Miracle-dz/UniFAR-FTUs huggingface dataset:
Suppression Method of Harmonic Disturbance Caused by Misalignment in MLRMS Using Improved Fractional-Order Repetitive Controller

In a large magnetic levitation rotating mechanical system (MLRMS) with multi-span rotors (MSR), the misalignment of the coupling will disturb the safe operation. To compensate the interference caused by misalignment of the coupling, a rotor disturbance suppression method using a resonant controller based on parallel fractional order repetitive control is proposed. In this paper, the mathematical model of coupling misalignment disturbance is established, and the influence of unbalanced mass is investigated. The stability analysis of the system with proposed disturbance suppression algorithm is carried out. The simulation results show that the method can effectively suppress the disturbance of unbalanced mass and misalignment.
Maize Leaf Disease Identification Method Based on Improved Faster R-CNN

Maize leaf disease is a common problem in the process of crop growth. It is of great significance to identify and monitor the disease timely and accurately for the benefit and yield of agricultural production. This paper aims to propose a method of maize leaf disease recognition based on improved Faster R-CNN. First, Region Proposal Network (RPN) is introduced in Faster R-CNN to generate candidate regions to reduce the computational load in the identification process. Secondly, the network uses Adam optimizer to optimize the training process of the model to improve the convergence speed and performance. Finally, in order to better deal with multiple categories of diseases, we use the binary cross entropy loss function to classify them, and introduce the box regression loss function to accurately predict the disease boundary box. The experimental results show that the method can improve the accuracy of disease identification and recall rate significantly. It has important application value for crop disease monitoring and agricultural production.
The Study of an Impact of Alcohol Intoxication on Voice Parameters in Biometric Authentication Systems

Changing person voice characteristics under the influence of alcohol intoxication affects the effectiveness of the voice identification procedures. Therefore, determining the level of alcohol in the blood remotely allows not only to identify the fact that the subject is in an altered psychophysiological state but also makes it possible to adjust the settings of the decision-making algorithm to increase the reliability of voice authentication. The purpose of the work is to analyze existing scientific works and results in the field of assessing the level of alcohol intoxication by voice and the analysis of data sets that make it possible to train artificial intelligence models to identify the fact of intoxication, as well as the analysis of factors influencing the psychophysiological state of the speaker on the parameters of his speech. The study includes a comparative analysis of scientific works from databases such as Scopus, Web of Science, and articles included in the list of Higher Attestation Commissions and takes into account factors such as gender, age, stages of alcohol intoxication, recording quality, and ambient noise level. Based on an analysis of scientific publications, the study identifies random forest as one of the most effective machine learning methods, demonstrating an accuracy of 95.3% on proprietary speech datasets and 80% on the widely used alcohol language corpus.
Efficiency Optimal Predictive Control of Induction Motor Based on Loss Model Method

To address the issue of low efficiency in induction motors under light load conditions, we propose a predictive control method based on the loss model approach for efficiency optimization. Current research on magnetic flux optimization often overlooks the impact of iron loss resistance current, which can affect model robustness and accuracy. Therefore, we introduce a robust loss model that considers the iron loss resistance current for motor efficiency optimization. For the control strategy, we choose model predictive control (MPC) for its fast dynamic response and simple structure. To enhance the robustness of the loss model approach, we incorporate a model reference adaptive system (MRAS) based resistence identification method. Experimental results confirm the effectiveness of our proposed method.
A Novel Real-Time Control Strategy for Smart-Multi-motor System Based on a Hybrid FPGA Gateway

The multi-motor system is an electromechanical system that finds application in various fields such as electric vehicles, robotics, and industrial machinery. It consists of multiple servo motors that work collaboratively to accomplish predetermined tasks. With the continuous upgrading and optimization of the system, the number of motors in the system increases, resulting in added strain on the communication network and consumption of control resource, which affects the synchronization of the system. In this paper, we proposed a novel control strategy based on a heterogeneous field programmable gate array (FPGA) gateway and designed smart micro motors that ensured the communication performance while minimizing the requirement for substantial control resources. By combining the two mentioned above, we have developed a Smart-Multi-Motor system. Additionally, a series of tests are conducted to verify the performance of the smart motors, and to demonstrate the system’s feasibility.
A Novel Hybrid Reluctance Machine using Synthetic Slot PMs and Zero-Sequence Current Excitation

This paper proposes a novel hybrid reluctance machine with zero-sequence current and dual-layer slot PMs excitation. Dual slot PMs are utilized to evoke DC-saturation-relieving and flux modulation effect, respectively. By introducing zero-sequence current excitation acting as invented DC field source, extra DC field winding can be removed. Moreover, the flux can be flexibly controlled through regulating zero sequence current for the machine speed range extension. In this paper, the machine configuration, DC-saturation-relieving principle, and flux modulation principle are introduced. In addition, the usage of dual-layer slot PMs and zero sequence current ratio are considered to obtain optimal torque performance. By using finite element analysis, the overall electromagnetic performance of the proposed machine is evaluated, including flux-linkage, no-load back-EMF, as well as steady torque with different zero-sequence currents. Further, the excellent flux regulation ability of zero-sequence current is verified, which can extend the speed range of proposed machine for electric vehicle propulsion.
Improved Strategy for Initial Point Generation of Intelligent Algorithm for Battery Operation Optimization in Microgrid System

With the rapid development of distributed renewable energy and energy storage, microgrids have emerged as a new energy system model. To better utilize peak-valley electricity prices or absorb renewable energy, it is necessary to optimize the operation of batteries in the system. This usually involves constructing a mathematical model of the system and applying optimization algorithms. Among various types of optimization algorithms, intelligent optimization algorithms are widely used due to their superior adaptability to models. However, for energy storage batteries, due to their complex constraint conditions, it is often challenging to ensure that intelligent optimization algorithms can converge quickly or find feasible regions. Therefore, considering the modelling characteristics of the constraints of energy storage batteries, this paper proposes an initial point generation strategy based on a divide-and-conquer approach. This strategy ensures that all initial points of the intelligent optimization algorithm meet the battery model constraints and exhibit excellent randomness and diversity. Subsequent case tests show that, compared with intelligent optimization algorithms not adopting this strategy, those using this strategy converge faster and deliver better optimization results.
Large-Scale Benchmark for Uncooled Infrared Image Deblurring

Infrared images are increasingly adopted in various applications. Therefore, motion deblurring for infrared images is also receiving growing interest. However, deep-learning-based deblurring techniques for infrared images have yet to be deeply studied, since there is no publicly available dataset for training and evaluating the networks. In this article, we introduce a large-scale dynamic scene deblurring dataset for microbolometer-based uncooled infrared detectors named uncooled infrared image deblurring (UIRD), which reflects their unique blur characteristics. The dataset is synthetically generated from cooled midwave infrared (MWIR) camera images using a combination of frame interpolation, IR band conversion, and a unique blur accumulation model for the microbolometer. The dataset consists of more than 30k blur-sharp image pairs, and we show the effectiveness of our dataset by showing deblurring results on real uncooled infrared images with the deblurring algorithms trained with our dataset. Our dataset is publicly released to facilitate future research in this area.
Attention-based Mechanism for Anomaly Time Slice Detection in DNS Tunnel Communication

In recent years, there has been a growing number of network attacks using DNS tunneling technology. Traditional machine learning classification and clustering algorithms do not consider the temporal relationships between time slices, leading to the issue of false negatives and reduced overall recall in DNS tunnel detection. To address this problem, this paper proposes a DNS tunnel communication anomaly time slice detection method based on attention mechanisms. It introduces BiGRU and attention mechanism techniques for detecting abnormal time slices in this scenario. Experimental results demonstrate that the method proposed in this paper not only improves accuracy but also reduces the false negative rate. Compared to traditional UDP-based DNS communication scenarios, the false negative rate is reduced to 0.98%, accuracy is increased to 99.91%, with a false positive rate of 3.61%.
Axial Split-phase Flux-switching Permanent-magnet Motor with Complementary Structure

In this paper, a novel method is presented to improve the torque density of flux-switching permanent Magnet Motor (FSPM), which has been proposed to meet the demands of electric vehicle drive system. In order to improve the utilization rate of permanent magnets, the full pitch salient rotor and axial split-phase structure are implemented. Since the number of salient poles of the rotor is the same as the number of stator slots, the fundamental magnetic field is utilized, and the stator windings on the same cross-section should be of the same phase. And the axial split-phase structure is introduced. In order to realize the winding complementarity, the different operation principles between the proposed FSPM and the traditional FSPM are illustrated, and the complementary structure with magnetic circuit is constructed. Finally, the performances of the proposed FSPM are analyzed by the finite element analysis and the results verify the effectiveness of the proposed methods.
Enhanced Ventilation and Thermal Performance by Skewed Through Holes on Rotor Yoke in Forced Air-Cooled Permanent Magnet Wind Generators

To enhance the ventilation and thermal performance of forced air-cooled permanent magnet synchronous wind generators (PMSWGs), this paper proposes a novel ventilation structure consisting of skewed through radial holes on the rotor yoke. 3-D coupled fluid-thermal computational fluid dynamics (CFD) models are developed for this study. The electromagnetic losses are considered as the heat source and appropriate boundary conditions are determined. Utilizing the CFD method, the fluid and temperature fields of generators with various rotor structures are computed. The simulation results show that the proposed through holes can reduce pressure loss and temperature of the winding significantly. Additionally, the influence of the hole structural parameters on the ventilation and electromagnetic performance is investigated.
Fast Semi-analytical Calculation of AC Copper Loss in Flat Wire Winding of YASA AFPM Motors

This article proposes a fast semi-analytical calculation method for AC copper loss of flat wire winding based on a new dual module YASA motor. This method is based on the FEMM open-source finite element platform, combined with a semi-analytical computational model. It improves computational efficiency from both time and space dimensions by reducing computational cycles and simplifying the number of computational grids. In this method, the influence of curvature effect, core saturation, magnetic leakage, and end region of YASA motor on AC copper loss are considered. To verify the effectiveness of this method, a comparison was made with the three-dimensional finite element method (3-D FEM). The proposed fast semi-analytical calculation method for AC copper loss of flat wire winding is consistent with the results of 3-D FEM calculation, and reduces the calculation time to 1/54.
On the Applicability of a Markov Virus Spread Model to E-mail Graphs

Modeling of computer virus epidemics is the most important area of theoretical research in the field of information security. The objective of this paper is to analyze the applicability of the modified Reed–Frost model for epidemic analysis on real life graphs. We compare the results calculated using the model with the results obtained during the simulation with different values of epidemic parameters. As a result, the values of epidemic parameters, in which both methods are in good agreement with each other, have been identified.
Design of A Wireless Switched Reluctance Motor and Drive System Using Switch-Controlled Capacitors

This paper proposes a new wireless SRM (switched reluctance motor) and drive system using SCC (switch-controlled capacitor) and AVC control (asymmetrical voltage-cancellation control). The power is transferred to the motor through coils instead of traditional wires. The output voltage is regulated by the pulse width of the inverter, and the resonant frequency of the transmitting side is tightly regulated by the equivalent capacitance of the switched capacitor. By exciting the winding phases of the SRM according to the rotor position, continuous positive torque is produced. A prototype has been constructed with SIMULINK to validate the system proposed in this paper.
The Analysis and Application of Face Recognition Technology

At present, face recognition technology is developing rapidly, it has a broad future prospect and many scholars have formed some scientific research results, which makes the technology develop rapidly in various industries. By reviewing several literatures, there is still a lack of relevant research that elaborates on face recognition technology, so this paper will be based on an in-depth study of face recognition technology. This research will provide a comprehensive analysis of face recognition systems, their main components and principles, some examples of face recognition algorithms and an analysis of their advantages and disadvantages, and a comprehensive analysis of the problems that exist at this stage of the technology. The article concludes with a summary and outlook of the whole paper.
Sensorless Adaptive PI Control of High-Power Propulsion PMSM Based on Sliding-mode Extremum Seeking Algorithm

This paper proposes a multivariable-sliding-mode extremum-seeking (MSES)-based adaptive PI method for sensorless speed control of high-power propulsion permanent magnet synchronous motor (PMSM) applications. Unlike the offline-tuning PI controllers, the proposed controller incorporates adaptive tuning laws that dynamically adjust the control gains in real-time using the gradient descent method. Using a speed feedback error term and a suitable sliding surface, the MSES tunes the PI gains during operation with a low computational load. Real-time tuning of the PI gains can reduce the system sensitivity against the variation of both the motor parameters and the load torque disturbances, and therefore, a satisfying tracking performance can be achieved. This method is easy to implement on a medium-performance micro controller unit (MCU). In order to verify the performance of the proposed control method, a simulation study is conducted using MATLAB/Simulink software, which shows that the proposed method has relatively high accuracy as well as satisfactory performance in terms of robustness against load torque perturbation and independence of parametric variations.
Research on Sentiment Analysis of Tibetan Short Text Based on Dual-channel Hybrid Neural Network

In response to the problem of varying degrees of loss of textual semantic information with the increase of model depth in a single-channel hybrid neural network model, this paper proposes a dual-channel hybrid neural network model-ALDCBAT based on the idea of multi-channel hybrid neural network, using ALBERT pre-training model, convolutional neural network and bidirectional gated unit network. The model first vectorizes Tibetan texts using ALBERT pre-training model, and then inputs the word vectors into the TextCNN model and the BiGRU model respectively. Secondly, an attention mechanism is introduced to enhance the text feature extraction ability of the BiGRU model. Finally, the output of the TextCNN model is concatenated with the output of the BiGRU model and the attention mechanism as the final output. Experimental results show that the classification accuracy of the dual-channel hybrid neural network model proposed in this paper is 91.12%, which partly solves the problem of loss of semantic information and effectively improves the classification accuracy of Tibetan sentiment analysis.
Investigation of short-circuit fault control for aerospace dual three-phase motor drive

This paper will focus on the research of short-circuit faults in dual three-phase electric drive bridge for aviation applications. First, the dynamic process of system under the condition of a single power device short circuit is analyzed. Then, the circuit fault detection and fault -tolerance method is proposed to reduce the braking torque generated from the fault side. The effectiveness of proposed method has been verified by the simulations and experiments. The research will provide theoretical and technical support for the functional safety of aviation electric drive systems.
Model-Free Predictive Current Control for Three-Phase AC/DC Inverters with LCL Filter Based on Hybrid SVM

The technology of space vector modulation has been commonly adopted in model-free method for AC/DC inverters based on LCL-type filter, which can achieve strong robustness against parameters disturbance and good steady-state performance. However, the grid-side current harmonics of model-free method with SVM is commonly higher than that of model-based method with SVM when accurate system parameters are known. To reduce the current THD while maintaining the strong robustness, an improved model-free method has been proposed in this paper, which adopts modified ultra-local model and hybrid SVM to reduce the steady-state harmonics of grid-side currents. The robustness and current harmonics reduction of the proposed method have been verified by the simulated results.
Static nonlinear errors compensation for RF front-end circuit on high speed RF sampling (Ti)ADCs

RF receivers can be divided into two main parts: the RF front-end circuit and the quantization part. In wideband receiver technology, the direct RF digitization architecture is extensively used. This allows for the digitization of the entire bandwidth before any processing, but it places significant demands on the RF front-end circuit. Active components such as Low Noise Amplifier (LNA) and Track & Hold (THA) can introduce nonlinear errors that propagate throughout the sampling and quantization process.Purpose: The aim of our work is to develop a background solution for estimating and correcting these errors primarily through digital calculations. We are investigating the potential benefits and limitations of dithering methods to determine their compatibility with the analog constraints imposed by the RF front-end circuit.Methods: We represent the static nonlinear errors using a third-order polynomial model. We develop an estimation algorithm utilizing the dithering method to determine the coefficients of this polynomial. We examine the implications of the errors introduced during the incorporation of the dithering signals.Results: Our study shows that the estimation algorithm works when the errors on the dithering signals are ignored. The practical limitations depend on the necessary accuracy of the dithering signals for the estimation algorithm to function effectively, in comparison to the achievable accuracy on real circuits.
A Series Compensated 27.5kV to 10kV Power Supply Device

A series compensated 27. 5kV/10kV power supply device is proposed to solve the difficulties of power supply and power quality problems of 10kV voltage level in railroad network. The device includes a power transformer, an AC/DC converter and a series transformer. The power transformer includes two 27. 5kV inputs. One set of three-phase 10kV outputs to the through or self-closed line, and the other set of three-phase low-voltage outputs to the AC-DC converter. The AC/DC converter can provide grid voltage drop compensation and low harmonic suppression to the 10kV grid through the series transformer with 10kV voltage coordination control strategy. Simulation and experimental results show that the topology and control method proposed in this paper have a good compensation effect.
Leveraging AIoT for Advanced Quality Control in Production Lines

Nowadays, edge computing has emerged as a crucial component in the fourth industrial revolution, effectively merging data processing in close proximity to its source. This approach not only enhances the efficiency of data processing but also integrates data acquisition, analysis capabilities, sensing, and communication. Additionally, the Internet of Things (IoT) holds substantial importance within Industry 4.0, serving as a fundamental technology for wireless connectivity, data collection, and real-time monitoring. Simultaneously, while edge computing offers numerous advantages, it also presents challenges such as security concerns, data-intensive services, handling incomplete data, and notably, substantial investment and maintenance costs. To address these issues, cloud computing technology emerges as the optimal solution. This article aims to to propose an innovative approach called Artificial Intelligence of Things (AIoT) for optimizing production management. The focus of this project is the development of a production line for quality control capable of classifying gears as well as detecting defective ones. The production line collects comprehensive data, which is subsequently transmitted to a server for further processing. The results are then displayed through various interfaces, including an a web dashboard, A desktop interface and an Android app, providing analytics insights. This project leverages the integration of AI and IoT technologies within the AIoT framework to create a fully autonomous environment that significantly enhances overall efficiency.