Miracle-dz/UniFAR-FTUs huggingface dataset:
Power Loss Analysis of Symmetrical Multilevel Boost Converter and Three-Level Boost Converter Compared to Classic Boost Converter

This paper investigates power losses of three boost converters-Symmetrical multilevel boost converter, three-level boost converter, and classic boost converter. The three converters are designed and simulated under the same specifications. Moreover, to ensure performance comparison, power losses are determined by referring to simulation data results and parameters of the devices provided by the manufacturer’s data sheets. The selection of each component is done based on design and simulation results. The result shows that for input power with 1041.52W, a symmetrical multilevel boost converter reduces total losses by 0.020% and 2.78% respectively compared to classic boost and three-level boost converter converters. As a result, the symmetrical multilevel boost converter provides an efficiency of 97.31% while the three-level boost converter produces an efficiency of 95.69% and the classic boost converter efficiency goes to 97.29%. The symmetrical multilevel boost converter advantages consist of providing low losses for active and passive components. Symmetrical multilevel boost converter transistors (two Mosfets) switching and conduction losses represent only 0.25 and 0.09% respectively, while the transistors losses of the classical boost converter constitute 1.08%. Similarly, in a symmetrical multilevel boost converter, the inductors $\left(\mathrm{L}_{1}, \mathrm{~L}_{2}\right)$ and capacitors $\left(C_{1}, \ldots, C_{6}\right)$ power losses decrease by 0.009% and 0.24% respectively compared to the classical boost converter. With these results, a suggestion is proposed to enhance the symmetrical multilevel topology of the boost converter’s efficiency by reducing diodes losses which consist of 82% of the total power loss.
A Fault-Tolerance MPPT Design and Model Analysis for Hydrokinetic Turbine Systems

This work proposes a converter-generator fault-tolerance strategy for a fixed-pitch hydrokinetic turbine (HKT) with a converter 4th leg connected to the generator neutral point. A maximum power point tracking (MPPT) control for post-fault operation is also investigated. A PLECS model is used to verify the fault-tolerance strategy's practicality, and the controller is verified with hardware-in-the-loop (HIL) on fault transition and post-fault steady-state operations. The MPPT power control combined with the fault-tolerance strategy can maximize the HKT operating range under various conditions. The HKT system is also simulated on a MATLAB multi-physics model over a one-year water mission profile. Then a case study is made to look into the impact of the fault-tolerance strategy on the system's reliability and cost-effectiveness. The case study shows that the fault-tolerant system has a 14.51% energy generation increase compared with a non-fault-tolerant system.
Image Encryption Research based on Key Extracted from Iris Feature

The encryption algorithm has disadvantages like the long key making memory difficult and uneasy safekeeping, which causes a potential threat to the information security. Therefore, a new direction of the encryption method research is to combine the biometric information with the traditional encryption algorithm. The key extracted from the iris and AES encryption algorithm are used in the image encryption algorithm. The db2 wavelet decomposition to the iris region is performed, and the third level high frequency coefficient is extracted as the iris feature codes, from which a 192 bit key is generated by using the stochastic mapping function. The randomness of the key extraction is analyzed. The proposed algorithm is employed to do the encryption test to the image. The encryption effect is compared with the scrambling encryption effect of the classic Arnold method. The experiment results show that security of the encryption image gained by using the proposed algorithm is higher, achieving the purpose of protecting image information.
Experimental Verification of a Hybrid DC Breaker’s Solid-State Branch, Utilizing Discrete IGBTs, for LVDC Systems

Hybrid direct current (DC) breakers are essential for fast fault current interruption in low voltage direct current (LVDC) systems. This study investigates the current interruption capabilities of a hybrid DC breaker’s solid-state branch by using a programmable flyback power supply to generate different gate voltages. The results demonstrate that a single discrete insulated gate bipolar transistor (IGBT) in a TO-264 package can interrupt 1300 A by increasing the gate voltage from 16 V to 30 V, 12 times its rated value. By connecting four of these IGBTs in parallel with a symmetric layout, equal current sharing is achieved, allowing currents up to 4000 A to be successfully interrupted. These findings pave the way for small, efficient, cost-effective hybrid DC breakers that do not require expensive IGBT modules.
Design of Dual-Three-Phase Synchronous Reluctance Starter Generator for Aircraft

More electric" is becoming the mainstream of novel aircraft development. A high-performance and reliable starter generator is essential to achieving this goal. Dual-three-phase synchronous reluctance starter generators (D3φSynRSGs) are considered a potential solution that combines both advantages. This paper proposes the design of a 25 kW D3φSynRSG. The Particle Swarm Optimization (PSO) algorithm is used to optimize the machine geometry. An analytical approach based on the fundamental magnetic circuit is proposed to help understand the effectiveness of a dual-three-phase (D3φ) winding. The nonlinear electromagnetic characteristics of the machine are investigated and validated with finite element analysis (FEA).
Detection and Prevention of TCP DoS/DDoS Attacks in Software Defined Network

Software Defined Networks (SDN) is an emerging network architecture that allows software programs to govern and configure the network intelligently and centrally. Although it offers numerous benefits such as controlling data traffic and improving security, it possesses vulnerabilities across its various levels, which can make it a tempting target to attackers who use Distributed Denial-of-Service (DDoS) attacks. This matter triggers till date field researchers and developers to keep on developing methodologies and applications to detect attacks and secure the Software Defined Networks. Our paper bridges the gap between the developing environment of SDNs and the ongoing security challenges they face by describing major DDoS detection techniques as well as introducing two novel methods to enhance the security posture of SDN environments. We propose two lightweight and practical approaches to detect and prevent attacks. The first approach targets TCP SYN flood attack with a TCP Proxy approach. The second approach prevents data plane saturation from 3-way complete handshaking TCP attacks with a hashing approach. Our work involves creating a set of firewall rules to spot TCP SYN flood attacks, as well as developing an application to evaluate the hash values for the different source IP address set of data. Simulation results shows insights into identifying and protecting against potential attacks on SDNs.
Parallel-Input Series-Output Z-Source Converters for High Voltage DC Power Supplies

High Voltage DC power supplies typically employ a high voltage high frequency transformer to step-up the voltage generated by a high frequency inverter. Generally, the step-up ratio of the transformers is high and therefore the effect of the stray capacitance and inductance values is amplified, deteriorating the inverter operation. LCC converters utilizing these stray capacitors are suggested to generate soft switching for better efficiency, but this increases the complexity. Also, the varying nature of the stray capacitance may complicate the control. In this paper, a modular, Parallel-Input Series-Output converter utilizing two z-source dc-dc converters is proposed to step a 350V input to generate 2-kV, 2-kW output. Simulation and experimental results prove that impedance source converters which are generally used in renewable energy applications can also be used at high voltage applications.
Core-as-a-Sensor: Ferrite DC-Resistance-Based Core Temperature Measurement of Magnetics

Future power electronic systems should feature continuous in-situ monitoring of components to facilitate, e.g., predictive maintenance and lifetime prognostics. Temperatures of magnetic components (i.e., transformers and inductors) are typically measured with thermocouples attached to the core surface. It is known from literature that the electrical conductivity of ferrite, typically used as core material of high-frequency magnetics, is temperature dependent. Hence, this paper proposes a method to observe the magnetic component temperature based on a ferrite core resistivity measurement, advantageously allowing to track the average core temperature (which is typically above the core surface temperature). First, methods for electrically contacting the core surface are discussed and common magnetic materials (MnZn ferrites) are characterized. Possible disturbance factors such as dc and ac magnetic flux occurring during operation of power electronic converters are experimentally found to have little impact on the measured resistivity. Finally, a smart inductor prototype with core-resistivity-based temperature estimation is presented and experimentally verified, and the applicability of the method to high-frequency NiZn and powder cores is indicated.
A Grid-Supporting Rectifier with Frequency and Voltage Regulation Capabilities

In recent years, with the increasing number of grid-interfaced converters on both the supply side and the demand side, the reduction of conventional synchronous machines in the energy share has caused a significant loss of the total system inertia, which can make the power system less stable and more susceptible to variations. This paper proposed a grid-interfaced three-phase rectifier featuring grid-supporting capabilities. It can alleviate the grid side frequency and voltage variations by adjusting the active power and reactive power drawn from the grid side. To realize the power balance synchronization under system dynamics, an additional battery energy storage system (BESS) was connected to the rectifier DC side to handle the excess or shortage of energy during frequency variations, while maintaining the DC-link voltage. The proposed rectifier’s grid-support capabilities are verified by MATLAB-Simulink simulation results. The stability enhancement merit of the proposed rectifier compared to the conventional grid-tracking rectifier is shown by stability assessments based on the general Nyquist criterion (GNC).
SLAH: A Siamese Network Vision Tracking Algorithm Using LSTM Adaptive Head

In the field of single object tracking, conventional methods often rely on correlation filters or visual image processing. However, these approaches typically focus solely on extracting target object features and consider only the position information in the current frame. They lack integration of contextual and spatial-temporal information, which can limit tracking accuracy.Our method takes inspiration from single object detection and tracking techniques. It combines efficient model-based object detection with the spatial position of the final target in the image. This approach offers significant advantages in reducing tracking drift (deviation from the target object) and improving tracking accuracy (predicting bounding boxes that closely follow the target). To extract spatial information, we utilize a three-layer ResNet and Convolutional Block Attention Module (CBAM) in conjunction with a siamese network. This combination effectively captures the spatial characteristics of the target object. Additionally, we employ an adaptive processing head with an internal structure of Long Short Term Memory (LSTM) to capture temporal information. By integrating spatial and temporal cues, our method achieves more robust and accurate tracking.Through comprehensive comparison and ablation experiments across multiple datasets, we have demonstrated notable improvements with our method. It tightly connects the predicted bounding box with the tracking target and effectively combines spatial and temporal information for precise object tracking.
A Novel Hybrid Core Structure for 100kW Medium Frequency Transformers

Nanocrystalline has been a promising magnetic material for high power medium frequency transformers (MFT). However, the high conductivity and the laminated core structure make the leakage flux induced eddy current loss a major limitation for the nanocrystalline-based MFT at higher operation frequencies, especially when a certain amount of leakage inductance is needed by the topology. This paper proposes a novel nanocrystalline ferrite hybrid core that eliminates the local hot spots caused by the eddy current loss and reduces the overall core loss. Several different hybrid core structures are compared through finite element analysis (FEA) to show the superior performance of the proposed structure. The validity of the proposed structure is verified by experimental results, where a maximum temperature reduction of 39.9°C and almost 70% reduction in transformer loss is observed. This new hybrid core structure is therefore an important concept for better transformer design in applications such as the solid-state transformer (SST).
Differential Phase Correction of Dual-polarization Weather Radar with Slotted Waveguide Antenna

The dual-polarization phased array weather radar (DPPAWR) uses a slotted waveguide antenna with high gain and low sidelobe radiation characteristics. However, the waveguide structure, slot position, and processing errors cause distance differences in the echoes of the horizontally (H) polarized channel and the vertically (V) polarized channel, which can lead to errors in the measured differential phase. Moreover, this error varies with the DPPAWR’s electronical scanning angle. Based on the slow change of the differential phase in the small precipitation area, this paper estimates and corrects the differential phase error using close-range weak precipitation echoes. The dependence of the differential phase error on the elevation angle (electronic scanning in elevation) is clarified, and a segmented formula for the initial differential phase and the elevation angle is obtained by quadratic polynomial fitting, which can correct the differential phase at close range and small precipitation area to 0. Finally, the effectiveness of the proposed method is verified using precipitation data of different reflectivity observed on another day.
A Non-Local Means Denoising Co-Processor with Data Reuse Scheme and Dual-Clock Domain for High Resolution Image Sensor

This paper presents a non-local means (NLM) co-processor with data reuse scheme and dual-clock domain for high resolution image sensor The key features are as follows: 1) The NLM is accelerated by the parallel pixel-level pipeline architecture with a compact single-port SRAM-based line buffer for serial-parallel conversion. 2) The algorithm is optimized by using L1 loss calculation instead of the L2 loss calculation for Similarity computing, and the fitted quantized look-up-table for exponential operations to reduce logic utilization; 3) the data reuse method for the weights calculation reduces the area of the corresponding circuit by 6/7; 4) Most of the parallel computing are implemented in the fast core, which runs at twice the pixel clock frequency, thus halve the amount of computation per cycle. The architecture achieves a real-time denoising at 60 frames/second for full high definition (FHD $\mathbf{1920}\times \mathbf{1080}$) videos with the energy efficiency of only 36.22 mW/frame.
Modified Space Vector Modulation and Voltage Balancing of Multiphase Neutral Point Clamped Rectifier

This study describes a control approach that balances the DC-Link capacitors voltage in a multiphase neutral-point-clamped (NPC) rectifier by applying optimized switching sequences. First a modified multiphase space vector modulation (SVM) control scheme has been proposed, which reduce the voltage fluctuations across the DC-Link capacitors. Based on the proposed SVM, each sector and its regions have different levels of effectiveness for small and medium vectors for the same switching state, which results in a better understanding of the problem. In the next step, a voltage balancing algorithm is presented and unified with the proposed space vector modulation in order to make sure that the best switching sequence with the least amount of energy deviation is chosen. The performance of the presented controller is evaluated through a comprehensive series of simulations.
Incidences des conditions d'élevage et d'alimentation en post-sevrage sur les performances en engraissement

This study compared animal performances and carcass characteristics in pigs run through different post-weaning management regimes. The different treatments led to a tangible liveweight differential when transitioned to finishing. The study used four batches of finisher pigs. Post-weaning experiments focused on livestock management system or diet amino acid composition. Finisher performances were cross-compared according to post-weaning treatment. It appears that the nutritional properties of the feed distributed post-weaning have a limited impact on the pace of weight gain during finishing. The results suggest that any liveweight differential recorded at the end of the post-weaning period will tend to reproduce a same-degree differential at finishing. This findings open perspectives for de-intensifying diet practices in the post-weaning period, which is typically when pigs at this stage of growth are most vulnerable to digestive disorders. However, pigs that are lighter at early finishing sometimes present a lower feed conversion ratio due to their lower dietary maintenance needs. A slide in the quality of livestock management conditions in the post-weaning period can have either positive or negative impacts, which reflects the complexity ofthe interactions between health status and nutritional status. Post-weaning diet and farm management conditions ultimately appear to have only a limited impact on carcass quality at slaughter.
Analytical Hierarchy Process (AHP) Analysis for Load Shedding Scheme in Islanded Distribution System Connected with Mini Hydro Generation

The likelihood of islanding events is one of the main concerns with the growing usage of renewable energy sources in distribution systems, such as Distributed Generations (DGs). In this condition, the grid is disconnected from the distribution system during the occurrence of disturbances in the system network. Due to the mismatch between power generation and load demands, the system voltage is greatly perturbed. To restore system stability, it was necessary to reduce a specific number of loads depending on the imbalance power value. This paper focused on the analysis to select the best combination of load to be shed based on 2 main criteria: Voltage Stability Index (VSI) value and real power value based on The Analytical Hierarchy Process (AHP) technique. In this work, the PSCAD/EMTDC program is used to simulate the efficacy of the proposed plan using an existing Malaysian network (a 25-bus system connected to micro hydro generating). Based on the finding, the AHP method can provide an enhancement of the VSI for the system network after being subjected to disturbances.
Power System Transient Stability Prediction Using Convolution Neural Network and Saliency map

Transient stability of a power system is one of the most important indices in power system operation, as it represents the criterion for determining whether the power system can continue to operate without losing synchronization in the event of accidents or faults occurring in the system. This paper proposes a convolutional neural network (CNN) model combined with a saliency map to predict the transient stability of a power system. The CNN model learns power system transient stability using the saliency map as input. The proposed method shows higher accuracy than the existing method, which uses RGB images as input. The accuracy of the proposed method is verified using simulation data obtained from the IEEE 39-bus system through Matlab/Simulink.
The Optimization Method of Reactive Power Control for Distribution Network Based on Two-Stage XGBoost

A large number of distributed photovoltaic(PV) power sources are connected to the distribution network, and their volatility and intermittency easily lead to frequent voltage fluctuations in the distribution network. The traditional volt/var optimization method using the heuristic algorithm to solve the model is highly dependent on the accurate modeling of the power grid. Besides, the solution accuracy and calculation speed are difficult to respond to the voltage control requirements of the new energy distribution network. Aiming at the volt/var optimization problem in the distribution network, a method for establishing a two-stage volt/var optimization model is proposed. Based on the XGBoost algorithm, this method considers the lifetime of the PV inverters and combines the historical optimal strategy library to perform two-stage optimization of the XGBoost model, to obtain the volt/var optimization model. It is verified that this method can improve the online operation speed of the volt/var optimization model and prolong the lifetime of IGBT, to update the optimization strategy of the distribution network in time and reduce device investment.
Fixed Frequency Operation in Critical Conduction Mode with Voltage-Controlled Variable Inductor

This study introduces a novel voltage-controlled variable inductor (VI) that effectively fixes the switching frequency of DC-DC converters operating in the critical conduction mode (CRM). The proposed VI addresses the limitations encountered with conventional current-controlled VIs, such as complex winding structures, control challenges, and reliance on magnetic saturation. In the proposed voltage-controlled VI, the primary-side inductance of a coupled inductor is regulated by an auxiliary voltage source on the secondary side. The validity of the proposed VI is experimentally verified using a prototype CRM boost converter with an output voltage of 400 V. Experimental results demonstrate that the switching frequency remains fixed at 100 kHz, and the main converter maintains soft switching characteristics regardless of variations in output power.
Mobile Threat Detection System: A Deep Learning Approach

Even though there have been numerous approaches to tackle the security and privacy issues in mobile systems, there is still potential for improvement. Long lists of permissions are typically ignored by mobile users since they are confusing. Therefore, it is crucial to assess Android applications by determining whether they are safe or malicious and by ensuring that the likelihood of each permission request is well understood. In this paper, we propose an approach that combines dynamic analysis and deep learning techniques to tackle the security problems of Android devices. By applying deep learning algorithms on the famous Drebin dataset, we successfully recorded a record high accuracy rate of 99.20% with our DrebinDNN model in comparison to other deep learning models such as Recurrent Neural Networks(RNN), Radial Basis Function Networks (RBFN), and Self-Organizing Maps (SOM) and machine learning models including Random Forest (RF), Support Machine Vector (SVM), Decision Tree, and Naïve Bayes. With this encouraging performance, we believe that our approach holds the promise of constituting a reliable option to mitigate cyber threats targeting the Android operating system.