Miracle-dz/UniFAR-FTUs huggingface dataset:
Maximum Power Point Tracking Control Strategy Based on the Hydrodynamic Model of the Pendulum Wave Energy Generation System

This paper proposes a new pendulum wave energy generation device, which is a direct-drive power generation structure with a rack and pinion drive mechanism. In order to improve the power generation efficiency of the pendulum wave energy generation system (PWEGS), this paper presents a maximum power point tracking (MPPT) control strategy. Firstly, the basic conditions for maximum capture power can be solved according to the hydrodynamic model of the pendulum body. On the one hand, a resonance control method is proposed to make the system resonate with the wave in real-time so as to increase the mechanical energy captured by the device. The specific implementation mode is to change the position of the counterweight mass block according to the wave cycle, so as to change the natural cycle of the device. On the other hand, a torque control method is proposed to improve the conversion efficiency of mechanical energy to electric energy. In essence, the optimal power take-off (PTO) damping torque is generated to match the radiation damping torque of the system by controlling the torque current of the permanent magnet synchronous generator, so that the equivalent resistance of the PTO system can be consistent with the radiation damping of the system. Based on the two control methods, the PWEGS can generate the maximum power. Finally, the feasibility and accuracy of the proposed MPPT control strategy are verified by simulation results.
Detection of Attacks on Industrial Internet of Things Using Fewer Features

Malicious attack detection becomes a critical issue in Industrial IoT(IIoT) environments. Meanwhile, the IoT market is constantly growing, and new IoT devices are connected to the Internet day by day, causing a rapid increase in network traffic. To enable IDS to detect malicious attacks in high-load network environments, a lightweight IDS is required. Therefore, Machine Learning (ML) based intrusion detection systems (IDS) with fewer features to meet the lightweight IDS are applied to the TON_IoT dataset. A Pearson correlation coefficient (PCC) is applied to calculate correlations among features, followed by Jamovi analysis software’s frequency table to analyze the core features of the TON_IoT dataset. Finally, the original 45 features are reduced to 10 core features for IDS to detect malicious activity. To verify the performance of malicious attack activities with the reduced 10 core features, four evaluation criteria are used: accuracy, precision, recall, and F1 score. Two ML techniques, KNN and RF, are applied for testing. According to experimental results, both ML techniques can detect multiple types of attacks with an accuracy of over 99%, indicating that using the proposed 10 core features for attack detection can still yield high accuracy.
Study of Spacer Etching with PR Approach

Over the past decade, advances in CMOS FEOL process technology nodes have made each generation of technology to achieve higher integration density and performance. However, the gate oxide thickness will lead to an exponential increase of leakage current which is usually caused by Lightly-doped drain (LDD) process. Furthermore, this appearance makes the some region insufficient to meet the electrical performance requirements. In order to solve the leakage issue caused by LDD, we develop a new spacer etch with Photo Resist (PR) approach, but this approach will cause some problems such as profile, width and uniformity issue. The formation mechanism of profile and improvement of uniformity are analyzed in this paper. The new approach shows better performance in the spacer etch process with PR. This paper provides an effective strategy for spacer etch process in the PR environment.
High Voltage Gain Interleaved DC-DC Converter with Voltage-Lift and Three-Winding Coupled-Inductor Techniques

A new high voltage gain interleaved DC-DC converter is proposed for the renewable energy systems. The configuration is composed of two-phase boost converter integrating a voltage-lift capacitor and three-winding coupled-inductor-based voltage multiplier modules to achieve high voltage gain without operating at extreme duty ratio. The switch voltage stress is greatly lower than the output voltage such that MOSFETs with low on-resistance are available to reduce the conduction losses. Due to the leakage inductances of the coupled inductors, the switches can turn on with zero-current switching (ZCS), and the diode reverse-recovery problem is alleviated. The input current ripple is decreased due to the interleaved operation. The operational principle, steady-state analysis and design considerations of the proposed converter is presented. In addition, a closed-loop controller is designed to. Finally, experimental a 1000 W prototype with 24-400 V conversion is built and tested to validate the performance of the proposed converter.
Seamless Transfer Control of Single-Phase Energy Storage System with AC Power Supply Function

This paper proposed the seamless transfer control algorithm of the single-phase energy storage system with ac power supply function. The proposed control scheme has parallelly connected voltage controller which control dc-link and grid voltage. During the transient in mode changes, the anti-windup scheme prevents the confliction or divergence of two controllers. It does not require artificial controller structure changing according to grid mode. To design the robust controller, the open loop transfer function is derived and analyzed to select proper gains. Simulation and experimental results on the 2kVA energy storage system (ESS) prototype with critical load verify the performance of the proposed control strategy.
Novel Technologies of Exoskeleton Systems Applied to Rehabilitation for Hand Therapies: A Technological Review

Movement disorders and injuries affect partial or full control of the parts of the body that affect your movement. Among the affected parts are the hands, which are vital to carrying out daily activities. According to the World Health Organization (WHO) around 15% of the world’s population, that is, 1000 million people, suffers from some type of disability. Only in Peru, there are around 1 million 575 thousand people who suffer from a variety of deficiencies, according to the National Institute of Statistics and Informatics (INEI). As a solution to this problem, hand exoskeletons emerge, which are devices that restore or improve the motor skills of the affected hand. In this review, 64 publications on hand exoskeletons were analyzed and classified into the following categories: trade names, acronyms or the name of the main author of the device, the degrees of freedom, the type of exoskeleton that can be passive or active, the mechanism that covers the parts of the hand, the types of movements that are performed, the kind of rehabilitation, the pathology that causes the injury or disability in the hand and the technology readiness level.
Design and Implementation of On-Device AI for Dangerous Driving Behavior Recognition

Recognizing dangerous driving behaviors is crucial considering the significant number of casualties in road accidents. This paper proposes an on-device AI for recognizing drivers' dangerous driving behaviors during vehicle operation. The driving behaviors are recognized using deep learning and implemented as a compact device inside the vehicle. The deep learning model utilizes the vehicle internal sensors to classify dangerous driving behaviors in real-time, distinguishing a total of 17 driving behaviors, including 9 normal driving behaviors and 8 dangerous driving behaviors. Due to the use of low-spec processors in the on-device AI system, lightweight techniques are applied to reduce the size of the deep learning model by over 50%. Nonetheless, the system achieves an accuracy of 83.24% and an F1-Score of 0.81. The lightweight deep learning model implemented in the on-device AI demonstrates an inference time of 13.6ms, enabling real-time operation. This allows for various applications, such as recognizing dangerous driving behaviors in real-time and providing drivers with warnings and encouraging safe driving.
An Iterative Meta-Testing Framework for Few-Shot Segmentation

Substantial Few-Shot Segmentation (FSS) methods adopt iterative manner to boost segmentation performance. Specifically, the traditional meta-testing can be turned into an iterative mode and the prediction result is iteratively refined. However, the abovementioned method shares the problem of Random Harmful Expansion (RHE). In the study, we propose a novel framework named Iterative Meta-Testing (IterMeta-Testing) to solve the RHE problem. In IterMeta-Testing, a Back-Off Module (BOM) is designed. The BOM can conduct a "back-off" operation to intelligently reject harmful expansion prediction results. Extensive experiments on PASCAL-5i and COCO-20i proved that the proposed IterMeta-Testing outperformed the baseline method. The proposed method can be integrated in an automated system of a robot for target detection and segmentation when sufficient fully-labeled image data are not available.
Reducing Coil Characteristics Deterioration by Using Insulated Rebar Testbody in Dynamic Wireless Power Transfer

In dynamic wireless power transfer (DWPT), it is essential to embed the power transmission coils in the road. Previous studies have reported that the properties deteriorate during embedment in reinforced concrete pavements because of rebar and concrete. In this paper, a reinforced concrete testbody was used to simulate the embedment of the coils in the pavement and to measure the properties of the coils because of the embedment. We propose a method to reduce the deterioration of properties by changing the rebar used in the testbody to be insulated rebar. As a result of measurements with a vector network analyzer (VNA), the use of insulated rebar reduced the Q-value of the coil by about 30 % compared to that of normal rebar. Furthermore, a transmission efficiency of 93.2 % was obtained at an air gap of 170 mm.
Impact of Interface Traps Generation on Flicker Noise Degradation in SI pMOSFETs

In this study, we examined the impact of interface states to the low frequency in Si pMOSFETs by applying a negative bias temperature instability (NBTI) stress. The subthreshold swing (S factor) degradation, the effective carrier mobility ($\mu_{eff}$) and the normalized drain current noise power spectral density ($S_{Id}$ 1/f) under different NBTI stress voltage ($V_{S}$) were characterized in devices. It is found that the $S_{Id}/Id^{2}$ increase with the stress voltage increases, due to the variation of interface trap density ($D_{it}$) increase.
Sensitivity Based Reduction of Equivalent Electrical Circuits in Power Electronics Application

This paper presents an application of an equivalent electric circuit reduction technique to improve the runtime of transient simulations. The approach is based on efficient adjoint sensitivity analyses of the initial electric circuit. Focus is in particular on electric circuits that contain a manageable number of functional elements, potentially of a non-linear type like semiconductor switches, and a much larger number of linear elements that are manifestation of "parasitic effects" as a result of the actual layout and design of the device. The reduction will retain the interpretability of the circuit elements whereas the complexity of the equivalent electric circuit is dramatically reduced. This has the beneficial side effect of stabilizing the transient simulation.The advantage of the proposed method is demonstrated in the conducted emissions simulation of a prototypical inverter. The accuracy of the model is good enough to predict the conducted emissions up to 30 MHz.
Implementation and Validation of a Long-Term Measurement System for Single Event Burnout at High Altitude

In electrified aircraft, cosmic radiation can be a potential hazard to power electronic systems like drive train inverters and dc/dc converters. As the increased failure rate at high altitudes usually requires a voltage derating, a detailed knowledge on the failure rate of the individual power semiconductors is necessary to enable an optimal compromise between failure rate and power density. This data is usually gained by accelerated measurements, which could omit certain influences of the high altitude. Therefore, this paper presents the implementation of a fully automatic long-term measurement system for the determination of the failure rate due to single event burnout under real conditions for power semiconductors.
A Novel SA-LIGBT with Dielectric Isolation

A novel shorted-anode lateral insulated gate bipolar transistor with dielectric isolation (DI-SA LIGBT) is proposed in this paper. The DI-SA structure is formed by introducing a dielectric trench between P+ and N+in the SA devices. The DISA structure isolation consists of vertical and lateral parts. The lateral dielectric isolation trench increases the anode current path, reduces the resistance ratio between the drift region and the collector, and effectively suppresses the snapback phenomenon in the SA LIGBT unipolar-to-bipolar mode. In the DI-SA LIGBT structure, the snapback and switching speed of the device can be optimized by adjusting the width and length of the vertical and lateral dielectric isolation trench. This paper compares the controllable advantages of DISA LIGBT and conventional SA LIGBT through Technology Computer-Aided Design (TCAD) simulation.
Short-circuit Current Improvement Control of Grid-forming Controlled Wind Turbine

Grid-forming controlled wind turbine is the key equipment to build the new power system. However, under the grid fault, the short-circuit current of it is limited, far less than the value that synchronous generator can output to support fault voltage recovery. The key is that the power devices of converter can bear limited thermal stress. The thermal model of switching devices is established and the thermal behavior of grid-forming controlled wind turbine is researched. It has been found that adjusting the switching frequency and adopting to discontinuous PWM(DPWM) can effectively reduce the junction temperature of power devices, thus increasing the short- circuit current of wind turbine to 4 times during the fault. The effectiveness of the proposed method is validated in PLECS software.
A 3D Reconstruction Method for Sheet Parts Using Binocular Camera

In automated and intelligent machining processes, it is often necessary to reconstruct a 3D model of the workpiece before machining to generate machining trajectories offline or to identify the workpiece. The geometric information of the workpiece can be derived from the point cloud data obtained by the binocular camera. In this paper, a binocular-based point cloud data processing method for 3D reconstruction and positioning of the workpiece. The clustering algorithm is introduced to extract the workpiece point cloud and reduce noise from the point cloud data collected by the binocular camera. The traditional boundary fitting algorithm of the PCD is investigated for its flaws. Then a contour classification method is applied in contour fitting and the contour fitting stratagem is proposed. The experimental system was set up to validate the suggested 3D reconstruction technique. Experimental results demonstrate the effectiveness of the method.
Online Battery Data Analytics Pipeline using Bigdata Tools for Electric Vehicles

This paper proposes an online big data analytics pipeline for machine learning (ML)-based battery health monitoring in electric vehicles (EVs). The proposed battery data analytics pipeline systematically integrates open-source big data tools including: 1) Apache Kafka (confluent) and Zookeeper for online battery data ingestion from multiple data sources, 2) KSQL database for streaming and sending multiple rows of data to the ML feature engineering stage, 3) Jupyter notebook training/running ML algorithms, and 4) Docker container to run all the pipeline software images. The proposed nearly real-time ML pipeline platform will enable a new level of battery intelligence system used for battery product development and lifetime battery health monitoring using the open-source bigdata tools. The feasibility of the proposed pipeline for an EV battery cell is validated by experiments.
Transformer Saturation Analysis and Mitigation for MMC-Based Grid Emulator

The modular multilevel converter (MMC)-based grid emulator emerges as a favorable solution for testing and validation of medium-voltage high-power wind turbines. The interface transformer of the grid emulator can be saturated when emulating voltage sags/swells in the faulted grids. The magnetic saturation can lead to severe inrush current, which may degrade the lifetime of transformer windings and even adversely affect the operation of grid emulator. This paper analyzes the dynamics of grid emulator under the interface-transformer saturation. It reveals the effect of the dc and harmonic components of magnetizing current on the MMC internal dynamics. An inrush current suppression technique is applied to mitigate the transformer saturation during the transient states, which guarantees the safety operation of the grid emulation system.
Graph Neural Network based Approach for Rumor Detection on Social Networks

In today’s society, social media usage has resulted in several challenges, including the widespread dissemination of rumors - unverified or false information that can significantly impact public perception and decision-making. As a result, detecting and preventing the spread of rumors is essential. Recent scientific studies have proposed various solutions that use both machine learning and deep learning techniques to identify rumors. In this paper, we investigate an approach that employs graph neural networks to detect rumors. Specifically, we represent posts and their responses as graphs, which are then processed through a Graph Attention Network (GAT) layer. The resulting representations are fed into a dense neural network for classification. Our experiments on the PHEME dataset show that our approach achieves satisfactory performance in identifying rumors. This study also provides a promising avenue for future research in the field of rumor detection using graph neural networks.
EEG and Fractal Dimension for Epileptic Seizures Detection

Epilepsy is a common neurological disorder that can cause seizures and unconsciousness. Early detection and treatment are crucial for reducing the severity of the injury. Commonly used to diagnose epilepsy, electroencephalograms (EEG) take a lot of time, are susceptible to interference, and can reduce accuracy. This research provides a reliable method for diagnosing epilepsy in EEG data. Each EEG segment is converted into an image using the short-time Fourier transform (STFT). It generates an 8-bit binary image for each TFI. Then, the FDs of the EEG data are estimated and determined using a box-counting approach. The least square-support vector machine (LS-SVM) classifier is employed to identify patients with epilepsy. This study uses original EEG signal data from Bonn University to test the suggested technique, which was 99% accuracy 98% sensitivity, and 99% specificity. Showing the provided method offers a high-accuracy classification and supports neurologists in making an epileptic condition diagnosis and subsequently proposing the proper treatment.
Current Source High Step-Up Push-Pull Resonant Converter for Low Wide-Input Source Based on Front-End Photovoltaic Cells Application

This paper present current source high step-up push-pull resonant converter (CSPPRCs) for low wide input source based on front-end photovoltaic cells application. The power state receives DC voltage of 10.5-12 VDC, which two switches (MOSFETs) would operate under zero current switching (ZCS) conditions with a frequency of 260 kHz. The both switches would operate alternately half-period. The output voltage is 330-370 VDC, the approximate around efficiency of the prototype is 92 percentage, the output power is 270 watts at maximum load. Experimental results for prototype was built and operated at fixed switching frequency, 330 VDC output voltage, and 180 W output power at 10.5 VDC input voltage, the efficiency is 90 percentage. From state-of-the-art power state connected to DC voltage is supplied to the both switches. which have a characterization of operated under ZCS conditions. The AC high frequency voltage of power state is provided to the high frequency transformer to matching circuit. To generate a current signal as a sinusoidal signal to be used as an input signal to a bridge full-wave rectifier. Consider, the voltage drops and the current flows through the diode in a bridge full-wave rectifier, While the diode are operated. A voltage drop will occur, it can turn-off under zero current switching. To reduced transition losses of diode. Overall efficiency in both input voltage of the prototype more than are 90 percentage during in full load range.
A Digital Controlled of Duty-ratio Feedforward for Four-level Flying Capacitor Boost PFC

This paper proposes a flying capacitor multilevel (FCML) power factor correction (PFC) with phase-shifted pulse width modulation (PSPWM) technology to achieve voltage balance of the flying capacitors and increase the equivalent frequency of the inductor. Under the rated current ripple, a smaller inductance can be used to reduce the size of the inductor. With an FCML architecture design, the voltage rating of the power devices can be reduced, which decreases the switching loss. Based on PSPWM control, the small-signal model is a detailed analysis, and the compensator is designed considering the phase delay caused by digital control. The duty-ratio feedforward (DFF) control reduces the harmonic distortion of the inductor current. Finally, the 350 W four-level flying capacitor boost PFC with DFF control is implemented with an inductor current frequency of 500 kHz, an input voltage of 90-264 Vac, and an output voltage of 390 V.
DC link Current Ripple Reduction for Five-level Hybrid ANPC Converters Using Double-based PWM Switching method

In many applications, the reduction of the current ripple is regarded as a crucial issue due the harmful effects of this parameter on the system. In power converters where higher order of the multilevel inverters is being used, the effects of the current ripple have higher influence and would leads to reducing the expected life-span of the DC link capacitors. In these regards, this paper proposed a creative method to reduce the ripple in the neutral point current for five-level hybrid neutral point clamped (ANPC) inverters. The proposed method depends on modifying the conventional switching sequences by creating a new switching technique. The proposed method results in efficient reduction in the current ripple and extend the life-span of the DC link capacitors. In addition, the proposed method does not require any change on the hardware or using extra devices. Moreover, the inverter is able to operate at the maximum capability without any limitation over the modulation index. The proposed method is verified using simulation results.