Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of A Family of High Voltage Gain Step-Up Multi-Port DC-DC Converters for Fuel Cell-based Hybrid Vehicular Power Systems

Battery assisted fuel cell based vehicular power systems are feasible solutions for transportation electrification. Step up DC-DC converters play an important role in energy conversion process of these systems. Due to uncertain nature and low voltage level of fuel cells, large voltage gain converters are desired. This paper proposes a family of non-isolated step-up DC-DC multi-port converters for hybrid FC + battery vehicular power systems with smart grid services capability. Fewer active and passive components compared to other step-up topologies in the literature, extendibility, simple power flow management, and low current and voltage ripples are among the benefits of this multi-port converter family. Design and control of these converters are discussed, and the simulations are conducted for one of the proposed topologies. In the simulation scenario, a 60-kW FC + battery vehicle system is used as the baseline for real-world application. The simulation results demonstrate the effectiveness of this step-up multi-port converter and the peak efficiency of 95.6% was recorded. Also, a lab-scale prototype of the converter is built and tested under various power flow scenarios to validate the simulation results.
A Convenient Temperature Estimation Method for Half-Bridge of RC-IGBT with One Monitoring Unit

Temperature monitoring of power devices is critical for control optimization and health management. However, existing monitoring methods require a monitoring unit for each device, which increases complexity and cost. The reverse conducting insulated gate bipolar transistor (RC-IGBT) has great advantages in practical application, such as high power density. A convenient temperature estimation method for RC-IGBT half-bridge using one monitoring unit is proposed in this paper. The peak reverse recovery current of the diode increases with the junction temperature. The peak reverse recovery currents of two bridge arms can be measured via turn-on collector current overshoot and turn-off collector current overshoot of one bridge arm. The temperatures of IGBT and free-wheeling diode (FWD) are close for RC-IGBT. Therefore, the temperatures of two bridge arms can be estimated with one monitoring unit. The method proposed provides a simple and convenient monitoring structure. The experimental results validate the feasibility of the proposed technique.
Positive volatility simulation in the Heston model

In the Heston stochastic volatility model, the transition probability of the variance process can be represented by a non-central chi-square density. We focus on the case when the number of degrees of freedom is small and the zero boundary is attracting and attainable, typical in foreign exchange markets. We prove a new representation for this density based on sums of powers of generalized Gaussian random variables. Further we prove Marsaglia's polar method extends to this distribution, providing an exact method for generalized Gaussian sampling. The advantages are that for the mean- reverting square-root process in the Heston model and Cox-Ingersoll-Ross model, we can generate samples from the true transition density simply, efficiently and robustly.
Noise, Device and Room Robustness Methods for Pronunciation Error Detection

In this work, we address the problem of audio classification operating on signals recorded with various mobile devices in challenging environments. We propose a method for device, room and noise robust pronunciation error detection. It involves a data augmentation pipeline of convolution operations with room impulse responses and mobile device microphone impulse responses, and addition of background noise. A dataset of impulse responses of a diverse set of mobile devices, rooms and noises is collected. The method is evaluated in a pronunciation error detection task. The data consists of Finnish people uttering various English words accompanied by expert annotations of pronunciation errors. Classification accuracy is shown to improve by up to 12.9 percentage points as the amount of generated training data is increased. Given the large diverse set of collected impulse responses, we demonstrate that robustness is achieved consistently for new rooms and devices, excluded from the training set.
PinPoint: An SMD Pin Localization Method

Automated optical inspection (AOI) is used to verify quality of printed circuit board (PCB) assembly and has been proposed for detecting counterfeit components and malicious "trojan" PCB modifications. Component pin localization and characterization is an important step in both of these processes. We present PinPoint: a computer vision algorithm which extracts pin information from surface-mount device (SMD) contours. PinPoint is robust against contour noise, component size, and package type. We evaluate PinPoint against a sample of SMD contours and show that it achieves remarkable performance. Our algorithm could serve as an efficient pin localization step in traditional assembly quality checks and can support future efforts to extract expensive-to-forge characteristics of SMD packages to improve optical assurance.
ATMAS: Airplane Trajectory Missing Alarm System based on Deep Learning

With the rapid development of civil aviation, more emphasis has been placed on airplane safety. However, the traditional method of dealing with the airplane vanishing from the radar is to rely on the controllers to contact the pilot when the disappearances occur, which brings a safety risk owing to the time delay. This paper proposes an automatic alarm system called Airplane Trajectory Missing Alarm System (ATMAS). ATMAS is made up of two components: a Long Short-Term Memory (LSTM) neural network and a Multi-Layer Perceptron (MLP). LSTM extracts the semantic context in trajectory, and MLP determines whether to alarm based on the context. By applying the airplane trajectory in Qingdao controlled airspace as a case study, ATMAS takes the records in the last minute as input and indicates whether the airplane will vanish from the radar in the following minute. The accuracy of the alarm reaches 90.15%.
Robust Tensor Tracking With Missing Data Under Tensor-Train Format

Robust tensor tracking or robust adaptive tensor decomposition of streaming tensors is crucial when observations are corrupted by sparse outliers and missing data. In this paper, we introduce a novel tensor tracking algorithm for factorizing incomplete streaming tensors with sparse outliers under tensor-train (TT) format. The proposed algorithm consists of two main stages: online outlier rejection and tracking of TT-cores. In the former stage, outliers affecting the data streams are efficiently detected by an ADMM solver. In the latter stage, we propose an effective recursive least-squares solver to incrementally update TT-cores at each time $t$. Several numerical experiments on both simulated and real data are presented to verify the effectiveness of the proposed algorithm.
Standardised switching cell building block for converter design optimisation with detailed electro-thermal model

The design of power electronic systems is typically performed based on optimisation procedures. In this paper, the switching cell of the converter is introduced as a new building block in the optimisation procedure of the converter systems. A standardised half-bridge arrangement is modelled and optimised in terms of efficiency, volume, parasitics, and thermal performance.
Distributed Denoising over Simplicial Complexes using Chebyshev Polynomial Approximation

In this work, we focus on denoising smooth signals supported on simplicial complexes in a distributed manner. We assume that the simplicial signals are dominantly smooth on either the lower or upper Laplacian matrices, which are used to compose the so-called Hodge Laplacian matrix. This corresponds to denoising non-harmonic signals on simplicial complexes. We pose the denoising problem as a convex optimization problem, where we assign different weights to the quadratic regularizers related to the upper and lower Hodge Laplacian matrices and express the optimal solution as a sum of simplicial complex operators related to the two Laplacian matrices. We then use the recursive relation of the Chebyshev polynomial to implement these operators in a distributed manner. We demonstrate the efficacy of the developed framework on synthetic and real-world datasets.
Region-free Safe Screening Tests for $\ell_{1}$-penalized Convex Problems

We address the problem of safe screening for $\ell_{1}$-penalized convex regression/classification problems, i.e., the identification of zero coordinates of the solutions. Unlike previous contributions of the literature, we propose a screening methodology which does not require the knowledge of a so-called “safe region”. Our approach does not rely on any other assumption than convexity (in particular, no strong-convexity hypothesis is needed) and therefore applies to a wide family of convex problems. When the Fenchel conjugate of the data-fidelity term is strongly convex, we show that the popular “GAP sphere test” proposed by Fercoq et al. can be recovered as a particular case of our methodology (up to a minor modification). We illustrate numerically the performance of our procedure on the “sparse support vector machine classification” problem.
The analysis of repeated ordinal data using latent trends

This thesis presents methodology to analyse repeated ordered categorical data (repeated ordinal data), under the assumption that measurements arise as discrete realisations of an underlying (latent) continuous distribution. Two sets of estimation equations, called quasiestimation equations or QEEs, are presented to estimate the mean structure and the cutoff points which define boundaries between different categories. A series of simulation studies are employed to examine the quality of the estimation processes and of the estimation of the underlying latent correlation structure. Graphical studies and theoretical considerations are also utilised to explore the asymptotic properties of the correlation, mean and cutoff parameter estimates. One important aspect of repeated analysis is the structure of the correlation and simulation studies are used to look at the effect of correlation misspecification, both on the consistency of estimates and their asymptotical stability. To compare the QEEs with current methodology, simulations studies are used to analyse the simple case where the data are binary, so that generalised estimation equations (GEEs) can also be applied to model the latent trend. Again the effect of correlation misspecification will be considered. QEEs are applied to a data set consisting of the pain runners feel in their legs after a long race. Both ordinal and continuous responses are measured and comparisons between QEEs and continuous counterparts are made. Finally, this methodology is extended to the case when there are multivariate repeated ordinal measurements, giving rise to inter-time and intra-time correlations.
One Step Ahead Energy Load Forecasting: A Multi-model approach utilizing Machine and Deep Learning

Emerging Energy Load Forecasting (ELF) methodologies assist Distribution System Operators (DSOs) and Aggregators. Energy imbalance among consumption and generation could also be managed with high prediction accuracy, as well as smart grid applications, like Demand Response (DR) events. This study aims to test several algorithms as a solution for ELF. The proposed methodology utilizes machine/deep learning models for time-series forecasting in the domain of energy consumption. Via result comparison it has been illustrated that Neural Networks (NNs), both artificial NNs such as Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) recurrent NNs with Extreme Gradient Boosting (XGBoost) were the more accurate ones among other models, showcasing Mean Absolute Error (MAE), R-squared (R2), Root Mean Squared Error (RMSE) and Coefficient Variation of Root Mean Squared Error (CVRMSE) values equal to 1.281, 0.98, 2.238 and 0.147, respectively.
Music Recommendation System Based on Facial Mood Detection

A person’s mood is mainly affected by the surrounding atmosphere and the type of company they keep around them. Its repercussions are seen in the person’s day-to-day life, socially and career-wise, leading to a positive or negative mindset. Our proposed system recommends appropriate songs to the customers of their liking based on their current dominant mood to emphasize this idea. Here, the customer’s face is detected using the OpenCV library, and different expressions are analyzed using CNN (Convolutional Neural Network) classifier and FER (Facial Expression Recognizer). In the end, appropriate songs get recommended to the customer based on their dominant mood detected. To make sure the songs recommended are of the customer’s liking, the customer selects their playlists at the starting. The system’s overall idea is to ensure that the customer gets to listen to their preferred songs to help them relate to their current feelings and help them reduce them to some extent by relaxing and stabilizing their emotions.
A Decentralized and Communication-free Control Algorithm of DC Microgrids for the Electrification of Rural Africa

Following the United Nation Sustainable Development Goals of ensuring universal access to basic and modern electric services by 2030, a strong research interest has emerged in the power grid community to design microgrids adapted for rural electrification of Sub-Saharan countries. Their optimal topology, their stability as well as their control architecture are still open to debate. However, DC microgrids with decentralized storage and production are increasingly gaining attention as they enable the progressive building of electric infrastructure in a bottom-up manner, which increases the economic viability, the modularity and the scalability of such rural electrification scheme. In addition, there is a growing consensus in terms of control for decentralized and communication-free algorithms. The absence of a centralized controller is crucial to avoid a single point of failure and to enable plug & play feature within the microgrid. Moreover, DC microgrids must be robust and affordably deployable even in areas with limited or no telecommunication signals. Therefore, this paper proposes the design through software simulations of a decentralized and communication-free control algorithm of DC microgrids adapted for the rural electrification of Africa. The proposed control is then thoroughly validated on a lab test bench and extensive results are presented.
Investigating Nonnegative Autoencoders for Efficient Audio Decomposition

Nonnegative Matrix Factorization (NMF) is a powerful technique for decomposing a music recording's magnitude spectrogram into musically meaningful spectral and activation patterns. In recent years, musically informed NMF-based audio decomposition has been simulated using neural networks, which opens up new paths of exploiting recent deep learning frame-works, including libraries for efficient gradient computations. In this article, we continue this strand of research by considering Nonnegative Autoencoders (NAE) in combination with gradient projection and structured dropout techniques. Conducting experiments based on piano recordings, we compare the decomposition results of NAE-based approaches with those obtained from a score-informed NMF variant. In this context, we examine various gradient descent methods using fixed and adaptive learning rates for deriving the NAE encoder and decoder parameters. Among others, we show how the famous multiplicative update rules for NMF can be transferred to the case of NAEs. The overall goal of our contribution is to illustrate the benefits and limitations of the various techniques concerning implementation issues (CPU, GPU), convergence speed, and overall runtime.
Deep Learning for scalp High Frequency Oscillations Identification

Since last 2 decades, High Frequency Oscillations (HFOs) are studied as a promising biomarker to localize the epileptogenic zone of patients with refractory focal epilepsy. As HFOs visual detection is time consuming and subjective, automatization of HFO detection is required. Most HFO detectors were developed on invasive electroencephalograms (iEEG) whereas scalp electroencephalograms (EEG) are used in clinical routine. In order HFO detection can benefit to more patients, scalp HFO detectors has to be developed. However, HFOs identification in scalp EEG is more challenging than in iEEG since scalp HFOs are of lower rate, lower amplitude and more likely to be corrupted by several sources of artifacts than iEEG HFOs. The main goal of this study is to explore the ability of deep learning architecture to identify scalp HFOs from the remaining EEG signal. Hence, a binary classification Convolutional Neural Network (CNN) is learned to analyze High Density Electroencephalograms (HD-EEG). EEG signals are first mapped into a 2D time-frequency image, several color definitions are then used as an input for the CNN. Experimental results show that deep learning allows simple end-to-end learning of preprocessing, feature extraction and classification modules while reaching competitive performance.
Echo Cancelation and Noise Suppression by Training a Dual-Stream Recurrent Network with a Mixture of Training Targets

Nonlinear echo in presence of background noise can degrade the performance of digital signal processing algorithms. Deep neural networks with their ability to model complex nonlinear functions can potentially address this issue. In this paper, a deep and causal neural network based on dual streaming of the near-end microphone and far-end speech signals is employed to leverage the real-time nonlinear echo cancellation and noise suppression. The extracted features of two streams are coupled into a shared neural network for joint echo and noise cancellation. The training target is a mixture of spectral mapping and masking-based targets which are gated through a feedforward neural network. The model is evaluated in terms of both signal-level and perception-level metrics for different scenarios with a range of SI-SDR as low as −25 dB. Furthermore, the effect of mixing of training targets is assessed by evaluating different models.
Positional Tracking of a Moving Microphone in Reverberant Scenes by Applying Perfect Sequences to Distributed Loudspeakers

Moving microphones allow for the fast acquisition of sound-field data. Such continuous sampling procedures require trajectory knowledge for relating the dynamic samples to a positional context and solving the involved spatio-temporal channel estimation problem. Having one non-uniformly moving microphone, the hardware effort is heavily dominated by additional tracking equipment. In this paper, we present a low-cost tracking method using the microphone signal itself, provided that loudspeakers at surrounding reference points are excited by perfect sequences. As this is often the actual measurement setup, the proposed technique can be seamlessly incorporated into such dynamic sampling procedures. The acoustic tracking system employs a polyphase decomposition and resampling scheme for obtaining low-frequency estimates of the varying impulse responses to the loudspeaker positions and applying multilateration.
Elliptically Contoured Alpha-Stable Representation for MUSIC-Based Sound Source Localization

This paper introduces a theoretically-rigorous sound source localization (SSL) method based on a robust extension of the classical multiple signal classification (MUSIC) algorithm. The original SSL method estimates the noise eigenvectors and the MUSIC spectrum by computing the spatial covariance matrix of the observed multichannel signal and then detects the peaks from the spectrum. In this work, the covariance matrix is replaced with the positive definite shape matrix originating from the elliptically contoured $\alpha$ -stable model, which is more suitable under real noisy high-reverberant conditions. Evaluation on synthetic data shows that the proposed method outperforms baseline methods under such adverse conditions, while it is comparable on real data recorded in a mild acoustic condition.
Optimal design of integrated motor drives - Comparison of topologies (2L/3L/modular), PWM variants, and switch technologies (Si/SiC/GaN)

In this paper, a new design procedure for the optimal design of an integrated motor drive is presented, including an extended iron loss model. The design procedure is based on a multi-objective optimization of power density, efficiency, and cost. In the optimization, a large design space is covered, including the inverter topology, the PWM scheme, the chip technology (Si/SiC/GaN), the winding scheme, the chip area/cost, and the switching frequency. In addition to power density/efficiency/cost, the system reliability is investigated. Considering a 1.5 kW IMD as example, the optimal design in terms of efficiency and cost is achieved using a modular topology, GaN HEMTs, and a 9-phase motor winding. This design enables an efficiency increase of +2.26 % at 36 % higher cost compared to the cost-optimal design that is achieved with the standard 2L-topology, Si IGBTs, and a 3-phase motor winding.
Research on Segmentation Method of Civil Construction Image based on Unet Algorithm Model

In order to accurately segment buildings and features in high-resolution remote sensing images. First, the boundary distance map is generated according to the ground truth map of the remote sensing image, and the remote sensing image and the ground truth map are used as the input of the Unet network; then, the building feature prediction layer is added to the end of the Unet network constructed based on the ResNet network. With the boundary distance prediction layer, a multi-task network is built; finally, the loss function of the multi-task network is defined, and the Adam optimization algorithm is used to train the network. Experiments are carried out on the Inria aerial remote sensing image building and landmark data set. The results show that compared with the fully convolutional network combined with the multi-layer perceptron method, the intersection ratio of VGG16 network, VGG16+ boundary prediction, ResNet50 and this method are improved by 5.15, 6.94, 6.41 and 7.86 percentage points, the accuracy is increased to 94.71%, 95.39%, 95.30% and 96.10% respectively, which can achieve high-precision extraction of architectural features.
Physical origin of the permanent components of the positive charge buildup resulting from NBTI/PBTI stress in nMOS/pMOS transistors

It is well established that the bias temperature instability (BTI) mechanism alters the Vth distribution with reliability implications to balanced analog circuits. This paper presents a deep understanding of the mechanisms involved in the permanent components of BTI effects that dominate the long-term reliability projections. By use of CV measurements, the energetic distribution of traps in the bandgap was studied where hydrogen reactions are linked to the positive charge buildup.
Perfect Incompressible Fluids

Introduction 1. Presentation of the equations 2. Littlewood-Paley theory 3. Around Biot-Savart's law 4. The case of a smooth initial data 5. When the vorticity is bounded 6. Vortex sheets 7. The wave front and the product 8. Analyticity and Gevrey regularity 9. Singular vortex patches References