Exploring the Performance of an Evolutionary Algorithm for Greenhouse Control

Evolutionary algorithms for optimization of dynamic problems have recently received increasing attention. Online control is a particularly interesting class of dynamic problems, because of the interactions between the controller and the controlled system. In this paper, we report experimental results on two aspects of the direct control strategy in relation to a crop-producing greenhouse. In the first set of experiments, we investigated how to balance the available computation time between population size and generations. The second experiments were on different control horizons, and showed the importance of this aspect for direct control. Finally, we discuss the results in the wider context of dynamic optimization

Agricultural Structures and Mechanization

In our globalized world, the need to produce quality and safe food has increased exponentially in recent decades to meet the growing demands of the world population. This expectation is being met by acting at multiple levels, but mainly through the introduction of new technologies in the agricultural and agri-food sectors. In this context, agricultural, livestock, agro-industrial buildings, and agrarian infrastructure are being built on the basis of a sophisticated design that integrates environmental, landscape, and occupational safety, new construction materials, new facilities, and mechanization with state-of-the-art automatic systems, using calculation models and computer programs. It is necessary to promote research and dissemination of results in the field of mechanization and agricultural structures, specifically with regard to farm building and rural landscape, land and water use and environment, power and machinery, information systems and precision farming, processing and post-harvest technology and logistics, energy and non-food production technology, systems engineering and management, and fruit and vegetable cultivation systems. This Special Issue focuses on the role that mechanization and agricultural structures play in the production of high-quality food and continuously over time. For this reason, it publishes highly interdisciplinary quality studies from disparate research fields including agriculture, engineering design, calculation and modeling, landscaping, environmentalism, and even ergonomics and occupational risk prevention

On the Recognition of Emotion from Physiological Data

This work encompasses several objectives, but is primarily concerned with an experiment where 33 participants were shown 32 slides in order to create ‗weakly induced emotions‘. Recordings of the participants‘ physiological state were taken as well as a self report of their emotional state. We then used an assortment of classifiers to predict emotional state from the recorded physiological signals, a process known as Physiological Pattern Recognition (PPR). We investigated techniques for recording, processing and extracting features from six different physiological signals: Electrocardiogram (ECG), Blood Volume Pulse (BVP), Galvanic Skin Response (GSR), Electromyography (EMG), for the corrugator muscle, skin temperature for the finger and respiratory rate. Improvements to the state of PPR emotion detection were made by allowing for 9 different weakly induced emotional states to be detected at nearly 65% accuracy. This is an improvement in the number of states readily detectable. The work presents many investigations into numerical feature extraction from physiological signals and has a chapter dedicated to collating and trialing facial electromyography techniques. There is also a hardware device we created to collect participant self reported emotional states which showed several improvements to experimental procedure

Environmental Technologies

This book on Environmental Technology takes a look at issues such as air, soil and noise pollution problems, environmental quality assessment, monitoring, modelling and risk as- sessment, environmental health impact assessment, environmental management and envi- ronmental technology development. It represents institutional arrangements, financial mechanisms and some sustainable technologies. The user can always count on finding both introductory material and more specific material based on national interests and problems. The user will also find ample references at the end of each chapter, if additional information is required. For additional questions or comments the user is encouraged to contact the author

AMoEBA: the adaptive modeling by evolving blocks algorithm

This dissertation presents AMoEBA, the Adaptive Modeling by Evolving Blocks Algorithm. AMoEBA is an evolutionary technique for automatic decomposition of data fields and solver/descriptor placement. By automatically decomposing a numerical data set, the algorithm is able to solve a variety of problems that are difficult to solve with other techniques. Two key features of the algorithm are its ability to work with discrete data types and its unique geometric representation of the domain. AMoEBA uses genetic programming generated parse trees to define data segregation schemes. These trees also place solver/descriptors in the decomposed regions. Since the segregation trees define the boundaries between the regions, discrete representations of the data set are possible. AMoEBA is versatile and can be applied to many different types of geometries as well as different types of problems. In this thesis, three problems will be used to demonstrate the capabilities of this algorithm. For the first problem, AMoEBA used approximated algebraic expressions to match known profiles representing a steady-state conduction heat transfer problem and the fully-developed laminar flow through a pipe. To further illustrate the versatility of the algorithm, an inverse engineering problem was also solved. For this problem, AMoEBA placed different materials in the segregated regions defined by the trees and compared this to known temperature profiles. The final demonstration illustrates the application of AMoEBA to computational fluid dynamics. In this implementation, AMoEBA segregated an elbow section of pipe and placed numerical solvers in the regions. The resulting solver networks were solved and compared to a known solution. Both the time and accuracy of the networks were compared to determine if a faster solution method can be found with a reasonably accurate solution. Although AMoEBA is adapted for each application, the core algorithm of AMoEBA is unaltered in each application. This illustrates the flexibility of the algorithm

The role of selected metal ions in the growth and physiology of wine yeasts

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