. We show several examples that optimize for both reduced heat fluxes and good quasi-symmetry as a proxy for low neoclassical transport. Finally, we run full transport simulations using the T3D stellarator transport code to evaluate the changes in the macroscopic profiles.

HK-SPSA based performance optimization method for steam generator liquid level control

Steam generator level control systems play a crucial role in ensuring safe, economical, and stable operation of nuclear power plants. However, traditional control systems exhibit limited efficiency in commissioning and are susceptible to human error. Additionally, traditional parameter tuning methods are typically experienced-based, cumbersome, and time-consuming, making it challenging to obtain optimal parameters. To address these issues, we propose a hybrid knowledge-guided improved simultaneous perturbation stochastic approximation algorithm (HK-SPSA) to optimize the control parameters of steam generator level control systems. Firstly, the algorithm utilizes iteration point adjacency information to approximate the current optimization process status and guide the adaptive adjustment of the iteration step size. Secondly, it utilizes composite gradient information generated by the estimated historical and current gradients to guide the optimization direction and the tuning of iteration step size. Simulation experiments have shown that HK-SPSA improves optimization performance and reduces the costs of optimization and adjustment compared to traditional methods.

Autonomous flight performance optimization of fixed-wing unmanned aerial vehicle with morphing wingtip

PurposeThe purpose of this paper is to improve autonomous flight performance of a fixed-wing unmanned aerial vehicle (UAV) via simultaneous morphing wingtip and control system design conducted with optimization, computational fluid dynamics (CFD) and machine learning approaches.Design/methodology/approachThe main wing of the UAV is redesigned with morphing wingtips capable of dihedral angle alteration by means of folding. Aircraft dynamic model is derived as equations depending only on wingtip dihedral angle via Nonlinear Least Squares regression machine learning algorithm. Data for the regression analyses are obtained by numerical (i.e. CFD) and analytical approaches. Simultaneous perturbation stochastic approximation (SPSA) is incorporated into the design process to determine the optimal wingtip dihedral angle and proportional-integral-derivative (PID) coefficients of the control system that maximizes autonomous flight performance. The performance is defined in terms of trajectory tracking quality parameters of rise time, settling time and overshoot. Obtained optimal design parameters are applied in flight simulations to test both longitudinal and lateral reference trajectory tracking.FindingsLongitudinal and lateral autonomous flight performances of the UAV are improved by redesigning the main wing with morphing wingtips and simultaneous estimation of PID coefficients and wingtip dihedral angle with SPSA optimization.Originality/valueThis paper originally discusses the simultaneous design of innovative morphing wingtip and UAV flight control system for autonomous flight performance improvement. The proposed simultaneous design idea is conducted with the SPSA optimization and a machine learning algorithm as a novel approach.

Optimization of CO2 EOR and geological sequestration in high-water cut oil reservoirs

In terms of the collaborative optimization of CO2 flooding for Enhanced Oil Recovery (EOR) and CO2 sequestration, previous studies have co-optimized both cumulative oil production and CO2 sequestration by various algorithms. However, these solutions fail to optimize the CO2 injection schemes for high-water cut oil reservoirs. This paper presents an optimization methodology for CO2 flooding and sequestration in high-water cut oil reservoirs. The production optimization was carried out by adjusting the injection and production rate. To solve the proposed objective functions, the simultaneous perturbation stochastic approximation (SPSA) algorithm is applied in this paper, and the CMG-GEM module is utilized to simulate the reservoir production performance. A typical high-water cut reservoir in the Shengli oilfield was used to verify the feasibility of the presented methodology. In this paper, the production performance and net present value (NPV) for continuous gas injection under different water cuts were analyzed. The optimal timing of transforming from water flooding to gas displacement for the high-water cut reservoir was optimized. In addition, the optimal water–gas ratios for Water-Alternating-Gas (WAG) flooding were determined. The sensitivity of NPV to gas injection price and carbon subsidy was analyzed. The results show that when the gas price is 0.178 $/m3 and the carbon subsidy is 0.0169 $/m3, the optimal timing of transforming from water flooding to gas injection should be earlier than the time when the water cut is 0.82. Through the combination of NPV, cumulative oil production rate, and CO2 sequestration volume for WAG flooding, the optimal WAG ratio should be 1:2. The presented method in this paper considers various economic indicators and can optimize CO2 flooding and sequestration in high-water cut oil reservoirs efficiently, which can provide some guidance for the design of CO2 flooding schemes in high-water cut oil reservoirs.

Technical Note—On the Convergence Rate of Stochastic Approximation for Gradient-Based Stochastic Optimization

Finite-Time Performance of Gradient-Based Stochastic Approximation Algorithms Stochastic approximation (SA) algorithms are among the most commonly used methods for addressing stochastic optimization problems. In “Technical Note: On the Convergence Rate of Stochastic Approximation for Gradient-Based Stochastic Optimization,” J. Hu and M. C. Fu provide a new error bound for SA algorithms when applied to a class of problems with convex differentiable structures. The bound allows a detailed characterization of an algorithm’s finite-time performance through directly analyzing the bias and variance of the employed gradient estimator. The main result is then applied to study and compare the efficiency of traditional Kiefer-Wolfowitz algorithms and those based on random direction finite-difference gradient estimators such as simultaneous perturbation SA. The analysis leads to new insights into when it may be advantageous to use such randomized gradient estimates under various problem and algorithm parameter settings.

A Hybrid Energy Storage System Integrated with a Wave Energy Converter: Data-Driven Stochastic Power Management for Output Power Smoothing

Beyond solar and wind energy, wave energy is gaining great interest due to its very high theoretical potential, although its stochastic nature causes intermittent and fluctuating power production. Energy storage system (ESS) integration to wave energy converter (WEC) plants represents a promising solution to mitigate this issue. To overcome the technological limits of the single storage devices, the hybridization of complementary ESSs represents an effective solution, extending the operating range over different timeframes. This paper analyzes the benefits of Li-ion battery–supercapacitor hybrid ESS integration into a grid-connected WEC, aiming at smoothing the produced power oscillations. The hybridization concept involves coupling a power-intensive technology, such as a supercapacitor devoted to managing fluctuations at higher frequency, with a battery technology exploited to manage power variations over longer timeframes to mitigate degradation issues. In this study, a multi-objective data-driven power management strategy, based on the simultaneous perturbation stochastic approximation (SPSA) algorithm, is implemented to minimize power fluctuations in terms of power ramp (representing the power variation between two consecutive values with a 1 s time step), both at the Point of Common Coupling (PCC) and the Li-ion battery terminals, thanks to the supercapacitor peak-shaving function. The SPSA management strategy, together with a suitable sizing procedure, allows a reduction of more than 70% in the power oscillations at the PCC with respect to those at the WEC terminals, while decreasing battery stress by more than 25% if compared to a non-hybrid ESS consisting of a Li-ion battery. This shows how supercapacitor features can extend battery lifespan when integrated in a hybrid ESS.

A 0–1 linear relaxation and stochastic approximation method for real-time pricing in a smart grid with non-convex constraints

In this article, real-time pricing (RTP) for a smart grid with complicated non-convex and/or non-smooth constraints is explored. First, piecewise linear functions are adopted to approximate any kinds of utility function and RTP is formulated into bilevel non-convex programming. Then, a 0–1 relaxation method is used to linearize sparse constraints and piecewise linear utility functions, to obtain mixed integer linear programming. Finally, a distributed simultaneous perturbation stochastic approximation (DSPSA) method is designed to solve the bilevel non-convex programming. The RTP strategy is generalizable because sparse constraints and non-differentiable and/or non-concave utility functions are widespread in practice, and the piecewise linear functions approximation method is applicable to all kinds of utility function, not just to non-smooth and/or non-concave utility functions. In addition, the 0–1 linear relaxation and stochastic approximation method make the bilevel non-convex programming easily solvable by DSPSA, which converges rapidly while maintaining the high accuracy of solutions.

Automatic History Matching for Adjusting Permeability Field of Fractured Basement Reservoir Simulation Model Using Seismic, Well Log, and Production Data

Developing automatic history matching (AHM) methods to replace the traditional manual history matching (MHM) approach in adjusting the permeability distribution of the reservoir simulation model has been studied by many authors. Because permeability values need to be evaluated at hundreds of thousands of grid cells in a typical reservoir simulation model, it is necessary to apply a reparameterization technique to allow the optimization algorithms to be implemented with fewer variables. In basic reparameterization techniques including zonation and pilot point methods, the calibrations are usually based solely on the production data with no systematic link to the geological and geophysical data, and therefore, the obtained permeability distribution may be not geologically consistent. Several other reparameterization techniques have attempted to preserve geological consistency by incorporating 4D seismic data; however, these techniques cannot be applied to our fractured basement reservoirs (FBRs) as they do not have 4D seismic data. Taking into account these challenges, in this study, an AHM methodology and workflow have been developed using a new reparameterization technique. This approach attempts to minimize the potential for geological nonconsistency of the calibrated results by linking the permeability to geophysical data. The proposed methodology can be applied to fields with only traditional geophysical data (3D seismic and conventional well logs). In the proposed workflow, the spatial distributions of seismic attributes and geomechanical properties were calculated and estimated from 3D seismic data and well logs, respectively. After that, a feed-forward artificial neural network (ANN) model trained by the back-propagation algorithm of the relationship between initial permeability with seismic attributes and geomechanical properties of their grid cell values is developed. Then, the calibration of the permeability distribution is performed by adjustment of the ANN model. Modification of the ANN model is performed using the simultaneous perturbation stochastic approximation (SPSA) algorithm to calibrate transmission coefficients in the ANN model to minimize the discrepancy between the simulated results and observed data. The developed methodology is applied to calibrate the permeability distribution of a simulation model of Bach Ho FBR in Vietnam. The effectiveness of the methodology is evident by comparing the historical matches with an available manually history-matched simulation model. The application shows that the proposed methodology could be considered as a suitable practical approach for adjusting the permeability distribution for FBR reservoir simulation models.

GK-SPSA-Based Model-Free Method for Performance Optimization of Steam Generator Level Control Systems

The Steam Generator (SG) is a crucial component of a Nuclear Power Plant (NPP), generating steam to transfer heat from the primary loop to the secondary loop. The control performance of the Steam Generator Level Control System (SGLCS) plays a crucial role in the normal operation of the SG. To improve the system’s performance, the parameters of the control system should be optimized. However, the steam generator and its corresponding control system are highly complex, exhibiting nonlinearity and time-varying properties. Conventional parameter-setting methods mainly rely on engineers’ experience, and are laborious and time-intensive. To tackle the aforementioned challenges, a Model-Free Optimization (MFO) method based on Knowledge-informed Historical Gradient-based Simultaneous Perturbation Stochastic Approximation (GK-SPSA) is applied to the performance optimization of the steam generator level control system. The GK-SPSA algorithm is a variant of the traditional SPSA algorithm. The fundamental idea of this revised algorithm is to maximize the utilization of historical gradient information generated during the optimization process of the SPSA algorithm, with the aim of enhancing overall algorithm performance in a model-free optimization context. Based on the effective utilization of historical gradient information, the GK-SPSA algorithm exhibits two improvements over the SPSA algorithm. The first improvement is related to the recognition of the online optimization progress, utilizing the state of the optimization progress to dynamically adjust the optimization step size. The second improvement is related to gradient estimation compensation, employing compensation rules to enhance the accuracy of gradient estimation, thus improving the optimization efficiency. Through simulation experiments, it can be observed that there is not much difference in the final iteration values among the GK-SPSA, IK-SPSA, and SPSA methods. However, the iteration count of GK-SPSA is reduced by about 20% compared to SPSA and by 11.11% compared to Knowledge-informed SPSA (IK-SPSA). The results indicate that this method can significantly improve the efficiency of parameter tuning for the liquid level control system of a steam generator.

A Modified Simultaneous Perturbation Stochastic Approximation Algorithm for Active Noise Control

Secondary path modeling is an important step in active noise control. Keeping the active noise control system stable, when the secondary path changes over time, is a widely studied problem. Using the simultaneous perturbation stochastic approximation algorithm, can update the control filter without secondary path modeling, avoiding the problem of increased noise caused by secondary path modeling. In this paper a variant of the simultaneous perturbation stochastic approximation algorithm is proposed, which incorporates historical coefficients of the control filter, to correct the direction of the gradient calculation. Based on an experimental setup with a realistic transfer path, this paper demonstrates that the proposed algorithm has superior performance in terms of convergence speed and noise reduction amount, than the general simultaneous perturbation stochastic approximation algorithm.

Developed Computational Methodology for the Most Effective Design of Open Channel Cross Sections

Open channel is the most important way to convey water for various purposes. Channels cost a lot of money to construct. The optimal design means delivering water with efficiency, safely, and economical costs of construction. The determinant of minimum cost must take into account the effect of manning coefficient variation between the sides and the bed. The proposed program is solved using the Shuffled complex evolution method developed at the University of Arizona (SCE-UA) algorithm. The results obtained in this study are compared with existing models developed by others in the past using either the complex equations or the Simultaneous Perturbation Stochastic Approximation (SPSA) technique. The result shows that the existing optimization problem formulations, when solved using SCE-UA, provided results compared to those solved using the Lagrange multiplier method. The results obtained in this study indicate that approximately 34% of cost savings can be achieved. Also, the provided results were compared to those obtained by others in the past using (SPSA) technique. The results obtained in this study indicate that approximately 36.5% of cost savings can be achieved. It was found that the (SCE-UA) technique is more accurate than (SPSA) and has the lowest total construction cost.