Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Resonant Dynamics and Voltage Balancing of Multiphase Coupled Inductor FCML Converters

This paper systematically investigates coupled inductor balancing of flying capacitor multilevel (FCML) converters. Coupled inductors synergize with FCML converters by reducing inductor current ripple, reducing switch stress, and providing stronger and faster balancing of flying capacitors, including in the presence of disturbances, parameter uncertainties, and circuit asymmetries. The multi-resonant balancing dynamics of multiphase FCML converters are explored, connecting the balancing speed, frequency, and initial conditions. Simulations and experimental results are used to evaluate the effectiveness of coupled inductor balancing. Monte Carlo methods are employed to demonstrate how coupled inductors provide robust balancing even with varying parameters and disturbances.
Comparison and Improvement of ZVS Operation Under Different Modulation Strategies for Modular Multi Active Bridge Converters

Modular multi active bridge (MMAB) converter, which is the core of high-frequency-link-based power electronic transformers, can provide multiple dc ports for flexible interconnection among regional power grids with different voltage levels and forms. Besides merits of galvanic isolation and exceptional scalability, MMAB converters have inherent zero-voltage-switching (ZVS) turn-on operation under phase-shift modulation, which substantially reduces switching losses and leads to high efficiency. However, ZVS characteristics of the converters cannot be guaranteed under traditional single phase-shift modulation. Therefore, modulation techniques with additional control degrees of freedom, such as common phase-shift and universal phase-shift modulation strategies, are introduced to improve the ZVS performance of the converters over a wide operation range. A comparison of ZVS operation under diverse modulation strategies is analyzed in detail, and experimental results with tested efficiencies under extreme conditions have verified the ZVS analysis and improved effects.
Adaptive Course Tracking Control of an Unmanned Surface Vehicle with State and Input Quantization

This paper considers the course tracking control of an unmanned surface vehicle with state and input quantization, aiming to saving communication resources and improving system efficiency. The radial basis function neural network (RBF - NN) is used to approximate the model uncertainty. A linear analytical model is used to describe the input quantization process. The adaptive neural network quantization controller does not need the prior information of quantization parameters. Then the stability of the unquantized system and the boundedness of the quantification error in the closed-loop control system are proved by means of some lemmas. On this basis, the stability of the designed system with state and input quantization is proved. Finally, the effectiveness and feasibility of the proposed algorithm are verified by simulation experiments.
Selection of HV SiC MOSFET Modules for MV-CHB for Distribution System Applications

Distribution system applications based on power electronics, including energy storage systems and STATCOMs, are experiencing rapid growth. These applications traditionally depend on bulky line-frequency transformers to connect with medium-voltage (MV) distribution systems. Alternatively, the MV cascaded H-bridge (MV-CHB) converter offers a transformerless solution by utilizing series-connected cells. The number of cells required is inversely proportional to the dc-link voltage of each cell. Utilizing high-voltage (HV) silicon carbide (SiC) MOSFET power modules reduce the requirement for a large number of cells. With the possibility of several options for these HV modules, an extensive comparison is essential to select the most suitable module for a particular application. Consequently, an algorithm is proposed to investigate and evaluate various 13.8 kV, 8.5 MVA MV-CHB converter designs. The comparison is based on designer-selected figures of merit (FOMs), particularly system power losses, volumetric power density, gravimetric power density, and system power complexity. To classify these MV-CHB designs, a multi-attribute decision-making (MADM) method is employed, considering the relative importance of each FOM. Based on this analysis, it is determined that the 3.3 kV and 6.5 kV SiC MOSFET-based MV-CHB designs exhibit the most favorable performance according to the selected FOMs.
Logical IoT Cloud - Integrated Systems Management for Cloud and IoT

Cloud has been adopted for many production systems and becomes a common IT infrastructure for production systems. IoT applications provide services based on sensors and actuators that are interconnected between cyber and physical spaces. The integrated systems management for Cloud and IoT is required to manage IoT resources as well as CPU, Memory, Storage, and Network. Systems management for IoT resources needs to support the same level as ordinary cloud resources. This paper proposes the integrated systems management for Cloud and IoT called Logical IoT Cloud (LIC). LIC enables Logical Sensors (LSs) with multiple virtual sensors and Logical Actuators (LAs) with multiple virtual actuators. They can act as the same functions as physical sensors and actuators in cloud-native environments. LIC can support multiple IoT applications that require receiving sensor data and sending physical actuators the control commands for production usage.
Using weather-based machine learning approach to estimate retail sales and interpret weather factors

We constructed a model that estimates consumer spending (retail sales), a key determinant of underlying economic trends, based on a machine learning model using weather data as an attribute. We also used our estimate model to analyze the impact of temperatures and rainfall on our sales estimates based on the SHAP values we calculated. Our results confirmed the effectiveness of weather data as an attribute in estimating retail industry food & beverage sales. We also identified patterns where weather has a nonlinear impact on sales. For example, lower rainfall results in higher estimated sales, but a moderate increase in rainfall begins to depress sales. Similarly, during extremely cold periods, temperature depresses sales, but a moderate increase in temperature contributes to higher sales.
U-net Model with Particle Swarm Optimization for Floating Raft Information Extraction

Extracting the sea area of Marine floating raft culture is contributed to the rational use of aquaculture resources. The availability of remote sensing technology can dynamically detect aquaculture rafts. As a classical semantic segmentation model, U - net can also be beneficial to extract the synthetic aperture radar images of marine cultivation. However, the parameters of U - net are customarily set by experts based on experience and cannot be changed dynamically. In this paper, a method for flexibly designing U - net model parameters by particle swarm optimization is presented. Treat individuals with diverse parameters as a swarm and randomly initialize the encoded information, allowing each individual to apply appraisal indi-cators for fitness evaluation. Implementing particle updates and iteratively improving model parameters in interactive learning between individuals and populations. The experimental results demonstrate that the model can obtain competitive outcomes on multiple evaluation indicators after parameter optimization.
Utilizing a Micro-Fluxgate Magnetometer in Dual-Path Configuration for Fast Switch-Current Sensing

There has been a growing interest in hybrid current sensors since they can combine a DC/low-frequency detector with a high-frequency current transducer to cover an ultrawide range of frequencies. Hybrid current sensors are used in a variety of applications, such as power electronics, motor control, and medical technology. They are also used for safety monitoring and power quality measurements. As a result of their size/bulkiness, EMI immunity, intrusiveness (or the introduction of parasitic elements and losses), nonlinearity (due to magnetic core saturation or temperature drift), cost, accuracy, and others, many of these devices are not appropriate for measuring switching currents (particularly in wide bandgap switches). In order to overcome the problems mentioned above, the present work combines an on-chip fluxgate magnetometer and a Rogowski coil embedded on the same PCB. Both of these sensing elements have the ability to measure the current accurately and have low parasitic elements and losses. This combination results in a compact, inexpensive, and accurate current measurement system. As the low-pass-filtered fluxgate element measures current from DC to around 16 kHz, the embedded Rogowski coil is underutilized for sensing higher frequencies starting from 16 kHz to tens of MHz. Based on a set of power electronics converter experiments, their added output provides superior performance for capturing the switching current waveform of wide bandgap semiconductors.
Optimal Allocation of D-STATCOM Devices with Improved Quantum Brain Storm Optimization in Hybrid Code

In this paper, a method is proposed to determine the optimal allocation of D-STATCOM with Improved Quantum Brain Storm optimization (IQBSO) in distribution networks. The use of distributed energy resources (DERs) such as renewables is well-spread in distribution networks so that uncertainties increase significantly. As a result, distribution system operators (DSOs) need to deal with such uncertainties by applying Distribution Flexible AC Transmission System (D-FACT) devices to the networks. This paper proposes a method for determining the location and output variables of D-STATCOM devices of D-FACTS. A nonlinear Robust optimization is presented to handle the uncertainties. As an optimization method, this paper proposes IQBSO that is an extension of Quantum Brain Storm optimization (QBSO) in Evolutionary Computation. Also, this paper introduces hybrid code into IQBSO to enhance the solution search process. The proposed method is successfully applied to the 69-node distribution network.
109.9-dB DR and 17.9-Bit ENOB Dynamic Zoom CDC Design for Audio Applications

Capacitive sensors are one of the most widely used sensors today. In recent years, High resolution has assumed a paramount role in sensor design, guaranteeing precise measurement outcomes. Quantification to the aF level is urgently needed. This paper presents the first dynamic Zoom capacitance to digital converter (CDC) incorporating noise coupling (NC) technology, which is intended for high-precision sensor applications in the audio domain. The proposed dynamic CDC is a hybrid of a high-resolution $\mathbf{\Delta}\mathbf{\Sigma}$ modulator $(\mathbf{\Delta}\mathbf{\Sigma}\mathbf{M})$ and a high-efficiency SAR-based CDC to achieve better performance. It is implemented in the TSMC 180 nm process, and the simulation results show that it achieves 109.9dB DR and 109.3dB SNR within the bandwidth of 20kHz, consuming $\mathbf{81}\boldsymbol{\mu}\mathbf{W}$ from a 1.2V supply. These advancements are accomplished through the combined utilization of coarse and fine conversions, noise coupling techniques, SAR-assisted multi-bit quantizers, and operational transconductance amplifiers (OTAs) based on dynamic bias inverters.
Real-Time Deep Learning Framework for Dermatology Image Classification on Low-Power Embedded Devices

The utilization of deep learning (DL) in medical research and industry has witnessed substantial growth in recent years. A pivotal application involves employing DL for dermatology image classification tasks. However, the major challenges in such tasks, in terms of the scarcity and bias of high-quality labeled data, significantly hinder further advancement in this domain. Such data insufficiency gives rise to concerns regarding accuracy disparities across different demographic groups, which may ultimately lead to unfair outcomes. Additionally, complex and effective DL models are often unsuitable with low-power embedded devices, which hinders their usability in resource-limited environments. In this paper, we propose a DL framework to address these issues. Our major approach involves augmenting data with Gaussian white noise to generate synthetic data samples and employing knowledge distillation techniques to transfer valuable knowledge from a larger and more complex model to a smaller and more efficient counterpart. Through comprehensive experimentation on an open-access skin disease classification dataset, we demonstrate that our proposed framework significantly enhances the performance of DL models on low-power embedded devices, thereby optimizing the trade-offs among overall accuracy, fairness for different demographic groups, and inference latency on low-power embedded devices.11Our code and experiments can be reproduced by utilizing the details provided in the Methodology section on image preprocessing and augmentation, model architecture, and training configurations. (https://github.com/yixinli19/Dermatology-image-classification). The MobileNet V3 is available at(https://pytorch.org/vision/stable/models/mobilenetv3.html). The Swin Transformer V2 is available at (http-s://pytorch.org/vision/stable/models/swin_transformer.html). The dermatology image dataset is available upon request from ESFair 2023. They can be reached at: https://esfair2023.github. io/ESFair/.
Net Load Forecasting Based on Residual Attention CNN-BiLSTM Model

Net load forecasting is a crucial task in power system operation and energy management. Accurate prediction of net load helps power companies optimize their generation plans, enhance energy utilization efficiency, and ensure a balance between power supply and demand. To improve the accuracy and precision of net load forecasting, this study proposes a solution based on the Residual Attention CNN-BiLSTM (RA-CNN-BiLSTM) model. The proposed model combines Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) and incorporates the residual attention mechanism to capture important features and temporal relationships in net load forecasting. Experimental comparisons and evaluations demonstrate that the RA-CNNBiLSTM model achieves lower prediction errors in net load forecasting and outperforms other models significantly.
Simulation and experiment of microstrip lines temperature rise based on COMSOL Multiphysics

Conductor loss and dielectric loss of the microstrip line can lead to temperature rise and limit the power capacity of the microstrip circuit. This involves the interaction (coupling) between the electric and temperature fields. In this study, the factors affecting the temperature rise of the microstrip line were analyzed by means of the parallel plate model. In addition, based on COMSOL Multiphysics simulation software, the influence of dielectric dissipation factor tanδ of microstrip lines of FR4 and RO4350B on the temperature rise per unit input power (TRPIP) was simulated. The simulation results under the set basic parameters show that the TRPIP of the strip conductors of the FR4-substrate microstrip lines with tanδ of 0.02 is about 4.8 °C, and that of RO450B-substrate microstrip lines with tanδ of 0.0037 is about 1.38 °C. Finally, in order to verify the simulation results, a non-invasive measuring device with an infrared camera was constructed to measure the heat distribution on the surface of the microstrip line.
A Driving Direction Estimation Algorithm for Obstacle Avoidance of a Wheel-less Snake Robot

This paper describes a driving direction estimation algorithm of a wheel-less snake robot and verifies that the algorithm allows the robot to locomote as desired in ROS-based simulation. Snake robots have serious operational difficulties since their driving directions are not intuitive. Therefore, a driving direction should be estimated for autonomous locomotion, such as path tracking and obstacle avoidance. A driving direction estimation algorithm based on the kinematics of the snake robot is used with path planning and tracking algorithms. The suggested driving direction estimation algorithm allowed straightness improvement and path tracking in the simulation environment.
Inverter Controls For Smooth Transition Operation And Voltage Balancing Of Networked Microgrids

With increasing penetrations of distributed energy resources (DERs), researchers have extensively studied the formation and networking of microgrids to improve reliability during distribution system outages. However, these operations might require DERs in the microgrids to switch between grid-following (GFL) and grid-forming (GFM) modes of operation. A smooth transition between the operation modes should be ensured to have a stable system operation and to avoid nuisance tripping. Additionally, as we network microgrids, inverter control schemes with voltage-balancing controls are needed for unbalanced distribution networks. This paper presents the design of a GFM and GFL capable inverter control scheme for reliable operation in networked microgrids with mechanisms for 1) smooth transition between the GFL and GFM modes, and 2) negative sequence voltage control for voltage balancing. Using electromagnetic transient (EMT) simulations, the control scheme is first tested on a simple two-source power system using a digital real-time simulator. Next, a real-world distribution feeder with unbalanced loading and multiple DERs is used to demonstrate stable operation during the networking of microgrids with voltage balance support.
A Heuristic Sparsification Method to Improve the Performance of Neural Networks on Autism Spectrum Disorder Identification Tasks

Deep learning (DL) has been widely used in the detection of neurological diseases such as Autism spectrum disorder (ASD). Most studies in this field manually designed and adjusted the hyperparameters of DL models, but due to the huge search space, few studies have attempted to optimize the topology of neural networks which is another important factor related to the performance of DL models. This paper proposes a heuristic sparsification method named the Evolutionary Connection Sparsification Algorithm (ECSA) which is used to optimize the topology of fully connected neural networks (FC-NNs) and generate the corresponding sparsely connected neural networks (SC-NNs). The algorithm is based on the workflow of the genetic algorithm but with improved coding strategy, fitness evaluation approach, crossover and mutation operator, and including an effective convergence acceleration strategy. Experiments show that the SC-NNs generated by ECSA significantly outperform the original FC-NNs in terms of accuracy, F1-score, and AUC, and the model complexities are greatly reduced.
Structural Feasibility and Electromagnetic Analysis of an Ironless Rotor Axial Flux Permanent Magnet Synchronous Machine

This paper presents an electromagnetic and structural study of an ironless-rotor axial flux permanent magnet synchronous machine with two different materials and manufacturing techniques. The machine features a disc-type non-ferromagnetic structure with a double-stator and a single rotor configuration. The rotor core is originally made of polyamide (PA) 6 and the permanent magnets (PMs) are bonded on the rotor disc. Results indicate that the use of PLA should be preferred over PA6 for use in the coreless rotor structure obeying thermal reasons, since it exhibits higher mechanical resistance in several operating points.
Alzheimer's Detection Using Demographic Information with a Combined Approach of Neural Network and Grid Search CV

Around the world, many are suffering from Demen-tia. It is mainly due to the neurological disorder and the onset of Alzheimer's Disease. Numerous studies have been conducted on its biomarker, Magnetic Resonance Imaging (MRI). Over the years, many algorithms, such as Neural Networks, Support Vector Machines, and various other Machine Learning algorithms, have been implemented to classify Alzheimer's Disease (AD) or Normal Control (NC) from the input MRI images. This paper focused on an integrated framework that achieved 92.26% accuracy in differentiating AD from NC using a Neural Net-work (NN), Grid Search Cross-Validation with Random Forest, and a few demographic factors related to Alzheimer's disease. The accuracies of the other three models-NN with Principal Component Analysis (85.32%), NN with t-SNE (82.57%), and Convolution Neural Networks (77.06%)-have been compared with it. Finer results than all were displayed by the suggested model. As a future extension to this model, we can install different IoT sensors on home appliances and attach them to AD patients themselves to continuously collect heterogeneous data from their behavioural patterns of daily activities and analyse them in the cloud to trigger other alarm sensors for family members or health workers when the AD patients are alone at home. IoT sensors can help confirm the sickness and protect the patient. Hence, the transformation of current healthcare facilities from hospital-centred to patient-centred is available.
Design of Filtering Power Amplifier Based on a Novel Band-pass Impedance Transformer

This article presents a novel band-pass filtering impedance transformer (BFIT) with the transformer ratio from 0.1 to 10. This BFIT is composed of two stepped impedance resonators (SIRs) which are cascaded and electric coupled. The analytical formulas and design steps of BFIT are given, and the impedance transformer (IT) is used in the design of a band-pass filtering power amplifier (PA). Finally, a 2.31-2.36 GHz band-pass filtering PA with measured drain efficiency of 54%-64% and large-signal gain of 11.2-11.5 dB is designed, fabricated and measured. Besides, the designed PA shows a good band-pass filtering characteristic, and achieves out-of-band suppression of 25 dB and 53 dB in the upper and lower sidebands.
Exploring the Efficacy of Deep CNN Algorithms for Pulmonary Infections Detection using X-rays: Case Study COVID-19

This paper explores the efficacy of various deep convolutional neural network methods for detecting and diagnosing COVID-19 induced pulmonary infections using X-ray images. The study involves training and testing different deep learning models on a consistent dataset. Model performance is evaluated based on accuracy, training time, and complexity, providing valuable insights into their suitability for this critical medical application. To ensure the robustness of the trained models, typical challenges arising from improper dataset handling, such as overfitting and underfitting, are addressed and mitigated through appropriate dataset splitting techniques. This approach enhances the reliability of the results and improves the generalizability of the models. The results show a 96% accuracy using ResNet50V2 model. Furthermore, a diagnosis App based on ResNet50V2 model is developed. This application serves as a practical diagnosis tool, facilitating the early detection and diagnosis of COVID-19 induced pulmonary infections.
First Results of DVB-S Based Passive Polarimetric Measurements of micro-Doppler Signatures of a Helicopter

The utilization of space-based transmitters as Illuminators of opportunity (IoO) offers many desirable characteristics for passive radar applications: wider, continuous and all-weather coverage including remote and offshore areas, higher reliability against natural disasters or physical attacks, and less sensitivity to multipath effects than terrestrial-based transmitters. DVB-S (Digital Video Broadcasting-Satellite) represents a widely used transmission standard enabling satellite-delivered broadcasting for digital television and radio services from geostationary orbits. As IoO for passive radar applications it offers comparably high bandwidths and provides dual polarized transmissions, which opens new possibilities in polarimetric passive radar applications. This paper describes the results of the deployment of such a radar for the close range surveillance of ground and air-traffic on the Frankfurt International airport.