Miracle-dz/UniFAR-FTUs huggingface dataset:
Personalized Estimation and Modification of Elbow Angular Displacement for Upper Limb Exoskeletons:A Fuzzy Logic-Based Approach

This study introduces an innovative method for estimating and modifying elbow angular displacement using surface electromyography (sEMG) signals, with the goal of achieving transparency between the wearer and upper limb exoskeleton robots while enhancing human mobility. The approach presented in this research offers a personalized control system that is driven by the user's preferences. The control system incorporates a fuzzy logic-based approach, which dynamically adjusts the estimated elbow angle based on the user's desired interaction with the rehabilitation device. Three distinct fuzzy systems are developed: a) one for estimating the joint angle trajectory, b) another for estimating the user's preferences, and c) a third system to control the angular displacement of the actuator. To evaluate the performance of the control strategy, an experimental setup is designed and tested using surface electromyography (sEMG) signal and external user input. The experimental results demonstrate that the proposed controller is capable of modifying the angle trajectory of the exoskeleton robot in accordance with the user's preferences
A DDoS Attack Detection System for Industry 5.0 using Digital Twins and Machine Learning

Industry 5.0 is a new paradigm that seeks to improve production via the use of cutting-edge technologies like the Internet of Things (IoT) and cloud computing. Distributed denial of service (DDoS) assaults, for example, may severely harm industrial processes and disrupt the whole supply chain, but this integration of technology also presents significant security issues. Therefore, it is crucial in the age of Industry 5.0 to create reliable DDoS attack detection systems. In this research, we offer a DDoS attack detection system for the Industry 5.0 environment that makes use of Digital Twins (DTs) and Machine Learning (ML). Our method takes use of the strengths of DTs to model the production flow virtually, which can then be used to track the system’s behaviour and spot any hiccups. For further analysis of the DTs’ output data in order to spot possible DDoS assaults, we use explicable ML algorithms like the decision tree classifier.
I’m not a human: A comparison of bot and human self-presentation

Social bots play a pivotal role in content creation and dissemination across social networks, often shouldering repetitive and mundane tasks. However, the emergence of malicious social bots has posed a significant threat to the online ecosystem, as they actively disseminate misinformation and disinformation. Many research efforts have been directed towards the detection of Twitter bots, a challenging endeavor given their non-identifiable nature through account names alone. This study takes a unique approach, focusing exclusively on the analysis of self-presentation by bots versus human users through linguistic analysis of written text on Twitter. We meticulously examine the linguistic characteristics of Twitter content generated by social bots and delve into the distinctions and commonalities within the content they produce. Employing a logistic regression model, we demonstrate the feasibility of distinguishing between text generated by bots and humans. Surprisingly, our findings reveal that bots generate content strikingly similar to that of humans, a phenomenon we elucidate through the lens of authorial agency theory. This research bears significant implications for social network designers, urging vigilance against the infiltration of malicious and fictitious accounts. Furthermore, it offers valuable insights into the characterization of social bots, which can be harnessed to enhance their detection mechanisms.
Lab VIEW and Deep Learning Based Combustion Flame Monitoring and Fuel Flow Measurement in a Thermal Power Station

Thermal power station performs a crucial function in generation of power. The furnace burns coal to produce heat energy. In order to reduce fuel waste, combustion quality is essential. Flame image monitoring system is crucial in assessing the quality of combustion because the price of fossil fuels is higher. The primary focus of this effort aims at determining the flame temperature and its corresponding fuel flow of a combustion flame captured online in video using two external cameras. Those videos are fused to create a single image frame. Pre-processing technique median filtering is executed and approximation wavelet transformation is applied to improve image quality and recover weak signals from noise. To increase efficiency and accuracy, the image is segmented using fuzzy k-means clustering. Discrete Fourier Transformation (DFT) is then applied to the clustered image to obtain integer values in pixel form. Finally the combustion flame temperature and its fuel flow will be determined using machine learning algorithm CNN (Conventional Neural Network).
A Fault Diagnosis Method for Inverter Based on Data Augmentation with IGA Specific Coefficient Wavelet Reconstruction

To solve the problem of insufficient fault samples for inverter fault diagnosis in weakly supervised small sample scenario, this paper proposed a fault diagnosis method for inverter based on data augmentation (DA) with improved genetic algorithm-specific coefficient wavelet reconstruction (IGA-SCWR). First, IGA is used to find the optimal fine-tuning coefficient (FTC) k, which is the key to ensure the quality of DA. Second, through wavelet packet decomposition, the wavelet coefficients are changed under the guidance of the optimal FTC k, and sufficient samples of new synthesized data were obtained. Then the “end-to-end” feature extraction and fault classification are realized based on convolutional neural networks (CNNs). Finally, the effectiveness of the proposed method is verified by a series of experiments.
Interval Barrier Lyapunov Function and Its Application to Wheeled Mobile Robot

Since the classical barrier Lyapunov function (BLF) suffers from the restriction on the availability of constrained signals, this work proposes the concept of the interval BLF through introducing the nonlinear interval observer (NIO) technology. Specifically, the velocity-constrained tracking control design of the wheeled mobile robot (WMR) is studied without the linear velocity measurement. To accomplish the constraint objective on the unmeasured linear velocity, the NIO is developed to estimate the upper and lower boundaries of that. Through tactfully using these boundaries and designing the interval BLF, the tracking control scheme is developed for the WMR to accomplish the velocity constraint objective for both the linear and angular velocities. Finally, the proposed control scheme is demonstrated through numerical validation.
Discrete-Time Modeling of PMSM for Parametric Estimation and Model Predictive Control Tasks

This paper presents novel implicit and explicit discrete-time permanent magnet synchronous motor models. Both derived models solve the problem of numerical instability and poor precision of motor currents' discrete-time prototypes formed using the forward Euler method and preserve the tolerable complexity of resulting descriptions. Discrete-time models of currents are derived based on the linear time-varying systems approach, considering the electrical angular velocity time-varying parameter. Angular velocity and angle are discretized by using the linear multistep methods. The implicit variant of the model is dedicated to parametric estimation tasks, and the explicit variant is to model predictive control. The derived descriptions are validated within the simulation by comparing the original continuous-time model and Euler approximation with the explicit model. Furthermore, the prediction capabilities of the explicit model and Euler approximation are compared as well.
Characteristic-Model Based Discrete-Time Sliding Mode Control for Attitude Tracking of Quadrotor Under Unknown Dynamics and Input Saturation

In this paper, the attitude tracking control problem for quadrotor with unknown dynamics and input saturation is investigated. First, characteristic model is applied to the quadrotor modeling with unknown dynamics. Second, a novel discrete-time sliding mode controller (SMC) built in characteristic model is proposed to achieve attitude tracking and eliminate the negative effect caused by input saturation. Besides, the proposed characteristic-model based discrete-time SMC has a strong robustness against disturbance. It is proved that the convergence of tracking error can be guaranteed. Finally, the effectiveness and superiority of the proposed scheme are illustrated by simulation.
Forecasting Insolation Shaded by Solar Panels for Optimal Layout in Agrivoltaic System

Agrivoltaics which install solar panels on farmland, have been expanding due to land competition in the global expansion of solar power generation. Insolation is an essential parameter for crop growth so it is crucial to forecast insolation under solar panels. However, previous examples of insolation estimation do not consider shading differences due to panel layout. In this paper, we constructed a machine-learning model with panel layout features to forecast the insolation shaded by solar panels. As a simulation result, the accuracy was greatly improved compared to the current method using coefficients. This method can realize the optimization of panel placement and contribute to the expansion of agrivoltaics.
Deep learning for speech emotion recognition: a technical tutorial on the state of the art

Speech is the simplest, most natural and most direct way of communication between people, and the rise of smart devices makes speech emotion recognition (SER) technology the most important part of human-computer interaction (HCI). This paper will start from the concept of language emotion recognition, and research the systematic research on speech emotion recognition in five aspects. First, we review the classification of some phonetic emotional features. Secondly, we introduce some of the latest technologies for speech emotion recognition based on traditional and machine learning. Furthermore, we summarize the classification of some common datasets. Finally, we discuss the practical application and future research direction of speech emotion recognition.
Analysis of DMS-Controlled Three-Phase Single-Stage SEPIC Differential-Mode Grid-Connected Inverter

Analysis and performance of a three-phase single-stage SEPIC differential-mode inverter (DMI) using a discontinuous modulation scheme (DMS) are presented in this paper. Since the DMS was previously suggested in many inverter topologies to reduce the voltage stress of utilized switching devices and enhance inverter efficiency, a simple design process to integrate DMS into the SEPIC DMI is proposed in this work. With this modulation strategy, the discontinuity of the duty cycle for one-third of the entire grid frequency cycle thoroughly minimizes the conduction losses and switching losses into the SEPIC converters. Also, it is noted that the voltage stress is significantly reduced in passive elements as well as switching devices. Furthermore, the compensation of the negative sequence harmonic components is maintained without adding extra sensors. The inverter operation along with mathematical analysis of the DMS and grid current control is presented. A PSIM simulator is used to evaluate the behavior of the proposed modulation strategy. Finally, a comparison with the conventional continuous modulation scheme (CMS) is performed for a better assessment.
Efficient computational methods for understanding human behaviour from mobile phone data

Smartphones and similar mobile devices present an unprecedented opportunity to collect data about human behaviour. Integrated into daily life, devices are privy to information about who we communicate with, where we are, and how we spend our time. However, without the proper tools for analysis and the ability to scale up to large amounts of data and large numbers of users, this data is of limited value. This thesis presents tools for extracting information about activities and location from sensor data available on commodity smartphones. We present an algorithm for comparing time series and demonstrate on a large number of benchmark problems that it is competitive with, and often exceeds, the performance of existing algorithms, while being efficient enough to run in real time on a standard smartphone. Activity and gait recognition are presented as applications of our approach, and the performance on these tasks is shown to exceed that of state-of-the-art approaches. For location detection, we present a thorough study of wifi signals in dynamic, nonstationary environments, and conclude that a new multinomial model is preferable to the standard Gaussian model used to model wifi measurements on a mobile terminal. We present a clustering algorithm for wifi signals that uses an underlying multinomial model, and demonstrate on a large dataset that this approach performs favourably on the task of identifying physical locations from wifi data. By basing our approach on the hierarchical Dirichlet process, the number of identifiable locations scales based on the data, and does not need to be prespecified. This allows for accurate, real-time localization of a mobile device both indoors and outdoors, in a manner that, contrary to existing approaches, protects the privacy of the user. Even detailed information is of limited value without the ability to convey it to the user in a meaningful way. We present a framework for translating from sensor data into human-readable descriptions of the events taking place. This framework is evaluated on a corpora of 150 GB of sensor data recorded from 38 users over 14 months, and provides for convenient visualization of large amounts of data and accurate reporting, in natural language, of events transpiring in the data. %%%%Les telephones intelligents et les appareils mobiles semblables nous fournissent une occasion sans precedent de recueillir des donnees a propos du comportement humain. Puisqu'ils font partie integrante de notre vie quotidienne, ces appareils ont acces a des informations privilegiees concernant les personnes avec qui nous communiquons, les endroits ou nous nous trouvons, et les facons dont nous passons notre temps. Cependant, sans outils d'analyse adequats et la capacite de traiter de grandes quantites de donnees et de grands nombres d'usagers, ces donnees ont une valeur limitee. Cette these presente des outils capables d'extraire des informations concernant les activites et l'emplacement d'une personne a partir des donnees amassees par les capteurs integres dans les…
Microwave Sensors: Novel Techniques, Topologies, and Manufacturing Technologies

This paper presents an overview of the recent advances we made in the field of microwave sensors. Sensors for different physical quantities are presented, ranging from structures for the retrieval of the electric and magnetic characteristics of materials, to devices for the determination of position and angular rotation. These sensors are based on various fabrication technologies, including planar structures, additive manufacturing, and hybrid solutions, which take advantage of the best features of different technologies.
An Observation Method of Vehicle Lateral Position Based on Map Matching in Winter Road Condition

In recent years, advanced driver assistance system (ADAS) is becoming standard equipment. However, there are still some challenges that need to be addressed, particularly in winter road conditions. Especially, since drivers in winter road conditions feel comfort by utilizing different lateral positions with the other seasons, the realization of a novel ADAS which equips its function is expected. Therefore, the characteristics evaluation of human driving behavior in winter road conditions is one of the key challenges. By taking into account the preferences of drivers in winter road conditions, autonomous driving cars can be developed that are more effective at keeping passengers safe and comfort, regardless of the weather or road conditions. As one method of collecting data to understand human driving behavior in winter road conditions, a method is proposed for observing the lateral position of vehicles throughout the year, including winter, using data obtained from a GNSS receiver installed in a vehicle. Experimental results demonstrate the effectiveness of this method of observation.
Mission Profile Based Optimal Design Approaches for Dual Active Bridge Converters

Proper selection of hardware parameters and modulation variables is critical to ensure optimal performance of dual active bridge (DAB) converters. While several optimization approaches of DAB converters exist in literature, the impact of the operation profile pertinent to the application where a converter is employed is seldom considered. Towards this end, this paper describes the methods of adopting a mission profile based approach for designing DAB converters. Optimal converter parameters are identified via numerical optimization, which are then used in simulation for validation. Results obtained by the mission profile based design are compared with that for the single operating point based design for three specific application scenarios. The comparative analysis highlights the applicability of the proposed mission profile based design strategy for a DAB converter based on its operational requirements and profile.
Comprehensive Comparison of Permanent Magnet Synchronous Machine and Vernier Machine

Permanent magnet vernier machines (PMVMs) are recognized as the most promising candidates for high-torque direct-drive applications. However, their market penetration is hindered by the low power factor. This paper aims to address the low-power-factor challenge, by employing the concept of low-coupling winding. As compared with two conventional PMVMs, the proposed PMVM with low-coupling winding exhibits a substantially improved power factor. To objectively evaluate the potential of the proposed PMVM, a comprehensive comparison between the proposed PMVM and the conventional permanent magnet synchronous machine (PMSM) is conducted. The no-load back-EMF, output torque, power factor, core losses, and efficiency are compared under various operating conditions, encompassing the entire torque-speed range. The strengths and weaknesses of the PMVM are analyzed, and the most promising application scenarios are suggested. Finally, a prototype of the proposed PMVM is fabricated to validate the analysis and comparison results.
Understanding Student Success Prediction Using SHapley Additive exPlanations

Predicting student success is an important task in educational institutions, as it allows for targeted interventions and support systems to enhance educational outcomes. This paper explores the use of SHAP (SHapley Additive exPlanations) model-agnostic method in understanding and interpreting student success prediction. The predictive model was built using Multi-Layer Perceptron neural network algorithm on a large public dataset. By shedding light on the underlying factors driving student success, this research contributes to the advancement of data-driven decision-making in education.
RS-MVSNet: Inferring the Earth's Digital Surface Model from Multi-View Optical Remote Sensing Images

3D modeling of the Earth's surface is an important topic and finds its various applications in the remote sensing communities. Due to the imaging complexity of optical remote sensing images, the process of creating a digital surface model of the Earth from multi-view optical remote sensing images is both time-consuming and challenging, especially when dealing with large areas. In this work, we propose a deep learning-based approach, called RS-MVSNet, for inferring a digital surface model from multi-view optical remote sensing images. In order to extend state-of-the-art learning-based multi-view stereo techniques to optical remote sensing images, a differentiable affine warping is designed for the first time, which utilizes an affine to Euclidean upgraded camera model to model the mapping relationship between small-sized remote sensing image tiles and their corresponding 3D local scenes in Euclidean space. Based on the differentiable affine warping, RS-MVSNet is abstracted from the complexities associated with remote sensing imaging and inherits generic components for deep learning based multi-view stereo, including multi-scale deep feature extraction, pyramid cost volume construction, regularization and regression. Moreover, an affine epipolar guided feature aggregation module is constructed in the proposed RS-MVSNet framework to accurately aggregate high-resolution remote sensing image features along affine epipolar lines into a finite cost volume. Extensive experiments are conducted on two public datasets, namely MVS3DM and US3D datasets, and our proposed RS-MVSNet shows promising results in terms of accuracy and efficiency.
Magnetic Field Generator for Testing the Operation of Vehicle Detection Sensors

This research paper focuses on the design of a magnetic field generator that utilizes a microcontroller, solenoid, and capacitors. The microcontroller generates a PWM signal of variable frequency and fill factor that excites the solenoid and generates a magnetic field of a different waveform. The waveforms generated for this study are sinusoidal, triangular, pulse, and one that represents the vehicle’s magnetic signature. The maximum achievable frequency with this setup is 10 Hz. The experimentally obtained results for 4 Hz waveforms confirm that the generator has satisfactory precision to generate magnetic waveforms. This type of generator has potential applications in the fields such as creating vehicle magnetic signatures and other scientific endeavors.
Simple and Effective Spatial-Attended Graph CNN with Learnable Aggregation for Classification in Point Clouds

The main task of point cloud classification is to extract unique features from each point cloud and to accurately distinguish individual point clouds. How to extract meaningful features in point clouds to improve recognition accuracy has always been a challenge. In this work, the double-KNN dynamic graphs with different neighbor sets are proposed to process the input point cloud separately and to extract respective features. These two separate features are individually gone through the convolutional block attention module (CBAM) [1], followed by learnable aggression mechanisms to construct the final salient features for classification. The simulation shows that the proposed architecture has better performance in overall accuracy and average accuracy when testing in the ModelNet40 dataset.
Multi-Instruments Music Generation Based on Chord Input

Although conventional music generation methods have improved with deep learning, there are still issues with the quality of multi-instruments music generation under some certain conditions such as specified chord sequence or mutilated music score. In this paper, we propose a new method based on Transformer to generate high-quality multi-instruments music from a chord sequence input. The proposed method outperforms a simple combination of single-instrument music in both subjective and objective evaluations.
A Hybrid Approach to Modeling of Cyber-Physical Distribution Grids Considering Packet Losses & Delays

Increased use of distributed renewable energy resources in low-voltage distribution grids open up for communication-based voltage control. However, non-ideal communication can affect the system and cause damage to the system. In particular, long update intervals, delays, and packet losses can be seen as imperfections of communication and should be compensated for. One solution is modeling these features and compensating for them in the control method. In this paper, a hybrid method to model the communication features and electrical grid together is presented. The model considers continuous dynamics of the electrical grid, discrete dynamics of the communication, as well as long update rates of communication, delays, and packet losses.