Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Analysis of a Novel Segmented PM Variable Leakage Flux Machine Considering Variable Flux Range

This paper proposes a novel variable leakage flux machine (VLFM) with segmented permanent magnets (SPMs), which is geometrically characterized by distinct flux-leakage bypass and has SPMs mounted on the rotor. The magnetic saturation status of the flux-leakage bypass and hence the leakage flux can be flexibly changed by controlling the load current. Thus, the air-gap flux regulation can be realized. The proposed SPM-VLFM has the advantages of low iron loss and high efficiency over a wide speed range, in which the required flux-weakening current and the associate excitation loss can be reduced. The machine topology and operating principle are introduced, respectively, and the multi-objective optimization is carried out to obtain the optimal design. Then, in order to validate the feasibility of the proposed design, the preliminary electromagnetic characteristics of the SPM-VLFM are compared with those of conventional machines with particular reference to the variable leakage flux property.
A Review of Ground Segmentation Methods for Vehicle-Mounted LiDAR Point Clouds

With the rapid development of the autonomous driving industry, the robustness requirements for autonomous vehicle perception systems are increasing, and in-vehicle LiDAR has gradually become an essential sensing device. This article first introduces the types of vehicle-mounted laser mines and discusses their advantages and disadvantages; Then, a comprehensive summary and classification of existing ground segmentation methods based on LiDAR point clouds were conducted. Finally, the advantages and disadvantages of these methods in various road environments were compared and analyzed comprehensively, and the future development direction of such research was prospected.
Analysis of Loss and Temperature of a High Speed Motor with Composite Magnetic Fiber Rotor

In this paper, a high speed motor with composite magnetic fiber rotor is proposed. Then the influence of three different proportions of magnetic powder and carbon fiber on the rotor on the electromagnetic properties is analyzed by 2D finite element method (FEM), including the rotor eddy current loss, the core loss and the copper loss. Finally, the 3D models of three different proportions are built in ANSYS Fluent to analyze the heat transfer characteristics of different proportions of magnetic powder and carbon fiber.
The Parameter Adaptive Sensorless Control Scheme for Permanent Magnet Synchronous Motor in a Wide-speed Range

In order to reduce the parameter sensitivity of sensorless control for permanent magnet synchronous motor (PMSM) driving system, a parameter adaptive sensorless control scheme is proposed. The parameter identification is implemented by the heterodyne demodulation method in the standstill status based on the rotating high-frequency signal injection method, which could also be used for rotor position estimation in the low-speed range at the same time. In the high-speed range, the sensorless control with respect to the back EMF calculation is carried out based on the identified parameters, then, a parameter adaptive sensorless control scheme could be obtained to achieve normalized sensorless control for PMSM in a wide-speed range. Finally, the effectiveness of the proposed method is verified by the experiment results.
Multi-robot Task Allocation based on Improved ADMM algorithm

Task allocation has significant implications for robots performing tasks. For the task allocation problem of indoor robots, this paper uses the improved ADMM allocation algorithm to allocate tasks to heterogeneous robots, and also validates the algorithm with its own created multi-round dataset to get good results in simulation. Meanwhile, this paper also further verifies the algorithm in the physical environments to make the algorithm have a good feasibility.
Design and implementation of a global illumination rendering system based on Surfels

We present the design and implementation of a novel rendering system utilizing a surfel-based global illumination caching method to enhance real-time rendering efficiency. By employing fixed surfels for static objects and updating those for dynamic entities in real-time, the system optimizes the balance between space and computational effort. The implementation, grounded in the Vulkan 1.2 API, demonstrates minimal quality loss compared to traditional path tracing methods while achieving significant improvements in time performance. Experimental evaluations, including diverse scenes and complexity levels, validate the system's effectiveness, marking it as a promising solution for intricate and dynamic rendering environments.
Hot Topic Clustering based on Gaussian Mixture Model built-in DTW

A hot topic clustering method based on Gaussian Mixture Model (GMM) built-in DTW is proposed in this paper. As a typical application of time series clustering, the accuracy of hot topic clustering has been a challenge. Compared with Euclidean distance, DTW distance prioritizes feature alignment in the time dimension by warping the samples to a common time axis, which will make the clustering model focus on the feature differences between sequences and ignore the interference of feature time. Compared with K-Shape, a mainstream time series clustering method, DTW has more specific sequence alignment ability, which greatly improves the accuracy of the algorithm. The traditional DTW algorithm suffers from curse of dimensionality when aligning multiple vectors, so a novel DTW multi-vector consistency algorithm based on existing research is also mentioned in this paper. Compared with the traditional GMM, the fusion of this algorithm can effectively improve the accuracy of hot key words trends clustering from Google Trends by 19.9%, while maintaining the advantage of GMM over K-Means. The improved GMM has 29.9% improvement in accuracy over K-shape, which indicates that it can be well adapted to time series datasets and used in hot topic clustering.
Blockchain in Education 4.0: A Review

As the Fourth Industrial Revolution marks the beginning of an era of digitization, key principles like inter-operability, decentralisation, and virtualisation are significantly impacting a variety of fields, including education. Aiming to combine traditional educational techniques with cutting-edge ICT-enabled tools, including artificial intelligence, automation, and blockchain, this paradigm coined as “Education 4.0” intertwines with “Industry 4.0”. With its intrinsic qualities of decentralisation, immutability, consensus, and transparency, blockchain stands up as an effective solution to problems like governance and credential credibility of the courses in which the student is enrolled can be resolved. This paper has contribution in four folds. This paper makes four significant contributions to literature. It begins by highlighting the goals of Education 4.0 and the relationship between modern teaching methods with cutting-edge technology. It then delves into an analysis of blockchain protocols, spotlighting their alignment with the DeFi protocol stack. The next part divides blockchain projects into four distinct categories, following which reviews diverse blockchain-powered projects in education, evaluating their implementations, advantages, and potential drawbacks.
Classification of Inflammatory Gene Expression Patterns with Machine Learning Models

Dementia is a complex neurological disorder characterized by a progressive decline in cognitive functions. Recent studies have suggested a link between neuroinflammation and the development of dementia, particularly in Alzheimer’s disease. This study focuses on the analysis of inflammatory gene expression patterns in the parietal cortex (PCx) and temporal cortex (TCx) from a human brain RNA sequence data set, aiming to derive insights into the underlying genetic link associated with dementia, as well as prediction of dementia. Using five machine learning and statistical methods, our study reveals better detection and classification of dementia patients using PCx-related gene patterns, as compared with those from the TCx. Our findings support the hypothesis that the PCx plays a significant role in neuroinflammatory responses associated with dementia. Based on this preliminary study, machine learning models offer a useful approach to recognize inflammatory gene expression patterns and providing a valuable biomarker analysis and prediction for dementia diagnosis.
Capacity Optimization Configuration of Grid- Connected Microgrid Considering Green Certificate Trading Mechanism

The optimization of capacity configuration is an important step in the planning and design phase of microgrids. In this paper, a capacity optimization configuration model of a grid-connected microgrid is proposed in order to maximize annual profit while meeting constraints. In this configuration model, the green certificate trading mechanism is considered to consume more renewable energy. By adopting the improved PSO algorithm, the optimized capacity of each device is obtained. By comparison, the impact of green certificate trading and optimization effects of different PSO algorithms on configuration results are analyzed.
Method for Improving Dynamic Characteristics of Electromechanical Transducer Based on Induction Motor

This paper proposes a universal method for improving the dynamic characteristics of induction motors. The transient processes here fluctuate greatly, which reduces the energy efficiency of the electromechanical converter. A flexible feedback on flux linkage is proposed, which makes it possible to suppress oscillations. A numerical experiment was carried out for the mode of starting an induction motor in relative variables.
Object Localization Using Non-Euclidean Metrics

In this paper, we proposed to use non-Euclidean statistical metrics to localize multiple 3D anatomical structures by estimating the object's position, orientation, and size in med-ical images. Precise orientation estimation is extremely important especially for model-based image segmentation algorithms because even a very small change in shape model orientation can lead to an inaccurate localization and segmentation. We statistically evaluated accuracy of orientation estimation using various metrics: Euclidean, Mean Hermitian, Log-Euclidean, Root-Euclidean, Cholesky decomposition, and Procrustes Size-and-Shape. Experimental results showed that non-Euclidean metrics, particularly Mean Hermitian and Cholesky decomposition, provided more accurate estimates than Euclidean metrics. We presented the effectiveness of the proposed method using abdominal and hand computed tomography (CT) images and magnetic resonance (MR) images of the foot. We obtained 3-to-6 times smaller RMSE (root mean square error) object localization errors with Non-Euclidean metrics compared to Euclidean metric.
Method of Intelligent Management of Aviation Maintenance Tools Based on RFID Technology

The aviation industry heavily relies on efficient maintenance processes to ensure safe and reliable operations. However, the management and organization of maintenance tools in aviation are often time-consuming and prone to errors. To realize the efficient and accurate management of the service tools and avoid the unsafe incidents caused by human error, this paper proposed an intelligent cabinet using RFID (Radio Frequency Identification) technology, which could automatically detect and track each tool and report the real-time result to the web-based management system.
The Mid-Frequency Magnetic Property Modeling of an Electrical Steel Sheet Using a Dynamic JA model Considering Magnetic Domain Energy and Fractional Derivative Operator

This paper proposes a dynamic Jiles-Atherton (JA) model considering magnetic domain energy and fractional derivative operator, and this model is verified to improve the modeling accuracy of magnetic property and calculate the magnetic field strength accurately under the frequency from 600Hz-1.8kHz. This approach was applied to estimate the magnetic property of a small scale, lkHz medium frequency transformer.
Unsupervised Low Illumination Enhancement Model Based on YCbCr Color Space

Since most of the current Low Light Image Enhancement(LLIE) models are trained under RGB channel, it has problems such as training process difficulties and color deviation of enhancement results. We propose an unsupervised low illumination enhancement model based on YCbCr color space. This model maps the input low illuminance original image based on RGB space to YCbCr space, and then carries out feature learning on the brightness channel Y in YCbCr. First, extract low-level features through standard convolution dimension elevation, and then divide the network into light-enhancement module and detail-recovery module. In the light-enhancement module, conduct deep-global feature extraction and shallow-local feature extraction respectively , to obtain the feature map after light enhancement. Then the feature map is connected and fused with low-level features in the detail-recover module to restore the texture and details lost in the light enhancement process. After obtaining the feature map after detail-recovery module, the new Y connect the feature map with Cb and Cr channels after dimensionality reduction to obtain the final enhanced image. In the process of model training, a new comprehensive loss function is used to realize the constraints on each component of the model. At the same time, the unsupervised learning process is realized by using the generative adversarial network, and the channel attention mechanism is introduced to achieve a better denoising effect. Through experimental comparison, the average NIQE and LOE values of the new model on the test dataset reach 3.88 and 146.2, which has a competitive enhancement effect.
Enhancing Stock Price Prediction Using Hybrid Auto Regressor and Machine Learning Models

The prediction or estimation of stock prices remains a complex and formidable task for humans, given the volatile price trends of stocks attributed to various factors. Although traditional methods, such as Fundamental Analysis and Technical Analysis, are in use, there is a pressing need to enhance these methods to safeguard funds from potential losses due to unavoidable factors in the stock market. To this end, we have proposed a novel method that predicts stock trends using an Auto Regressor machine learning model that leverages time series data. The suggested model presupposes that a time series' present value depends on its previous values. , and the relationship between them is linear, with the strength between them being determined by an autoregressive coefficient.
Unleashing digital wallet expansion in the post-pandemic: A study using Technology Continuance Theory

The goal of this study is to explore if consumers would continue to use digital wallets after the Covid-19 crisis. The Technology Continuance Theory (TCT) is used as the foundation for the research. The e-wallet has become popular and millions of people use it in daily basis during the pandemic in 2020-2021. However, the pandemic has passed and is now under control. To test the hypothesis, around 250 people took part in the study. The dataset was examined using the structured equation modelling (SEM) and partial least squares (PLS). The study's results show that the link between confirmation and continued use of the digital wallet and between perceived usefulness and continued use is mediated by consumer’s satisfaction. This confirms the notion that people value the quality and usefulness of e-wallets and will continue to use the technology in their everyday lives even after the pandemic is ended.
Low Light Image Enhancement Through A Combination of Retinex Based Decomposition and Curve Estimation

In this paper, we propose a machine learning approach for enhancing photos taken in low light conditions. We use symmetrical convolutional neural networks inspired by the Zero-DCE method to learn a curve for enhancing photos. We also use existing models for decomposition based on the retinex theory, including reflectance and illumination networks inspired by the Kindling the Darkness approach. In addition, we experiment with custom weighted loss functions to optimize accuracy on metrics such as structural similarity. Our approach yields slight improvements in structural similarity while maintaining peak signal-to-noise ratio comparable to Kindling the Darkness 1. Additionally, our method produces more natural-looking colors, as subjectively evaluated by human observers. Our results demonstrate the effectiveness of our approach for enhancing low-light photos and suggest potential avenues for future research in this area.
Forecasting Stock Market Alternations Using Social Media Sentiment Analysis and Deep Neural Networks

Forecasting stock market volatility is a challenging task, primarily due to the influence of non-financial factors such as public opinion and sentiment. Social media platforms, particularly Twitter, have the potential to sway public sentiment and impact stock market prices. In this paper, we present a novel approach for predicting the volatility of multiple indices in the U.S. stock market by integrating historical closing prices with sentiment analysis of Twitter data using deep neural networks. Our approach involves extracting features from tweet metadata and analyzing the overall presence of authors on the platform to estimate future changes in index closing prices. Through our experiments, we observed a strong correlation between the predicted closing prices of an index, the sentiment expressed in tweets, and the extracted features. Despite the sparsity of the dataset, our applied neural networks exhibited robust per-formance. This study contributes to the expanding research on predicting stock market volatility through the utilization of social media sentiment analysis and deep learning techniques. The proposed approach holds promise for applications in financial decision-making and risk management.
A Highly Reliable Naturally Clamped Current-fed Dual Active Bridge Converter

This paper proposes a new naturally clamped Current-fed Dual Active Bridge (CF-DAB) hybrid converter topology to optimize the performance of current-fed DC/DC converters and broaden their application range. Combined with the new topology, four operating modes of the hybrid converter are proposed, which are the full-rating mode, composite mode, full-bridge mode and half-bridge mode. The respective operating intervals are divided according to the voltage gain and the transmitted power range. The multiple operating modes make the proposed topology more widely and flexibly applied. In addition, the reliability of the converter can be improved to a certain extent. Finally, the feasibility of the proposed topology is verified by building a 200W experimental prototype.
Research on Intrusion Detection Method Based on Recurrent Neural Network

Intrusion Detection System (IDS) scans the network or system for intrusion activities or security vulnerabilities, protects the network or host from damage, and detects intrusion behaviors by looking for the characteristics of known attacks or differences from normal activities. Compared with traditional machine learning methods, deep learning algorithms are more effective in intrusion detection. This paper presents a deep learning method based on Long Short-Term Memory (LSTM) to detect attacks. Principal Component Analysis (PCA) and Mutual Information (MI) are used for reduction and feature selection. The proposed intrusion detection method is tested on the KDDCUP '99 dataset, and the results show that the intrusion detection method based on deep learning and PCA achieves better training and testing accuracy in binary classification and multi-feature classification.
Numerosity Adaptation Effect with Three-Surface Stimuli

Humans and animals can rapidly estimate the approximate number of items in a scene. Recent studies have shown that numerosity, like many other perceptual attributes, is susceptible to adaptation. The main purpose of this study was to gain a better understanding of the basic mechanisms governing number perception by conducting experiments on numerosity adaptation using multi-surface stimuli, in particular three-surface stimuli. In the experiments, adaptation experiments were conducted using a combination of one-surface and three-surface stimuli. We confirmed an adaptation effect when the number of surfaces of the adaptation, test, and comparison stimuli was not equal, and no adaptation effect was confirmed when the number of surfaces was equal. These results support the prediction. Overall, this study provides new insights into numerosity adaptation with three-surface stimuli and demonstrates the possible influence of depth perception on the processing mechanism of number perception.
Markov Stochastic Processes Application Tools to Improve the Characteristics of Distributed Systems Synchronization Procedures

Appling mathematical modeling methods for analyses synchronization processes of distributed data processing systems makes it possible to significantly optimize these processes and guarantee the required level of consistency. To analyze stochastic distributed synchronization procedures, mathematical modeling tools are needed to adequately reflect their stochastic nature and form a probabilistic time characteristics assessment of the shared resource ownership distributed between the parallel processes. The research carried out within the current paper shows that stochastic modeling tools are able to solve a number of complex problems in this area and in particular to adapt synchronization algorithms for use in heterogeneous computing systems with a different set of hardware components and providing an acceptable synchronization time for a large amount of data. The paper essentially analyzes Markov synchronization models with group maintenance and considers tools for bringing these models to the case of arbitrary service time.
On Beat Tracking and Tempo Estimation of Musical Audio Signals via Deep Learning

Automatic beat tracking and tempo estimation constitute important tasks in the Music Information Retrieval (MIR) field, with numerous relevant applications being developed. In this paper, a deep learning methodology for beat tracking and tempo estimation is proposed, ex-ploiting the Onset Strength Signal (OSS) of a musical audio signal. Specifically, the presented approach involves trans-forming the audio signal into its corresponding OSS and feeding it into a neural network to predict the occurrence of beats in time. Notably, the implemented system has a relatively simple structure and avoids higher complexity. The proposed method is evaluated on two benchmark datasets and compared to existing methods exhibits equiva-lent performance in terms of both beat tracking and tempo estimation accuracy.
Virtual Reality Based Simulation of Key Technology of Coal Mine Power Supply Teaching Experiment

Coal mine power supply technology is the core course of electrical engineering in mining colleges and universities. Due to the complexity and particularity of power supply environment, it is hard to put into practice in teaching. So that to boost students' experimental level and engineering design ability, a virtual simulation system of coal mine power supply is built on account of virtual reality technology. The system introduces Quadric Error Metrics algorithm for model simplification, and an improved folding cost is adopted to keep the details of the model. The system simulates the key technology of coal mine power supply teaching experiment, and realizes the functions of theory learning, scene roaming, equipment maintenance, knowledge examination and so on. The experiment can motivate students' hobby in studing, enrich the teaching experiment course, and has practical application value.