Miracle-dz/UniFAR-FTUs huggingface dataset:
SIR-Net: Self-Supervised Transfer for Inverse Rendering via Deep Feature Fusion and Transformation From a Single Image

Measuring the material, geometry, and ambient lighting of surfaces is a key technology in the object’s appearance reconstruction. In this article, we propose a novel deep learning-based method to extract such information to reconstruct the object’s appearance from an RGB image. Firstly, we design new deep convolutional neural network architectures to improve the performance by fusing complementary features from hierarchical layers and different tasks. Then we generate a synthetic dataset to train the proposed model to tackle the problem of the absence of the ground-truth. To transfer the domain from the synthetic data to the specific real image, we introduce a self-supervised test-time training strategy to finetune the trained model. The proposed architecture only requires one image as input when inferring the material, geometry, and ambient lighting. The experiments are conducted to evaluate the proposed method on both the synthetic data and real data. The results show that our trained model outperforms the existing baselines in each task and presents obvious improvement in final appearance reconstruction, which verifies the effectiveness of the proposed methods.
An Investigation on Mechanical Properties of PLA Produced by 3D Printing as an Implant Material

Polylactic acid (PLA) is a thermoplastic polyester produced from lactic acid. It has become a popular material due to its economic cost and renewable resources. It is the most widely used filament material in 3D printing process. Its biocompatibility and biodegradability made it a perfect choice for medical implants. Its usage in 3D printing also provided flexibility in implant design and production. The aim of the study was to investigate the effect of printing parameters, namely layer height, print speed, infill and shell number, on the tensile and flexural properties of PLA test samples produced by 3D printing. Taguchi method was used to design the experiments. During the optimization of the parameters larger the better signal-to-noise ratio (S/N ratio) and analysis of variance (ANOVA) were used with the aid of Minitab 2019 software. The most effective first and second parameters for tensile loading were infill and number of shells and for bending they were number of shells and infill then followed by printing speed and layer height for both. The optimum parameters for tensile properties were obtained as 0.15 mm layer height, 60 mm/s print speed, 50 % infill and 5 lines of shell. For flexural properties they were 0.10 mm, 100 mm/s, 25% and 5 lines, respectively.
A Novel Third-Harmonic Elimination Method for VOC-Based Three-Phase DC/AC Inverter

Virtual oscillator control (VOC) has been proposed for Microgrids, since compared to the droop control method, VOC has a faster transient response. However, the output voltage of the conventional VOC always contains the third-harmonic. Thus, in the grid-connected mode, the third-harmonic voltage causes the generation of significant third-harmonic current which is injected into the power grid. In this paper, by analyzing the nonlinear oscillator and simplifying the nonlinear current source in the oscillator, a novel VOC for three-phase DC/AC inverter is proposed, where the third-harmonic of the oscillator output voltage can be successfully eliminated, whether in the islanded or grid-connected mode of operation. In addition, compared with the traditional VOC, the dynamic response of the proposed VOC-based inverter can be significantly improved, especially in the islanded mode. Experimental device designed on the DSPACE DS1202 is developed to verify the feasibility of the proposed strategy.
Enhancement of Integrated Navigation System for high-speed flying vehicles' Navigation Applications

The Inertial Navigation Systems (INS), Global Navigation Satellite System (GNSS) integration becomes very important for high speed flying vehicles as a navigation solution. In this paper, a derivation and modeling of a system model of the integrated system for a 27-states Kalman filter is presented. Sensors (gyroscopes and accelerometers) errors, and GNSS errors are characterized and modeled as well. Results show that some parameters of estimated gyroscopes (gyros) errors such as vertical and east gyro drifts and the estimated east accelerometer bias are not observables. The simulation shows that in the integrated system and the navigation errors in both the INS and GNSS can be estimated with high accuracy. Results analysis proves that the development of state estimation for an Inertial Measuring Unit (IMU) can efficiently supply current motion information (position and attitude states). This efficient information can be used to carry out accurate guidance and control strategies for such a hi-speed flight bodies.
Non-Line-of-Sight Identification Without Channel Statistics

Identifying non-line-of-sight (NLOS) conditions is important to discard, or improve, any location estimates that have been estimated with NLOS ranges. Typically, NLOS identification relies on channel statistics that have been collected for both LOS and NLOS channels. We investigate NLOS identification using distance residuals instead. The results show that distance residuals can be used to identify location estimates with NLOS ranges with very high accuracy, and that in some cases, individual NLOS ranges can also be identified.
Slicer: A network hypervisor with an automatic mapping of virtual network topology

This paper is dedicated to the problem of dividing the physical network into virtual layers with the different provided quality of service. Currently, this problem is relevant due to the development of 5G networks. Our proposed solution — Slicer is a hypervisor with the capabilities to isolate traffic, to automatically map a virtual topology and to provide required QoS in a virtual layer. One of the key results of this paper is the mapping algorithm, that allows to aggregate several physical switches into virtual one. To reduce the algorithm complexity the heuristic based on Steiner trees is used in the proposed algorithm. The experimental study of Slicer and our mapping algorithm was conducted for the topologies from Topology Zoo library.
Measurement of antiresonant frequency during DC bias voltage application for analysis of second harmonic response of ScAlN on SMR

Second harmonic generation (H2) is not desirable for BAW filter applications. Recent BAW filter requires reduction of H2 to achieve high receiver sensitivity. In order to reduce H2, nonlinear parameters of AlN and ScAlN are important because it is widely used in BAW filters. Irieda et al. indicated that second order nonlinear piezoelectric coefficients $\chi_{11}{}^{\mathrm{T}}$ and dielectric $\chi_{02}{}^{\mathrm{E}}$ are dominant for H2 [1], [2]. They showed these nonlinear coefficients $\chi_{11}{}^{\mathrm{T}}$ and $\chi_{02}{}^{\mathrm{E}}$ can be approximately estimated from the change of parallel resonant frequency $f_{\mathrm{p}}$ and inverse dielectric constant $\beta^{\mathrm{S}}$ during a DC bias voltage application. In this study, AlN and ScAlN films were grown on Al bottom electrode/Ta2O5/SiO2 Bragg reflector (SMR) by RF magnetron sputtering. $f_{\mathrm{p}}$ and $\beta^{\mathrm{S}}$ of AlN/SMR and ScAlN/SMR as a function of the DC bias voltage were measured. Nonlinear parameter $\chi_{11}{}^{\mathrm{T}}$ and $\chi_{02}{}^{\mathrm{E}}$ of films were estimated. $\chi_{11}{}^{\mathrm{T}}$ was decreased with increasing of Sc concentration.
Analysis of the Isolated Modular Multilevel DC-DC Converter by Considering the Impact of Magnetizing Inductance

Interconnection of Medium Voltage DC (MVDC) grids necessitates reliable and efficient DC-DC converters. Among the existing topologies, the Isolated Modular Multilevel DC-DC (IMM DC-DC) converter is a promising solution. In the technical literature, the impact of the magnetizing inductance of the High Frequency Transformer (HFT) on the operation and control of the converter is neglected. This paper analyzes the IMM DC-DC converter by focusing on the impact of the magnetizing inductance on the operation and control of the converter. Power transfer capability, arm current, reactive power, and soft-switching are discussed by considering the impact of the magnetizing inductance. This paper demonstrates that the magnetizing inductance of the HFT is a key parameter that needs to be carefully selected, as it can improve soft-switching operation, while reducing the power transfer capability of the converter. Simulation studies of a test system in the Matlab/Simulink software environment verify the presented modeling and analysis of the IMM DC-DC converter.
Soft–Switching EV Traction Inverter Exploiting Full Potential of Wide Bandgap Devices

This paper presents an implementation of wide bandgap devices in soft–switching inverter architectures to fully exploit the benefits of high switching frequencies and fast switching transients without the adverse effects of motor insulation degradation and elevated levels of electromagnetic interference. Circuit simulation tools and analytical loss models are utilized to validate the advantages of the auxiliary resonant commutated pole soft–switching converter architecture against the implementation of wide bandgap devices in a conventional two–level hard–switching inverter where techniques are employed to control the dV/dt of the semiconductor devices. In addition, it is found that high–frequency operation is possible without the penalty in efficiency since switching losses are minimized in soft–switching inverter topologies. This characteristic leads to increased inverter efficiency and reduced harmonic losses in the motor which makes the architecture attractive for future electric vehicle traction applications.
Conditional Tabular GAN-Based Two-Stage Data Generation Scheme for Short-Term Load Forecasting

Load forecasting is one of the critical tasks for enhancing the energy efficiency of smart grids. Even though recent deep learning-based load forecasting models have shown excellent forecasting performance, one of the common problems they faced was that their forecasting accuracy was highly dependent on the data quality and quantity available for the model training. Collecting a sufficient amount of high-quality data is expensive and time-consuming. Recently, a generative adversarial network (GAN) has shown its potential as a solution to the data shortage problem by generating virtual data based on a small amount of real data, and several studies have used GAN to generate electric load data for training forecasting models. However, due to the noise data problem of GANs, their predictive performance also deteriorated. To solve this problem, in this study, we propose a two-stage data generation scheme that more effectively generates input and output variables for short-term load forecasting. In the first stage, we generate virtual calendar and temperature data used as input variables using a conditional tabular GAN (CTGAN). In the second stage, we generate electric load data corresponding to the input variables using a deep learning-based regression model. Lastly, we construct our forecasting model by training another regression model using a mixture of generated data and real data. To verify the effectiveness of our scheme, we conducted extensive experiments using various datasets and data generation models. We report some of the results.
Anti-rollover motion planning for heavy-duty vehicle

The rollover dynamics has been few touched on during the study of path planning in current researches. This paper proposed an anti-rollover motion planning for heavy-duty vehicle. Taking the coupling of roll motion of sprung mass of the front axle with that of the drive axle into consideration, 7 degrees of freedom vehicle rollover model is established, and an evaluation index that can accurately describe the rollover motion state is derived. Then, the Model Predictive Control (MPC) is designed for motion planning which combining the rollover dynamics, artificial potential field for obstacle avoidance and trajectory tracking. Thus, the optimal path without collision risk, rollover trend and vehicle dynamics constraint is calculated. Finally, some typical scenarios are applied to validate the performance of the proposed motion planning. Results state that the anti-rollover motion planning can avoid collisions effectively and reduce the rollover risk simultaneously.
Resolution Measured as Separability Compared to Full Width Half Maximum for Adaptive Beamformers

Spatial resolution is defined as a system's ability to separate targets using some kind of criterion, such as the Rayleigh criterion. However, in practice, lateral resolution is often evaluated by measuring the Full Width Half Maximum (FWHM at -6dB) of the point spread function (PSF). We hypothesize that FWHM overestimates the system resolution for some adaptive beamformers, compared to using the Rayleigh criterion. Simulation results seem to confirm this hypothesis.
A Recommendation System for Diabetes Detection and Treatment

Detection, and recommendation systems are widely used in many companies and organizations in favor of giving their employees or even customers a better service. The importance of the systems motivated the investigation of the existing approaches to test its efficiency and possible usage. In this article, the aim is to integrate both systems to create a system that is able to detect diabetes and then recommend a proper plan or medication to overcome diabetes. So, we conducted a study of different approaches of both detection and recommendation systems in order to select the appropriate one. Recommendations systems have different methods. In this paper, we focused on the content-based recommendation method, in which we evaluated using a movie recommender application and movie dataset. We also evaluated and tested four different detection approaches, in which we found that the most accurate approach is the random forest with an accuracy of 79.2% and F-measure of 0.787. A discussion of the usage of a collaborative recommendation system is also introduced to give a wide vision and comparison of the two methods to decide which one is better for a diabetes treatment recommendation system.
Research on Self-synchronization Strategy of Grid-connected Inverters in Weak Power Grid

In order to ensure good power quality, the grid-connected inverter mostly uses phase-locked loop (PLL) to maintain synchronization with large power grid. But the PLL will cause a negative-impact on the stability of grid-connected inverter under weak grids. The paper analyzes the equivalent impedance of the PLL under the weak grid firstly. Then the paper proposed a self-synchronization strategy using DC bus voltage and reactive power control to synchronize the frequency and amplitude with the grid-connected inverter instead of the applying PLL. Applying the proposed method to the on/off grid switching condition, a pre-synchronization control strategy based on virtual power is proposed to achieve seamless switching of the grid-connected inverter. At last, the designed self-synchronization control strategy and pre-synchronization strategy are verified on the Plecs simulation platform.
Cascade GW Controllers for Speed Ripple Minimization at Low Speed Operation of PMSM Drives for EV

This paper proposes a control strategy to reduce speed ripples at low speed working conditions of Permanent Magnet Synchronous Machines (PMSMs) for Electric Vehicle Applications. The treated issue is related to the periodic torque ripples which induce speed oscillation that deteriorate the drive performance. To ensure a high-performance control regarding this issue, the reported work proposes an original control method based on Grey Wolf (GW) algorithm. The key idea of the proposed approach is to incorporate the benefit of fast optimization process of the GW optimizer in order to find input controls which satisfy the speed tracking and minimize speed ripples. The proposed method is described and the speed ripple issue is analyzed. Experimental results show that the proposed control method can be implemented in real-time on embedded hardware, offering high performance in both steady and transient states of the PMSM drives even at low speed range.
Improved Semantic Segmentation of Low-Resolution 3D Point Clouds Using Supervised Domain Adaptation

One of the key challenges in applying deep learning to solve real-life problems is the lack of large annotated datasets. Furthermore, for a deep learning model to perform well on the test set, all samples in the training and test sets should be independent and identically distributed (i.i.d.), which means that test samples should be similar to the samples that were used to train the model. In many cases, however, the underlying training and test set distributions are different. In such cases, it is common to adapt the test samples by transforming them to their equivalent counterparts in the domain of the training data before being processed by the deep learning model. In this paper, we perform domain adaptation of low-resolution 8, 16 and 32 channels LiDAR 3D point clouds projected on 2D spherical images in order to improve the quality of semantic segmentation tasks. To achieve this, the low-resolution 3D point clouds are transformed using an end-to-end supervised learning approach to spherical images that are very similar to those obtained by projecting high-resolution 64 channels LiDAR point clouds, without changing the underlying structure of the scene. The proposed framework is evaluated by training a semantic segmentation model on 64 channels LiDAR clouds from the Semantic KITTI dataset [1] and using this model to segment 8, 16 and 32 channel point clouds after adapting them using our framework. The results obtained from carried out experiments demonstrate the effectiveness of our framework where segmentation results surpassed those obtained with nearest neighbor interpolation methods.
A Nine-Level Inverter Topology with Equal Source Utilization

The maximum amount of load demand is supplied through batteries instead of a PV array for standalone applications in rural areas. This demands proper and equal battery source utilization for multilevel inverters (MLIs) with multiple sources for maintaining a better life of the battery. In this regard, a two-source nine-level MLI topology with the characteristics of equal source utilization is proposed. The number of voltage levels further increased either by increasing the number of switches or cascaded connection of the cell for improving the quality of the waveform. The proposed topology has the capability of equal voltage source utilization, which increases the balance in the charge of batteries by proposing a new switching method for generating gate pulses. The effectiveness and robustness of the developed topology are carried out under various operating conditions in the Simulink environment. Finally, an experimental test bench of the developed topology is built and the acquired experimental results validate the simulation results.
Energy Spectrum Analysis for Optimal Design of Ultra-High Frequency (UHF) Piezoelectric Resonators Leveraging 3D FEA Domain Decomposition Method with Cloud HPC

RF filter application requires high Q resonator to meet the necessary performance specifications. To achieve high Q resonators, analysis of electrical, mechanical properties for the Surface Acoustic (SAW) and Bulk Acoustic (BAW) mode and its harmonics are required to assess loss mechanisms to optimize device behavior. In this paper, we present a time domain (TD) method that can be used to extract frequency domain (FD) energy spectra. TD approach coupled with domain decomposition and cloud HPCs allows for efficient analysis of large (∼billion DOF) devices. TD method provides a quantitative insight on the energy spectrum associated with modes and the corresponding Q-factor values for the main resonance modes and its harmonics.
Neural Machine Based Mobile Applications Code Translation

Although many cross platform mobile development software used a trans-compiler-based approach, it was very difficult to generalize it to work in both directions. For example, to convert between Java for Android Development and Swift for iOS development and vice versa. This is due to the need of writing a specific parser for each source language, and a specific code generator for each destination language. Neural network-based models are used successfully to translate between natural languages, including English, French, German any many others by providing enough datasets and without the need of adding language specific code for understanding and generation. In this paper, a source code converter based on the Neural Machine Translation Transformer Model that can translate from Java to Swift and vice versa is introduced. A synthesized dataset is used to train the model, the pipeline used for the translation as well as the code synthesis procedure throughout the work are illustrated. Initial results are promising and give motivation to further enhance the proposed tool.
A Nine-Bridge Single-Stage Isolated Three-Phase Bidirectional AC/DC Converter

This paper proposes a nine-bridge single-stage isolated three-phase bidirectional AC/DC converter, which realizes buck-boost voltage regulation, high-frequency electrical isolation, and bidirectional power transmission. The converter adopts three transformers to distribute the total power equally, and the number of switches is significantly reduced by optimizing the topology. The Y-Δ connection of the transformer can provide an additional step-down conversion ratio. The improved Space Vector Pulse Width Modulation (SVPWM) algorithm is proposed to guarantee the voltage-second balance of the transformers and unity power factor control of the AC side. The proper selection of the switching states of the zero voltage vector contributes to an increase in the operating frequency of the transformers, which is 1.5 times that of the switches connected with them. The operating principles of the converter are presented in detail as well as its SVPWM algorithm. The simulation results under the rectifier mode and the inverter mode in MATLAB/Simulink validate the theoretical analysis well.
Remaining Useful Life Estimation of Rotating Machines using Octave Spectral Features

Bearing failure is one of the most common causes of failure for electric machines. Acquiring the vibration data of a machine with suitably placed accelerometers is a noninvasive and widely adopted approach for obtaining information regarding the health condition of the mechanical bearings. This paper presents a robust condition monitoring method for wear state classification and remaining useful life estimation of the mechanical rolling element bearings using orthogonal vibration signals. This proposed method uses non-linear signal processing techniques in the frequency domain for feature subset selection of short-time Fourier Transform (STFT) spectra and non-linear temporal class boundaries for classification using Coarse and Weighted K-Nearest Neighbour. This method has been tested and validated using the IEEE PHM PRONOSTIA challenge dataset. The signal processing and ML based approach presented here has performed extremely well with correct classification results of up to 75.6% being achieved. This work is of significant merit and will be highly valuable for the electric machines community allowing for the implementation of a robust condition monitoring system for many industrial applications using vibration sensors.