Artificial Immune Systems: Principle, Algorithms and Applications

The present thesis aims to make an in-depth study of adaptive identification, digital channel equalization, functional link artificial neural network (FLANN) and Artificial Immune Systems (AIS).Two learning algorithms CPSO and IPSO are also developed in this thesis. These new algorithms are employed to train the weights of a low complexity FLANN structure by way of minimizing the squared error cost function of the hybrid model. These new models are applied for adaptive identification of complex nonlinear dynamic plants and equalization of nonlinear digital channel. Investigation has been made for identification of complex Hammerstein models. To validate the performance of these new models simulation study is carried out using benchmark complex plants and nonlinear channels. The results of simulation are compared with those obtained with FLANN-GA, FLANN-PSO and MLP-BP based hybrid approaches. Improved identification and equalization performance of the proposed method have been observed in all cases

Development of Advanced Mathematical Morphology Algorithms and their Application to the Detection of Disturbances in Power Systems

This thesis is concerned with the development of Mathematical morphology (MM)-based algorithms and their applications to signal processing in power systems, including typical power quality disturbances such as low frequency oscillations (LFO) and harmonics. Traditional morphological operators are extended to advanced ones in the thesis, including multi-resolution morphological gradient (MMG) algorithms, envelope extraction morphological filters (MF), LFO extraction MF and convolved morphological filters (CMF). These advanced morphological operators are applied to the detection and classification of power disturbances, detection of continuous and damped LFO, and the detection and removal of harmonics in power systems

A hybrid intelligent technique for induction motor condition monitoring

The objective of this research is to advance the field of condition monitoring and fault diagnosis for induction motors. This involves processing the signals produced by induction motors, classifying the types and estimating the severity of induction motors faults. A typical process of condition monitoring and fault diagnosis for induction motors consists of four steps: data acquisition, signal analysis, fault detection and post-processing. A description of various kinds of faults that can occur in induction motors is presented. The features reflecting faults are usually embedded in transient motor signals. The signal analysis is a very important step in the motor fault diagnosis process, which is to extract features which are related to specific fault modes. The signal analysis methods available in feature extraction for motor signals are discussed. The wavelet packet decomposition results consist of the time-frequency representation of a signal in the same time, which is inherently suited to the transient events in the motor fault signals. The wavelet packet transform-based analysis method is proposed to extract the features of motor signals. Fault detection has to establish a relationship between the motor symptoms and the condition. Classifying motor condition and estimating the severity of faults from the motor signals have never been easy tasks and they are affected by many factors. AI techniques, such as expert system (ES), fuzzy logic system (FLS), artificial neural network (ANN) and support vector machine (SVM), have been applied in fault diagnosis of very complex system, where accurate mathematical models are difficult to be built. These techniques use association, reasoning and decision making processes as would the human brain in solving diagnostic problems. ANN is a computation and information processing method that mimics the process found in biological neurons. But when ANN-based methods are used for fault diagnosis, local minimums caused by the traditional training algorithms often result in large approximation error that may destroy their reliability. In this research, a novel method of condition monitoring and fault diagnosis for induction motor is proposed using hybrid intelligent techniques based on WPT. ANN is trained by improved genetic algorithm (IGA). WPT is used to decompose motor signals to extract the feature parameters. The extracted features with different frequency resolutions are used as the input of ANN for the fault diagnosis. Finally, the proposed method is tested in 1.5 kW and 3.7 kW induction motor rigs. The experimental results demonstrate that the proposed method improves the sensitivity and accuracy of the ANN-based methods of condition monitoring and fault diagnosis for induction motors.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Seed orchards

Presentations published in proceedings: DNA and seed orchards - Darius Danusevicius, Yousry El-Kassaby, Maria Gaspar, Øystein Johnsen and Xiao-Ru Wang Seed Orchard Planning and Management in Turkey - Murat Alan, Hikmet Ozturk and Sadi Siklar Synchronization and Fertility Variation Among Pinus nigra Arn. Clones in a Clonal Seed Orchard - P.G. Alizoti, K. Kilimis and P. Gallios Practical use of GA4/7 to stimulate flower production in Picea abies seed orchards in Sweden - Curt Almqvist Seed orchards and seed collection stands of Scots pine in Turkey - Nebi Bilir and M. Denizhan Ulusan Do we need flower stimulation in seed orchards? - Władysław Chałupka Using SYNCHRO.SAS, a program to facilitate phenological data processing, in a radiata pine seed orchard in northern Spain. - Veronica Codesido and Josefina Fernández-López. A New Generation of Clonal Seed orchards of wild cherry - Bart de Cyuper PROSAD a tool for projecting and managing data about seed orchards - Vladimír Foff and Elena Foffová The Swedish Scots Pine Seed Orchard Västerhus - Anders Fries, Dag Lindgren and Bengt Andersson Coancestry among wind pollinated progenies from a Pinus pinaster seed orchard in a progeny trial. - Maria João Gaspar; Ana de-Luca; Santiago C González-Martínez; Jorge Paiva; Elena Hidalgo José Lousada and Helena Almeida Contribution of seed orchards to timber harvest in the short-run and in the long-run - Peichen Gong, and Ola Rosvall Planter's guide - a decision support system for the choice of reforestation material - Mats Hannerz and Tore Ericsson Pomotechnical treatments in the broadleave clonal seed orchards - Davorin Kajba, Nikola Pavičić, Saša Bogdan and Ida Katičić Mixing of seed crops from different years is an effective management strategy for enhancing effective population size in Eucalyptus seedling seed orchard crops - R. Kamalakannan and M. Varghese Management of Seed Orchards considering Gain and Diversity and how it is Applied in Korea - Kyu-Suk Kang and Chang-Soo Kim Gene conservation through seed orchards - a case study of Prunus spinosa L. - Jörg R.G. Kleinschmit, Ludger Leinemann and Bernhard Hosius Combining production of improved seeds with genetic testing in seedling seed orchards - Jan Kowalczyk Deployment of clones to seed orchards when candidates are related - Dag Lindgren and Darius Danusevičius The Swedish seed orchard program for Scots pine and Norway spruce - Dag Lindgren, Bo Karlsson, Bengt Andersson and Finnvid Prescher Advanced-Generation Seed Orchard Designs - Milan Lstibůrek and Yousry A.El-Kassaby Problems with seed production of European larch in seed orchards in Poland - Piotr Markiewicz A review of the seed orchard programme in Poland - Jan Matras Seed Orchard Management Strategies for Deployment of Intensively Selected Loblolly Pine Families in the Southern US - Steven E. McKeand, Davis M. Gerwig, W. Patrick Cumbie, and J.B. Jett Paternal gene flow in Cryptomeria japonica seed orchards as revealed by analysis of microsatellite markers - Yoshinari Moriguchi, Hideaki Taira and Yoshihiko Tsumura Fertility Variation across Years in Two Clonal Seed Orchards of Teak and its Impact on Seed Crop. - Abel Nicodemus, Mohan.Varghese, B. Nagarajan and Dag Lindgren A review of Scots pine and Norway spruce seed orchards in Finland - Teijo Nikkanen Finnish Birch Seed Production 1970-2007 - Sirkku Pöykkö British Columbia’s Seed Orchard Program: Multi Species Management With Integration To The End User - David J.S. Reid Pest insects and pest management in Swedish spruce seed orchards - Olle Rosenberg and Jan Weslien New Swedish Seed Orchard Program - Ola Rosvall and Per Ståhl Comparison of seed orchard and stand seed of Scots pine in direct seeding - Seppo Ruotsalainen Temporal and Spatial Change of the Mating System Parameters in a Seed Orchard of Pinus tabulaeformis Carr. - Xihuan Shen, Dongmei Zhang, Yue Li and H. X. Zhang Challenges and Prospects for Seed Orchard Development in South China - Run-Peng Wei Factors affecting effective population size estimation in a seed orchard: a case study of Pinus sylvestris - Dušan Gömöry, Roman Longauer, Ladislav Paule and Rudolf Bruchánik Pollen contamination and after-effects in Scots pine - Jan-Erik Nilsso

On Applications of New Soft and Evolutionary Computing Techniques to Direct and Inverse Modeling Problems

Adaptive direct modeling or system identification and adaptive inverse modeling or channel equalization find extensive applications in telecommunication, control system, instrumentation, power system engineering and geophysics. If the plants or systems are nonlinear, dynamic, Hammerstein and multiple-input and multiple-output (MIMO) types, the identification task becomes very difficult. Further, the existing conventional methods like the least mean square (LMS) and recursive least square (RLS) algorithms do not provide satisfactory training to develop accurate direct and inverse models. Very often these (LMS and RLS) derivative based algorithms do not lead to optimal solutions in pole-zero and Hammerstein type system identification problem as they have tendency to be trapped by local minima. In many practical situations the output data are contaminated with impulsive type outliers in addition to measurement noise. The density of the outliers may be up to 50%, which means that about 50% of the available data are affected by outliers. The strength of these outliers may be two to five times the maximum amplitude of the signal. Under such adverse conditions the available learning algorithms are not effective in imparting satisfactory training to update the weights of the adaptive models. As a result the resultant direct and inverse models become inaccurate and improper. Hence there are three important issues which need attention to be resolved. These are : (i) Development of accurate direct and inverse models of complex plants using some novel architecture and new learning techniques. (ii) Development of new training rules which alleviates local minima problem during training and thus help in generating improved adaptive models. (iii) Development of robust training strategy which is less sensitive to outliers in training and thus to create identification and equalization models which are robust against outliers. These issues are addressed in this thesis and corresponding contribution are outlined in seven Chapters. In addition, one Chapter on introduction, another on required architectures and algorithms and last Chapter on conclusion and scope for further research work are embodied in the thesis. A new cascaded low complexity functional link artificial neural network (FLANN) structure is proposed and the corresponding learning algorithm is derived and used to identify nonlinear dynamic plants. In terms of identification performance this model is shown to outperform the multilayer perceptron and FLANN model. A novel method of identification of IIR plants is proposed using comprehensive learning particle swarm optimization (CLPSO) algorithm. It is shown that the new approach is more accurate in identification and takes less CPU time compared to those obtained by existing recursive LMS (RLMS), genetic algorithm (GA) and PSO based approaches. The bacterial foraging optimization (BFO) and PSO are used to develop efficient learning algorithms to train models to identify nonlinear dynamic and MIMO plants. The new scheme takes less computational effort, more accurate and consumes less input samples for training. Robust identification and equalization of complex plants have been carried out using outliers in training sets through minimization of robust norms using PSO and BFO based methods. This method yields robust performance both in equalization and identification tasks. Identification of Hammerstein plants has been achieved successfully using PSO, new clonal PSO (CPSO) and immunized PSO (IPSO) algorithms. Finally the thesis proposes a distributed approach to identification of plants by developing two distributed learning algorithms : incremental PSO and diffusion PSO. It is shown that the new approach is more efficient in terms of accuracy and training time compared to centralized PSO based approach. In addition a robust distributed approach for identification is proposed and its performance has been evaluated. In essence the thesis proposed many new and efficient algorithms and structure for identification and equalization task such as distributed algorithms, robust algorithms, algorithms for ploe-zero identification and Hammerstein models. All these new methods are shown to be better in terms of performance, speed of computation or accuracy of results

Meta Heuristics based Machine Learning and Neural Mass Modelling Allied to Brain Machine Interface

New understanding of the brain function and increasing availability of low-cost-non-invasive electroencephalograms (EEGs) recording devices have made brain-computer-interface (BCI) as an alternative option to augmentation of human capabilities by providing a new non-muscular channel for sending commands, which could be used to activate electronic or mechanical devices based on modulation of thoughts. In this project, our emphasis will be on how to develop such a BCI using fuzzy rule-based systems (FRBSs), metaheuristics and Neural Mass Models (NMMs). In particular, we treat the BCI system as an integrated problem consisting of mathematical modelling, machine learning and classification. Four main steps are involved in designing a BCI system: 1) data acquisition, 2) feature extraction, 3) classification and 4) transferring the classification outcome into control commands for extended peripheral capability. Our focus has been placed on the first three steps. This research project aims to investigate and develop a novel BCI framework encompassing classification based on machine learning, optimisation and neural mass modelling. The primary aim in this project is to bridge the gap of these three different areas in a bid to design a more reliable and accurate communication path between the brain and external world. To achieve this goal, the following objectives have been investigated: 1) Steady-State Visual Evoked Potential (SSVEP) EEG data are collected from human subjects and pre-processed; 2) Feature extraction procedure is implemented to detect and quantify the characteristics of brain activities which indicates the intention of the subject.; 3) a classification mechanism called an Immune Inspired Multi-Objective Fuzzy Modelling Classification algorithm (IMOFM-C), is adapted as a binary classification approach for classifying binary EEG data. Then, the DDAG-Distance aggregation approach is proposed to aggregate the outcomes of IMOFM-C based binary classifiers for multi-class classification; 4) building on IMOFM-C, a preference-based ensemble classification framework known as IMOFM-CP is proposed to enhance the convergence performance and diversity of each individual component classifier, leading to an improved overall classification accuracy of multi-class EEG data; and 5) finally a robust parameterising approach which combines a single-objective GA and a clustering algorithm with a set of newly devised objective and penalty functions is proposed to obtain robust sets of synaptic connectivity parameters of a thalamic neural mass model (NMM). The parametrisation approach aims to cope with nonlinearity nature normally involved in describing multifarious features of brain signals

Curvature-based sparse rule base generation for fuzzy rule interpolation

Fuzzy logic has been successfully widely utilised in many real-world applications. The most common application of fuzzy logic is the rule-based fuzzy inference system, which is composed of mainly two parts including an inference engine and a fuzzy rule base. Conventional fuzzy inference systems always require a rule base that fully covers the entire problem domain (i.e., a dense rule base). Fuzzy rule interpolation (FRI) makes inference possible with sparse rule bases which may not cover some parts of the problem domain (i.e., a sparse rule base). In addition to extending the applicability of fuzzy inference systems, fuzzy interpolation can also be used to reduce system complexity for over-complex fuzzy inference systems. There are typically two methods to generate fuzzy rule bases, i.e., the knowledge driven and data-driven approaches. Almost all of these approaches only target dense rule bases for conventional fuzzy inference systems. The knowledge-driven methods may be negatively affected by the limited availability of expert knowledge and expert knowledge may be subjective, whilst redundancy often exists in fuzzy rule-based models that are acquired from numerical data. Note that various rule base reduction approaches have been proposed, but they are all based on certain similarity measures and are likely to cause performance deterioration along with the size reduction. This project, for the first time, innovatively applies curvature values to distinguish important features and instances in a dataset, to support the construction of a neat and concise sparse rule base for fuzzy rule interpolation. In addition to working in a three-dimensional problem space, the work also extends the natural three-dimensional curvature calculation to problems with high dimensions, which greatly broadens the applicability of the proposed approach. As a result, the proposed approach alleviates the ‘curse of dimensionality’ and helps to reduce the computational cost for fuzzy inference systems. The proposed approach has been validated and evaluated by three real-world applications. The experimental results demonstrate that the proposed approach is able to generate sparse rule bases with less rules but resulting in better performance, which confirms the power of the proposed system. In addition to fuzzy rule interpolation, the proposed curvature-based approach can also be readily used as a general feature selection tool to work with other machine learning approaches, such as classifiers

Genetics and Improvement of Forest Trees

Forest tree improvement has mainly been implemented to enhance the productivity of artificial forests. However, given the drastically changing global environment, improvement of various traits related to environmental adaptability is more essential than ever. This book focuses on genetic information, including trait heritability and the physiological mechanisms thereof, which facilitate tree improvement. Nineteen papers are included, reporting genetic approaches to improving various species, including conifers, broad-leaf trees, and bamboo. All of the papers in this book provide cutting-edge genetic information on tree genetics and suggest research directions for future tree improvement