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This paper presents a comprehensive exploration of the Advanced Impedance Mismatch Technique (AIMT), a novel approach designed for the accurate detection of simultaneous and varied faults within photovoltaic (PV) systems. This investigation integrates a spectrum of fault detection strategies, pinpointing reflectometry as a notably effective tool. Despite its utility, conventional reflectometry applications face critical constraints, notably the limitation to identify only the primary fault location within a PV array and the inability to distinguish between different fault types. This work introduces an innovative mathematical model that estimates the impedance of PV modules, enhancing the reflectometry method to enable the precise identification and localization of multiple defective modules within a string. The proposed technique exhibits a remarkable sensitivity to detect slight impedance differences between a functional PV string and one with defective modules. The validity of the AIMT's mathematical model is corroborated through simulation experiments on a string of seven PV modules afflicted with multiple simultaneous faults. These experiments rigorously evaluate the technique's accuracy in pinpointing the locations of defective modules within a PV string. The outcomes of our proposed N $N$‐times faster AIMT reveal a strong concordance between the simulated reflective signals and the impedance values forecasted by the model, highlighting the proposed method's proficiency in the detailed detection and diagnosis of progressive faults within PV systems. [ABSTRACT FROM AUTHOR]

With the fast development of large-scale Photovoltaic (PV) plants, the automatic PV fault identification and positioning have become an important task for the PV intelligent systems, aiming to guarantee the safety, reliability, and productivity of large-scale PV plants. In this paper, we propose a residual learning-based robotic (UAV) image analysis model for low-voltage distributed PV fault identification and positioning. In our target scenario, the unmanned aerial vehicles (UAVs) are deployed to acquire moving images of low-voltage distributed PV power plants. To get desired robustness and accuracy of PV image detection, we integrate residual learning with attention mechanism into the UAV image analysis model based on you only look once v4 (YOLOv4) network. Then, we design the sophisticated multi-scale spatial pyramid fusion and use it to optimize the YOLOv4 network for the nuanced task of fault localization within PV arrays, where the Complete-IOU loss is incorporated in the predictive modeling phase, significantly enhancing the accuracy and efficiency of fault detection. A series of experimental comparisons in terms of the accuracy of fault positioning are conducted, and the experimental results verify the feasibility and effectiveness of the proposed model in dealing with the safety and reliability maintenance of low-voltage distributed PV systems. [ABSTRACT FROM AUTHOR]

This paper addresses the challenges modelled by collecting and storing large volumes of big data, focusing on mitigating data errors. The primary goal is to propose and evaluate an enhanced K-means algorithm for big data applications. This research also aims to design an extensive energy data system to demonstrate the improved algorithm's practical utility in monitoring power equipment. The research begins with an in-depth analysis of the traditional K-means algorithm, culminating in the proposal of an improved version. Subsequently, the study outlines developing a comprehensive, extensive energy data system, encompassing architectural aspects such as data storage, mechanical layers, and data access structures. The research also involves the development of a power big data analysis platform, incorporating the improved algorithm for clustering and analyzing power equipment monitoring data. Experimental results reveal that the proposed improved K-means algorithm outperforms the traditional version, with significantly improved accuracy and reduced classification errors, achieving an error rate of less than one. The improved K-means algorithm showcased remarkable enhancements, achieving a meagre misclassification rate of just 0.08% while substantially boosting accuracy levels, consistently exceeding 95% across all datasets. Moreover, the power big data system developed in this study to meet practical requirements while enhancing storage and processing efficiency effectively. [ABSTRACT FROM AUTHOR]

Recent advances brought the performance of MEMS-based varifocal mirrors to levels comparable to conventional ultra-high-speed focusing devices. Varifocal mirrors are becoming capable of high axial resolution exceeding 300 resolvable planes, can achieve microsecond response times, continuous operation above several hundred kHz, and can be designed to combine focusing with lateral steering in a single-chip device. This survey summarizes the past 50 years of scientific progress in varifocal MEMS mirrors, providing the most comprehensive study in this field to date. We introduce a novel figure of merit for varifocal mirrors on the basis of which we evaluate and compare nearly all reported devices from the literature. At the forefront of this review is the analysis of the advantages and shortcomings of various actuation technologies, as well as a systematic study of methods reported to enhance the focusing performance in terms of speed, resolution, and shape fidelity. We believe this analysis will fuel the future technological development of next-generation varifocal mirrors reaching the axial resolution of 1000 resolvable planes. [ABSTRACT FROM AUTHOR]

Based on the q -shifting operator, we introduce the concept of Riemann–Liouville fractional quantum integration for interval-valued functions (IVFs), and establish new Riemann–Liouville fractional q -Hermite–Hadamard (q -HH) and q -HH-Fejér inequalities for left and right h -convex IVFs (LR- ℏ -convex-IVFs). The findings obtained generalize known results in the literature and serve as a foundation for future studies in inequalities for interval-valued functions and fractional quantum calculus. The results are illustrated with examples. [ABSTRACT FROM AUTHOR]

The objective of this paper is to explore novel unified continuous and discrete versions of the Trapezium-Jensen-Mercer (TJM) inequality, incorporating the concept of convex mapping within the framework of q-calculus, and utilizing majorized tuples as a tool. To accomplish this goal, we establish two fundamental lemmas that utilize the 1q and 2q differentiability of mappings, which are critical in obtaining new left and right side estimations of the midpoint q-TJM inequality in conjunction with convex mappings. Our findings are significant in a way that they unify and improve upon existing results. We provide evidence of the validity and comprehensibility of our outcomes by presenting various applications to means, numerical examples, and graphical illustrations. [ABSTRACT FROM AUTHOR]

A review of results on Hermite–Hadamard (H-H) type inequalities in quantum calculus, associated with a variety of classes of convexities, is presented. In the various classes of convexities this includes classical convex functions, quasi-convex functions, p-convex functions,  (p , s) -convex functions, modified  (p , s) -convex functions,  (p , h) -convex functions,  t g s -convex functions,  η -quasi-convex functions,  ϕ -convex functions,  (α , m) -convex functions,  ϕ -quasi-convex functions, and coordinated convex functions. Quantum H-H type inequalities via preinvex functions and Green functions are also presented. Finally, H-H type inequalities for  (p , q) -calculus, h-calculus, and  (q − h) -calculus are also included. [ABSTRACT FROM AUTHOR]

Quantum calculus provides a significant generalization of classical concepts and overcomes the limitations of classical calculus in tackling non-differentiable functions. Implementing the q-concepts to obtain fresh variants of classical outcomes is a very intriguing aspect of research in mathematical analysis. The objective of this article is to establish novel Milne-type integral inequalities through the utilization of the Mercer inequality for q-differentiable convex mappings. In order to accomplish this task, we begin by demonstrating a new quantum identity of the Milne type linked to left and right q derivatives. This serves as a supporting result for our primary findings. Our approach involves using the q-equality, well-known inequalities, and convex mappings to obtain new error bounds of the Milne–Mercer type. We also provide some special cases, numerical examples, and graphical analysis to evaluate the efficacy of our results. To the best of our knowledge, this is the first article to focus on quantum Milne–Mercer-type inequalities and we hope that our methods and findings inspire readers to conduct further investigation into this problem. [ABSTRACT FROM AUTHOR]

We derive two new fractional quantum integral identities. Using these identities we obtain several new fractional quantum estimates of trapezoid like inequalities essentially using the class of preinvex functions. [ABSTRACT FROM AUTHOR]

Background: The lack of evidence on complications using mitral valve approaches leaves the choice of risk exposure to the surgeon's preference, based on individual experience, speed, ease, and quality of exposure. Methods: The present study analysed patients undergoing mitral valve surgery using a superior transseptal approach or a left-atrial approach between 2006 and 2018. We included first-time elective mitral valve procedures, isolated, or combined, without a history of rhythm disturbances. We used propensity score matching based on 26 perioperative variables. The primary endpoint was the association between the superior transeptal approach and clinically significant adverse outcomes, including arrhythmias, need for a permanent pacemaker, cerebrovascular events, and mortality. Results: A total of 652 patients met the inclusion criteria; 391 received the left atrial approach, and 261 received the superior transseptal approach. After matching, 96 patients were compared with 69 patients, respectively. The distribution of the preoperative and perioperative variables was similar. There was no difference in the incidence of supraventricular tachyarrhythmias or the need for treatment. The incidence of nodal rhythm (p = 0.008) and length of stay in intensive care (p = 0.04) were higher in the superior transseptal group, but the need for permanent pacemaker implantation was the same. Likewise, there was no difference in the need for anticoagulation due to arrhythmia, the incidence of cerebrovascular events or mortality in the postoperative period or in the long-term follow-up. Conclusion: We did not find an association with permanent heart rhythm disorders or any other significant adverse clinical outcome. Therefore, the superior transeptal approach is useful and safe for mitral valve exposure. [ABSTRACT FROM AUTHOR]

One of the most important conditions for the efficient operation of solar power plants with a large installed capacity is to ensure the systematic monitoring of the surface condition of the photovoltaic modules. This procedure is aimed at the timely detection of external damage to the modules, as well as their partial shading. The implementation of these measures solely through visual inspection by the maintenance personnel of the power plant requires significant labor intensity due to the large areas of the generation fields and the operating conditions. Authors propose an approach aimed at increasing the energy efficiency of high-power solar power plants by automating the inspection procedures of the surfaces of photovoltaic modules. The solution is based on the use of an unmanned aerial vehicle with a payload capable of video and geospatial data recording. To perform the procedures for detecting problem modules, it is proposed to use "object-detection" technology, which uses neural network classification methods characterized by high adaptability to various image parameters. The results of testing the technology showed that the use of a neural network based on the R-CNN architecture with the learning algorithm—Inception v2 (COCO)—allows detecting problematic photovoltaic modules with an accuracy of more than 95% on a clear day. [ABSTRACT FROM AUTHOR]

In recent years, the theory of convex mappings has gained much more attention due to its massive utility in different fields of mathematics. It has been characterized by different approaches. In 1929, G. H. Hardy, J. E. Littlewood, and G. Polya established another characterization of convex mappings involving an ordering relationship defined over R n known as majorization theory. Using this theory many inequalities have been obtained in the literature. In this paper, we study Hermite–Hadamard type inequalities using the Jensen–Mercer inequality in the frame of q ˙ -calculus and majorized l-tuples. Firstly we derive q ˙ -Hermite–Hadamard–Jensen–Mercer (H.H.J.M) type inequalities with the help of Mercer's inequality and its weighted form. To obtain some new generalized (H.H.J.M)-type inequalities, we prove a generalized quantum identity for q ˙ -differentiable mappings. Next, we obtain some estimation-type results; for this purpose, we consider q ˙ -identity, fundamental inequalities and the convexity property of mappings. Later on, We offer some applications to special means that demonstrate the importance of our main results. With the help of numerical examples, we also check the validity of our main outcomes. Along with this, we present some graphical analyses of our main results so that readers may easily grasp the results of this paper. [ABSTRACT FROM AUTHOR]

Transferable adversarial attacks against Deep neural networks (DNNs) have received broad attention in recent years. An adversarial example can be crafted by a surrogate model and then attack the unknown target model successfully, which brings a severe threat to DNNs. The exact underlying reasons for the transferability are still not completely understood. Previous work mostly explores the causes from the model perspective, e.g., decision boundary, model architecture, and model capacity. Here, we investigate the transferability from the data distribution perspective and hypothesize that pushing the image away from its original distribution can enhance the adversarial transferability. To be specific, moving the image out of its original distribution makes different models hardly classify the image correctly, which benefits the untargeted attack, and dragging the image into the target distribution misleads the models to classify the image as the target class, which benefits the targeted attack. Towards this end, we propose a novel method that crafts adversarial examples by manipulating the distribution of the image. We conduct comprehensive transferable attacks against multiple DNNs to demonstrate the effectiveness of the proposed method. Our method can significantly improve the transferability of the crafted attacks and achieves state-of-the-art performance in both untargeted and targeted scenarios, surpassing the previous best method by up to 40% in some cases. In summary, our work provides new insight into studying adversarial transferability and provides a strong counterpart for future research on adversarial defense. [ABSTRACT FROM AUTHOR]

In this paper, we derive two new identities involving q-Riemann-Liouville fractional integrals. Using these identities, as auxiliary results, we derive some new q-fractional estimates of trapezoidal-like inequalities, essentially using the class of generalized exponential convex functions. [ABSTRACT FROM AUTHOR]