Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of Stress Over Social Interactions Using Machine Learning and Natural Language Processing

In the field of natural language processing, the investigation of stress is turning out to be increasingly critical. The advancement of recommender frameworks and computerized emotional well-being assessments can profit from the investigation of stress discovery for psychological wellness expectation, as per earlier examination. In this study, we enhance sentiment and emotion analysis to measure the stress levels of people by analyzing their social networking posts and comments. Using machine learning techniques and the deep learning sentiment analysis model BERT, we analyze sentiment using enormous datasets of tweets. We also used an unsupervised machine learning technique called Latent Dirichlet Allocation to scan a set of papers to find word and phrase patterns and create word groups and related phrases that most accurately represented the documents. This aids in predicting the subject that is connected to the textual information. We will be able to identify users' emotions online with the help of these models. Additionally, stress or depression can be studied using these feelings. In summary, the BERT model and ML models have very high detection rates. The benefits of this research can be shown in terms of mental wellness.
Adaptive Locomotion Control of Hexapod Robot Based on Deep Reinforcement Learning and Proprioception

The hexapod robot is widely used for outdoor missions due to its superior traversability. At present, hexapod robots use external sensors to obtain environmental information, but external sensors are susceptible to natural factors such as illumination, which will lead to sensor failure in serious cases. This paper investigates the normal movement of hexapod robot in unstructured environment solely relying on internal sensors. The information obtained from internal sensors meets minimum requirements for normal locomotion of hexapod robot, just like walking with closed eyes for human beings. This method uses deep reinforcement learning to train the locomotion strategy of hexapod robot in simulation environment, which enables hexapod robot to walk steadily at a fixed speed in an unstructured environment and has certain obstacle avoidance ability.
Enhancement Of Fault Diagnosis In Mechanical Systems Using Deep Learning Techniques

Intelligent defect conclusion can possibly be a valuable device for dealing with mechanical large information because of its speed and exactness in breaking down signs and making analyse. Be that as it may, in conventional intelligent determination draws near, highlights are extricated physically based on gathered information and symptomatic experience. Such systems are tedious and work concentrated, yet they exploit human innovativeness. The concept of unaided component realizing, which utilizes artificial intelligence techniques to gain highlights from crude information, fills in as inspiration for the recommended two-stage learning strategy for intelligent machine conclusion. At long last, an original demonstrative model is constructed involving melded profound highlights as contribution to multiple DNNs (MDNNs) and SoftMax. To measure the adequacy of the recommended innovation, it is utilized to intelligent disappointment recognition for auto last drive.
PRECISE: A 5Gbps Serialiser for Scientific Detectors in a 180nm CMOS Image Sensor Process

This paper describes a serialiser system tailored for scientific CMOS image sensor applications. The system first structures the data using the Xilinx Aurora 64B/66B protocol, thus enabling rapid system startup, skew compensation without an additional clock output, and transmission of several types of packets. The data is passed on to a 32-to-1 multiplexer working at a maximum data rate of 5 Gbps which is followed by a CML driver for off-chip data transmission. Including a PLL for timing signals generation, the system has been implemented in a 180 nm CMOS Image Sensor technology, occupying 0.265 mm2 of silicon area, and consuming 185 mW at 5 Gbps. The PLL allows clock signal generation within a frequency range of 1.25 GHz and 5.0 GHz. The full system was successfully tested at data rates ranging from 2.5 Gbps to 5.0 Gbps, with measured bit error rates better than 10-15 for data rates up to 4.3 Gbps and 10-13 for 5 Gbps.
The Demonstrator of the Instrumented Decay Tunnel for the ENUBET Monitored Neutrino Beam

The NP06/ENUBET project is designing a facility with unprecedented control on the neutrino flux for high precision (O(1%)) cross section measurements. An instrumented decay tunnel represents the key part of the facility, measuring large angle charged leptons from kaon decays and thus constraining the associated neutrino fluxes. It is composed by a sampling calorimeter with longitudinal, radial and azimuthal segmentation for e/μ/π separation and a rings of plastic scintillator doublets inside the calorimeter for the rejection of photons coming from π0 decays. The configuration of the calorimeter readout is the result of an intense prototyping activity. The final one is based on WLS fibers running on the frontal faces of the tiles. SiPMs collect the light from the fiber and are placed above a borated polyethylene layer, that shields against radiation damage of the sensors. To demonstrate the viability and effectiveness of the approach, a 1.65 m long section of the instrumentation with 45 deg azimuthal coverage have been built and then tested in the renovated East Area at CERN-PS. The summary of the prototyping activities, an overview of the Demonstrator design and construction and preliminary results of the test beam campaign are reported in this contribution.
A Resonant Switched-Capacitor Converter with Step-Up/Down Capabilities Based on Extended Full-Range Regulation Method

Voltage regulation methods of resonant switched-capacitor converters have drawn attention to extend the regulation range. In this paper, a resonant switched-capacitor converter with both step-up and step-down capabilities is proposed while most of the switched-capacitor converters can only boost or lower the voltage for a given power transfer direction. The output voltage can be regulated continuously by extended full-range regulation (FR) method and several switching techniques are derived for different voltage gain ranges. With fixed-frequency modulation and discontinuous conduction mode (DCM) operation, high efficiency conversion can be achieved due to the simple control scheme and zero current switching (ZCS). A comprehensive analysis of operation principle, voltage gain curves, and closed-loop implementation is given. The analysis is verified by simulation results.
Optimization Study for the Influence of Parasitic Parameters in Aerospace Power Supply Based on GaN

Gallium Nitride (GaN) power devices are conducive to the development of high-frequency and high-efficiency of aerospace power supply. However, the inevitable parasitic parameters in the circuit are easy to cause oscillation and lead to the deterioration of circuit reliability. An active clamp forward DC/DC converter with parasitic parameters is proposed, using GaN power device as the main switch to improve the switching frequency and efficiency of the power supply. The main operating modes of the circuit are deduced theoretically, and the topology of the circuit containing three kinds of parasitic inductors is analyzed dynamically by simulation. Finally, according to the simulation results, the optimization measures for the parasitic parameters causing different oscillation problems are proposed. An experimental prototype is designed and experimentally tested, proved the effectiveness of the suppression methods. Promote the high-reliability application of GaN power devices in aerospace power supply.
Constrained Sliding Mode Control Strategy for Boost Converter

In the realm of power electronic converters, challenges arise in terms of weakened system damping capability under constant power loads (CPL) and increased system complexity under multiple constraints. To address these issues, a constrained sliding mode control strategy, which involves the reconstruction of the mathematical model of the Boost converter through the incorporation of a nonlinear function, is proposed in this study. By integrating the design of sliding mode control, a controller is achieved that effectively satisfies the full-state constraints. In the control parameters, a small signal analysis method is designed and used. The designed controller is implemented using pulse modulation technique to regulate the on/off operation of the switching tubes in the Boost converter. The effectiveness and feasibility of the proposed method is verified via simulations.
Cloud Computing’s Effect on Video Games Streaming

The disruptive power of cloud computing is altering the gaming industry. Real-time gaming is now feasible because to advancements in streaming algorithms, further blurring the lines between the digital and physical worlds. Understanding the potential of Cloud computing to change the gaming industry is the emphasis which investigates the complicated relationship between numerous technologies. By using cutting-edge streaming protocols and techniques like Adaptive Bitrate Streaming (ABS), buffering, caching, and compression, users may have uninterrupted, high-quality gaming experiences. The study’s findings herald a new era of interactive entertainment and shed light on the transformative possibilities of these technologies. Asynchronous Bit-Rate (ABR) encoding, buffering, caching algorithms, video compression standards, and streaming protocols are just few of the numerous digital components that work together to provide uninterrupted data transmission. The rapid progress of ABR algorithms has made it possible to dynamically alter video quality to maximize the user experience across a broad variety of network scenarios. Buffering and caching algorithms play crucial roles by proactively managing data flow and storage to avoid disruptions in the streaming experience.
Real-Time Innovation in Future Generation Security for Data Privacy Intrusion System in Machine Learning Approach

Programming called an intrusion discovery system watches over a solitary PC or an organization of PCs for pernicious movement that intends to take or edit data or change network conventions. Most of intrusion recognition methods utilized today are unequipped for guarding against the dynamic and perplexing nature of cyber-attacks on PC organizations. Albeit successful versatile procedures, for example, unique machine learning calculations, may prompt more noteworthy discovery rates, diminished deception rates, and suitable calculation and transmission costs. Data mining might be utilized to make miniature data streams, classification, and example mining rather frequently. The real-time intrusion identification system and related intrusion counteraction system are introduced in this work utilizing machine learning. To prepare the model, a convolutional brain organization (CNN) is utilized. A 5G data network assessment of the innovation was directed. The improvement of programming characterized networks is thought about by brilliant intrusion location systems (IDS). It utilizes man-made reasoning based demonstrating. Convolutional brain network-based programming parts empower the system to distinguish intrusions that were not recently recognized. Intrusion Prevention System “IPS” is to stop malicious traffic. Based on the evaluation, this solution provides more buffers than comparable alternatives, facilitating real-time management in 5G settings.
Experimentation of Code Smells Using Deep Learning Techniques

A code smell is a measurable indicator that highlights significant issues in the software development process caused by inadequate programming practices. These are usually introduced in a software program during the design or implementation phases, and may lead to more significant problems during software maintenance. The existing methods to detect smells based on metrics, heuristics or machine learning have certain limitations. In this paper, we suggest two models built on deep learning for predicting code smells: convolutional neural network (CNN) and recurrent neural network (RNN). These models use features extracted from source code to detect six different code smells. We then evaluate and compare their results. The proposed approach performs remarkably well for single code smell identification according to the experimental results obtained.
A System Based on AI and M1 Enhanced to Investigate Physiological Markers for User Forecasting Decision-Making

By creating an explicatory AI report for stress prediction using wearable sensors, we fill this gap in our work. We based the explanatory AI's appearance and feel on a typical blood test report because medical professionals and patients are probably familiar with theseUsing physiological data, we provide in this study a novel, comprehensible AI technique for stress prediction. Users and medical professionals may decide which biological characteristics, in addition to any anomalies connected to health, have the greatest influence on the prediction of stress based on the report. Both quantitative and qualitative indicators were used to assess the usefulness of the study on explainable AI. It was demonstrated that the stress prediction accuracy was on par with modern standards. There was shown to be a correlation between the ratios of the individual physiological indicators used to make the stress prediction and the actual results. A qualitative survey with psychiatrists was also conducted in addition to these quantitative evaluations to further bolster the credibility and efficacy of the AI system's explanatory report on the stress. Additional explanatory elements pertaining to the patient's various emotional states, such as melancholy, relaxation, anxiety, or happiness, will be added in future study.
Physical-Information-Based Neural Network Grid-Connected Inverter Impedance Model

The integration of grid-connected inverters in large numbers poses challenges to the stability of the power grid. Impedance-based analysis methods are effective means to study the small-signal stability of power electronic systems. However, traditional impedance modeling approaches have limitations when dealing with black-box or grey-box models, especially for grid- connected inverters with unknown control structures and parameters. In this paper, a novel approach is proposed to address the challenges posed by black/grey-box models in grid-connected inverters. The approach is based on a neural network that incorporates physical information and utilizes measured control data. Compared to conventional neural network impedance identification methods, this approach exhibits significantly improved accuracy.
Harnessing E-Portfolio Creation for Exam Success, Student Engagement, and Satisfaction

Incorporating e-portfolio creation into the educational process can result in substantial gains for students. Yet the literature has presented limited empirical evidence on how to implement e-portfolio creation in classrooms. In this mixed-methods study conducted at a public Japanese university, the performance and participation of 17 students in an online Moodle course where e-portfolio creation was the central learning activity were observed and evaluated. The course adopted a new instructional design model (the CLEAR model) that promotes a constructivist, creation-based learning approach. Students were required to create their own e-portfolio using Mahara, discuss peers’ e-portfolios, and post written self-reflections on Moodle forums. As a pre-test–post-test intervention, the course significantly increased students’ exam scores on the same test, engaged them intensively online and offline without instructor supervision, and was rated as a positive and satisfying learning experience by participants. Practical implications, limitations, and future research directions are discussed in closing.
An Emotion-Aware Multitask Approach to Fake News and Rumor Detection Using Transfer Learning

Social networking sites, blogs, and online articles are instant sources of news for internet users globally. However, in the absence of strict regulations mandating the genuineness of every text on social media, it is probable that some of these texts are fake news or rumors. Their deceptive nature and ability to propagate instantly can have an adverse effect on society. This necessitates the need for more effective detection of fake news and rumors on the web. In this work, we annotate four fake news detection (EFN) and rumor detection datasets with their emotion class labels using transfer learning. We show the correlation between the legitimacy of a text with its intrinsic emotion for fake news and rumor detection, and prove that even within the same emotion class, fake and real news are often represented differently, which can be used for improved feature extraction. Based on this, we propose a multitask framework for fake news and rumor detection, predicting both the emotion and legitimacy of the text. We train a variety of deep learning models in single-task (STL) and multitask settings for a more comprehensive comparison. We further analyze the performance of our multitask approach for EFN in cross-domain settings to verify its efficacy for better generalization across datasets, and to verify that emotions act as a domain-independent feature. Experimental results verify that our multitask models consistently outperform their STL counterparts in terms of accuracy, precision, recall, and F1 score, both for in-domain and cross-domain settings. We also qualitatively analyze the difference in performance in STL and multitask learning (MTL) models.
A Misalignment-Insensitive Dual-Channel Electric Vehicle Wireless Charging System with a Novel Antiparallel Cross-Shaped Solenoid Pad

The electric vehicle wireless charging (EVWC) system needs to maintain stable output even when the coil offset occurs. In this paper, a combination of a square coil and a novel cross-shaped solenoid pad with antiparallel windings is proposed. The former is used to pick up the vertical magnetic flux, and the latter is to receive the horizontal magnetic $\mathbf{flux}$ generated in the offset case to compensate for the decrease in the vertical magnetic flux. A dual-channel inductor-capacitor-capacitor-series (LCC-S) topology with series connected diode rectifiers is utilized to form two energy transfer paths with opposite misalignment characteristics to achieve a stable output. The proposed magnetic couplers can achieve a smooth mutual inductance fluctuation in the horizontal $X$ and $Y$ directions. The fluctuation of the equivalent total mutual inductance is 4.39% from $-210$ mm to 210 mm (52.5% of the pad size) in both directions. The experimental results show that within the 52.5% offset range of the pad size, the output voltage fluctuates by only 4.72%.
Multi-sensor Calibration System for Mobile Robots

Multi-sensor calibration is crucial for mobile robots, especially in logistics and warehouse environments. While calibration techniques exist for individual sensors, industry-level calibration of the multiple sensors on mobile robots covering the entire operational life cycle is rarely explored. To bridge the gap, we present a fast, accurate, and scalable calibration system supporting both manufacturing and operation phases for mobile robots with diverse sensors like odometers, IMUs, LiDARs, and cameras. Specifically, we propose an online yaw angle estimation approach for IMU by fusing odometer data as compensation. Additionally, we pioneer an approach to calibrate 2D LiDAR using surrounding objects’ reflectance strength as the calibration metric in the manufacturing phase calibration, and the iterative closest point method for refinements during operation phase calibration. Furthermore, we develop a least squares formulation to calibrate cameras’ intrinsic and extrinsic parameters, achieving rapid convergence. The system has been used by Geekplus across factories worldwide including Asia, Europe, America, and Oceania, successfully calibrating and deploying over 30,000 mobile robots.
RL Based Queue Selection Algorithm for Input Queued Switches: A Theoretical Approach

In modern computer networks, packet switching is the predominant method of transmitting data between devices. As a result, efficient packet queuing and routing strategies are essential to ensure high network performance and user satisfaction. Traditionally, queue selection algorithms have been designed using static rules and heuristics that do not adapt to changes in network traffic and topology. However, the increasing complexity and dynamism of modern networks require more sophisticated approaches that can learn and adapt to changing conditions. The main purpose of this article proposes a new queue selection algorithm that utilizes $RL$ to learn optimal policies for selecting output ports. The algorithm we need is based on Q-learning, a popular $RL$ algorithm that learns the action value by updating the best estimate of the $Q$ value for each (state, action) pair. We present a high-level algorithmic description of our approach and discuss its key components and parameters. We also describe a simulation -based evaluation of our algorithm using a realistic network model and compare its performance with several baseline methods.
Improve Handling Stability for Path Tracking of a 4WIS Vehicle Based on Sliding-mode Method

Four-wheel-independent-steering (4WIS) vehicle with more degrees of freedom in steering control satisfies the high demand on handling performance of autonomous driving application. This paper proposes a control framework to improve the handling stability for path tracking of a 4WIS vehicle. Based on the lateral and heading error model in Frenet coordinate system, a LQR controller gives four-wheel steering angles to follow the reference path. With the ideal vehicle state gains generated by kinetic constrains and ideal steering center offset, the sliding-mode state feedback controller can effortlessly deal with the path disturbances and model uncertainties. Rear-wheel compensation angles which aims to adjust the vehicle state are added with the four-wheel steering angle above to compose 4WIS angles. Simulation results in CarSim-Simulink environment show that the proposed control framework is effective in enhancing the smoothness of the steering behavior and then the handling stability for path tracking of 4WIS vehicle.
Control for Nonlinear Electric Drive System of the Lower Limb Exoskeleton

The paper presents a method of sliding control for nonlinear electrical drive system to eliminate the influence of nonlinear factors and external disturbances on stability and position tracking of movement of the lower limb exoskeleton. Sliding control solves a powerful non-linear problem of a moving exoskeleton object with uncertain dynamic model parameters, friction elements, and external disturbance to ensure convergence and stability of the joint angle position tracking error. Modelling the dynamic movement of the lower limb exoskeleton by the mathematical method is established, which includes five links with the consideration of nonlinear elements of friction, elasticity and external disturbance to evaluate the effectiveness of the recommended control method. The stability of the recommended controller and the tracking efficiency of the control system are demonstrated by applying the theorem of Lyapunov stability and simulation in the Matlab/Simulink environment.