Novel analysis–forecast system based on multi-objective optimization for air quality index

© 2018 Elsevier Ltd The air quality index (AQI) is an important indicator of air quality. Owing to the randomness and non-stationarity inherent in AQI, it is still a challenging task to establish a reasonable analysis–forecast system for AQI. Previous studies primarily focused on enhancing either forecasting accuracy or stability and failed to improve both aspects simultaneously, leading to unsatisfactory results. In this study, a novel analysis–forecast system is proposed that consists of complexity analysis, data preprocessing, and optimize–forecast modules and addresses the problems of air quality monitoring. The proposed system performs a complexity analysis of the original series based on sample entropy and data preprocessing using a novel feature selection model that integrates a decomposition technique and an optimization algorithm for removing noise and selecting the optimal input structure, and then forecasts hourly AQI series by utilizing a modified least squares support vector machine optimized by a multi-objective multi-verse optimization algorithm. Experiments based on datasets from eight major cities in China demonstrated that the proposed system can simultaneously obtain high accuracy and strong stability and is thus efficient and reliable for air quality monitoring

Identification of continuous-time model of hammerstein system using modified multi-verse optimizer

his thesis implements a novel nature-inspired metaheuristic optimization algorithm, namely the modified Multi-Verse Optimizer (mMVO) algorithm, to identify the continuous-time model of Hammerstein system. Multi-Verse Optimizer (MVO) is one of the most recent robust nature-inspired metaheuristic algorithm. It has been successfully implemented and used in various areas such as machine learning applications, engineering applications, network applications, parameter control, and other similar applications to solve optimization problems. However, such metaheuristics had some limitations, such as local optima problem, low searching capability and imbalance between exploration and exploitation. By considering these limitations, two modifications were made upon the conventional MVO in our proposed mMVO algorithm. Our first modification was an average design parameter updating mechanism to solve the local optima issue of the traditional MVO. The essential feature of the average design parameter updating mechanism is that it helps any trapped design parameter jump out from the local optima region and continue a new search track. The second modification is the hybridization of MVO with the Sine Cosine Algorithm (SCA) to improve the low searching capability of the conventional MVO. Hybridization aims to combine MVO and SCA algorithms advantages and minimize the disadvantages, such as low searching capability and imbalance between exploration and exploitation. In particular, the search capacity of the MVO algorithm has been improved using the sine and cosine functions of the Sine Cosine Algorithm (SCA) that will be able to balance the processes of exploration and exploitation. The mMVO based method is then used for identifying the parameters of linear and nonlinear subsystems in the Hammerstein model using the given input and output data. Note that the structure of the linear and nonlinear subsystems is assumed to be known. Moreover, a continuous-time linear subsystem is considered in this study, while there are a few methods that utilize such models. Two numerical examples and one real-world application, such as the Twin Rotor System (TRS) are used to illustrate the efficiency of the mMVO-based method. Various nonlinear subsystems such as quadratic and hyperbolic functions (sine and tangent) are used in those experiments. Numerical and experimental results are analyzed to focus on the convergence curve of the fitness function, the parameter variation index, frequency and time domain response and the Wilcoxon rank test. For the numerical identifications, three different levels of white noise variances were taken. The statistical analysis value (mean) was taken from the parameter deviation index to see how much our proposed algorithm has improved. For Example 1, the improvements are 29%, 33.15% and 36.68%, and for the noise variances, 0.01, 0.25, and 1.0 improvements can be found. For Example 2, the improvements are 39.36%, 39.61% and 66.18%, and for noise variances, the improvements are by 0.01, 0.25 and 1.0, respectively. Finally, for the real TRS application, the improvement is 7%. The numerical and experimental results also showed that both Hammerstein model subsystems are defined effectively using the mMVO-based method, particularly in quadratic output estimation error and a differentiation parameter index. The results further confirmed that the proposed mMVObased method provided better solutions than other optimization techniques, such as PSO, GWO, ALO, MVO and SCA

Fuzzy logic based adaptive vibration control system for structures subjected to seismic and wind loads

In this study, an attempt has been made to develop a Fuzzy Logic Multi Verse Optimal Control (FLMVOC) system as a new adaptive real-time vibration control mechanism for structures subjected to seismic excitation and wind load by utilizing the capability of the stochastic optimization method and fuzzy logic technique.The magnetorheological damper (MR) is deployed as a controllable vibration damping system in this study due to its excellent damping performance and low energy consumption. Therefore, the analytical model for the MR damper is formulated and integrated with the developed fuzzy logic optimal control (FLOC) algorithm. The story drift and absolute acceleration have been defined as the inputs of the fuzzy logic controller (FLC), while the MR commanding voltage is considered as the controller’s output. Then, the membership functions and fuzzy rule base have been formulated. To derive the optimal controller, the FLC with full parameters has been trained with multi objective multi verse algorithm (MOMVO). For this purpose, the MATLAB program and its Simulinks have been integrated and hybridised with finite element package to simulate and evaluate structure response for various input parameters.The developed FLMVOC system has been implemented in three story shear building subjected to seismic load and 60 story wind induced high rise building in order to evaluate its efficiency in diminishing the dynamic response of the structure.The result revealed that FLMVOC system successfully reduced structural drifts by 60%, 53%, and 41% under the effect of El Centro, Kobe, and Northridge earthquakes, respectively, while the floor absolute acceleration was reduced by 38%, 17%, and 10%, respectively. For the wind induced structure, the proposed system showed the ability to maintain the floor acceleration within people’s comfort criterion in addition to the reduction in story drift

Analyse multidisciplinaire des assemblages plafond à plancher dans les bâtiments en bois et développement d'une stratégie d'optimisation multi-objectif

La conception de l'assemblage plafond-plancher dans un bâtiment en bois peut être un défi en raison de ses aspects multidisciplinaires et des limitations d'espace. En tant qu'un composant du bâtiment, cet espace possède un rôle structural et représente un volume dans lequel passent les systèmes de la mécanique du bâtiment. L'objectif de ce travail est de démystifier l'assemblage plafond-plancher à travers une recherche bibliographique et des entrevues semi-dirigées et de développer ensuite une méthodologie d'optimisation multi-objectif pour une conception optimale de ce sandwich. Le modèle développé intègre tant la partie structurale que les systèmes de la mécanique du bâtiment, dont un but de minimiser l'épaisseur de l'assemblage tout en optimisant le système de ventilation qui passe dedans. L'algorithme NSGA-II est utilisé dans le processus d'optimisation. Une étude de cas sur un assemblage plafond-plancher dans un bâtiment en bois a été réalisée pour évaluer le modèle développé, où trois configurations d'assemblage ont été testées. Dans la première configuration, le réseau de ventilation passe à travers la structure, soit en parallèle aux poutres, soit en les traversant. Dans ce cas, des ouvertures seront nécessaires à condition que le diamètre de la conduite ne dépasse pas 15% de la hauteur des poutres. Dans la deuxième configuration, on considère que les poutres sont renforcées, pour donner une tolérance supplémentaire relative à l'ouverture au niveau des poutres allant jusqu'à 30% de leur hauteur. La troisième configuration est la méthode traditionnelle où aucune ouverture n'est autorisée et le réseau de ventilation passe en dessous de la structure. Pour les trois configurations, deux types de dispositions des gaines de ventilation ont été évaluées. Les meilleures solutions sont présentées sous la forme de fronts de Pareto. L'analyse des résultats montre que l'optimisation de la configuration traditionnelle de l'assemblage donne toujours de meilleures solutions (où l'épaisseur de l'assemblage varie de 0.65 m à 0,87m et la perte de pression varie de 50 Pa à 105 Pa) comparativement aux deux autres configurations.The design of the ceiling-to-floor assembly in timber buildings can be challenging due to its multidisciplinary aspects, space limitations, and requirements of timber constructions. As a component of the building, this space has a structural role. It ensures the safety and comfort of occupants and represents a volume through which building services systems pass. The objective of this work is to demystify the ceiling-to-floor assembly in timber buildings and to develop a multi-objective optimization method for an optimum design. In order to do this, bibliographical research was carried out in various databases. This step was accompanied by a series of semi-structured interviews with wood construction experts. Then a multi-objective optimization strategy for the ceiling-to-floor assembly was developed. It integrates both structures and building mechanical systems to minimize the thickness of the ceiling-to-floor assembly and optimize the pressure drops in the air distribution system that passes through it. The multi-objective genetic algorithm (NSGA-II) is used in the optimization process. Design variables related to the structure and ventilation network are taken into account. A case study of a ceiling-to-floor assembly in a timber building was carried out to evaluate the developed model, where three assembly configurations were tested: (i) the diameter of an aperture in a beam to let a duct pass is limited to 15% of the beam height, (ii) the aperture diameter limitation is 30% of the beam height, corresponding to a beam with reinforcement, (iii) no apertures are allowed and the ducts are below the beams. Best solutions are presented through the Pareto fronts and the optimal dimensions of the structure and air distribution ducts are generated. For the case study, results show that the optimization algorithm gives better results in terms of thickness and pressure drops in the third configuration where ducts pass through beams (assembly thickness ranges from 0.65 to 0.87 m, pressure drops from 50 Pa to 105 Pa), compared to the configuration where the duct passes through the structure

Algoritmos metaheurísticos para la segmentación de imágenes

Uno de los temas más tratados en la comunidad de procesamiento de imágenes es la segmentación, consistente en obtener estructuras subyacentes para facilitar su interpretación, por ejemplo, obtener bordes o agrupaciones de píxeles que forman regiones con alguna propiedad. Dado que se utiliza como un paso de preprocesamiento antes de las tareas de visión por computador de alto nivel, como el reconocimiento de objetos y la representación de imágenes, se han propuesto diversos enfoques para la segmentación, que se centran en la mejora de la calidad de los procesos aplicados para conseguir los mejores resultados posibles. Sin embargo, en muchos casos el coste computacional de estas técnicas puede ser elevado, limitando su uso. En el área de la optimización global se han propuesto una gran cantidad de algoritmos metaheurísticos (AM) para resolver problemas complejos de ingeniería en un tiempo razonable. Los AMs son algoritmos de búsqueda estocásticos que utilizan reglas o heurísticas aplicables a cualquier problema para acelerar su convergencia a soluciones cercanas al óptimo. Es común observar que los AMs emulan procesos y comportamientos inspirados por mecanismos presentes en la naturaleza, como la evolución..