Fuzzy Logic

The capability of Fuzzy Logic in the development of emerging technologies is introduced in this book. The book consists of sixteen chapters showing various applications in the field of Bioinformatics, Health, Security, Communications, Transportations, Financial Management, Energy and Environment Systems. This book is a major reference source for all those concerned with applied intelligent systems. The intended readers are researchers, engineers, medical practitioners, and graduate students interested in fuzzy logic systems

Quantifying and Mitigating Debris-Induced Bias in Radar Measurements of Tornadic Winds

The centrifuging of lofted tornadic debris is known to cause bias in Doppler radar measurements of tornado wind speeds. Debris presence in a radar volume is associated with anomalous radial divergence, underestimation of azimuthal wind speeds, and negative bias in vertical velocities, potentially resulting in erroneous interpretations of tornado structure. Using a simulation-based framework to study these errors, a variety of polarimetric radar time-series simulations from SimRadar are analyzed and compared in order to establish the relationships between debris field characteristics---such as debris size and number concentration---and the magnitude of bias in Doppler velocity and retrieved wind fields. Since debris characteristics also influence polarimetric measurements, we additionally seek to assess the relationships between velocity bias magnitude and relevant polarimetric variables. Establishing such relationships could support the development of a new moment-based approach to Doppler velocity bias correction for mobile research radars. The latter half of this work introduces an alternative method for Doppler velocity bias mitigation utilizing novel spectral filtering techniques. Since debris is associated with unique polarimetric signatures as well as substantial velocity bias, this method incorporates dual-polarization spectral density (DPSD) estimation and fuzzy logic scatterer classification to identify debris-dominated signal contributions in a Doppler spectrum based on the velocity distribution of polarimetric characteristics. Outputs from the scatterer classification algorithm are used to suppress and filter the identified debris contributions within the original Doppler spectrum. New Doppler velocity estimates are recalculated from the filtered signals, and comparisons are made against both the original velocity estimate and the true Doppler velocity to evaluate the effectiveness of these spectral filtering methods at reducing debris-related bias. In the future, these algorithms will be applied to observational data sets from mobile research radars

Vision-based representation and recognition of human activities in image sequences

Magdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2013von Samy Sadek Mohamed Bakhee

Measurements, Models, Systems and Design

531 s.

Online Multi-Stage Deep Architectures for Feature Extraction and Object Recognition

Multi-stage visual architectures have recently found success in achieving high classification accuracies over image datasets with large variations in pose, lighting, and scale. Inspired by techniques currently at the forefront of deep learning, such architectures are typically composed of one or more layers of preprocessing, feature encoding, and pooling to extract features from raw images. Training these components traditionally relies on large sets of patches that are extracted from a potentially large image dataset. In this context, high-dimensional feature space representations are often helpful for obtaining the best classification performances and providing a higher degree of invariance to object transformations. Large datasets with high-dimensional features complicate the implementation of visual architectures in memory constrained environments. This dissertation constructs online learning replacements for the components within a multi-stage architecture and demonstrates that the proposed replacements (namely fuzzy competitive clustering, an incremental covariance estimator, and multi-layer neural network) can offer performance competitive with their offline batch counterparts while providing a reduced memory footprint. The online nature of this solution allows for the development of a method for adjusting parameters within the architecture via stochastic gradient descent. Testing over multiple datasets shows the potential benefits of this methodology when appropriate priors on the initial parameters are unknown. Alternatives to batch based decompositions for a whitening preprocessing stage which take advantage of natural image statistics and allow simple dictionary learners to work well in the problem domain are also explored. Expansions of the architecture using additional pooling statistics and multiple layers are presented and indicate that larger codebook sizes are not the only step forward to higher classification accuracies. Experimental results from these expansions further indicate the important role of sparsity and appropriate encodings within multi-stage visual feature extraction architectures

Advanced Knowledge Application in Practice

The integration and interdependency of the world economy leads towards the creation of a global market that offers more opportunities, but is also more complex and competitive than ever before. Therefore widespread research activity is necessary if one is to remain successful on the market. This book is the result of research and development activities from a number of researchers worldwide, covering concrete fields of research

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

IMPACT OF CLIMATE CHANGE ON WILDLAND FIRE THREAT TO THE AMUR TIGER AND ITS HABITAT

Global biodiversity is increasingly threatened by combined pressures from human- and climate-related environmental change. Projected climate change indicates that these trends are likely to continue and may accelerate by the end of this century leading to large scale modification of species habitats. Such modification will be amplified by an increase in catastrophic natural events such as wildland fire - one of the dominant disturbance agents in boreal and temperate forests of the Russian Far East (RFE). In the RFE, large fire events lead to abrupt, extensive, and long-term conversion of forests to open landscapes, thus considerably impacting the habitat of the critically endangered Amur tiger (Panthera tigris altaica). A remotely sensed data-driven regional fire threat model (FTM) is developed to assess current and projected fire threat to the Amur tiger under scenarios of climate change. The FTM is parameterized to account for regional specifics of fire occurrence in the RFE and fire impacts on the Amur tigers, their main prey, and their habitat. Fire regimes are shown to be strongly influenced by anthropogenic use of fire and the monsoonal climate of the RFE, with large fire seasons observed during uncharacteristically dry years. Even with a large proportion of human ignition sources and periodic extreme events, fire currently poses a limited threat to the Amur tiger meta-population. The observed peaks in high fire threat conditions are localized in space and time and are likely to impact a small number of individual tigers. Under the wide range of the IPCC climate change scenarios, no considerable change in fire danger is expected by the mid-21st century. However, by the end of the 21st century under the A2 (regional self-reliance) scenario of the IPCC Special Report on Emissions, fire danger over the southern part of the RFE is predicted to increase by nearly 15%. An overlap of areas of likely increase in fire danger with areas of highest tiger habitat quality results in a 20% mean yearly increase in fire threat with a mean monthly increase of ~40% in August. The results have implications for conservation strategies aimed at securing long-term habitat availability