Miracle-dz/UniFAR-FTUs huggingface dataset:
Three Branch net Lane Detection on Complex Road Conditions

Lane detection task is the most necessary part in modern intelligent driving technology. However, there still have many challenge need to be conquered. In this paper, we proposed a three-branch neural network, in this framework, there are several new techniques are used. Including multi-branch, lightweight module, feature recalibration and decoder module, named Three Branch Net. Furthermore, A new dataset have been used, which includes much more complex situation and more close to the real world. Compared with other newest method, experiment results shows the proposed approach is the most effective method in complex road conditions task.
Learning to Solve Linear Inverse Problems in Imaging with Neumann Networks

Recent advances have illustrated that it is often possible to learn to solve linear inverse problems in imaging using training data that can outperform more traditional regularized least squares solutions. Along these lines, we present some extensions of the Neumann network, a recently introduced end-to-end learned architecture inspired by a truncated Neumann series expansion of the solution map to a regularized least squares problem. Here we summarize the Neumann network approach, and show that it has a form compatible with the optimal reconstruction function for a given inverse problem. We also investigate an extension of the Neumann network that incorporates a more sample efﬁcient patch-based regularization approach
On-task theta power is correlated to motor imagery performance

This study aimed to evaluate on-task electroencephalographic spectral measures and its correlation to performance during a motor imagery (MI) task. By investigating this aspect, we hope to understand what makes some individuals MI “illiterates”. Eighteen healthy subjects performed an experimental task whereby a cursor was moved to one of two targets (left and right) using only MI of the left and right hands. To evaluate the effect of aptitude, performance was measured as percentage of correct movement to target, and Mahalanobis distances were calculated between whole-scalp spectral patterns during left and right motor imagery. Then the correlation between performance and Mahalanobis distance was investigated for central, and whole-head topographies using Spearman’s correlations. In central topographies, distances on alpha band were positively correlated with performance ($\rho = 0.562, p =0.032$), while distances on theta band were negatively correlated to performance ($\rho = -0.648, p =0.018$) in whole-head maps. The investigation of on-task whole-scalp differences allows a holistic comprehension of the neural basis of motor imagery, as well as how this leads to performance variations.
Lane markings-based relocalization on highway

In this paper we propose a method to create maps of lane markers and a relocalization method using these maps on highways. We use a three-steps sub-pixellic detection process to locate the edges of lane markers with centimetric precision using images from a front-facing camera. We propose a simple mapping process using RTK-GNSS data to create lane marker edge maps to be used in the relocalization task. Given such a map, we then use transferable belief model to associate observed markers to those stored in the prior map, and a symbolic registration algorithm is then used to estimate the raw pose of the vehicle. Finally, this raw pose measurement is fused with odometric data to improve the global pose estimate precision. This method is evaluated on an open highway driving sequence of 10km on the outskirts of Paris. The proposed approach works in real time and yields lateral and longitudinal errors with standard deviations of respectively 8 and 13 centimeters.
Intelligent Transformer Unit Topology Using Additional Small Power Converter Based on Conventional Distribution Transformer

This paper proposes a new Intelligent Transformer Unit (ITU) topology. The proposed intelligent transformer unit can be constructed just adding a three-leg converter to the conventional distribution transformer. With the proposed topology, the magnitude of the distribution voltage can be kept constant and harmonics of the distribution voltage can be reduced. Also, transmission efficiency can be improved by improving power factor of the high voltage side of the distribution transformer. Furthermore, compared to the previous intelligent transformer unit based on full-power ratings of power semiconductors, the proposed topology is more efficient because most of the power is delivered through conventional distribution transformer. Simulations and experiments were performed and the result reveals the effectiveness of the proposed topology.
Novel Switching Control Method for Synchronous Rectifier of Phase-Shifted Full-Bridge Converter in Light-Load Conditions

This paper proposes the new switching control method for the synchronous rectifier (SR) of phase-shifted full-bridge (PSFB) converter in light-load conditions, where it usually operates in discontinuous conduction mode (DCM) to reduce the effective duty ratio and core loss caused by reverse current. However, it has the problem that the current flows through the body diode instead of the channel of SR. This causes undesirable conduction loss. The proposed switching control method of SR reduces the conduction loss by optimizing its turn-on time to minimize the conduction ranges of body diode without an auxiliary circuit. In particular, its boundary condition and loss calculation is mathematically analyzed. Then, its practical effectiveness is verified by experimental tests based on hardware implementation for the PSFB converter of 400 W.
Deep Learning for Bipartite Assignment Problems*

Many assignment problems cannot be solved to optimality in real-time. The existing literature tends to focus on the development of handcrafted heuristics that exploit the structure of a particular assignment problem. We instead seek a general-purpose approach that can automatically learn such heuristics. In this work, we present a deep learning approach that is designed to automatically learn heuristics for a large class of assignment problems. We demonstrate the effectiveness of our approach on the weapon-target assignment (WTA) problem, which is nonlinear and NP-complete. We take non-trivial WTA problem instances and show that our approach reliably finds near-optimal solutions in milliseconds.
Effect of Ultrasound Treatment on Chitosan-Silver Nanoparticles Antimicrobial Activity

Nanoparticles have good prospects as an alternative to antibiotics. Achieving the stability of nanoparticle solutions over time is one of the important tasks for researchers as well. Preparation of new antimicrobial release systems based on Ag NP and biological polymers will contribute to create effective antimicrobial agents. In current paper we evaluate antibacterial activity of chitosan-Ag NPs treated by ultrasound against Gram-positive and Gram-negative microorganisms. Ag NPs were synthesized via polyol method. 2% chitosan solution used to produce polymer-Ag NPs composite. X-ray diffraction (XRD) technique, scanning electron microscopy (SEM), and UV-Vis spectroscopy were used for NPs characterization. The antimicrobial activity of the Ag NPs and Ag NP/chitosan solution was tested against S. aureus, S. pyogenes, E. coli,. K. pneumonia, P. aeruginosa, P. vulgaris and Candida spp. Pure Ag NPs demonstrate high antimicrobial activity against all microorganisms. The ultrasonic treatment of nanoparticles makes them more active in relation to gram-negative bacteria but sonicated composition of the Ag NPs and 2 % chitosan gel express the highest antimicrobial activity against all types of microorganisms.
Voltage-Controlled Tunable Capacitor based Resonant Power Converter

The conventional resonant converters are based on fixed passive components which are required to be operated by variable switching frequency control with limited regulation capabilities under changing operating conditions. This includes the variations of load, source, and components. The variable switching frequency operation not only increases the size of the EMI and the harmonic filters but also reduces the efficiency of a converter. To address these challenges, a fixed frequency resonant power converter based on voltage-controlled tunable capacitor is proposed in this work. In the proposed converter, the capacitance of the resonant tank is varied to control the gain of the resonant converter by changing the dc control voltage across a tunable capacitor bank. Detailed analysis and design of the proposed converter, utilizing LLC resonant tank structure, for achieving high efficiency and fixed frequency operation are presented. Experimental results are presented to validate the operation and high performances of the proposed resonant converter.
Investigate of a Flux Switching Permanent Magnet Machine with Alternative Flux Bridges

In this paper, a novel flux switching permanent magnet (FSPM) machine with alternative flux bridges is proposed to achieve a larger torque density than regular FSPM machine, especially for high pole ratio (ratio of rotor tooth number to winding pole pair number) FSPM machine. The proposed FSPM machine has the same rotor, PMs, and winding structure, but different stator yoke configuration with the conventional FSPM machine. In the proposed FSPM machine, the flux bridges are placed at the top of stator yoke as "C"shaped, which can increase the main working harmonic field and reduce the flux barrier effect. First, the operational principle and influence of pole ratio on flux barrier effect are introduced. Then, three proposed FSPM machines with different pole ratios are investigated in torque characteristics considering the effects of split ratio and flux bridge width factor. Finally, the electromagnetic performances of the conventional and proposed FSPM machines are compared by finite element analysis (FEA) in terms of flux density, back-electromotive force (EMF), torque characteristics and overload capability.
Impact of converter control parameters on the high-frequency resonance stability of a wind-farm connected to an ac grid

The transient stability of a wind-farm connected to an ac grid depends on the control settings of the involved converters, the grid resonances and the system's operating conditions. This paper focuses on investigating the impact of converter control algorithms on possible high-frequency resonance interactions that could endanger the overall stability of these kinds of systems. Mainly, two control strategies namely the conventional cascade control structure, consisting of an inner current-control loop and various outer-loop controls, and the immediate voltage control structure, which does not include a current-control loop, are compared. The impact of control parameters for both methods will be investigated as well. For this purpose, the ac-side input admittance of a wind-turbine converter is derived for the two control strategies and passivity-based frequency-domain analysis is used to assess their impact on system stability. Finally, the analytical findings are verified through detailed time-domain simulations and recommendations to increase system stability are given accordingly.
Robust Cooperative Load-Frequency Tracking Protocols for Leader-Follower Smart Power Grid Networks With Uncertainty

This paper introduces consensus based leader-follower robust cooperative load frequency tracking control(LFTC) protocols for multi-area smart power grid networks. The LFC protocols combines local states with the states of the neighboring areas with directed communication topology. We first develop Robust LFTC protocols by assuming that the bounds of the uncertainty associated with power network dynamics and disturbance are known a priori. Lyapunov and graph theory used to show the finite-time convergence of the states of the follower control area power grid networks to the states of the leader control area. Then, we relax the demand of the bound on the uncertainty from LFTC protocols by integrating a robust adaptive learning algorithms. Robust adaptive learning control uses to deal with the presence of uncertainty associated with the power grid networks and disturbance. Convergence analysis of the closed-loop multi-area power grid networks are shown by using Lyapunov and graph theory. Analysis shows that the states of the follower control areas can reach an agreement and track the states of the leader control area asymptotically. Evaluation results on a four-area interconnected power grid networks are presented to show the effectiveness of the proposed consensus based distributed robust LFTC protocols for real-time applications.
Design of Loosely Coupled Transformer of Wireless Power Transfer for Higher Misalignment Tolerance of System Efficiency

The offset between the primary and secondary couplers of the Loose-Coupled Transformer (LCT) results in deviation in the efficiency of the wireless transmission system. In order to obtain higher efficiency deviation tolerances, a three-loop LCT structure is proposed. The LCT's intermediate couplers are slightly larger than primary and secondary couplers. A 30mm air gap between primary and intermediate couplers exists. The compensation capacitance of the proposed intermediate coupler is variable. Moreover, when the horizontal or vertical offset between the primary and secondary couplers reaches 200 mm or 50 mm, respectively, the intermediate couplers can move with the secondary couplers. At the same time, the proposed LCT control strategy is optimized. A 3-kW prototype was developed for experimental verification, which proved the effectiveness of proposed LCT and control strategy.
An Ultralow Power Burst-Chirp UWB Radar Transceiver for Indoor Vital Signs and Occupancy Sensing in 40-nm CMOS

This letter presents an ultrawideband (UWB) radar for occupancy and vital signs detection. The proposed burst-chirp operation achieves <inline-formula> <tex-math notation="LaTeX">$> 30\times $ </tex-math></inline-formula> power reduction and abundant margin for the UWB spectrum regulations. With the proposed Domino chirp generation and time-domain predistortion, the radar has a fast chirp slope of 0.7 GHz/40 <inline-formula> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula> and a low RMS error of 0.5 MHz. Fabricated in a 40-nm CMOS process, the presented radar consumes a record-low power of 680 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> and can detect human respiration and heartbeat up to 15 and 5 m, respectively.
High-Fidelity Liquid-cooling Thermal Modeling of a WBG-based Bidirectional DC-DC Converter for Electric Drivetrains

The advent of Wide-Bandgap (WBG) semiconductors, e.g., Silicon Carbide (SiC) and Gallium Nitride (GaN), power electronics E-drive converters are projected to obtain an increase in power density as $\sim 2\mathrm{x}$ for SiC devices and ~4x for GaN devices, which demand detailed thermal modeling and analysis of power semiconductors and cooling systems. This paper has proposed high-fidelity (HiFi) modeling of bidirectional DC-DC converter coupled with liquid cooling system providing detailed information with higher accuracy and less complexity to determine performance during conceptual modeling in electric vehicle drivetrain with minimum testing and development effort.
Defect Detection and Classification for Plain Woven Fabric Based on Deep Learning

Defect detection for plain woven fabric is investigated in this paper. The original RGB images are converted into gray scale images and further enhanced by gray level adjustment. Several different filtering methods are used to denoise for comparison and ideal low pass filter is selected for the filtering of adjusted gray scale images. Deep learning based on convolutional neural network is suggested for the detection and classification of the filtered images using the VGG model. Experiment results verified the effectiveness of the proposed method.
Underdetermined Source Separation Based on Generalized Multichannel Variational Autoencoder

This paper deals with a multichannel audio source separation problem under underdetermined conditions. Multichannel non-negative matrix factorization (MNMF) is a powerful method for underdetermined audio source separation, which adopts the NMF concept to model and estimate the power spectrograms of the sound sources in a mixture signal. This concept is also used in independent low-rank matrix analysis (ILRMA), a special class of the MNMF formulated under determined conditions. While these methods work reasonably well for particular types of sound sources, one limitation is that they can fail to work for sources with spectrograms that do not comply with the NMF model. To address this limitation, an extension of ILRMA called the multichannel variational autoencoder (MVAE) method was recently proposed, where a conditional VAE (CVAE) is used instead of the NMF model for expressing source power spectrograms. This approach has performed impressively in determined source separation tasks thanks to the representation power of deep neural networks. While the original MVAE method was formulated under determined mixing conditions, this paper proposes a generalized version of it by combining the ideas of MNMF and MVAE so that it can also deal with underdetermined cases. We call this method the generalized MVAE (GMVAE) method. In underdetermined source separation and speech enhancement experiments, the proposed method performed better than baseline methods.
Multi-port Converter with Square-wave-voltage Multilevel Converter and Active Power Filter Connected in Series

This paper proposes a multi-port converter, which has multiple power sources with a buck converter or inverter connected in series, with an active power filter for a reduction of power conversion loss and circuit volume of an energy storage system. The proposed circuit consists of a multilevel converter, a series-active power filter, and an unfolder. In the proposed circuit, the number of inductors is reduced from four to two compared to the conventional circuit. Moreover, the proposed circuit reduces harmonic components of output voltage with a multilevel topology in order to reduce the volume of a filter inductor. In addition, a voltage control system is proposed for the converter to regulate the capacitor voltage of the active power filter. It is demonstrated that a 1-kW prototype circuit with Si-MOSFETs achieves a maximum efficiency of 99.3%. Furthermore, the capacitor voltage is regulated to constant even when the load power is stepped from no-load to full load.
Comparative Analysis on Gain Feature between CLTC and CDT-LC

A resonant DC-DC converter with dual-transformer structure is proposed. The converter improves the efficiency and gain characteristics. It has the ability to achieve wide range voltage regulation in a narrow frequency range. At the same time, a simplified method of double transformers and its gain characteristics are analyzed in details. Finally, a 2.5 kW prototype is built to verify the theoretical analysis, and the maximum efficiency is 97%.
PCB Layout Based Short-Circuit Protection Scheme for GaN HEMTs

Gallium Nitride Enhancement-Mode High Electron Mobility Transistors (GaN HEMTs) are superior to other power transistors in terms of efficiency, package size and switching speed which leads to increased power density in power converter applications. However, GaN HEMTs have much shorter short-circuit withstand time compared to the conventional devices, which is limited to several hundred nanoseconds. Therefore, reliable and fast protection solutions are required to protect GaN HEMTs from fatal over-current failures. In this paper, a novel short-circuit (SC) protection scheme based on fault current sensing by using Printed Circuit Board (PCB) layout parasitics is proposed. The proposed scheme uses the voltage drop on the parasitic inductance of the PCB trace to detect very intense high slew rate SC faults. In addition, the voltage drop on the parasitic resistance of the PCB trace is utilized to detect relatively slow over-current (OC) faults. Once a fault is detected, a soft turn-off mechanism is initiated by the proposed circuit to turnoff devices gradually to eliminate over-voltage breakdown risk. The proposed circuit is verified by both SPICE simulations and hardware implementation. The experimental results show that both SC and OC faults can be detected and GaN HEMTs can be protected. The total operation duration for the circuit is 370 ns during a SC fault. The SC fault can be detected within 30 ns and the soft turn-off mechanism is initiated within 80 ns to terminate the SC current flowing through the GaN HEMTs within 290 ns.
A Performance Comparison of Deep Learning Methods for Real-time Localisation of Vehicle Lights in Video Frames

A vehicle’s braking lights can help to infer its future trajectory. Visible light communication using vehicle lights can also transmit other safety information to assist drivers with collision avoidance (whether the drivers be human or autonomous). Both these use cases require accurate localisation of vehicle lights by computer vision. Due to the large variation in lighting conditions (day, night, fog, snow, etc), the shape and brightness of the light itself, as well as difficulties with occlusions and perspectives, conventional methods are challenging and deep learning is a promising strategy. This paper presents a comparison of deep learning methods which are selected based on their potential to evaluate real-time video. The detection accuracy is shown to have a strong dependence on the size of the vehicle light within the image. A cascading approach is taken, where a downsampled image is used to detect vehicles, and then a second routine searches for vehicle lights at higher resolution within these Regions of Interest. This approach is demonstrated to improve detection, especially for small objects. Using YOLOv3 for the first stage and Tiny_YOLO for the second stage achieves satisfactory results across a wide range of conditions, and can execute at 37 frames per second. The ground truth for training and evaluating the methods is available for other researchers to use and compare their results.
Design and Comparison of Single-Layer Dual-Stator 6/4 FSPM Machine with Toroidal Winding

Single-layer winding structure is able to increase the winding factor of dual-stator 6/4 flux-switching permanent magnet (FSPM) machine compared to conventional double-layer winding. The use of toroidal winding in the single-layer winding structure significantly reduces the manufacturing complexity and the risk of turn-to-turn short. This paper investigates the design and performance of single-layer winding, dual-stator 6/4 FSPM machine equipped with toroidal winding. Because of the higher winding factor, the single-layer topology can achieve the same power with smaller size or lower electric loading compared to the double-layer topology. In addition, the single-layer winding topology is shown to have better field weakening capability than the double-layer winding topology.
Multiphysics Optimization of Air-Cooled PFC Rectifiers with Wide Bandgap Devices

This work presents a multiphysics optimization method to identify an optimized design for single-phase, air-cooled PFC rectifiers applying wide bandgap devices. The method includes analytical models for thermal behavior, semiconductor losses, filter, and passive components. Combining all models, a minimized volume is found by choosing an optimized switching frequency and current ripple. Unlike the state of art research, semiconductor losses and the size of cooling system is considered in the optimization. An experimental evaluation confirms the validity of the presented method, stating maximum power densities of approximately 11.5 kW/dm3.