Miracle-dz/UniFAR-FTUs huggingface dataset:
A Boost Half-Bridge LC Resonant DC-DC Converter

A boost half-bridge LC resonant dc-dc converter is proposed in this paper. The proposed converter adds two power switches to the conventional half-bridge (HB) LLC converter, which forms the boost-bridge-arm. This converter can work in high-voltage-gain (HG) mode and low-voltage-gain (LG) mode according to whether the boost-bridge-arm works. In the HG mode, the converter can work with boost energy storage through the boost-bridge-arm, which makes the proposed converter acquire higher voltage gain than the conventional HB LLC converter. In the LG mode, the operating principle is close to the conventional HB LLC converter, but the proposed converter has a smaller circulating current. Finally, a 400V input/180-300V output experimental prototype was built to prove the theoretical analysis.
An Implementation Method of a High-power Harvester from High-voltage Transmission Lines

The current transformer (CT) is an effective harvester for high-voltage transmission Lines. However, when the traditional CT supplies power to the online monitoring equipment, the large-scale fluctuation of the transmission line current will not only lead to insufficient power supply of the CT (when the current is low), but also saturate the iron core of the CT (when the current is high). In view of the above problems, this paper proposes a method for realizing the high-power harvester based on the impedance converter. According to the level of the transmission line current, control the impedance converter works in different models to realize the dynamic adjustment of the output impedance from capacitive to inductive, and ensures that CT maintains stable output voltage and power when the current of the transmission line changes in a large range. At the same time, the equivalent modeling of the CT is carried out through simulation and experiment, which verifies the effectiveness of the scheme proposed in this paper. The CT power density at 60 A is 9.91 W/kg.
Developing Skills for Industry 4.0: Challenges and Opportunities in Engineering Education

This Research to Practice Work in Progress Paper focuses on the Education 4.0 Framework, based on built-in skill development mechanisms and innovative learning pedagogies related to Active Learning approaches. Practicing engineers are increasingly expected to act as boundary spanners who manage and participate in diverse interdisciplinary global teams, leverage cultural differences, and draw on expert knowledge from multiple fields and specialities to translate competing stakeholders' needs into practical design solutions. This demand for a holistic type of engineer stems from the requirements of the Fourth Industrial Revolution: engineers who assume the role of leaders can integrate knowledge and work seamlessly across intellectual, social, and cultural challenges. Our exploratory study aims to identify the implications for practice, challenges, and opportunities in Higher Education. More specifically, we look at two engineering programs of two universities, one located in Mexico and the other in the United States of America. Both universities have implemented massive Experiential and Challenge-Based Learning efforts with industry and external organizations as educational partners to immerse students in real-life contexts. The methodology used was qualitative-experimental, involved more than 120 students, and included a post-test for skills assessment with different instruments such as rubrics, questionnaires, interviews, and surveys. The preliminary findings of this work-in-progress study reveal that the role of Higher Education professors in the training of future engineering professionals within the Education 4.0 Framework has different degrees of complexity related to the cross-border nature of educational innovation, that requires the commitment and alignment of multiple actors: students, professors, organizations, and industry.
A novel approach to Real-time contract based reasoning for Hybrid Systems

Worst Case Execution Time (WCET) analysis of large and complex hybrid systems can be time consuming. Contract based design allows for compositional reasoning of complex systems. Contracts justify the behavior of a system by way of assumptions (which are to be satisfied by the system environment) and guarantees, which are to be met by the system. Contracts also play a major role in compositional reasoning, refinement and re-usability of the system components. In this paper, we present a formal framework to enforce real-time contracts using Hoare triples, for synchronous system design and verification. In that regard, we propose real-time Hoare rules which are based on the WCET of the system and its components. We verify the real-time behavior of the system by applying these rules. These rules not only justify the system behavior and the behavior of its components but their timing as well. We also show that these Hoare rules are sound. Then we show that the synchronous composition of component level Hoare rules based contracts justify a system level contract. This real-time contract composition and reasoning technique which is based on real-time Hoare logic rules is the first ever attempt in synchronous system design and verification.
An any-cell-to-any-cell equalization based on half-bridge CLLC converters for Lithium-ion battery strings

In this paper, a charge equalization converter using a multi-winding transformer is proposed. The proposed scheme achieves the cell-to-cell charge transportation by LC resonant tank and half-bridge CLLC converter. In this operation, adjacent two cells share a LC resonant tank and a half-bridge CLLC converter. Therefore, the number of windings cuts in half in comparison to conventional multi-winding charge equalization converter, resulting small circuit size. To verify the operation of the proposed charge equalization converter, simulation for six lithium-ion batteries stack is given.
Modeling and Analysis of Bridge-Leg Crosstalk of GaN HEMT Considering Staged Effect of Common-Source Inductance

Wide band-gap (WBG) field-effect power devices such as gallium nitride (GaN) devices are being widely used for a better system-level performance as a result of their figures of merit compared to counterpart silicon devices. But they could also suffer from more severe bridge-leg crosstalk issue as a result of higher switching speed and special device features. To suppress the crosstalk phenomenon of GaN-based bridge-leg configurations, modeling and theoretical analysis of crosstalk voltage are necessary. However, the superposition of two noise excitations (di/dt and dv/dt) in time domain and the nonlinearities in capacitance-voltage (C-V) of GaN HEMTs bring difficulties to its modeling and analysis. In addition, as switching speed enhanced, the influence of parasitic inductance cannot be ignored. This paper proposes an analytical model of crosstalk voltage, considering staged effect of common-source inductance. By the physical behaviors of different switching stages, the two excitations are integrated into one-the current through common-source inductor in order to simplify the analysis and bridge direct relationship between two variables. Based on that, an investigation into the impact of circuit parameters on crosstalk voltage is conducted. Besides, the nonlinear characteristic of junction capacitances is taken into account in the model. The proposed model can be applied to guide GaN device selection and PCB design. The accuracy of model and the effectiveness of analysis results are verified.
Unsupervised anomaly detection for multilevel converters based on wavelet transform and variational autoencoders

This paper proposes an anomaly detection scheme for multilevel converters based on a wavelet packet transform and variational autoencoder (WPT-VAE). The wavelet packet transform is used for dimensionality reduction and feature extraction of raw signals. The extracted features are normalized and then sent to the VAE to perform further feature extraction and waveform regeneration. Based on a five-level nested neutral-point-piloted (NNPP) converter, the effectiveness of the proposed method is verified by experiments. The normal dataset is used for model training, while a mixed dataset composed of normal and abnormal data is used for testing. The results show that the proposed WPT-VAE exhibits superior performances in anomaly detection compared with a widely used classification algorithm. Abnormal data can be quickly and accurately distinguished from normal data for early intervention to prevent serious faults, which has good practical value.
The behavior of the pointing vector in the area of elementary polarization singularities

The behavior of the Pointing vector in the area of elementary polarization singularities with one or two C-points, which are bounded by regular shape s-contour is considered. It was shown that the disclinations, which move, are born and annihilate along s-contour, correspond to the singularities of the distribution of the parameters of instantaneous Poynting vector. C-points are associated with the "vortex" kind singularities of the averaged Pointing vector field if the handedness factor and topological charge of C-point are characterized by the different signs. "Impassive" Pointing singularities arise in the area, if the signs are the same. Elementary topology for the Pointing vector field is formulated. The results of the computer simulation are presented.
Power Decoupling Three-Port Converter with Integrated Transformer

This paper proposes a power decoupling three-port converter to integrate the on-board charger and on-board auxiliary power module. A shared switching leg is employed to provide an extra degree of freedom to regulate the output power of different ports. Hence, control decoupling can be achieved even if all ports operate at the same time. In addition, a four-leg integrated matrix transformer is also provided to reduce the losses of the magnetic component. A comparison between the discrete and the integrated transformer is offered, and the employment of the integrated transformers can effectively reduce the core losses under different output conditions. Experimental validation is tested on an integrated 6.6kW+3kW prototype.
Coupled Electro-Thermo-Mechanical Analysis to Understand Fuse Element Ageing by Finite Element Method

This article discusses the ageing of electrical fuses and proposes a novel method for estimating the fuse's lifetime. Fuse ageing is primarily caused by repeated cyclic thermal loads under AC/DC currents. Temperature variations cause the development of thermo-mechanical stresses due to the geometric constraints in the fuse, resulting in damage that accumulates over time. As a result, fuses may blow after a series of current pulses with values that deviate from the original I-t curve. Because of the above, the lifetime estimation of the fuse is essential for improving the reliability of electrical systems. A coupled electro-thermo-mechanical methodology using finite element simulations has been proposed to predict fuse lifetime. For the given pulse current input, the evolution of Joule's heat, temperature, stress, strain, and lifetime prediction has been described. The present study's findings can be used to evaluate the phenomenon of fuse ageing and make recommendations for fuse replacement.
A Unified Model Predictive Control for the Grid Integration of Vanadium Redox Flow Batteries

This paper proposes a unified model predictive control algorithm for a double-stage isolated bidirectional converter interfacing a vanadium redox flow battery with the power grid. The proposed control algorithm relies on a single cost-function optimization routine to control both the grid-side and the battery-side converters. A dynamic model of the battery based on a variable-parameter electrical circuit equivalent is included in the predictive controller to improve the predictions, and thus the control performance, and to explicitly integrate advanced battery management features to fully exploit the potential of the vanadium redox flow technology. The proposed model predictive controller is compared with a conventional PI-based control system to assess its performance. Furthermore, an example of flow rate optimization based on the integrated battery model is presented, which aims at maximizing the system efficiency over a battery charge and discharge cycle.
Multidirectional Gravity-Assist and Active-Following Lower-Limb Exoskeleton Robot for Gait Neurorehabilitation

Gravity-supported gait interactions and active user-participation have promoted the gait recovery for individuals with spinal cord injury or stroke. However, devices or systems that concurrently integrated with these functions have received comparatively little attention. In this paper, a versatile lower-limb rehabilitation exoskeleton robot system with gravity-assist and active-following is developed for the gait training, which enables the subject with motor disorders to walk naturally. The developed system consists of three main components: 1) an active-following walker based on the visual feedback, 2) a gravity-assist cable-driven parallel platform with multiple tension sensors, 3) an eight degree of freedoms (DoFs) lower-limb exoskeleton. Further, the kinematics and dynamics of the whole system are analyzed and a robust adaptive control strategy is designed. Finally, an experiment of the human gait trajectory-tracking is performed to verify the effectiveness of the proposed system.
A Semi-supervised SAR Image Classification with Data Augmentation and Pseudo Labeling

We present a semi-supervised Synthetic Aperture Radar (SAR) image classification method to improve the SAR classification performance with a small number of datasets. To overcome the disadvantage of a small number of SAR data, we apply FixMatch method to SAR image dataset. We further augment data by adding speckle noise to handle the speckle noise in SAR image. By leveraging the augmented data as strongly augmented data in FixMatch method, we effectively learn the network with small amount of labelled data. Experimental results demonstrated that the proposed method outperforms baseline.
Ultra-short-term Forecasting Method of Photovoltaic Power Generation Based on Combined Deep Neural Network

With the aggravation of the energy crisis and environmental pollution, the proportion of renewable energy generation is gradually increasing. Thus, accurate power forecasting is essential for safe access and reliable consumption of the power grid. The network structure of deep learning technology with multiple hidden layers makes it have excellent target data feature extraction ability for massive sample data and has particular applications in power prediction. In this paper, the feature of time series data of photovoltaic power generation is analyzed, and an ultra-short-term forecasting method of photovoltaic power generation based on a convolutional neural network and gated recurrent unit (CNN-GRU) is proposed. The influences of network model input factors, timestep value, and single-step and multi-step forecasting on forecasting results are analyzed. The simulation results of actual data show that the proposed model has good forecasting performance.
Modeling Student Collaboration Network to Enhance Student Interactions

The Covid-19 pandemic, as Henry Kissinger mentions, will not only "forever alter the world order," but also potentially transform the ever-changing higher education world. The recent increase in technological innovations in information, communications, and computer technology has profoundly transformed traditional teaching-learning processes and peer-to-peer interactions for knowledge transfer. One such radical change in technology that researchers are continually working on is motivating collaborative learning and student interactions to improve their learning experiences. Collaborative Learning (CL), where students work in groups to achieve a specific learning objective, can facilitate a deep learning activity that promotes student participation. However, the potential of discussion forums is limited due to their unstructured nature in LMSs like Canvas.We propose and develop a structured discussion forum that can offer a platform to communicate and discuss problems and receive feedback, discuss solutions, and suggestions online. Students who participate in these discussion forums can benefit in multiple ways, including increased class preparedness and more active learning. The twofold objectives and outcomes include 1) analyzing discussion board data to reveal students’ interaction and their degree of participation in the course, and 2) developing a toolset to draw useful inferences from such collaboration networks. Specifically, our schema-based model can help students visualize the discussion board networks creating an engaged learning environment. Furthermore, the model can help draw valuable inferences of the patterns of student interactions and assess student participation and belonging in the course with greater precision.This paper demonstrates a schema-based discussion board model that can allow researchers to collect better-formatted discussion data and more reliable information about the posts, such as the type of posts and the relationships of each post with others. The reimagined discussion boards include the ability to classify discussion posts using various parameters, visualize the posts’ patterns of interactions, identify their relationships with other discussion posts, and precisely evaluate student participation in discussions to monitor the major topics of discussion. We believe that the result of increased participation in discussions with other students will have the effect of increasing students’ sense of belonging to the community of scholars.
A Sequential Network-model Alliance Module for Lithium-ion Battery Temperature Prediction

The lithium-ion battery is an important energy storage means, so the monitoring of its state is very important. In particular, temperature variations of lithium-ion batteries seriously affect their performance and safety. It is necessary to predict the temperature change in advance to take corresponding strategies to prevent the danger occurrence. Both long and short-term memory networks and temporal convolutional networks are effective methods to deal with sequence regression problems. However, the method based on deep learning will reduce its generalization ability when there is overfitting or data change. Therefore, a sequential network-model alliance module (SNMAM) is established in this paper, which combines a thermal model and advanced neural network to improve generalization ability and prediction accuracy. In addition, SNMAM applies an denoising autoencoder to reduce the impact of measurement noise. Complete experiments are presented to verify the effectiveness of the SNMAM designed in the paper.
Investigations in Psychological Stress Detection from Social Media Text using Deep Architectures

Psychological stress is a feeling of mental stress and pressure. Within this context we try to understand the tweets which express psychological stress. Psychological stress detection is a complex task majorly because the stressed community contains mostly introverts. With the increase in people using social-media sites, the information related to mental health of the people extracted from their posts are increasing day-by-day. We use this information to design an AI enabled framework for automatic stress detection. However these data are noisy and complex, therefore deep learning based models are utilized for automatic extraction of features rather than manual extraction of features. Several deep learning based architectures including Multichannel CNN, CNN, GRU, Capsule network and BERT model are explored for solving this task of detecting tweets having mentions about mental stress. Experimental results on a standard Twitter dataset reveal that Multichannel CNN attains the best performance with accuracy of 97.5%, precision, recall and f-score values of 96.8%, 97.5% and 97.2%, respectively.
Design and Parametric Analysis of Dual Mechanical Port Field Excited Flux Switching Generator for Wind Turbine Applications

In this paper, a novel dual mechanical port field excited flux switching generator (DMPFEFSG), which incorporates inner and outer rotors, is proposed for wind turbine applications. The proposed DMPWFFSG is composed of a dual armature single tooth non-overlapped winding and shared toroidal field excitation coils wound on the stator yoke. As a result, a shortened flux path that improves electromagnetic performance is conceived. To further improve overall performance, parametric analysis is performed on geometric parameters based on finite element method (FEM). In comparison with initial design, the analysis reveals that that the inner machine armature flux can be improved by 7.39%, the back-EMF can be increased by 11.74% while cogging torque ($T_{cog}$) can be suppressed by 31.51%, whereas for the outer machine armature, the flux can be increased by 61.95%, the back-EMF can be increased 41.88% while $T_{cog}$ can be suppressed by 36.63%.
RaRWS: A Radar-assisted Real-time Water Segmentation Network to Meet the Autonomous Navigation of USV in Inland Waterways

Water segmentation is essential for autonomous navigation of Unmanned Surface Vehicles (USV) in inland waterways. However, most of the existing methods are suitable for maritime environments. Due to their poor real-time performance and a high false-positive rate of water surface reflection and water-sky interference in inland waterways, we propose a radarassisted real-time water segmentation network (RaRWS) to solve the above problems. We obtain pseudo-waterlines based on radar data by deleting points, fitting them multiple times, and generating a radar mask. While simplifying the encoder backbone network, we use the Attention Refinement Module (ARM) to fuse radar masks to improve detection and waterside accuracy. In addition, a Feature Fusion Module (FFM) is introduced to help the decoder fuse high- and low-level features and further fuse radar data. RaRWS is tested in both normal and bad weather. The results show that RaRWS can achieve higher performance compared with the current state-of-the-art methods (F1 is above 99%), while gaining real-time performance (41.3fps).
Active-Relay-Based Wireless Power Transfer System With Efficiency Optimization Over Load Variation

To achieve an efficiency improvement for a threecoil wireless power transfer (WPT) system with the load variations, an active-relay-based WPT system has been proposed. In the proposed system, the relay coil is in the same plane as the transmitting coil with tightly wound, which can be regarded as part of the transmitting coil without affecting the transmission distance. In order to realize a continuous impedance matching of different load conditions, the switchingcontrol capacitor (SCC) is used in the relay coil, which makes the relay coil an active structure. A collaborative control method is proposed for SCC to obtain the maximum efficiency of the system with load variations. Moreover, the additional losses of SCC have been analyzed. Finally, the effectiveness of the proposed system is verified experimentally.