Miracle-dz/UniFAR-FTUs huggingface dataset:
Four-Port Modular Multilevel AC/AC Converter in Continuous Co-phase Traction Power Supply Application

Continuous co-phase traction power supply system is an effective method to eliminate neutral sections and provide high power quality for both the public grid and the catenary. Modular multilevel converter (MMC) can be applied in co-phase power supply system to perform three-phase/single-phase conversion. However, the existing schemes of MMC have some problems including high cost, bulky size and coupling between public grid and catenary. This paper proposed a new main circuit scheme based on four-port MMC (4P-MMC) and three-phase Y/D transformer, which significantly reduces the cost and realizes the decoupling of three-phase and single-phase sides. Simulation results are presented to verify the validation of the proposed scheme.
Hornbill: A Self-Evaluating Hydro-Blasting Reconfigurable Robot for Ship Hull Maintenance

Maintenance of ship hull involves routine tasks during dry-docking that includes inspection, paint stripping, and re-painting. Among those, paint stripping is always seen as harmful for human operators and a time-consuming task. To reduce human risk, cost, and environmental cleanliness, the shipping maintenance industries started using robotic solutions. However, most of such robotic systems cannot operate fully autonomous since it requires human in the loop to monitor the cleaning efficiency. To this end, a novel autonomous self-evaluating hull cleaning robot called Hornbill is presented in this paper. The proposed robot is capable of navigating autonomously on the hull surface and perform water jet blasting to strip off the paint coating. The robot is also enabled with a Deep Convolutional Neural Network (DCNN) based self-evaluating scheme that benchmarks cleaning efficiency. We evaluated the proposed robot’s performance by conducting experimental trials on a metal plate under three different paint coatings. While performing the paint stripping task in every experimental trial, the self-evaluating scheme would generate the heat map that depicts the plate’s cleanliness. The results indicate that the proposed self-evaluating system could successfully generate high accurate cleanliness heat maps in all considered scenarios, which simplifies the checkup process for paint inspectors.
A Novel Parametric Model for the Prediction and Analysis of the COVID-19 Casualties

Coronavirus disease (COVID-19) outbreak has affected billions of people, where millions of them have been infected and thousands of them have lost their lives. In addition, to constraint the spread of the virus, economies have been shut down, curfews and restrictions have interrupted the social lives. Currently, the key question in minds is the future impacts of the virus on the people. It is a fact that the parametric modelling and analyses of the pandemic viruses are able to provide crucial information about the character and also future behaviour of the viruses. This paper initially reviews and analyses the Susceptible-Infected-Recovered (SIR) model, which is extensively considered for the estimation of the COVID-19 casualties. Then, this paper introduces a novel comprehensive higher-order, multi-dimensional, strongly coupled, and parametric Suspicious-Infected-Death (SpID) model. The mathematical analysis results performed by using the casualties in Turkey show that the COVID-19 dynamics are inside the slightly oscillatory, stable (bounded) region, although some of the dynamics are close to the instability region (unbounded). However, analysis with the data just after lifting the restrictions reveals that the dynamics of the COVID-19 are moderately unstable, which would blow up if no actions are taken. The developed model estimates that the number of the infected and death individuals will converge zero around 300 days whereas the number of the suspicious individuals will require about a thousand days to be minimized under the current conditions. Even though the developed model is used to estimate the casualties in Turkey, it can be easily trained with the data from the other countries and used for the estimation of the corresponding COVID-19 casualties.
Development of 3-phase Current-fed Dual Active Bridge Converter for Bi-Directional Battery Charger Application

This paper proposes a novel control method for reducing power loss on the 3-phase dual active bridge (DAB). The proposed control method is control both duty of switches and phase between primary and secondary stage simultaneously. In conventional control method, the system efficiency can be decreased because of the reactive power in the converter, when the converter operating voltage is changed. This problem can be solved by adopting proposed control method by controlling constant voltage of auxiliary capacitor. The proposed control method is verified by experimental results using 10kW prototype modular power stack.
Boosting User Experience via Foreground-Aware Cache Management in UFS Mobile Devices

Mobile devices today often have multiple applications running simultaneously in the background. These background applications could rapidly consume storage cache resources, thus degrading the performance of foreground applications as well as the user experience. This issue could get worse as modern mobile devices are employing universal flash storage (UFS), which supports faster transmission speed and full-duplex transmission. In this article, a foreground application-aware cache management approach, FOAM, is proposed to address this issue. Through adaptive management of storage cache resources with the awareness of I/O workload patterns, UFS device features, and foreground/background information, I/O performance of foreground application is significantly improved. Experimental results show that the proposed approach could boost the performance of foreground read I/O by 45.9%, foreground write I/O by 18.4% on average compared with the existing approach.
Optimal Design of a 3-Coil Wireless Power Transfer System for Deep Micro-Implants

This paper presents a design methodology for a 3-coil magnetic resonance wireless power transfer (WPT) system with a long transfer distance (up to 20 cm) and a small implanted receiver (RX) (2mm in diameter) at a specific frequency. The methodology aims to find out the optimal value of dimensional parameters (i.e., coil diameter, gap interval and coil turn number) of the transmitter (TX) coils to maximize the magnetic field strength at the target distance while keeping the coil self-resonant frequency (SRF) twice of the target operational frequency. Firstly, the circuit model of the TX circuits is developed, which include a single-turn coupling coil and a multi-turn primary coil. Secondly, the co-dependences between the dimensional parameters are analyzed, which shows the dominant factors and secondary factors of each dimensional parameter, and how the optimal values of dimensional parameters are changed by these factors. Based on the analysis, design flow of the TX circuit is proposed to decide the optimal values of dimensional parameters given the transfer distance, source voltage, operational frequency and wire diameter. Using the design flow, optimal values of dimensional parameters are predicted for 20-cm transfer distance and 16-cm transfer distance and are verified with finite element analysis (FEA) software COMSOL Multiphysics. With the optimal TX design, the power received by a 2-mm ferrite core solenoid RX is calculated. At 20 cm transfer distance, up to 4.3 mW can be achieved in air, and 0.8 mW can be achieved in conductive human tissue.
Predicting Future Potential Flight Routes via Inductive Graph Representation Learning

Air transport activity has been considered as an important part of modern societies with high demand for massive scale mobilizations. One of the fundamental challenges in air transportation is to predict future potential flight routes connecting newly developed cities around the world. In this paper, we aim to solve the flight routes prediction problem under graph theory perspective as a link prediction task by mapping the existing airports as graph vertices and flight routes as graph links. We generate low-dimensional feature vectors of vertices by learning the embedding function. For each selected vertex in the input network, we sample the neighborhood and aggregate feature information from neighbors to predict and generalize to unseen links and vertices. The effectiveness of our prediction approach is shown using the Open Flights database containing 568 airlines with 65,535 routes between 3,425 airports. The promising performance of 90% accuracy is gained by the prediction model.
Optimal Design of Control Strategy for Full-Bridge LLC Converter

Based on the full-bridge LLC converter, this paper proposes a hybrid control strategy for input voltage fluctuations. The specific control strategy is as follows: 1) When the input voltage is stable, the PID controller is used. Combine the FM control working mode with the phase shift control working mode; 2) When the input voltage fluctuates, the digital direct controller is used. By direct frequency modulation or direct phase shift control, the fluctuation of the resonant current and the resonant capacitor voltage is reduced, and the output voltage is quickly adjusted.
Clustering Paths With Dynamic Time Warping

Studying software visualization often includes the evaluation of paths collected from participants of a study (e.g., eye tracking or movements in virtual worlds). In this paper, we explore clustering techniques to automate the process of grouping similar paths. The heart of the evaluated approach is a distance metric between paths that is based on dynamic time warping (DTW). DTW aligns two paths based on any given distance metric between their data points so as to minimize the distance between those paths—alignment may stretch or compress time for best fit. With a data set of 127 paths of professional software developers exploring code cities in virtual reality, we evaluate the clustering based on objective quality indices and manual inspection.
Design and Optimization Methodology of Transformer for 700/400 V Series Resonant DC/DC Converters with Enhanced Power Density

This paper investigates the potential volume reduction of a transformer for a Series Resonant Converter (SRC), using silicon carbide semiconductors, in a Ground Power Unit (GPU) application. Using an optimization based theoretical and empirical transformer design methodology, it is shown that the series resonant transformer volume has a large potential volume reduction below 50-60 kHz operation, but limited potential reduction above these operating frequencies. A 15 kW, 50 kHz transformer is designed and the simulated/modelled efficiency of the full SRC converter, shows a peak efficiency of 98.45 %.
Choosing a Means of Implementing an Information System to Minimize the Risks of Personnel Management

The use of information technologies to mitigate the risks of forming and managing human resources of the company is considered. This work analyzes modern information systems for monitoring and management of personnel. Alternative means for realization of the information system project considering the peculiarities of the scope are considered. The hierarchy of the general purpose of choosing the optimal means of implementing the information system for minimizing the risks of personnel management is presented. The most appropriate project implementation for such a system is to use the WordPress platform. A complex of UML diagrams was developed for the project implementation.
Memory-Augmented Convolutional Neural Networks With Triplet Loss for Imbalanced Wafer Defect Pattern Classification

A wafer bin map (WBM) represents the wafer testing results for individual dies on a wafer using a binary value that represents pass or fail. WBMs often have specific defect patterns, which occur because of assignable causes. Therefore, the identification of defect patterns in WBMs aids in understanding the root causes of process failure. Previous studies on the classification of WBM defect patterns have demonstrated effective performances. However, in previous studies, the effect of class imbalance on the WBM defect patterns was not considered, although in practice, it is more reasonable to assume that there is a significantly large number of WBMs that lack defect patterns because they occur when there are process faults. In this paper, we propose memory-augmented convolutional neural networks with triplet loss for classifying defect patterns in highly imbalanced WBM data. We use a triplet loss-based convolutional neural network as an embedding function to obtain a well-separated low-dimensional space according to the defect patterns. We then use a memory module to balance the number of WBMs between defect-pattern classes. We train the proposed model end-to-end to learn the embedding function and update the memory simultaneously. We then validate the proposed model using simulated WBM data. The proposed model demonstrates a high classification performance and effective embedding results for imbalanced WBM data.
High Voltage Direct Driven Wind Turbine Generator

This paper reports the design consideration for a high voltage direct drive generator suitable for a HVDC offshore wind park application. The study considers both concentrated overlapping and non overlapping windings in different slot/pole combinations for the design of a 35kV , 20MW permanent magnet synchronous machine. The analysis has been executed by finite element analysis. Among different slot/pole number solutions, two has been identified as worth of attention: q = 1, 42 pole-pairs machine and q = 2/5, 50 pole-pairs machine. The paper reports the results of the comparison in terms torque performances, power factor, efficiency, and annual energy production and annual energy losses.
Automatic Search Method of Robust Gate Driving Vectors for Digital Gate Drivers Against Variations in Operating Conditions of IGBT’s

To solve the trade-off between the switching loss and the current/voltage overshoot of power transistors, a digital gate driver is effective. A temperature and load current dependent optimization of the gate driving vectors (GV) for the digital gate drivers, however, is required [7]. Robust gate driving vectors (RGV) to the temperature and load current variations are proposed in [9]. The test cost to search RGV, however, is very high. To reduce the test cost, a robust simulated annealing (RSA) for an automatic search method of RGV is proposed. In the proposed RSA, instead of changing the operating conditions, GV is varied in a single operating condition. Compared with the conventional method to search RGV [9], the proposed RSA reduces the measurement time by more than 85 % (from more than 5.5 hours to less than 50 minutes), which results in the reduced test cost.
Detection of False-Data Injection Attacks in Supercapacitor Charging Systems

For charging energy storage systems of urban light trams, multiple modules are required in parallel to jointly bear the charging current. However, the traditional parallel multi-module charging system faces the risk of current fluctuation and imbalance caused by false-data injection attacks, reducing the reliability. This paper proposes a detection technology of false-data injection attacks in supercapacitors charging process based on adaptive cooperative control. The controller can dynamically analyze current volatility of each module in the process of charging current volatility. When the controller detects the output current fluctuation caused by injection attack, it executes the adaptive current equalization algorithm, automatically adjusts the current output value to approximate the reference current, so as to eliminate or reduce the impact of injection attack on the system. The experimental results verify the anti-interference capability, feasibility and practicability of the proposed distributed flow sharing control method.
A Comprehensive Survey on “Various Decoupling Mechanisms With Focus on Metamaterial and Metasurface Principles Applicable to SAR and MIMO Antenna Systems”

Nowadays synthetic aperture radar (SAR) and multiple-input-multiple-output (MIMO) antenna systems with the capability to radiate waves in more than one pattern and polarization are playing a key role in modern telecommunication and radar systems. This is possible with the use of antenna arrays as they offer advantages of high gain and beamforming capability, which can be utilized for controlling radiation pattern for electromagnetic (EM) interference immunity in wireless systems. However, with the growing demand for compact array antennas, the physical footprint of the arrays needs to be smaller and the consequent of this is severe degradation in the performance of the array resulting from strong mutual-coupling and crosstalk effects between adjacent radiating elements. This review presents a detailed systematic and theoretical study of various mutual-coupling suppression (decoupling) techniques with a strong focus on metamaterial (MTM) and metasurface (MTS) approaches. While the performance of systems employing antenna arrays can be enhanced by calibrating out the interferences digitally, however it is more efficient to apply decoupling techniques at the antenna itself. Previously various simple and cost-effective approaches have been demonstrated to effectively suppress unwanted mutual-coupling in arrays. Such techniques include the use of defected ground structure (DGS), parasitic or slot element, dielectric resonator antenna (DRA), complementary split-ring resonators (CSRR), decoupling networks, P.I. N or varactor diodes, electromagnetic bandgap (EBG) structures, etc. In this review, it is shown that the mutual-coupling reduction methods inspired By MTM and MTS concepts can provide a higher level of isolation between neighbouring radiating elements using easily realizable and cost-effective decoupling configurations that have negligible consequence on the array’s characteristics such as bandwidth, gain and radiation efficiency, and physical footprint.
Digital active gate control for a three-phase inverter circuit for a surge voltage suppression and switching loss reduction

This paper proposes a new gate control method for the digital active gate drivers, which are installed in a three-phase PWM inverter. The proposed method changes the control parameters of the digital active gate driver according to the instantaneous value of an ac current. This control method enables to shape the gate voltage so as to reduce the surge voltage and switching loss with the consideration of their dependency on the output current. This paper also proposes a new optimization platform for automatically adjusting the control parameters of the digital active gate driver with a three-phase PWM inverter. This method applies the searching parameters for only any duration in an ac current cycle and fixed parameters for the other duration. Thus this method can search the optimal gate drive pattern according to the instantaneous value of the ac current. The validity of the proposed gate control method and the optimization platform were evaluated by using a 5-kW three-phase PWM inverter with digital gate drive ICs. As a result, it is confirmed that the combination of the proposed gate control method and optimization platform can reduce the surge voltage by 23% compared to conventional gate drivers under the condition of the same power loss.
A Multispectral Light Field Dataset and Framework for Light Field Deep Learning

Deep learning undoubtedly has had a huge impact on the computer vision community in recent years. In light field imaging, machine learning-based applications have significantly outperformed their conventional counterparts. Furthermore, multi- and hyperspectral light fields have shown promising results in light field-related applications such as disparity or shape estimation. Yet, a multispectral light field dataset, enabling data-driven approaches, is missing. Therefore, we propose a new synthetic multispectral light field dataset with depth and disparity ground truth. The dataset consists of a training, validation and test dataset, containing light fields of randomly generated scenes, as well as a challenge dataset rendered from hand-crafted scenes enabling detailed performance assessment. Additionally, we present a Python framework for light field deep learning. The goal of this framework is to ensure reproducibility of light field deep learning research and to provide a unified platform to accelerate the development of new architectures. The dataset is made available under dx.doi. org/10.21227/y90t-xk47. The framework is maintained at gitlab.com/iiit-public/lfcnn.
UltraTrail: A Configurable Ultralow-Power TC-ResNet AI Accelerator for Efficient Keyword Spotting

Recent advances in machine learning show the superior behavior of temporal convolutional networks (TCNs) and especially their combination with residual networks (TC-ResNet) for intelligent sensor signal processing in comparison to classical CNNs and LSTMs. In this article, we propose UltraTrail, a configurable, ultralow-power TC-ResNet AI accelerator for sensor signal processing and its application to efficient keyword spotting (KWS). Following a strict hardware/model co-design approach, we have derived an optimized low-power hardware architecture for generalized TC-ResNet topologies consisting of a configurable array of processing elements and a distributed memory with dynamic content reallocation. We additionally extend the network with conditional computing to reduce the number of operations during inference and to provide the possibility for power-gating. The final accelerator implementation in Globalfoundries’ 22FDX technology achieves a power consumption of 8.2 $\mu \text{W}$ for the task of always-on KWS meeting the real-time requirement of 100 ms per inference with an accuracy of 93% on the Google Speech Command Dataset.
A High Performance Feedback Quantized Predictive Control of Induction Machine Drives

This paper proposes a high performance feedback quantized model predictive control (FBQ-MPC) method for induction motor (IM) drives fed by a voltage source inverter (VSI). The proposed FBQ-MPC method includes reference voltage determination, selection of the ’best’ voltage from a Finite Set, and application of this voltage via a nearest neighbour quantizer (NNQ) with a feedback loop around it.The following objectives are achieved by the proposed method: (a) elimination of the use of weighting factors in the cost function, (b) improved tracking performance; (c) improved harmonic performance; (d) reduction of torque ripple; and (e) mitigation of common-mode voltage (CMV). The paper compares the proposed FBQ-MPC method with two classical MPC strategies for IM drives and shows a superior performance. To confirm its effectiveness, the paper includes validation of the proposed method by extensive simulations in Matlab/Simulink, and experiments using T MS320F 28377D platform.