561,010 research outputs found
A New Time Series Similarity Measure and Its Smart Grid Applications
Many smart grid applications involve data mining, clustering, classification,
identification, and anomaly detection, among others. These applications
primarily depend on the measurement of similarity, which is the distance
between different time series or subsequences of a time series. The commonly
used time series distance measures, namely Euclidean Distance (ED) and Dynamic
Time Warping (DTW), do not quantify the flexible nature of electricity usage
data in terms of temporal dynamics. As a result, there is a need for a new
distance measure that can quantify both the amplitude and temporal changes of
electricity time series for smart grid applications, e.g., demand response and
load profiling. This paper introduces a novel distance measure to compare
electricity usage patterns. The method consists of two phases that quantify the
effort required to reshape one time series into another, considering both
amplitude and temporal changes. The proposed method is evaluated against ED and
DTW using real-world data in three smart grid applications. Overall, the
proposed measure outperforms ED and DTW in accurately identifying the best load
scheduling strategy, anomalous days with irregular electricity usage, and
determining electricity users' behind-the-meter (BTM) equipment.Comment: 7 pages, 6 figures conferenc
Diffeomorphic Transformations for Time Series Analysis: An Efficient Approach to Nonlinear Warping
The proliferation and ubiquity of temporal data across many disciplines has
sparked interest for similarity, classification and clustering methods
specifically designed to handle time series data. A core issue when dealing
with time series is determining their pairwise similarity, i.e., the degree to
which a given time series resembles another. Traditional distance measures such
as the Euclidean are not well-suited due to the time-dependent nature of the
data. Elastic metrics such as dynamic time warping (DTW) offer a promising
approach, but are limited by their computational complexity,
non-differentiability and sensitivity to noise and outliers. This thesis
proposes novel elastic alignment methods that use parametric \& diffeomorphic
warping transformations as a means of overcoming the shortcomings of DTW-based
metrics. The proposed method is differentiable \& invertible, well-suited for
deep learning architectures, robust to noise and outliers, computationally
efficient, and is expressive and flexible enough to capture complex patterns.
Furthermore, a closed-form solution was developed for the gradient of these
diffeomorphic transformations, which allows an efficient search in the
parameter space, leading to better solutions at convergence. Leveraging the
benefits of these closed-form diffeomorphic transformations, this thesis
proposes a suite of advancements that include: (a) an enhanced temporal
transformer network for time series alignment and averaging, (b) a
deep-learning based time series classification model to simultaneously align
and classify signals with high accuracy, (c) an incremental time series
clustering algorithm that is warping-invariant, scalable and can operate under
limited computational and time resources, and finally, (d) a normalizing flow
model that enhances the flexibility of affine transformations in coupling and
autoregressive layers.Comment: PhD Thesis, defended at the University of Navarra on July 17, 2023.
277 pages, 8 chapters, 1 appendi
Deep Level Transient Spectroscopy (DLTS) System And Method
A computer-based deep level transient spectroscopy (DLTS) system (10) efficiently digitizes and analyzes capacitance and conductance transients acquired from a test material (13) by conventional DLTS methods as well as by several transient methods, including a covariance method of linear predictive modeling. A unique pseudo-logarithmic data storage scheme allows each transient to be tested at more than eleven different rates, permitting three to five decades of time constants τ to be observed during each thermal scan, thereby allowing high resolution of closely spaced defect energy levels. The system (10) comprises a sensor (12) for detecting capacitance and/or conductance transients, a digitizing mechanism (14) for digitizing the capacitance and/or conductance transients, preamplifiers (16a, 16b) for filtering, amplifying, and for forwarding the transients to the digitizing mechanism (14), a pulse generator (18) for supplying a filling pulse to the test material (13) in a cryostat (24), a trigger conditioner for coordinating the timing between the digitizing mechanism (14) and the pulse generator (18), and a temperature controller (26) for changing the temperature of the cryostat (24).Georgia Tech Research Corporatio
Simulation of MEMRISTORS in the presence of a high-frequency forcing function
This reported work is concerned with the simulation of MEMRISTORS when they are subject to high-frequency forcing functions. A novel asymptotic-numeric simulation method is applied. For systems involving high-frequency signals or forcing functions, the superiority of the proposed method in terms of accuracy and efficiency when compared to standard simulation techniques shall be illustrated. Relevant dynamical properties in relation to the method shall also be considered
Uncertainty Analyses in the Finite-Difference Time-Domain Method
Providing estimates of the uncertainty in results obtained by Computational Electromagnetic (CEM) simulations is essential when determining the acceptability of the results. The Monte Carlo method (MCM) has been previously used to quantify the uncertainty in CEM simulations. Other computationally efficient methods have been investigated more recently, such as the polynomial chaos method (PCM) and the method of moments (MoM). This paper introduces a novel implementation of the PCM and the MoM into the finite-difference time -domain method. The PCM and the MoM are found to be computationally more efficient than the MCM, but can provide poorer estimates of the uncertainty in resonant electromagnetic compatibility data
Identification of heat exchange process in the evaporators of absorption refrigerating units under conditions of uncertainty
Проведено аналіз функціонування випарників абсорбційно-холодильних установок блоку вторинної конденсації типового для України агрегату синтезу аміаку. Обґрунтована необхідність мінімізації температури вторинної конденсації за рахунок створення автоматизованої адаптивної системи оптимального програмного управління. Встановлені рівняння для чисельної оцінки невизначеності теплового навантаження випарника та коефіцієнту теплопередачі. Розроблено алгоритмічне забезпечення щодо розв’язання задач ідентифікації та створення математичної моделі. Визначена технічна структура автоматизованої системи для їх реалізації
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