Miracle-dz/UniFAR-FTUs huggingface dataset:
A Type-2 Fuzzy PID-Based System of Surface FES with Pulse Width Modulation for Stroke Rehabilitation

To help stroke patients perform smooth and stable limb movement, and promote the recovery of lost limb function, this paper designed a closed-loop surface functional electrical stimulation (FES) rehabilitation training system based on a type-2 fuzzy PID controller. Firstly, the system detected movement angle error between the desired trajectory and the actual trajectory of wrist extension motion through two gyroscope modules real-time. Then, the MCU controller adjusted the pulse width of the applied electrical stimulation using the type-2 fuzzy PID controller according to the angle error and achieved the goal of regulating the stimulus intensity. The MCU controller also instructed the stimulating drive circuit to generate biphasic charge-balanced constant current stimulation pulses. Finally, through hydrogel electrodes, the stimulation pulse was applied to stimulation sites on the surface of forearm extension muscles, which induced the wrist to follow the desired trajectory. The system sent data, including the expected angle, actual angle, and time-varying pulse width, to a MATLAB script program for visualization through the esp8266 module. In addition, we tested and verified the performance of our system through an organized human experiment. The result shows the average Pearson correlation coefficient (PCC) between the trajectory of the controlled limb and the target trajectory is 0.93 ± 0.02. Therefore, the FES rehabilitation training system based on the type-2 fuzzy PID controller in this paper could enable the paralyzed limb of hemiplegia patients to perform the same trajectories as the healthy limb.
Real-Time People Counting System using YOLOv8 Object Detection

This paper presents a comprehensive real-time people counting system that utilizes the advanced YOLOv8 object detection algorithm. The proposed method aims to accurately track individuals within a video stream and provide precise counts of people entering and exiting specific areas of interest. The system combines state-of-the-art computer vision techniques, leveraging the robust object detection capabilities of YOLOv8, along with efficient tracking mechanisms and region-based analysis. The system demonstrates exceptional accuracy and robustness in people-counting tasks through extensive experimental evaluations conducted across various scenarios. The results highlight the system's effectiveness in crowd management, occupancy analysis, and surveillance applications.
Density Peaks Clustering Algorithm Based on Two-step Allocation Strategy

Density peaks clustering is a simple and efficient density-based clustering algorithm and has attracted wide attention in recent years. However, due to the lack of error correction, the one-step allocation strategy for typical density peaks clustering tends to cause the Domino effect. To solve this problem, this paper proposes a density peaks clustering algorithm based on a two-step allocation strategy. According to the original one-step allocation strategy, we first assign labels to all points. In the first step, we assign the trusted label or the pending label, whether the information of the point and its K-nearest neighbors is consistent, to each point. In the second step, we focus on the trusted labels of the K-nearest neighbors to correct the remaining points with pending labels. Finally, the density peaks clustering algorithm based on the two-step allocation strategy achieves excellent clustering performances on benchmark datasets. The code can be downloaded from https://github.com/Gedanke/DPC-TAS.
Machine Learning Approach to Estimate Ultraviolet Irradiance from Solar Radiation Data

One primary cause of degradation for materials exposed to the outdoors is ultraviolet (UV) light. To estimate their rate of degradation and determine their longevity (service life), truthful quantification of the UV irradiation incident on their surfaces is needed. In this paper, several neural models are proposed to estimate three UV components based on the broadband global horizontal irradiance and various atmospheric constituents. Using spectral results obtained with the SMARTS model for a large variety of atmospheric conditions and sun positions, three groups of experiments are carried out: the first one is designed to estimate the total UV irradiance at the surface (280–400 nm), whereas the second and third groups aim at estimating its UV-A and UV-B fractions, respectively. The results generally show a good prediction performance, particularly for the first two groups.
Extraction and Analysis of Hazardous Patterns in Truck-Involved Crashes via a Text Mining and Network Topology Framework

The proliferation of text data in traffic crash reports makes it imperative for utilization in effective ways. However, crash reports are often underutilized considered as there is a lack of effective text mining and analysis tools. This study proposes a framework integrating text mining and network topology theory to discover the latent hazardous patterns in crash reports. Based on text mining techniques, the most relevant keywords in police reports were identified. The association rule mining (ARM) is used to judge the strong correlation relationship between keywords. Additionally, the strong correlation relationship was combined with the network topology method to construct a thematic network of the text data. With the thematic network, the Louvain algorithm is applied to infer the latent hazardous patterns in crash reports. The approach was validated by the reports of severe truck-involved crashes in China from 2013 to 2020. Several latent patterns were identified from the crash reports. The modularity measure of the network with lift value outperforms that with traditional co-occurrence frequency, showing that the proposed method can effectively extract risk factors and hazardous patterns from crash reports. The framework can be incorporated into intelligent transportation systems to cope with increasing textual data.
SUBARCTIC AND SUBALPINE: WHERE AND WHAT?

A review is given of the origin, history, and use of the terms subarctic and subalpine. It is suggested that North American scientists join in the European trend towards a standardization of the nomenclature for latitudinal zones and altitudinal belts. The subarctic zone and the subalpine belt are redefined as covering the forest-tundra ecotone at high latitudes and
An Improved Active Feedforward Compensation Based on Model Predictive Control for Electrical Test Bench

The load generator system is widely used to perform dynamic and static load tests of electric machines or combustion motors. Due to their simple structure and mature theory, classical control theories, such as the Root Lucas method and the state feedback method, have been widely used in power electronics and electric drives. However, in classical control strategies, controller parameters are designed by trial and error, which cannot achieve optimal design. As an optimal controller, Model Predictive Control (MPC) does not seek only control, but optimal control. MPC can be divided into continuous control set model predictive control (CCS-MPC) and finite control set model predictive control (FCS-MPC). Now, the fact that the dynamometer is a precision instrument and should have a wide operating range of speed-torque, in this paper based on the existent weakness in the MPC, An Improved Active Feedforward Compensation Based on Model Predictive Control proposed to provide better robustness against uncertainties caused by parameters mismatch between the plant model and the model used in the predictive controller. With the evaluation done with the help of simulation, visible performance improvement in the system occurs.
Efficiency Evaluation of a dual-inverter supplied open-end three-phase 5 Hp Induction motor drives

This paper discusses efficiency of Variable Speed Drive (VSD) of dual-inverter fed a three-phase 5 Hp open-end winding induction motor. The motor is controlled using equivalent Space Vector Carrier Based PWM. VSD System is modelled using Matlab/Simulink meanwhile inverter and motor are modelled using Simscape Blockset. Simulation result shows that efficiency VSD using dual-inverter is significantly improved in comparison with VSD using two-level inverter. At modulation indices 0.15 – 1.15, efficiency of the system using two-level inverter is in the range of 20% - 82% which is improved by 36% to 97% for the system fed by dual Inverter at full Load (20 N-m). The improvement of efficiency of VSD system with dual-inverter is caused by improvement of inverter efficiency by 76%-98%, compared with efficiency of two-level inverter (44%-93%) while efficiency of motor is relatively the same in the range of 45% to 89% for both inverter structures under investigation. Improvement of efficiency is in line with improvement of quality of inverter output voltage of the dual-inverter over the two-level inverter. Furthermore, it is also found that the efficiency of system is reduced by the reduction of motor load. At very low load, the reactive power (Q) becomes higher than the active power (P), which means the power factor of system become very low (less than 0.5)
A Lane Change Assisting Framework for Large Vehicles

The trend of intelligent transportation has been leading the world for years, and human acceptance of applying Advanced Driver Assistance Systems (ADAS) in driving is increasing. Among all types of human driving, large vehicle driving is the one that requires the assistance of ADAS the most. It is imperative to use automatic driving technology to assist large vehicle driving and further integrate those successful cases into ADAS to increase safety and driving experience. According to [1] and [2], improper lane changes are one of the top 10 driver-related factors involved in large truck crashes. Due to their size and weight, large vehicles require more space and time to change lanes. To fundamentally improve the safety of their lane changes, this paper proposes a Lane Change Assisting Framework (LCAF) for large vehicles. It helps to reduce the potential risks of large vehicles in the lane-changing process by predicting the driving intention of surrounding vehicles and evaluating the safety of potential routes. By assisting the lane change decision-making of large vehicles, while not affecting their passing rate, the passive influence of their lane changes on the whole traffic flow is minimized, and the speed and capacity of the traffic system are improved.
An Optimization Technique for Improving Parameters of the Inverse Jiles-Atherton Hysteresis Formulation in Case of Steel Sheet Fe3%Si

In a current study, we present an innovative model for optimization of hysteresis cycle parameters. Proposed model is applied to a steel sheet. In doing so, we have defined the interest of the use of Jiles-Atherton phenomenological model and we have specified our study on the case of the inverse model. The exploit of Particle Swarm Optimization technique was discussed with details. B(H) curves produced by modeling and experimentation are shown for the quasi-static frequency under sinusoidal magnetic excitation applied to a GNO Fe3%Si steel sheet. Simulation results are analyzed and compared with experimental obtained B(H) curve. In a first situation, a study was conducted after optimization, while in the second one; the specified investigation was carried out with optimization. The simulated and optimized B(H) curve and the experimental data show and confirm an excellent agreement. A highly successful performance has been made, validating our proposed Model.
Comparative Performance Analysis of Constant and Variable DC Bus Current CSI-Fed PMSM Drive in a Wide Range of Speed and Torque

Current source-fed (CSI-fed) PMSM drive has advantages over VSI due to its higher power density, higher efficiency, high temperature, lower noise level, and EMC, and replacing DC link capacitor by inductor (as a result reducing volume and weight). By considering the increasing use of high switching frequency wide band gap (WBG) switches (silicon carbide and gallium nitride switches) in new generation converters, the CSI type is preferred due to its special advantages. Different constructions of CSI-fed PMSM drives have been proposed, in all of which the feeding of the CSI is provided through a voltage-to-current converter. In this paper, Comparative performance analysis of constant and variable DC bus current Buck-CSI-fed PMSM drive are evaluated in the view of power losses and efficiency, DC link current ripple, as well as thermal stresses of switches. For accurate modeling of the WBG switches, the simulation of the drive system has been carried out in PLECS software. The simulation results show that the variable DC bus current Buck-CSI fed PMSM drive configuration has better performance for a wide range of torque and speed.
Understanding Vehicle Interaction in Driving Video with Spatial-temporal Deep Learning Network

This paper develops a deep learning method to identify vehicle interactions from driving video and creates a dataset for network training and testing. This is an original work targeting qualitative vehicle interactions from continuous visual sensing of image velocity, position, and vehicle width/depth. The motion profile is obtained from driving video as a subspace for learning and classification. A spatial-temporal 2D CNN Network (named ST2CN) scans streaming frames to detect the vehicle status at every frame without latency from a short history. The motion continuity and temporal dependency are well preserved by this ST2CN based network. The labeled interaction dataset has 300-minute videos sampled at dense rate to include 13 classes of interactions. Our initial results serve as a benchmark for sensing vehicle interactions. The detection of surrounding vehicle's action helps qualitative planning and smooth driving of intelligent vehicles.
Suppression of Epileptiform Activity in a Wilson-Cowan Network Model by Distributed Linear Delayed Feedback Control

Electrical brain stimulation (EBS) has been established as a potent instrument for modulating brain activities. This paper presents a spatially distributed linear delayed feedback control (LDFC) approach for EBS, designed to suppress epileptiform activity within a Wilson-Cowan network model of the cortex. The EBS is administered distributively via a grid of stimulating electrodes positioned on the cortex, and we evaluate the efficacy of LDFC in suppressing epileptiform activity. In a large range of control parameter space (feedback gain and delay time), the overall pathological oscillations can be substantially reduced, although not entirely suppressed.
Banana Plant Disease Detection-Hybrid Machine Learning Approach

Artificial Intelligence has been creating a revolution in agriculture by changing traditional methods with new and efficient methods to improve overall production and quality. If farmers are getting a way to identify early and exactly about the infections on plants, the spreading of diseases can be limited and farmers can apply appropriate pesticides and fertilizers so that the risks and costs will be minimized and yield will be maximized. Here we propose a hybrid approach for the automatic detection and classification of Banana leaf diseases. Ant Colony Optimization has been used for the feature selection and Convolutional Neural Networks have been used for the classification of leaf images. A performance analysis of a novel CNN and the hybrid approach has been conducted and found that the hybrid approach is providing greater accuracy.
VSSeg: A Point Cloud Segmentation Approach for Unstructured Region in Surface Mine

Point cloud segmentation for unstructured regions is challenging. Especially, the diversity and complexity of the terrains in surface mine make point cloud segmentation more difficult, which brings potential risks for autonomous driving. To solve this problem, a Variance and Slope ratio based Segmentation method, named VSSeg was proposed. The proposed VSSeg method takes advantage of the different distributions of scatter diagrams projected in the height direction to obtain the variance-based discriminant slope characteristics. And it uses the slope changes at critical points of the ground and the unstructured regions to achieve slope-ratio-based segmentation. Experiments were conducted on the mining datasets with annotation, and the results showed the effectiveness and robustness of our proposed VSSeg method. Especially, for dumping berm segmentation, the accuracy reached 95% on average with a real-time speed.
Évaluation d’une supplémentation chronique en nitrate et citrulline sur la performance à l’effort et la fonction cardiovasculaire du sujet âgé

Introduction : La biodisponibilite accrue du monoxyde d’azote (NO) peut ameliorer les performances physiques et la fondtion cardiovasculaire. Il n’est pas clair si les personnes âgees presentant une diminution de biodisponibilite en NO peuvent beneficier d'une supplementation chronique en precurseurs de NO. Materiel et methode : Cet essai en double aveugle randomise a pour objectif d'evaluer l'effet de la consommation chronique de precurseurs de NO sur la fonction vasculaire et la performance physique chez les adultes âges (60 a 70 ans). Vingt-quatre sujet âges sains (12 femmes) ont effectue une evaluation de la fonction vasculaire ainsi que des tests physiques locaux (extension du genou) et du corps entier (cycle incremental) jusqu'a epuisement avant et apres un mois de consommation quotidienne d'un placebo (PLA) ou d'un complement riche en nitrate et en citrulline (N + C, 520 mg de nitrate et 6 g de citrulline). La pression arterielle (PA) et la rigidite arterielle, les reponses vasculaires post-ischemiques, hypercapniques et hypoxiques ont ete evaluees. Le cortex prefrontal et l'oxygenation des quadriceps ont ete surveilles par spectroscopie dans le proche infrarouge. Resultats : La supplementation en N + C a reduit la PA moyenne (- 3,3 mmHg; p = 0,047) sans modifier les autres parametres de la fonction vasculaire et de la cinetique d'oxygenation. La supplementation en N + C a reduit la frequence cardiaque et la consommation d'oxygene sous-maximales et a augmente la puissance maximale de 5.2% (p <0.05), Neanmoins elle n'a eu aucun effet sur les performances de la force d’extension du genou. Conclusion : Ces resultats suggerent que la supplementation chronique en precurseurs du NO chez les personnes âgees en bonn sante peut reduire la PA au repose et augmenter les performances du velo en ameliorant les reponses cardiorespiratoires.
Improving K-Nearest Neighbor Performance in Footwear Classification Using Leave One Out Cross Validation

Footwear is a tertiary need for humans in this world. The fashion world will continue to evolve, including the development of footwear fashion. In addition, based on the Criminal Investigation Agency of the Republic of Indonesia, footwear can be used as evidence of a crime, and can be used to identify a crime. Computer vision still has difficulty categorizing fashion, even though fashion plays a huge role in everyday life. Therefore, footwear image classification will be applied to help identify crimes and meet human needs in categorizing desired footwear and can be used to identify potential suspects, identify brands and models of shoes found at crime scenes. A series of methods are applied, starting from pre-processing, including image labelling and converting the image from RGB to grayscale, Histogram of Oriented Gradients (HOG) feature extraction, edge detection using the Canny Edge Detection algorithm, and to enhance the accuracy of KNN classification, the method applied is Leave One Out Cross Validation (LOOCV) the method works by splitting the data and save the accuracy in each iteration, which results in an increase in accuracy of 4%, from 94% using the KNN algorithm alone and with a combination of KNN and LOOCV resulting in the highest accuracy of 98%.
Human-Like Decision Making and Planning for Autonomous Driving with Reinforcement Learning

One of the main challenges faced by autonomous vehicles operating in mixed traffic scenarios pertains to ensuring safe and efficient navigation, particularly adhering to the implicit rules obeyed by human drivers. In this study, an Adaptive Socially-Compatible Hierarchical Behavior and Motion Planning (ASC-HBMP) framework is proposed to tackle the issue of socially-compatible navigation. ASC-HBMP comprehensively captures the attributes of other traffic participants to guide autonomous vehicles in devising human-like, safe, and efficient trajectories in a socially-compatible manner, striking a balance between safety and efficiency within complex multi-scenarios. Hierarchical Behavior and Motion Planning (HBMP) establishes driving tasks as high-level behavioral decision-making processes that emphasize efficiency, as well as low-level motion planning methods that prioritize safety. HBMP accepts the guidance provided by Adaptive Socially-Compatible Module (ASCM) to generate trajectories with diverse driving style characteristics. Finally, cross-platform simulation experiments are conducted on the SUMO and ROS simulators to validate the navigation performance and generalization capability of our approach in comparison to other baseline methods.
Research on Intelligent Noise Reduction Method for CT Images Based on Attention Mechanism

With the widespread use of computed tomography (CT) technology, the demand for high-quality CT images is increasing, and how to achieve efficient and high-quality image noise reduction has been a hot research topic in the field of CT. In this paper, we focus on the intelligent noise reduction method of CT images based on attention mechanism. In this paper, U-Net is selected as the base network, and the channel attention module and pixel attention module are chosen for the study. To further improve the performance of pixel attention, we propose to introduce multi-scale convolution and residual modules to optimize it. By adding the channel attention and the optimized pixel attention, a lightweight noise reduction network is proposed in this paper. Compared with CBDNet, this network is lighter and faster, and the peak signal-to-noise ratio of noise reduction results is improved by 0.14 dB on average and the model is more stable under the simulated dataset, and the structural similarity of noise reduction results is improved by 0.036% under the real sample dataset.