Ice buildup on wind turbine blades is a significant issue, leading to operational risks and reduced efficiency. Conventional ice detection methods, such as visual inspection, power curve analysis or specialised sensors, are often slow, inefficient, or costly. This paper proposes an approach using 0-dimensional persistence homology from topological data analysis (TDA) applied to tower and blade vibration spectra. This method extracts key features representing the lifespan of the sub-level sets of the spectra, allowing the formulation of a clearer supervised learning problem. The resulting persistence diagrams are embedded into persistence images and persistence rank functions. Persistence images are employed alongside convolutional neural networks (CNN) to distinguish asymmetrical ice distribution on one or two blades as well as symmetrical ice distribution across three blades from normal conditions. For the symmetrical ice distribution scenario, persistence rank functions with functional principal component analysis (FPCA) and support vector machines (SVM) offer a simpler classification. This approach not only improves ice detection accuracy but also reduces equipment costs and maintenance, promising enhanced wind turbine blade monitoring and maintenance efficiency. Note to Practitioners-This study is motivated by the need for an accurate ice detection strategy in wind turbine blades that ensures precise and reliable detection. It utilises standard data from wind turbine structural vibrations to avoid additional expenses associated with specialised sensors. Unlike traditional faults, the gradual accumulation of ice on wind turbine blades permits the use of more intricate data transformations such as topological data analysis (TDA), which would otherwise be too computationally intensive for anomalies requiring detection within seconds. Additionally, this approach enables a more refined detection algorithm, crucial for identifying cases in which the ice distribution is symmetric across the wind turbine rotor, which does not produce imbalance loads in the structure and therefore has a subtler effect.

The present paper examines the possibility of a smart grid to the nearest destinations in the Milky Way, incorporating research data of astrobiology, biosignatures, habitability criteria, proposals for terraforming terrestrial planets, and innovations in interplanetary robotic exploration, energy production and spacecraft motion. We focus on the dynamics of modelling and experimentation, with projects such as interstellar chemical engineering, bio-manufacturing, space sails, fusion, ion thrusters and antimatter propulsion, into the surrounding environments ranging from Kuiper Belt and Oort Cloud to Proxima and Alpha Centauri. The research task is the experimental modelling of current real-world situations in alignment with the past, the present and the future of space exploration.

Wearable devices and medical sensors revolutionize health monitoring, raising concerns about data privacy in Machine Learning (ML) for healthcare. This tutorial explores Federated Learning (FL) and Blockchain (BC) integration, offering a secure and privacy-preserving approach to healthcare analytics. FL enables decentralized model training on local devices at healthcare institutions, keeping patient data localized. This facilitates collaborative model development without compromising privacy. However, FL introduces vulnerabilities. BC, with its tamper-proof ledger and smart contracts, provides a robust framework for secure collaborative learning in FL. After presenting a taxonomy for the various types of data used in ML in medical applications, and a concise review of ML techniques for healthcare use cases, this tutorial explores three integration architectures for balancing decentralization, scalability, and reliability in healthcare data. Furthermore, it investigates how Blockchain-based Federated Learning (BCFL) enhances data security and collaboration in disease prediction, medical image analysis, patient monitoring, and drug discovery. By providing a tutorial on FL, blockchain, and their integration, along with a review of BCFL applications, this paper serves as a valuable resource for researchers and practitioners seeking to leverage these technologies for secure and privacy-preserving healthcare ML. It aims to accelerate advancements in secure and collaborative healthcare analytics, ultimately improving patient outcomes.

Cyber security for power systems is becoming a concern for many researchers. However, the impact of cyberattacks on actual power systems is unclear. This paper discusses analytically the significance of cyber security for power systems and evaluates whether cyber-attacks are a real threat, or are just a hypothetical concept. First, this paper categorizes the different types of power system attacks and then systematically reviews the cyber-attack modeling, detection, and mitigation approaches for bulk power systems. This paper also discusses prominent cyber and physical attacks on actual power systems and draws a comparison among them based on economical and societal impacts. Based on the facts uncovered in the paper, when compared to physical attacks, cyber-attacks on power systems have had negligible economic and societal impacts. In fact, there is only one documented case (the Ukrainian cyber-attack by Russia) that successfully caused customer disconnections.

Large abscesses are walled-off collections of pus and bacteria that often do not respond to antibiotic therapy. Standard of care involves percutaneous placement of indwelling catheter(s) for drainage, a long and uncomfortable process with high rehospitalization rates. The long-term goal of this work is to develop therapeutic ultrasound approaches to eradicate bacteria within abscesses as a noninvasive therapeutic alternative. Inertial cavitation induced by short pulses of focused ultrasound (histotripsy) is known to generate lethal mechanical damage in bacteria. Prior studies with Escherichia coli (E. coli) in suspension demonstrated that bactericidal effects increase with increasing peak negative pressure amplitude, treatment time and duty cycle. The current study investigated correlates of bactericidal activity with histotripsy bubble cloud size produced for these treatment parameters at several ultrasound frequencies. Histotripsy was applied to E. coli suspensions in 10-mL sample vials at 810 kHz, 1.2 MHz, or 3.25 MHz for 40 minutes. The resulting cavitation activity in the sample vials was separately observed with high-speed photography. The bubble cloud area was quantified from those images. A linear relationship was observed between log kill and cavitation cloud size (R2 = 0.96), regardless of the acoustic parameters (specifically frequency, pulse duration and pressure amplitude) used to produce the cloud. These findings suggest a role for in-situ treatment monitoring using B-mode imaging.

Emotions arise from a complex interplay of various factors, including conscious experience, physiological processes, and contextual elements. Although emotions are inherently dynamic processes, this aspect is oftentimes neglected in experimental protocols. In this study, we employed dynamical systems theory to investigate the time-varying self-assessed emotion ratings. We used the continuous ratings of the publicly available CASE dataset, in which thirty individuals rated their level of arousal and valence while watching videos designed to evoke four different emotions. Firstly, we analyzed the univariate dynamics by reconstructing the phase space from the arousal and valence series separately and quantified their regularity and spatial complexity by using three metrics: Fuzzy, Sample, and Distribution Entropy. Then, we combined the arousal and valence series by proposing a novel index, the Multichannel Distribution Entropy (MDistEn), to estimate the complexity of the bivariate phase space. By coupling the two dimensions, we found that MDistEn resulted as an effective marker of fear, showing patterns statistically different from all of the other stimuli (p-value≤0.001). These findings support the investigation of the time-varying dynamics of annotated emotion ratings, as a promising pathway to discriminate the onset of fear-related pathological states.

The Wavelet Entropy Clustering (WEC) technique enhances the pattern recognition (PR) methods in science and engineering domains. WEC helps identify hidden patterns in time series (TS) data. State-of-the-art WEC methods rely on advanced algorithms and wavelet function selection. Selecting pre-defined wavelets for PR might not capture all the patterns in the TS data. This limitation affects the accuracy of the TS data analysis. Existing data-driven-based PR methods handle complex TS data. They construct wavelets tailored to the data. This approach improves the accuracy of PR in complex TS datasets. Balancing all the desired wavelet properties like orthogonality and compact support becomes challenging. This inherent trade-off in achieving all properties limits the effectiveness of data-driven methods. Wavelet set theory (WST) offers a framework for addressing these challenges. WST tailors wavelets from a TS signal. Tailor wavelets perform better signal decomposition, capture complex features, and conduct multi-scale analysis. In this work, we propose WaveClust, a new approach which combines WST into the WEC framework. WaveClust extracts hidden features from TS data using multi-level wavelet packet entropy. We compare the performance of WaveClust with existing wavelet-based methods. WaveClust outperforms conventional approaches in PR tasks. It improves the ability of PR tasks to identify complex patterns in TS data. WaveClust aids researchers in analyzing complex TS data for scientific and engineering applications.

Synchronous machines (SMs) are characterized as nonlinear dynamic systems, with stator flux linkages being critical for controller design. Among the developed methods for flux linkage estimation, the disturbance observer-based flux linkage estimator (DOB-FLE) is recognized as the state-of-theart. However, DOB-FLE faces challenges in ensuring exponential convergence during transient states. To address this, this paper introduces an extended state observer-based flux linkage estimator (ESO-FLE), which represents an advancement over DOB-FLE. This novel approach utilizes extended states to model the nonlinear disturbance term as time-varying ramp signals, offering a contrast to the constant assumption employed in DOB-FLE. It concurrently estimates both the extended states and the flux linkages through an ESO. Simulation results from a 35-kW SM drive demonstrate that ESO-FLE provides superior transient performance under dynamic conditions compared to DOB-FLE.

We can calculate n fold integrals and derivative exponentially faster using fractional calculus. Integral of some functions are NP problem meaning it will take long time (Non polynomial time). By calculating n fold integrals faster we can achieve real time simulations.we can also solve hyper dimensional problems and to achieve hyper dimensional computing.we can reduce space and time complexity of an algorithm using this method.we also compare results with normal n fold integrals.we got 100 % accurate result.

During fast charging of Lithium-Ion batteries (LIB), cell overheating and overvoltage increase safety risks and lead to faster battery deterioration. Moreover, in conventional Battery Management Systems (BMS), the cell balancing, charging strategy and thermal regulation are treated separately at the expense of faster cell deterioration. Hence, this paper proposes an optimized fast charging and balancing strategy with electrothermal regulation of LIB packs. Thereby, the power dissipation constraints of the passive balancing are introduced in the proposed integrated optimal framework and cell balancing is achieved by bypassing the extra charging current. The electrothermal model of the cells, along with a battery pack formed by a string of cells, is implemented. Extensive experiments are carried out to identify the coefficients for the Lithium-Ion cell model, i.e. Samsung-INR18650-20R, and the charging current trajectory as well as the balancing signals are generated with Model Predictive Control (MPC). The pack level simulations and experiments show that the proposed algorithm maintains the electro-thermal boundaries throughout the charging process, increasing the safe charge acceptance of the battery pack.

A document by Mahmood Mohammadzadeh. Click on the document to view its contents.

Efficient heat management and control in optical devices, facilitated by advanced thermo-optic materials, is critical for many applications such as photovoltaics, thermal emitters, mode-locked lasers, and optical switches. Here, we investigate the thermooptic properties of 2D graphene oxide (GO) films by precisely integrating them onto microring resonators (MRRs) with control over the film thicknesses and lengths. We characterize the refractive index, extinction coefficient, thermo-optic coefficient, and thermal conductivity of for the GO films with different layer numbers and degrees of reduction, including reversible reduction and enhanced optical bistability induced by photo-thermal effects. Experimental results show that the thermo-optic properties of 2D GO films vary widely with the degree of reduction, with significant polarization anisotropy, enabling efficient polarization sensitive devices. The versatile thermo-optic response of 2D GO substantially expands the scope of functionalities and devices that can be engineered, making it promising for a diverse range of thermo-optic applications.

This paper introduces the application of a reduced-order modeling technique for accurate temperature monitoring in Power Electronics modules. The methodology involves coupling the Finite Element Method with the radiosity equation to obtain a high-fidelity representation of the physical behavior of the device. These models account also for surface-to-surface radiation, an aspect that can have a high impact when the operating temperatures increase, and the components are close to each other, which is a common scenario for high power density and compact modules. The Model Order Reduction is performed via the Proper orthogonal decomposition, coupled with the Discrete Empirical Interpolation Method to handle the nonlinearity introduced by the surface-to-surface radiation. This approach allows to reduce the computation time with a limited effect on the accuracy of the prediction. The method is applied to reduce the order of the thermal model of a DC-DC converter. Numerical and experimental results demonstrate the approach’s effectiveness, showcasing high accuracy with minimal computation time and memory cost.

Counterfeit electronic components are known to enter supply chains through recycling, with these already-aged components creating serious reliability risks, particularly for critical infrastructure systems. A number of recycled integrated circuit (IC) risk mitigation approaches have been proposed, but these generally lack pragmatic feasibility. This work proposes a novel real-world deployable on-chip sensor that: 1) is tamper-resistant by exploiting persistent changes caused by hot carrier injection (HCI); 2) generates a DC signal measurable by common low-cost test equipment; and 3) reuses an existing I/O interface, including existing pins; while 4) requiring a very small footprint. Combining this sensor with a random sample-based testing strategy allows for low-cost and time efficient detection of fraudulently recycled batches of ICs. Through simulation-based validation using process-accurate models of a 65 nm technology we show that employing a random sample size as small as 130 is sufficient for identifying such batches with a statistical significance level of 0.01.

A document by Jafar Shamsi. Click on the document to view its contents.

In this article, we discuss the concept of infinity (∞), infinitesimals □ , and a new mathematical operation that expands calculations into the realm of the infinite. This breakthrough allows for the computation of infinite numbers, leading to new possibilities for mathematical research and practical applications. By introducing infinite numbers, numerous advantages arise, such as accurately representing limits, divergences in integrals and infinite series, and extending calculations to infinity. This mathematical advancement opens doors to exploring uncharted territories, offering valuable insights and breakthroughs.

Field oriented control (FOC) and Direct torque control (DTC) are the two most popular vector control methods for electric motor drives. FOC uses linear controllers and pulse width modulation (PWM) to control the fundamental components of the load voltage. On the other hand, DTC is a nonlinear strategy that directly generates the voltage vectors in the absence of a modulator. This paper presents a comparative study between the two control schemes. The pros and cons of both the methods are separately discussed for two candidate machines: Induction Machine (IM) and Permanent Magnet Synchronous Machine (PMSM). The comparison is based on various criteria including basic control characteristics, dynamic performance, parameter sensitivity, and implementation complexity. The discussion is supported with results from various articles, and a criterion to draw a fair comparison between the performance of the two schemes is also presented.

Meeting the targets of Sustainable Development Goal (SDG) 7, which focuses on ensuring access to affordable, reliable, sustainable, and modern energy for all, poses significant challenges. Overcoming these hurdles requires innovative solutions that can bridge the gap between current capabilities and future needs. Swarm electrification emerges as a promising concept that could accelerate progress towards achieving SDG 7 goals by leveraging the collective power of decentralized energy resources. This review delves into the concept of swarm electrification, placing it within the context of the prevailing trends in the power system sector: decentralization, decarbonization, and digitalization. It examines the role of digital technologies in enhancing swarm electrification and categorizes application areas according to the phases of swarm electrification. Particular attention is given to the technologies underpinning Deep Digitalization, such as distributed ledger technology, notably blockchain, and artificial intelligence, with a focus on machine learning. These technologies play pivotal roles in advancing swarm electrification. The review demonstrates how deep digitalization can facilitate the improvement of swarm electrification and ultimately support the integration of bottom-up initiatives with top-down grid expansion efforts over time.