251 research outputs found
Complex-valued embeddings of generic proximity data
Proximities are at the heart of almost all machine learning methods. If the
input data are given as numerical vectors of equal lengths, euclidean distance,
or a Hilbertian inner product is frequently used in modeling algorithms. In a
more generic view, objects are compared by a (symmetric) similarity or
dissimilarity measure, which may not obey particular mathematical properties.
This renders many machine learning methods invalid, leading to convergence
problems and the loss of guarantees, like generalization bounds. In many cases,
the preferred dissimilarity measure is not metric, like the earth mover
distance, or the similarity measure may not be a simple inner product in a
Hilbert space but in its generalization a Krein space. If the input data are
non-vectorial, like text sequences, proximity-based learning is used or ngram
embedding techniques can be applied. Standard embeddings lead to the desired
fixed-length vector encoding, but are costly and have substantial limitations
in preserving the original data's full information. As an information
preserving alternative, we propose a complex-valued vector embedding of
proximity data. This allows suitable machine learning algorithms to use these
fixed-length, complex-valued vectors for further processing. The complex-valued
data can serve as an input to complex-valued machine learning algorithms. In
particular, we address supervised learning and use extensions of
prototype-based learning. The proposed approach is evaluated on a variety of
standard benchmarks and shows strong performance compared to traditional
techniques in processing non-metric or non-psd proximity data.Comment: Proximity learning, embedding, complex values, complex-valued
embedding, learning vector quantizatio
Myoelectric forearm prostheses: State of the art from a user-centered perspective
User acceptance of myoelectric forearm prostheses is currently low. Awkward control, lack of feedback, and difficult training are cited as primary reasons. Recently, researchers have focused on exploiting the new possibilities offered by advancements in prosthetic technology. Alternatively, researchers could focus on prosthesis acceptance by developing functional requirements based on activities users are likely to perform. In this article, we describe the process of determining such requirements and then the application of these requirements to evaluating the state of the art in myoelectric forearm prosthesis research. As part of a needs assessment, a workshop was organized involving clinicians (representing end users), academics, and engineers. The resulting needs included an increased number of functions, lower reaction and execution times, and intuitiveness of both control and feedback systems. Reviewing the state of the art of research in the main prosthetic subsystems (electromyographic [EMG] sensing, control, and feedback) showed that modern research prototypes only partly fulfill the requirements. We found that focus should be on validating EMG-sensing results with patients, improving simultaneous control of wrist movements and grasps, deriving optimal parameters for force and position feedback, and taking into account the psychophysical aspects of feedback, such as intensity perception and spatial acuity
Innovative Solutions for Navigation and Mission Management of Unmanned Aircraft Systems
The last decades have witnessed a significant increase in Unmanned Aircraft Systems (UAS) of all shapes and sizes. UAS are finding many new applications in supporting several human activities, offering solutions to many dirty, dull, and dangerous missions, carried out by military and civilian users. However, limited access to the airspace is the principal barrier to the realization of the full potential that can be derived from UAS capabilities. The aim of this thesis is to support the safe integration of UAS operations, taking into account both the user's requirements and flight regulations. The main technical and operational issues, considered among the principal inhibitors to the integration and wide-spread acceptance of UAS, are identified and two solutions for safe UAS operations are proposed: A. Improving navigation performance of UAS by exploiting low-cost sensors. To enhance the performance of the low-cost and light-weight integrated navigation system based on Global Navigation Satellite System (GNSS) and Micro Electro-Mechanical Systems (MEMS) inertial sensors, an efficient calibration method for MEMS inertial sensors is required. Two solutions are proposed: 1) The innovative Thermal Compensated Zero Velocity Update (TCZUPT) filter, which embeds the compensation of thermal effect on bias in the filter itself and uses Back-Propagation Neural Networks to build the calibration function. Experimental results show that the TCZUPT filter is faster than the traditional ZUPT filter in mapping significant bias variations and presents better performance in the overall testing period. Moreover, no calibration pre-processing stage is required to keep measurement drift under control, improving the accuracy, reliability, and maintainability of the processing software; 2) A redundant configuration of consumer grade inertial sensors to obtain a self-calibration of typical inertial sensors biases. The result is a significant reduction of uncertainty in attitude determination. In conclusion, both methods improve dead-reckoning performance for handling intermittent GNSS coverage.
B. Proposing novel solutions for mission management to support the Unmanned Traffic Management (UTM) system in monitoring and coordinating the operations of a large number of UAS. Two solutions are proposed: 1) A trajectory prediction tool for small UAS, based on Learning Vector Quantization (LVQ) Neural Networks. By exploiting flight data collected when the UAS executes a pre-assigned flight path, the tool is able to predict the time taken to fly generic trajectory elements. Moreover, being self-adaptive in constructing a mathematical model, LVQ Neural Networks allow creating different models for the different UAS types in several environmental conditions; 2) A software tool aimed at supporting standardized procedures for decision-making process to identify UAS/payload configurations suitable for any type of mission that can be authorized standing flight regulations. The proposed methods improve the management and safe operation of large-scale UAS missions, speeding up the flight authorization process by the UTM system and supporting the increasing level of autonomy in UAS operations
Using Artificial Neural Networks for Digital Soil Mapping – a comparison of MLP and SOM approaches
Portuguese soil map coverage remains incomplete, while the existing cartography has some shortcomings. Artificial Neural Networks (ANN) are advanced computer-based techniques which are being used for Digital Soil Mapping (DSM). These techniques allow mapping soil classes in a cheaper, more consistent and flexible way, using surrogate landscape data. This work compares the performance of two ANN approaches, Multi-layer Perceptron (MLP) and Self-Organizing Map (SOM), for DSM. The tests were carried out in IDRISI Taiga for three catchments in Portugal and one in Spain, using different sampling designs to obtain the training sets. Results reveal that best ANN performance is obtained with a MLP model rather than a SOM model, independently of data transformation and sampling method. However, MLP is also the most sensitive method to the data used to develop the models.info:eu-repo/semantics/publishedVersio
Artificial neural networks to detect forest fire prone areas in the southeast fire district of Mississippi
An analysis of the fire occurrences parameters is essential to save human lives, property, timber resources and conservation of biodiversity. Data conversion formats such as raster to ASCII facilitate the integration of various GIS software’s in the context of RS and GIS modeling. This research explores fire occurrences in relation to human interaction, fuel density interaction, euclidean distance from the perennial streams and slope using artificial neural networks. The human interaction (ignition source) and density of fuels is assessed by Newton’s Gravitational theory. Euclidean distance to perennial streams and slope that do posses a significant role were derived using GIS tools. All the four non linear predictor variables were modeled using the inductive nature of neural networks. The Self organizing feature map (SOM) utilized for fire size risk classification produced an overall classification accuracy of 62% and an overall kappa coefficient of 0.52 that is moderate (fair) for annual fires
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