Miracle-dz/UniFAR-FTUs huggingface dataset:
Suppression of Detent Torque for Arc Permanent Magnet Synchronous Motor Based on Variable-Height Tooth

In multi-axis machining systems, compared to traditional rotary motors, the drive system using arc permanent magnet synchronous motor (A-PMSM) can significantly improve machining efficiency. The existing A-PMSM has the problem of large torque ripple, which restricts the development of machining technology for complex structures. This article proposes one A-PMSM with variable-height tooth. The slot parameters that minimize the cogging torque are obtained while keeping the coil spacer factor equal at different radii. The cogging torque of the A-PMSM is analyzed and calculated using two-dimensional equivalent and energy method. The design criteria for the notch width that suppresses the cogging torque is obtained. The structure of the variable-height tooth is proposed to address the performance waste caused by inconsistent slot area when using the structure of non-parallel slot for A-PMSM. The cogging torque is effectively suppressed while maintaining a high coil spacer factor, which provides experience for the design of the multi-axis machining systems.
Smart Identity Management System Using Blockchain Technology

In the digital era, identity management is essential due to the growing cyber threats and weaknesses in the national ID systems now in place. It is difficult to get and update IDs using the current manual procedure. With an emphasis on data security and transparency, this article offers a blockchain-based identity management system to address these concerns. The suggested system auto-starts from birth, creating distinctive IDs and keeping extensive data, such as national ID information, academic credentials, and more. It is built using blockchain technology, which offers security and user control over identification data. This technology provides a secure, decentralized platform that improves privacy and identity data security. It has the potential to transform identity management across multiple sectors, notwithstanding implementation difficulties. The paper provides a comprehensive analysis of our projected smart identity management system for Bangladesh, beginning with an overview of the system's architecture, proof-of-work, workflow, and required procedures for developing a functional prototype. The system's architecture is based on blockchain technology, which offers a secure and decentralized platform for keeping identity information. In order to increase privacy and security, the architecture is designed to give users control over their identity information. The system is intended to offer a decentralized, safe, and open platform for handling identity data while protecting the confidentiality of individual user information. The advantages of employing this system make it a potential alternative for strengthening the security and privacy of identification information, notwithstanding the difficulties connected with its implementation.
A SiC MOSFET-Based Auxiliary Resonant Commutated Pole Inverter with Simple Auxiliary Resonant Circuit and Improved Modulation Strategy

In this paper, a simple auxiliary resonant circuit is adopted to realize an auxiliary resonant commutated pole soft-switching inverter, and its modulation strategy is improved simultaneously. It can realize ZVZCS turn-on and ZVS turn-off of main switch, and ZCS turn-on and ZVS turn-off of auxiliary switch. Due to the characteristic that the turn-off loss of SiC MOSFET is far lower than the turn-on loss, the use of capacitor is avoided, the volume of inverter is reduced, and there is no neutral point potential drift. The modulation strategy of soft-switching inverter is simple, the control is reliable, and the voltage stress is not higher than the DC power supply voltage. In this paper, a modulation strategy is confirmed and then improved, the operation modes and equivalent circuit of soft-switching inverter under the two modulation strategies are analyzed. Further, the method of designing various electronic components and modulation strategy parameters is proposed. At last, taking SiC MOSFET as switching device, a simulation platform is built, and the effectiveness of the inverter is verified by simulation.
Electric Vehicle Charging Load Forecasting Model Considering Users’ Travel Rules

The proportion of EV in total number of motor vehicles in China has increased rapidly. Under this background, it is of great significance to predict the EV charging load reasonably and accurately. By studying the travel rules of car used by personal objective, the EV charging load prediction is carried out. According to the user 's travel statistics, travel laws of EV are analyzed by the combination of user travel chain and Markov process (MP). Moreover, the charging load forecasting model is established under the two conditions which including EV charging at home and EV charging on the way. Finally, the Monte Carlo (MC) simulation method which used widely is adopted to calculate the charging load. In addition to this, the EV charging load distribution at different times and different regions is obtained. The simulation results in the software show that the established forecasting model can make accurate and reasonable predictions of charging loads for a large number of randomly dispersed electric private car.
COVID-19 Detection Using Fusion-Based Deep Learning Models

The COVID-19 pandemic has been one of the most challenging crises attacking the world in the last three years. Many systems have been introduced in the field of COVID-19 detection. In this research, deep learning models for the detection of COVID-19 with a probability of the presence of COVID-19 are proposed. The COVID-19 dataset is split into 70% training and 30% testing using the holdout method. For the deep learning model, the CT images are fed into the model without feature extraction, and three different deep learning DL models (convolutional neural networks (CNN), GoogleNet, and residual nets (ResNet50)) are trained and evaluated. The three deep learning models are fused to obtain high accuracy, and get better performance. The trained models are evaluated using accuracy, precision and recall evaluation metrics. The experiments show that the best model is the deep-based fusion model by which the system achieved 96.4%, 96.2%, and 96.2% for accuracy, precision, and recall, respectively. Comparing our study with the related work proves the powerful and high performance of the proposed methodologies.
Sentiment Analysis to Find Out Positive or Negative Opinions on Ride Hailing Application

Ride-hailing services, particularly in the context of the Gojek app, have become a major talking point on social media platforms, especially Twitter. This phenomenon is not surprising, given that so many people now rely heavily on on-demand ride-hailing services, both as drivers and customers. In an effort to understand the growing sentiment towards these services, a system has been proposed with a classification approach using the Support Vector Machine (SVM) method. The results of this study show that this system is able to provide a fairly accurate picture of the sentiment related to Gojek services. In a series of tests conducted, the Confusion Matrix produced an accuracy rate of 0.803. This accuracy score reflects the model's ability to correctly attribute the appropriate sentiment label for about 80.3% of the overall test data used. The system is therefore instrumental in providing valuable insights into users' perceptions and responses to on-demand ride-hailing services, which is highly relevant in the evolving online transportation industry.
Research and Application of Cybersecurity Situation Awareness for Smart Grid Power Control System

Cybersecurity situational awareness technology holds paramount significance within the network environment of power monitoring systems. It serves to promptly detect and address various network threats, thereby safeguarding sensitive information and data against malicious activities. Leveraging innovative technologies such as machine learning, artificial intelligence, and big data, the cybersecurity situational awareness of power monitoring systems can unravel inherent logical relationships among datasets. Presently, the cybersecurity of power monitoring systems confronts an array of risks, exposing certain inadequacies in corresponding defense measures. In response to these challenges, this paper introduces cutting-edge technologies pertaining to the cybersecurity situational awareness platform for power monitoring systems. These encompass a multidimensional security event correlation analysis model, an anomaly detection methodology founded on "baseline learning" for abnormal traffic and behaviors, a model for recognizing attack chains based on attack scenarios, and a power remote control security technology predicated on "address self-verification". We employ data situational awareness, data classification and grading, and data leakage traceability technologies to safeguard the data security of the power control system. Finally, we encapsulate the situational awareness solution for power monitoring systems and its applications, culminating in a synthesis of prospective research for the future.
Mongolian Compound Word Semantic Web Word Vector Evaluation Research

Semantic dictionaries are the basis of natural language processing tasks. With the construction of WordNet, CCD, HowNet and other semantic dictionaries, the semantic resources of Chinese and English have been greatly improved, and so has the construction of Mongolian lexical semantic network. As an important part of the lexical system, the construction project of Mongolian lexical semantic network is an essential step to fill in the perfect compound words. In this study, the Mongolian compound word semantic web based on the optimal word vector model is automatically generated by comparing the Word2Vec model, Glove model and FastText model for Mongolian word vectors in different dimensions and performing similarity and similarity on the test set.
The prediction model of the equivalent thermal conductivity of stator winding

The thermal management has become a more and more important topic in the development of traction machines for electric vehicles. In order to achieve good thermal management, the accurate thermal calculation is necessary. This paper investigates the equivalent thermal conductivity of the stator winding by proposing three equivalent models. The equivalent thermal conductivity of single copper conductor with insulation is deduced. After that, the equivalent thermal conductivity of the impregnated stator winding is calculated by Hashin-Shtrikman and effective medium theory equations. Based on finite element simulations, the correctness and accuracy of the proposed models is verified.
Effects of Unequal-tooth Stator on Electromagnetic Vibration of Fractional-slot Concentrated Winding Permanent Magnet Motors

This paper investigates the effects of unequal-tooth width stator on radial force harmonics and cogging torque in fractional-slot concentrated winding permanent magnet (FSCW-PM) motors. Firstly, considering unequal-tooth width stator, the cogging torque and radial force are derived analytically through the magnetomotive force-permeance model. The general analytical relationship is revealed for the slot/pole combination and cogging torque and radial force in FSCW-PM motors with unequal-tooth width stator. Subsequently, the effects of the unequal-tooth width stator on the radial force harmonics and cogging torque are verified by finite element analysis (FEA), respectively. Finally, the effects of unequal-tooth width stator on the torque ripple, vibration and cogging torque are validated in FSCW-PM motors through the simulation and experimentation.
Epilepsy Database Collection Implementing Smartphone Inertial Sensors

Epilepsy is a neurological disorder characterized by recurrent seizures and these seizures require ongoing monitoring. Data collection is a crucial first step to improve our understanding of this condition. Implementing smartphone inertial sensors is a new approach to epilepsy data collection that involves using the accelerometer and gyroscope sensors in a smartphone to record physical movements during a seizure episode. The aim of data collection is to gain a deeper insight into the characteristic patterns of seizures and to improve its management. Because there was not any available dataset for epilepsy detection and all the researchers in the previous studies used locally produced datasets so, an Android application was developed that can collect raw data from motion sensors (accelerometer and gyroscope) and experiments were conducted to determine the best sample rate for epilepsy cases. In this paper, we presented an application for gathering a dataset of sensor data from 30 people 15 male and 15 females between the age of 18-45, and each participant were asked to perform 9 activities. The sampling rate used in collecting data has been investigated and analyzed using Fast Fourier Transform technique resulting a suitable sampling rate (50 samples/second) for capturing motion sensor data. The results show that smartphones can accurately record physical movements during seizures and can efficiently be utilized to collect a sizable and diverse dataset for enhancing seizure detection algorithms.
Image-to-image Translation for Enlargement of Aircraft Skin Defect Datasets

With the size and diversity of a dataset being integral to the accuracy of aircraft skin defect detection models, it can be argued that the currently available small aircraft skin defect datasets need to be greatly increased. In this paper, we suggest employing image-to-image translation techniques, specifically the CycleGAN network combined with perceptual loss. By exploiting the CycleGAN network’s ability to generate images similar to those fed into the network, we aim to increase the size of an available aircraft skin damage dataset through the generation of synthetic images. The addition of perceptual loss further enhances the quality and realism of generated images. Through image evaluation with Fréchet Inception Score, we showcase how this method has the capacity to address the challenge posed by limited datasets for small aircraft skin damage.
A Two-term Analytical Iron Loss Model of High-Speed Motor Considering Abnormal Eddy Current Loss

In this paper, a two-term analytical iron loss model considering the abnormal eddy current loss (ECL) is established. Based on the traditional two-term iron loss model (TTILM), an abnormal eddy current loss (AECL) coefficient is added to the ECL term, which changes with the change of the magnetic flux density (MFD) and frequency. In order to avoid the complicated post-processing process of finite element method, the MFD waveform of one-third position of the tooth and half position of the yoke are extracted, and substituted into the model as the average MFD of the tooth and yoke respectively, and a fast two-term analytical iron loss calculation model was established. By comparing with the finite element model (FEM), it is found that the proposed model has accurate calculation accuracy at high frequency.
Multi-Scale Convolutional Vision Transformer for Semantic Segmentation

Semantic segmentation is a fundamental task in computer vision, and the development of encoder networks remains one of the primary directions in this field. Encoder networks play a crucial role since the final result of segmentation directly depends on the quality of the extracted image features. This paper presents a novel hierarchically structured encoder architecture that integrates Convolutional Neural Networks with multi-scale feature capturing and Transformers. The proposed model introduces three significant architectural solutions. First, it introduces a new Self-Attention Block that includes an effective Inception-like multi-branch convolution layer with a Multi-Head Self-Attention layer. Second, the computational complexity of Multi-Head Self-Attention is efficiently reduced by resizing the spatial dimensions of Key and Value tokens using Average Pooling. Third, it abandons positional encoding and adopts a Depth-Wise convolutional layer in the Feed-Forward Network instead. We conducted experiments on datasets of varying complexity for image classification, such as CIFAR-100 and Caltech-256, to assess the generalization ability and feature extraction efficiency of the network. It demonstrates competitive performance, achieving an accuracy of 0.775 on CIFAR-100 and 0.665 on Caltech-256, compared to state-of-the-art Transformers, while having lower model complexity. These results demonstrate the great potential of our model and underline the need for further research aimed at its refinement, the search for the best architecture characteristics, pre-training on the ImageNet-1K dataset, and its eventual integration into semantic segmentation tasks.
An Improved Strategy for Direct Instantaneous Torque Control of Four-Phase Switched Reluctance Motor

A direct torque transient control based on the relationship between inductance and torque of switched reluctance motors(SRM) is proposed for application to a four-phase 16/12 switched reluctance motor. Based on the direct transient torque control by applying the hysteresis torque controller, a new control region is added, which is located near the minimum value of phase inductance, considering the effect of phase inductance on torque. The control of the new region can prepare for the subsequent torque build-up. Compared with the conventional direct transient torque control, it can provide torque and control torque pulsation at higher speed and higher load.
A Review of Fraudulent Practices in Healthcare Insurance and Machine Learning-Based Investigation Approaches

Healthcare insurance fraud is a complex and costly problem that has become a concern globally. Traditional methods of detecting fraudulent claims and requests are time-consuming and often ineffective. Machine learning methods offer potential solutions to this problem by improving fraud investigation and prevention in health insurance systems. This paper presents a comprehensive review of machine learning-based approaches for addressing healthcare insurance fraud, as well as associated challenges and limitations. Despite limitations, our findings suggest that fraud could be effectively tackled by addressing the challenges identified. Areas for further research were also highlighted.
Vibration Sources and Minimization Techniques in Permanent Magnet Synchronous Machines: A Review

The topic of vibration and noise in permanent magnet synchronous machines (PMSM) is paramount due to its convolution with not only the aging of windings and bearings but also the user experience in applications such as electric vehicles and wind power generation. In this paper, a detailed review of the state-of-the-art in this field with specific focus on the vibration sources and minimization methods is presented. The paper lays the foundation for an up-to-date and more thorough knowledge of the topic which is critical to the engineering design and innovations in the area.
Research on High-Efficiency Control Strategy for Aircraft Electric Propulsion Motor System

In order to improve the efficiency of aircraft electric propulsion motor systems, this paper proposes a minimum loss discontinuous pulse width modulation(DPWM) algorithm and a minimum loss control algorithm based on virtual high-frequency signal injection(VHFSI). Firstly, the harmonic voltage and current of the motor are analyzed based on the double Fourier decomposition, and an accurate loss model including high-frequency harmonic losses is established. On this basis, a normalized minimum loss DPWM modulation method is proposed to reduce the overall loss of the system. In addition, in order to operate the motor at the most efficient current angle, a minimum loss control method based on VHFSI is proposed. The simulation results show that the proposed modulation method and control algorithm can effectively improve the overall efficiency of the system.
Loss Analysis of a Free-Piston Transverse-Flux Linear Generator under Reciprocating Variable Speed Motion

The Free-Piston linear generators work in reciprocating variable-speed motion, but they are often simplified and equivalent to uniform motion, which leads to inaccurate losses calculation of the generator, and thus affects the calculation accuracy of efficiency. The transverse-flux linear generator (TFLG) is very suitable for free-piston power generation due to its outstanding performance. However, its magnetic circuit is relatively complex and thus its loss calculation is quite special. Therefore, the loss characteristics of the free-piston transverse-flux linear generator (FPTFLG) require in-depth analysis. Firstly, this paper delves into the effects of variable speed motion on stator core loss, copper loss, and permanent magnet loss. Then, the precise transient loss models have been proposed to accurately evaluate the loss changes of linear machines under reciprocating variable speed conditions. Finally, compared to uniform motion, the impact of electromagnetic performance such as efficiency and output power of FPTFLG under reciprocating variable speed motion is thoroughly analyzed.
Distributed Sensing of Fiber-Optic Communication Lines Using Brillouin Scattering

This paper deals with main aspects of distributed sensing of fiber-optic communication lines using Brillouin scattering. The results of investigation of the main functionalities of the coherent reflectometry method and the method of counter propagating waves are presented. Special attention is paid to the principles of construction of reflectometry systems and analysis of requirements to optical radiation sources. In conclusion, the main problems and prospects of practical implementation of the method under study in the practice of monitoring fiber-optic communication lines are formulated.
Sensorless Starting Method for High-Speed Brushless DC Motor Based on Quasi-Current Source Inverter

During the sensorless starting process of highspeed brushless DC motors (HS-BLDCM), the small stator resistance and inductance will cause large starting current, and the speed estimation error will cause incorrect switching of self-synchronous operation. Focused on these problems, a constant current frequency up acceleration starting method based on quasi-current source inverter (QCSI) is proposed in this paper. First, a QCSI is applied to directly control the current amplitude of the constant current inductance, and the motor can be accelerated with constant torque by keeping the constant inductance current of QCSI. Then, the commutation instants are calculated based on this uniform acceleration process, and the number of acceleration steps is designed to achieve self-synchronous operation switching, which avoids incorrect switching caused by speed estimation error during the starting process. Finally, the experimental results verify the effectiveness of the proposed method.
Substrate and Macroalgae Instance Segmentation Based on Deep Learning with Background Filter

The health of a coral reef directly reflects the state of the local marine ecosystem as corals and macroalgae compete for living space. Therefore, in addition to the distribution of corals, the distribution of macroalgae is an important indicator in coral reef ecological surveys. However, previous computer vision research has mainly focused on coral classification with limited attention to macroalgae classification. Thus, we proposed a substrate and macroalgae instance segmentation based on deep learning with a background filter using the YOLACT deep learning algorithm to perform instance segmentation on various types of substrate and macroalgae. A Gray-level Co-occurrence Matrix (GLCM) [7] and the appropriate threshold were also employed to determine if YOLACT misclassified the background and improve the accuracy. According to the experimental results, the proposed approach of using the GLCM and appropriate threshold showed a 29% increase in precision.