Miracle-dz/UniFAR-FTUs huggingface dataset:
AI Techniques for Software Vulnerability Detection and Mitigation

The growth of the Internet of Things (IoT) is leading to some restructuring and transformation of everyday lives. The number and diversity of IoT devices have increased rapidly, enabling the vision of a smarter environment and opening the door to further automation, accompanied by the generation and collection of enormous amounts of data. The automation and ongoing proliferation of personal and professional data in the IoT have resulted in countless cyber-attacks enabled by the growing security vulnerabilities of IoT devices. Therefore, it is crucial to detect and patch vulnerabilities before attacks happen in order to secure IoT environments. One of the most promising approaches for combating cybersecurity vulnerabilities and ensuring security is through the use of artificial intelligence (AI). In this paper, we provide a review in which we classify, map, and summarize the available literature on AI techniques used to recognize and reduce cybersecurity software vulnerabilities in the IoT. We present a thorough analysis of the majority of AI trends in cybersecurity, as well as cutting-edge solutions.
Study on Core Loss of Switched Reluctance Motor by Considering Stator Polarity Arrangements

Core loss of switched reluctance motor (SRM) is very difficult to analyze due to serious non-sinusoidal and nonlinear properties of magnetic field which is caused by motor structure and various complex operation conditions. Flux density is affected by magnetic field distribution and rotating speed, which generates different core loss, and further results in different performances. Aiming at studying the effects of stator polarity arrangements (SPAs) on the operation performance of SRM and optimizing the motor structure for further improving operation efficiency, this paper investigates the characteristics of flux density and the influence on the distribution of core loss of a 12/10 poles SRM. The simulation results indicate that the tangential component of flux density at the stator and rotor yoke dramatically varies due to the stator polarity arrangements, which produces the distinction of distribution of core loss for typical parts.
Research and implementation of onboard heterogeneous resource scheduling

For cutting-edge fields such as aerospace exploration that require high real-time and low power consumption, the scheduling of onboard resources is closely related to the configuration ability and management efficiency of spatial information networks. Therefore, designing a reasonable resource scheduling plan has great practical significance. The open-source container orchestration management system represented by Kubernetes cannot fully meet the needs of heterogeneous resource scheduling in onboard environments. It is not lightweight enough for onboard embedded environments, and scheduling strategies have problems such as a large gap between requested and actual resources, and node resource imbalance. Therefore, this article proposes a lightweight onboard heterogeneous resource scheduling scheme and designs a resource balancing scheduling algorithm based on historical load. Through experiments, it has been shown that this scheme has certain effects in improving the degree of cluster resource balancing and reducing resource waste.
Text-Independent Speaker Verification Based on 3D Inception-Resnet

This paper proposed a new method using the 3D Inception-ResNet architecture for text-independent speaker verification. The method of creating a speaker model through utterance-level features ignores the speaker’s internal features and time dimension features. More specifically, this paper introduced the 3D-ResNet module with the convolution operation capture the time and frequency features of the audio signal. Better model the spatio-temporal characteristics of audio data, model and analyze time series data, and effectively improve the performance of the speaker verification system. Furthermore, in order to capture the detailed characteristics of the speaker, this paper adopted the strategy of multiple receptive fields. In the proposed method, the data obtained through the 3D Inception-ResNet architecture was averaged to create an independent model for each speaker. Experiments demonstrate that our proposed method significantly outperforms traditional d-vector and 3D-CNN architectures.
Research on Federated Learning Data Management Method Based on Data Lake Technology

With the diversified development of data information in the era of big data, the scale of data is also increasing. Due to the multi-source and heterogeneous data characteristics in the information age, the data sources are also more diverse. It is difficult to identify valid data, and data consistency cannot be guaranteed. Based on the above background, we gained a complete understanding of enterprise requirements and challenges after in-depth research on the federated learning data for data lake technology. This paper designs and implem12 -*+ents a data lake federated learning and data management system. Firstly, the application of enterprise big data is analyzed in detail, and the many deficiencies in its sources and query methods are clarified. On this basis, through in-depth customization of the unified query system, based on data lake technology and federated learning technology, the enterprise's multi-heterogeneous data system is re-planned to run stably and reliably and manage enterprise data more efficiently.
Study of an Improved STPMSM for Improving Linearity of Torque/Current

Compared with interior permanent magnet synchronous motors (IPMSMs), the conventional spoke-type permanent magnet synchronous motors (STPMSMs) have a problem of poor linearity of torque. The main reason is that the working point of PMs for STPMSMs under load drops significantly. In response to this problem, this paper proposes an improved STPMSM. The article introduces the structure of the motor in detail, and compares the difference in linearity of torque for the improved STPMSM, conventional STPMSM and IPMSM. Finite element analysis (FEA) and experimental results show that the linearity of torque under heavy load for the improved STPMSM is improved significantly, and the output capacity of torque is enhanced.
Mixed Attention Interleaved Execution Cascade Architecture for Fittings Instance Segmentation in Overhead Transmission Line Images

Recognition and localization of the fittings in overhead transmission line (OTL) images are the basis of fittings status detection and fault diagnosis. Most of the current recognition and localization of the fittings are based on object detection methods, which cannot accurately locate the fittings, and existing instance segmentation methods have limited accuracy for instance segmentation of fittings in complex OTL scenarios. To solve these problems, in the interleaved execution part, we inherit the idea of Hybrid Task Cascade (HTC) and add a direct information path to the same-stage box and mask branches to reinforce the coupled relationship between detection and segmentation; in the mask branch part, we sequentially apply the efficient channel attention (ECA) module and the dilated spatial attention (DSA) module and then insert them into the mask branch to improve the cross-stage information communication in the cascade architecture and mask prediction. Combining them results in Mixed Attention Interleaved Execution Cascade (MAIEC), a new cascade architecture for instance segmentation. Extensive experiments on the OTL fittings dataset reveal the effectiveness of the proposed method. The proposed MAIEC improves the AP of the box and mask predictions by respectively 2.0% and 1.5% compared to the strong HTC baseline.
An Efficiency Optimization Strategy for PMSM Drives by Combination of LMC and Multi-Vector-MPC

This paper presents an efficiency optimization control strategy for two-level three-phase inverter-fed interior permanent magnet synchronous motors (IPMSMs). The loss minimization control (LMC) is combined with the three-vector-based finite control set model predictive control (FCS-MPC) in this method. Through comparing the four hybrid control methods combined by LMC, maximum torque per ampere (MTPA), field oriented control (FOC) and MPC, simulation tests have been carried on a 6.4 kW IPMSM. The results evaluate that the presented method have good dynamic performance and reduce machine’s loss during transient further while maintaining relative low harmonics.
Effect of Cooling Strategies on Performance of Rotor PM and Stator PM Machines

This paper investigates the effect of various cooling strategies, including forced air, water jacket, and shaft cooling, as well as their combinations, on the output torque and power of rotor PM and stator PM machines. Finite element analysis and computational fluid dynamics are used for analyzing the torque and power capabilities accounting for the thermal limits of PM and winding insulation. It is shown that hybrid water jacket and shaft cooling can significantly extend the operational region of surface-mounted PM machines, while only water jacket cooling is required in switched-flux PM machines to produce a competitive torque.
Digital Control Challenges in a Single-Phase CCM Totem-Pole PFC Rectifier with GaN Devices

This paper surveys the main challenges regarding the PFC totem pole digital control design and implementations, mainly addressing the problems related to the voltage controller design and handling of the zero-crossing point of the ac voltage. For a dual control loop totem pole PFC, analysis and design of the inner and outer control loop is given in detail. The voltage controller is implemented as a time delay filter (TDF) in series with a PI controller. A combination of feedback and feedforward control is used for the current control. This paper also provides a detailed analysis and proposes a simple and robust method for reducing the current spike at the zero-crossing point of the ac voltage, which appears intrinsically for this topology. In order to verify the theoretical analysis and to test proposed control methods, an experimental model of the totem pole PFC rectifier using GaN devices and operating at 250W/100kHz was developed and tested.
Punch Types and Range Estimation in Boxing Bouts Using IMU Sensors

In the field of competitive boxing, performance is often evaluated by analyzing punch frequency and type, a process now enhanced by machine learning and Inertial Measurement Unit sensor data. Despite these advancements, punch range, a crucial factor influencing strategy and punch effectiveness, has been largely overlooked. To address this aspect, our work focuses on classifying punch types and ranges using various ML techniques trained on IoT sensors like IMU sensor data. We utilize spatiotemporal features, specifically power spectral density extracted from the four types of punch data, as input for the ML models. The models employed include Fine Decision Tree, Coarse Decision Tree, Linear Discriminant, Quadratic Discriminant, and Random Forest. In our comparative analysis of these ML models, we have found that the Random Forest classifier achieves the highest accuracy, accurately predicting punch types and their corresponding ranges with an impressive accuracy rate of 96.5%. This breakthrough in punch classification enables coaches and trainers to comprehensively assess a boxer's performance and design tailored training regimens for further improvement.
Field-Harmonic-Oriented Design of Vernier Permanent Magnet Machine Towards High Torque Density and Low Torque Ripple

Vernier permanent magnet machine (VPMM) has been a promising candidate for low-speed large-torque applications owing to high torque density based on multiple-magnetic field-harmonics working principle However, rich field harmonics also cause large torque ripple, meanwhile the regular reducing methods will also undermine torque density. In this paper, the field-harmonic-oriented design approach is proposed for the VPMM towards high torque density and low torque ripple. Based on the permeance unit model, the mapping relationship between torque output and field harmonics is built. Explicit design regulations are established for the airgap structure so as to modulate the optimal field harmonics composition. Via the proposed method, a surface-mounted VPMM is purposely designed and the electromagnetic performance is verified by FEA. Finally, a prototype of the proposed machine has been manufactured and tested. Both analytical and experimental results validate the feasibility of the proposed design method.
New Triple-Output Quad-Active-Bridge DC/DC Converter Employing a Four-Leg Inverter Input Stage

Charging stations for electric vehicles require electrical isolation between the charging ports. Therefore, this paper proposes a multi-port DC/DC converter structure that combines three conventional dual-active bridge converters (DABCs) where the primary side switching stages are integrated into a single four-leg inverter, and three individually isolated output ports (4L3) are provided. This quad-active bridge converter (QABC) structure features fewer semiconductor devices than three parallel connected single-phase DABCs with a six-leg inverter $(3\times 2\mathrm{L}3$, resulting in 6L3). The 4L3 QABC is also advantageous compared to a known variant where the primary side switching stages are integrated into a three-leg inverter (3L3), as the 4L3 QABC does not limit the duty cycles of the primary side transformer voltages. Therefore, the input voltage can be fully utilized, which allows lower transformer RMS currents compared to the conventional 3L3 QABC. Our analysis explains the operation and modulation and underscores the 4L3 QABC’s enhanced performance that consistently diminishes transformer current across all operational ranges, achieving a 20% reduction in the sum of squared transformer RMS currents at rated operation compared to the conventional 3L3 QABC. Furthermore, we show a virtual prototype with an integrated cooling system, which reaches a power conversion efficiency of 98.4% for 750V/400V primary/secondary voltages at 120kW and a power density of $10\mathrm{k}\mathrm{W}/\mathrm{d}\mathrm{m}^{3}$.
Optimal Design of a High Temperature Superconducting Homopolar Inductor Machine

A high temperature superconducting homopolar inductor machine (HTS-HIM) is optimally designed considering the effect of magnetic field on HTS coil in this paper. Firstly, the structure and operation principle of HTS-HIM are presented. The three-dimensional HTS-HIM finite element analysis model and two-dimensional axisymmetric direct coupling model of HTS-HIM based on T-A formulation are established. Secondly, the excitation window parameters, the excitation current and number of turns of HTS coil are optimized, taking into account the HTS-HIM performance and the safety of HTS coil. Thirdly, the magnetic field weakening capabilities of the U-shaped flux diverter and copper layer are analyzed and their parameters are optimized. Finally, the optimal design scheme and the critical current of HTS coil in HTS-HIM are obtained.
Construction of Knowledge Graph Based on the Four Treatises of Tibetan Medicine and Its Searching System

Using Neo4j combined with Py2neo knowledge graph technology, the knowledge graph was constructed based on the data of "The Four Medical Tantra" translated and annotated by Ma Shilin, Luo Dashang, etc., and the secret book with more than 240,000 words was marked as entity-relationship-entity triplet by manual labeling group, and entered it into the Neo4j graph database, created 6 entity labels and 6 relationship types, including 2411 nodes and 4106 relationships, and developed the retrieval system of "The Four Medical Tantra" based on the knowledge graph, realizing the input of patient symptoms and disease names Call the knowledge graph to visualize the disease diagnosis and treatment plan, diet and daily life precautions provided by the "Four Medical Classics". As one of the most important classics in Tibetan medicine, "The Four Medical Tantra" is displayed in a visual way, and a knowledge search system for "The Four Medical Tantra" is built, so that Tibetan medicine scholars can understand and use knowledge more intuitively. To provide Tibetan medicine scholars with more comprehensive, accurate and convenient medical services can better inherit and develop Tibetan medicine culture.
Comparative Study of Machine Learning Algorithms and Text Vectorization Methods for Fake News Detection

The detection of fake news is a crucial task in today's society, given the widespread use of social media and online platforms. In this study, we investigate the application of Machine Learning (ML) algorithms for the detection of fake news. We consider two different datasets of categorized news articles of various sizes and apply various ML algorithms, along with two methods of text vectorization. Specifically, we examine Bag of Words and Tf-Idf, with the use of stemming and with different n-gram values. The resulting vectors are processed by Naive Bayes algorithms, Linear algorithms, Support Vector Machines, and Random Forest classifiers. F1-score and computational time for each algorithm-vectorization combination were recorded. Our results have shown that Linear Algorithms and Support Vector Machines combined with Tf-Idf vectors and n-gram value of (1,2) produced the highest accuracies, with an F1-score up to 96.8%.
Capacitor Energy Based DC-Link Voltage Control Method for Dual Three-Phase PM Assisted SynR Starter/Generator

This paper proposes a voltage control method based on the DC-link capacitor energy concept for dual three-phase (DTP) permanent-magnet-assisted synchronous reluctance starter/generator (PMa-SynRS/G). Conventional voltage control methods suffer from poor dynamic performance and the control parameters are sensitive to speed and load. To solve this problem, the DC-link capacitor energy is taken as controlled plant in the proposed method, where the control model of the generation system is linearized. The energy and power relationship between motor and rectifier is analyzed and a load power feed-forward strategy is applied to reduce the impact of load uncertainty. Control parameters are designed based on mathematical analyzing of the system. Finally, simulation and experimental results verify the proposed method.
Fault-Tolerant Control of Dual Three-Phase Permanent Magnet Synchronous Machine under Open Phase Fault

In order to solve the asymmetry and coupling problem of the fault mathematical model, and improve the fault-tolerant control performance of the dual-three-phase permanent magnet synchronous machine (D3-PMSM) without shift phase, a post-fault reduced-order decoupling model of D3-PMSM under single phase open circuit fault (OCF) is derived through the coordinate transformation from $d-q$ to $s-e$ axis. A post-fault Field Orient Control (FOC) is proposed and investigated on basis of the derived reduced-order decoupling mathematical model and the minimum copper loss criterion. Meanwhile, the post-fault $2^{\text{nd}}$ and $4^{\text{th}}$ harmonic flux and back electromotive force (EMIF) are considered, and a PIR controller is used to suppress the $2^{\text{nd}}$ harmonic current to reduce torque ripple. Finally, the experimental results demonstrate the fault-tolerant control strategy can keep the torque and speed constant before and after fault, and suppress the torque ripple after single phase OCF happens.
Control Strategy for High Lift Motors in Distributed Electric Propulsion Aircraft

Distributed electric propulsion (DEP) aircraft utilize numerous high-lift propellers across their wingspans to enhance airflow during low-speed flight, which effectively boosts their aerodynamic efficiency. The efficiency improvement of high-lift systems plays a pivotal role in expanding the flight range of DEP aircraft. This study introduces a maximum torque per ampere (MTPA) control strategy, premised on model reference adaptive control (MRAC), to address this critical concern. By deploying permanent magnet synchronous motors (PMSM) in the high-lift systems, the proposed MTPA control strategy facilitates automatic identification and updates of motor parameters, resulting in a significant increase in the efficiency of the high-lift systems. The simulation results demonstrate the superior static and dynamic performance of the MRAC-based MTPA control system.
Analytical Model Transfer-Based Surrogate-Assisted Design Optimization for SPMVM Using Structural Similarity

This paper proposes an efficient surrogate-assisted design optimization method for a surface-mounted permanent-magnet vernier machine (SPMVM) based on subdomain model transfer learning. In the design optimization of SPMVM with parallel slot structure with asymmetric circumferential structure, a fictitious motor model with similar structure and identical parameters except for the parallel tooth structure is created. This fictitious motor with symmetric circumferential structure is used to build a subdomain model, so that a high-precision surrogate model can be built for optimization with the aid of analytic model data to reduce the size of the finite element dataset required for the training process, and accelerate the optimization process.
Improving Single-Image 3D Reconstruction of Porous Media Using Generator with Four-Region Loss Cuts

With the improvement of computer performance, the use of deep learning technology for 3D digital rock core reconstruction has gradually become a hot topic. However, there still remain significant challenges in achieving high-quality imaging using single-slice reconstruction for 3D rock core images. This paper proposes a method that combines four-region random cutting to enhance the performance of single-image reconstruction for 3D images. The following is the structure arrangement of this paper: In the first introduction section, we introduce the background and significance of 3D digital rock cores, and review several representative generative network models. In the second method section, we detail the ease of use of the method adopted in this paper.In the third part, we conduct rigorous experiments to demonstrate the effectiveness of the improved method proposed in this paper. Experimental results show that the method proposed in this paper is effective, greatly improves the training speed and reconstruction quality.
An Optimization Design System of Medium Frequency Transformer with Non-Ideal Geometry Structure

In the region of electrified transportation, applications such as electric vehicles and high-speed trains have created a demand for miniaturization of power conversion devices. Magnetic components, as important parts of power conversion devices, face challenges in accurately analyzing performance parameters such as efficiency due to increased operating frequency. Additionally, the increase in power density exacerbates heat dissipation issues. To address these challenges, three analytical models are proposed in this paper: one for analyzing leakage inductance of medium frequency transformers (MFTs), another for analyzing winding losses, and the third for steady-state temperature rise analysis. These models exhibit fast calculation speed and high accuracy. Furthermore, based on these analytical models, a transformer optimization design system is proposed that offers fast computation speed and high accuracy. Finally, the accuracy of the proposed analytical models and optimization design system is verified by experiments and simulations.