135 research outputs found
Selection of sensors by a new methodology coupling a classification technique and entropy criteria
Complex industrial processes invest a lot of money in sensors and automation devices to monitor and supervise the process in order to guarantee the production quality and the plant and operators safety. Fault detection is one of the multiple tasks of process monitoring and it critically depends on the sensors that measure the significant process variables. Nevertheless, most of the works on fault detection and diagnosis found in literature emphasis more on developing procedures to perform diagnosis given a set of sensors, and less on determining the actual location of sensors for efficient identification of faults. A methodology based on learning and classification techniques and on the information quantity measured by the Entropy concept, is proposed in order to address the problem of sensor location for fault identification. The proposed methodology has been applied to a continuous intensified reactor, the "Open Plate Reactor (OPR)", developed by Alfa Laval and studied at the Laboratory of Chemical Engineering of Toulouse. The different steps of the methodology are explained through its application to the carrying out of an exothermic reaction
Autonomic management of a building's multi-HVAC system start-up
Most studies about the control, automation, optimization and supervision of building HVAC systems concentrate on the steady-state regime, i.e., when the equipment is already working at its setpoints. The originality of the current work consists of proposing the optimization of building multi-HVAC systems from start-up until they reach the setpoint, making the transition to steady state-based strategies smooth. The proposed approach works on the transient regime of multi-HVAC systems optimizing contradictory objectives, such as the desired comfort and energy costs, based on the "Autonomic Cycle of Data Analysis Tasks" concept. In this case, the autonomic cycle is composed of two data analysis tasks: one for determining if the system is going towards the defined operational setpoint, and if that is not the case, another task for reconfiguring the operational mode of the multi-HVAC system to redirect it. The first task uses machine learning techniques to build detection and prediction models, and the second task defines a reconfiguration model using multiobjective evolutionary algorithms. This proposal is proven in a real case study that characterizes a particular multi-HVAC system and its operational setpoints. The performance obtained from the experiments in diverse situations is impressive since there is a high level of conformity for the multi-HVAC system to reach the setpoint and deliver the operation to the steady-state smoothly, avoiding overshooting and other non-desirable transitional effects.European CommissionJunta de Comunidades de Castilla-La ManchaMinisterio de Ciencia e Innovació
Cell formation problem - A Lagrangean relaxation to mathematical programming approach and a linear performance measure
Two topics in the part-machine cell formation problem are discussed: In the first part, a Lagrangean relaxation in a mathematical programming model is proposed to simultaneously set machines into groups and parts into families in a cellular manufacturing system. The objective of this model is to find the optimal number of cells while minimizing inter-cellular part moves and increasing utilization of machines within the cells. The method uses a 0-1 integer programming model. The Lagrangean relaxation relaxes the model through an iterative search. In the second part, we introduce a new performance measure and compare it to some known performance measures. The new measure preserved some important features of previous performance measures and overcomes a number of drawbacks. Both the measure and the model are applied to benchmark problems as well as randomly generated problems. The new measure and model are comparable to the existing models and measures
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Automating pilot function performance assesssment using fuzzy systems and a genetic algorithm
Modern civil commercial transport aircraft provide the means for the safest of all forms of transportation. While advanced computer technology ranging from flight management computers to warning and alerting devices contributed to flight safety significantly, it is undisputed that the flightcrew represents the most frequent primary cause factor in airline accidents. From a system perspective, machine actors such as the autopilot and human actors (the flightcrew) try to achieve goals (desired states of the aircraft). The set of activities to achieve a goal is called a function. In modern flightdecks both machine actors and human actors perform functions. Recent accident studies suggest that deficiencies in the flightcrew's ability to monitor how well either machines or themselves perform a function are a factor in many accidents and incidents. As humans are inherently bad monitors, this study proposes a method to automatically assess the status of a function in order to increase flight safety as part of an intelligent pilot aid, called the AgendaManager. The method was implemented for the capture altitude function: seeking to attain and maintain a target altitude. Fuzzy systems were used to compute outputs indicating how well the capture altitude function was performed from inputs describing the state of the aircraft. In order to conform to human expert assessments, the fuzzy systems were trained using a genetic algorithm (GA) whose objective was to minimize the discrepancy between system outputs and human expert assessments based on 72 scenarios. The resulting systems were validated by analyzing how well they conformed to new data drawn from another 32 scenarios. The results of the study indicated that even though the training procedure facilitated by the GA was able to improve conformance to human expert assessments, overall the systems performed too poorly to be deployed in a real environment. Nevertheless, experience and insights gained from the study will be valuable in the development of future automated systems to perform function assessment
A Survey of Machine Learning Techniques for Behavioral-Based Biometric User Authentication
Authentication is a way to enable an individual to be uniquely identified usually based on passwords and personal identification number (PIN). The main problems of such authentication techniques are the unwillingness of the users to remember long and challenging combinations of numbers, letters, and symbols that can be lost, forged, stolen, or forgotten. In this paper, we investigate the current advances in the use of behavioral-based biometrics for user authentication. The application of behavioral-based biometric authentication basically contains three major modules, namely, data capture, feature extraction, and classifier. This application is focusing on extracting the behavioral features related to the user and using these features for authentication measure. The objective is to determine the classifier techniques that mostly are used for data analysis during authentication process. From the comparison, we anticipate to discover the gap for improving the performance of behavioral-based biometric authentication. Additionally, we highlight the set of classifier techniques that are best performing for behavioral-based biometric authentication
GAELS Project Final Report: Information environment for engineering
The GAELS project was a collaboration commenced in 1999 between Glasgow University Library and Strathclyde University Library with two main aims:· to develop collaborative information services in support of engineering research at the Universities of Glasgow and Strathclyde· to develop a CAL (computer-aided learning package) package in advanced information skills for engineering research students and staff The project was funded by the Scottish Higher Education Funding Council (SHEFC) from their Strategic Change Initiative funding stream, and funding was awarded initially for one year, with an extension of the grant for a further year. The project ended in June 2001.The funding from SHEFC paid for two research assistants, one based at Glasgow University Library working on collaborative information services and one based at Strathclyde University Library developing courseware. Latterly, after these two research assistants left to take up other posts, there has been a single researcher based at Glasgow University Library.The project was funded to investigate the feasibility of new services to the Engineering Faculties at both Universities, with a view to making recommendations for service provision that can be developed for other subject areas
Mathematical Modelling and Control of Renewable Energy Systems and Battery Storage Systems
PhD, 262ppIntermittent nature of renewable energy sources like the wind and solar energy poses new
challenges to harness and supply uninterrupted power for consumer usage. Though, converting
energy from these sources to useful forms of energy like electricity seems to be promising, still,
significant innovations are needed in design and construction of wind turbines and PV arrays
with BS systems. The main focus of this research project is mathematical modelling and control
of wind turbines, solar photovoltaic (PV) arrays and battery storage (BS) systems. After careful
literature review on renewable energy systems, new developments and existing modelling and
controlling methods have been analysed. Wind turbine (WT) generator speed control, turbine
blade pitch angle control (pitching), harnessing maximum power from the wind turbines have
been investigated and presented in detail. Mathematical modelling of PV arrays and how to
extract maximum power from PV systems have been analysed in detail.
Application of model predictive control (MPC) to regulate the output power of the wind turbine
and generator speed control with variable wind speeds have been proposed by formulating a
linear model from a nonlinear mathematical model of a WT.
Battery chemistry and nonlinear behaviour of battery parameters have been analysed to present
a new equivalent electrical circuit model. Converting the captured solar energy into useful
forms, and storing it for future use when the Sun itself is obscured is implemented by using
battery storage systems presenting a new simulation model.
Temperature effect on battery cells and dynamic battery pack modelling have been described
with an accurate state of charge estimation method. The concise description on power
converters is also addressed with special reference to state-space models. Bi-directional
AC/DC converter, which could work in either rectifier or inverter modes is described with a
cost effective proportional integral derivative (PID/State-feedback) controller
Genetic and Swarm Algorithms for Optimizing the Control of Building HVAC Systems Using Real Data: A Comparative Study.
Buildings consume a considerable amount of electrical energy, the Heating, Ventilation,
and Air Conditioning (HVAC) system being the most demanding. Saving energy and maintaining
comfort still challenge scientists as they conflict. The control of HVAC systems can be improved by
modeling their behavior, which is nonlinear, complex, and dynamic and works in uncertain contexts.
Scientific literature shows that Soft Computing techniques require fewer computing resources
but at the expense of some controlled accuracy loss. Metaheuristics-search-based algorithms show
positive results, although further research will be necessary to resolve new challenging multi-objective
optimization problems. This article compares the performance of selected genetic and swarmintelligence-
based algorithms with the aim of discerning their capabilities in the field of smart buildings.
MOGA, NSGA-II/III, OMOPSO, SMPSO, and Random Search, as benchmarking, are compared
in hypervolume, generational distance, ε-indicator, and execution time. Real data from the Building
Management System of Teatro Real de Madrid have been used to train a data model used for the
multiple objective calculations. The novelty brought by the analysis of the different proposed dynamic
optimization algorithms in the transient time of an HVAC system also includes the addition,
to the conventional optimization objectives of comfort and energy efficiency, of the coefficient of
performance, and of the rate of change in ambient temperature, aiming to extend the equipment
lifecycle and minimize the overshooting effect when passing to the steady state. The optimization
works impressively well in energy savings, although the results must be balanced with other real
considerations, such as realistic constraints on chillers’ operational capacity. The intuitive visualization
of the performance of the two families of algorithms in a real multi-HVAC system increases
the novelty of this proposal.post-print888 K
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