scholarly journals Sobre la síntesis óptima de mecanismos

2019 ◽  
pp. 16-22
Author(s):  
Ixshel Jhoselyn Foster-Vázquez ◽  
Rogelio De Jesús Portillo-Vélez ◽  
Eduardo Vazquez-Santacruz
Keyword(s):  
Optimization Problem ◽  
Optimization Problems ◽  
Great Influence ◽  
General Rule ◽  
Linear Constraints ◽  
Synthesis Problem ◽  
Mechanism Synthesis ◽  
Problem Statement ◽  
Engineering Design Process ◽  
The Impact

In the engineering design process, it is of particular relevance the problem statement that has to be solved to guarantee an optimal design. There is no general rule for this, and in the particular case of the synthesis of flat mechanisms, the solution strongly depends on the problem statement for the design or mechanism synthesis. The object this paper is presenting one proposal at synthesis problem of a four-bar flat mechanism for cartesian trajectory tracking. The mechanism synthesis problem is stated as a nonlinear optimization problem with non linear constraints. Four different approaches are considered in order to demonstrate the impact of the considered statement of the optimization problem for its solution. The solution of the four optimization problems is obtained by means of numerical calculations using genetic algorithms. The numerical results of the four optimization problem statemens are compared under fair circumstances and they depict the great influence of the initial problem statement for its solution.

A Dynamics-Based Optimal Trajectory Generation for Controlling an Automated Excavator

2010 ◽  
Vol 224 (10) ◽  
pp. 2109-2119 ◽  
Author(s):  
S Yoo ◽  
C-G Park ◽  
S-H You ◽  
B Lim
Keyword(s):  
Optimal Trajectory ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Linear Constraints ◽  
Control Points ◽  
Weighting Functions ◽  
B Splines ◽  
Finite Dimensional ◽  
Motion Optimization ◽  
Save Energy

This article presents a new methodology to generate optimal trajectories in controlling an automated excavator. By parameterizing all the actuator displacements with B-splines of the same order and with the same number of control points, the coupled actuator limits, associated with the maximum pump flowrate, are described as the finite-dimensional set of linear constraints to the motion optimization problem. Several weighting functions are introduced on the generalized actuator torque so that the solution to each optimization problems contains the physical meaning. Numerical results showing that the generated motions of the excavator are fairly smooth and effectively save energy, which can prevent mechanical wearing and possibly save fuel consumption, are presented. A typical operator's manoeuvre from experiments is referred to bring out the standing features of the optimized motion.

Topology Optimization Design of Bars Structure Based on SKO Method

2013 ◽  
Vol 394 ◽  
pp. 515-520 ◽  
Author(s):  
Wen Jun Li ◽  
Qi Cai Zhou ◽  
Xu Hui Zhang ◽  
Xiao Lei Xiong ◽  
Jiong Zhao
Keyword(s):  
Global Optimization ◽  
Topology Optimization ◽  
Optimization Design ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Topology Optimization Problem ◽  
Proposed Model ◽  
Continuum Structure ◽  
The Impact

There are less topology optimization methods for bars structure than those for continuum structure. Bionic intelligent method is a powerful way to solve the topology optimization problems of bars structure since it is of good global optimization capacity and convenient for numerical calculation. This article presents a SKO topology optimization model for bars structure based on SKO (Soft Kill Option) method derived from adaptive growth rules of trees, bones, etc. The model has been applied to solve the topology optimization problem of a space frame. It uses three optimization strategies, which are constant, decreasing and increasing material removed rate. The impact on the optimization processes and results of different strategies are discussed, and the validity of the proposed model is proved.

scholarly journals Numerically Efficient Fuzzy MPC Algorithm with Advanced Generation of Prediction—Application to a Chemical Reactor

Algorithms ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 143 ◽  
Author(s):  
Piotr M. Marusak
Keyword(s):  
Optimization Problem ◽  
Optimization Problems ◽  
Control Plant ◽  
Chemical Reactor ◽  
Linear Constraints ◽  
Continuous Stirred Tank Reactor ◽  
Practical Applications ◽  
Quadratic Optimization Problem ◽  
Control Signals ◽  
Advanced Generation

In Model Predictive Control (MPC) algorithms, control signals are generated after solving optimization problems. If the model used for prediction is linear then the optimization problem is a standard, easy to solve, quadratic programming problem with linear constraints. However, such an algorithm may offer insufficient performance if applied to a nonlinear control plant. On the other hand, if a model used for prediction is nonlinear, then non–convex optimization problem must be solved at each algorithm iteration. Then the numerical problems may occur during solving it and the time needed to calculate the control signals cannot be determined. Therefore approaches based on linearized models are preferred in practical applications. A fuzzy algorithm with an advanced generation of the prediction is proposed in the article. The prediction is obtained in such a way that the algorithm is formulated as a quadratic optimization problem but offers performance very close to that of the MPC algorithm with nonlinear optimization. The efficiency of the proposed approach is demonstrated in the control system of a nonlinear chemical control plant—a CSTR (Continuous Stirred–Tank Reactor) with van de Vusse reaction.

Role of Brand experience in Fashion brands

2019 ◽  
Vol 118 (1) ◽  
pp. 57-64
Author(s):  
G. Aiswarya ◽  
Dr. Jayasree Krishnan
Keyword(s):  
Factor Analysis ◽  
Purchase Intention ◽  
Great Influence ◽  
Brand Experience ◽  
Brand Preference ◽  
Mediating Role ◽  
Confirmatory Factor ◽  
Selling Strategies ◽  
The Impact

Traditionally the products were pushed into the hands of customers by production and selling strategies; then the marketing strategy evolved which gained momentum by understanding the customer needs and developing products satisfying those needs. This strategy is most prevalent and what should be done to stand up in this most competitive scenario? The answer to this key question is to create an experience. The customers now also seek good experiences than other benefits. Brand experience has gained more attention, especially fashion brands. Previous studies demonstrate the role of the brand experience in brand equity and other consumer behavior constructs. But very little is known about the impact of brand experiences on fashion brands. The aim of this study is to develop a model which makes our understanding better about the role of Brand preference and Brand experience and its influence on purchase intention of the brand. An initial exploratory study is conducted using a focus group to generate items for the study. The items, thus generated are prepared in the form of a questionnaire and samples were collected.  Exploratory factor analysis is conducted and the reliability of the constructs is determined. These constructs are loaded onto AMOS to perform Confirmatory factor analysis. The results confirmed the scales used. We also noticed that Brand preference has a great influence on the Brand experience. Thereby the finding supports the role of the brand experience which tends to have a mediating role in influencing the purchase intention.

scholarly journals Co-Simulation Framework for Optimal Allocation and Power Management of DGs in Power Distribution Networks Based on Computational Intelligence Techniques

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1648
Author(s):  
Marinko Barukčić ◽  
Toni Varga ◽  
Vedrana Jerković Jerković Štil ◽  
Tin Benšić
Keyword(s):  
Power System ◽  
Power Management ◽  
Computational Intelligence ◽  
Input Data ◽  
Optimization Problem ◽  
Optimal Allocation ◽  
Distribution Networks ◽  
Distributed Generations ◽  
Data Resolution ◽  
The Impact

The paper researches the impact of the input data resolution on the solution of optimal allocation and power management of controllable and non-controllable renewable energy sources distributed generation in the distribution power system. Computational intelligence techniques and co-simulation approach are used, aiming at more realistic system modeling and solving the complex optimization problem. The optimization problem considers the optimal allocation of all distributed generations and the optimal power control of controllable distributed generations. The co-simulation setup employs a tool for power system analysis and a metaheuristic optimizer to solve the optimization problem. Three different resolutions of input data (generation and load profiles) are used: hourly, daily, and monthly averages over one year. An artificial neural network is used to estimate the optimal output of controllable distributed generations and thus significantly decrease the dimensionality of the optimization problem. The proposed procedure is applied on a 13 node test feeder proposed by the Institute of Electrical and Electronics Engineers. The obtained results show a huge impact of the input data resolution on the optimal allocation of distributed generations. Applying the proposed approach, the energy losses are decreased by over 50–70% by the optimal allocation and control of distributed generations depending on the tested network.

Practical Tools for Reasoning About Linear Constraints

1991 ◽  
Vol 15 (3-4) ◽  
pp. 357-379
Author(s):  
Tien Huynh ◽  
Leo Joskowicz ◽  
Catherine Lassez ◽  
Jean-Louis Lassez
Keyword(s):  
Logic Programming ◽  
Intelligent Systems ◽  
Optimization Problem ◽  
Spatial Reasoning ◽  
Quantifier Elimination ◽  
Canonical Representation ◽  
Linear Constraints ◽  
Practical Applications ◽  
Variable Elimination ◽  
Fourier Algorithm

We address the problem of building intelligent systems to reason about linear arithmetic constraints. We develop, along the lines of Logic Programming, a unifying framework based on the concept of Parametric Queries and a quasi-dual generalization of the classical Linear Programming optimization problem. Variable (quantifier) elimination is the key underlying operation which provides an oracle to answer all queries and plays a role similar to Resolution in Logic Programming. We discuss three methods for variable elimination, compare their feasibility, and establish their applicability. We then address practical issues of solvability and canonical representation, as well as dynamical updates and feedback. In particular, we show how the quasi-dual formulation can be used to achieve the discriminating characteristics of the classical Fourier algorithm regarding solvability, detection of implicit equalities and, in case of unsolvability, the detection of minimal unsolvable subsets. We illustrate the relevance of our approach with examples from the domain of spatial reasoning and demonstrate its viability with empirical results from two practical applications: computation of canonical forms and convex hull construction.

scholarly journals Optimal Pricing Strategy of Electric Vehicle Charging Station for Promoting Green Behavior Based on Time and Space Dimensions

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Xiaomin Xu ◽  
Dongxiao Niu ◽  
Yan Li ◽  
Lijie Sun
Keyword(s):  
Electric Vehicles ◽  
Optimization Problems ◽  
Power Grid ◽  
Economic Benefits ◽  
Pricing Strategy ◽  
Optimal Pricing ◽  
Electricity Pricing ◽  
Optimal Pricing Strategy ◽  
Charging Stations ◽  
The Impact

Considering that the charging behaviors of users of electric vehicles (EVs) (including charging time and charging location) are random and uncertain and that the disorderly charging of EVs brings new challenges to the power grid, this paper proposes an optimal electricity pricing strategy for EVs based on region division and time division. Firstly, by comparing the number of EVs and charging stations in different districts of a city, the demand ratio of charging stations per unit is calculated. Secondly, according to the demand price function and the principle of profit maximization, the charging price between different districts of a city is optimized to guide users to charge in districts with more abundant charging stations. Then, based on the results of the zonal pricing strategy, the time-of-use (TOU) pricing strategy in different districts is discussed. In the TOU pricing model, consumer satisfaction, the profit of power grid enterprises, and the load variance of the power grid are considered comprehensively. Taking the optimization of the comprehensive index as the objective function, the TOU pricing optimization model of EVs is constructed. Finally, the nondominated sorting genetic algorithm (NSGA-II) is introduced to solve the above optimization problems. The specific data of EVs in a municipality directly under the Central Government are taken as examples for this analysis. The empirical results demonstrate that the peak-to-valley ratio of a certain day in the city is reduced from 56.8% to 43% by using the optimal pricing strategy, which further smooth the load curve and alleviates the impact of load fluctuation. To a certain extent, the problem caused by the uneven distribution of electric vehicles and charging stations has been optimized. An orderly and reasonable electricity pricing strategy can guide users to adjust charging habits, to ensure grid security, and to ensure the economic benefits of all parties.

ONE IMPROVED AGENT GENETIC ALGORITHM — RING-LIKE AGENT GENETIC ALGORITHM FOR GLOBAL NUMERICAL OPTIMIZATION

2009 ◽  
Vol 26 (04) ◽  
pp. 479-502 ◽  
Author(s):  
BIN LIU ◽  
TEQI DUAN ◽  
YONGMING LI
Keyword(s):  
Genetic Algorithm ◽  
Numerical Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Genetic Operators ◽  
Global Numerical Optimization ◽  
Candidate Solution ◽  
Low Dimension ◽  
Multi Agent ◽  
Agent Structure

In this paper, a novel genetic algorithm — dynamic ring-like agent genetic algorithm (RAGA) is proposed for solving global numerical optimization problem. The RAGA combines the ring-like agent structure and dynamic neighboring genetic operators together to get better optimization capability. An agent in ring-like agent structure represents a candidate solution to the optimization problem. Any agent interacts with neighboring agents to evolve. With dynamic neighboring genetic operators, they compete and cooperate with their neighbors, and they can also use knowledge to increase energies. Global numerical optimization problems are the most important ones to verify the performance of evolutionary algorithm, especially of genetic algorithm and are mostly of interest to the corresponding researchers. In the corresponding experiments, several complex benchmark functions were used for optimization, several popular GAs were used for comparison. In order to better compare two agents GAs (MAGA: multi-agent genetic algorithm and RAGA), the several dimensional experiments (from low dimension to high dimension) were done. These experimental results show that RAGA not only is suitable for optimization problems, but also has more precise and more stable optimization results.

The Science of Goal Setting

2012 ◽  
Vol 19 (1) ◽  
pp. 13-25 ◽  
Author(s):  
Daniel Hayden ◽  
Fangzhou Deng
Keyword(s):  
Goal Setting ◽  
Diffusion Of Innovation ◽  
Social Marketing ◽  
Collective Behavior ◽  
Great Influence ◽  
Environmental Conservation ◽  
Marketing Campaign ◽  
Social Marketing Campaign ◽  
Social Marketing Campaigns ◽  
The Impact

Goal setting within social marketing campaigns is art and science. An analysis of Rare Pride conservation campaigns shows the quantitative, replicable relationship among the impact of these conservation campaigns with diffusion of innovation theory, and collective behavior theory that can guide marketers to set better goals. Rare is an environmental conservation organization that focuses on reducing community-based threats to biodiversity through a social marketing campaign called Pride. Pride campaigns work by removing barriers to change (whether they are technical, social, and political or something else) and inspiring people to make change happen. Based on the analysis of historical Pride campaign survey data, we found that the starting percentage of engagement has a great influence on the percentage change at the end of the campaign: The higher the initial adoption level of knowledge, attitude, and behavior change, the easier these measures are to improve. The result also suggests a difference in the potential of change with different audience segments: It is easiest to change influencer, then general public, and finally resource user who are the target of the social marketing campaign. In this article, we will analyze how to use diffusion of innovation and collective behavior theories to explain the impact of campaigns, as well as how to set more attainable goals. This article is consistent with similar research in the field of public health, which should help marketers set goals more tightly, allocate resources more effectively, and better manage donor expectations.

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