Miracle-dz/UniFAR-FTUs huggingface dataset:
Schema Matching Based On Deep Learning Using LSTM Model

schema matching aims to find the correspondence between two or more schemas. It is needed in many systems, such as data warehouses or data integration systems, as well as in many database applications. That is why we need to use better and more efficient methods. Different types of approaches are taken to unify the data collected and merge it into a single schema by using deep learning methods. A neural network attempts to map, and highlight the information that is needed to be expected with a function and therefore, achieve a more acceptable predicted output. In this research, a new assignment between data and schema is proposed using a popular model: the recurrent neural network (RNN) long Short term memory model (LSTM). Through a set of simulations, we choose better hyperparameters to create a model that allows us to predict the correct schema for our training data, where we established the acceptable model based on accuracy. Thus, these results are conducted using free datasets from the Udemy course platform.
Linear Extended State Observer based Anti-Interference Robust Position Tracking Control for Two-Inertia Systems with Uncertain Load Disturbance

In order to suppress the load position control error due to torsional stiffness of two-inertia systems, a linear extended state observer based anti-interference robust position control is proposed to overcome this issue. It is composed of a back-stepping position controller (BPC) and a linear extended state observer (LESO), and validated on a permanent magnet synchronous machine (PMSM) based two-inertia servo platform. The test result shows superior performance in the suppression of position error and good robustness against uncertain parameter changes and load interference.
Net-bit rate of >562-Gb/s with 32-GBaud Probabilistically Constellation-Shaped 1024QAM Signal Based on Entropy and Code-Rate Optimization

We achieved a 17.57-bit/4Dsymbol information rate with >562-Gb/s net rate based on precisely entropy and code-rate optimized 32-GBaud probabilistically constellation-shaped (PCS-)1024QAM with an ultra-narrow-linewidth 1-Hz laser. A net rate of >542-Gb/s with an optimized PCS-1444QAM-based signal was also demonstrated for 30-km transmission.
Feedback Power Cost Optimization in Power Distribution Networks With Prosumers

In this article, we propose a feedback control approach for solving optimal power flow (OPF) problems in power distribution networks (DNs) based on a projected gradient scheme, where the cost is given by the sum of functions of local power injections. This approach does not require detailed knowledge of the grid model and enables real-time tracking of the optimal generation configuration. Moreover, the communication burden is minimal: only a broadcast from a central entity, called the Network Supervisor (NS), to the agents in the network is needed. Agents do not have to share local information that can be sensitive, e.g., their power injections or the value of the local costs. Our approach is used to solve a particular OPF problem targeting DNs with high penetration of distributed energy resources (DERs) in which the energy cost is given by the remuneration that the DERs’ owners receive for the service they provide.
Efficient spectrum estimation of noise using line spectral pairs for robust speech recognition

A novel method for estimating the power spectral density of acoustic background noise is proposed. The spectral peak frequencies are approximated by the roots of the P polynomial, which constitute half of the line spectral pairs. The probability distributions of the magnitude values at the spectral peaks are modelled by a mixture of two univariate Gaussian functions, where the Gaussian with smaller mean is considered as noise and the other as speech. The validity of the proposed method is exhibited by the experimental results evaluated on a simple speech recognition task.
The importance of reduced order modeling for building a digital twin

The paper is focused on the digital twin concept in the automotive industry. It is an actual topic, which mainly speeds up the development process, increases saving of costs, and enhances product quality. Building a digital twin is a complex discipline requiring the mutual connection of many engineering domains as virtual prototyping, system modeling, lean validation, artificial intelligence, and many others. The core of the digital twin is a precise system model, which consists of reduced order models (ROMs). An example of thermal ROM creation for an inverter component is detailly described.
Scattering of electromagnetic waves by a sinusoidally stratified half-space. I. Formal solution and analytic approximations

A detailed investigation of the scattering of a plane wave incident on a periodically stratified medium is presented. The periodic medium occupies the x > 0 region, and the stratification occurs along the z direction and is produced by a sinusoidal modulation of the dielectric constant. By choosing the polarization parallel to the y coordinate, the fields in the stratified medium appear as solutions of a wave equation of the Mathieu type. The geometry employed herein is shown to serve as a basic configuration for a large number of applications dealing with optical–acoustic interactions, reflection gratings, periodic antennas, and others. The modulated half-space therefore represents a canonical problem which leads to rigorous solutions for the present polarization and to good approximations for the alternative polarization.By viewing the solution in terms of waves of the Floquet type, it is shown that the field consists of modes that travel independently and are coupled to each other only at the air–diele.
Static Analysis Methods For Memory Leak Detection: A Survey

Memory leaks are still one of the common bugs in programs written in languages without garbage collectors. There is a list of methods to detect such bugs. One of the approaches is to use static analysis. This paper discusses static analysis methods for memory leak detection. Mainly those methods use two stages. First, they use different types of graphs to find memory leaks faster but with less precision. Then, for the detected cases they use path-sensitive analysis to increase precision. Additionally, we tested available tools on Juliet test suite. We chose false positive rate (FPR) and false negative rate (FNR) as metrics. On average SMOKE and Clang static analysis tools show the best FPR and FNR.
Life after Future Combat System: a family of ground robotic systems

Until recently, the Army Future Combat System (FCS) was the future of Army ground robotics hallmarked by system of systems interoperability for manned and unmanned platforms. New missions, threats, and realities have caused the Army to restructure the Army Future Combat System, but still require unmanned systems interoperability without the FCS system of system interoperability architecture. The result is the Army material developer has no overarching unmanned ground vehicle (UGV) interoperability standards in place equal to the Army unmanned aircraft system (UAS) community. This paper will offer a Life After the FCS vision for an Army family of common ground robotics and payload standards with proposed IEEE, STANAG, SAE, and other standards to potentially achieve common ground robotics interoperability to support the Army and Army Maneuver Support Center of Excellence (MSCoE) Chemical, Engineer, and Military Police mission needs.
A Study on Operation Characteristics According to Rib Thickness for Each Layer of Double V-type IPMSM

In this paper, the electromagnetic field characteristics according to the rib thickness of each layer of IPMSM having a double V-type structure were analyzed. Using an 8-pole 72-slot 300kW class IPMSM as a standard model, the tendency of electromagnetic field characteristics according to rib thickness was confirmed through RSM, and two comparative models with different rib thicknesses were selected for comparison and analysis with the standard model. Finally, the optimization was carried out within the thickness range close to the standard model’s rib thickness by considering the minimum to maximum thickness of the rib and the stress of the rotor. As a result of the analysis, when the rib thickness of the 1 layer was thin and the rib thickness of the 2-layer was relatively thick, the electromagnetic field characteristics were improved in low-speed operation. In the case of high-speed, when the rib of the 2-layer was thinner than that of the 1-layer, the electromagnetic field characteristics were improved.
Sensorless Active Damping Control for Three-Phase LCL Grid-Tied Converters With State-Estimation of Grid Voltage and Capacitor Current

LCL filters have been widely used between grids and inverters due to their excellent filtering performance and low cost. However, in order to ensure the stability of the system, the traditional damping control scheme usually requires a large number of sensors which makes extra cost. In order to solve this malpractice, a model reference based active damping control strategy is proposed for a three-phase LCL-type grid-tied inverter, which is able to stabilize the system, and accurately control the active and reactive power with only the inverter-side current sensor. Instantaneous power theory is used to estimate the capacitor current and the grid voltage. Simulation results verify the feasibility of this scheme.
A Retinal Image Restoration Method based on Generative Adversarial Network

The basis of a good generalization neural network is a large number of learning samples with label. The lack of data will lead to over-fitting in practice. Due to the particularity of medical images, it is very difficult to obtain data sets with real segmentation labels in the field of segment medical images. Each segmentation label of medical image needs doctors' manual segment, which is time-consuming. Thus, how to obtain effective medical images with label to amplify data set has become a key research issue in deep learning. This paper proposes a model, called MixGAN, to recover the corresponding realistic retinal images according to the existing segmentation labels. MixGAN is based on generative adversarial ideas, including a generator that can extract multiple features and a discriminator using patch judgment. The experiments shows that our MixGAN can generate images with a structural similarity of 0.835 to real retinal images, and each image has segmentation label, which can double the size of the DRIVE data set.
Analyzing Power Decisions in Data Center Powered by Renewable Sources

Both academics and industry have engaged their efforts in reducing greenhouse gas (GHG) emissions of Information and Communications Technology (ICT). Data centers are one of the most electricity-expensive ICT actors due to their uninterrupted service. Reducing the usage of brown energy or migrating to green energy using renewable sources (RES) is a way to reduce these emissions. However, this migration is not straightforward due to intermittence from these sources. This work is part of Datazero 2 ANR project. This project aims to design a data center powered only by RES production and storage elements. This architecture requires several decisions at different levels of management. This project divides the problem into two groups: offline and online. On the offline side, it uses renewable and workload predictions to prepare an offline plan with a power envelope (power delivered to the servers) and hints on how to manage the storage (batteries and hydrogen). Given the offline plan, the article's contribution is how to deal with real and dynamic power constraints online while keeping the planned storage level at the end. So, this article proposes policies to modify the plan according to the changes in predictions. We evaluate these policies in a homogeneous and heterogeneous data center. The results demonstrate that our policies could approach the storage level and improve Quality of Service (QoS) independently of data center infrastructures.
Deep Learning System with Data Augmentation for Electric Machinery Fault Diagnosis from Vibration Signals

A challenging aspect of applying Artificial Intelligence (AI) for electric machinery fault diagnosis systems in practical engineering scenarios is to collect adequate cases with sufficient faulty conditions for training and validation. In particular, imbalanced data sets, such as motor vibration signals having much more data toward no-fault or common fault conditions than the rare ones, usually lead to biased predictions or misdiagnosis. In this paper, a statistical feature extraction model, the proposed Synthesis Data Augmentation Method and the Deep Neural Network (DNN) are investigated and implemented to enhance the classification accuracy from vibration signal measurements. The experimental results have shown an increase in prediction accuracy from 70.48% to 92.38%.
Pipelined Memristive Analog-to-Digital Converter With Self-Adaptive Weight Tuning

Benefiting from area and power efficiency, memristors enable the development of neural network analog-to-digital converter (ADC) to break through the limitations of conventional ADCs. Although some memristive ADC (mADC) architectures have been proposed recently, the current research is still at an early stage, which is mainly on the simulation and requires numerous target labels to train the synapse weights. In this paper, we propose a pipelined Hopfield neural network mADC architecture and experimentally demonstrate that such mADC has the capability of self-adaptive weight tuning. The proposed training algorithm is an unsupervised method originated from the random weight change (RWC) algorithm, which is modified to reduce the complexity of error feedback circuit to make it more hardware friendly. The synapse matrix could be adapted to the 1T1R crossbar array. For an 8-bit two-stage pipelined mADC, the proposed architecture in the simulation could achieve 7.69 fJ/conv FOM, 7.90 ENOB, 0.1 LSB INL, and 0.1 LSB DNL. And the experimental performance only achieves 1.56 pJ/conv FOM, 7.59 ENOB, 0.21 LSB INL, and 0.29 LSB DNL, which is mainly limited by the comparator’s switching time.
Ontological modeling for the evaluation of Information System Success

Some several models and methods that aim to assess the performance of information systems, but they do not take into account the specific aspects of each case. Moreover, even the most flexible models are based solely on input criteria chosen in advance, hence the impossibility of adding other criteria (sub-criteria). This article presents ontology to evaluate the information system’s success. This ontology aims to facilitate the evaluation by using a hierarchical model based on a set of criteria (sub-criteria). Experiments were performed using the Protégé editor. The results show that the ontology can evaluate the IS according to the sector of the company and even the criteria (sub-criteria) chosen by the decision-makers.
Optical Fibre FPI End-Tip based Sensor for Protein Aggregation Detection

The detection of protein aggregates and its concentration, plays a relevant role in several fronts, namely at the detection of neurodegenerative diseases such as Alzheimer's, and the assessment of cellular stress linked to cell death. Evaluation of protein aggregation is an essential step of quality control in all stages of biopharmaceutical synthesis and transport, in order to guarantee the activity and desired immunogenicity of their products. The detection of protein aggregates often requires the use of expensive and complex techniques. Here we propose a fast and easy implementation solution, based on an optical fibre Fabry-Perot Interferometer (FPI) end tip resonator. The solution proposed revealed to be effective on the detection of protein aggregates, providing similar results as the ones obtained using a commercial spectrometer for fluorescence detection.
Denoising Low-Dose CT Images Using a Multi-Layer Convolutional Analysis-Based Sparse Encoder Network

Imaging in the field of low-dose computed tomography (LDCT) tend to be rather noisy and artificial but is diagnostically useful. One approach to improve the quality of LDCT images is to use deep learning (DL) techniques. DL-based methods produce state-of-the-art performance in low-level medical image restoration tasks but remain defect to interpret due to their black-box constructions. In this paper, we present a simple yet effective LDCT image denoising model by combining the advantages of a residual strategy and a multilayer convolutional analysis-based sparse encoder (CASE). Inspired by convolutional sparse coding (CSC), we constructed a multilayer CASE to sufficiently capture and represent hierarchical image features and designed CASE-net to achieve improved LDCT noise artifact suppression. Moreover, a hybrid loss function, e.g. mean absolute error (MAE) loss, edge loss and perceptual loss, was used to achieve better denoising effects. Experiments on the MAYO and UIH datasets demonstrated the performance of our framework. The results prove that the proposed approach can restrain noise and artifacts and maintain tissue structure during the LDCT imaging.
Compressed Sensing of Sparse Spectrum Using Distributed Sound-to-Light Conversion Device Blinkies

In this study, we propose a method for estimating a sparse spectrum of sound using multiple sound-to-light conversion devices called Blinkies. A Blinky is a compact device that converts sound information into the light with a programmable operation. By distributing multiple Blinkies and monitoring them with a video camera, we can obtain acoustic information from a wider area without wired or wireless communication. However, due to the limited frame rate of the video camera, the bandwidth of the observed video signals is significantly narrower than that of the original sound. To restore the spectrum of an original sound signal, we design the entire signal acquisition process with multiple Blinkies and a video camera to be regarded as compressed sensing. We show that a sparse sound spectrum can be restored with the proposed method by numerical experiments.
A comparative analysis of metaheuristic techniques for improving PTE and PDL of a wireless power transfer system

Wireless Power Transfer (WPT) with inductive coupling is the one most advanced techniques for powering biomedical implants, in recent decades has been the transmission of energy without the need of cables. The importants elements (indicators), power transfer efficiency (PTE) and power delivered to load (PDL), of a wireless power transfer systems . These keys are dependent on several design parameters of WPT system, such as the geometrical parameters of the coils, the separation of the sender (TX) and the receiver (RX). And also the operating frequency. The invention, design, and optimization of coils square spirals in a wireless energy transfer system using a resonant inductive link are the emphasis of this paper. Metaheuristic algorithms are among the optimization techniques used.
Turbomachinery Mechanical Oscillations Analysis and Active Compensation Design by EESS in Thermal Power Plant

There are analyzed a turbomachinery mechanical oscillations which are excited in the synchronous generator caused by electrical transients in the electrical grid. The higher voltage harmonics and interharmonics affecting the mechanical stability are identified and discussed. Then, there is presented a methodology using Fourier harmonic analysis of the generator voltage to identify potentially harmful frequencies of the voltage. Moreover, there is presented design of active protection system using Electronic Energy Storage System (EESS) which is a specifically designed Battery Energy Storage System (BESS) extended by superior control unit handling mathematical model of turbomachinery and control algorithms commanding directly the power converter. The EESS is connected to the point of common coupling (PCC) with the synchronous generator. The protection system generates electric currents based on the designed methodology to compensate identified harmful voltage frequencies. The state of the art of the project and the future development is summarized in the conclusion.
Autonomous vehicles lane detection using particle filters

Lane detection belongs among many others crucial tasks which an autonomous vehicles traveling in urban roads need to take care of. We present a lane detection method which uses particle filters combined with visual information from onboard camera to control the vehicles direction. Our initial experiments shows promising results as applied and tested in urban roads with focus on real-time data processing.
Online Parameter Identification Method using Neural Network for IPMSM

This paper proposes a new neural network based estimator to identify in real time dq-axis inductances, stator resistance and the rotor flux linkage of the interior permanent magnet synchronous machines (IPMSM). The parameters of the tested IPMSM, which were first experimentally measured by utilizing the offline standard method, are used to construct the IPMSM model in Matlab/Simulink. The training data of the neural network model are taken from the simulation of the IPMSM control system under different working conditions. The estimation accuracy and fast updating capability of the proposed solution were compared with the existing online methods including Adaline Neural Network, Recursive Least Square and Extended Kalman Filter. Extensive numerical simulations have been carried out to assess the robustness and the accuracy of the proposed online parameters identification solution.
Comparative Study of Radial-Flux Dual-Rotor Fractional-Slot Permanent Magnet Machines with Series and Parallel Magnetic Circuits

In this paper, electromagnetic performances of radial-flux dual-rotor (DR) fractional-slot concentrated-winding permanent magnet (PM) machines with series and parallel magnetic circuits are compared based on the optimised designs using finite-element analysis based genetic algorithm. It is found that under the same copper loss, the series magnetic circuit DRPM machine has higher output torque and lower iron loss due to yokeless structure. The influences of critical design parameters on both machines, e.g., PM thicknesses and pole arcs, inner and outer split ratios, and stator tooth width, are also investigated. It shows that both machines tend to sacrifice the inner rotor in order to enlarge the slot area, and the outer rotor makes a major contribution to the resultant torque.
A Novel Magnetic-Field-Shifting Method for Improving the Torque Density of Interior Permanent Magnet Machines

In this paper, a novel magnetic-field-shifting (MFS) method for an interior permanent magnet (IPM) machine is proposed to improve torque performance. By the superposition of the d-axis and q-axis permanent magnetic fields, the permanent magnetic field is shifted to 45° axis apart from origin position, d-axis normally. After using the proposed method, the reluctance torque and permanent magnet torque can obtain maximum simultaneously. Therefore, the torque density of the IPM machine can be improved, and more flexible topology design method can be achieved.
Multi-branch Learning for Noisy and Reverberant Monaural Speech Separation

With the rapid development of deep learning approaches, much progress has been made on speech enhancement, speech dereverberation, and monaural multi- speaker speech separation to solve the cocktail problem. Some excellent methods have been proposed to solve the monaural speech separation in a noisy and reverberant environment. However, few studies exploit the correlations between anechoic speech and reverberant speech. In this work, the structure of a popular separation system is deconstructed, and a multi-branch learning method is proposed to enforce the network to exploit the correlations between anechoic speech and the corresponding reverberant speech. The results show that using multi-branch learning can improve the separation performance of different networks by 0.7dB with conv-tasnet on the WHAMR! dataset.