A sparse semi-blind source identification method and its application to Raman spectroscopy for explosives detection

Rapid and reliable detection and identification of unknown chemical substances are critical to homeland security. It is challenging to identify chemical components from a wide range of explosives. There are two key steps involved. One is a non-destructive and informative spectroscopic technique for data acquisition. The other is an associated library of reference features along with a computational method for feature matching and meaningful detection within or beyond the library. In this paper, we develop a new iterative method to identify unknown substances from mixture samples of Raman spectroscopy. In the first step, a constrained least squares method decomposes the data into a sum of linear combination of the known components and a non-negative residual. In the second step, a sparse and convex blind source separation method extracts components geometrically from the residuals. Verification based on the library templates or expert knowledge helps to confirm these components. If necessary, the confirmed meaningful components are fed back into step one to refine the residual and then step two extracts possibly more hidden components. The two steps may be iterated until no more components can be identified. We illustrate the proposed method in processing a set of the so called swept wavelength optical resonant Raman spectroscopy experimental data by a satisfactory blind extraction of a priori unknown chemical explosives from mixture samples. We also test the method on nuclear magnetic resonance (NMR) spectra for chemical compounds identification. © 2013 Published by Elsevier B.V

Joint signature of two or more systems with applications to multistate systems made up of two-state components

The structure signature of a system made up of $n$ components having continuous and i.i.d. lifetimes was defined in the eighties by Samaniego as the $n$-tuple whose $k$-th coordinate is the probability that the $k$-th component failure causes the system to fail. More recently, a bivariate version of this concept was considered as follows. The joint structure signature of a pair of systems built on a common set of components having continuous and i.i.d. lifetimes is a square matrix of order $n$ whose $(k,l)$-entry is the probability that the $k$-th failure causes the first system to fail and the $l$-th failure causes the second system to fail. This concept was successfully used to derive a signature-based decomposition of the joint reliability of the two systems. In the first part of this paper we provide an explicit formula to compute the joint structure signature of two or more systems and extend this formula to the general non-i.i.d. case, assuming only that the distribution of the component lifetimes has no ties. We also provide and discuss a necessary and sufficient condition on this distribution for the joint reliability of the systems to have a signature-based decomposition. In the second part of this paper we show how our results can be efficiently applied to the investigation of the reliability and signature of multistate systems made up of two-state components. The key observation is that the structure function of such a multistate system can always be additively decomposed into a sum of classical structure functions. Considering a multistate system then reduces to considering simultaneously several two-state systems

2D Transformations of Energy Signals for Energy Disaggregation

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/)The aim of Non-Intrusive Load Monitoring is to estimate the energy consumption of individual electrical appliances by disaggregating the overall power consumption that has been sampled from a smart meter at a house or commercial/industrial building. Last decade’s developments in deep learning and the utilization of Convolutional Neural Networks have improved disaggregation accuracy significantly, especially when utilizing two-dimensional signal representations. However, converting time series’ to two-dimensional representations is still an open challenge, and it is not clear how it influences the performance of the energy disaggregation. Therefore, in this article, six different two-dimensional representation techniques are compared in terms of performance, runtime, influence on sampling frequency, and robustness towards Gaussian white noise. The evaluation results show an advantage of two-dimensional imaging techniques over univariate and multivariate features. In detail, the evaluation results show that: first, the active and reactive power-based signatures double Fourier based signatures, as well as outperforming most of the other approaches for low levels of noise. Second, while current and voltage signatures are outperformed at low levels of noise, they perform best under high noise conditions and show the smallest decrease in performance with increasing noise levels. Third, the effect of the sampling frequency on the energy disaggregation performance for time series imaging is most prominent up to 1.2 kHz, while, above 1.2 kHz, no significant improvements in terms of performance could be observed.Peer reviewe

Double Fourier Integral Analysis based Convolutional Neural Network Regression for High-Frequency Energy Disaggregation

© 2021 IEEE. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/ 10.1109/TETCI.2021.3086226Non-Intrusive Load Monitoring aims to extract the energy consumption of individual electrical appliances through disaggregation of the total power load measured by a single smart-meter. In this article we introduce Double Fourier Integral Analysis in the Non-Intrusive Load Monitoring task in order to provide more distinct feature descriptions compared to current or voltage spectrograms. Specifically, the high-frequency aggregated current and voltage signals are transformed into two-dimensional unit cells as calculated by Double Fourier Integral Analysis and used as input to a Convolutional Neural Network for regression. The performance of the proposed methodology was evaluated in the publicly available U.K.-DALE dataset. The proposed approach improves the estimation accuracy by 7.2% when compared to the baseline energy disaggregation setup using current and voltage spectrograms.Peer reviewe

Signature Verification

Handwriting recognition is a process in recognizing handwritten letter images. For this project, the main purpose is to identify signature's owners to prevent from skilled forger. Therefore, the project is more focused on the signature verification rather than the character recognition. Signature verification can prevent falsification by detecting the flow ofthe curve ofthe signature and using the distance similarity. The main objective of this project is to verify the signature. The secondary objectives are to make life easier by having the signature verification system to identify the skilled forger from using someone else's credit card and to increase the security measure on credit card. The method that will be used in this project is using the neural network classification in the backpropagation network. This is because backpropagation can get the input to give the correct output, which has been used by many researchers. In implementing the prototype, a distance measure is used as the verification method but backpropagation is one of the suitable methods in designing it for future expansion. Matlab software is used in developing the system. Basically, this system will be using the Matlab software and for the hardware part by using the digitized tabiet with the pen tip in order to capture the user signature image. Beside that, some calculations will be used in measuring the signature attributes andas for the error part; there will bethepercentage ofthe error occurs

Concerning the formation of chains of particles in the Kob-Andersen (4:1) and Wahnstr{\"o}m (1:1) model liquids on coolind and supercooling

Investigations of structural changes happening with liquids on cooling and supercooling continue to attract significant attention as there is still no sufficient understanding of the connection between the structural changes and the dynamic slowdown. Previously, liquids' structures were usually discussed from the local perspective of individual particles. Here, we report on the structural evolutions of the binary Kob-Andersen (KA) (4:1) and Wahnstr\"{o}m (1:1) model liquids using a different approach. The approach is based on the previously not discussed observation that in the liquid and supercooled liquid states some particles form nearly linear chains in the temperature region where the crystallization process has not been observed. Depending on the chain definition, it is possible to speak about the chains containing more than 8 particles. The average number of chains monotonically increases as the temperature of the liquid decreases. Considerations of the inherent structures show that the number of chains remains nearly constant in the inherent structures (IS) obtained from the parent structures (PS) above the potential energy landscape (PEL) crossover temperature (PELCT). Below the PELCT, the average number of chains in the IS increases, as the temperature of the PS decreases. Counter-intuitively, for the KA system, below the PELCT the number of chains in the PS can be larger than the number of chains in the corresponding IS. The distributions of the potential energies (PE) of the particles in chains show that the particles forming the chains tend to have higher PE than the particles which are not in the chains. We also found that the particles forming the chains diffuse at a slightly lower rate than the global average. We also discuss the lifetimes of the chains. We compare some of our results with the results obtained within the topological cluster classification approach.Comment: 21 pages, 15 figures (with supplemental materials

Efficiently Estimating Survival Signature and Two-Terminal Reliability of Heterogeneous Networks through Multi-Objective Optimization

The two-terminal reliability problem is a classical reliability problem with applications in wired and wireless communication networks, electronic circuit design, computer networks, and electrical power distribution, among other systems. However, the two-terminal reliability problem is among the hardest combinatorial problems and is intractable for large, complex networks. Several exact methods to solve the two-terminal reliability problem have been proposed since the 1960s, but they have exponential time complexity in general. Hence, practical studies involving large network-type systems resort to approximation methods to estimate the system\u27s reliability. One attractive approach for quantifying the reliability of complex systems is to use signatures, but even signature-based approaches in computing exact network reliability may become computationally prohibitive as the number of components grows, and simulation-based approximations, such as Monte Carlo algorithms, are generally required. Nonetheless, the computation of the network\u27s signature poses a majorchallenge in terms of computational time, especially when considering large, heterogeneous networks. Motivated by this, we propose a MC-survival signature based method to estimate two-terminal reliability for heterogeneous networks through multi-objective optimization. We formulate the problem of estimating the multi-dimensional survival signature of a network with heterogeneous components as a repeated multi-objective maximum capacity path problem and we present a fast and memory-efficient, Dijkstra-like algorithm to solve it. To the best of our knowledge, this is the first work to point out the relationship between the multi-dimensional survival signature computation and a multi-objective optimization problem. We empirically validate our method and perform computational experiments to compare its performance against two other approaches. The results of the experiments shows that our method is much faster than the other two approaches and can be used with a larger number of replications so to improve the accuracy of the reliability estimation

Probability signatures of multistate systems made up of two-state components

The structure signature of a system made up of $n$ components having continuous and i.i.d. lifetimes was defined in the eighties by Samaniego as the $n$-tuple whose $k$-th coordinate is the probability that the $k$-th component failure causes the system to fail. More recently, a bivariate version of this concept was considered as follows. The joint structure signature of a pair of systems built on a common set of components having continuous and i.i.d. lifetimes is a square matrix of order $n$ whose $(k,l)$-entry is the probability that the $k$-th failure causes the first system to fail and the $l$-th failure causes the second system to fail. This concept was successfully used to derive a signature-based decomposition of the joint reliability of the two systems. In this talk we will show an explicit formula to compute the joint structure signature of two or more systems and extend this formula to the general non-i.i.d. case, assuming only that the distribution of the component lifetimes has no ties. Then we will discuss a condition on this distribution for the joint reliability of the systems to have a signature-based decomposition. Finally we will show how these results can be applied to the investigation of the reliability and signature of multistate systems made up of two-state components