Fuzzy spectral and spatial feature integration for classification of nonferrous materials in hyperspectral data

Hyperspectral data allows the construction of more elaborate models to sample the properties of the nonferrous materials than the standard RGB color representation. In this paper, the nonferrous waste materials are studied as they cannot be sorted by classical procedures due to their color, weight and shape similarities. The experimental results presented in this paper reveal that factors such as the various levels of oxidization of the waste materials and the slight differences in their chemical composition preclude the use of the spectral features in a simplistic manner for robust material classification. To address these problems, the proposed FUSSER (fuzzy spectral and spatial classifier) algorithm detailed in this paper merges the spectral and spatial features to obtain a combined feature vector that is able to better sample the properties of the nonferrous materials than the single pixel spectral features when applied to the construction of multivariate Gaussian distributions. This approach allows the implementation of statistical region merging techniques in order to increase the performance of the classification process. To achieve an efficient implementation, the dimensionality of the hyperspectral data is reduced by constructing bio-inspired spectral fuzzy sets that minimize the amount of redundant information contained in adjacent hyperspectral bands. The experimental results indicate that the proposed algorithm increased the overall classification rate from 44% using RGB data up to 98% when the spectral-spatial features are used for nonferrous material classification

Plastic nets in agriculture ; a general review of types and applications

At the moment, there are a large number of agricultural net types on the market characterized by different structural features such as type of material, type and dimensions of threads, texture, mesh size, porosity / solidity and weight; by radiometric properties like color, transmissivity/reflectivity/shading factor; by physical properties like air permeability and several mechanical characteristics such as tensile stress, strength, elongation at break, and durability. Protection from hail, wind, snow, or strong rainfall in fruit-farming and ornamentals, shading nets for greenhouses and nets moderately modifying the microenvironment for a crop are the most common applications. A systematic review of the current state-of-the-art of structural parameters, standard and regulations, most common agricultural net applications, and their supporting structures has been developed by means of a literature study, technical investigations, concerning characteristics and use of nets. As a result, the survey highlighted that in many cases different, not even similar, net types were adopted for the same application and the same cultivations by various growers. Results show that neither growers nor net producers have clear ideas about the relationship between the net typology optimization for a specific application and the construction parameters of the net. The choice often depends on empirical or economic criteria and not on scientific considerations. Moreover, it appears that scientifically justified technical requirements for nets used in specific agricultural applications have not been established yet

Material recognition using tactile sensing

Identification of the material from which an object is made is of significant value for effective robotic grasping and manipulation. Characteristics of the material can be retrieved using different sensory modalities: vision based, tactile based or sound based. Compressibility, surface texture and thermal properties can each be retrieved from physical contact with an object using tactile sensors. This paper presents a method for collecting data using a biomimetic fingertip in contact with various materials and then using these data to classify the materials both individually and into groups of their type. Following acquisition of data, principal component analysis (PCA) is used to extract features. These features are used to train seven different classifiers and hybrid structures of these classifiers for comparison. For all materials, the artificial systems were evaluated against each other, compared with human performance and were all found to outperform human participants' average performance. These results highlighted the sensitive nature of the BioTAC sensors and pave the way for research that requires a sensitive and accurate approach such as vital signs monitoring using robotic systems

A soil temperature and energy balance model for integrated assessment of Global Change impacts at the regional scale

The investigation of the impact of Global Change on the basic resources on which life, and man, depends, is the main objective of the environmental science community at the beginning of the 21st century. Advances in information technology, new methods of spatially distributed data retrieval and increased understanding of the physical, chemical and biological processes in the Earth system facilitate integrative models of the dynamic processes under change. Together with the integration of deep actors models from social and economical sciences into a common model framework, scenario runs based on inputs from Regional Climate Models (RCMs) and constrained by prognoses of the future developments in demography, economy and human behaviour are now possible. The objective of the integrative project GLOWA-Danube is the development of such a modelling system and its application on the mesoscale catchment of the Upper Danube river with an area of about 77,000 km2. The decision support system DANUBIA is designed for plausible predictions of the impact of changes in climate, human behaviour and land use on the future of the water and related matter cycles. DANUBIA is able to assist knowledge-based management decisions, by predicting the effects of adaptation and mitigation strategies on the natural resources of the Upper Danube basin. The closure of the water, energy, nitrogen and carbon cycles in the soil-vegetation-atmosphere system relies on the adequate representation of all processes involved and their interaction. To close the energy cycle in the soil-vegetation-atmosphere system and provide valuable input data for biochemical models of soil nitrogen and carbon transformation, this thesis presents the Soil Heat Transfer Module (SHTM) together with an energy balance algorithm of the soil surface for regional scale simulations. SHTM combines simplified physical algorithms for the computation of the actual temperature in the upper soil layers and a dynamic lower boundary condition to represent Climate Change conditions. Changes in soil moisture and soil freezing are explicitly taken into account. The surface ground heat flux as the driving force of the model is provided by an explicit solution of the soil surface energy balance and a snow-soil coupling algorithm, respectively. This thesis shows, that the soil temperature and energy balance modules developed as extensions of PROMET (PROcesses of Matter and Energy Transfer) are ready to bridge the gap between regional scale (up to 100,000 km2) application and the requirement of physical process models in predictive, coupled modelling systems like DANUBIA

Eddy current pulsed thermography for non-destructive evaluation of carbon fibre reinforced plastic for wind turbine blades

PhD ThesisThe use of Renewable energy such as wind power has grown rapidly over the past ten years. However, the poor reliability and high lifecycle costs of wind energy can limit power generation. Wind turbine blades suffer from relatively high failure rates resulting in long downtimes. The motivation of this research is to improve the reliability of wind turbine blades via non-destructive evaluation (NDE) for the early warning of faults and condition-based maintenance. Failure in wind turbine blades can be categorised as three types of major defect in carbon fibre reinforced plastic (CFRP), which are cracks, delaminations and impact damages. To detect and characterise those defects in their early stages, this thesis proposes eddy current pulsed thermography (ECPT) NDE method for CFRP-based wind turbine blades. The ECPT system is a redesigned extension of previous work. Directional excitation is applied to overcome the problems of non-homogeneous and anisotropic properties of composites in both numerical and experimental studies. Through the investigation of the multiple-physical phenomena of electromagnetic-thermal interaction, defects can be detected, classified and characterised via numerical simulation and experimental studies. An integrative multiple-physical ECPT system can provide transient thermal responses under eddy current heating inside a sample. It is applied for the measurement and characterisation of different samples. Samples with surface defects such as cracks are detected from hot-spots in thermal images, whereas internal defects, like delamination and impact damage, are detected through thermal or heat flow patterns. For quantitative NDE, defect detection, characterisation and classification are carried out at different levels to deal with various defect locations and fibre textures. Different approaches for different applications are tested and compared via samples with crack, delamination and impact damage. Comprehensive transient feature extraction at the three different levels of the pixel, local area and pattern are developed and implemented with respect to defect location in terms of the thickness and complexity of fibre texture. Three types of defects are detected and classified at those three levels. The transient responses at pixel level, flow patterns at local area level, and principal or independent components at pattern level are derived for defect classification. Features at the pixel and local area levels are extracted in order to gain quantitative information about the defects. Through comparison of the performance of evaluations at those three levels, the pixel level is shown to be good at evaluating surface defects, in particular within uni- directional fibres. Meanwhile the local area level has advantages for detecting deeper defects such as delamination and impact damage, and in specimens with multiple fibre orientations, the pattern level is useful for the separation of defective patterns and fibre texture, as well as in distinguishing multiple defects.Engineering and Physical Sciences Research Council(EPSRC), Frame Programme 7(FP7