This paper presents detailed Markov models for the reliability assessment of multilevel neutral-point-clamped (NPC) converter leg topologies, incorporating their inherent fault-tolerance under open-circuit switch faults. The Markov models are generated and discussed in detail for the three-level and four-level active NPC (ANPC) cases, while the presented methodology can be applied to easily generate the models for higher number of levels and for other topology variants. In addition, this paper also proposes an extremely fast calculation method to obtain the precise value of the system mean time to failure from any given formulated system Markov model. This method is then applied to quantitatively compare the reliability of two-level, three-level, and four-level ANPC legs under switch open-circuit-guaranteed faults and varying degrees of device paralleling. The comparison reveals that multilevel ANPC leg topologies inherently present a potential for a higher reliability than the conventional two-level leg, questioning the suitability of the traditional search for topologies with the minimum number of devices in order to improve reliability. Experimental results are presented to validate the fault-tolerance assumptions upon which the presented reliability models for the three-level and four-level ANPC legs are based.

Road accidents caused by human error are among the main causes of the death in the world. Specifically, drowsiness and unconsciousness while driving are responsible for many fatal accidents on highways. Accuracy and performance are key metrics related to many researched techniques for the detection of drivers’ drowsiness. To improve these metrics, in this paper, a new method based on image processing and deep learning is proposed. The proposed method is based on facial region diagnosing using the Haar-cascade method and convolutional neural network for drowsiness probability detection. Evaluation analysis of the proposed method on the UTA-RLDD dataset with stratified 5-fold cross-validation showed a high accuracy of 96.8% at a speed of 10 frames per second, which is higher than those that have previously been reported in the literature. For further investigation, a custom dataset including 10 participants in different light conditions was collected. The result of all experiments showed the great potential of the proposed method for practical applications in intelligent transportation systems

Hyper-parameter optimization is an essential task in the use of machine learning techniques. Such optimizations are typically done starting with an initial guess provided to hyperparameter values followed by optimization (or minimization) of some cost function via gradient-based methods. The initial values become crucial since there is every chance for reaching local minimums in the cost functions being minimized, especially since gradient-based optimizing is done. Therefore, initializing hyper-parameters several times and repeating optimization to achieve the best solutions is usually attempted. Repetition of optimization can be computationally expensive when using techniques like Gaussian Process (GP) which has an O(n3) complexity, and not having a formal strategy to initialize hyperparameter values is an additional challenge. In general, reinitialization of hyper-parameter values in the contexts of many machine learning techniques including GP has been done at random over the years; some recent developments have proposed some initialization strategies based on the optimization of some meta loss cost functions. To simplify this challenge of hyperparameter initialization, this paper introduces a data-dependent deterministic initialization technique. The specific case of the squared exponential kernel-based GP regression problem is focused on, and the proposed technique brings novelty by being deterministic as opposed to random initialization, and fast (due to the deterministic nature) as opposed to optimizing some form of meta cost function as done in some previous works. Although global suitability of this initialization technique is not proven in this paper, as a preliminary study the technique’s effectiveness is demonstrated via several synthetic as well as real data-based nonlinear regression examples, hinting that the technique may have the effectiveness for broader usage.

This tutorial introduces state-of-the-art methods for tracking multiple spatially extended objects based on unlabeled noisy point clouds, e.g., from radar or lidar sensors. In the first part, the focus lies on tracking a single extended object, i.e., the objective is to simultaneously estimate the shape and position of a moving object based on spatially distributed noisy detections. Model-based approaches for tracking elliptical and star-convex shaped objects are treated. Furthermore, recent learning-based approaches are discussed, which learn the spatial distribution of detections from real data. The second part considers the track management and data association problem, i.e., the initialization and termination of tracks as well as the association of detections to objects. After an in-depth analysis of the data association problem for extended objects, various data association approaches are discussed. Finally, recent frameworks for multiple extended object tracking are introduced such as the random finite set-based Poisson multi-Bernoulli mixture framework.

All existing solutions to distributed consensus are organised around a Paxos-like structure wherein processes contend for exclusive leadership in one phase, and then either use their dominant position to propose a value in the next phase or elect an alternate leader. This approach may be characterised as adversarial and phase-asymmetric, requiring distinct message schemas and process behaviours for each phase. In over three decades of research, no algorithm has diverged from this basic model, alluding to it perhaps being the only viable solution to consensus. This paper presents a new consensus algorithm named Spire, characterised by a phase-symmetric, cooperative structure. Processes do not contend for leadership; instead, they collude to iteratively establish a dominant value and may do so concurrently without conflicting. Each successive iteration is structured identically to the previous, employing the same messages and invoking the same behaviour. By these characteristics, Spire buckles the trend in protocol design, proving that at least two disjoint cardinal solutions to consensus exist. The resulting phase symmetry halves the number of distinct messages and behaviours, offering a clear intuition and an approachable foundation for learning consensus and building practical systems.

Circuit models for multiphase coupled inductors are summarized, compared, and unified. Multiwinding magnetic structures are classified into parallel-coupled structures and series-coupled structures. For parallel-coupled structures used for multiphase inductors, the relationships between a) inductance-matrix models, b) extended cantilever models, c) magnetic-circuit models, d) multiwinding transformer models, e) gyrator-capacitor models, and f) inductance-dual models are examined and discussed. These models represent identical physical relationships in the multiphase coupled inductors, but emphasize different physical aspects and offer distinct design insights. The circuit duality between the series-coupled structure and the parallel-coupled structure is explored. Design equations for interleaved multiphase buck converters based on these models are streamlined and summarized, and a simplified equation showing the relationships between current ripple with and without coupling is presented. The models and design equations are verified through theoretical derivation, SPICE simulation, and experimental measurements.

The 3rd Generation Partnership Project (3GPP) specification of the fifth generation (5G) New Radio (NR) allows for a highly scalable and flexible radio access technology to cater to network operators with different requirements. Such scalability and flexibilities in network configurations inevitably translate to complications in the design and implementation of 5G-NR systems. Radio access in 5G-NR is much more complex and involved than its predecessor, 4G long term evolution (LTE) and LTE-Advanced technology. Therefore, the 5G-NR specifications turn out to be quite dense. Specifically, the specifications are concise, design motivations rarely explained, and the information can be convoluted or distributed across several documents. Moreover, there are several key design details associated with the access layer procedures for any given physical layer channel, which are often omitted in the specifications. For example, design motivation aspects of initial access channels or signal generation can be quite difficult to follow or understand in 5G-NR. In this paper, all the design details associated with initial access channels and signal generation in 5G-NR specifications are laid out. The contributions of the paper are three folds. First, the design details and justifications associated with both downlink and uplink access channels are presented along with signal generation details. Secondly, receiver design aspects of NR PRACH short formats are discussed. Lastly, PRACH receiver implementation aspects and performance reports from different network operators are presented and compared with 3GPP specified Radio Performance and Protocol aspect requirements[1] for millimeter wave (mmW) access. The work in this paper is of significant value to researchers and system engineers looking to design and build initial access algorithms as part of 5G NR systems.   [1] Radio Performance and Protocol aspect requirements are specified by the 3GPP Radio Access Network working group 4, also known as RAN4.

Proton exchange membrane fuel cells (PEMFCs) are promising devices for conversing chemical energy into electrical energy due to their versatile properties, such as high power density, quick start-up, lower operating temperature, and portability, etc. For PEMFC technology to outperform the incumbent technologies, artificial intelligence (AI) based multi-objective optimisation (AI-MOO) has been employed to facilitate the design and applications of PEMFC since AI-MOO is flexible enough to consider various factors simultaneously in the customized multiple objective functions and under new or updated case situations. This review provides a comprehensive literature survey on AI-MOO employed in PEMFC field. Firstly, AI-MOO were introduced in detail, including the definition, categories and framework. Then the objectives, intelligent algorithms and trade-off methods that are commonly used in PEMFC were tabularised and evaluated. The application of AI-MOO in PEMFC were summarised systematically based on the application areas, including the PEMFC components, kinetics and thermodynamics, control and monitoring systems, the overall performance, and the hybrid systems. The related studies were tabularised and discussed, especially algorithms, variables, objectives and optimisation results. Finally, this review addressed the current challenges in the research area and proposed research implications for future investigations.

In recent years, numerous public, private, and non-governmental organizations (NGOs) have produced documents addressing the ethical implications of artificial intelligence (AI). These normative documents include principles, frameworks, and policy strategies that articulate the ethical concerns, priorities, and associated strategies of leading organizations and governments around the world. We examined 112 such documents from 25 countries that were produced between 2016 and the middle of 2019. While other studies identified some degree of consensus in such documents, our work highlights meaningful differences across public, private, and non-governmental organizations. We analyzed each document in terms of how many of 25 ethical topics were covered and the depth of discussion for those topics. As compared to documents from private entities, NGO and public sector documents reflect more ethical breadth in the number of topics covered, are more engaged with law and regulation, and are generated through processes that are more participatory. These findings may reveal differences in underlying beliefs about an organization’s responsibilities, the relative importance of relying on experts versus including representatives from the public, and the tension between prosocial and economic goals. [This article is an accepted version. The final published version is available at https://doi.org/10.1109/TTS.2021.3052127. © 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.]

This paper will discuss the key applicable North American Standards defining performance, testing, installation and routine field maintenance of liquid-immersed distribution substation transformers. These transformers, typically applied in commercial and industrial power systems, can be installed either in outdoor or indoor locations, applied as remote or close-coupled to either primary or secondary switchgear, or both. Recommendations based on these standards for selection, installation and factory/field testing industry users should specify to maximize reliability, operational efficiency and safety will follow.

Relative time emerges from relative frequencies. It is the phase shift in relative frequencies due to infinitesimal loss in wave energy and corresponding enlargement in the wavelengths of oscillations; which occur in any clock between relative locations due to the relativistic effects or difference in gravitational potential; result error in the reading of clock time; which is wrongly presented as time dilation.

— Skin-cancer is the most common type of cancer among all other types of cancers spreading both developed and developing countries. In this paper, a web service is developed in order to help physicians as well as individuals to upload an image and diagnose the particular types of lesion. Computer vision is used to analyse lesions on images by providing computers with somewhat similarity as humans do. For this a Convolution Neural Network (CNN) is used with multi classification on International Skin Imaging Collaboration (ISIC) 2018 dataset with HAM10000 images. This dataset is a meta-data which has various types of images with seven different labels. At first, the model is trained with a larger training set and saved in a zip folder. Secondly, a web service is created where users or a doctor can upload an image for classification. Thirdly, the images uploaded are pre-processed as there is noise, hairs on image. Techniques like resizing, normalisation, thresholding, black-hat filtering and inpainting are used for this purpose. After this, the saved model is called to define whether the uploaded image is benign or malignant. The experimental results reveal that the proposed model is superior in terms of detection and diagnosis accuracy as compared to modern methods.

Big Data faces many challenges with respect to the security while storing them on a cloud server. There is high chance of getting data viewed by the hacker and the server for performing various operations. In order to provide high level of confidentiality and integrity, a new Encryption technique is introduced known as Attribute Based Encryption (ABE), which instead of making use of the receiver’s public key for encryption, uses various attributes. As a future scope, ABE can be combined with Homomorphic Encryption (HE) to provide a secure transfer of the identity. In order to provide more privacy of data, web log Analyzer is used to find out the loopholes and the unauthorized access to the web data

The reliable determination of earthquake source parameters is a relevant task of seismological investigations which ground nowadays on high quality seismic waveforms collected by near-source dense arrays of ground motion sensors. Here we propose a parametric modelling technique which analyzes the time-domain P-wave signal recorded in the near-source range of small-to-large size earthquakes. Assuming a triangular moment-rate function and a uniform speed, circular rupture model, we develop the equations to estimate the seismic moment, rupture radius and stress-drop from the corner-time and plateau level of the average logarithm of the P-wave displacement vs time curves (LPDT). The constant-Q, anelastic attenuation effect is accounted by a post-processing procedure that evaluates the Q-unperturbed moment-rate triangular shape. The methodology has been validated through the application to the acceleration records of the 2016-2017 Central Italy and 2007-2019 Japan earthquake sequences covering a wide moment magnitude range (Mw 2.5 - 6.5) and recording distance < 100 km. After correcting for the anelastic attenuation function, the estimated average stress-drop and the confidence interval (〈∆σ〉=0.60 (0.42-0.87) MPa and 〈∆σ〉=1.53 (1.01-2.31) for crustal and subcrustal events of Japan and 〈∆σ〉=0.36(0.30-0.44) MPa for Central Italy) show, for both regions, a self-similar, constant stress-drop scaling of the rupture duration/radius with seismic moment. The smaller sensitivity of the spatially averaged, time-varying peak displacement amplitude to the radiation from localized high slip patch on the fracture surface, could explain the retrieved smaller average stress-drops for sub-crustal earthquakes in Japan and M>5.5 events in Central Italy relative to previous estimates using spectral methods.

Presenting a novel set generalizing the usual base representation of floating point numbers. This “base-prime” set was identified in patented research on nonuniform data sampling and self-stabilizing computer arithmetic.

With the emergence of Internet of Things that allows communications and local computations, and with the vision of Industry 4.0, a foreseeable transition is from centralized system planning and operation toward decentralization with interacting components and subsystems, e.g., self-optimizing factories. In this paper, a new “price-based” decomposition and coordination methodology is developed to efficiently coordinate subsystems such as machines and parts, which are described by Mixed-Integer Linear Programming (MILP) formulations, in a distributed and asynchronous way. To ensure low communication requirements, exchanges between the “coordinator” and subsystems are limited to “prices” (Lagrangian multipliers) broadcast by the coordinator, and to subsystem solutions sent to the coordinator. Asynchronous coordination, however, may lead to convergence difficulties since the order in which subsystem solutions arrive at the coordinator is not predefined as a result of uncertainties in communication and solving times. Under realistic assumptions of finite communication and solve times, convergence of our method is proved by innovatively extending Lyapunov Stability Theory. Numerical testing of generalized assignment problems through simulation demonstrates that the method converges fast and provides near-optimal results, paving the way for self-optimizing factories in the future.

The increasing popularity of ChatGPT and its widespread adoption across various industries have brought forth new and evolving challenges. In this research paper, our primary focus is to investigate how ChatGPT can benefit the Information Technology (IT) sector while exploring the challenges it presents. Given the significance of this topic, numerous Chief Information Security Officers (CISOs) are currently working on understanding and implementing ChatGPT. Thus, we embarked on this research endeavor to delve deeper into this subject. Our paper, titled “Unleashing the Potential: Overcoming Hurdles and Embracing Generative AI in IT Workplaces: Advantages, Guidelines, and Policies,” aims to examine the advantages, hurdles, and potential of Generative AI (GenAI) and its applications in IT environments. To accomplish our research goals, we employed multiple GenAI tools and conducted extensive investigations to explore their benefits and identify strategies to mitigate associated challenges. By leveraging these tools, we aimed to unlock the full potential of GenAI in the IT landscape. Our research paper delves into various aspects, including the advantages offered by GenAI, the hurdles encountered during its implementation, and the potential it holds for transforming IT workplaces. Furthermore, we provide guidelines and policies to ensure the responsible and effective utilization of GenAI within IT organizations. Through this research, we aim to contribute to the growing body of knowledge surrounding GenAI, enabling IT professionals to make informed decisions and harness the benefits of this emerging technology. By addressing the challenges and providing guidelines, we aim to facilitate the seamless integration of GenAI into IT workplaces, fostering innovation and efficiency in the industry. Please see attached PDF for result.

Bacterial extracellular vesicles (EVs) are nanoscale lipid-enclosed packages that are released by bacteria cells and shuttle various biomolecules between bacteria or host cells. They are implicated in playing several important roles, from infectious disease progression to maintaining proper gut health, however the tools available to characterise and classify them are limited and impractical for many applications. Surface-enhanced Raman Spectroscopy (SERS) provides a promising means of rapidly fingerprinting bacterial EVs in a label-free manner by taking advantage of plasmonic resonances that occur on nanopatterned surfaces, effectively amplifying the inelastic scattering of incident light. In this study, we demonstrate that by applying machine learning algorithms to bacterial EV SERS spectra, EVs from cultures of the same bacterial species Escherichia coli can be classified by strain, culture conditions, and purification method. While these EVs are highly purified and homogeneous compared to complex samples, the ability to classify them from a single species demonstrates the incredible power of SERS when combined with machine learning, and the importance of considering these parameters in future applications. We anticipate that these findings will play a crucial role in developing the laboratory and clinical utility of bacterial EVs, such as the label-free, noninvasive, and rapid diagnosis of infections without the need to culture samples from blood, urine, or other fluids.