Miracle-dz/UniFAR-FTUs huggingface dataset:
Autonomous Optimal Voltage Support Scheme of Two-Stage PV System for Grid Fault Ride Through

Symmetric or asymmetric grid fault can cause faulty phase sudden voltage dip and the unexpected trip of local generation units. Also, fault-ride-through requirements bring challenges to inverter’s operation, such as active power oscillation due to unbalanced voltage and over-current injection in low-voltage phase. To address above issues, an autonomous optimal voltage support scheme which aims to support point of common coupling (PCC) voltage amplitudes by two-stage photovoltaic (PV) system during fault is proposed. The scheme can determine optimal positive and negative sequence reference current considering fault characteristics, grid impedance condition, maximum available PV generation as well as inverter’s physical and operational constraints, by calculating the analytical solution of a two-variable nonlinear programming problem. And simulation results demonstrate the usefulness of proposed voltage support strategy for avoiding six-PV solar farm unexpected trip during short-time symmetric and asymmetric faults.
SOLAR SPECTRAL IRRADIANCE CHANGES DURING CYCLE 24

We use solar spectra obtained by the Ozone Monitoring Instrument (OMI) on board the Aura satellite to detect and follow long-term (years) and short-term (weeks) changes in the solar spectral irradiance (SSI) in the 265–500 nm spectral range. During solar Cycle 24, in the relatively line-free regions the SSI changed by ∼0.6% ± 0.2% around 265 nm. These changes gradually diminish to 0.15% ± 0.20% at 500 nm. All strong spectral lines and blends, with the notable exception of the upper Balmer lines, vary in unison with the solar “continuum. ” Besides the lines with strong chromospheric components, the most involved species include Fe i blends and all prominent CH, NH, and CN spectral bands. Following the general trend seen in the solar “continuum,” the variability of spectral lines also decreases toward longer wavelengths. The long-term solar cycle SSI changes are closely, to within the quoted 0.1%–0.2% uncertainties, matched by the appropriately adjusted short-term SSI variations derived from the 27 day rotational modulation cycles. This further strengthens and broadens the prevailing notion about the general scalability of the UV SSI variability to the emissivity changes in the Mg ii 280 nm doublet on timescales from weeks to years. We also detect subtle deviations from this general rule: the prominent spectral lines and blends at λ ≳ 350 nm show slightly more pronounced 27 day SSI changes when compared to the long-term (years) trends. We merge the solar data from Cycle 21 with the current Cycle 24 OMI and GOME-2 observations and provide normalized SSI variations for the 170–795 nm spectral region.
Object Detection To Enable Autonomous Vessels On European Inland Waterways

To enable autonomous vessels to operate on inland waterways, they need to detect, track and localize objects at close range to safely navigate. We deployed current deep learning techniques to detect and track these objects. As there are no large labeled datasets of European inland waterways, we used transfer learning to overcome the lack of data. By using preexisting similar datasets, we were able to significantly decrease the required amount of labeled data from the target distribution. Furthermore, we improved the mean Average Precision from 0.461 to 0.814 by using a limited number of labeled target data samples. We estimated the relative distance of the objects based on the generated bounding boxes. The information from the camera is then combined with LiDar data to generate a top-view map of the environment which is used as input for an object-avoidance control agent. All these methods can run in real-time on the vessel with an fps of 1.83 on a 2.7GHz vCPU.
Lyapunov Function Construction using Constrained Least Square Optimization

In this study, the Lyapunov function is constructed for polynomial dynamical systems using Constrained Least Square Optimization. In literature, the least square method is used to determine the coefficients of the Lyapunov function to make its derivative negative semi-definite. Then the coefficients are inserted in the Lyapunov function to check its positive definiteness. This way requires many iterations for finding the Lyapunov function. However, in the proposed method, a single optimization program is used to search the optimal coefficients to make the Lyapunov function positive definite and its derivative negative semi-definite. In the proposed method the polynomial Lyapunov function of a particular degree is selected with unknown coefficients. The least-square problem is solved to ensure that the coefficients of monomials that have odd power are zero and the coefficients of monomials that have even power are positive. Moreover, the least-square problem is solved under the constraint to make the derivative of the Lyapunov function along the system trajectories negative semi-definite. The proposed method is illustrated with few examples, and the results show that it successfully identifies the Lyapunov function for globally asymptotically stable systems.
An autonomous system whose solutions are uniformly ultimately bounded but not uniformly bounded

(ii) Conversely, if every solution is continuable up to t = T, then there is an L(tf s, r) for which (2) holds. (iii) Especially, the solution operator T(t, s) defined by x(t) — T{t, s)x(s) (under the uniqueness) is completely continuous, that is, for any bounded set BaR, T(t, s)B is bounded if T(t, s) is defined on B. (iv) If the solutions of (1) are uniformly ultimately bounded, then they are uniformly bounded when the system (1) is autonomous. It is quite natural to expect the same properties for functional differential equations
Energy Neutral Urban Noise Monitoring and Classification With LoRaWAN Based IoT

Internet of Things (IoT) technology paves the way for novel smart city applications. Noise monitoring systems are among these applications, and they help local authorities enforce noise limits more effectively. While some monitoring systems lack easy deployment due to the requirement of physical connection to electric grid or communication network, others need high maintenance because of quick battery depletion. The latter problem becomes even more challenging for the applications with noise classification capability due to higher consumption figures. This paper presents a noise monitoring and classification (NMC) system that enables energy neutral operation using solar harvester and exploiting low power capabilities of LoRaWAN.
Performance of Intrusion Detection System Using Bagging Ensemble with SDN-Base Classifier

Intrusion Detection System (IDS) is a system for detecting suspicious activity on a network. Many machine learning-based IDS approaches have been built to detect intrusion. However, along with the development of types of attacks, currently the application of IDS has not been maximally successful when detecting various types of attacks. To overcome this problem, better techniques are needed to detect attacks. This research applies an ensemble-based Bagging-SDN machine learning model. Bagging-SDN is built from three base-learners, namely Support Vector Machine (SVM), Decision Tree (DT), and Naïve Bayes (NB). These three base learners are known as SDN (SVM, DT, NB). Furthermore, Bagging-SDN was validated using the UNSW-NB15 dataset. Based on the experiment, it was found that the Bagging-SDN performance was superior to the old method based on single classifier. Best Bagging-SDN performance when using Decision Tree (DT) as a base-learner. Bagging-SDN performance was able to increase the accuracy 80.09% compared to the earlier technique which only reached 75.89%. As for further research, the development of machine learning-based IDS still needs to be improved. For example, build ensemble classifier methods to improve performance using other technique like boosting, stacking and another base-learner. So, the IDS still has satisfactory performance in detecting new types of attacks.
Iron Loss Measurement Segregation between an Assembled Stator Core and Tester

For accurate prediction of iron losses in steel laminations, manufacturing effects and actual flux distributions has to be considered in the measurement process. This work aims at proposing a systematic iron loss measurement segregation approach between a tester and an assembled stator core. The tested stator core is exposed to different magnetization levels and frequencies along with different pole configurations produced by the magnetizer to assess the proposed approach. The effects of stator assembly, as well as the rotating nature of the excitation flux, are considered. It is shown that the employment of manufacturer steel data at high flux densities results in a considerable underestimation of the assembled stator core losses. Therefore, a comparison has been conducted between measured specific iron loss data of the stator core and the standard Epstein frame data for the particular M36G29 steel lamination comprising the tested stator.
A General Data-driven Design Methodology of Magnetic Couplers for Wireless Power Transfer Systems

With the rise of the electric vehicle market, wireless charging technology for electric vehicles as a convenient, fast and safe new charging technology has been focused on research in recent years. Compared with traditional wired charging, wireless charging has great advantages in terms of economy, convenience, safety, and adaptability. To improve the performance of inductive power transfer (IPT) systems, it is essential to pursue an optimized design of the magnetic couplers. Although the result of the Traditional finite element method (FEM) is very accurate, it needs a lot of time. Hence, for the first time, this paper proposes a general data-driven coil design approach based on the combination of a neural network and the multi-objective optimization algorithm. The proposed method significantly accelerates the design process and provides highly compliant design results. A design example of an integrated magnetic coupler is provided to validate the superiority of the proposed method in time-saving and accuracy, where the FEM simulation results show that the design deviation is within 10%.
High-Performance Multistage Constant Current Charging for Wireless Power Transfer Systems

In lithium-ion (li-ion) battery charging process, multistage constant-current (MSCC) charging can effectively increase the charging capacity with less charging time compared with the conventional constant current-to-constant voltage (CC-to-CV) charging in wired charging. This paper proposes and designs a MSCC charging strategy for the wireless power transfer system. By using the LCC-S compensation topology for the inductive power transfer (IPT) converter, the current gain is designed with mathematical derivation to achieve load-independent current output with zero phase angle (ZPA) according to the MSCC charging pattern. Phase-shift modulation (PSM) is adopted to control the input DC-link of the WPT system to achieve different current outputs according to different CC stages. Finally, the charging system is simulated to verify the correctness of the design and the feasibility of the proposed WPT system with MSCC charging. The maximum efficiency of the system is 92.4% and the time-averaged efficiency is 91.3%.
iqcToolbox: An Open-Source Robustness Analysis Toolbox based on Integral Quadratic Constraints

We present iqcToolbox, an open-source robustness analysis toolbox based on integral quadratic constraint (IQC) theory. To better enable collaborative contributions, this MATLAB toolbox is modularly designed and avoids dependencies on other closed-source toolboxes, excepting the Control System Toolbox. In contrast to existing robustness analysis packages, iqcToolbox is capable of analyzing discrete-time time-varying systems as well as continuous-time systems. iqcToolbox also allows the specification of time-varying IQC multipliers, and we demonstrate how the incorporation of time-varying systems in the toolbox can generate new uncertainties (such as eventually time-invariant parameters), express new disturbance signal sets (such as constant or periodically updated signals), and enable new analysis tools (such as worst-case reachability analysis). Both the expressivity and modularity of our core analysis objects provide a rich and stable framework for extensions by current and future developers.
Evaluation of ORB-SLAM based Stereo Vision for the Aircraft Landing Status Detection

When encountering emergency conditions, the pilot may lose the help from the modern navigation systems and be forced to perceive the surrounding environment only through vision, which means the safety of manual landing depends heavily on human factors. To provide the pilot with additional information about the status of the aircraft, helping increase the safety during the landing procedure, ORB-SLAM2, a state-of-the-art SLAM algorithm, is used in this paper to estimate the 6DoF localization of the aircraft. To evaluate the performance for aircraft landing, this work used Unreal Engine to generate the simulation runway scene with built-in stereo cameras. The results show that for the landing scenario, ORB-SLAM2 can provide the pilot with an alarm message when the descending speed or the glide angle of the aircraft is inappropriate. Also, as a lightweight, real-time, and stand-alone features, ORB-SLAM2 could be easily applied to other smart and capability-limited unmanned aerial vehicles.
Vibration Anomaly Detection using Deep Autoencoders for Smart Factory

Early fault detection in production is crucial for manufacturing facilities to prevent unplanned downtimes and maximise the operational life of equipment. The aim of this paper is to present a method of anomaly detection for an in-service motor using self-supervised learning. The authors have developed a condition monitoring system for a Smart Factory using deep autoencoders. The system was installed in a live production facility with the goal of improving site maintenance.
Measurement of Magnetic Particle Concentrations in Wildfire Ash via Compact NMR

Wildfire ash plumes deposit magnetic particles (nanoscale and larger) into rivers and streams. Post-fire runoff contains increased levels of suspended particles and contaminants; including traces of metals, nutrients, and total suspended solids that affect the water quality and aquatic species. This work reports on a compact and portable Nuclear Magnetic Resonance (NMR) system capable of measuring the magnetic particle content in wildfire ash. The system is comprised of a custom NMR device with a specially designed permanent magnet that shapes the magnetic field to provide the basis of a compact and robust NMR-based sensing system for the future in-field measurements of ash in aqueous solutions. The system is set up to measure transverse relaxation processes and can acquire sufficient data in as little as two minutes. Experimental results confirmed the linear relationship between magnetic content in water and transverse relaxation rates, and can therefore be used as a method for estimating magnetic content in wildfire ash.
Sustainable and Efficient Fog-Assisted IoT Cloud Based Data Collection and Delivery for Smart Cities

The Internet of Things (IoT) aims to enhance the quality of human life by sensing, processing and delivering data from the physical world. The Internet of Things Cloud (IoTC) is able to enhance sensing capabilities of IoT devices while fog computing can overcome computation and storage limitations of IoT devices. Based on this observation, we propose a fog-assisted IoTC data collection and delivery framework to reduce IoTC-based data communication costs and enhance success rates. The framework exploits the named data networking to deliver IoTC-based data so that multiple IoT devices can retrieve data from the nearest provider via one data delivery process. Moreover, the framework achieves mobility support for IoT devices to guarantee successful receipt of data. The experimental results show that this framework reduces data communication costs by nearly 37.15% and improves success rates by nearly 9.25%.
Student Satisfaction and Churn Predicting using Machine Learning Algorithms for EdTech course

EdTech courses are believed to provide industry-oriented practical skills to the students to bridge the gap between academics and the industry. They also provide supporting education to the students to crack competitive exams in various sectors. With education institutions being shut down due to lockdown and moved to online mode, many students enrolled themselves in EdTech courses online creating competition amongst EdTech organizations. Reduced prices at times lead to reduced quality of the course content and the delivery. Students are often the prey in the competition who are left unsatisfied with the course and would never return to the organization for further courses leading to a churn. At least 50% of college students enroll in online courses in India but the completion rate is only about 13% which is very low. Customer churn is a major challenge to an organization reducing their growth as well as revenue. Two different machine learning algorithms viz. K-Nearest Neighbor (KNN) and Support Vector Machines (SVM) are proposed in this paper to predict the churn rate of students for EdTech courses based on the feedback of the students collected through the course end survey. The model also tells if the student is interested to enroll further in any other courses offered by the organization or refer the courses to their friends or relatives. The data is collected in real-time based on the online courses offered by a startup company and the churn rate for the organization is calculated.
A Novel SVPWM Control Strategy for High-Frequency Link Dual Matrix-Type Inverter

Aiming at the topology of high-frequency link dual matrix inverter (HFLDMI), an intermediate hexagonal modulation strategy based on topology decoupling and vector decoupling is proposed, which effectively suppresses the common mode voltage (CMV) of the system and realizes soft switching of switches. Firstly, the working mode of HFLDMI under the proposed modulation strategy is analyzed in detail. After that, the accurate switching models of CMV and zero sequence voltage (ZSV) of HFLDMI are established and the CMV and ZSV of HFLDMI are compared with the CMV and ZSV of a conventional dual inverter. Finally, the correctness of the proposed modulation strategy is verified by simulation software. The results show that the CMV amplitude of HFLDMI is suppressed below Vdc/6 and the ZSV is zero.
Sensitivity of SEM Fit Indexes With Respect to Violations of Uncorrelated Errors

This simulation study investigated the sensitivity of commonly used cutoff values for global-model-fit indexes, with regard to different degrees of violations of the assumption of uncorrelated errors in confirmatory factor analysis. It is shown that the global-model-fit indexes fell short in identifying weak to strong model misspecifications under both different degrees of correlated error terms, and various simulation conditions. On the basis of an example misspecification search, it is argued that global model testing must be supplemented by this procedure. Implications for the use of structural equation modeling are discussed.
Analysis and Testing of a Four Coil Magnetic Levitation Configuration

Magnetic levitation in general is relevant to applications which require fine control of motion, vibration, force and torque. One common method for magnetic levitation is to use four iron core coils connected in pairs, two Hall effect sensors, and an outer ring of permanent magnets to levitate disk magnets from below. This method uses only two feedback control loops to stabilize rotation and translation of the magnet together in both horizontal directions, where vertical translation is passively stable and its vertical axis yaw rotation is left uncontrolled to rotate freely. The advantageous features of this levitation configuration is that only two sensors and actuators are needed to levitate a magnet above an array of coils.To investigate the characteristics of this magnetic levitation method, we have modeled the forces and torques produced on the levitated magnet from the fixed magnets and induced magnetization of the coil cores according to its position and orientation. This model will enable modification of the system design parameters to optimize performance in terms of levitated weight, height, stability or any other desired criteria of the levitation system. Our control implementation demonstrates that simple proportional-derivative (PD) control of the two feedback loops is sufficient to stabilize the motion of the levitated magnet.
Hybrid Approach for Emotion Recognition Using CNLSTM in Video Expressions

Emotional awareness has been the subject of research in the field of Human-Computer Interaction in recent years. Computers have become an integral part of human life. Users need human-like interactions to better communicate with computers. Many researchers have done so be interested in emotional recognition and classification using a variety of sources. For recognizing emotions, we proposed a machine learning and deep learning-based approach namely CNLSTM in video expressions. The hybrid approach is employed to dissect the video data and perform recognition of emotions. The CNN extracts the features from the corresponding frames in the video and LSTM is used to explore the temporal dynamics of the video. To validate the proposed approach, two datasets namely CK+, JAFFE, and Aff-wild2 have been taken. Different experiments have been performed to validate the results of the proposed approach. The experimental results show that our proposed approach achieves accuracy in the angry mode of the CK+ dataset. Also, the proposed model shows better results in unbalanced data.
Magnitude to Digital Converter with Latch-Type Comparator and Dynamic Switching Current Scheme

A magnitude to digital converter is proposed using a latch-type comparator to replace the conventional opamp-based comparator. The PVT-dependent timing error can be relieved by employing the latch-type comparator and rearranging the decision control circuits. Besides, the power efficiency can be improved within the low and high speed operations. For increasing the linearity of the converting process, a dynamic current source is also developed to obtain the best coefficient of determination. A prototype of 10-bit converter was designed to operate at 40-kS/s with only 56.S-$\mu$W of power dissipations, respectively.