A Nonlinear Optimization Design Algorithm for Nearly Linear-Phase 2D IIR Digital Filters

An effective method is proposed for the design of recursive digital filters in the time/spatial domain. The method is based on the computation of an eigenvalue and its corresponding eigenvector of an appropriate real symmetric and positive-definite matrix derived from the objective error function of the impulse response in the least-squares sense. The method can be used to design 1D IIR digital filters as well as 2D IIR digital filters. The method is easy and the performance is comparable to those of the existing methods. One of the main advantages of the proposed method is that the solution is obtained directly without the need for iteration.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In the current scenario Economic Dispatch problem has taken a suitable twist as the public has become increasingly concerned with environmental matters. The dispatch of systems to minimise pollutants, as well as to achieve minimum cost are the two main constraints to be dealt with. In this paper, Global optimization techniques such as Honey Bee’s and Simulated Annealing techniques are employed together (HB-SA) to solve the Combined static Economic and Emission dispatch (CEED) problems in power system. This paper clearly is justified with the results, separately obtained for the above two techniques and is also provided with the results on applying both the algorithms separately. With practical consideration, Economic Dispatch will have non-smooth cost functions with equality and inequality constraints that make the problem, a large-scale highly constrained non linear optimization problem. As to demonstrate the effectiveness of the proposed method, it is subjected to the CEED problems considering (or) handling valve point loading effects. Comparison with other optimization techniques showed the superiority of the proposed HB-SA approach and confirmed its potential for solving nonlinear CEED problems.

In real‐time two‐dimensional digital filters using a special‐purpose processor with fixed‐point arithmetic, the word length should be as small as possible to realize the desired processing accuracy. A digital filter which is affected least by quantization should be used. From such a viewpoint, this paper aims at the synthesis of the two‐dimensional digital filter structure with the separable denominator and with the minimum round‐off error. As the first step, the two‐dimensional digital filter with the separable denominator is represented by the local state‐space model. The result of analysis for the round‐off error already made by the authors is outlined. Based on these results, the synthesis of the structure with the minimum round‐off noise is formulated by equivalent transformation. A method of synthesis is presented. The two‐dimensional digital filter with the separable denominator and with the minimum roundoff noise, which is obtained by the synthesis method in this paper, is shown to be of a structure which does not produce an overflow oscillation for zero input. Finally, the effectiveness of the proposed method is shown by a numerical example.

The intermittent nature of wind power generation induces great challenges for power bidding in the electricity market. The deployment of battery energy storage can improve flexibility for power bidding. This paper investigates an optimal power bidding strategy for a wind–storage hybrid power plant in the day-ahead electricity market. To handle the challenges of the uncertainties of wind power generation and electricity prices, the optimal bidding problem is formulated as a risk-aware scenario-based stochastic programming, in which a number of scenarios are generated using a copula-based approach to represent the uncertainties. These scenarios consider the temporal correlation of wind power generation and electricity prices between consecutive time intervals. In the stochastic programming, a more practical but nonlinear battery operation cost function is considered, which leads to a nonlinear constrained optimization problem. To solve the nonlinear constrained optimization problem, an ensemble differential evolution (EDE) algorithm is proposed, which makes use of the merits of an ensemble of mutant operators to generate mutant vectors. Moreover, a problem-specific constraint-handling technique is developed. To validate the effectiveness of the proposed EDE algorithm, it is compared with state-of-the-art DE-based algorithms for constrained optimization problems, including a constrained composite DE (C2oDE) algorithm and a novel DE (NDE) algorithm. The experimental results demonstrate that the EDE algorithm is much more reliable and much faster in finding a better bidding strategy against benchmarking algorithms. More precisely, the average values of the success rate are 0.893, 0.667, and 0.96 for C2oDE, NDE, and EDE, respectively. Compared to C2oDE and NDE, the average value of the mean number of function evaluations to succeed with EDE is reduced by 76% and 59%, respectively.

This paper proposes a robust parameter estimator to deal with the problem of inaccurate power losses calculated from the estimated state variables. These power loss inaccuracies result from some incorrect branch impedance due to non-stationary ambient conditions or geometrical constraints unreported in the database. Specifically, we use the projection statistics and the coupling relationship between parameter errors and the measurements to detect incorrect branch parameters associated with leverage points, which are proved to show the largest influence on the state estimates. Then these suspicious parameters are augmented with the original system state variables for the generalized estimation. Finally, the generalized robust estimator is executed as a nonlinear constrained optimization problem. The necessary condition for the primal-dual interior point method-based solver is derived. Our proposed method is able to enforce each suspicious parameter within its validity domain expressed as inequality constraints. Thanks to the robustness of the proposed Schweppe-type Huber (SHGM)-estimator and the enhanced measurement redundancy using multiple night off-peak snapshots of a nearly constant power system state, incorrect parameter values and erroneous zero injections are reliably detected and effectively corrected and validated. The performance of the weighted least squares estimator, the generalized least absolute value estimator, and the SHGM-estimator are evaluated and compared with regards to the estimated values of impedances and losses. Field test results on the realistic 2000-bus French power system show that the SHGM-estimator outperforms the other two estimators.

Effective and comprehensive evaluation of cold stress is critical for healthy dairy cow breeding in the winter. Previous studies on dairy cow cold stress have considered thermal environmental factors but not physiological factors or air quality. Therefore, this study aimed to propose a multilevel fuzzy comprehensive evaluation (FCE) method for cold stress in dairy cows based on the analytic hierarchy process (AHP) and a genetic algorithm (GA). First, the AHP was used to construct an evaluation index system for cold stress in dairy cows from 3 dimensions: thermal environment (temperature, relative humidity, wind speed, and illumination), physiological factors (respiratory rate, body surface temperature), and air quality [NH3, CO2, inhalable particulate matter (PM10)]. Second, the consistency test of the judgment matrix was transformed into a nonlinear constrained optimization problem and solved using the GA. Next, based on fuzzy set theory, the comment set and membership function were established to classify the degree of cold stress into 5 levels: none, mild, moderate, high, and extreme. Then, the degree of cold stress in cows was obtained using multilevel fuzzy comprehensive judgment. To investigate the effect of illumination indicators on cold stress in dairy cows, 24 prelactation cows from the south and north sides were selected for a 117-d comprehensive cold stress evaluation. The results showed that the mean mild cold stress durations were 605.3 h (25.22 d) and 725.5 h (30.23 d) and the moderate cold stress durations were 67.2 h (2.8 d) and 96 h (4.0 d) on the south and north sides, respectively. Simultaneously, generalized linear mixed model showed that there were significant correlations between the daily cold stress duration and milk yield, feeding time, lying time, and active steps in the cows on both sides. This method can reasonably indicate cow cold stress conditions and better guide cold protection practices in actual production.

Legged mechanisms can walk and run but sometimes encounter a risk of falling. In this article, a general numerical framework of balance criteria for a single-support legged mechanism is proposed and demonstrated. Explicit forms of necessary and sufficient conditions for balancing are identified and the balanced state manifold is constructed accordingly in the extended phase space of joint angle, joint velocity, and actuation limit. Within the iteration loops for partitioned joint angle and actuation limit, a nonlinear constrained optimization problem is formulated where the dynamic models of the legged mechanism are incorporated. The necessary conditions for balancing, such as the Zero-Moment Point, positive normal reaction, friction, and the ability to end up at a final static equilibrium, are implemented along with the system parameters for generality. The sequential quadratic programming method numerically solves for the velocity extrema within the complete feasible domain to construct the balanced state manifold as a viability kernel, which is a reachable superset of all possible controller-based domains. The balanced state manifold, along with its demonstration using the proposed optimal balancing motion for minimum energy and biped walking motions in sagittal plane, shows valid features that are physically consistent. The framework can be extended to systems in three-dimension with higher complexity, both in single and double support phases, for the development and stability analysis of walking robots and humans.

A Design Optimization Procedure for Efficient Turbine Airfoil Design

Crank block steering mechanism optimization, which is a nonlinear constrained optimization problem, is important for forklift truck to get preferable steering performance. Vector particle swarm optimization (VPSO) is a swarm intelligent algorithm proposed to solve constrained optimization problems with simple concept and easy implementation. Therefore, VPSO was proposed to optimize crank block steering mechanism of forklift truck in this paper, which will be intended for minimizing the maximal error of outer wheel steering angle, maximizing the minimal transmission angle and making the transmission capability of the force better. The experimental results obtained by VPSO are compared with the results obtained by enumeration algorithm which are reliable for optimization problems. The comparison results showed that vector particle swarm optimization can get same optimal results as enumeration algorithm in much less calculation times.

&amp;lt;p&amp;gt;Thermodynamics plays an increasingly important role in computational geodynamics, as the community moves towards including chemical reactions in mechanical models of deformation. An example is mineral reactions that induce volume changes which affect the local state of stress that may trigger nonlinear feedbacks. Another example is numerical simulations of magmatic systems where the chemistry of melts changes dramatically as the melt differentiates on its way up through the lithosphere. In order to compare the results of numerical models with geochemical and petrological field data, it is crucial to predict the rock types and major element chemistry from numerical simulations of magmatic systems. Precomputing phase diagrams and including them as a lookup table, which is the current standard approach, works when the chemistry is more or less constant, but is nontrivial for magmatic systems where the chemistry changes drastically and involves a 13-dimensional system (11 oxides, plus pressure and temperature). Parameterizing the main reactions is a possibility, but this renders the comparison of simulation results with available data difficult.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In the context of magmatic systems, significant progress has been made in recent years and we now have thermodynamic melting models that are consistent with experiments and can simulate the full compositional range from mafic to felsic melt compositions. Yet, in order to be useful for geodynamic applications, we also need sufficiently fast Gibbs energy minimization software tools that can automatically determine the most stable assemblage for a given pressure, temperature and chemistry. This is a nonlinear constrained optimization problem, for which we have developed a new, parallel, solution approach. One novelty of our approach is that it can efficiently make use of good initial guesses, for example obtained from the previous timestep of a geodynamic simulation or from nearby points in pressure and temperature space. Yet, as with any gradient-based method, a risk remains to be trapped in a local minimum in the solution space that is not the overall most stable assemblage. It is thus important to explore a sufficiently broad range of starting parameters to ensure convergence toward the global minimum. Whereas this problem is well-known among users of existing thermodynamic software (such as THERMOCALC, where the starting values have to be adjusted manually), it is much less clear how nonlinear the parameter space actually is for real applications. Do we have thousands of local minima, or are there only a few (and if yes, can we precompute some of these)?&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Here, we will discuss several examples of melting models and map out the nonlinearity of the parameter space for these cases to get better insights in how to further speed up such calculations. We also discuss how shallow and deep neural networks can be trained and implemented as part of the workflow.&amp;lt;/p&amp;gt;

Optimum design of continuous stirred tank reactors (CSTRs) in series to biodegrade organics in wastewater was carried out. It was assumed that cell growth kinetics follows Contois model with endogenous decay. The optimal design was based on the minimum overall reactors volume required for a certain degree of substrate removal. The effect of operating parameters such as substrate inlet feed concentration to the first reactor, substrate removal efficiency, and number of CSTRs in series on the optimum design were investigated. The non-linear constrained optimisation problem was solved using the MATLAB function fmincon . For equal CSTRs, the system of non-linear equations was solved by ‘fsolve’ MATLAB function. Results indicate that the optimum multi-stage bioreactors are beneficial only at high substrate conversion. The substrate concentration in the feed to the first reactor has little effect on the total reactors volume. Recycle of biomass to the first reactor reduces the required total reactors volume. In general, at high percentage of substrate removal the optimum reactors design requires smaller volume compared to equal-size reactors.

Identification of dynamic parameters of a 3-DOF RPS parallel manipulator

Integrated design of radial active magnetic bearing systems using genetic algorithms

In this paper, a novel Trust Region based Sequential Linear Programming (TRSLP) method is proposed for unscheduled intentional islanding in microgrid. In unscheduled islanding, the solution must be obtained with minimum time and minimum load curtailment by maintaining all the system parameter constraints intact. First, the unscheduled intentional islanding is formulated as a nonlinear constrained optimization problem. Then, the TRSLP based optimization method is employed to obtain the final switching sequence of the network so that intentional islanding can be done in optimal manner. The objective function of the optimization problem is to minimize the amount of load curtailment. To validate the proposed technique, a 7-bus microgrid test system is simulated in Real-Time Digital Simulator (RTDS) and corresponding system parameters are further used in the TRSLP optimization technique to get the optimal islanding area.

An effective cellular particle swarm optimization for parameters optimization of a multi-pass milling process