Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Sensorless Synergetic Control of Electric Vehicle Using Algebraic Observer

To Improve the traction chain system reliability, and distribution rejection, and reduce the component cost. A good choice of sensorless control of Electric Vehicle drives ensures that. In this paper, an original Algebraic Observer (AO) was developed to estimate the speed of an Electric Vehicle (EV) associated with a six-Phase Permanent Magnet Synchronous Motor PMSM. The suggested AO has shown a suitable solution for multi-phase machine assortment, because it does not depend on the model of the drive system, and provides satisfaction estimates even in the existence of considerable perturbations. Moreover, in order to improve the dynamic performance of EV, a robust nonlinear based on synergetic theory was developed. Synergetic control offers a high dynamic performance, and robustness and reduces the chattering phenomenon. In order to confirm the effectiveness of the suggested sensorless based on synergetic control and the algebraic observer. A real model of a lightweight electric vehicle was carried out and evaluated under the NEDC driving cycle. Simulation results show the durability and high performance given by the sensorless control presented using synergetic control and algebraic observer.
ATmega2560 MCU Space Vector Modulation Implementation on Three-phase Inverter and Validation via Typhoon HIL 402

In this paper, a space vector modulation implementation is done via ATmega2560 microcontroller with the Arduino development board. The control parameters are defined by the user in MATLAB software and sent to Arduino to start or stop the controller or to change the controller parameters. Space vector switching times for chosen parameters are calculated and given. The implemented algorithm is validated via Typhoon HIL 402 system on a three-phase inverter model for two different operation points. Voltage and current waveforms are obtained in Typhoon HIL. Line and phase voltage, as well as the phase current spectrum analysis is done via Typhoon HIL and processed in MATLAB for given system parameters. The measurement results validates the implemented space vector control algorithm.
Coordinated PI-based frequency deviation control of isolated hybrid microgrid: An online multi-agent tuning approach via reinforcement learning

Numerous remote area applications welcome standalone renewable energy power generation systems or isolated microgrids (MGs). Due to the nature of solar and wind energy, the frequency deviation control (FDC) in hybrid MGs has become more complicated and critical than the conventional grid for power quality purposes. By using a coordination control strategy between a double-layered capacitor and a fuel cell, our mission here is to design a FDC system based on the PI controller which is tuned by an artificial neural network (ANN) in a multi-agent structure. To achieve this aim, a reinforcement learning technique is applied to train the ANN-based tuners. The performance of the proposed FDC system has been verified under different conditions by using real data to demonstrate the stability and robustness of the proposed controller.
Recurrent Neural Network based Partially Observed Feedback Control of Musculoskeletal Robots

The musculoskeletal robot has become a promising research direction. However, the control problem of the musculoskeletal robot still limits its application. Especially in the real-world application, only some incomplete and imprecise feedback states could be obtained due to the limitation of sensors. Therefore, this paper proposes a recurrent neural network (RNN) based partially observed feedback control method of musculoskeletal robots. The RNN has the ability of working memory and can realize muscle control through implicitly inferring sufficient states from partially observed states. It can also generate muscle excitations with synergies to guarantee great generalization. The effectiveness of the proposed method is verified on a simulated musculoskeletal system. Compared with other deep reinforcement learning method, the proposed method achieves similar performance under sufficient feedback states and better performance under partially observed feedback states.
Using AI-based NiCATS System to Evaluate Student Comprehension in Introductory Computer Programming Courses

This Research to Practice Full Paper presents the use of data collected by our Non-Intrusive Classroom Attention Tracking System (NiCATS) to evaluate student comprehension. Quantifying students' cognitive processes in classrooms in a non-intrusive way is challenging. By analyzing various aspects of the eye metrics against defined regions of interest (ROI), instructors can better understand students’ cognitive processes as they acquire new knowledge. Eye-tracking studies primarily define ROIs based on commonly used metrics (source code complexity, significant fixation durations, etc.). While helpful, these metrics, when used independently, do not accurately represent their comprehension patterns. This paper contributes an alternative, multilayered approach for calculating gaze metrics against automatically defined ROIs. The work utilizes the AI-based Non-Intrusive Classroom Attention Tracking System (NiCATS - developed by the researchers), collecting raw-gaze data in real-time as information is presented on a computer screen. This paper reports the results of a study in which undergraduate students in a CS programming course were asked to identify defects seeded in Java programs. Each JAVA program included its own unique sets of ROIS defined using two different granularities: lexer-based and line-based. The ROI sets were then used to calculate relevant eye metrics in the context of each ROI layout. The results of the eye metric analysis at specific ROIs w.r. t their code review task provide insights into the cognitive processes students undergo when trying to comprehend new material. Subdividing this region into lexer-based regions, we determined “content topics” students struggled with (e.g., using complex data types) in a specific area. This feedback is valuable to the instructor as it enables the ability to identify hard-to-comprehend content topics post-hoc and gives the ability to validate student learning in the classroom. While this experiment focused on students in introductory programming courses, we intend to conduct experiments in other learning settings where students are expected to read material on a computer screen or solve actual problems. To summarize, the analysis of these eye metrics using more fine-grained ROIs (lexer-based, line-based) as an extension of complexity-based ROIs provides instructors with deeper insights into the cognitive processes used by students when compared to the current state-of-the-art techniques.
FPGA Implementation of Permanent Magnet Synchronous Motor Control and Modulation

Most current traction control systems use digital signal processing as their core, and their inherent latency and sequential instruction execution characteristics do not satisfy some applications that require higher performance. To solve this problem, it is desirable to use field programmable gate arrays (FPGA) with hardware parallelism and quasi-analog circuit characteristics to build high-performance control systems. In this paper, an FPGA implementation for permanent magnet synchronous motor (PMSM) control is proposed and a model-based design approach (Xilinx System Generator) is used to reduce the development time of the algorithm. First, the components and design ideas of each module of the vector control system are introduced. The PI, modulation, and coordinate transformation modules are introduced, and comparative simulation experiment s are conducted to verify the dynamic and steady-state performance of the motor control system in agreement with Simulink simulation results. Next, the generated code is analyzed by post-implementation timing simulation, and the results show that the runtime of the FPGA-based program is significantly shorter than the runtime of programs in digital signal processors (DSP) and the generated code can achieve the correct functions. Finally, hardware resource consumption and design optimization are analyzed, and the results show that the optimized control system achieves improved performance and a balance with resource consumption. The above verifies the correctness and feasibility of the proposed implementation method.
Analysis of Computed Tomography Images of Lung Cancer Patients with The Marker Controlled Based Method

In this study, it was aimed to obtain the tumor region from computed tomography images after a number of pre-processes using the Marker-Controlled watershed segmentation. In accordance with this purpose, tumor segmentation was performed using four different data sets. Segmentation success was analyzed with the Jaccard index method in terms of similarity rates to the reference images. The index was calculated as average 0.8231 for the RIDER lung CT dataset, 0.8365 for the lung 1 dataset, 0.8578 for the lung 3 dataset and 0.8641 for the LIDC-IDRI dataset. Our current work on the practical and successful segmentation of lung tumor has been promising for next steps.
Submillimetre continuum observations of NGC 253

The authors have mapped the 350-μm emission along the major axis of the dusty galaxy NGC 253 and find this to have an extent of about 95 arcsec. The total mass of interstellar material in the central kpc is ≡3×108M_sun;. Comparison of the results with CO observations suggests that the interstellar material in NGC 253 is in the form of massive clouds, similar to those in our Galaxy.
Generalized Roughness Bearing Fault Diagnosis Using Time Series Analysis and Gradient Boosted Tree

Generalized roughness bearing faults are the most commonly reported ones in rotating machine failures. Early detection of bearing faults plays a vital role in minimizing operation losses. This paper aims to develop a real-time condition monitoring for bearing faults and for this purpose a sensory system to measure 3 axes vibration is developed. A time series model is proposed for vibration signals to extract features accurately. These features are classified using gradient boosted trees (GBT). GBT is an ensemble of trees and is optimal for parallel evaluation with relatively low cost. The proposed feature extraction algorithm also has a low computational cost. Overall, the proposed algorithm has very low latency and computational cost which enables fault diagnosis through embedded processors. This algorithm can diagnose five different faults with a promising accuracy of 98.5%, which is calculated for different load and speed conditions.
A New Video Dataset for Recognizing Intake Gestures in a Cafeteria Setting

Intake gesture detection plays an important role in dietary monitoring systems to help individuals objectively quantify their eating behavior. Intake gestures include motions related to consumption, such as ingestion of a bite (food) and drink (liquid). Video provides an automatic and non-contact way to detect gestures. Previous studies have evaluated video-based detectors in laboratories where participants were invited to consume meals containing a limited number of food and beverage choices. Because of this, previous datasets have limited variability in food and beverage appearances, and types of intake tools and gestures. Cameras were positioned conspicuously close to participants. To address these limitations, we describe a new video dataset that was collected by recording the meals of 264 participants in a real restaurant. Participants consumed 374 different types of foods and beverages. The camera was installed on the ceiling of the restaurant to minimize the effect of an experimental observer. In this study, we present the details of collecting and labeling the data for video-based research. To explore the feasibility of detecting intake gestures using our dataset and establish baseline performance, we implemented three state-of-art models, namely CNN-LSTM, SlowFast and X3D-L. Results indicate that the best F1 scores were 0.899 and 0.778 on bite and drink gesture detection respectively.
A MSTUKF-based Technique for SOC Estimation of Li-ion Batteries for Electric Vehicles

Lithium-ion batteries have received widespread use by electric vehicles because of their high energy density, and so forth. Accurate estimation of state of charge (SOC) is very important for the high efficiency use and lifetime prolong of batteries. In this work, a strong tracking unscented Kalman filter method using multiple sub-optimal fading factors (MSTUKF) is employed to estimate lithium ion battery SOC. This method introduces multiple sub-optimal fading factors to traditional unscented Kalman filter algorithm, which effectively solves the robustness problem of the unscented Kalman filter method facing with the state mutation, and the model mismatch for battery degradation. Simulation results prove that this MSTUKF based method has a smaller prediction error rate than the traditional unscented Kalman filter method. The results also showed that this MSTUKF based method can be used for lithium-ion battery management of electric vehicles.
Transformer-based Residual Network for Hyperspectral Snapshot Compressive Reconstruction

The core problem of hyperspectral snapshot compressive imaging is to achieve high-quality reconstruction from a single snapshot measurement. Existing reconstruction methods are mainly based on convolutional networks. Inspired by the exciting success of Transformer in high-level vision tasks, we introduce Transformer into hyperspectral snapshot compressive imaging, and propose the Transformer-based residual network to learn reconstruction mapping. Specifically, the proposed network cascades multiple basic modules, each of which consists of two local-enhanced window (Lewin) Transformer blocks and two residual blocks. The advantage of this basic module is ability to exploit both local feature maps and long-range dependencies for resonstruction. The Transformer blocks treat the feature map at each pixel location as a token and capture the spatial-spectral context of each pixel within a local window through self-attention, thus effectively improving the spectral fidelity of reconstructed hyperspectral images at modest computational cost. We conduct extensive experiments on both simulation and real data. The experimental results show that the proposed network is concise and effective, achieving the best reconstruction results compared with several state-of-the-art methods.
A 650 V, 2.1 mohm GaN Half-bridge Power Module for 400V EV Traction Inverter Application

Compared with silicon and silicon carbide vertical power devices, the lateral structure of gallium nitride (GaN) high electron mobility transistors (HEMT) has a very different packaging style with all electrodes on one side of the package. This makes it harder to realize high power GaN modules. This paper presents the design and development of a compact 650 V, 2.1 mohm GaN half-bridge power module. The overall dimension of the GaN power module is 64mm x 98mm x 4.7mm. 24 GaN devices are placed symmetrically on two sides of a printed circuit board (PCB) substrate, enabling liquid cooling from both sides. The PCB layout is designed to reduce the stray inductance of the power loop to 0.42 nH. The module demonstrates a small 18.5% voltage overshoot when working at 400V/200A, and the turn-on, turn-off energy at 400V, 300A are 6.8 mJ and 0.7 mJ, respectively. The tested junction to coolant thermal resistance is less than 100K/kW.
Efficient Training of EfficientNetV2-S Using AdaBelief Optimizer

The EfficientNetV2 architectures have classification accuracies exceeding 80% on the Imagenet-1K/21K datasets. Here, the networks used to be trained using stochastic gradient descent with momentum ($SGD+M$) as the optimizer. Such a method is known to be effective but takes too much time, making it impractical to train the networks from scratch especially when computational power is limited. To address this, we provide a guide on how to use the AdaBelief Optimizer for training EfficientNetV2-S. Results show that, even without complex training configurations, using AdaBelief can lead the EfficientNetV2-S network to achieve a top-1 accuracy as high as 80% on the Imagenet-1K validation set within 80 epochs of training.
Fault Reconfiguration of Series-Connected Dual-Transformer Active Bridge Converter for Reliable Shipboard DC System

This article proposes a dynamic fault reconfiguration method based on a multi-port converter named dual-transformer series-connected active bridge converter (DT-SCAB converter) which interconnects DC bus and integrated energy storage devices and is applied in ship board system. The proposed converter enables to clear short-circuit fault at the output port connected with DC bus and effectively improves reliability of shipboard power system under complicated conditions. In condition of short-circuit fault occurrence at one of the output terminals, the proposed fault reconfiguration method isolates the faulty terminal and maintains constant transmission power between energy storage device and healthy output terminal. Based on normal mode and fault mode, power transmission model and zero-voltage-switch (ZVS) operation boundaries of all switches are analyzed for circuit parameters design. Meanwhile the control strategies under two kinds of working modes are analyzed respectfully based on the dynamic characteristics. Finally, performance of the proposed fault reconfiguration method has been tested with the laboratory prototype to demonstrate reliability and flexibility of the converter system.
Detection of Visual Impairment From Retinal Fundus Images with Deep Learning

Nowadays, there are challenges in terms of eye care, including the treatment of visual impairment, quality of prevention and vision rehabilitation services. Since the eye is an organ that gives information about other diseases due to its structure, examinations have an important role. In this study, a solution is sought with AlexNet, ResNet and Xception architectures based on deep learning to predict the presence or absence of visual impairment from retinal fundus images. Thus, the possibility of vision loss of patients can be reduced by detecting people with visual impairments and by early diagnosis, and thus, significant increases in the patient's quality of life can be observed.
A Single-Stage Multilevel AC-DC Bidirectional Converter With Natural Grid Harmonic Elimination

Typically, AC-DC converters have an AC-DC stage followed by a DC-DC stage. In these two stage approaches, the AC-DC stage is hard-switched, whereas the DC-DC stage is soft-switched. Therefore, the devices in the AC-DC stage can be switched only at tens of kHz to avoid excessive switching losses, thus limiting the bandwidth of the current control loop. Hence, the control of these converters with distorted grid voltages requires multiple harmonic compensation loops or other complex control algorithms in order for the grid currents to comply with IEEE standards. By integrating both the stages, the devices in the AC-DC stage can also be soft-switched, thus facilitating higher switching frequency, and thereby higher current control bandwidth. This paper proposes an integrated multilevel bidirectional AC-DC converter with improved control bandwidth and disturbance rejection. It is shown experimentally that the high bandwidth current control loop of the converter can reject grid harmonic current disturbances, without the need for any compensation loops or complex control techniques, and comply with IEEE standards.
Modeling and design of an observer-based robust controller for a low-cost inverted pendulum based on the H∞ approach

The inverted pendulum is a mechanical system with a simple configuration that carries a non-linear, unstable nature widely used in control theory as a benchmark for research. The current research presents the modeling of a low-cost commercially available inverted pendulum and the design of a robust state-feedback controller and a robust observer, following the H∞ principle and using a low-cost commercially available inverted pendulum system as a reference. Simulated results compare the performance of the designed controller and observer with a conventional LQR controller and observer.
Financial Market Prediction Using Deep Neural Networks with Hardware Acceleration

Financial market prediction has long been a hard-to-solve problem. Recently many machine learning and deep learning approaches have been taken into account to predict various properties of financial markets, namely volatility, price, and trend prediction. This paper proposes a novel multi-timeframe 1-D convolutional neural network to predict market movements. The model is trained with actual foreign exchange data in an offline setting. The trained model is then deployed on a Xilinx Zedboard Zynq-7000 SoC field programmable gate array (FPGA) to accelerate the calculation and also reducing the power consumption compared to the known approaches. We evaluate the performance of our model on test-set data new to the model; the results show that the proposed model outperforms commonly used models such as long short term memory (LSTM), multi-layer perceptron (MLP), and single-time-scale convolutional neural networks (CNN) with an accuracy of 76.60%. Although deploying the model on FPGA causes a slight accuracy reduction of 2.56%, the evaluations show that the accuracy of the hardware model is still higher than other models. Moreover, according to the evaluation results, performing the inference phase on hardware dramatically reduces latency which is a vital feature for this kind of applications with a factor of roughly 380. Power consumption is also compared and reduced when compared to an Intel server with Xeon ES-2658 processor.
Optimization on Structure and Parameter of Magnetic Coupler in Wireless Charging System

Aiming at the problems of large size and high cost of the magnetic coupler using ferrite and aluminum plate as the shielding layer of the DD coil in the wireless power transfer system, this paper proposes a new type of magnetic coupler composed of ferrite, nanocrystalline and aluminum plate. The three-layer magnetic coupler shield structure is optimized for structure and parameters. Through simulation and experiments, it is proved that the proposed composite shielding layer has the advantages of small size and low cost under the premise of achieving the same coupling coefficient.
Reliability-Oriented Multi-Objective Design Optimization of Electric Aircraft Propulsion Drives

The recently emerging field of electrified aviation requires design automation which is able to quickly and accurately create light-weight, efficient, and reliable electric propulsion drives for potential mass production. This paper proposes a systematic multi-objective design methodology intended for aviation applications, specifically focusing on power electronic drives. Due to the high priority of reliability in such safety-critical applications, the lifetime estimations due to three major contributing factors are considered during the design optimization, namely, semiconductor power cycling lifetime, semiconductor cosmic radiation susceptibility, and DC-link capacitor power cycling lifetime. When used to design a back-to-back voltage source converter, the design optimization algorithm produced 114,817,329 solutions within approximately 2 hours. The resulting optimized performance with respect to power density, efficiency, and reliability was 11.66 kW/kg, 98.81%, and 223.48 Failures-in-Time (FIT), respectively.