Miracle-dz/UniFAR-FTUs huggingface dataset:
PCB-Based Magnetics Integration and Common-Mode Noise Suppression for A High-Frequency PFC

Magnetics with embedded windings in printed circuit board (PCB) reduces labors in power-supply manufacturing and enables implementation of advanced electromagnetic interference (EMI) suppression techniques. However, planar inductors with embedded PCB windings could potentially suffer higher winding loss due to the accumulation of magnetomotive force (MMF) and the skin effect, compared to conventional litz wire-based designs. One approach to reduce winding loss is to interleave windings of different phases in a form of coupled inductor. In this way, however, the available coupling coefficient is usually constrained to a certain high level that limited by the specific winding structure. This paper proposes a winding interleaving approach that extends the lower limit of coupling coefficient to zero while still maintaining the benefits of winding interleaving. Furthermore, balance technique is integrated with PCB winding-based inductors for common-mode (CM) noise suppression. A 2.2 kW GaN-device based totem-pole power-factor correction (PFC) converter is demonstrated as an example. The proposed PCB-based inductor structure enables a peak efficiency of 99% and a $20 \ \mathbf{dB}\mu\mathrm{V}$ CM noise suppression in experiment.
ARDL-IDS: Adversarial Resilience in Deep Learning-based Intrusion Detection Systems

With the growing complexity of computer networks and malicious attacks, countermeasures for prevention, detection, and protection are in high demand. Intrusion Detections Systems(IDS) have shown a lot of potential in detecting these attacks, but an effective and adaptive IDS that can be scaled and updated systematically is essential, and the efficiency of deep learning methods for the same has been rapidly increasing. ARDL-IDS identifies the need for a high-performing network intrusion detection system that sustains itself in the rapidly growing network environments and uses Deep Learning techniques to help in handling the volume and variety of these intrusions that are prevalent. The KDDCUP'99 dataset is used to classify the various types of attacks using a Deep Neural Network(DNN). To prevent possible attacks on the trained model, the system is made more robust through adversarial training with FGSM, BIM, MIM, and PGD attacks.
Array Faulty Element Diagnostics by Few Phaseless Data and Convex Optimization

The problem of detecting defective turned-off elements in antenna arrays from amplitude-only data is addressed.Commonly used antenna diagnostics methods, such as the back transformation method (BTM) and the matrix method (MM), exploit amplitude and phase field data. Here, instead, the diagnostics is cast as the recovery of a real (binary) signal from few amplitude measurements of the near-field. Therefore, it is basically a phase retrieval problem.Taking inspiration from the recently introduced PhaseMax algorithm, the phase retrieval is formulated as a convex optimization. In particular, PhaseMax is adapted to the faults detection problem by removing the need to estimate a reference solution. Moreover, it is shown that the convex optimization is equivalent to a sparse minimization problem which allows to employ all the powerful tools of compressive sensing realm.Preliminary numerical results are presented to assess the achievable performance as the number of faulty elements increase. Finally, a strategy that reduces the number of measurement points by employing steering diversities is presented and checked.
Transient Thermal Modeling of Power Semiconductors for Long-Term Load Profiles

This paper presents an improved thermal analysis method focusing on the long-term profiles of power modules. A shift-invariant dictionary method is developed to extract the critical atoms to sparsely represent time series signals. Then the temperature response of the power module can be approximated by a sparse linear combination of basis functions. The performance of the proposed method is compared with the convolution method, the result indicates that the transient junction temperature of the power module can be predicted with less computational effort. The simulation and experimental results also prove that the proposed method can effectively reconstruct the thermal behavior, exhibits good predictive capability and is suitable for temperature prediction under long time scales.
On the Fault-Ride-Through Dynamics of Grid-Forming Converters — A Multi-dimensional Adaptive Inertia Approach

Grid-forming (GFM) converters are gaining momentum as one of the keys to enabling large-scale integration of distributed energy resources (DER) with the grid. Among the desired functionalities, GFM converters must demonstrate a stable fault-ride-through (FRT) behavior, i.e., during and after a fault is cleared in the system. As GFMs exhibit voltage source behavior, current needs to be limited under fault conditions. In addition, the GFM control should ensure that the load angle does not go into an unstable region. Also, once the fault is cleared, the converter should return to the nominal operating point swiftly. Analysis of GFM during and after faults reveals a dichotomy in the inertia required for a smooth and stable FRT. Consequently, this paper proposes a multi-dimensional adaptive inertia function for GFM converters to increase the critical clearing time while reducing the post-fault recovery/resynchronization time. The efficacy of the proposed method is analyzed in MATLAB/Simulink EMT simulations using a virtual synchronous machine GFM controller as the benchmark control scheme and comparing it with a fully adaptive inertia/damping GFM controller.
High Power Density Three-Phase LLC DC/DC Converter with Coupled Resonant Inductor and Wye-Delta Transformer for Aircraft Applications

In applications with a high step-down voltage as in aircraft applications, finding a topology able to achieve high power density and high efficiency while begin EMI-friendly is crucial. Gallium Nitride devices enable high frequency switching, allowing the shrinkage of the magnetic components. The drawback however is the lower efficiency due to switching losses and electromagnetic interference constraints. The focus of this research work is the evaluation of a Three-Phase LLC converter, which, due to its inherent step-down nature (wye-delta transformer), integration capability, low input and output current ripple and high efficiency (ZVS on the entire load conditions), may be a promising solution for a high power density and modular DC/DC converter. Specifications in avionics are very challenging and include a wide input voltage range (235–300 V), a large step-down to the fully controlled 28 V in a half-brick form factor (47 cm3) for a nominal output power of 1 kW leading to a power density of 20 kW/L.
Hexagonal Split Ring Resonator for 5G Applications

This research design presents microstrip-patchantenna(MPA) for many shapes of antenna, in this paper with and without cuts ofHexagonal Split Ring Resonator at24.5 GHz for 5G applications. The substrate, FR4, has a relative permittivity of 4.4 and a height of 1.6 mm, respectively used in Microstrip feed. The designs are simulated using Ansys HFSS Software, and the antenna parameters, such as gain, SII& VSWR, band width, and directivity, have been researched. According to the results, when the number of rings increases, resonance is attained at the high of gain with a bandwidth ranging from 223. 2MHz to 1021. 5MHz at the very least.
Dual-Band Metal-Only Antenna Combining Reflectarray and Reflector Functionalities

This paper presents a dual band metal-only antenna that operates at 20/40GHz. The proposed metal-only antenna combines the functionalities of reflectarray and parabolic reflector antennas. The unit cell consists of a square waveguide with short circuit termination and a square metallic block in the center. At 20GHz, the reflected beam is determined by a parabolic surface. At 40GHz, the reflected beam is controlled by the phase distribution of cells in the antenna. The gain increases with frequency in the lower frequency band, which is similar to what happens in a parabolic reflector antenna. The aperture efficiency at 20/40GHz is about 22.99/43.79%.
Novel High-Frequency Transient Techniques in Non-Intrusive Load Monitoring Techniques

The steady-state power signatures have the characteristics of addition and variation when the loads are operated at a simultaneous time and the system voltage changes, respectively. Therefore, it is difficult to identify individual loads in a steady-state system. The Discrete Wavelet Transform (DWT) has the characteristics of processing unstable and transient signals, analyzing time-frequency domain signals, and analyzing load signal characteristics. This paper proposes the analysis of DWT as a feature extraction technique for load identification. The proposed methods can improve the accuracy of load identification, shorten the computation time, reduce the memory sizes occupied by data processing, and overcome the difficulty of using steady-state features for non-intrusive load monitoring (NILM). This paper develops high-frequency transient sampling technology and cooperates with machine learning algorithms to establish a high-accuracy NILM model.
Direct Digital Control and Capacitor Current Compensation to Improve Grid-Current Distortion for Three-Phase Three-Wire LCL Grid-Connected Inverter Under Distorted Grid Voltages

Grid voltage is always distorted which results in grid-current distortion when there is no control at the capacitor current. This paper presents the grid-current improvement with direct digital control and capacitor voltage feedforward (CVF) for 3Φ3W LCL grid-connected inverter under distorted grid voltages. With the inverter-side current feedback, the simple direct digital control can cover inductance variation and determine the control laws to track inverter-side inductor current, achieving robustness. A simple hysteresis repetitive predictor with finite impulse response low-pass filter and compensator term is introduced to the CVF loop which can stabilize the system and compensate the distorted grid current with effective control. Therefore, the overall control system becomes simple, robust and effective. The virtual impedance method is adopted to confirm the stability when the filter inductances drop and control parameters vary. Simulated and experimental results from 12 kW are used to verify the theoretical analyses and discussions.
Demand Response Planning via Home Energy Management System for Residential Aggregators in a Virtual Power Plant

This paper proposes a demand-responsive virtual power plant framework to address the energy management issues in distribution networks, considering the lack of distributed energy resources among apartment residents. In the proposed interaction mechanism, a price-based demand response strategy is utilized to develop load scheduling for residential aggregators, thereby achieving the goal of peak shaving and valley filling of the power system. Bi-level optimization problems are solved via bilevel particle swarm optimization for aggregation agents and users, separately. With the help of agents, load schedules reduce the peak-valley difference. For evaluating the effectiveness of the proposed framework, experiments are carried out for 135 loads in 15 houses from a publicly available dataset. The results show load scheduling succeeds in demand response.
Millimeter-Wave MIMO Antenna With Decoupling Structures for Isolation Enhancement

MIMO antenna of 4 elements with decoupling structures is presented for millimeter-wave applications. The antenna consists of radiating elements and decoupling structures such as defective ground plane and metasurfaces individually or combined. First, an optimized defected ground surface (DGS) is introduced to improve the isolation over 60-75 GHz (22%) bandwidth with an inner edge separation of 0.55 mm (0.13λ0) at 70 GHz. Furthermore, metasurface realized by split ring resonators at 0. 6λ0 from the antenna elements improved the isolation of the 4 elements by a maximum of 25 dB. The proposed antenna’s gain, efficiency, and surface currents are compared to the initial 4 elements to verify the proposed decoupling technique for MIMO applications.
Summary of Research on Key Technologies of 5G Base Station Flexible Resources

As a key technology of the fifth-generation communication technology, 5G base stations bring high-speed communication and high electricity costs. The current development situation of 5G base stations is the first part of this paper, which focuses on the regulation potential of the flexibility resources of 5G base stations. The results of some researchers' studies are then provided in terms of how the base station uses its flexibility resources and how those resources are used in other contexts. Lastly, it is anticipated that technical innovation and the application of 5G base station flexibility resources will benefit both 5G base station operators and grid operators.
Contact Resistances to n- and p-type 2D Semiconductors MoS2 and WSe2 with Moire Lattice Interfaces

We show how Moire lattices between metal contacts and n-type MoS2 or ambipolar WSe2 layers can favor physisorptive sites with weaker Fermi level pinning rather than chemisorptive sites. This distinction causes a sharp difference in Schottky pinning factor, S. S for physisorbed interfaces is ∼0.36, while S is a strongly pinned 0.21 for chemisorbed sites. Thus, Ef lies close to the conduction band for physi-In or Ag on MoS2, while Pd lies near the valence band for p-type WSe2. This explains the experimental results for In, Ag and Pd contacts.
Smart Agro-Industrial Monitoring System Using Multi-Sensors and ESP-NOW Protocol

The multisensor-based embedded system monitors a few sets of sensing parameters continuously and simultaneously with different data rates. These systems are highly required for remote monitoring. Constantly monitoring multisensor data leads to a high energy budget, limiting resource-constrained device usage. Hence, it is necessary to develop an energy-efficient signal quality-aware wireless sensor network system for remote monitoring and controlling Agro-Industrial based applications. A data logger for a wireless sensor network is designed using ESP-NOW protocol for collecting data from multiple sensors. ESP8266 is used for transmitting and receiving the data. The challenges in agro-industrial applications may vary with time, so this needs monitoring and leveling them. Collecting the data from the sensors and applying compression techniques minimizes cost, power consumption, speed, and latency. Run-Length Encoding (RLE) and K-RLE compression techniques are used and they are compared.
Multi-PET Cooperative Autonomous Navigation Based on Multi-agent Deep Deterministic Policy Gradient

Monte-Carlo(MC) Methods and Temporal-Difference(TD) modeling techniques are often employed for predicting the charging load of conventional electric cars. In solving discrete and low-dimensional state and motion space variables, they have obtained outstanding results. However, when confronted with the travel characteristics of a plug-in electric taxi (PET) in a more complex environment, the absence of commercial competition between taxis (including commercial electric vehicles with the same travel characteristics, such as online ride-hailing) frequently leads to poor convergence and inaccurate prediction. To address the aforementioned issues, a Multi-Agent Deep Deterministic Policy Gradient (MADDGP) strategy reinforcement learning(RL) method was presented to mimic PET charging decision. The simulation results demonstrate that the approach is more accurate at predicting load and can converge to the Nash equilibrium point with full information even in an environment with imperfect information.
Antenna Array Based on 3D-printed Plastic BoR Elements Coated with Conductive Paint

The body-of-revolution (BoR) Vivaldi arrays are usually all-metal, including both antenna elements and the antenna base. However, despite the robustness and low losses of the all-metal designs, they are often heavy and expensive. In this paper, we investigate 3D-printed, dual-polarized BoR antenna elements operating at 2±6 GHz. By 3D printing the antenna elements from plastic, we can make them less expensive, hollow, and thus lightweight. The elements are made conductive by painting them with silver paint. The performance of the antenna array is evaluated with simulations and measurements. Due to the relatively low conductivity of the paint, resistive losses of the array are higher than those of the all-metal counterparts. Nevertheless, the measured total efficiency of the array is still high, 95% at the highest, which confirms the applicability of the proposed manufacturing technique. Furthermore, we compare the proposed design to a previously published inverted BoR antenna array.
Achieving Full Azimuth Coverage in a Planar Lens Antenna

In this paper we present a lens antenna that can scan in the whole azimuthal plane. This is achieved by using two planar generalized Luneburg lenses that are connected through multiple 3 dB 90 degrees hybrid couplers. The antenna is designed for the 3GPP n260 band operating from 37 to 40 GHz making it suitable for indoor 5G coverage. Simulation results of the antenna are presented to illustrate that it can scan in the full azimuth plane.
Dot Product of Confocal Images for Portable Radar-Based Microwave Imaging

Objective: The purpose of this study is to develop accurate radar-based microwave imaging. Method: A portable radar-based detector is placed on an excised breast tissue of total mastectomy. Measurements are conducted twice with different orientations for the breast. A two-stage rotational clutter suppression (TSR) algorithm is carried out for the multi-static rotational configuration of the radar-detector. It consists of a signal selection and an adaptive filtering. Dot product is carried out for the two sets of confocal images measured at different orientations to remove clutters. Conclusion: The dot product of two confocal images with different orientations can reconstruct more accurate images than conventional confocal images. It can improve not only image contrast but also the accuracy of tumor location.
Development of computer vision based assistive software for accurate analysis of autistic child stereotypic behavior

Many children with autism most frequently exhibit stereotypical movements (SM) such as arm flapping, spinning, body rocking, etc., which are controllable at early stages with proper detection and treatment procedure. Though, the detection of the SM relies on careful observations and time consuming manual quantification, an intelligent assistive diagnostic tool to capture the SM could aid the therapist for better provision of treatment. This study presents a stereotypic movement detection and analyzing (SMDA) software that apprehended SM of selected body part or whole body in terms of intensity and frequency. The SMDA is powered by (i) computer vision (CV), a video pre-processing module; and (ii) media pipe library (MPL) with a machine learning (ML) framework, to process recorded videos and provide vital information to therapist. Using CV and MPL, landmarks of body parts (LBP) are identified and subsequently labeled them. Further, the labelled landmarks (example: wrist) are tracked during the onset of SM in recorded videos and extracted the X- Y coordinates. Using, data peak filtering algorithm (DPFA), the intensity of SM and its frequency are estimated. Quick detection of vital parameters of SM and versatile LBP selection, features SMDA standout to be a better diagnostic assistive tool to the therapist.
Diamond-Window Resonant Inductor with Significant AC Flux

Resonant inductor with significant ac flux at MHz switching frequency is challenging because of high core loss and winding loss due to skin and proximity effect. Air gap brings in fringing magnetic field resulting in uneven current distribution and therefore higher winding loss. The conventional rectangular-window core is replaced by the diamond-window core to reduce the fringing flux on the winding and therefore results in smaller inductor loss. An inductor prototype was built and was placed into a 200W Series-Resonator Buck (SRB) converter at 48V input voltage and 40A output. Experimental results are discussed to verify the theoretical analysis.
Scene Generation Method of Wind-solar Joint Output Based on Generative Adversarial Network

With the increasing penetration of renewable energy in the power system, the uncertainty of the output of renewable energy units should be fully considered in the planning and operation process to improve the efficiency and practicability. Therefore, this paper proposes an improved scenario generation method for joint wind-solar output with gradient penalty for generative adversarial networks, Wasserstein distance as the discriminant loss function and month label information being added to the model for generating planning-oriented wind-solar joint output scenarios. The uncertainty range of wind power output is determined based on the generated scenarios. Finally, compared with the traditional scene generation method, the results show that the proposed method can more accurately describe the uncertainty range of renewable energy output and provide operation boundary for power system planning.
Classification of Mpox Images Using Transfer Learning on Lightweight Models

With the fall of COVID-19 viral infections around the world, Mpox cases are increasing daily. Patients afflicted with the disease frequently exhibit a variety of skin lesions and can widespread community transmission. The use of deep learning technologies has been widely applied in the field of medicine in recent years such as in the areas of medical imaging and diagnosis. The purpose of this study is to create an image classification model using a transfer learning method to distinguish the Mpox disease. Four pre-trained models were selected for the experiment on the criteria of memory-constrained architecture and the lightweight of the model including ResNet50V2, InceptionV3, MobileNetV3, and EfficientNetB4. The result found that the EfficientNetB4 architecture training with a set consisting of optimizer = Adam, batch size = 32, and learning rate = 1e-4 yielded the highest accuracy among others, with a validation accuracy, precision, recall, and F1-score of 95.19%, 95.00%, 95.00%, and 95.00% respectively.
Optical Design of Arctic Weather Satellite Microwave Sounder

The Arctic Weather Satellite (AWS) mission by the European Space Agency is a prototype mission for a constellation of small meteorological satellites. The AWS mission is intended to be a lower cost mission at a faster development rate than previous meteorological missions. The payload is a cross-track scanning radiometer which operates in the 50-57, 89, 165-183 and 325 GHz frequency bands. This paper presents the optics of the AWS radiometer. The calibration concepts for in flight and onground are also discussed.
Analysis of Resonant PWM Active-clamp Ćuk DC/DC Converter

In this paper, a novel steady-state modeling technique is introduced for resonant pulse width modulation (PWM) active-clamp (PAC)-Ćuk dc/dc converters. The PAC-Ćuk dc/dc converter is equipped with two clamping circuits which enable ZVS turn-on of all the switches resulting in high-efficiency operation of the converter. A resonant form of PAC-Ćuk dc/dc converter is introduced and studied using the modeling technique to develop the output power and series inductor current dependency on the control parameters. As the resonance introduces high nonlinearity in the analysis, the traditional steady-state modeling generates erroneous results. Hence, a closed-form LC resonant modeling technique developed in this paper is used to analyze the impact of resonance on the PAC-Ćuk dc/dc converter. The resonant model is developed using the superimposition and state-plane trajectory technique. The developed resonant modeling technique delivers an exact solution for a resonant LC link connected across two active voltage sources which applies to other resonant converters.