Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust system identification for hysteresis-controlled devices using SINDy

For advanced control of technical systems reliable system identification methods are essential. The data-driven framework Sparse Identification of Nonlinear Dynamics (SINDy) by Kutz and Brunton is extended in order to tackle hysteresis-controlled systems. In order to gain robustness, a so called proximity hysteron is introduced. This paper presents this extension and documents experiments with simulations of an academic example and an industrial chiller system. A proof of concept is followed by experiments which show that strong nonlinearities as well as inadequate sampling rates can critically impair the algorithm.
Sizing and Operation of a Wireless Power Transfer System for Bidirectional V2V Energy Exchange

Limited on-board available charge is one of the most important concerns in the wide acceptance of Electric Vehicles (EVs). EVs can be charged either at home or at charging stations. Another solution that appears promising is wireless charging while in motion to extend the range of a vehicle. Concern in this case is availability of the charging infrastructure all along the high-way. There is always a possibility that even though vehicle is enabled for wireless charging, the source system is not available. Under such a situation, an on-board wireless charging system with bi-directional power flow capability can be useful for vehicle to vehicle (V2V) energy exchange. Present paper considers the case of bidirectional V2V energy exchange system. At first such a system is explained and then discussion is extended to the sizing of coil system, circuital arrangements and explanation of different modes of energy exchange. Operation of the system is explained with the help of simulation results to justify the possibility.
On recursive temporal difference and eligibility traces

This work studies a new reinforcement learning method in the framework of Recursive Least-Squares Temporal Difference (RLS-TD). Differently from the standard mechanism of eligibility traces, leading to RLS-TD(λ), in this work we show that the forgetting factor commonly used in gradient-based estimation has a similar role to the mechanism of eligibility traces. We adopt an instrumental variable perspective to illustrate this point and we propose a new algorithm, namely — RLS-TD with forgetting factor (RLS-TD-f). We test the proposed algorithm in a Policy Iteration setting, i.e. when the performance of an initially stabilizing controller must be improved. We take the cart-pole benchmark as experimental platform: extensive experiments show that the proposed RLS-TD algorithm exhibits larger performance improvements in the largest portion of the state space.
A New Eight Switch Seven Level Boost Active Neutral Point Clamped (8S-7L-BANPC) Inverter

A new boost Active-Neutral-Clamped (ANPC) voltage boosting inverters are appealing for low dc-link voltage demand. Recent boost ANPC topology has demonstrated a 1.5 times voltage gain, but the number of switches has a higher number. This paper proposed an improved boost ANPC topology with 1.5 voltage gain using the least number of switches. Further, the number of conducting switches has been reduced remarkably which enhances the system efficiency. The seven-level (7L) output voltage levels are achieved using a floating capacitor with self-voltage balancing capability. The proposed topology has been analyzed and compared to the ANPC topologies proposed in recent years. For the validation of the theoretical aspect of the proposed topology, the experimental findings have been compiled in the paper.
E-mobility and PV Solar challenge in Peruvian radial distribution feeders

Smart grids have big challenges for incorporation of new technologies, in the last years, many faults have been detected in inverters, station chargers, photovoltaic (PV) generation and distribution transformers; because, the medium voltage grids have been designed for radial installation. Nevertheless, for smart grids, it requires connections between high voltage substations with medium voltage grids, high penetration of photovoltaic generation and station chargers, required for new emerging countries. One of the most important aspects are electrical mobility (E-mobility) in urban cities, ultra-grid penetration, the medium voltage grids have to consider new improvements. In this research, a new methodology is proposed with seven steps. For electrical mobility in Peru, the electrical networks with photovoltaic Solar panels and fast charging stations for emerging countries have new effects as overload in transformers, inverter control and modelling for station chargers and distribution transformers should be review, due to loads transfer, voltage sag and losses, the impact is high in a short term. These results provide challenges for government, industry and prosumers. Results have demonstrated voltage sag are from 5.68% to 3.81%, losses from 1,120kW to 860kW, voltage transformer from 1.21% to 1.09% and transformers rotations with a detailed load density analysis with 20% penetration with fast charging stations and 40% of ultra grid penetration with photovoltaic generators.
Local COVID-19 Severity and Social Media Responses: Evidence From China

Unexpected but exceedingly consequential, the COVID-19 outbreak has undermined livelihoods, disrupted the economy, induced upheavals, and posed challenges to government decision-makers. Under various behavioural regulations, such as social distancing and transport limitations, social media has become the central platform on which people from all regions, regardless of local COVID-19 severity, share their feelings and exchange thoughts. Our study illustrates the evolution of moods expressed on social media regarding COVID-19-related issues and empirically confirms the hypothesis that the severity of the pandemic substantially correlates with these sentiments by analysing tweets on Sina Weibo (China’s central social media platform). Methodologically, we leveraged Sentiment Knowledge Enhanced Pre-training, the most state-of-the-art natural language processing pre-trained sentiment-related multipurpose model, to label Sina Weibo tweets during the most distressed period in 2020. Given that the model itself does not provide a feature explanation, we utilize a random forest and linear probit model with the labelled sample to demonstrate how each word plays a role in the prediction. Finally, we demonstrate a strong negative linear relationship between the local severity of COVID-19 and the local sentiment response by incorporating miscellaneous geo-economic control variables. In short, our study reveals how pandemics affect local sentiment and, in a broader sense, provides an easy-to-implement and explanatory pipeline to classify sentiments and resolve related socioeconomic issues.
Flexible Piezoelectric Micro Ultrasonic Transducer Based on a Laser Processed Substrate

With advancements in the material and fabrication of wearable electronics, the development of flexible ultrasonic devices has been greatly improved. The substrates are the key components of any flexible devices. However, conventional piezoelectric micro ultrasonic transducers are mostly based on rigid substrates. In this work, a flexible piezoelectric micro ultrasonic transducer (PMUT) with a laser processed substrate is presented. The resonant frequency of the PMUT is 199.37 kHz, and the device has good conformal contacting with concave and convex surfaces. The mechanical and acoustic characteristics were illustrated, and the fabricated device was used to capture the time of flight (TOF) assisted by a commercial ultrasonic transducer in air medium. These results indicate that the presented flexible PMUT has the potential for wearable applications such as ultrasonic range finder or position sensing.
Towards Robust Certification of Computer-Vision-Based Detect and Avoid in UAS

Computer-vision-based Detect and Avoid (DAA) promises to enable large-scale unmanned BVLoS drone flights in the near future, which would have enormous economic benefits. However, quite a bit of work remains to be done before it can be certified. We describe AirMap's ongoing research into computer-vision-based DAA.
UAV and SWIPT Assisted Disaster Aware Clustering and Association

In an event of a disaster, the connectivity of on-scene available User Equipment (UE) to the first responders is important because of the unavailability of conventional networks. Therefore, in this paper, considering the deployment of both the Unmanned Aerial Vehicle (UAV) and Mobile Command Center (MCC), we investigate end to end connectivity of UEs to the MCC in terms of the outage. Specifically, various disaster aware clustering schemes are proposed that utilize the UAV and MCC position for the association. These schemes include multiple degrees of freedom to manage intra-cluster distances along with the flexibility to restructure the clusters. In addition, we assume the provision of simultaneous wireless information and power transfer (SWIPT) at Cluster Heads (CHs) through the UAV and MCC. The results show that the association of a UE to MCC or UAV prior to clustering can be optimized to achieve better performance. Without SWIPT at CH, the minimum distance metric to the UAV provides less outage. However, with SWIPT a weighted compromise between intra-cluster distance and CH distance to the UAV achieves less outage. We applied our proposed methods on a real man-made disaster scenario layout and determined their efficacy.
Graph Regularized Deep Discrete Hashing for Multi-Label Image Retrieval

Multi-label hashing is a new research topic in image retrieval. As images are usually associated with multiple semantic labels, there is multi-level semantic similarity such as very similar, normally similar and dissimilar among multi-label images. In order to obtain the multi-level semantic similarity, this letter constructs a hypergraph in label space by creating a hyperedge for each semantic label and including all images annotated with a common label into one hyperedge. In this way, the number of common hyperedges shared by the vertices in hypergraph can be used to encode the high-order semantic relations among multiple images. Considering the useful similarity information hidden in the instance space, a <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>NN graph in instance space is further constructed. By learning from both the hypergraph and <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>NN graph with spectral learning strategy, a graph regularized deep discrete hashing is developed which updates graph regularized binary codes and deep neural network based robust features iteratively in a discrete optimization framework. The results in comparison with nine state-of-the-art hashing methods on two multi-label image datasets such as MIRFLICKR-25 K and NUS-WISE demonstrate its effectiveness.
SOH analysis of Li-ion battery based on ECM parameters and broadband impedance measurements

The impedance of a Li-ion battery is an important parameter for the battery state-of-health (SOH) estimation. The dependency of the battery impedance to the SOH can be monitored by fitting an equivalent-circuit-model (ECM) to the impedance data and observe the changes in the ECM parameters. The ECM is typically fitted by the complex-nonlinear-least-squares (CNLS) algorithm which requires accurately chosen initial conditions for the parameters to guarantee the consistent performance of the algorithm. In order to use the ECM parameters for SOH estimation in practical applications, the impedance measurements should be fast and simple to implement to the battery system. This paper demonstrates the utilization of practical and fast pseudo-random-sequence (PRS) impedance measurements to the SOH analysis of a nickel manganese cobalt Li-ion battery by observing the variations in the ECM parameters. The measured impedances are fitted to the ECM by using the CNLS with adaptively obtained initial conditions. It is shown that the ECM parameters are changing as the battery capacity degrades. In addition, it is observed that some parameters are able to indicate a drastic reduction in the battery capacity and SOH.
A Secure Gateway for the Cooperation of Information Technologies and Industrial Automation Systems

Promoting digitalization in industrial automation drives usage of new technologies, communication and information models. Thereby providing a platform for industrial automation to interact cooperatively with information technologies plays a crucial role to increase its digitalization and intelligence. An important factor in this interaction is security. The current automation systems do not cooperate in a high interconnected environment but offer a high security level. In contrast to the current automation systems, the future automation systems in context of Industry 4.0 offer high interconnectedness but require a security solution. The interaction deals with retrieving and processing data from the process control system and sending feedback to it. For retrieving data from automation systems different concepts are discussed such as the NAMUR Information diode. The goal of this paper is to develop a realization of a secure gateway for the interaction of information technology and automation systems. Furthermore, an approach to integrate feedback of information technology into process control is discussed.
Fast and Efficient PMSM Electromagnetic Parameter Identification Methodology from the Efficiency Map

This paper presents a fast and efficient methodology to extract the electromagnetic magnitudes of a PMSM from its torque-speed-efficiency map. Thus, having the efficiency distribution along the torque-speed region, the algorithm is able to determine the electromagnetic parameters of the PMSM with no any geometry analysis involved. A d-q electrical model is used to compute the PMSM performance. The electromagnetic magnitudes to be modelled are the stator winding resistance, iron losses and d-q linkage flux, which jointly with the d-q currents, allows determining the d-q inductances. This approach is well suited as a fast electromagnetic parameter identification method to determine the electromagnetic magnitudes with no prior knowledge of the PMSM geometry. The obtained data can be helpful to perform a customized control algorithm of the PMSM. If the motor is available, by testing different operating points and extracting the efficiency data, it is possible to extract full information about the electromagnetic characteristics of the PMSM while adjusting the control algorithm to improve the motor performance. Finally, the magneto-static magnitudes are validated by means of FEA, using the public experimental data available of Toyota Prius 2004 PMSM.
Finite-Set Predictive Control with Disturbance Rejection Capability for PMSGs in Wind Turbine Applications

A finite-set predictive control (FSPC) approach is designed by considering the discrete-time model of the system under control. Therefore, variations of the model parameters and disturbances due to un-modeled dynamics deteriorate the performance of the FSPC. In order to overcome this problem, this paper proposes a FSPC strategy with an equivalent input disturbance (EID) observer for permanent-magnet synchronous generators (PMSGs) in wind turbine applications. The proposed EID observer estimates the total disturbance due to variations of the model parameters and un-modeled dynamics and considers it in the design of the controller. Experimental results are given to validate the performance of the proposed FSPC scheme.
Experimental Path tracking optimization and control of a nonlinear skid steering tracked mobile robot

The skid steering tracked robot is consider one of the famous robots that used in the autonomous agricultural field. The robot model is considered as a coupled nonlinear model. So, a real kinematic model is required to develop the robot motion which will improve the high quality and quantity of the cultivated crops. So, in this research a mathematical model for the skid steering mobile robot (SSMR) and a mathamtical model has been presented to simulate the robot. The model has been validated based on experimental data for the Skid Steering model. The robot motion as position and velocity has been measured using Inertial Measurement Unit (IMU) and fused with the External Camera measurements. These data used to train a neural network model to develop the equivalent kinematic model that replace the nonlinear model. Then, PID controller was used to perform position and speed control for the two tracks and then to the whole robot body pose. Moreover, metaheuristic techniques were used to improve the PID response by tuning gains based on Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC). The system response for the path tracking and its precision has been optimized based on the developed techniques. Moreover, the extracted results show the high performance of tracking path based on a tuned PID controller based on ABC optimization technique.
A CFAR-based faults detector for Induction Motors

Despite the rich literature dealing with induction motor faults detection, the automatic decision on the health operating condition has received scarce attention. This paper aims at demonstrating the usefulness of the combination of the Maximum Likelihood (ML) and the detection theory for induction motor faults detection. In particular, it proposes to use stator currents as medium for faults detection. Indeed, under faulty conditions, additional spectral components appear in the stator currents spectrum. This paper proposes to use a constant false alarm rate (CFAR) detector of these components. The objective is to decide between two hypothesis; the motor is healthy or faulty. The generalized likelihood ratio test (GLRT) is used to tackle this binary hypothesis testing problem.Monte Carlo simulations have been performed in order to evaluate the statistical performance of the proposed approach. Then, the proposed algorithm has been evaluated on experimental data for broken rotor bars fault detection.
Hybrid position/force control of manipulator using series elastic actuator with position controller

This paper proposes a method for designing a hybrid position/force control of a manipulator with a series elastic actuator (SEA) mounted on each joint. The actuator used in the SEA is assumed to be a low-cost servomotor with a built-in position controller. The hybrid position/force control is achieved by controlling the angle of the servomotor and the angular displacement of the SEA. Also, an algorithm is proposed to generate a target value for each of these states according to the position/force target value. To control these states, a state feedback system with an observer is designed for an extended system containing a position controller. Simulation results confirmed that hybrid control can be realized by giving the servomotor an appropriate target input according to the posture of the manipulator during the task execution. In addition, it is confirmed that the proposed control system has a certain robustness even when the initial posture and the environment parameters are different from the controller design.
A Machine Learning-Based Recommender System for Improving Students Learning Experiences

Outcome-based education (OBE) is a well-proven teaching strategy based upon a predefined set of expected outcomes. The components of OBE are Program Educational Objectives (PEOs), Program Outcomes (POs), and Course Outcomes (COs). These latter are assessed at the end of each course and several recommended actions can be proposed by faculty members’ to enhance the quality of courses and therefore the overall educational program. Considering a large number of courses and the faculty members’ devotion, bad actions could be recommended and therefore undesirable and inappropriate decisions may occur. In this paper, a recommender system, using different machine learning algorithms, is proposed for predicting suitable actions based on course specifications, academic records, and course learning outcomes’ assessments. We formulated the problem as a multi-label multi-class binary classification problem and the dataset was translated into different problem transformation and adaptive methods such as one-vs.-all, binary relevance, label powerset, classifier chain, and ML-KNN adaptive classifier. As a case study, the proposed recommender system is applied to the college of Computer and Information Sciences, Jouf University, Kingdom of Saudi Arabia (KSA) for helping academic staff improving the quality of teaching strategies. The obtained results showed that the proposed recommender system presents more recommended actions for improving students’ learning experiences.
Stochastic Modeling of Content-Dependent Scheduling in D2D Cache-Enabled Networks

In this work, we aim at characterizing the aver-age success probability of content delivery in cache-equipped device-to-device (D2D) network under content-dependent channel access probability. We adopt retransmissions-upon-decoding-errors in a slotted-Aloha system, and account for the temporal interference correlation. We study the impact of the content-dependent access probabilities on the overall performance of the network. We verify the analytical results of this work via intensive Monte-Carlo simulations.
Autonomous Face Detection and Tracking Using Quadrotor UAV

In this paper, human face detection and tracking system with a camera of the Quadrotor UAV is proposed. During flight, Quadrotor takes photos of the human face, records videos and sends these photos and videos to the computer with Wi-fi connection. Face detection algorithm detects human face using Viola Jones algorithm. Face detection algorithm can detect multiple faces at the same time. Face tracking algorithm identifies feature points of the face in first frame and track these features in following frames in a video recorded by the Quadrotor UAV’s camera. Face detection and face tracking algorithms’ performances are evaluated by using photos and videos of the human faces. It could be interpreted that face detection algorithm successfully detect single and multiple faces and face tracking algorithm is competent to track the human face.
Precision Four-Dimensional Digital Mapping Teaches Humans and Machines to See In the Dark

Safe and efficient aircraft navigation in challenging environments such as congested airports and metropolitan areas demands mapping solutions with the highest possible precision. Advances in imaging, onboard sensors, and always-connected aircraft provide the real time accuracy needed to operate in such conditions. This paper presents the results of over seven years of research, development, and commercial production of high precision digital mapping for piloted and autonomous aerospace systems and provides examples of the application of those systems to meet the navigation challenges of today and tomorrow. We review new developments using sub-centimeter survey techniques and proprietary tool chains to integrate data collected from in-flight and space-based sensors. These techniques have achieved sub 1-meter accuracy, producing mapping databases of airports and urban flying areas that are five to ten times more accurate than the aircraft mapping solutions flying today. We also discuss the essential role that temporal (4D) mapping will play as the industry strives to achieve not only greater accuracy, but greater responsiveness to aviation environments in which mapped artifacts and surrounding obstacles shift with time. Representing ground transport vehicles in an airport, or the installation of a new building crane in an urban setting, not only requires dramatically more frequent mapping database updates than the current 28-day cycle, but also near real-time integration of dynamic environmental data. To support the 4D perspective, a cloud-based repository has been prototyped that stores digital maps that are continuously updated with data drawn from a wide universe of sources ranging from Notices to Airmen (NOTAMS) to sensor data “crowd sourced” from vehicles in flight.