A novel approach for multispectral satellite image classification based on the bat algorithm

Amongst the multiple advantages and applications of remote sensing, one of the most important use is to solve the problem of crop classification, i.e., differentiating between various crop types. Satellite images are a reliable source for investigating the temporal changes in crop cultivated areas. In this work, we propose a novel Bat Algorithm (BA) based clustering approach for solving crop type classification problems using a multi-spectral satellite image. The proposed partitional clustering algorithm is used to extract information in the form of optimal cluster centers from training samples. The extracted cluster centers are then validated on test samples. A real-time multi-spectral satellite image and one benchmark dataset from the UCI repository are used to demonstrate robustness of the proposed algorithm. The performance of the Bat Algorithm is compared with the traditional K-means and two other nature-inspired metaheuristic techniques, namely, Genetic Algorithm and Particle Swarm Optimization. From the results obtained, we can conclude that BA can be successfully applied to solve crop type classification problems

A novel approach for multispectral satellite image classification based on the bat algorithm

Amongst the multiple advantages and applications of remote sensing, one of the most important use is to solve the problem of crop classification, i.e., differentiating between various crop types. Satellite images are a reliable source for investigating the temporal changes in crop cultivated areas. In this work, we propose a novel Bat Algorithm (BA) based clustering approach for solving crop type classification problems using a multi-spectral satellite image. The proposed partitional clustering algorithm is used to extract information in the form of optimal cluster centers from training samples. The extracted cluster centers are then validated on test samples. A real-time multi-spectral satellite image and one benchmark dataset from the UCI repository are used to demonstrate robustness of the proposed algorithm. The performance of the Bat Algorithm is compared with the traditional K-means and two other nature-inspired metaheuristic techniques, namely, Genetic Algorithm and Particle Swarm Optimization. From the results obtained, we can conclude that BA can be successfully applied to solve crop type classification problems

A new VRPPD model and a hybrid heuristic solution approach for e-tailing

We analyze a business model for e-supermarkets to enable multi-product sourcing capacity through co-opetition (collaborative competition). The logistics aspect of our approach is to design and execute a network system where “premium” goods are acquired from vendors at multiple locations in the supply network and delivered to customers. Our specific goals are to: (i) investigate the role of premium product offerings in creating critical mass and profit; (ii) develop a model for the multiple-pickup single-delivery vehicle routing problem in the presence of multiple vendors; and (iii) propose a hybrid solution approach. To solve the problem introduced in this paper, we develop a hybrid metaheuristic approach that uses a Genetic Algorithm for vendor selection and allocation, and a modified savings algorithm for the capacitated VRP with multiple pickup, single delivery and time windows (CVRPMPDTW). The proposed Genetic Algorithm guides the search for optimal vendor pickup location decisions, and for each generated solution in the genetic population, a corresponding CVRPMPDTW is solved using the savings algorithm. We validate our solution approach against published VRPTW solutions and also test our algorithm with Solomon instances modified for CVRPMPDTW

Applications of biased-randomized algorithms and simheuristics in integrated logistics

Transportation and logistics (T&L) activities play a vital role in the development of many businesses from different industries. With the increasing number of people living in urban areas, the expansion of on-demand economy and e-commerce activities, the number of services from transportation and delivery has considerably increased. Consequently, several urban problems have been potentialized, such as traffic congestion and pollution. Several related problems can be formulated as a combinatorial optimization problem (COP). Since most of them are NP-Hard, the finding of optimal solutions through exact solution methods is often impractical in a reasonable amount of time. In realistic settings, the increasing need for 'instant' decision-making further refutes their use in real life. Under these circumstances, this thesis aims at: (i) identifying realistic COPs from different industries; (ii) developing different classes of approximate solution approaches to solve the identified T&L problems; (iii) conducting a series of computational experiments to validate and measure the performance of the developed approaches. The novel concept of 'agile optimization' is introduced, which refers to the combination of biased-randomized heuristics with parallel computing to deal with real-time decision-making.Las actividades de transporte y logística (T&L) juegan un papel vital en el desarrollo de muchas empresas de diferentes industrias. Con el creciente número de personas que viven en áreas urbanas, la expansión de la economía a lacarta y las actividades de comercio electrónico, el número de servicios de transporte y entrega ha aumentado considerablemente. En consecuencia, se han potencializado varios problemas urbanos, como la congestión del tráfico y la contaminación. Varios problemas relacionados pueden formularse como un problema de optimización combinatoria (COP). Dado que la mayoría de ellos son NP-Hard, la búsqueda de soluciones óptimas a través de métodos de solución exactos a menudo no es práctico en un período de tiempo razonable. En entornos realistas, la creciente necesidad de una toma de decisiones "instantánea" refuta aún más su uso en la vida real. En estas circunstancias, esta tesis tiene como objetivo: (i) identificar COP realistas de diferentes industrias; (ii) desarrollar diferentes clases de enfoques de solución aproximada para resolver los problemas de T&L identificados; (iii) realizar una serie de experimentos computacionales para validar y medir el desempeño de los enfoques desarrollados. Se introduce el nuevo concepto de optimización ágil, que se refiere a la combinación de heurísticas aleatorias sesgadas con computación paralela para hacer frente a la toma de decisiones en tiempo real.Les activitats de transport i logística (T&L) tenen un paper vital en el desenvolupament de moltes empreses de diferents indústries. Amb l'augment del nombre de persones que viuen a les zones urbanes, l'expansió de l'economia a la carta i les activitats de comerç electrònic, el nombre de serveis del transport i el lliurament ha augmentat considerablement. En conseqüència, s'han potencialitzat diversos problemes urbans, com ara la congestió del trànsit i la contaminació. Es poden formular diversos problemes relacionats com a problema d'optimització combinatòria (COP). Com que la majoria són NP-Hard, la recerca de solucions òptimes mitjançant mètodes de solució exactes sovint no és pràctica en un temps raonable. En entorns realistes, la creixent necessitat de prendre decisions "instantànies" refuta encara més el seu ús a la vida real. En aquestes circumstàncies, aquesta tesi té com a objectiu: (i) identificar COP realistes de diferents indústries; (ii) desenvolupar diferents classes d'aproximacions aproximades a la solució per resoldre els problemes identificats de T&L; (iii) la realització d'una sèrie d'experiments computacionals per validar i mesurar el rendiment dels enfocaments desenvolupats. S'introdueix el nou concepte d'optimització àgil, que fa referència a la combinació d'heurístiques esbiaixades i aleatòries amb informàtica paral·lela per fer front a la presa de decisions en temps real.Tecnologies de la informació i de xarxe

A hybrid metaheuristic for the time-dependent vehicle routing problem with hard time windows

This article paper presents a hybrid metaheuristic algorithm to solve the time-dependent vehicle routing problem with hard time windows. Time-dependent travel times are influenced by different congestion levels experienced throughout the day. Vehicle scheduling without consideration of congestion might lead to underestimation of travel times and consequently missed deliveries. The algorithm presented in this paper makes use of Large Neighbourhood Search approaches and Variable Neighbourhood Search techniques to guide the search. A first stage is specifically designed to reduce the number of vehicles required in a search space by the reduction of penalties generated by time-window violations with Large Neighbourhood Search procedures. A second stage minimises the travel distance and travel time in an ‘always feasible’search space. Comparison of results with available test instances shows that the proposed algorithm is capable of obtaining a reduction in the number of vehicles (4.15%), travel distance (10.88%) and travel time (12.00%) compared to previous implementations in reasonable tim

The Greystar prototype : designing a multidetector for automatic isotope identification

L’utilisation de la spectroscopie nucléaire comme outil de détection est limitée par le ratio du signal sur bruit. Les méthodes traditionnelles qui permettent d’outrepasser cette difficulté sont d’augmenter l’intensité du signal ou de diminuer le bruit. Pour amplifier le signal, il existe deux méthodes : avoir un émetteur plus intense ou avoir un détecteur de meilleur rapport signal sur bruit . Les techniques typiques de réduction de bruit de fond sont : l’application de filtres numériques et l’ajout de systèmes, tels des circuits de coïncidence, pour favoriser l’étude du signal. Dans le cadre de notre recherche, nous cherchons à détecter des traces d’éléments en utilisant l’activation par neutron. Nous sommes donc limités à avoir des émetteurs d’intensité faible. Beaucoup de chercheurs se penchent déjà sur la création et l’amélioration de détecteurs ayant de meilleures résolutions en énergie. L’approche que nous proposons utilise des détecteurs typiques ayant des résolutions standards et aucun système de suppression de bruit de fond. Nous avons développé un montage expérimental astucieux qui se charge de récolter un maximum d’information. Nous le couplons par la suite à un système d’analyse statistique robuste basé sur l’estimation du maximum de vraisemblance d’un modèle de mixture pour extraire les paramètres d’intérêt. Cette méthode mène à une amélioration flagrante du pouvoir de détection d’un tel système. Les résultats de cette recherche sont par la suite très utiles à tous les systèmes de spectroscopie semblables. Les méthodes d’acquisition et d’analyse développées sont en parfaite synergie avec les travaux de recherche qui se font ailleurs sur des systèmes de détecteurs plus exotiques.The use of nuclear spectroscopy as a detection tool is limited by signal to noise ratio. The most common ways of overcoming this difficulty are either boosting the signal or dampening the noise. Boosting the signal can either be done by having a more powerful emitter or by having a better signal to noise ratio. Applying digital filters and having special experimental setups are the typical ways of enhancing the signal. In our case, the detection of trace elements with neutron activation analysis, we are limited by the nature of the problem to having small emitter signals. A good amount of research is already being done to develop new and exotic detectors to improve their energy resolution. The approach we propose uses typical detectors with average resolution and no background suppression. We propose an intelligent experimental design intended to extract a maximum of information from the experiment and an analysis algorithm based on a rigorous statistical model using a maximum likelihood approach to solving a mixture model in order to drastically improve detection efficiency. This method can be combined with more complex setups that use higher resolution detectors. As such the results of this project synergize well with what is being done elsewhere in the fields of spectroscopy and detector physics

A Novel Path Planning Optimization Algorithm Based on Particle Swarm Optimization for UAVs for Bird Monitoring and Repelling

Bird damage to fruit crops causes significant monetary losses to farmers annually. The application of traditional bird repelling methods such as bird cannons and tree netting become inefficient in the long run, requiring high maintenance and reducing mobility. Due to their versatility, Unmanned Aerial Vehicles (UAVs) can be beneficial to solve this problem. However, due to their low battery capacity that equals low flight duration, it is necessary to evolve path planning optimization. A novel path planning optimization algorithm of UAVs based on Particle Swarm Optimization (PSO) is presented in this paper. This path planning optimization algorithm aims to manage the drone’s distance and flight time, applying optimization and randomness techniques to overcome the disadvantages of the traditional systems. The proposed algorithm’s performance was tested in three study cases: two of them in simulation to test the variation of each parameter and one in the field to test the influence on battery management and height influence. All cases were tested in the three possible situations: same incidence rate, different rates, and different rates with no bird damage to fruit crops. The field tests were also essential to understand the algorithm’s behavior of the path planning algorithm in the UAV, showing that there is less efficiency with fewer points of interest, but this does not correlate with the flight time. In addition, there is no association between the maximum horizontal speed and the flight time, which means that the function to calculate the total distance for path planning needs to be adjusted. Thus, the proposed algorithm presents promising results with an outstanding reduced average error in the total distance for the path planning obtained and low execution time, being suited for this and other applications.This research work is within the activities of PrunusBot project—Autonomous controlled spraying aerial robotic system and fruit production forecast, Operation No. PDR2020-101-031358 (leader), Consortium No. 340, Initiative No. 140, promoted by PDR2020 and co-financed by the EAFRD and the European Union under the Portugal 2020 program.info:eu-repo/semantics/publishedVersio

Sustainable Structural Design for High-Performance Buildings and Infrastructures

Exceptional design loads on buildings and structures may have different causes, including high-strain natural hazards, man-made attacks and accidents, and extreme operational conditions. All of these aspects can be critical for specific structural typologies and/or materials that are particularly sensitive. Dedicated and refined methods are thus required for design, analysis, and maintenance under structures’ expected lifetimes. Major challenges are related to the structural typology and material properties. Further issues are related to the need for the mitigation or retrofitting of existing structures, or from the optimal and safe design of innovative materials/systems. Finally, in some cases, no design recommendations are available, and thus experimental investigations can have a key role in the overall process. For this SI, we have invited scientists to focus on the recent advancements and trends in the sustainable design of high-performance buildings and structures. Special attention has been given to materials and systems, but also to buildings and infrastructures that can be subjected to extreme design loads. This can be the case of exceptional natural events or unfavorable ambient conditions. The assessment of hazard and risk associated with structures and civil infrastructure systems is important for the preservation and protection of built environments. New procedures, methods, and more precise rules for safety design and the protection of sustainable structures are, however, needed

A novel genetic algorithm-based optimization framework for minimizing seismic retrofitting interventions costs in existing masonry structures

The pressing necessity of enhancing the seismic safety of existing masonry structures in earthquake-prone areas has led, in recent years, the research to propose a vast amount of new retrofitting techniques. However, retrofitting interventions are generally associated with important costs. Currently, there are no formal methods to optimize these interventions thus, their design is entrusted only to engineers' intuition. This paper presents a novel optimization framework aimed at the minimization of seismic retrofitting-related costs by an optimal placement (topological optimization) of reinforced plasters in masonry structures. In the proposed framework a 3D equivalent masonry model implemented in OpenSees is handled by a genetic algorithm developed in MATLAB® routine that iterates reinforcement configurations to match the optimal solution. The feasibility of each solution is controlled by the outcomes of a seismic static equivalent analysis by controlling the safety check of masonry walls with respect to both flexural and shear collapse. It is also shown, through a case study, that the proposed approach is efficient to pinpoint optimal retrofitting configurations, significantly reducing invasiveness and downtime