Miracle-dz/UniFAR-FTUs huggingface dataset:
A Scalable VR-Based System to assess Driving-Related Abilities in People with Disability

Driving independently plays a crucial role for people with disability as it allows for social participation and autonomy in daily life. However, the safety of the driver him/herself, the other drivers, and pedestrians must be ensured. This paper presents VRoADS, a Virtual Reality (VR)-based tool to support assessors while evaluating the driving-related abilities of people with disability who want to (re)obtain a driving license. VRoADS allows the evaluation of the same driving-related abilities that are generally assessed before the release of the “special driving license” using standard equipment named Residual Capabilities Verifier (RCV). Although valid, this tool lacks realism and is only available in a few specialized centers on the Italian territory. VRoADS aims to fill these gaps by providing a driving-contextualized scenario and the possibility of being operated with different input controls: this would allow its use with simplified and cost-effective hardware setups that can be made available in smaller centers too. Before use, VRoADS must be validated with different hardware setups to assess whether its measures are comparable to those obtained with the RCV.
Deep RL-Enabled Inverters: Strengthening RES Integration in Grids with Electric Arc Furnaces

This research work presents the application of Deep Deterministic Policy Gradient (DDPG), a Reinforcement Learning (RL) approach, for grid modeling, voltage and phase angle estimation, and control of grid-supporting inverters. The goal is to develop a grid-supporting inverter that produces virtual inertia, stabilizing the grid frequency problems from heavy loads such as electric arc furnaces and enabling seamless integration of renewable energy sources (RES). By employing DDPG, we eliminate the need for multiple estimation tools, such as Fast Fourier Transform (FFT), Synchronous Reference Frame (SRF), or lowpass filters, typically used in traditional methods for determining controller loop setpoints. Moreover, the method reduces the necessity for extensive tunings of parameters, like PID controller coefficients and lowpass filter settings. Findings indicate that the DDPG-based control system offers a fast and efficient control mechanism for maintaining the frequency stability of power systems. By improving grid stability, this innovative approach can play a pivotal role in promoting the widespread adoption of RES.
Enhancing Power Quality Using 12-Pulse AC-DC Converter with DC Side Current Injection Scheme in More Electric Aircraft Applications

This research introduces a novel circuit arrangement design to minimize the presence of harmonics in the AC current mains of a 12-pulse based autotransformer with Diode Bridge Rectifier (DBR) utilizing a star configuration. The suggested circuit setup involves the utilization of a tapped interphase full bridge rectifier (TIFBR) on the DC side of the parallel connection of two 6-pulse DBRs. This modification leads to an increase in the number of pulses within the supply current. Consequently, it effectively mitigates the harmonic components, resulting in an enhancement of the overall power quality for the system that aligns with the stipulated criteria of the IEEE-519 standard across different load conditions. Notably, the outcomes indicate that the proposed arrangement of the 24-pulse AC-DC converter can achieve an input line current total harmonic distortion (THD) within specified limit under varying load circumstances suitable for More Electric Aircraft (MEA) applications. Additionally, the suggested setup undergoes power quality analysis in MATLAB/Simulink simulations. These results validate power quality enhancement within the incoming AC line current.
LUTNet-RC: Look-Up Tables Networks for Reservoir Computing on an FPGA

We propose look-up tables networks-based reservoir computing (LUTNet-RC). This work is the first trial of applying LUTNets to RC. LUTNet-RC consists of a LUT-based reservoir layer and a non-LUT-based output layer. LUTNets have disadvantages such as limited sparse connectivity and weights cannot be changed after implementation. However, when applied to a reservoir layer of RC (LUT-based reservoir layer), these disadvantages are eliminated, because this layer works with sparse connectivity and the weights are fixed, so only the advantage of small circuit resources is obtained. For the LUT-based reservoir layer, we propose and model a multi-bit weight reservoir, modifying the conventional binarized reservoir to improve calculation accuracy. In the case of LUTNets, the proposed multi-bit weight reservoir can be implemented without the increase in utilized circuit resources because LUTNets focus only on the input-output relationship on neurons. Additionally, we propose a speed-up method in the output layer with time division calculation, which compares the current network state with previous states and then calculates only status-changed neurons. As a result, we implement a LUTNet-RC with 1500 reservoir neurons on a field-programmable gate array (KR260) running at 100MHz. The utilized circuit resources are dominated by LUTs, which use approximately 26% of the total amount of LUTs. The LUTNet-RC can infer more than 106 data per second. We also verify the LUTNet-RC performance using nonlinear auto-regressive moving average 10 (NARMA10) and the performance is comparable to conventional works. We conclude that the LUTNet-RC is one of the highest-performance RC on an FPGA.
Positioning Laser Line Detection Algorithm of Construction Robot Based on LSD and Improved Least Squares Method

A laser line detection algorithm for the positioning of construction robots based on Line Segment Detector (LSD) and an improved least squares method (LSM) is proposed to address the line finding and localization requirements in actual construction operations. After preprocessing and edge detection of the input image, the LSD algorithm is utilized to detect line segments representing the laser lines for positioning. These line segments are then converted into a point set containing information about the laser lines' positions. To overcome the limitations of traditional least squares method that relies on vertical errors, an improved least squares method is employed to fit the centerline of the laser lines, which serves as a reference for subsequent robot localization. Experimental results demonstrate that the proposed algorithm accurately detects and fits the centerline of the laser lines in the input image, and exhibits high robustness and accuracy.
Investigating EV Charging Station in LAMBDA Microgrid for V2B Applications

The advent of bidirectional chargers in electric vehicles (EVs) has unlocked the potential for innovative technologies like vehicle-to-grid (V2G) and vehicle-to-building (V2B) systems. This paper focuses on the initial phase of the EV charging station at LAMBDA MG Lab, where the advantages of V2B technology are explored. By leveraging V2G capabilities through optimized operations, the implementation at LAMBDA MG Lab offers several key benefits, including enhanced energy flexibility, significant cost savings in energy bills, and efficient management of peak demand.
Direct speed predictive control of permanent magnet synchronous motor

The use of traditional Model predicts direct speed control (MPDSC) method for permanent magnet synchronous motors (PMSM) has the problems of large calculation amount and complicated design of weight factors , this paper uses the principle of beatless control to convert the reference speed and the reference flux into an equivalent reference voltage vector at the same time, and constructs a cost function that can track the reference voltage vector error while abandoning the design of the weight factor in the traditional MPDSC method. At the same time, the use of extended observers improves the control performance of MPDSC.
Research on Detection and Attack Strategy Against Swarm of Drones

With the gradual deployment of swarm of drones in combat, the research on high cost-effectiveness countermeasures against swarm of drones has become an urgent and necessary topic. This article first analyzes the challenges faced by ships against swarm of drones and the measures that need to be taken. Then, it explores the reconnaissance and early warning methods for swarm of drones, and proposes a multi-layer defense system for long-range, mid-range, and short-range countermeasures. This research provides valuable guidance for improving the capabilities of naval vessels against drone swarm.
Finite-Time Robust Controller Using Sliding Mode Approach for Grid-Connected Inverters

This article suggests and experimentally implements a new finite-time robust current controller for grid-connected three-phase two-level inverters through an RL filter under unknown parameter variations. New advanced types of nonlinear sliding mode control laws are defined to ensure the strong robustness of the closed-loop system under the proposed controller against the variations of the filter parameters. Chattering elimination is addressed in the proposed sliding mode control laws. The finite-time stability notion is considered in the proposed approach to guarantee stability within finite settling time. The stability proof of the closed-loop system is presented utilizing the Lyapunov theory. The effectiveness of the suggested approach in fast and robust dynamic responses and alleviation of the chattering compared with the conventional sliding mode controller is demonstrated using simulation and experimental results.
Prospect-theoretic Modeling of Team Cognition for Task Allocation Towards Human-automation Symbiosis

Collaboration between human and automation agents is identified essential for improving human-automation team performance, and task allocation is the direct operation that influences it during human-automation interaction. While conventional task allocation does not consider human-related factors in problem formulation, this paper proposes to include team cognitive performance in the optimization objective to allow adaptation to human agent states, thus facilitating human-automation symbiosis. To achieve this objective, this paper models different human-related factors during task allocation, including individual cognitive states, human trust, and level of automation. Team cognitive performance is proposed to be evaluated through cognitive load, confidence, and attention, which is used for team performance evaluation. Based on this, the team cognition-aware task allocation problem is formulated, and a two-dimensional genetic algorithm is proposed to solve this problem. A validation case in manufacturing assembly is presented to illustrate the feasibility and effectiveness of the proposed approach.
Postural Ergonomic Assessment of Construction Workers Based on Human 3D Pose Estimation and Machine Learning

Work-related musculoskeletal disorders (WMSDs) have been the major cause of occupational injuries among construction workers. The traditional observational assessment is time-consuming and subjective, while the sensor-based postural analysis is usually associated with high setup costs and intrusiveness. This study proposed an automated ergonomic risk assessment method based on computer vision and machine learning focusing on lower body postural risks. It provided a comprehensive risk dashboard, including posture detection and rule-based extreme flexion examination. Specifically, with raw video input, the postural feature extraction module can identify skeleton coordinates frame by frame by adopting a state-of-art 3D pose estimation algorithm. In the ergonomic assessment module, the knee angles can be calculated using skeleton coordinates, and the support vector machine (SVM) classifier was trained for posture recognition. The illustration based on a real-life example demonstrated the applicability and reliability of the proposed method, with nearly 95% accuracy for posture detection. In summary, the study provided a more comprehensive and in-depth postural analysis of construction activities, which has great potential to facilitate intervention strategies for WMSD prevention with quantified evidence.
Design of Face Image Super-Resolution Network Based on Multi-scale Perceptive Inception Dense Block

Within the realm of image data manipulation, enhancing resolution and reconstructing facial visuals stand as pivotal methods designed to restore poorly defined images impacted by unidentified deterioration mechanisms. Traditional non-generative adversarial network (non-GAN) models suffer from high-frequency information reconstruction deficiencies when restoring high-frequency information in images. Utilizing the adversarial learning process between generators and discriminators, GANs are capable of synthesizing high-definition imagery enriched with authentic texture and enhanced clarity. However, they have inherent limitations in terms of pixel-level changes, line distortions and detail recovery. In the context of Mixed Residual of Multi-scale Perceptive Inception Dense Block GAN (MRMPIDBGAN), a Multi-scale Perceptive Inception Block (MPIB) is engineered for the robust retrieval of features and multi-scale data from facial images. Based on MPIB, a Mixed Residual of Multi-scale Perceptive Inception Dense Block (MRMPIDB) is further constructed to form the generator, which facilitates better capture and reconstruction of facial image details. Secondly, to more accurately capture the local texture information of the image, the original VGG discriminator in ESRGAN is replaced by a U-Net discriminator, and spectral normalization techniques are applied to improve the stability of the discriminator. Subsequently, an innovative upsampling technique is formulated to optimize efficacy yet minimize computational demands. Further improvements in the model's efficacy are achieved through the replacement of the traditional VGG loss by a metric grounded in perceptual criteria. Experimental results confirm the effectiveness of this algorithm in super-resolution facial image restoration and demonstrate significant advantages in facial image reconstruction, clear texture capture and feature detailing compared to existing methods.
Design and Simulation of a Four-Wheeled Rover for Enhanced Lateral Stability

Ever since the onset of robotics, there has been a huge demand for all-terrain rovers that can operate in unstructured ground conditions. One of the major challenges for all-terrain rovers while traversing uneven ground conditions is to maintain longitudinal and lateral stability of their platform while maximizing their terrainability and minimizing actuation input for stabilization. In this paper, the conceptual design of a novel four-wheeled rover based on the DUNE rover architecture has been presented. An analytical model of the proposed rover is formulated in MATLAB to quantitatively compare the stability and mobility performance metrics on multiple terrain conditions with that of the conventional DUNE rover design. A multibody dynamic simulation of the proposed design is also performed in Msc.ADAMS to validate the analytical model. In comparison to the DUNE rover architecture, the proposed design utilizes only a single rotary actuator to maintain a more uniform distribution of wheel grip on the terrain through a reduced wheel load deviation. It also exhibits a significant increase in the stability margin and stabilizes the chassis roll angle in uneven terrains which have net non-zero lateral slopes.
Fault Tolerant Technique for 3n- Phase Induction Motor Drives under Open-Winding Fault Condition

The increasing capabilities of power electronics have led to the rise in usage of poly-phase machines for industrial applications. These are preferred over conventional three-phase IMs due to their superior performance and reliability. However, further research is needed to enhance the reliability of multi-phase IMs with faulty windings. This paper introduces a converter topology and a scheme for the reliable working of 3n-phase drives under open-winding fault condition. Verification of the results are made on the 9-Phase IM drive using Ansys Maxwell simulation and further evaluated by hardware implementation. The findings exhibit the proposed concepts are effective in maintaining the performance of the nine-phase IM even during fault conditions. This research contributes to the ongoing effort to enhance the reliability of multiphase IMs in industrial applications.
Aging Aware Retraining with a Sparse Update for Neuromorphic Computing

A low-power system with minimal area and high inference accuracy defines the performance of the neural network accelerators. This tradeoff between accuracy and hardware performance is challenging for software hardware co-designers to build an optimum design due to various hardware variabilities. A compressed model can reduce the area and power consumption without compromising accuracy. Pruning is a commonly used method of removing unwanted weight values. After pruning, the distribution of redundant weight values for different neural architectures shows different hardware performances. A highly pruned neural network can be easily affected by hardware variability issues. Analyzing the hardware variability with sparse updates can minimize the area, power, and computational complexity. This co-design approach can decide the level of pruning required for different neural network architectures based on their hardware implementation. Here in this paper, aging-aware retraining with sparse updates on pre-trained neural networks is analyzed. Model compression techniques are analyzed for different neural architectures with hardware variability challenges. Neural network performance analysis and hardware-optimized parameters are compared with other decomposition techniques, such as tensor decomposition and quantization.
Enormous-Scale Quantum State Vector Calculation with FPGA-accelerated SATA storages

Gate-based quantum computers, poised to surpass their classical counterparts, drive intense research interest. Emulating quantum systems relies on state vector-type simulators, hampered by soaring memory demands, especially for systems beyond 40 qubits. Our solution called Trefoil system combines multiple FPGAs and SATA disks. Trefoil features a main unit and eight storage subsystems, each with 32 SATA disks, providing 2 PBytes of memory for up to 47 qubits. Sector-level block transfers enable efficient quantum operations such as H, Z, CNOT, and unitary matrices. The results show an H gate for 30 qubits executes in 10 minutes without increased times using parallel processing. Our study advances scalable quantum computing simulations, surmounting memory limitations.
Time Series Classification Method Based on Multi-Scale Convolution with LSTM

To solve the problem that both local and temporal features of time series can affect the classification accuracy, a Multi-scale Convolutional Neural Network with LSTM (LSTM-MCNN) is proposed. Firstly, in the full convolution part of the model, multi-size convolutions are used to extract local features of different scales from the input time series signals. Secondly, the SE (Squeeze-and-excitation) module was added after the convolutional layer to reduce the influence of useless channel information, so that valuable features of different sizes could be extracted. Finally, the local features obtained are combined with the temporal features extracted by the long short-term memory network to further mine the temporal feature information. The experimental results show that the accuracy of the proposed method on multiple standard time series datasets is higher than that of the compared model, which also proves that the proposed method has versatility and accuracy in the time series classification task.
Wireless Power Transfer Platform to Re-charging UAVs using Sandwich Coils with Planar Ferrite Cores and Renewable Source

The limitations of UAVs in terms of their endurance or flight time are largely determined by the battery capacity. Because the weight of the battery unit is a significant factor, there is a trade-off between battery capacity and weight. This has led to the need for recharging or battery swap during UAV operations. To overcome this limitation, improving the potential battery energy density per kilogram or enhancing the ease of recharging. One solution is wireless charging system using sandwich coils. Magnetic coupler is the key component to reduce leakage flux that is harmful for human body and surrounding equipment. This paper presents real experimental data of sandwich coils performance with different alignment and ferrite I core orientations, and evaluations of the minimum leakage flux and maximum efficiency. Based on the experimental data, we successfully built a smart 5W prototype to test the concept of conveniently charging small UAVs using solar panels and a battery with sandwich coils and ferrite I cores.
Edge Node Cooperation and Resource Sharing in Mobile Edge Computing

As edge computing undergoes ongoing development propelled by the advancements in Internet of Things and mobile communication technologies, the issue of edge node cooperation and resource sharing needs to be investigated. In this paper, the issue of edge node cooperation and resource sharing is modeled as a two-layer framework. In the lower layer, a heuristic matching algorithm between users and edge nodes is developed, and a resource sharing algorithm among edge nodes in the same coalition is proposed. In the upper layer, a coalition formation algorithm is designed based on the coalition game. Simulation results show that the proposed algorithms reduce the network cost effectively compared with non-cooperative scheme.
Research on the Application of 5G mMTC in the Smart Grid

5G is the latest generation of cellular mobile communication technology that enables a shift from personal mobile applications to industry applications. The three typical application scenarios for 5G are enhanced mobile broadband, ultra-reliable low-latency communication, and massive machine-type communications. In particular, 5G mMTC aligns well with the demands of the power grid, which requires extensive coverage and connectivity for efficient business transmission. It also meets the needs of the integrated power Internet of Things in smart grids. , allowing for deep access coverage of power terminals in the transmission and distribution process without the need for extensive local network wiring and carrier equipment deployment. Therefore, this paper focus on how to construct a wireless communication network for the power Internet of Things using 5G mMTC technology, while prioritizing power security protection, to support applications such as intelligent information collection, monitoring, and control in the power grid.
A DC/DC Converter for 400 V and 800 V EVs Fast Charging Stations

This paper presents a DC/DC converter, which can be used in 400 V and 800 V EVs fast charging stations. The proposed topology is based on the conventional PSFB converter with a simples clamping circuit, and a auto-configurable output switching mechanism. This converter can operate under high power conversion efficiency because it had zero-voltage switching for primary power switches and soft-commutation for output rectifiers. Also, this proposal uses SiC MOSFET to reduce losses and improve converter performance. In addition, the proposed topology does not require any snubber circuits to reduce the voltage stress of the switching devices because the switch voltage of the primary and secondary power stage is confied by the input and clamping circuit voltage, respectively. Finally, the simulation results show the reliability of this design method.