Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 10 of 10 for “"Time series signals"”.
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A modified weight optimisation for higher-order neural network in time series prediction
Most of time series signals are difficult to predict as consist of non-linear, high complexity (noise) and chaotic processes. The challenges in time series prediction are to provide a technique to better understand a dataset. In line with this, the Cuckoo Search (CS) learning algorithm, a kind of …
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Dynamic Time Warping Constraints for Semiconductor Processing
… However, a notable challenge for monitoring time series signals are the nonlinear variations in signal timing. These small, but acceptable, temporal variations are typically caused by small run-to-run differences that are inherent to the process. Dynamic time warping (DTW) can be used for …
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Application of maximal information coefficient and affinity propagation to characterizing seismic time series associated with earthquakes
… representations are significant for time series analysis and subsequent machine learning applications. A low-dimensional set of comprehensive features is instrumental to improving the efficiency and accuracy of classification. The main contribution of this work is to develop a new …
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An improved Pi-Sigma neural network using error feedback for time series prediction
Time series prediction grabs much attention because of its effect on the vast range of real-life applications. Traditional time series forecasting tools have some limitations like slow training process, less efficient training methods that decrease the performance of the model. Higher Order Neural …
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Applied Machine Learning with Latent Space Representation and Manipulation
… image classification, object detection, and time-series signals prediction, etc. Latent space is a concept that is hidden but significant to machine learning, which helps extract features of data from different dimensions. In this dissertation, we try to apply machine learning with latent …
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Higher order neural networks for financial time series prediction
… to be a promising tool for forecasting financial times series. Numerous research and applications of neural networks in business have proven their advantage in relation to classical methods that do not include artificial intelligence. What makes this particular use of neural networks so attractive …
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Sparse Representation and its Application to Multivariate Time Series Classification
… sparsely represented features of complex data signals, including temporal data analysis. Under reasonable conditions, many signals are assumed to be sparse within a domain, such as spatial, time, or timefrequency domain, and this sparse characteristics of such signals can be obtained through …
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Sparse Representation and its Application to Multivariate Time Series Classification
… sparsely represented features of complex data signals, including temporal data analysis. Under reasonable conditions, many signals are assumed to be sparse within a domain, such as spatial, time, or timefrequency domain, and this sparse characteristics of such signals can be obtained through …
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Visual Computing and AI Technologies to Analyse Movement Disorders Associated with Parkinson’s Disease for Diagnostic Purposes
… classification and correlation analysis. A real-time computer vision method used a custom-trained YOLO model to evaluate finger tapping videos, analysing computer features and their association with clinical ratings using Spearman coefficients. An automated framework employing MediaPipe Hands …
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Techniques for determining hidden properties of large-scale power systems
… for simultaneous modal analysis of multiple time-series signals is presented. Here, Dynamic Mode Decomposition (DMD) is successfully applied towards transmission-level power system measurements in an implementation that is able to run in real-time. Since power systems are considered as …