Three-phase asynchronous motor are fundamental components in industrial systems, and their failure can lead to significant operational downtime and economic losses. Vibration and current signals are effective indicators for monitoring motor health and diagnosing faults. However, motors in real applications often operate under variable conditions such as fluctuating speeds and loads, which complicate the fault diagnosis process. This paper presents a comprehensive dataset collected from a three-phase asynchronous motor under various fault types and severities, operating under diverse speed and load conditions. The dataset includes both single faults and mechanical-electrical compound faults, such as rotor unbalance, stator winding short circuits, bearing faults, and their combinations. Data were acquired under both steady and transitional conditions, with signals including triaxial vibration, three-phase currents, torque, and key-phase signals. This dataset supports the development and validation of robust fault diagnosis methods for electric motors under realistic operating conditions.
Unilateral limb motor imagery (MI) plays an important role in upper-limb motor rehabilitation and precise control of external devices, and places higher demands on spatial resolution. However, most existing public datasets focus on binary- or four-class left-right limb paradigms that mainly exploit coarse hemispheric lateralization, and there is still a lack of multimodal datasets that simultaneously record EEG and fNIRS for unilateral multi-directional MI. To address this gap, we constructed MIND, a public motor imagery fNIRS-EEG dataset based on a four-class directional MI paradigm of the right upper limb. The dataset includes 64-channel EEG recordings (1000 Hz) and 51-channel fNIRS recordings (47.62 Hz) from 30 participants (12 females, 18 males; aged 19.0-25.0 years). We analyse the spatiotemporal characteristics of EEG spectral power and hemodynamic responses, and validate the potential advantages of hybrid fNIRS-EEG BCIs in terms of classification accuracy. We expect that this dataset will facilitate the evaluation and comparison of neuroimaging analysis and decoding methods.
Shashwat Kumar, Arafat Rahman, Robert Gutierrez
et al.
Clinical assessments for neuromuscular disorders, such as Spinal Muscular Atrophy (SMA) and Duchenne Muscular Dystrophy (DMD), continue to rely on subjective measures to monitor treatment response and disease progression. We introduce a novel method using wearable sensors to objectively assess motor function during daily activities in 19 patients with DMD, 9 with SMA, and 13 age-matched controls. Pediatric movement data is complex due to confounding factors such as limb length variations in growing children and variability in movement speed. Our approach uses Shape-based Principal Component Analysis to align movement trajectories and identify distinct kinematic patterns, including variations in motion speed and asymmetry. Both DMD and SMA cohorts have individuals with motor function on par with healthy controls. Notably, patients with SMA showed greater activation of the motion asymmetry pattern. We further combined projections on these principal components with partial least squares (PLS) to identify a covariation mode with a canonical correlation of r = 0.78 (95% CI: [0.34, 0.94]) with muscle fat infiltration, the Brooke score (a motor function score), and age-related degenerative changes, proposing a novel motor function index. This data-driven method can be deployed in home settings, enabling better longitudinal tracking of treatment efficacy for children with neuromuscular disorders.
MD Zobaer Hossain Bhuiyan, Abir Bin Faruque, Mahtab Newaz
et al.
This paper presents the design and development of a low-cost fuel dispensing system prototype based on the STM32 microcontroller and L298N motor driver. The system aims to provide an affordable and scalable solution for fuel delivery in remote or small-scale environments where conventional, high-cost systems are not feasible. The core control unit is built using an STM32 microcontroller, which manages user input through a 4x4 matrix keypad and displays operational data on a 16x4 LCD screen via I2C communication. A 12V DC pump motor is used to simulate the fuel dispensing mechanism, precisely controlled via the dual H-bridge L298N motor driver. The system is powered by a 11.1V battery and is designed for ease of deployment and portability. The keypad allows users to input the desired fuel amount, while the system ensures accurate motor runtime corresponding to the volume to be dispensed. This project demonstrates how embedded systems can be leveraged to build cost-effective, user-friendly, and energy-efficient solutions. The proposed design can be further enhanced with flow sensors, GSM connectivity, RFID cards, and payment integration for real-world applications in fuel stations or agricultural use.
Brushless DC (BLDC) motors are increasingly used in various industries due to their reliability, low noise, and extended lifespan compared to traditional DC motors. Their high torque-to-weight ratio and impressive starting torque make them ideal for automotive, robotics, and industrial applications. This paper explores the multi-objective tuning of BLDC motor controllers, focusing on position and torque ripple. A state-space model of the BLDC motor and the entire control system, including the power stage and control structure, is developed in the Simulink environment. Two common control mechanisms, trapezoidal and Field Oriented Control (FOC), are implemented and optimized. Both mechanisms utilize a cascaded closed-loop position control, providing fair disturbance rejection but requiring challenging tuning of the controllers. To address these challenges, the non-dominated sorting genetic algorithm II (NSGA-II) is used for optimization. This study demonstrates the effectiveness of optimization techniques in enhancing the performance of control systems.
Aiming at the optimization of autonomous docking trajectory of unmanned receiver in the in-flight refueling, a high-precision computational fluid dynamics method was used to calculate the dangerous area behind the refueling aircraft as the obstacle in the flight environment in the docking trajectory planning. An improved ant colony algorithm is proposed, which uses reverse learning to form a better initial population and greatly improves the convergence speed of the algorithm. Then the weight of the two cost functions of track safety and track distance is adaptively adjusted by using the fuzzy control, and the track distance is shortened on the premise of keeping the flight safety of the oil receiving aircraft. The simulated results show that the improved ant colony algorithm can realize the autonomous docking trajectory planning of single unmanned oil receiver and multi-aircraft formation refueling, and has higher track safety and faster convergence speed than the traditional ant colony algorithm.
Connected and automated vehicles (CAVs) represent the future of transportation, utilizing detailed traffic information to enhance control and decision-making. Eco-driving of CAVs has the potential to significantly improve energy efficiency, and the benefits are maximized when both vehicle speed and powertrain operation are optimized. In this paper, we studied the co-optimization of vehicle speed and powertrain management for energy savings in a dual-motor electric vehicle. Control-oriented vehicle dynamics and electric powertrain models were developed to transform the problem into an optimal control problem specifically designed to facilitate real-time computation. Simulation validation was conducted using real-world data calibrated traffic simulation scenarios in Chattanooga, TN. Evaluation results demonstrated a 12.80-24.52% reduction in the vehicle's power consumption under ideal predicted traffic conditions, while maintaining benefits with various prediction uncertainties, such as Gaussian process uncertainties on acceleration and time-shift effects on predicted speed. The energy savings of the proposed eco-driving strategy are achieved through effective speed control and optimized torque allocation. The proposed model can be extended to various CAV and electric vehicle applications, with potential adaptability to diverse traffic scenarios.
Syed Saim Gardezi, Soyiba Jawed, Mahnoor Khan
et al.
A multitude of individuals across the globe grapple with motor disabilities. Neural prosthetics utilizing Brain-Computer Interface (BCI) technology exhibit promise for improving motor rehabilitation outcomes. The intricate nature of EEG data poses a significant hurdle for current BCI systems. Recently, a qualitative repository of EEG signals tied to both upper and lower limb execution of motor and motor imagery tasks has been unveiled. Despite this, the productivity of the Machine Learning (ML) Models that were trained on this dataset was alarmingly deficient, and the evaluation framework seemed insufficient. To enhance outcomes, robust feature engineering (signal processing) methodologies are implemented. A collection of time domain, frequency domain, and wavelet-derived features was obtained from 16-channel EEG signals, and the Maximum Relevance Minimum Redundancy (MRMR) approach was employed to identify the four most significant features. For classification K Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Naïve Bayes (NB) models were implemented with these selected features, evaluating their effectiveness through metrics such as testing accuracy, precision, recall, and F1 Score. By leveraging SVM with a Gaussian Kernel, a remarkable maximum testing accuracy of 92.50% for motor activities and 95.48% for imagery activities is achieved. These results are notably more dependable and gratifying compared to the previous study, where the peak accuracy was recorded at 74.36%. This research work provides an in-depth analysis of the MI Limb EEG dataset and it will help in designing and developing simple, cost-effective and reliable BCI systems for neuro-rehabilitation.
Slender vehicles often encounter significant aeroservoelastic challenges due to their low elastic mode frequencies and wide servo control system bandwidths. Traditional analysis methods have limitations, including low modeling accuracy for real vehicles in numerical methods, scale errors in wind tunnel tests, and significant risks in flight tests. The ground aeroelastic stability test is an innovative experimental method designed to address these challenges. This novel method employs shakers to apply condensed unsteady aerodynamic forces in real-time to actual vehicles, serving for both the ground flutter test (GFT) and the ground aeroservoelastic test (GAT). While extensive research exists on the GFT, there is limited exploration of the GAT. For the GAT of a slender vehicle in this paper, the condensed aerodynamic forces are calculated using the quasi-steady aerodynamic derivative method. An improved, partially decoupled inverse model controller is designed for force control, guided by an assessment of coupling strength among different shakers. Ground experiments under various flight control laws and flight dynamic pressures produce accurate results. Numerical simulations and experimental results demonstrate high precision, with excitation force amplitude deviations within ±10% and phase deviations within ±5° within the frequency range relevant to aeroservoelastic stability.
Chi Zhang, Amir Hossein Kalantari, Yue Yang
et al.
Predicting pedestrian behavior when interacting with vehicles is one of the most critical challenges in the field of automated driving. Pedestrian crossing behavior is influenced by various interaction factors, including time to arrival, pedestrian waiting time, the presence of zebra crossing, and the properties and personality traits of both pedestrians and drivers. However, these factors have not been fully explored for use in predicting interaction outcomes. In this paper, we use machine learning to predict pedestrian crossing behavior including pedestrian crossing decision, crossing initiation time (CIT), and crossing duration (CD) when interacting with vehicles at unsignalized crossings. Distributed simulator data are utilized for predicting and analyzing the interaction factors. Compared with the logistic regression baseline model, our proposed neural network model improves the prediction accuracy and F1 score by 4.46% and 3.23%, respectively. Our model also reduces the root mean squared error (RMSE) for CIT and CD by 21.56% and 30.14% compared with the linear regression model. Additionally, we have analyzed the importance of interaction factors, and present the results of models using fewer factors. This provides information for model selection in different scenarios with limited input features.
This article provides a review on X-ray pulsar-based navigation (XNAV). The review starts with the basic concept of XNAV, and briefly introduces the past, present and future projects concerning XNAV. This paper focuses on the advances of the key techniques supporting XNAV, including the navigation pulsar database, the X-ray detection system, and the pulse time of arrival estimation. Moreover, the methods to improve the estimation performance of XNAV are reviewed. Finally, some remarks on the future development of XNAV are provided.
This paper studies several problems that may exist in the aerodynamic design of low Mach number general aircraft, and discusses the design characteristics of the pressure distribution of some related airfoils while part of the characteristics is verified by a multi-point laminar airfoil design case. Firstly, several issues which exist in the aerodynamic design of low Mach number general aircraft, such as relatively large zero-lift drag coefficient, high stall performance requirements, and large variation range of Reynolds number, are discussed and analyzed. Then, to address these issues, the paper extracts some useful design features that are beneficial to the airfoil aerodynamic performance from the study on the design characteristics of the pressure distribution of several turbulent flow airfoils and laminar flow airfoils that are designed for low Mach number general aircraft. Finally, by considering the typical design conditions of a light multi-purpose single-turboprop-engine general aircraft, including the cruise, climb and stall conditions, a multi-point laminar airfoil optimization is offered by using the GAW-1 airfoil as the baseline. The optimized foil has a 9.6 improvement in the lift-to-drag ratio of the cruise condition, a 16.3 improvement in the lift-to-drag ratio of the climb condition but a 0.115 decrease in the maximum lift coefficient of the stall condition. The results show that the requirements from the cruise, climb and stall conditions are contradictory to some degree in the considering multi-point optimization case, and the designers should deal with this trade-off carefully based on the features of the low Mach number general aircraft.
Kalchenko Volodymyr, Venzhega Volodymyr, Kuzhelnyi Yaroslav
et al.
Problem. The most high-precision parts of the car engine are camshaft and crankshaft, parts that require two-sided processing of the end surfaces: piston pins, valve springs. Traditional processing methods are used in their manufacture and repair. Therefore, taking into account the large volumes of car production, the improvement of existing and development of new processing methods is
very important. Goal. The purpose of this work is the analysis and improvement of existing methods and the implementation for new ones of processing the specified parts to increase the productivity and accuracy of their processing. Methodology. The processing of support necks and cams is expedient to
be carried out in one installation during deep high-speed grinding with crossed axes of the circle and camshaft due to stabilization of the cutting depth and feed along the contour with uneven rotation of the part. The method of high-speed milling of camshaft cams with crossed axes of the tool and parts on CNC machines is also promising. Root and connecting rod necks of crankshafts can be processed in one installation by deep high-speed grinding with crossed axes of the circle and the part due to stabilization of the depth of cutting and feed along the contour with uniform rotation of the part. For end surfaces, it is possible to introduce single-pass processing on double-sided end grinding machines
with oriented grinding wheels with calibration areas. Results. The obtained results indicate a 25-30% increase in productivity and processing accuracy of the specified parts. Originality. It is used when processing the effect of the intersection of the axes of the tool and the part and carrying out a special
correction of the tool, which allows to obtain a calibration area. Practical value. The proposed methods improve the parameters of accuracy, roughness and productivity of processing, which is an important scientific and practical task, whose solution will allow to increase the service life of car components and assemblies, reduce the cost of their production, and raise the level of competitiveness of products and services.
Oleksandr Dveirin, Viktor Riabkov, Liudmyla Kapitanova
et al.
The subject of study is the process of forming the parameters of modification changes in a heavy transport aircraft. The goal is to develop the subject area of modification changes with the help of design models. The task is to develop models for determining the parameters of modification changes of the base aircraft based on the subject area of the modification project, namely: a model of the decomposition of modification changes of the base aircraft with the selection of parameter levels, which made it possible to apply more reliable assessment methods for different groups of parameters; a model of displaying the structure of the base aircraft in the subject area of the modification project, which uses the concepts of similarity and equivalence, it allows to highlight the necessary modification changes of the base aircraft; a temporal model of tracking efficiency changes, taking into account the time interval of creating modifications; a model for assessing the feasibility of modification changes in lower-level parameters, taking into account additional labor costs for their implementation. All this makes it possible to increase the reliability of the technical and economic justification of the modification. The methods used are following: an analytical method of defining the subject area using a system of new criteria that allow evaluating the integral characteristics of the aircraft modification for the benefit of different participants at different stages of the creation of the modification: for project developers, specific cost criteria are adopted as the ratio of all types of costs to all useful work, performed by the modification at the stage of its operation; for the stage of flight tests and production development in modifications – the criterion for the expediency of modification changes, taking into account the labor costs for their implementation; for operating airlines – the technology of formation of flight parameters, which provide a "niche" of competitiveness of modifications and temporary tariffs for the benefit of the consumer. The obtained scientific results: allow to form subject areas of promising modifications of heavy transport aircraft, which can reasonably compete in the world markets of aircraft and air transportation. Conclusion. The scientific novelty of the obtained results is as follows: the proposed model for determining the subject area of modification changes of the base aircraft. This makes it possible to set the parameters of modification changes in the aircraft at the stage of aircraft design, and thereby ensure its competitiveness in the aircraft and air transportation markets.
Machine Learning Inter-atomic Potentials (MLIPs) have become a common tool in use by computational chemists due to their combination of accuracy and speed. Yet, it is still not clear how well these tools behave at or near transitions states found in complex molecules. Here we investigate the applicability of MLIPs in evaluating the transition barrier of two, complex, molecular motor systems: a 1st generation Feringa motor and the 9c alkene 2nd generation Feringa motor. We compared paths generated with the Hierarchically Interacting Particle Neural Network (HIP-NN), the PM3 semi-empirical quantum method (SEQM), PM3 interfaced with HIP-NN (SEQM+HIP-NN), and Density Functional Theory calculations. We found that using SEQM+HIP-NN to generate cheap, realistic pathway guesses then refining the intermediates with DFT allowed us to cheaply find realistic reaction paths and energy barriers matching experiment, providing evidence that deep learning can be used for high precision computational tasks such as transition path sampling while also suggesting potential application to high throughput screening.
Recently, aerodynamic performance analysis has been widely studied due to its importance in aircraft design. Most works adopted computational fluid dynamics (CFD) simulation to compute the aerodynamic forces, which is time consuming. To reduce the simulation time, several works proposed to use deep learning model as the surrogate model of CFD simulation. However, the explainability of deep learning models is poor and has been widely criticized, which limits the further development of deep learning in aerodynamic performance analysis. In this paper, a novel neural network is proposed to predict the aerodynamic forces of airfoils. To improve the explainability, the circular padding is proposed to replace traditional zero padding in the convolutional layers. Moreover, the saliency map of the predicted aerodynamic force on the input airfoil is shown in a more intuitive way. In this manner, the influence of different parts of airfoil on the final aerodynamic force can be easily analyzed. Extensive experiments on different data sets show that our work is efficient and effective. Most importantly, these results explain the potential relationship between the airfoil and the aerodynamic force.