Coverage, Continuity and Visual Cortical Architecture

The primary visual cortex of many mammals contains a continuous representation of visual space, with a roughly repetitive aperiodic map of orientation preferences superimposed. It was recently found that orientation preference maps (OPMs) obey statistical laws which are apparently invariant among species widely separated in eutherian evolution. Here, we examine whether one of the most prominent models for the optimization of cortical maps, the elastic net (EN) model, can reproduce this common design. The EN model generates representations which optimally trade of stimulus space coverage and map continuity. While this model has been used in numerous studies, no analytical results about the precise layout of the predicted OPMs have been obtained so far. We present a mathematical approach to analytically calculate the cortical representations predicted by the EN model for the joint mapping of stimulus position and orientation. We find that in all previously studied regimes, predicted OPM layouts are perfectly periodic. An unbiased search through the EN parameter space identifies a novel regime of aperiodic OPMs with pinwheel densities lower than found in experiments. In an extreme limit, aperiodic OPMs quantitatively resembling experimental observations emerge. Stabilization of these layouts results from strong nonlocal interactions rather than from a coverage-continuity-compromise. Our results demonstrate that optimization models for stimulus representations dominated by nonlocal suppressive interactions are in principle capable of correctly predicting the common OPM design. They question that visual cortical feature representations can be explained by a coverage-continuity-compromise.Comment: 100 pages, including an Appendix, 21 + 7 figure

Adaptive networks for robotics and the emergence of reward anticipatory circuits

Currently the central challenge facing evolutionary robotics is to determine how best to extend the range and complexity of behaviour supported by evolved neural systems. Implicit in the work described in this thesis is the idea that this might best be achieved through devising neural circuits (tractable to evolutionary exploration) that exhibit complementary functional characteristics. We concentrate on two problem domains; locomotion and sequence learning. For locomotion we compare the use of GasNets and other adaptive networks. For sequence learning we introduce a novel connectionist model inspired by the role of dopamine in the basal ganglia (commonly interpreted as a form of reinforcement learning). This connectionist approach relies upon a new neuron model inspired by notions of energy efficient signalling. Two reward adaptive circuit variants were investigated. These were applied respectively to two learning problems; where action sequences are required to take place in a strict order, and secondly, where action sequences are robust to intermediate arbitrary states. We conclude the thesis by proposing a formal model of functional integration, encompassing locomotion and sequence learning, extending ideas proposed by W. Ross Ashby. A general model of the adaptive replicator is presented, incoporating subsystems that are tuned to continuous variation and discrete or conditional events. Comparisons are made with Ross W. Ashby's model of ultrastability and his ideas on adaptive behaviour. This model is intended to support our assertion that, GasNets (and similar networks) and reward adaptive circuits of the type presented here, are intrinsically complementary. In conclusion we present some ideas on how the co-evolution of GasNet and reward adaptive circuits might lead us to significant improvements in the synthesis of agents capable of exhibiting complex adaptive behaviour

Transient stability assessment of hybrid distributed generation using computational intelligence approaches

Includes bibliographical references.Due to increasing integration of new technologies into the grid such as hybrid electric vehicles, distributed generations, power electronic interface circuits, advanced controllers etc., the present power system network is now more complex than in the past. Consequently, the recent rate of blackouts recorded in some parts of the world indicates that the power system is stressed. The real time/online monitoring and prediction of stability limit is needed to prevent future blackouts. In the last decade, Distributed Generators (DGs) among other technologies have received increasing attention. This is because DGs have the capability to meet peak demand, reduce losses, due to proximity to consumers and produce clean energy and thus reduce the production of CO₂. More benefits can be obtained when two or more DGs are combined together to form what is known as Hybrid Distributed Generation (HDG). The challenge with hybrid distributed generation (HDG) powered by intermittent renewable energy sources such as solar PV, wind turbine and small hydro power is that the system is more vulnerable to instabilities compared to single renewable energy source DG. This is because of the intermittent nature of the renewable energy sources and the complex interaction between the DGs and the distribution network. Due to the complexity and the stress level of the present power system network, real time/online monitoring and prediction of stability limits is becoming an essential and important part of present day control centres. Up to now, research on the impact of HDG on the transient stability is very limited. Generally, to perform transient stability assessment, an analytical approach is often used. The analytical approach requires a large volume of data, detailed mathematical equations and the understanding of the dynamics of the system. Due to the unavailability of accurate mathematical equations for most dynamic systems, and given the large volume of data required, the analytical method is inadequate and time consuming. Moreover, it requires long simulation time to assess the stability limits of the system. Therefore, the analytical approach is inadequate to handle real time operation of power system. In order to carry out real time transient stability assessment under an increasing nonlinear and time varying dynamics, fast scalable and dynamic algorithms are required. Transient Stability Assessment Of Hybrid Distributed Generation Using Computational Intelligence Approaches These algorithms must be able to perform advanced monitoring, decision making, forecasting, control and optimization. Computational Intelligence (CI) based algorithm such as neural networks coupled with Wide Area Monitoring System (WAMS) such as Phasor Measurement Unit (PMUs) have been shown to successfully model non-linear dynamics and predict stability limits in real time. To cope with the shortcoming of the analytical approach, a computational intelligence method based on Artificial Neural Networks (ANNs) was developed in this thesis to assess transient stability in real time. Appropriate data related to the hybrid generation (i.e., Solar PV, wind generator, small hydropower) were generated using the analytical approach for the training and testing of the ANN models. In addition, PMUs integrated in Real Time Digital Simulator (RTDS) were used to gather data for the real time training of the ANNs and the prediction of the Critical Clearing Time (CCT)

Ballistics Image Processing and Analysis for Firearm Identification

Firearm identification is an intensive and time-consuming process that requires physical interpretation of forensic ballistics evidence. Especially as the level of violent crime involving firearms escalates, the number of firearms to be identified accumulates dramatically. The demand for an automatic firearm identification system arises. This chapter proposes a new, analytic system for automatic firearm identification based on the cartridge and projectile specimens. Not only do we present an approach for capturing and storing the surface image of the spent projectiles at high resolution using line-scan imaging technique for the projectiles database, but we also present a novel and effective FFT-based analysis technique for analyzing and identifying the projectiles

Development of Some Efficient Lossless and Lossy Hybrid Image Compression Schemes

Digital imaging generates a large amount of data which needs to be compressed, without loss of relevant information, to economize storage space and allow speedy data transfer. Though both storage and transmission medium capacities have been continuously increasing over the last two decades, they dont match the present requirement. Many lossless and lossy image compression schemes exist for compression of images in space domain and transform domain. Employing more than one traditional image compression algorithms results in hybrid image compression techniques. Based on the existing schemes, novel hybrid image compression schemes are developed in this doctoral research work, to compress the images effectually maintaining the quality

Combined Wavelet-neural Clasifier For Power Distribution Systems

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2002Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2002Bu çalışmada, dağıtım sistemlerinde hibrid “Dalgacık-Yapay Sinir ağı (YSA) tabanlı” bir yaklaşımla arıza sınıflama işlemi gerçeklenmiştir. 34.5 kV “Sağmalcılar-Maltepe” dağıtım sistemi PSCAD/EMTDC yazılımı kullanılarak arıza sınıflayıcı için gereken veri üretilmiştir. Tezin amacı, on farklı kısa-devre sistem arızalarını tanımlayabilecek bir sınıflayıcı tasarlamaktır. Sistemde kullanılan arıza işaretleri 5 kHZ lik örnekleme frekansı ile üretilmiştir. Farklı arıza noktaları ve farklı arıza oluşum açılarındaki hat-akımları ve hat-toprak gerilimlerini içeren sistem arızaları ile bir veritabanı oluşturulmuştur. “Çoklu-çözünürlük işaret ayrıştırma” tekniği kullanılarak altı-kanal akım ve gerilim örneklerinden karakteristik bigi çıkarılmıştır. PSCAD/EMTDC ile üretilen veri bu şekilde bir ön islemden geçirildikten sonra YSA-tabanlı bir yapı ile sınıflama islemi gerçekleştirilmiştir. Bu yapının görevi çeşitli sistem ve arıza koşullarını kapsayan karmaşık arıza sınıflama problemini çözebilmektir. Bu çalışmada, Kohonen’in öğrenme algoritmasını kullanan bir “Kendine-Organize harita” ile “eğitilebilen vektör kuantalama” teknikleri kullanılmıştır. Bu “dalgacık-sinir ağı” tabanlı arıza sınıflayıcı ile eğitim kümesi için % 99-100 arasında ve sınıflayıcıya daha önce hiç verilmemiş test kümesi ile de %85-92 arasında sınıflama oranları elde edilmiştir. Elde edilen başarım oranları literatürdeki sonuçlara yakındır. Geliştirilen birleşik “dalgacık-sinir ağı” tabanlı sınıflayıcı elektrik dağıtım sistemlerindeki arızaların belirlenmesinde iyi sonuçlar vermiş ve iyi bir performans sağlamıştır.In this study an integrated design of fault classifier in a distribution system by using a hybrid “Wavelet- Artificial neural network (ANN) based” approach is implemented. Data for the fault classifier is produced by using PSCAD/EMTDC simulation program on 34.5 kV “Sagmalcılar-Maltepe” distribution system in Istanbul. The objective is to design a classifier capable of recognizing ten classes of three-phase system faults. The signals are generated at an equivalent sampling rate of 5 KHz per channel. A database of line currents and line-to-ground voltages is built up including system faults at different fault inception angles and fault locations. The characteristic information over six-channel of current and voltage samples is extracted by the “wavelet multi-resolution analysis” technique, which is a preprocessing unit to obtain a small size of interpretable features from the raw data. After preprocessing the raw data, an ANN-based tool was employed for classification task. The main idea in this approach is solving the complex fault (three-phase short-circuit) classification problem under various system and fault conditions. In this project, a self-organizing map, with Kohonen’s learning algorithm and type-one learning vector quantization technique is implemented into the fault classification study. The performance of the wavelet-neural fault classification scheme is found to be around “99-100%” for the training data and around “85-92%” for the test data, which the classifier has not been trained on. This result is comparable to the studied fault classifiers in the literature. Combined wavelet-neural classifier showed a promising future to identify the faults in electric distribution systemsYüksek LisansM.Sc

Analysing and simulating spatial patterns of crop yield in Guizhou Province based on artificial neural networks

Supplemental material for this article is available online.This is the author accepted manuscript, the final version is available from SAGE via the DOI in this record.The area of karst terrain in China covers 3.63×106 km2, with more than 40% in the southwestern region over the Guizhou Plateau. Karst comprises exposed carbonate bedrock over approximately 1.30×106 km2 of this area, which suffers from soil degradation and poor crop yield. This paper aims to gain a better understanding of the environmental controls on crop yield in order to enable more sustainable use of natural resources for food production and development. More precisely, four kinds of artificial neural network were used to analyse and simulate the spatial patterns of crop yield for seven crop species grown in Guizhou Province, exploring the relationships with meteorological, soil, irrigation and fertilization factors. The results of spatial classification showed that most regions of high-level crop yield per area and total crop yield are located in the central-north area of Guizhou. Moreover, the three artificial neural networks used to simulate the spatial patterns of crop yield all demonstrated a good correlation coefficient between simulated and true yield. However, the Back Propagation network had the best performance based on both accuracy and runtime. Among the 13 influencing factors investigated, temperature (16.4%), radiation (15.3%), soil moisture (13.5%), fertilization of N (13.5%) and P (12.4%) had the largest contribution to crop yield spatial distribution. These results suggest that neural networks have potential application in identifying environmental controls on crop yield and in modelling spatial patterns of crop yield, which could enable local stakeholders to realize sustainable development and crop production goals.Natural Environment Research Council (NERC)National Natural Science Foundation of ChinaChina Scholarship Counci

A novel swarm based feature selection algorithm in multifunction myoelectric control

Accurate and computationally efficient myoelectric control strategies have been the focus of a great deal of research in recent years. Although many attempts exist in literature to develop such strategies, deficiencies still exist. One of the major challenges in myoelectric control is finding an optimal feature set that can best discriminate between classes. However, since the myoelectric signal is recorded using multi channels, the feature vector size can become very large. Hence a dimensionality reduction method is needed to identify an informative, yet small size feature set. This paper presents a new feature selection method based on modifying the Particle Swarm Optimization (PSO) algorithm with the inclusion of Mutual Information (MI) measure. The new method, called BPSOMI, is a mixture of filter and wrapper approaches of feature selection. In order to prove its efficiency, the proposed method is tested against other dimensionality reduction techniques proving powerful classification accuracy. © 2009 - IOS Press and the authors. All rights reserved

Proceedings of the Third International Workshop on Neural Networks and Fuzzy Logic, volume 2

Papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by the National Aeronautics and Space Administration and cosponsored by the University of Houston, Clear Lake, held 1-3 Jun. 1992 at the Lyndon B. Johnson Space Center in Houston, Texas are included. During the three days approximately 50 papers were presented. Technical topics addressed included adaptive systems; learning algorithms; network architectures; vision; robotics; neurobiological connections; speech recognition and synthesis; fuzzy set theory and application, control and dynamics processing; space applications; fuzzy logic and neural network computers; approximate reasoning; and multiobject decision making