Miracle-dz/UniFAR-FTUs huggingface dataset:
MAGLEVs – an Overview in 2023

The energy crisis, unfortunately here to stay, imposes lower Wh/passenger/km for given travel time and distance. Besides electric cars, wheel on rail vehicles, for urban and high-speed interurban travel, MAGLEVs-vehicles, with controlled magnetic linear electric motor propulsion, suspension and guidance, are a viable option due to: friction-free propulsion levitation and guidance, with lower weight, lower noise too, and thus lower Wh/passenger/km for speeds up to 500+km/h, and lower maintenance costs, at the price of high investment costs. The present paper summarizes main MAGLEV solutions, in terms of topologies, principles, performance, some already commercial, noticing their main merits and demerits, with the hope of stirring more aggressive R&D and industrial efforts to apply MAGLEVs as sustainable e-transport national/ continental means.
Lane line detection algorithm based on improved UNet network

In order to solve the problems of loss of image information and insufficient acquisition of global context information in lane line detection, this paper proposes a lane line detection algorithm based on improved UNet network. On the basis of UNet, a CBAM module is introduced, which combines channel attention and spatial attention to enhance the perceptual and expressive abilities in the feature extraction process. By adaptively learning the importance weights of feature channels and spatial locations, the CBAM module is able to better capture the important features of lane lines and improve the attention to the region of interest. In addition, this paper optimizes the loss function by using the Dice loss function and the Focal loss function to solve the problems of sample imbalance and insufficient sensitivity to lane line boundary details. Experimental results show that the method proposed in this paper achieves superior performance on the Tusimple dataset.
BEKBQA: Improving Knowledge Base Question Answering with a Bidirectional Entity Scoring Model

Knowledge base multi-hop question answering must perform hop-by-hop reasoning on long paths to obtain answer entities. However, due to the lack of datasets with correct path annotations, the model’s training can only rely on the results of the last hop answer for updating. A more innovative approach is to apply the knowledge distillation method to the question-answering task, and the complex teacher model learns the entity distribution of each hop as the intermediate supervision signal of the student model. However, the structure of this dual model needs to be simplified. At the same time, in the backward reasoning process from the candidate answer entity to the topic entity, this method only uses the last hop result of the forward as the initialization, which does not guarantee that the backward reasoning process can conform to the semantic information of the question sentence. To address these challenges, we design a structurally simple model capable of constraining the intermediate reasoning process through forward and backward bidirectional reasoning. Moreover, we updated the initialization method of backward reasoning to make it more reasonable. At the same time, the main functions of the model are entirely based on the attention mechanism so that the results of each hop can be visualized and the reasoning process is transparent.
Road Side Perception Systems for Safer Intersections: Field Test

Intersections have been known as hazardous points of the road networks for over a century. A diversity of solutions have been developed to reduce the number of accidents at intersections, such as traffic lights, pedestrian crossings, as well as roundabouts. In the past decade, many cities have had to deal with a growing number of alternate means of transport such as bicycles, powered two-wheelers and electric personal transporters, which are both more mobile and more vulnerable. As for the announced automated vehicles, they might be less adaptive that human drivers. These changes again raise the question of how accidents can be prevented at intersections of roads, bus lanes, cycling lanes and walkways. In order to improve the awareness of road users, we propose a cooperative information system based on computer vision to monitor moving obstacles and on communication to warn road users facing imminent danger of collision. We have implemented and tested this system on a roundabout with a high traffic volume, and we report the difficulties that we experienced.
Reinforcement Learning-Based Intelligent Traffic Signal Control Considering Sensing Information of Railway

Traffic signal control plays a crucial role in ensuring efficient transportation in urban road networks, and recent advancements in deep reinforcement learning (RL) have shown promising potential in this domain. However, previous studies have given limited attention to the design of the reward function while also neglecting the impact of the railway information during urban transportation. To fulfill these research gaps, this study focuses on investigating RL-based traffic signal control considering the sensing information of railway. In this study, we employ the Simulation of Urban Mobility (SUMO) software, enabling the establishment of an intelligent transportation system (ITS) field consisting of two signalized intersections and two railroad crossings. Within this simulation environment, we utilize two RL algorithms [proximal policy optimization (PPO) and deep Q-networks (DQNs)] to establish an RL-SUMO model for conducting comprehensive simulation experiments under predefined traffic conditions. Simulation results demonstrate that the proposed RL-based traffic signal control method performs significantly better compared to the fixed control. In addition, the train flow has great effects on the whole traffic flow efficiency due to the opening/closing of the railroad crossing. These findings indicate the potential of RL method in advancing traffic control strategies, paving the way for more efficient and intelligent traffic management systems.
Study on the design of computation system for flight fatigue value

Flight fatigue is an important factor affecting civil aviation safety. In order to maintain a good level of crew alertness during the duty period, has introduced the Fatigue Risk Management System into civil aviation regulations. Fatigue risk management systems generally use fatigue bio-mathematical models to predict pilot fatigue before flight, and consider the impact of pilot fatigue during crew scheduling. This paper takes into account the differences in sleep duration requirements and circadian rhythms among individual pilots, improves the computation of circadian rhythms and homeostasis in the three-process model of alertness, and implements a method for computing fatigue values of flights. On this basis, we designs a system for fatigue value computation including input data module, a module for pilot fatigue computation and processing, a module for flight fatigue computation and an output module. The system realizes the automatic computation of the fatigue value of the flight before the flight. The fatigue computation method and system of fatigue value can be used to monitor the fatigue level of the flight crew before the flight, which is helpful for the relevant departments of the airline to make adjustments for the fatigue warning, this ensure that the flight crew maintains a high level of alertness during the flight.
Towards Contactless Fingerprint Presentation Attack Detection using Algorithms from the Contact-based Domain

In this work, we investigate whether contact-based fingerprint Presentation Attack Detection (PAD) methods can generalize to the contactless domain. To this end, we selected a state-of-the-art patch-based fingerprint PAD algorithm which achieved high detection performance in the contact-based domain and adapted it for contactless fingerprints. We train and test the method using three contactless fingerprint databases and evaluate its generalization capabilities using Leave-One-Out (LOO) pro-tocols. Further, we acquired a new PAD database and use it in a cross-database evaluation. The adopted method shows low error rates in most scenarios and can generalize to unseen contactless presentation attacks.
Neural Network Model of Decision Making in a Control System Based on Lidar Data*

This paper proposes the neural network regression model that simulates decision-making by a human operator. The method is proposed for processing the coordinates of point clouds from a lidar and automatically determining the model target output for use in deep learning. This model is intended for calculation of setpoints in an excavator automatic control system. The model recognizes the features of the surface based on lidar data and simulates the operator’s perception of the environment.
Multi-modal emotion recognition in e-commerce live broadcast scenarios

Multimodal sentiment analysis is an important branch of natural language processing. This paper reviews multimodal sentiment recognition in live broadcast scenarios. Firstly, the research basis of sentiment analysis and multimodal fusion is reviewed. Then, the research basis is summarized from the three single-modal sentiment analyses of text, expression, and speech. Finally, based on the existing research of multi-modal sentiment analysis and combined with the e-commerce live broadcast scene, the improvement and fusion strategies of three single-modal sentiment analyses in live broadcast are discussed.
Efficiency Characteristics Comparison of Surface and Interior Permanent Magnet Synchronous Machines in Electric Vehicle

The paper analyzes and compares the efficiency characteristics of the Surface Permanent Magnet Synchronous Machine (SPMSM) and the Interior Permanent Magnet Synchronous Machine (IPMSM) which applied in the electric vehicle (EV). The loss coefficient is introduced and employed to study the mechanism between the efficiency characteristics and the parameters which have great influence on the loss distribution. The driving cycle efficiency is analyzed and compared to evaluate the application in the EV. The conclusions obtained can be used to guide the initial design to match the object load characteristic.
Weightless Model Predictive Direct Speed Control Method Based on the PMSM

Model predictive control, as a popular motor control method in recent years, has the advantages of conceptual clarity, simple structure, fast dynamic response, and the ability to control multiple variables simultaneously. However, it has the problems of large amount of computation, complicated design of weighting factors and no clear theoretical indication. In order to eliminate the traditional double-closed-loop cascade structure and solve the cumbersome weight factor design problem in model prediction, this paper proposes weightless model predictive direct speed control method. The method can effectively solve the limitations of the double closed-loop structure on the dynamic performance during speed control and eliminate the repeated debugging process of the weighting factors. In this paper, the weightless prediction model of permanent magnet synchronous motor based on voltage vector selection is derived. The advantages of the control method are verified through simulation, which demonstrates the better performance of the method.
Experimental Results of Nonlinear Model-Based Predictive Controller for the Qbot2e Differential Drive Mobile Robot

Nonlinear Model Predictive Control (NMPC) is a powerful and versatile control technique widely applicable to various systems, including mobile robots. This paper introduces an NMPC controller specifically designed for the Qbot2e dif-ferential drive mobile robot. The controller is implemented in the MATLAB/Simulink environment, utilizing the NLMPC and FORCESPRO toolboxes for optimization and deployment. To ensure stability, terminal state equality constraints are incorpo-rated. Through a series of real-time simulations, the controller demonstrates exceptional performance in achieving both posture stabilization and trajectory tracking with remarkable accuracy. The findings of this study highlight the potential of NMPC as a promising control technique for mobile robots. The results not only indicate the efficacy and ease of implementation of NMPC, thanks to its constraint handling capabilities but also showcase its superiority over existing state-of-the-art methods. This work contributes to advancing the understanding and utilization of NMPC in the field of mobile robotics.
Attention Distillation with Sample Re-weighting and Adaptive Margin for Low-Resolution Face Recognition

Low-resolution face recognition poses challenges due to the loss of fine details and increased image noise. To address this, we propose an attention distillation framework enhanced with two novel modules. Firstly, the Sample Re-weighting (SRW) module assesses low-resolution sample quality by assigning importance weights based on image quality. This reduces the impact of low-quality samples during model training, enabling better handling of noisy samples. Secondly, the Adaptive Margin Module (AM) addresses intra-class variation by introducing category-dependent learnable parameters to adjust boundaries for each class. This captures variations within categories, resulting in improved recognition performance, especially for classes with limited samples. We evaluate our framework on AgeDB-30 for face verification and Tinyface for face recognition, demonstrating significant improvements at lower resolutions. Our method surpasses the state-of-the-art A-SKD approach, particularly in handling challenges posed by low-resolution images.
Optimization of the Power-to-Weight Ratio of an Electric Aircraft Propulsion System using Planetary Gearboxes

A novel approach to weight optimization of an aerospace electric drive using a planetary gearbox was developed. For this purpose, two different algorithms were developed for estimating the weight of an electric machine and a planetary gearbox. It is found that a drive train using a planetary gearbox decreases the total weight of the drive significantly in comparison with the direct electric drive. The total weight of the drive was reduced by all the gear ratios examined, which are greater than 3.8. The lightest configuration that includes a gearbox is 43 % lighter than the direct drive at a gear ratio of 4.59.
Research on Image Tibetan Caption Generation Method Fusion Attention Mechanism

Image caption generation combines computer vision and natural language processing to generate natural language descriptions of images. With the rapid development of deep learning, the performance of end-to-end image caption generation research based on Chinese and English datasets has been improved. However, there is a lack of Tibetan caption data samples for low-resource languages, and the research on image Tibetan caption generation is almost blank. Therefore, in order to promote the research and development of multilingual image generation and improve the accuracy of Tibetan image caption, VGG19, InceptionV3 and ResNet101 are used as the backbone network, and group convolution is used to replace the fully connected operation of the attention mechanism. A CNN-based An image Tibetan caption generation method that integrates with the LSTM encoding and decoding network to improve the attention mechanism. Experiments on the Flickr8K and Flickr30K-tic Tibetan caption datasets show that this method is superior to the traditional attention mechanism image caption generation method, and has achieved better performance improvement in BLEU and ROUGE-L image caption evaluation indicators.
Simulation Model and Data Acquisition Method for Wind Turbine Group

This article presents a novel approach to simulating and collecting data for a direct-drive wind turbine array. The simulation model for the direct-drive wind turbine array encompasses various components, including the direct-drive fan, a multiplier amplifier, a controlled current source, the line's equivalent impedance, and the power grid. The direct-drive fan is linked to the controlled current source and the line's equivalent impedance, serving to transmit the fan's current signal to the equivalent impedance of the line. Meanwhile, the multiplier amplifier connects to a controlled current source, tasked with multiplying and amplifying the external system's fan current signal based on the predefined number of fans. Subsequently, it transmits the multiplied and amplified fan current signal back to the controlled current source. The controlled current source then utilizes this amplified fan current signal as a reference value to deliver its controlled current source signal to the equivalent impedance of the line. Ultimately, both the controlled current source signal and the fan current signal are channeled into the power grid through the line's equivalent impedance. This innovative approach is highly applicable to conducting system simulation analyses for large-scale wind power integration, resulting in a significant reduction in computational workload and a substantial increase in practical engineering value.
Distribution Grid Compatible Renewable Energy Through Optimal Exploitation of Bottom Up Flexibility

The critical challenge that modern smart grids face in term of their management is the maximum possible exploitation of the energy that Renewable Energy Sources (RES) produce. The non-controllable nature of the rate that RES produce energy depends on the dynamic and non-accurately predictable weather conditions. In this context, flexibility noted as the modification of the energy demand, harmonizes (balances dynamically) energy production and energy consumption. Its advanced modeling and the optimization of its use highly affects energy cost in modern smart grids. In this paper we present a novel Distribution-Level Flexibility Market (DLFM) architecture which is noted Reactive DLFM (R-DLFM). R-DLFM operates after the clearing of the well-known day ahead energy market and/or balance market and ensures: i) the minimization of the energy cost and ii) the compliance of the dispatch with the constraints of the distribution system. The evaluation of R-DFLM depicts that its flexibility exploitation has high impact in the energy cost and compliance with distribution network constraints.
Investigation of Low-Speed High-Power Density Spoke-Type FSCW PMSM with Similar PM Volume and Different Shapes Considering Magnetic Gearing Effect

This article investigates the air-gap magnetic flux density and the generated torque response in spoke-type permanent magnet (ST-PM) machines with distinct permanent magnet shapes having similar volumes. The rectangular and trapezoidal shapes of the PM are investigated in this analysis, and the corresponding air-gap flux density harmonics and torque production mechanisms are investigated. The FSCW ST-PM machine contains abundant air-gap magnetic field harmonics, impacting the machine’s torque response. Firstly FEA is used to analyze the ST-PM machine’s air-gap magnetic field harmonics, and then the traditional torque-generating mechanism in PMSM along with the magnetic gearing effect contributions to the average torque, are separately investigated for the analyzed machine design cases. A brief comparative analysis of the analytical and simulation results for the ST-PM machine designs with different PM shapes and similar volumes is also presented.
Gold Price Prediction Based On Yahoo Finance Data Using Lstm Algorithm

Gold, a highly valued precious metal, has significant intrinsic value in contemporary society. Therefore, more and more people are embarking on precious metal investments known as gold. Individuals who wish to invest in gold should be vigilant in monitoring the fluctuations in this precious metal's buying and selling prices. Yahoo Finance is a leading online platform that can be used as a reliable reference for monitoring the fluctuations in gold's buying and selling prices. These factors mainly include changes in the closing price, opening price, highest value, and lowest value of gold.The observed phenomenon shows that gold prices exhibit high volatility, mainly due to frequent and repeated fluctuations. The LSTM (Long Short-Term Memory) technique, coupled with applying hyperparameter optimization through grid search, enables accurate gold price prediction using historical gold price data. This result is achieved after the grid search procedure, where the lowest error result is 0.00033, generated by the LSTM parameter unit of 200 with a dropout of 0.1.
Polymorphisms in the GNAS gene as predictors of ventricular tachyarrhythmias and sudden cardiac death Results from the DISCOVERY Trial and Oregon Sudden Unexpected Death Study

Background- — Population-based studies suggest that genetic factors contribute to sudden cardiac death (SCD). Methods and Results- — In the ﬁ rst part of the present study (Diagnostic Data In ﬂ uence on Disease Management and Relation of Genetic Polymorphisms to Ventricular Tachy-arrhythmia in ICD Patients [DISCOVERY] trial) Cox regression was done to determine if 7 single-nucleotide polymorphisms (SNPs) in 3 genes coding G-protein subunits (GNB3, GNAQ, GNAS) were associated with ventricular tachyarrhythmia (VT) in 1145 patients receiving an implantable cardioverter-de ﬁ brillator (ICD). In the second part of the study, SNPs signi ﬁ cantly associated with VT were further investigated in 1335 subjects from the Oregon SUDS, a community-based study analyzing causes of SCD. In the DISCOVERY trial, genotypes of 2 SNPs in the GNAS gene were nominally signi ﬁ cant in the prospective screening and signi ﬁ cantly associated with VT when viewed as recessive traits in post hoc analyses (TT vs CC/CT in c.393C > T: HR 1.42 [CI 1.11-1.80], P = 0.005; TT vs CC/CT in c.2273C > T: HR 1.57 [CI 1.18-2.09], P = 0.002). TT genotype in either SNP was associated with a HR of 1.58 (CI 1.26-1.99) ( P = 0.0001). In the Oregon SUDS cohort signi ﬁ cant evidence for association with SCD was observed for GNAS c.393C > T under the additive ( P = 0.039, OR = 1.21 [CI 1.05-1.45]) and recessive ( P = 0.01, OR = 1.52 [CI 1.10-2.13]) genetic models. Conclusions- — GNAS harbors 2 SNPs that were associated with an increased risk for VT in ICD patients, of which 1 was successfully replicated in a community-based population of SCD cases. To the best of our knowledge, this is the ﬁ rst example of a gene variant identi ﬁ ed by ICD VT monitoring as a surrogate parameter for SCD and also con ﬁ rmed in the general population.
Optimization of Tethered Power Supply System Based on High Frequency and High Voltage AC Transmission

This article studies a three-phase AC transmission architecture with high-frequency characteristics for high-power tethered unmanned aerial vehicles(UAV). Instead of the complicated DC/DC conversion architecture applied in traditional tethered DC transmission networks, an inverter that generates 1-2kHz three-phase AC power and high-frequency transformers are applied. In order to improve the power density of the onboard transformer, a design process is adopted to reduce the overall weight by replanning the core diameter size with the lowest weight as the optimization principle. Considering the transmission loss and weight, the appropriate length and specification of cable is configured. Finally, a transmission network model is established based on the cable distribution parameter model, and an inverter control approach that can compensate for system voltage drop is proposed.