Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparative Classification Study for Broken Rotor Bar Fault and its Severity Analysis using Machine Learning Approaches in Rotating Machines

Early fault detection in rotating machines reduces the amount of time, money, and labor needed to repair or replace the machine in the event of an abrupt breakdown that halts production. Due to an increase in the faults and the loss of valuable time and money, industries are now moving towards accurate, and automatic detection and diagnosis of faults in induction machines. Since broken rotor bar faults (BRBFs) occur inside a motor, it goes unidentified till the motor completely fails. This study presents a comparative approach to detecting broken rotor bar faults and assessing their corresponding severity levels. After acquisition of the experimental data (3-phase currents), the Extended Park’s Vector Approach (EVPA) has been utilized to transform the 3-phase currents, after which, the statistical time domain features have been used to develop the feature set. Thereafter, Principal Component Analysis (PCA), has been used to study the geometry of the data and also used as a dimensionality reduction tool to transform and reduce the feature set. A comparative study has been presented where a variety of neural and non-neural classification models have been trained and tested using PCA and non-PCA transformed feature-sets to determine the best model. Using the developed feature sets, best results are presented by the Ensemble classifier which stems from the Bagged Tree family.
Single Depot Heterogeneous Capacitated Vehicle Routing Problem with Simultaneous Delivery and PickUp for Disaster Management Systems

We address the challenge of cost management for pre-disaster emergency funds, with ample warning time available for completing emergency operations. We formulate a mathematical model for a complex vehicle routing problem involving a single depot, a fleet of heterogeneous vehicles with limited capacities, and simultaneous delivery and pickup tasks. Each vehicle type is assigned a specific road network based on vehicle-road compatibility. We develop heuristic approaches to generate high-quality solutions for this problem and compare them with a state-of-the-art commercial solver. Our findings reveal that our heuristics perform exceptionally well for very large problem instances, while the commercial solver outperforms them for smaller instances. Moreover, our algorithms can handle scenarios where customers have either delivery or pickup demands, as well as cases where both operations are required. We evaluate the performance of our exact formulations extending existing data, as well as generate new data sets demonstrating the effectiveness of our bioinspired heuristic methods in achieving satisfactory outcomes.
Influence of Irganox 1010 on Space Charge Behavior in Thermal-Oxidatively Aged PP/Elastomer Blends

Polypropylene (PP) has been considered as a promising insulation candidate for high voltage direct current (HVDC) cables for its overall low cost and recyclable. To address its high stiffness limiting application in cable system, a propylene-based elastomer (PBE), DOW VERSIFY™ 2200 was introduced. However, the potential lifetime of blends was confirmed to be reduced dramatically, to only one quarter of that of pure PP. Based on this observation and the fact that antioxidants (AO) can improve the thermal stability, an AO has been introduced to investigate its influence on thermal-oxidative ageing in PP/elastomer blends. According to chemical changes characterized by Fourier-Transform Infrared spectroscopy, the AO reacts with oxygen initially on the sample surface and is assumed to be consumed up within 3 to 4 weeks at $150 ^{\circ }\mathrm{C}$ in a fan oven. The space charge accumulation behaviour was estimated by the pulsed electro-acoustic method, where a resultant charge peak brought by the AO can be observed resulting in the positive charge movement and negative charge accumulation near the cathode, as a function of the poling time. It has been found that the DC breakdown strength (BDS) shows an increase with ageing times until 4 weeks.
Parsimonious Technologies for Sensing Upper Limb Muscles Activation

Low-cost sensing technologies are experiencing significant growth. They can offer cost-effective and simpler alternatives to existing systems at cost of accuracy. This has the potential to drive innovation and allow rehabilitation robotics to expand. This expansion would facilitate the integration of rehabilitation robotics into clinical contexts. This paper addresses the analysis of force myography (FMG) as an alternative to surface electromyography (sEMG) for sensing upper limb muscle activation in the case of elbow flexion and extension. This analysis will ultimately allow the design and proof of concept of a parsimonious wearable sensing technology that consists of a low-cost force sensor embedded in a proper ergonomic structure.
Clinical Indicators and Thresholds for Determining Discharge Destination from Acute Stroke Ward

Objective: The present study aimed to investigate the complex interaction of factors affecting discharge destination after the acute phase of stroke. Methods: In 304 patients with stroke who required rehabilitation, we focused on three discharge destinations (i.e., home, convalescent rehabilitation ward, and other facility). Sociodemographic, medical, and acute symptoms data, within the first week of stroke onset and discharge destination, were collected from acute stroke wards. The complex interaction of factors relating to discharge destination from an acute stroke ward was investigated by classification and regression tree analysis. Results: Patients with a total Functional Independence Measure (FIM) score > 35, a Brunnstrom recovery stage of the lower limb > V, and a comprehension FIM score > 5 had a 91.7% chance of home discharge. By contrast, patients with a total FIM score ≤ 35, age ≤ 79.5 years, and > 2.5 family members had an 80% chance of discharge to a convalescent rehabilitation ward. Finally, patients with a total FIM score ≤ 35, age > 79.5 years, and an expression FIM score ≤ 3 had a 78.6% chance of discharge to other facilities. Conclusion: The interaction of independence on FIM, function of the paralyzed lower limb, age, number of family members, and comprehension or expression function affect the choice of discharge destination from acute stroke wards.
Multi-Dimensional Clustering-Based Innovative Machine Learning Algorithm for Short-Term Load Forecasting

This paper presents a novel machine learning algorithm based on multi-dimensional clustering for short-term load forecasting. The proposed approach employs a combination of Catboost and LSTM techniques using the recursive least square algorithm (RLS). The primary objective of this paper is to clearly identify the Electric Peak demand as well as their occurring times so that energy control algorithms can optimize and set appropriate resources to reduce the peak load demand, which can result in improved efficiency of local distribution networks and reduced environmental impact of energy generation and distribution. Simulation experiments demonstrate that the proposed algorithm outperforms several benchmark methods in terms of key metrics such as mean absolute percentage error (MAPE), root mean square error (RMSE), and bias.
Matrix Completion via Coded Distributed Alternating Least Squares

Low-rank matrix factorization (MF) decomposes a matrix into two low-dimensional matrices. One key algorithm for MF is distributed alternating least squares (ALS), iteratively learning the matrices. However, hardware, software, or network problems can cause distributed nodes (stragglers) to fail computation or communication. Existing algorithms neglect stragglers as a design concern. To address this, we propose D-ALS-MC (Distributed ALS Matrix Completion), a coding-assisted algorithm for distributed matrix completion. We analyze the trade-off between the maximum tolerable number of stragglers (recovery threshold) and coding redundancy (code rate), along with computational costs. Moreover, we prove that incomplete matrices satisfying the Restricted Isometry Property (R.I.P.) condition can recover original values. The experimental results demonstrate the effectiveness of our proposed methods.
A probabilistic machine learning approach to detect severe sub synchronous oscillations in DFIG based wind farms

The dynamic properties of the power grid have become increasingly complex due to the continuous transition of the power grid towards the use of renewable energy sources for power generation. The emerging sub synchronous oscillation (SSO) conditions owing to the high penetration of renewable energy resources is one such issue and should be investigated rigorously to maintain the reliability and safety of the grid. Although wide area measurement system can detect instabilities in general, it is inadequate to detect the sub synchronous oscillations. Recent events that have occurred in USA and China bear evidence to this inadequacy. This article proposes a technique for the real time monitoring of the sub synchronous oscillations in DFIG based wind farms and early warning of severe SSO using an artificial intelligence-based approach. Accordingly, a probabilistic Naive Bayes algorithm is trained to anticipate severe SSO that could potentially disconnect wind turbines from the grid, thereby allowing the power system operators to take necessary actions to control the SSO without letting the disconnection of large number of wind turbines from the power grid. The proposed Naive Bayes classifier has a F1-Score, training time and prediction time of 0.97, 0.891 seconds, and 0.0238 seconds respectively. Furthermore, it can provide the confidence level of its prediction rather than a deterministic output as proposed in existing literature. The simulation results demonstrate the effectiveness of the proposed approach for SSO identification.
A Novel Distributed Secondary Control Method for Microgrids with Forced Deviation Constraint

Dynamic perturbations and power fluctuations in microgrids are hard to predict, leading to frequency and voltage overshoots beyond allowable limits. Aiming to solve this issue and address stable coordination, this work develops a distributed control scheme with forced deviation constraint capability. In the proposed scheme, an activation function is innovatively applied to the distributed algorithm to project tracking errors from the real number field to the expected range set. The limited control input obtained based on the constrained error can effectively constrain the output frequency and voltage state of the controlled distributed generators (DGs), thus avoiding unexpected fluctuations and overshoots.
Dielectric Response of PVDF-Al2O3 Nanocomposites

The dielectric response of poly(vinylidene fluoride) (PVDF) and of PVDF- $\mathrm{A}1_{2}\mathrm{O}_{3}$ nanocomposites was analyzed in this work. The influence of nano’ $\mathrm{A}1_{2}\mathrm{O}_{3}$ concentration, between 5 and 30 wt%, on enhancing the dielectric performances of PVDF is discussed in the paper regarding the potential applications of the studied nanocomposites as dielectrics for high energy density polymer capacitors and their use in advanced power systems and electronic devices. The experimental results show that the dielectric spectra emphasize higher real permittivity values for nanocomposites, while the dielectric losses decrease as compared with neat PVDF, especially between $10^{2}$ and $10^{4}$ Hz. The morphology of the nanocomposites seems to play a significant role in their dielectric behavior.
Robustness of Microservice Architecture Design, A Complex Network Approach

The number of businesses utilizing microservice architecture has grown rapidly in recent years. Due to its independence and scalability, one of the main challenges is designing a robust microservice architecture. A microservice architecture can be represented as a dependency graph, which is like a complex network. In this paper, the point is to simulate and compare the impact of various methods for measuring robustness in microservice architecture design for real-world application examples to determine which design is superior. As a result, this paper introduces a mathematical function that is made from the combination of existing formulas for complex networks.
Attention Mechanism Based CNC Milling Cutter Wear Detection Using Machine Vision

With the development of modern manufacturing industry, the requirements for machining accuracy of CNC machine tools are becoming increasingly high. Traditional wear detection technology is gradually unable to meet the requirements of detection accuracy and speed, so there is an urgent need for intelligent and efficient detection methods. A CNC milling tool wear detection system is developed to address the problem of tool wear during the CNC milling process. A scheme is designed for the image acquisition system of the side and bottom edges of the milling cutter based on machine vision. An attention mechanism-based method is proposed for milling cutter wear detection. An experimental platform and completed experiments were established on image registration and fusion, tool edge extraction, and tool wear classification. The experimental results show that: The method can classify the tool wear status, extract the side edge surface contour, and improve the accuracy and efficiency of digital milling.
Toward a taxonomy of asteroid spectra in the 3-µm region

Roughly one hundred low-albedo asteroids have been observed with SpeX in the 2-4 µm region in the past decade, and a variety of band depths and band shapes are found. We will present examples of this variety and discuss current and expected means of classifying spectra in the 3-µm region.
Effect of BTA on Charge Mobility in Insulating Oil and Oil-Immersed Paper

1,2,3 BTA) is a widely-used additive to limit the oil flow electrification in oil-paper insulation of HVAC transformers, but it is still indeterminate whether BTA is applicable to converter transformers. Our previous experiment shows that BTA promotes charge accumulation at the oil-paper interface under DC stress, and it cannot be explained by its effect on the electric double layer and the interface barrier. Considering that charge mobility is also an important factor that affects the interface charge characteristics, the effects of BTA on charge mobility in insulating oil and oil-immersed paper is investigated in this paper. The addition of BTA increases charge mobility in both insulating oil and oil-immersed paper, especially for positive charge, and it increases the charge mobility in insulating oil more significantly than in oil-immersed paper, widening the already huge mobility gap between insulating oil and oil-immersed paper, which causes the promoting effect of BTA on charge accumulation at the oil-paper interface. Reducing the disparity in charge mobility between insulating oil and oil-immersed paper is beatifical to suppress charge accumulation at the oil-paper interface.
Effect of Gamma Irradiation on Structure and Electrical Properties of PP Copolymer Films

Polypropylene (PP) is known as a very sensitive material to gamma radiations ($\gamma$ -rays). In this study the performances of a copolymer of PP are evaluated. In the field of electrical insulation, one must determine how electrical properties are modified after an irradiation with a ${}^{60}\text{Co}$ source and various doses. The effects on the conductivity and space charge distribution under electrical field application are analyzed. During this work, 180 $\mu \mathrm{m}$ -thick films of PP copolymer were exposed to $\gamma$ -rays in air at room temperature using ${}^{60}\text{Co}$ source (with a dose rate of 23 Gy/min) to reach doses up to 60 $\text{kGy}$. Pulsed Electro Acoustic method was used to measure space charge in the sample, at controlled temperature. The protocol was based on 1 h polarization under a DC field (5, 10 and 15 kV/mm) followed by 2 h relaxation. Current measurements were performed under the same polarization conditions but the relaxation was extended to 3 h. DSC analysis showed that while the crystallinity increases after irradiation at 30 kGy, it decreases for 60 kGy dose. On the other hand, at 60 kGy, the thermogram exhibit multiple melting peaks instead of one observed on the virgin sample and 30 kGy exposed sample. Transient charging currents decrease monotonically and rise slightly with dose. PEA measurements show that homocharges are the principal feature of space charge nature in irradiated sample compared to virgin one, even if it was barely noticeable. It appears that, for electrical properties, the PP copolymer exhibits a good resistance to $\gamma$ -rays in the explored dose range, even if a non-negligible change was detected in the crystallinity. This particular feature needs more explorations to better understand the structure-properties relationships.
Development of a New PEA Cell for Model Cables

In this work, a prototype of a new PEA cell configuration has been built and its principle of operation investigated and described. The reasons that led the authors to this work are due to the fact that the traditional cell still presents some issues. In particular, it known that that for cable specimens the reflection phenomenon and the signal attenuation are the main responsible factors that affecting the main output signal. Concerning the acoustic wave attenuation phenomenon, it was found that the signal magnitude can increase by replacing the metallic ground electrode with a polymeric acoustic coupler and the PVDF absorber with a metallic block. Based on this, after a description of the classic and the new PEA cell structure, in the present paper the acoustic wave behavior in the sensor/absorber interface has been simulated by means of a previously developed model. Simulation results show that in the new PEA cell the signal sensed by piezoelectric sensor is doubled in comparison with that detected in the traditional configuration. However, in the first experimental test carried out with the built PEA cell prototype in a model cable specimen it has been observed that the magnitude of the space charge profile needs to be further improved.
Sapphirine and högbomite in overprinted kyanite-eclogites of central Rhodope, N. Greece: first evidence of granulite-facies metamorphism

Sapphirine and hogbomite were found within overprinted kyanite-eclogites of central Rhodope. Sapphirine is characterized as peraluminous and occurs in association with rutile (+ ilmenite) + plagioclase. Hogbomite coexists either with kyanite + rutile (+ ilmenite) + plagioclase or with rutile + Fe-Mg-spinel. Sapphirine and, in part, hogbomite were probably formed by reaction between kyanite, omphacite and corundum, during the overprint. Some hogbomite also grew by reaction between rutile and Fe-Mg-spinel. Formation of the sapphirine is considered to have taken place above 700°C at high pressures (ca, 10-15 kbar), implying that the eclogites entered the granulite-facies field. The rocks studied record the highest overprinting temperatures in the Rhodope
Enhanced Discrete-Time Differentiators for State Observers of Sensorless Permanent Magnetic Synchronous Motor Drives

In sensorless motor drive applications, the rotor and the speed of the rotor are usually estimated through state observer algorithms. A key part of such algorithm is the computation of the derivative of the rotor position, resulting in the estimated rotor speed. Computing derivatives brings challenges due to their inherent noise amplification characteristic. This paper presents enhanced discrete-time differentiators for state observers of sensorless permanent magnetic synchronous motor drives. A class of discrete-time differentiators based on finite impulse response filters and one based on Kalman filter are proposed. Details about how to obtain the coefficients of the differentiators are presented. To verify the performance of the proposed differentiators, all of them were executed in F28379D LaunchPad microcontroller. Results show that the proposed discrete-time differentiators compute the derivative of the rotor position signal in a satisfactory manner, even in the presence of noise.
Using a Mixed-method Approach to Identify Urban Mobility Needs for the Development of Micromobility Solutions

Micromobiles provide a sustainable and space-saving alternative for mobility in urban areas. Although the global market potential for micromobility products is estimated at 400–500 billion USD in 2030, micromobility concepts are only occasionally finding their way into everyday urban life. It is therefore questionable, if the currently existing products meet the requirements of an urban society. To ensure that products are clearly customer-focused, situational customer needs analyses form the basis for decisions about product features and technical solutions. Therefore, a mixed methods approach adapted to the requirements of urban mobility will be used to identify the basic needs of potential urban mobility customers using the example of Germany. This mixed approach combines qualitative and quantitative research. Specifically, experts and potential customers of micromobiles are interviewed qualitatively and the derived results are validated and prioritised in two quantitative studies. The generated data forms a basis for the subsequent steering of the technical solution finding.
A Input-Parallel-Output-Series Multiphase Hybrid Boost Converter Applied to Hydrogen Fuel Cells

This paper presents a high conversion gain multi-phase DC/DC boost converter topology, applied to the hydrogen fuel cell. The hydrogen-based fuel cell presents a low voltage at its output with a current in DC. The purpose of this multiphase DC/DC boost converter topology is to divide the output current of the fuel cell in such a way that the nominal current that circulates through the branches is much lower, thus reducing the dimensioning of the cables, inductance, power losses and costs, in addition there is a high conversion gain in such a way to connect the output to a three-phase inverter. The electrical scheme of the proposed topology is validated through simulation results using the matlab/simulink computational tool.
Temporal Convolutional Network Ensemble for Day-Ahead and Week-Ahead Electricity Price Forecasting

Forecasting electricity prices is a key tool for power market participants in making market decisions which has received an increased importance with growing renewable market penetration and market deregulation. Artificial neural networks (ANNs) perform well with complex and non-linear tasks, leading to neural network solutions such as long short-term memory neural networks (LSTMs) becoming the favored option for electricity price forecasting over the past decade. Recent advances in temporal convolutional networks (TCN) have proven them competitive to other state-of-the-art neural networks (NNs), including LSTMs. This paper presents a hybrid TCN+SVR (support vector regression) model to forecast electricity prices using the PJM electricity market dataset. Historical loads and prices are used as inputs into the tested models. A comparison of the results to a base model TCN and a hybrid SVR+TCN model using identical inputs show improvement in price forecasting accuracy for different simulated cases.