Optimizing the operating conditions in a high precision industrial process using soft computing techniques

This interdisciplinary research is based on the application of unsupervized connectionist architectures in conjunction with modelling systems and on the determining of the optimal operating conditions of a new high precision industrial process known as laser milling. Laser milling is a relatively new micro-manufacturing technique in the production of high-value industrial components. The industrial problem is defined by a data set relayed through standard sensors situated on a laser-milling centre, which is a machine tool for manufacturing high-value micro-moulds, micro-dies and micro-tools. The new three-phase industrial system presented in this study is capable of identifying a model for the laser-milling process based on low-order models. The first two steps are based on the use of unsupervized connectionist models. The first step involves the analysis of the data sets that define each case study to identify if they are informative enough or if the experiments have to be performed again. In the second step, a feature selection phase is performed to determine the main variables to be processed in the third step. In this last step, the results of the study provide a model for a laser-milling procedure based on low-order models, such as black-box, in order to approximate the optimal form of the laser-milling process. The three-step model has been tested with real data obtained for three different materials: aluminium, cooper and hardened steel. These three materials are used in the manufacture of micro-moulds, micro-coolers and micro-dies, high-value tools for the medical and automotive industries among others. As the model inputs are standard data provided by the laser-milling centre, the industrial implementation of the model is immediate. Thus, this study demonstrates how a high precision industrial process can be improved using a combination of artificial intelligence and identification techniques

Optimizing the operating conditions in a high precision industrial process using soft computing techniques

This interdisciplinary research is based on the application of unsupervized connectionist architectures in conjunction with modelling systems and on the determining of the optimal operating conditions of a new high precision industrial process known as laser milling. Laser milling is a relatively new micro-manufacturing technique in the production of high-value industrial components. The industrial problem is defined by a data set relayed through standard sensors situated on a laser-milling centre, which is a machine tool for manufacturing high-value micro-moulds, micro-dies and micro-tools. The new three-phase industrial system presented in this study is capable of identifying a model for the laser-milling process based on low-order models. The first two steps are based on the use of unsupervized connectionist models. The first step involves the analysis of the data sets that define each case study to identify if they are informative enough or if the experiments have to be performed again. In the second step, a feature selection phase is performed to determine the main variables to be processed in the third step. In this last step, the results of the study provide a model for a laser-milling procedure based on low-order models, such as black-box, in order to approximate the optimal form of the laser-milling process. The three-step model has been tested with real data obtained for three different materials: aluminium, cooper and hardened steel. These three materials are used in the manufacture of micro-moulds, micro-coolers and micro-dies, high-value tools for the medical and automotive industries among others. As the model inputs are standard data provided by the laser-milling centre, the industrial implementation of the model is immediate. Thus, this study demonstrates how a high precision industrial process can be improved using a combination of artificial intelligence and identification techniques

Soft Computing Decision Support for a Steel Sheet Incremental Cold Shaping Process

It is known that the complexity inherited in most of the new real world problems, for example, the cold rolled steel industrial process, increases as the computer capacity does. Higher performance requirements with a lower amount of data samples are needed due to the costs of generating new instances, specially in those processes where new technologies arise. This study is focused on the analysis and design of a novel decision support system for an incremental steel cold shaping process, where there is a lack of knowledge of which operating conditions are suitable for obtaining high quality results. The most suitable features have been found using a wrapper feature selection method, in which genetic algorithms and neural networks are hybridized. Some facts concerning the enhanced experimentation needed and the improvements in the algorithm are drawn

Testing Ensembles for Intrusion Detection: On the Identification of Mutated Network Scans

In last decades there have been many proposals from the machine learning community in the intrusion detection field. One of the main problems that Intrusion Detection Systems (IDSs) - mainly anomaly-based ones - have to face are those attacks not previously seen (zero-day attacks). This paper proposes a mutation technique to test and evaluate the performance of several classifier ensembles incorporated to network-based IDSs when tackling the task of recognizing such attacks. The technique applies mutant operators that randomly modifies the features of the captured packets to generate situations that otherwise could not be provided to learning IDSs. As an example application for the proposed testing model, it has been specially applied to the identification of network scans and related mutations

Urban bicycles renting systems: Modelling and optimization using nature-inspired search methods

Urban Bicycles Renting Systems (UBRS) are becoming a common and useful component in growing modern cities. For an efficient management and support, the UBRS infrastructure requires the optimation of vehicle routes connecting several bicycle base stations and storage centers. In this study, we model this real-world optimization problem as a capacitated Vehicle Routing Problem (VRP) with multiple depots and the simultaneous need for pickup and delivery at each base station location. Based on the VRP model specification, two nature-inspired computational techniques, evolutionary algorithms and ant colony systems, are presented and their performance in tackling the UBRS problem is investigated. In the evolutionary approach, individuals are encoded as permutations of base stations and then translated to a set of routes subject to the constraints related to vehicle capacity and node demands. In the ant-based approach, ants build complete solutions formed of several subtours servicing a subset of base stations using a single vehicle based on both apriori (the attractiveness of a move based on the known distance or other factors) and aposteriori (pheromone levels accumulated on visited edges) knowledge. Both algorithms are engaged for the UBRS problem using real data from the cities of Barcelona and Valencia. Computational experiments for several scenarios support a good performance of both population-based search methods. Comparative results indicate that better solutions are obtained on the average by the ant colony system approach for both considered cities

Urban bicycles renting systems: Modelling and optimization using nature-inspired search methods

Urban Bicycles Renting Systems (UBRS) are becoming a common and useful component in growing modern cities. For an efficient management and support, the UBRS infrastructure requires the optimation of vehicle routes connecting several bicycle base stations and storage centers. In this study, we model this real-world optimization problem as a capacitated Vehicle Routing Problem (VRP) with multiple depots and the simultaneous need for pickup and delivery at each base station location. Based on the VRP model specification, two nature-inspired computational techniques, evolutionary algorithms and ant colony systems, are presented and their performance in tackling the UBRS problem is investigated. In the evolutionary approach, individuals are encoded as permutations of base stations and then translated to a set of routes subject to the constraints related to vehicle capacity and node demands. In the ant-based approach, ants build complete solutions formed of several subtours servicing a subset of base stations using a single vehicle based on both apriori (the attractiveness of a move based on the known distance or other factors) and aposteriori (pheromone levels accumulated on visited edges) knowledge. Both algorithms are engaged for the UBRS problem using real data from the cities of Barcelona and Valencia. Computational experiments for several scenarios support a good performance of both population-based search methods. Comparative results indicate that better solutions are obtained on the average by the ant colony system approach for both considered cities

Different approaches for the detection of SSH anomalous connections

The Secure Shell Protocol (SSH) is a well-known standard protocol, mainly used for remotely accessing shell accounts on Unix-like operating systems to perform administrative tasks. As a result, the SSH service has been an appealing target for attackers, aiming to guess root passwords performing dictionary attacks or to directly exploit the service itself. To identify such situations, this article addresses the detection of SSH anomalous connections from an intrusion detection perspective. The main idea is to compare several strategies and approaches for a better detection of SSH-based attacks. To test the classification performance of different classifiers and combinations of them, SSH data coming from a real-world honeynet are gathered and analysed. For comparison purposes and to draw conclusions about data collection, both packet-based and flow data are analysed. A wide range of classifiers and ensembles are applied to these data, as well as different validation schemes for better analysis of the obtained results. The high-rate classification results lead to positive conclusions about the identification of malicious SSH connections

Detection of heat flux failures in building using a soft computing diagnostic system

The detection of insulation failures in buildings could potentially conserve energy supplies and improve future designs. Improvements to thermal insulation in buildings include the development of models to assess fabric gain - heat flux through exterior walls in the building- and heating processes. Thermal insulation standards are now contractual obligations in new buildings, and the energy efficiency of buildings constructed prior to these regulations has yet to be determined. The main assumption is that it will be based on heat flux and conductivity measurement. Diagnostic systems to detect thermal insulation failures should recognize anomalous situations in a building that relate to insulation, heating and ventilation. This highly relevant issue in the construction sector today is approached through a novel intelligent procedure that can be programmed according to local building and heating system regulations and the specific features of a given climate zone. It is based on the following phases. Firstly, the dynamic thermal performance of different variables is specifically modeled. Secondly, an exploratory projection pursuit method called Cooperative Maximum-Likelihood Hebbian Learning extracts the relevant features. Finally, a supervised neural model and identification techniques constitute the model for the diagnosis of thermal insulation failures in building due to the heat flux through exterior walls, using relevant features of the data set. The reliability of the proposed method is validated with real data sets from several Spanish cities in winter time

Learning and training techniques in fuzzy control for energy efficiency in buildings

A novel procedure for learning Fuzzy Controllers (FC) is proposed that concerns with energy efficiency issues in distributing electrical energy to heaters in an electrical energy heating system. Energy rationalization together with temperature control can significantly improve energy efficiency, by efficiently controlling electrical heating systems and electrical energy consumption. The novel procedure, which improves the training process, is designed to train the FC, as well as to run the control algorithm and to carry out energy distribution. Firstly, the dynamic thermal performance of different variables is mathematically modelled for each specific building type and climate zone. Secondly, an exploratory projection pursuit method is used to extract the relevant features. Finally, a supervised dynamic neural network model and identification techniques are applied to FC learning and training. The FC rule-set and parameter-set learning process is a multi-objective problem that minimizes both the indoor temperature error and the energy deficit in the house. The reliability of the proposed procedure is validated for a city in a winter zone in Spain