Miracle-dz/UniFAR-FTUs huggingface dataset:
Microgrid Planning in Distribution Networks Considering Optimal Placement of Distributed Generation Units

Due to increasing penetration of renewable energy-based distributed generation (DG), conventional distribution networks are transformed into their active form, where microgrids are considered fundamental building blocks. Optimal placement of DG units is the primary step towards microgrid planning. Optimally placed and sized DGs can reduce the total power losses in distribution networks by localizing the power supply to loads. In this paper, a DG optimal placement method by minimizing the total power losses is proposed, where Brute Force search algorithm and Backward Forward Sweep method are used to solve the optimization and load flow problems, respectively. IEEE 33 bus radial distribution test system is used to validate the proposed method in MATLAB. Several case studies are conducted, dispatchable and non-dispatchable DGs and capacitor banks are used in the optimal placement and sizing. The proposed method is proved to be effective by comparing with an existing method.
Granular Reduction Based on Boolean Matrix in Formal Decision Contexts

Granular reduction is an effective dimension reduction method in formal decision contexts. However, it is a NP-hard problem to calculate the reducts through the discernibility matrix when the data is high-dimensional, which will lead to high time consumption. In this paper, we propose a granular based-matrix reduction in consistent formal decision contexts. First, the consistency of formal decision context is defined based on the object granular matrix. Next, to obtain a minimal granular reduct, we construct the dis-cernibility matrix and reduct algorithm (DARMT). Second, the implication rule approach is studied from the viewpoint of granular based-matrix reduct. Finally, we perform an experimental evaluation on eighteen data sets which explicate the feasibility and effectiveness of our proposed approach.
Early Diagnosis of ASD based on Facial Expression Recognition and Head Pose Estimation

Autism Spectrum Disorder (ASD) is one of the most common developmental disorders characterized by impairment of social interaction and communication skills, as well as stereotype behaviors. The early diagnosis has been focused on the analysis of EEG and MRI, which requires sophisticated medical equipment and the data collection process is cumbersome. Based on the differences of appearance characteristics between ASD and Typical Development (TD) children, we propose an efficient and effective method to diagnose autistic patients via facial expression recognition and head pose estimation. We apply the Conformer network to facial expression and head pose, respectively, to extract both local features and global features. In addition, we process the extracted features with accumulative histogram and adopt Long Short-Term Memory (LSTM) for classification. We verify the performance of the proposed method on our self-collected ASD video dataset (ACVD) and achieve a classification accuracy of 97.59%.
Waveform Correlation Based Harmonic Voltage Contribution Determination of Iron and Steel Plants Supplied From PCC

In this research work, a new method which determines the individual harmonic voltage contributions of the EAF plants supplied from a point of common coupling (PCC) to the PCC voltage is presented. EAFs are one of the most significant sources of the harmonics, especially the uncharacteristic ones, therefore it is important to be able to discriminate the amount of individual contributions from the feeders of a PCC supplying multiple EAFs. The proposed method uses the relationship derived between the correlation coefficient of the PCC voltage and the feeder current waveforms and the harmonic voltage contribution of each plant supplied from the PCC. The main idea is based on the fact that harmonic voltage at the PCC is a result of the additive effect of voltage drops on the source side impedance of the power system caused by the individual feeder currents. After computing the Pearson correlation coefficients at each harmonic frequency using 10-cycle synchronized feeder current and PCC voltage waveforms, harmonic voltage contribution of the related feeder is obtained using the proposed method. This procedure can be repeatedly used to obtain the contribution of each EAF plant at each frequency component. Field measurements from a PCC supplying multiple EAF plants are used to verify the results and the performance is compared with the previously proposed methods. With a specified source side impedance by the utility, the proposed approach is a fast method of obtaining the harmonic responsibilities, since no real-time impedance measurements are required and no need for the measurements of the other feeders to compute the contribution of a specific feeder in contrast to some other methods. The proposed method can be easily adapted as a real time harmonic contribution detection tool for the power quality analyzers, all of which have synchronized voltage and current waveform measurements.
Ordinal Distance-based Domain Adaptation Framework for Motion Sickness Classification

Many people experience motion sickness. In order to analyze a driver’s motion sickness state and prevent accidents, a method of estimating the degree of motion sickness based on bio-signals is emerging. The brain-computer interface (BCI) systems using electroencephalogram (EEG) are used as the most direct method of estimating motion sickness conditions. However, EEG-based systems suffer from variability between subjects and over time, so a calibration process is required for every use. To address this problem, we mitigate the need for calibration through cross-subject transfer learning between the target data and the multi-subjects source data. All experiments were conducted in a domain adaptation setting. Meanwhile, we assume that there is an ordinal relationship between motion sickness scores. Thus, we performed an ordinal classification task so that the feature vectors were mapped by reflecting the ordinal characteristics according to the motion sickness state. In this paper, we propose a motion sickness classification BCI framework in combination with ordinal classification, resting-state prototype-based ordinal distance learning, and a subject-specific embedding module. Taking into account constraints of ordinal rank, the feature extractor is trained with prototype-based ordinal distance learning to measure the relative distance between the resting-state and motion sickness state. We further utilize an embedding module that encodes subject-specific information combined with task discriminative features to be effective for domain adaptation tasks. The proposed framework achieved the highest performance (accuracy 60.21 %) through comparative experiments with other models.
Promoting operator’s wellbeing in Industry 5.0: detecting mental and physical fatigue

Building on the benefits of Industry 4.0, Industry 5.0 promotes human-centricity of factories, placing operator’s wellbeing at the center of production. Production processes are expected to be designed around operator’s needs, for a more sustainable contribution of industry to society. In this broad context, in this paper we consider the problem of monitoring operator’s condition and, specifically, detecting any mental or physical fatigue they might be experiencing in workplaces. Indeed, if the onset of any source of fatigue is monitored, it is possible to introduce assistive strategies that preserve productivity, on the one side, and operator’s wellbeing, on the other side. To achieve this goal, we detect mental and physical fatigue conditions via physiological monitoring, by means of a wearable device that measures cardiac activity. We design an experimental protocol such that test participants are exposed to mental and physical fatigue. Their heart rate variability is then extracted and analysed to discriminate among rest, mental fatigue, physical fatigue and joint mental and physical fatigue. The achieved results show that statistically significant differences can be found in time-domain metrics. Moreover, the analysis of the empirical distribution functions shows, for each metrics, the conditions that exhibit the greatest differences and, hence, that can be distinguished more accurately. However, results show also that, in the presence of physical fatigue, it is difficult to detect the presence of additional mental fatigue.
Improved Modelling of PDIV Evolution Over Time Under Multiple Ageing Factors for Insulation of Inverter-Fed Electrical Machines

This article describes research to model PDIV evolution under multi-factor stressing of the insulation system of an electrical machine. An attempt is made to establish whether a change of PDIV could be used to predict remaining life. Twisted pairs are used as samples of electrical motor insulation. They undergo accelerated ageing by exposure to high temperatures and the constant influence of inverter-style square-wave voltage. The ageing process lasts between weeks and months during which PDIV is measured periodically. The dedicated method of PDIV measurement with inverter-style voltage is presented. The evolution of PDIV is successfully modelled using an Inverse Power Model – observation and modelling of PDIV evolution is a novelty in the field. The ageing tests are performed according to the Design of Experiment method. The evolution of the parameters of the model at different factor levels can be extrapolated from the model obtained.
1D Atmosphere Models from Inversion of Fe i 630 nm Observations with an Application to Solar Irradiance Studies

Present-day semi-empirical models of solar irradiance (SI) variations reconstruct SI changes measured on timescales greater than a day by using spectra computed in one dimensional atmosphere models (1D models), which are representative of various solar surface features. Various recent studies have pointed out, however, that the spectra synthesized in 1D models do not reflect the radiative emission of the inhomogenous atmosphere revealed by high-resolution solar observations. We aimed to derive observation-based atmospheres from such observations and test their accuracy for SI estimates. We analyzed spectropolarimetric data of the Fe i 630 nm line pair in photospheric regions that are representative of the granular quiet-Sun pattern (QS) and of small- and large-scale magnetic features, both bright and dark with respect to the QS. The data were taken on 2011 August 6, with the CRisp Imaging Spectropolarimeter at the Swedish Solar Telescope, under excellent seeing conditions. We derived atmosphere models of the observed regions from data inversion with the SIR code. We studied the sensitivity of results to spatial resolution and temporal evolution, and discuss the obtained atmospheres with respect to several 1D models. The atmospheres derived from our study agree well with most of the 1D models we compare our results with, both qualitatively and quantitatively (within 10%), except for pore regions. Spectral synthesis computations of the atmosphere obtained from the QS observations return an SI between 400 and 2400 nm that agrees, on average, within 2.2% with standard reference measurements, and within −0.14% with the SI computed on the QS atmosphere employed by the most advanced semi-empirical model of SI variations.
Multi-UAV Trajectory Control, Resource Allocation, and NOMA User Pairing for Uplink Energy Minimization

In this work, we study the joint optimization of multiple unmanned aerial vehicles (UAVs)’ trajectories, power allocation, user-UAV association, and user pairing for UAV-assisted wireless networks employing the nonorthogonal multiple access (NOMA) for uplink communications. The design aims to minimize the total energy consumption of ground users while guaranteeing to successfully transmit their required amount of data to the UAV-mounted base stations. The underlying problem is a mixed-integer nonlinear program (MINLP), which is difficult to solve optimally. To tackle this problem, we derive the optimal power allocation as a function of other variables, which is used to transform the optimization problem into an equivalent form. We then propose an iterative algorithm to solve the resulting optimization problem by using the block coordinate descent (BCD) method where three subproblems are solved in each iteration and this process is repeated until convergence. Specifically, given the UAVs’ trajectories and data rates, we solve the NOMA user pairing, and user-UAV association subproblem optimally by exploiting its special structure. Then, we describe how to optimize the users’ data rates and tackle the UAV trajectory optimization in the second and third subproblems, respectively, by using the successive convex approximation (SCA) method. Numerical results show that our proposed algorithm can provide efficient active-inactive schedules (by setting user’s transmit powers to zero), and lower energy consumption compared to an existing baseline, and an OMA-based resource allocation and UAV-trajectory optimization strategy.
Passive BCI Oddball Paradigm for Dementia Digital Neuro-biomarker Elucidation from Attended and Inhibited ERPs Utilizing Information Geometry Classification Approaches

Brain-computer interface (BCI) and efficient machine learning (ML) algorithms belonging to the so-called ‘AI for social good’ domain contribute to the well-being improvement of patients with limited mobility or communication skills. We report preliminary results from a project focusing on developing a dementia digital neuro–biomarker for early-onset prognosis of a possible cognitive decline utilizing a passive BCI approach. We also report findings from two elderly volunteer pilot study groups in oddball paradigm EEG responses to attended (target) and inhibited (ignored) images in a classical short-term-memory evaluating oddball paradigm. We propose applying an information geometry approach employing Riemannian geometry tools for EEG covariance matrix-derived features used in subsequent shallow machine learning classification. The reported pilot study showcases the vital application of artificial intelligence (AI) for an early-onset mild cognitive impairment (MCI) prediction in the elderly.
On the effective turbulence driving mode of molecular clouds formed in disc galaxies

We determine the physical properties and turbulence driving mode of molecular clouds formed in numerical simulations of a Milky Way-type disc galaxy with parsec-scale resolution. The clouds form through gravitational fragmentation of the gas, leading to average values for mass, radii and velocity dispersion in good agreement with observations of Milky Way clouds. The driving parameter (b) for the turbulence within each cloud is characterised by the ratio of the density contrast (sigma_rho) to the average Mach number (Mach) within the cloud, b = sigma_rho/Mach. As shown in previous works, b ~ 1/3 indicates solenoidal (divergence-free) driving and b ~ 1 indicates compressive (curl-free) driving. We find that the average b value of all the clouds formed in the simulations has a lower limit of b > 0.2. Importantly, we find that b has a broad distribution, covering values from purely solenoidal to purely compressive driving. Tracking the evolution of individual clouds reveals that the b value for each cloud does not vary significantly over their lifetime. Finally, we perform a resolution study with minimum cell sizes of 8, 4, 2 and 1 pc and find that the average b value increases with increasing resolution. Therefore, we conclude that our measured b values are strictly lower limits and that a resolution better than 1 pc is required for convergence. However, regardless of the resolution, we find that b varies by factors of a few in all cases, which means that the effective driving mode alters significantly from cloud to cloud.
Portfolio optimization Decision-Making System by Quantum-inspired Metaheuristics and Trend Ratio

When investors invest in the stock market, the first decision is invested in which stock market and how to decide on the invested stock. The high return stock is accompanied by high risk. Constructing a portfolio can help to diversify the risk in the investment. This paper proposes a decision support system that can help select stocks and construct a portfolio with high return and low risk precisely in the Singapore stock market, which is one of the top performances in Asia. The proposed system uses the novel assessment indicator trend ratio to simultaneously consider the portfolio return and risk and then uses the global-best guided quantum-inspired tabu search algorithm with quantum-NOT gate (GNQTS) to search for the near-optimal solution. The decision-making system also includes 13 sliding windows to retain the freshest data and identify the suitable investment period. According to the experimental results, the system consisting of evolutionary computation can perform robust and rational decisions to efficiently construct a stable uptrend portfolio with the highest trend ratio in the Singapore stock market.
Coordinated Frequency Control Strategy for VSC-HVDC-Connected Wind Farm and Battery Energy Storage System

With the increasing penetration of renewable energy, power system inertia is reduced; thus, frequency stability faces tremendous challenges. Offshore wind farms (WFs) are often integrated to the grid through a voltage-source-converter-based high-voltage direct current (VSC-HVDC) transmission. However, traditional WFs cannot provide frequency support owing to the decoupling characteristics of VSC-HVDC. Modern WFs may support frequency regulation, but the recovery of rotor speeds of wind turbines (WTs) would cause a considerable second frequency drop (SFD). To resolve these problems, this paper presents a coordinated control strategy for a VSC-HVDC-connected WF with a battery energy storage system (BESS) for providing frequency support. The proposed strategy enhances the synthetic inertia by allowing WFs and BESS to participate in frequency regulation, in which the VSC-HVDC transmission supports frequency regulation by regulating its DC-link voltage, and BESS provides the required power during the rotor-speed recovery of WTs. Thus, SFD can be prevented and frequency deviation is minimized. In this study, the case study that considers the outage of synchronous generator and variable wind-speed scenario was conducted in PSCAD/EMTDC. The simulation results verify the effectiveness and the robustness of the proposed control strategy and demonstrate the superiority of the proposed strategy over other existing strategies.
Wearable Analytics and Early Diagnostic of COVID-19 Based on Two Cohorts

The outbreak of the COVID-19 pandemic forced a need to create screening tests to diagnose the disease. To answer this challenge, this paper introduces the support methodology for COVID-19 early detection based on wearable and machine learning likewise on two various cohorts. We compare the level of detection of the COVID-19 disease, Influenza, and Healthy Control (HC) thanks to the usage of machine learning classifiers likewise changes in heart rate and daily activity. The features obtained as the parameters of the ratio of heart rate to the variable of the number of steps proved to have the highest statistical importance. The COVID-19 cases versus HC were possible to be distinguished with 0.73 accuracy by the XGBoost algorithm, whereas COVID-19 cases, Influenza vs. HC were able to be differentiated on similar level of accuracy: in 0.72 by Support Vector Machine. The multiclass classification between the cases achieved a 0.57 F1-score for three classes by XGBoost. For early diagnosis, this solution could serve as an extra test for clinicians during the pandemic, and the result shows which metric could be useful for creating the machine learning model.
Toward construction of a consistent field theory with Poincare covariance in terms of step-function-type basis functions showing confinement/deconfinement, mass-gap and Regge trajectory for non-pure/pure non-Abelian gauge fields

This article is a review by the authors concerning the construction of a Poincaré covariant (owing to spacetime continuum) field-theoretic formalism in terms of step-function-type basis functions without ultraviolet divergences. This formalism analytically derives confinement/deconfinement, mass-gap and Regge trajectory for non-Abelian gauge fields, and gives solutions for self-interacting scalar fields. Fields propagate in spacetime continuum and fields with finite degrees of freedom toward continuum limit have no ultraviolet divergence. Basis functions defined in a parameter spacetime are mapped to real spacetime. The authors derive a new solution comprised of classical fields as a vacuum and quantum fluctuations, leading to the linear potential between the particle and antiparticle from the Wilson loop. The Polyakov line gives finite binding energies and reveals the deconfining property at high temperatures. The quantum action yields positive mass from the classical fields and quantum fluctuations produces the Coulomb potential. Pure Yang-Mills fields show the same mass-gap owing to the particle-antiparticle pair creation. The Dirac equation under linear potential is analytically solved in this formalism, reproducing the principal properties of Regge trajectories at a quantum level. Further outlook mentions a possibility of the difference between conventional continuum and present wave functions responsible for the cosmological constant.
Quantitative spectroscopy of extreme helium stars Model atmospheres and a non-LTE abundance analysis of BD+10°2179

Extreme helium stars (EHe stars) are hydrogen-deficient supergiants of spectral type A and B. They are believed to result from mergers in double degenerate systems. In this paper, we present a detailed quantitative non-LTE spectral analysis for BD+10°2179, a prototype of this rare class of stars, using UV-Visual Echelle Spectrograph and Fiber-fed Extended Range Optical Spectrograph spectra covering the range from ∼3100 to 10 000 A. Atmosphere model computations were improved in two ways. First, since the UV metal line blanketing has a strong impact on the temperature-density stratification, we used the ATLAS12 code. Additionally, We tested ATLAS12 against the benchmark code STERNE3, and found only small differences in the temperature and density stratifications, and good agreement with the spectral energy distributions. Secondly, 12 chemical species were treated in non-LTE. Pronounced non-LTE effects occur in individual spectral lines but, for the majority, the effects are moderate to small. The spectroscopic parameters give T_(eff) =17 300±300 K and log g = 2.80±0.10, and an evolutionary mass of 0.55±0.05 M_⊙. The star is thus slightly hotter, more compact and less massive than found in previous studies. The kinematic properties imply a thick-disc membership, which is consistent with the metallicity [Fe/H] ≈ −1 and α-enhancement. The refined light-element abundances are consistent with the white dwarf merger scenario. We further discuss the observed helium spectrum in an appendix, detecting dipole-allowed transitions from about 150 multiplets plus the most comprehensive set of known/predicted isolated forbidden components to date. Moreover, a so far unreported series of pronounced forbidden He I components is detected in the optical-UV.