Orthogonal subspace projection (OSP) is a versatile hyperspectral imaging technique which has shown great potential in dimensionality reduction, target detection, spectral unmixing, etc. However, due ...to its inherent requirement of prior target knowledge, OSP has not been explored in anomaly detection. This article takes advantage of an unsupervised OSP-based algorithm, automatic target generation process (ATGP), and a recently developed OSP-go decomposition (OSP-GoDec) along with data sphering (DS) to make OSP applicable to anomaly detection. Its idea is to implement ATGP on the background (BKG) and target subspaces constructed from the low-rank matrix L and sparse matrix S generated by OSP-GoDec to derive an OSP-based anomaly detector (OSP-AD). In particular, OSP-AD also includes DS to remove BKG interference from the target subspace so as to enhance anomaly detection. Surprisingly, operating data samples on different constructions of the BKG subspace and the target subspace yields various versions of OSP-AD. Experiments show that given an appropriate construction of the BKG subspace and the target subspace, OSP-AD can be shown to outperform existing anomaly detectors including Reed-Xiaoli anomaly detector and collaborative representation-based anomaly detector (CRD).
This paper develops a new Neyman-Pearson detection approach, to be called band-specified virtual dimensionality (BSVD), to estimating the number of bands required by band selection (BS), ...<inline-formula> <tex-math notation="LaTeX">n_{\mathrm {BS}} </tex-math></inline-formula>, as well as finding desired bands at the same time. Its idea is derived from target-specified virtual dimensionality (TSVD) where targets under hypotheses as signal sources in TSVD are replaced with bands as signal sources and the test statistics derived for a Neyman-Pearson detector (NPD) is signal-to-noise ratio (SNR) that is used to derive orthogonal subspace projection (OSP) approach for hyperspectral image classification and dimensionality reduction. Accordingly, the resulting virtual dimensionality is referred to as OSP-based BSVD. Several benefits resulting from BSVD cannot be offered by the traditional BS methods. One is its direct approach to dealing with <inline-formula> <tex-math notation="LaTeX">n_{\mathrm {BS}} </tex-math></inline-formula>. Another is no-search strategy needed for finding optimal bands. Instead, it uses NPD to determine and rank desired bands for band prioritization. Most importantly, it determines <inline-formula> <tex-math notation="LaTeX">n_{\mathrm {BS}} </tex-math></inline-formula> and finds desired bands simultaneously and progressively.
Orthogonal subspace projection (OSP) has found many applications in hyperspectral data exploitation. Its effectiveness and usefulness result from implementation of two stage processes, i.e., ...annihilation of undesired signal sources by an OSP via inverting a matrix in the first stage followed by a matched filter to extract the desired signal source in the second stage. This paper presents a theory of recursive OSP (ROSP) for hyperspectral imaging, which performs OSP recursively without inverting undesired signature matrices. This ROSP opens up many new dimensions in extending OSP. First of all, ROSP allows OSP to implement varying signatures via a recursive equation without reinverting undesired signature matrices. Second, ROSP can be further used to derive an unsupervised ROSP (UROSP) OSP, which allows OSP to find a growing number of unknown signal sources recursively while simultaneously determining a desired number of signal sources. As a result, the commonly used automatic target generation process (ATGP) can be extended to a recursive ATGP, which can be considered as a special case of UROSP. Third, for practical applications, UROSP can be also extended in two differ ent fashions to causal process and progressive process, which give rise to causal UROSP and progressive UROSP, respectively, both of which can be easily realized in hardware implementation. Finally, UROSP provides a feasible stopping rule via a recently developed UROSP-specified virtual dimensionality.
The orthogonal subspace projection (OSP) approach has received considerable interest in hyperspectral data exploitation recently. It has been shown to be a versatile technique for a wide range of ...applications. Unfortunately, insights into its design rationale have not been investigated and have yet to be explored. This work conducts a comprehensive study and analysis on the OSP from several signal processing perspectives and further discusses in depth how to effectively operate the OSP using different levels of a priori target knowledge for target detection and classification. Additionally, it looks into various assumptions made in the OSP and analyzes filters with different forms, some of which turn out to be well-known and popular target detectors and classifiers. It also shows how the OSP is related to the well-known least-squares-based linear spectral mixture analysis and how the OSP takes advantage of Gaussian noise to arrive at the Gaussian maximum-likelihood detector/estimator and likelihood ratio test. Extensive experiments are also included in this paper to simulate various scenarios to illustrate the utility of the OSP operating under various assumptions and different degrees of target knowledge.
Display omitted
•OsP2 nanoparticles anchored on N,P-doped carbon as new catalyst is synthesized.•The interaction between OsP2 nanoparticles and carbon is good to the catalysis.•Hydrogen evolution ...reaction proceeds by Volmer-Heyrovsky mechanism in acid media.•This new electrocatalyst exhibits excellent HER activity at all PH values.
Exploring hydrogen evolution reaction (HER) catalysts with high activity over the wide range of pH (0–14) is of great significance, but extremely challenging. Noble-metal phosphides are newly developed electrocatalysts that can function well at all pH values. Despite the pivotal role Os compounds have played in the progress of catalytic chemistry, its phosphides have never been demonstrated to mediate the HER. Herein, we report a new OsP2-based electrocatalyst that consists of fine OsP2 nanoparticles (NPs) dispersed over N,P co-doped carbon film (OsP2@NPC) using a combination of template and pyrolysis methods. Impressively, this novel OsP2@NPC exhibits improved HER activity compared with Os@NPC, with small overpotentials of 38, 54, and 70 mV at 10 mA cm−2 and Tafel slopes of 40, 82, and 67 mV dec−1, and better stability than commercial Pt/C in 0.5 M H2SO4, 1.0 M phosphate buffer solution, and 1.0 M KOH, respectively. The experimental and computational results indicate that both the unique structure of the porous interconnected network and the interaction between OsP2 NPs and NPC contribute to the robust activity. Meanwhile, the ∼40 mV dec−1 Tafel slope in 0.5 M H2SO4 and the density functional theory (DFT) calculations suggest the predominant Volmer-Heyrovsky mechanism for the OsP2-catalyzed HER, with electrochemical desorption of hydrogen as the rate-limiting step. This new electrocatalyst is expected to enlarge the growing family of transition metal phosphides for the HER.
Low-rank and sparsity-matrix decomposition (LRaSMD) has received considerable interests lately. One of effective methods for LRaSMD is called go decomposition (GoDec), which finds low-rank and sparse ...matrices iteratively subject to the predetermined low-rank matrix order <inline-formula> <tex-math notation="LaTeX">m </tex-math></inline-formula> and sparsity cardinality <inline-formula> <tex-math notation="LaTeX">k </tex-math></inline-formula>. This article presents an orthogonal subspace-projection (OSP) version of GoDec to be called OSP-GoDec, which implements GoDec in an iterative process by a sequence of OSPs to find desired low-rank and sparse matrices. In order to resolve the issues of empirically determining <inline-formula> <tex-math notation="LaTeX">p = m+ j </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">k </tex-math></inline-formula>, the well-known virtual dimensionality (VD) is used to estimate <inline-formula> <tex-math notation="LaTeX">p </tex-math></inline-formula> in conjunction with the Kuybeda et al. developed minimax-singular value decomposition (MX-SVD) in the maximum orthogonal complement algorithm (MOCA) to estimate <inline-formula> <tex-math notation="LaTeX">k </tex-math></inline-formula>. Consequently, LRaSMD can be realized by implementing OSP-GoDec using <inline-formula> <tex-math notation="LaTeX">p </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">k </tex-math></inline-formula> determined by VD and MX-SVD, respectively. Its application to anomaly detection demonstrates that the proposed OSP-GoDec coupled with VD and MX-SVD performs very effectively and better than the commonly used LRaSMD-based anomaly detectors.
Nanoparticles (NPs) show great promise as tools for designing formulations capable of meeting the challenge of targeted and non-targeted drug delivery to bone and cartilage tissue. NPs can also be ...employed to deliver regenerative materials, growth factors, and genes to bone and cartilage. Furthermore, the application of nanomaterials in tissue engineering has permitted scaffold preparation for delivery of growth factors, genes, and other agents required for bone and cartilage regeneration. The application of nanotechnology to different areas of medicine such as cancer therapy, drug delivery, nano-biosensing, and tissue engineering has recently generated a notable impact on diagnostics and treatments. Orthopedic applications of nanotechnology include drug delivery, surface modification of implants and prosthesis, tissue engineering for bone and cartilage regeneration, and novel diagnostics. Osteoarthritis (OA), rheumatoid arthritis (RA), and osteoporosis (OSP) are the three most common chronic orthopedic diseases. In this review, we discuss the application of nanotechnology in the diagnosis and treatment of OA, RA, and OSP. We also evaluate the limitations associated with existing treatments, as well as those related to drug delivery to orthopedic tissues.
Display omitted
Orthogonal subspace projection (OSP) has been widely used in many applications for hyperspectral data exploitation. However, its performance is sensitive to its used prior target knowledge, which is ...significantly affected by target background (BKG). To resolve this issue, this article develops three approaches to extend OSP in improving its performance. One is data sphering which can suppress BKG via removing the first- and second-order data statistics. Another takes advantage of a recently developed low-rank and sparse matrix decomposition (LRaSMD) to separate BKG and target signal sources in two subspaces characterized by the low-rank matrix and sparse matrix, respectively, and then annihilates BKG via the low-rank matrix, referred to as BKG-annihilated OSP (BA-OSP). A third approach combines data sphering and LRaSMD to further improve OSP over target detectability and BKG suppressibility. Experiments show that implementing OSP in conjunction with data sphering and LRaSMD significantly improves OSP in target detection and BKG suppression, and also performs as well as a widely used constrained energy minimization (CEM)-based subpixel target detection.
The low magnitude of fault current coupled with the non-linear dynamics makes detecting high impedance faults (HIF) challenging in hybrid microgrids. In addition to reduced fault current, the ...stochastic variation in the operation of the PV-based distributed energy resources (DERs) due to weather intermittency and network reconfiguration arising due to N-1 contingency further hinders achieving high reliability and accuracy in HIF detection. For increased sensitivity against HIF, a long short-term memory (LSTM) based protection scheme is proposed with improved immunity to weather intermittency and adaptiveness to N-1 contingencies. Using metrological data, a statistical model of solar irradiance is derived to examine the impact of weather variation on the voltage and current dynamics post fault. At the same time, the inclusion of contingency data in the learning of the LSTM model ensures adaptability to changes in the network topology. A limited set of critical sensors provides the information necessary to perform the protection tasks thereby avoiding the complexity of the communication network, cost associated with sensor installation and computational cost. The validation of the proposed hybrid microgrid protection scheme under diverse operating scenarios reflects its effectiveness in providing reliable and accurate protection with robustness against N-1 single-line contingencies and weather variation.
•Hybrid microgrid protection with high sensitivity during high impedance fault.•Imparting robustness to the protection scheme against N-1 contingency.•Immunity to high impedance fault detection against weather intermittency.•Avoiding redundancy in sensor information by identification of critical sensors.
PDF
