Miracle-dz/UniFAR-FTUs huggingface dataset:
Autonomous Motion Decision-making based on Deep Reinforcement Learning for Autonomous Driving

Motion decision-making is an open-challenging issue for autonomous driving, especially in the complex and diverse environment. A motion decision-making model based on deep reinforcement learning (DRL) is proposed in this work. To optimize the driving policy, a multi-objective reward function is designed to guide the autonomous driving system to explore optimal decision policy with the goal of safety, efficiency, and smoothness. A convolutional neural network (CNN) is designed as the backbone network of the DRL model to make full use of the observation information of environment. To enhance safety, a safety check module is designed for avoiding potential unsafe actions. A series of contrast experiments are conducted to justify the model performance of the proposed method on the DeepTraffic simulation environment. The experimental results demonstrate the effectiveness of the proposed DRL-based model in terms of the safe, efficient and smooth motion decision-making, and the generalization ability in different traffic conditions.
Fake News Detection using a Decentralized Deep Learning Model and Federated Learning

Social media has beneficial and detrimental impacts on social life. The vast distribution of false information on social media has become a worldwide threat. As a result, the Fake News Detection System in Social Networks has risen in popularity and is now considered an emerging research area. A centralized training technique makes it difficult to build a generalized model by adapting numerous data sources. In this study, we develop a decentralized Deep Learning model using Federated Learning (FL) for fake news detection. We utilize an ISOT fake news dataset gathered from "Reuters.com" (N = 44,898) to train the deep learning model. The performance of decentralized and centralized models is then assessed using accuracy, precision, recall, and F1-score measures. In addition, performance was measured by varying the number of FL clients. We identify the high accuracy of our proposed decentralized FL technique (accuracy, 99.6%) utilizing fewer communication rounds than in previous studies, even without employing pre-trained word embedding. The highest effects are obtained when we compare our model to three earlier research. Instead of a centralized method for false news detection, the FL technique may be used more efficiently. The use of Blockchain-like technologies can improve the integrity and validity of news sources.
Multi-Objective Optimization of Bi-directional On-Board Chargers Based on 650V GaN Power Transistors

High voltage GaN power transistors can significantly improve the key performance indices of a power electronic converter in terms of efficiency, reliability and power density. This brings a potentiality for widespread adoption of these semiconductors in e-mobility applications such as on-board chargers (OBCs) in electric vehicles. According to literature surveys, among all semiconductor technologies GaN-based high-electron-mobility transistors (HEMTs) devices are utilized to design compact, efficient and reliable OBCs. In this paper, a component-level (i.e., switch, capacitor and magnetics) optimization model is proposed based on Multi-objective Genetic Algorithm (MOGA) technique to assess the relationship between size (cm3), losses (W), and reliability (number of cycles) of the components used in OBCs. Besides, electrical circuit models establishing the relationship between these components for a dual-stage OBC architecture are presented. Finally, Pareto-front solutions for these components under the worst-case operation scenario are presented for the components used in the AC-DC stage of a bidirectional phase-modular three-phase OBC with a rated power of 11 kW.
Towards an IoT Framework for Wellness Assessment

Wellness assessment can provide important data to improve the health of an individual. However, constantly monitoring individual wellness has challenges due to the different and several activities people participate in throughout the day. The availability and popularity of low-cost and inexpensive Internet of Things (IoT) devices can alleviate this issue. In this work, an IoT framework is presented to examine the relationship between an individual's wellness and their surrounding environment. It uses an IoT device to collect data and forwards them using WiFi and Bluetooth Low Energy (BLE). The participants in this study are two groups of ten undergraduate students at the University of Guelph. Each group took the experiment at different times of the year. Each participant is given an Android smartphone equipped with an application, in order to complete a brief psychological survey three times per day. During the periods of completion, an IoT device in their possession is reading environmental data. The five environmental variables collected are temperature, humidity, air pressure, luminosity, and noise level. Upon submission of the survey, the results of the survey and the environmental data are sent to a server via WiFi. According to experimental results, the first group to complete the experiment indicated a correlation between stress and noise, while the second group indicated a correlation between distress and light.
A New Voltage Clamp Method for PV Maximum Power Tracking Under Shading Conditions

In this paper, a novel Voltage Clamp Method (VCM) for Maximum Power Point Tracking (MPPT) of a partially shaded PV string conventional structures is presented. In the proposed approach, clamp voltages are imposed on the PV array to measure the available currents of each panel. This allows the detection of the shading situations and the estimation of the optimal power profile. The clamp voltages are precisely generated by a switched electronic circuit that behaves as a variable Zenerdiode. This circuit is implemented by a digitally controlled closed-loop amplifier that is capable of handling high currents and high voltage. The suggested technique is evaluated by both simulations and experimental tests where various dynamic irradiance conditions have been considered. Our system is easy to implement since it does not require either irradiance, temperature sensors, extensive modeling, complex calculations, or an expensive setup.
Balanced repeated measurements designs

SUMMARY Repeated measurements designs are concerned with scientific experiments in which each experimental unit is assigned more than once to a treatment, either different or identical. An easy method of constructing balanced minimal repeated measurements designs when p, the number of periods, is less than t, the number of treatments, is given. Extra-balanced minimal repeated measurements designs are constructed whenever p < t.
Model Predictive Control of Two-Tier Converter for Maximum Power Extraction from Photovoltaic System

Renewable energy sources (RESs) have become the most emerging and cost-effective technology to generate electrical power for remote areas. Nevertheless, discontinuity and low performance of the power converters are considered challenges when RESs are used. This paper adopts a newly designed dc-dc converter, the so-called Two-Tier converter (TTC) in the photovoltaic (PV) system to supply dc-load with the required voltage values. Moreover, model predictive control (MPC) is proposed to extract the maximum power from the PV under various radiations. At the same time, a conventional PI controller is used for the dc-dc buck-boost converter to regulate the output dc voltage at the required value. Finally, the battery bank storage is employed to assist in extracting the maximum power through charging and discharging modes. To validate the integration of the system, the MATLAB/Simulink toolbox is used to simulate the dynamics of the entire system and the results confirm the capability of the proposed system with the control algorithms.
Fuzzy Adaptive Cruise Control of Autonomous Vehicle and Prescan-MATLAB Co-simulation Verification

This paper addresses the adaptive cruise control (ACC) problem for an automatic vehicle following system that only uses radar sensors to detect the distance and relative speed between vehicles. By considering the uncertainty and nonlinear characteristics of the longitudinal kinematics interaction between the following vehicle and the preceding vehicle, a fuzzy PID control strategy is designed. Compared with the PID controller, the designed controller is robust to modeling uncertainties and external disturbances caused by the changes in the road adhesion coefficient, the road slope angle and the acceleration of the preceding vehicle. The co-simulation system of PreScan and MATLAB with complex road scene is established. The effectiveness of the designed controller is verified by comparison with the traditional PID controller in the co-simulation platform.
Enhancing Microwave Sensor Performance With Ultrahigh Q Features Using CycleGAN

In this work, a microwave planar sensor is used for liquid material characterization. Two identical complementary split ring resonators (CSRRs) operating at 3 GHz are coupled to create a highly sensitive capacitive region. The moderate quality factor of the sensor <inline-formula> <tex-math notation="LaTeX">$\approx 230$ </tex-math></inline-formula> is significantly improved up to <inline-formula> <tex-math notation="LaTeX">$\approx 5040$ </tex-math></inline-formula> with loss compensation using a regenerative amplifier. The moderate quality factor restrains the passive mode sensor from distinguishing low concentrations of 1%–4% water in ethanol, while considerably distinct profiles are achievable using the active-mode sensor. The measured passive mode sensor response is then processed using CycleGAN, a machine-learning algorithm conventionally used for image-to-image translation. This strongly enhances the quality factor of the responses, effectively translating them to the active domain. This improvement reduces the limit of water detection down to 1% for the water-in-ethanol mixture. In addition, the sensor is used for noninvasive monitoring of glucose levels, in both passive and active modes. The resolution of the CycleGAN-boosted response approaches that of the active sensor (<inline-formula> <tex-math notation="LaTeX">$\approx 20$ </tex-math></inline-formula> mg/dL), showing a considerable enhancement when compared to the resolution of the passive sensor (<inline-formula> <tex-math notation="LaTeX">$\approx 70$ </tex-math></inline-formula> mg/dL).
Kinematic Modeling of Scissor-Mechanism-Based Curvilinear Actuator

Conventional electric motors usually provide rotary or linear motions to actuate mechatronic systems. In order to accomplish complex tasks, many research studies have been conducted to develop intelligent actuation mechanisms capable of converting simple input motions into complex target motions. In this paper, we propose a novel type of curvilinear actuator to achieve non-linear output motions, which is based on a scissor mechanism with off-centered link position in each unit. A kinematic modeling framework is also presented to compute the motion trajectories of the proposed scissor-mechanism-based curvilinear actuators (SMCA). Simulation results have demonstrated that, by changing the link position of each scissor unit, the created SMCA can achieve different types of output curves and poses.
Analyzing the Effectiveness of Extended Boolean Models in Information Retrieval

Many extended Boolean models such as fuzzy set, $p$-norm, et al. have been proposed in the information retrieval literature to support ranking facility for the Boolean retrieval system. They can be explained within the same framework, and each extended Boolean model is characterized by evaluation formulas for AND and OR operations. A variety of operators have been also developed in the area of fuzzy set theory for AND and OR operations, and can be used in extended Boolean models. In this paper we analyze the behavioral aspects of various operators for AND and OR operations, and address important properties in terms of retrieval effectiveness. Our analyses show that the four properties, namely single operand dependency, negative compensation, double operand dependency and unequal importance decrease retrieval effectiveness in some circumstances. This suggests that the two properties, namely positive compensation and equal importance might help retrieval effectiveness. We also provide the experimental results that coincide with our analyses.
Novel Approach to Robust Stability Criteria of Uncertain System with Time-Varying Delay

This paper considers the robust stability criteria of uncertain system with time-varying delay. Firstly, by exploiting a new Lyapunov function that optimizes the segment of time delay and using the convexity property and free-weight method of the Linear Matrix Inequality, delay-dependent stability condition can be obtained for the asymptotical stability of the nominal system. Secondly, basing on the obtained condition, the corresponding linear matrix inequality can be obtained for the uncertain system. Finally, an example is given to demostrate the effectiveness and the merit of the proposed method.
Optimum Configuration of Edge Computing Protocols for Industrial Internet-of-Thing Applications

Industrial Internet-of-Things (IIoT) technology has been rigorously developed in recent years, moving towards the ambitious goal of industry 4.0, Network Automation. However, there are a few critical challenges regarding the implementation of a reliable IIoT ecosystem for different applications; security, battery life, and bandwidth are controversial challenges. All the challenges regarding IIoT are mainly struggling within the edge computing smaller box of the big picture in which a cloud is an upstream object while sensors and actuators act as downstream devices. Therefore, having a reliable IIoT ecosystem necessitates focusing on the whole IIoT’s challenges in the edge computing smaller box; first realizing the vital, practical demands of a specific IIoT application, and then defining the compatible protocols to form the effective edge computing configuration. This paper reviews four IIoT case study applications with their specific requirements and their counterpart sensor/actuator properties to find the appropriate edge computing protocols satisfying their demands.
AC Grid-interface Bidirectional Buck-Type Converters for DC microgrids: A Comparative Study

This paper discusses the power converter interface between the 400 VDC DC microgrid and the European threephase grid (400 VLL). Therefore, power bidirectional step-down topologies are considered in this paper. Among the various topologies, three single-stage non-isolated topologies are compared in terms of semiconductor losses, magnetic component size and heat sink size. These topologies are the seven-switch buck converter, the SWISS converter and the Y-converter. The comparative evaluation is conducted for a 10-kW converter targeting residential or small commercial applications.
Raman Thermometry For Sensing Of Hot Carriers In Gold Nanoparticle-Based Bimetallic Photocatalysts

Raman thermometry is a distinctive method of measuring the local temperature in nanoplasmonic systems through acquisition and analysis of the Stokes and anti-Stokes scattering amplitudes in a Raman spectrum. In this work, we present a nanoscopic temperature sensor that estimates the local temperature during hot carrier-mediated photocatalytic degradation of methylene blue (MB) dye molecules on Au-Pt core shell nanoislands on a glass substrate. The temperature was recorded at different Raman modes and the local heating of chemical bonds in the molecules to strong interaction with plasmonic hot carriers, was validated.
Local Demagnetization Fault Detection in PMASynRM based on Finite Element Modeling and Characterisation

Good overall performances and efficiency of Permanent Magnet Synchronous Machines (PMSMs) are highly impacted by irreversible demagnetization faults. This paper proposes to analyze the electromagnetic behavior of a faulty Permanent Magnet Assisted Synchronous Reluctance Motor (PMASynRM) with neodymium-iron-boron (NdFeB) rare-earth magnets, in order to identify fault signatures and develop an efficient fault indicator. Finite Element Analysis (FEA) is used to model different demagnetization defects and evaluate precisely post-demagnetization characteristics of the baseline machine. The study focuses on flux and back-emf time and frequency analysis for its high potential to identify low severity fault signatures. The results show that the frequency spectrum of the Zero-Sequence Back-Emf Component (ZSBEC) calculated using fast Fourier transform (FFT) can be used to develop a fault detection index which allows quantifying, with high sensitivity, the severity of irreversible demagnetization faults in PMASynRMs.
A Sliding Mode Observer-based Limit Cycle Oscillation Suppression using a Robust Active Flow Control Technique

This paper presents a sliding mode observer (SMO)-based robust control method, which achieves simultaneous estimation, fluid flow velocity regulation and limit cycle oscillation (LCO) suppression in a flexible airfoil. The proposed control design is based on a dynamic model that incorporates the fluid structure interactions (FSI) in the airfoil. The FSI describe how the flow field velocity at the surface of a flexible structure gives rise to fluid forces acting on the structure. In the proposed control method, the LCO are controlled via control of the flow field velocity near the surface of the airfoil using surface-embedded synthetic jet actuators. The flow field velocity is expressed using a proper orthogonal decomposition based reduced-order flow model that formally incorporates the actuation effects of synthetic jet actuator. Specifically, this flow field velocity profile is driven to a desired time-varying profile, which results in a LCO-stabilizing fluid forcing function acting on the airfoil. A sliding mode observer is designed to estimate the unmeasurable states in the reduced-order model of the actuated flow field dynamics. The SMO is rigorously proven to achieve local finite-time estimation of the unmeasurable state in the presence of the parametric uncertainty in the SJA. A Lyapunov-based stability analysis is used to prove that the active flow control system asymptotically converges to the LCO-stabilizing forcing function that suppresses the LCO. Numerical simulation results demonstrate that the augmented observer provides a reduction in the control effort required to stabilize the LCO.
A Resilient Economic Dispatch Method for Power Grid under DoS Attacks

In this paper, we consider the problem of resilient economic dispatch for power grid under denial-of-service (DoS) attacks. By introducing a resilient distributed economic dispatch algorithm, the minimum of the generation cost can be found while balancing the supply and demand. The global information is estimated by the resilient consensus protocol in a distributed fashion and the optimal solution of the economic dispatch problem is searched by the saddle point dynamics. It is shown that the developed resilient algorithm can ensure the exponential convergence of the closed-loop systems by using the Lyapunov stability theory. Finally, an illustrative example based on the IEEE 9-bus attacked by multi-channels DoS is provided to verify the effectiveness of the proposed method.
A Study for Laser Additive Manufacturing Quality and Material Classification Using Machine Learning

This paper demonstrates the use of acoustic emissions (AEs) to monitor the quality, and material used, for the laser additive manufacturing (LAM) process with steel and copper wire. Layers of deposited material (steel or copper) were created using LAM. The quality of these layers was either good or unstable. The AEs were recorded using three sensors, one microphone, and two structure-borne sound probes. The recorded signals were processed and transformed using the fast Fourier method. Then models were trained with the processed data and evaluated using a fivefold cross-validation. Results show that it is possible to accurately classify the materials used during LAM (up to a balanced accuracy [BAcc] score of 0.99). Also, the process quality could be classified with a BAcc score of up to 0.81. Overall, the results are promising, but further research and data collection are necessary for a proper validation of our results.
Mobile Edge Computing for Rapid deployment Object Detection System

In the past, the process of transferring data collected by IoT devices or mobile devices has data leakage, personal privacy, and information security issues since object detection systems have mostly sent data to the cloud for processing and storage. To solve this problem, we implement object recognition and tracking function in JavaScript on the front end, and set up the CRUD operation API in Python on the back end, so that users can directly perform object recognition on the computer or mobile browser, and manage the database on the web through the API. It enables the process from deployment to data transfer in a very short time.
Long-time Driving Style Recognition Using Gated Recurrent Unit With Attention

Long-time driving style recognition can help automotive applications to better understand drivers' behavior and habits. Many existing driving style recognition models formulate the driving style recognition problem as a feature classification. But it leads to the inevitable problem of information loss in the feature extraction process. At the same time, the long-time sequence data input tends to cause information loss problem. In order to solve these two problems, a novel solution is proposed, using a gated recurrent unit (GRU) with attention model, to recognize long-time driving style. The recognition problem is formulated as a sequence data classification. Given a sequence driving data at each timestep of a driving journey, the model can recognize the driver's driving style (normal, aggressive and drowsy) accurately. The validity of the solution is tested in the public UAH-DriveSet dataset. The proposed solution is compared with some machine learning and deep learning models, and outperforms the other models in the UAH-DriveSet dataset, which reaches a recognition accuracy rate of 85.93%.
Simulation Environment for Modular Automation Systems

When developing products or performing experimental research studies, the simulation of physical or logical systems is of great importance for evaluation and verification purposes. For research-, and development-related distributed control systems, there is a need to simulate common physical environments with separate interconnected modules independently controlled, and orchestrated using standardized network communication protocols.The simulation environment presented in this paper is a bespoke solution precisely for these conditions, based on the Modular Automation design strategy. It allows easy configuration and combination of simple modules into complex production processes, with support for individual low-level control of modules, as well as recipe-orchestration for high-level coordination. The use of the environment is exemplified in a configuration of a modular ice-cream factory, used for cybersecurity-related research.
DDPG-guided Adaptive Equivalent Consumption Minimization Strategy for a Range-Extended Electric Vehicle

To improve the optimality without significantly increasing the computational burden, this paper proposes a DDPG-guided adaptive equivalent consumption minimization strategy (A-ECMS) for a range-extended electric vehicle. DDPG agent is trained to find the optimal equivalent factor sequence by trial and error and ECMS is used to achieve power distribution based on numerical optimization. In real-time applications, the actor network outputs the equivalent factors based on the vehicle states. This architecture can combine the exploration capability of DDPG with the stable and efficient optimization capability of ECMS. Simulation results show that the equivalent fuel consumption of DDPG-guided A-ECMS is reduced by 7.1% compared with that of PI-based A-ECMS.