Miracle-dz/UniFAR-FTUs huggingface dataset:
NVH-Simulation of Permanent Magnet Synchronous Traction Drives Including Torsional Mode Shapes

Apart from performance requirements like efficiency and thermal stability of electrical drives, NVH-criteria (noise vibration harshness) such as low acoustic noise emissions and vibrations are very important in vehicle applications. In this study, we present a method to reduce a 3D structural model with boundary conditions which consider the mounting of the machine and combine it with a 2D-FEA analysis to determine the spatial force harmonics. In addition, the zero-sequence currents of delta-connected machine are considered and their impact on the forces is discussed. Moreover, the simulation results are compared to experimental data. Through the boundary conditions of the 3D-modal analysis, torsional mode shapes are regarded for the first time in a not strictly FEM-based approach. As this process is a multi-physics approach, each step starting with the electromagnetic force calculation, the 3D-modal analysis and the analytical surface deflection calculation is introduced and explained.
State Estimation for Polysolenoid Linear Motor based on an Adaptive Unscented Kalman Filter with Unknown Load and Measurement Noises

The linear motor has been widely applied in industry to provide directly straight motion. The Polysolenoid Linear Motor (PLM), as its name, is one type of the synchronous linear machine. Toward the control problem of the PLM, state observation plays a crucial role due to the lack of measurement. In this paper, an Adaptive Unscented Kalman Filter (AUKF), which can provide reliable information of system state including applied current as well as position and velocity, is proposed. Furthermore, our observer can deal with the uncertainty load force and unknown unbias measurement noises adaptively, which contributes to robust and effective control of PLM with uncertain load condition. A scenario will be made to test the robutness of the algorithm under the value variation of measurement noise covariance. The performance of the system is verified by simulation in an illustrative example.
Discrete Current Regulator Design with Sensorless Drive for High-Speed Permanent Magnet Synchronous Machine

The high-speed electric machine has been widely used in uninterruptible power supplies, wind power plants, rail transits and hybrid vehicles due to the high power density and fast dynamic response. However, the dynamic characteristics of the current loop will deteriorate with the increase of the speed, and it is difficult for sensorless control methods to operate reliably under low frequency modulation index. Therefore, discrete current regulator and super-twisting sliding mode observer (ST-SMO) are proposed in this paper. The directly discrete model of permanent magnet synchronous machine (PMSM) is analyzed firstly in complex-coefficient. On this basis, using the zero point of the current regulator to eliminate the poles of the controlled object, the discrete current regulator is designed with the compensation of the inherent digital delay in controller. Then, the structural equation of discrete ST-SMO sensorless control method is expounded. In addition, a comparison between discrete ST-SMO and continuous ST-SMO is carried out. Finally, the proposed control strategy in this paper is verified by simulation results.
NLP Summarization: Abstractive Neural Headline Generation Over A News Articles Corpus

Most of NLP research fields (Translation, Classification, Dialogue Systems …) have been revolutionized by the rise of deep learning methods, which rely on the new dense and low-dimensional feature representation. We present in this article the basic training techniques of Word Embeddings as well as the recent works on Abstractive Neural Summarizers. We also introduce our trained French Word Embeddings, further used as the embedding layer to implement our baseline French Neural Summarizer for the headline generation task, using the RNN (Recurrent Neural Network) Encoder-Decoder architecture.
Towards GPU Accelerated HyperSpectral Depth Estimation in Medical Applications

HyperSpectral (HS) images are commonly used for classification tasks in different domains, such as medicine. In this field, a recent use is the differentiation between healthy tissues and different types of cancerous tissues. To this end, different machine learning techniques have been proposed to generate classification maps that indicate the type of tissue corresponding to each pixel in the image. These 2D representations can be used stand-alone, but they can not be properly registered with other valuable data sources like Magnetic Resonance Imaging (MRI), which can improve the accuracy of the system. For this reason, this paper builds the foundations of a multi-modal classification system that will incorporate 3D information into HS images. Specifically, we address the acceleration of one of the hotspots in depth estimation tools/algorithms.MPEG-I Depth Estimation Reference Software (DERS) provides high-quality depth maps relying on a global energy optimizer algorithm: Graph Cuts. However, this algorithm needs huge processing times, preventing its use during surgical operations. This work introduces GoRG (Graph cuts Reference depth estimation in GPU), a GPU accelerated DERS able to produce depth maps from RGB and HS images. In this paper, due to the lack of HS multi-view datasets at the moment, results are reported on RGB images to validate the acceleration strategy.GoRG shows a ×25 average speed-up compared to baseline DERS 8.0, reducing total computation time from around one hour for 8 frames to only a few minutes. A consequence of our parallelization is an average decrease of 1.6 dB in Weighted-to-Spherically-Uniform Peak-Signal-to-Noise-Ratio (WS-PSNR), with some remarkable disparities approaching 4 dB. However, using Structural Similarity Index (SSIM) as metric results come closer to baseline DERS. Effectively, an average decrease of only 1.20% is achieved showing that the obtained speed-up gains compesate the subjective quality losses.
A Hybrid Control of Hysteresis Using MDPI Model *

In the paper, a modified dynamic Prandtl-Ishlinskii (MDPI) model is proposed for describing hysteresis in piezoelectric actuators. In the model, the dynamic thresholds with input differences are proposed to replace the fixed thresholds in classical backlash operator. Then, the inverse MDPI model is constructed for hysteresis compensation. Afterward, a hybrid control scheme with the inverse MDPI model based hysteresis compensator is developed for the control of system with piezoelectric actuators. Finally, experimental results are presented to verify the proposed control scheme.
Deep Reinforcement Based Power Allocation for the Max-Min Optimization in Non-Orthogonal Multiple Access

NOMA is a radio access technique that multiplexes several users over the frequency resource and provides high throughput and fairness among different users. The maximization of the minimum the data-rate, also known as max-min, is a popular approach to ensure fairness among the users. NOMA optimizes the transmission power (or power-coefficients) of the users to perform max-min. The problem is a constrained non-convex optimization for users greater than two. We propose to solve this problem using the Double Deep Q Learning (DDQL) technique, a popular method of reinforcement learning. The DDQL technique employs a Deep Q- Network to learn to choose optimal actions to optimize users’ power-coefficients. The model of the Markov Decision Process (MDP) is critical to the success of the DDQL method, and helps the DQN to learn to take better actions. An MDP model is proposed in which the state consists of the power-coefficients values, data-rate of users, and vectors indicating which of the power-coefficients can be increased or decreased. An action simultaneously increases the power-coefficient of one user and reduces another user’s power-coefficient by the same amount. The amount of change can be small or large. The action-space contains all possible ways to alter the values of any two users at a time. DQN consists of a convolutional layer and fully connected layers. We compared the proposed method with the sequential least squares programming and trust-region constrained algorithms and found that the proposed method can produce competitive results.
The impact of degassing holes for millimeter-wave antenna-in-package application

This paper study an influence which is added degassing holes on the millimeter-wave(mmWave) antenna in package (AiP). In order to improve the yield loss in the substrate manufacturing, the designs have to add the plenty of degassing holes into solid metal layer. Because of broadband in 5G mmWave applications, this paper simulates five kinds of patch antennas with variant frequencies that it takes performance into account when adding ground plan with the degassing holes. Three antennas are operated in the n257/258/261 bands, others are operated in the n260 band. The dimension of antenna in package (AiP) is 6 mm $\times 6$ mm $\times 0.28$ mm, and is made up of two copper layers and one dielectric layer. This paper discovers two sizes of degassing holes into reference ground of patch antennas, they are not influencing the performance of mmWave AiP. For first design, the diameter of the degassing hole is 100 um, and the space between the degassing holes is 200 um. The second case is 150 um and 150 um, respectively. Shape of the degassing hole is octagon. Finally, this work not only summarizes a simply method to minimize the effects but also give a beneficial for the performance of the mmWave antennas and decrease design time.
Personalized Recommendation System with User Interaction based on LMF and Popularity Model

With the popularization of social media and similar web-based applications, the information generated by the users of these applications is growing exponentially. Thus, more attention is required on the “recommender system”. The recommender system fails to determine the real-time user interest. This paper uses an instant input mechanism to overcome this problem i.e. based on user interaction, a recommender system determines users' real-time interest. This paper uses “Significance analysis” to refine the recommender process performance. The most successful predictions are given to the client during communication to assess the real-time desires of the users and as soon as the model identifies the user it determines the expected recommendations for that user, thus, providing “Personalization of expectations. ” There are two problems in the recommender scheme “cold-start” or “potential-false termination”. This problem is dealt using the user interaction which provides interactive recommendation although the system doesn't have any information related to the user. Potential-false dismissal occurs due to “tag sparsity”. To overcome this problem, associations between various tags are taken into account, which refine the recommendations. The paper uses “low rank matrix factorization (LMF)” approach to determine the potential interests and personalize the recommendations during each iteration. This method overcomes the rating sparsity and is enhanced by embedding the similar user and resource information. For new users, real time input is used to recommend similar items.
Detection of ransomware in static analysis by using Gradient Tree Boosting Algorithm

Ransomware is the type of malware that encrypts the user data which cannot be accessed then the ransom demands to pay for decrypting key. Many organizations lose their data and money; lose their reputation as small organizations. So, detect the ransomware which affected the system before execution. Later, detection of ransomware was done by the decision tree algorithm method. In this work, we use a static detection of ransomware which extracts the features to classify whether it is ransomware, malware or benign before execution on the system by using gradient tree boosting algorithm. In the previous method, the detection of ransomware by using a decision tree method which achieved 98.98% with a detection rate of 0.2%, which ends with False Positive Rate (FPR) and the result is efficient for small dataset. Our proposed method the detection of the ransomware achieves 99.997% with a detection rate of 0.1% false positive rate again it results with less than 0.01% false positive rates with 98.3% of detection rate based on the 700,000 training and 400,000 testing samples from the dataset. Our method achieves more accuracy than the later algorithm while increasing the dataset for detecting the ransomware and also to identify the type of malware.
Analysis of a 48V-12V Hybrid Switched Capacitor Converter with DC Winding Current Autotransformer

A hybrid 48V-12V fixed ratio converter (DCX) is analyzed in this paper. It is composed of soft charging switched capacitors and an autotransformer with DC winding current, aiming at high output current applications. Its non-resonant operation and the self-balance between the capacitor and the autotransformer make the converter robust to parasitics and component tolerances. The transformer structure is simple and easy to be built since no interleaving is required. Operation of the circuit is explained, and the switching transition taking account of parasitics is analyzed, revealing a method to achieve ZVS on all switches by the stray inductance in the switched capacitor loop. The resonant problem at the switching nodes during the switching transition is discussed, a lossless snubber is proposed to clamp this voltage spike for the two low side switches. A proof-of-concept prototype of 250W (22A output current) is tested to verify the operation of this topology, achieving 97.1% peak efficiency.
An Effective Dynamic Sliding Mode Control Based Nonlinear Disturbance Observer for a Quadrotor UAV

This paper introduces an effective dynamic sliding mode control based on a nonlinear disturbance observer (NDO) for a quadrotor. The proposed algorithm is used to supply a fast adaptation and strictly robust stability for attitude control of the vehicle. First, a NDO is used to approximate the external disturbance. Second, an effective dynamic sliding mode controller (SMC) is designed by using the estimated values of disturbances to compensate for the effect of these perturbations. The stability of a control system is proven by Lyapunov theory. A numerical simulation was performed and compared to existing methods to demonstrate the robustness and effectiveness of the proposed algorithm.
An Expressed and Private Opinion Model on Influence Networks

This paper studies the evolution of an expressed and private opinion dynamics model with asynchronous updating. It is shown that under some mild connectivity and individual activation conditions, the expressed and private opinions of all individuals in the network converge to a common value exponentially fast when the susceptibility to influence of all individuals is equal to one. A numerical example verifies the theoretical result.
Synthesis and Design of Enhanced N-Path Filters With 60-dB/decade RF Selectivity

A “third-order” passive mixer-first receiver is presented to improve channel selectivity and resilience to close-in blockers, by building an $N$ -path filter which drives an impedance with 60-dB/decade roll-off. A step-by-step derivation is provided for the synthesis of the first ever driving point impedance with 60-dB/decade roll-off. An integrated circuit prototype was fabricated in 28-nm bulk CMOS as proof-of-concept and characterized. The receiver front-end is capable of broadband operation from 0.2 to 4.5 GHz, demonstrates third-order filtering of close-in blockers, and achieves an out-of-band IIP3 of +21 dBm and B1dB of greater than 6 dBm for blockers at the alternate channel. Additionally, it achieves a noise figure of 6.8 dB in the presence of a 0-dBm blocker.
Semianalytical Estimation of Nonlinear Distortion in Active Antennas

Active antennas are key enablers of new generation satellite applications, from Earth observation to telecommunications. In particular, high throughput satellite systems of next generation require the intrinsic flexibility of active antennas, making them one of the most relevant research topics of the recent years. At the best of the authors' knowledge, there is no literature investigating the signal-to-nonlinear-distortion ratio (SNDR) in active antennas. Hence, in this paper we propose a semianalytical model to evaluate the impact of the nonlinear distortion generated by solid-state power amplifiers (SSPAs) in direct radiating arrays (DRAs) for two very relevant waveforms: the DVB-S2X waveform, that is one of the most successful standards for satellite communications, and the New Radio waveform, that is going to integrate non-terrestrial networks in the 5G framework.
Novel Master/Slave Configurated Closed Loop Regulated Modular 320kW Isolated Symmetric CLLC Resonant DC/DC Converter

A phase shift modulation based closed loop voltage regulation and current balance scheme is proposed for a high power, modular, multi-power path symmetric isolated bidirectional capacitor-inductor-inductor-capacitor (CLLC) resonant DC/DC converter. Novel master/slave configuration facilitates connection of multiple symmetrical CLLC DC/DC converters either in parallel and/or in series combination to increase the voltage and current rating of the converter cluster. The unique daisy chain formation using optical fiber communication allows connection of up to 4 DC/DC converters in one converter cluster. The proposed control scheme is simulated in Matlab/Simulink©, the dynamic and endurance tests (750hours) carried out on the built 1200V-320kW hardware validates the proposed scheme. The soft switching at a high frequency makes the converter to operate with higher efficiency (up to 98%) at full load, while assuring smaller hardware footprint.
A Real-Time Quality Control System Based on Manufacturing Process Data

Quality prediction is one of the key links of quality control. Benefitting from the development of digital manufacturing, manufacturing process data have grown rapidly, which allows product quality predictions to be made based on a real-time manufacturing process. A real-time quality control system (RTQCS) based on manufacturing process data is presented in this paper. In this study, the relationship between the product real-time quality status and processing task process was established by analyzing the relationship between the product manufacturing resources and the quality status. The key quality characteristics of the product were identified by analyzing the similarity of the product quality characteristic variations in the manufacturing process based on the big data technology, and a quality-resource matrix was constructed. Based on the quality-resource matrix, the RTQCS was established by introducing an association-rule incremental-update algorithm. Finally, the RTQCS was applied in actual production, and the performance of RTQCS was verified by experiments. The experiments showed that the RTQCS can effectively guarantee the quality of product manufacturing and improve the manufacturing efficiency during production.
Dynamic Topology Transformation of Cloud-Network Computer Systems: Conceptual Level

Many users need the organization of complex virtual topologies, not just a simple hierarchical or client-server architecture, which makes it difficult to solve problems associated with data risk management. The subject of research is the processes of managing message flows in distributed computing and data exchange in cloud-grid systems with variable system and functional architecture. The purpose of the research is to improve data risk management as a result of the organization of distributed computing and data exchange in the cloud-grid and mobile distributed computing systems with a variable structure. In order to solve these problems the conceptual model of the cloud-grid system with several third party auditors, group managers, cloud services provider, user groups and user databases was built. In contradistinction to another concepts, it is proposed to implement network computing as a service organized on the user demand and implemented in a computer network, where one or another virtual system architecture is created.
Power and Performance optimized Domino Comparator in 7nm FinFET for TLBs

Translation look aside buffers (TLB) are used to translate virtual address to physical address in microprocessors. Translation Lookaside Buffers (TLB) make use of high-speed domino-based address comparator to generate sense address mismatches faster. Domino logic circuits are used in designs to achieve higher speeds but are prone to PVT variations. In advanced FinFET nodes, higher metal resistances and capacitances limit the performance and power reduction. This paper presents the optimization of domino address comparator in 7nm technology to overcome the speed and power limitations. The banking technique used in this study compares the performance and reliability of the domino comparator based on the domino node capacitance. The proposed technique improved power and performance significantly for 128-bit and 64-bit address comparators.
Video Object Detection Guided by Object Blur Evaluation

In recent years, the excellent image-based object detection algorithms are transferred to the video object detection directly. These frame-by-frame processing methods are suboptimal owing to the degenerate object appearance such as motion blur, defocus and rare poses. The existing works for video object detection mostly focus on the feature aggregation at pixel level and instance level, but the blur impact in the aggregation process has not been exploited well so far. In this article, we propose an end-to-end blur-aid feature aggregation network (BFAN) for video object detection. The proposed BFAN focuses on the aggregation process influenced by the blur including motion blur and defocus with high accuracy and little increased computation. In BFAN, we evaluate the object blur degree of each frame as the weight for aggregation. Noteworthy, the background is usually flat which has a negative impact on the object blur degree evaluation. Therefore, we introduce a light saliency detection network to alleviate the background interference. The experiments conducted on the ImageNet VID dataset show that BFAN achieves the state-of-the-art detection performance, exactly 79.1% mAP, with 3 points improvement compared to the video object detection baseline.
Towards a Global Big Data Maturity Model

This work is part of a global objective of creating an end-to-end practical framework for a better adoption and implementation of Big Data Technology, with a special focus on North African companies.This paper presents the first major step towards the goal mentioned above, which is, making the right assessment of the maturity and readiness of organizations to adopt Big Data, this is generally done through a maturity model.After extensive Analysis of the existing literature and software vendors documents regarding maturity models, fifteen Maturity models were scrutinized closely, and two major points where noted:1- There is no global consensus on the dimensions evaluated in the different models; in fact, there wasn’t even one dimension that was cited in all Models. This shows that from one model to another, there is a different mindset focusing on some aspects while ignoring others.2- No model incorporates the methodology dimension, a key factor in successfully driving Big Data projects.Hence, our proposition for a new exhaustive model we called GBDMM (Global Big Data maturity model), allowing a profound assessment of organization’s maturity by inspecting the maximum number of maturity dimensions and preparing the ground for a detailed view on how each dimension should evolve through Time.