Online In Real Time Research Articles

Technological Aspects of Distance Learning in Geogebra Classroom-Math Environment

The article discusses the technological aspects of distance learning in the GeoGebra Classroom virtual math platform environment, based on a practical model of using the GeoGebra Classroom. In particular, the main activities of Geogebra Classroom are presented in the following order: Activities - find / create an activity; GeoGebra class - create a GeoGebra class; Admission of students to the GoeGebra class; Appointment of a co-teacher; Monitoring student progress; GeoGebra classrooms in a distance learning environment; GeoGebra Applets (Graphing; CASE; Geometry; 3D Graphics; Spreadsheet; Probability; Notes) for GeoGebra Classroom; Creating a GeoGebra class from a GeoGebra book.Every activity in the work is explained and performed in practice, which is confirmed by the corresponding illustrations actually carried out in the GeoGebra classroom environment. The algorithm for carrying out each activity is explained in detail and analyzed from a technological point of view and the methodological value of a separate step is shown. The discussion of the individual activities is accompanied by the proper definitions and purposes of the GeoGebra Classroom environment interface.The article provides the recommendations for the necessary measures before starting the practical work in the GeoGebra Classroom,such as: Registration on the official GeoGebra website https://www.geogebra.org/; Download the GeoGebra Classic installation package and install it on the local computer; Have knowledge of using online and installed applications of GeoGebra Classic to create a resource.The main activities of the GeoGebra classroom in the work are carried out on the example of discussing a practical model, parallel lines, with virtual groups of students. The following steps are shown sequentially: Creating resources in local and online GeoGebra applications; Creation of activities and their enrichment with additional elements of educational goals (Text; GeoGebra Applet; Notes; Question: Open question, Multiple choice; Video; Picture; PDF file Web); Creating a GeoGebra class and engaging participants online in real time; interactive communication in the GeoGebra classroom with the joint participation of the teacher and sstudents, which does not require sharing of screens by students; Tracking students' progress in the educational process, conducting anonymous discussions and providing feedback.The article analyzes and confirms the success of using GeoGebra Classroom in teaching mathematics both in schools and in higher educational institutions.

Speed sensorless control of a bearingless induction motor based on fuzzy PI fractional MRAS scheme

ABSTRACT To handle the suspended rotor speed self-identification problem of a bearingless induction motor (BL-IM), a sensorless control strategy based on fuzzy PI fractional model reference adaptive system (MRAS) is proposed. First, based on the flux linkage error, fractional MRAS adaptive law is designed to accelerate the response speed of the controlled object, and Lyapunov theorem is used to prove the stability of the adaptive law. Then, the fuzzy control rules in the fuzzy PI controller are used to adjust PI parameters of adaptive law online in real time. Finally, the speed under no-load operation, abrupt speed change and abrupt change of load is estimated and sensorless control is realized. The simulation and experimental results show that the proposed fuzzy PI fractional MRAS control system of the BL-IM can not only effectively realize self-identification of speed under no-load, speed change and abrupt change of load, but also have good suspension characteristics of the motor suspended rotor, realizing high reliability and low costs of BL-IM.

ONLINE PROBABILISTIC FUZZY CLUSTERING METHOD BASED ON EVOLUTIONARY OPTIMIZATION OF CAT SWARM

Context. The problems of big data clustering today is a very relevant area of artificial intelligence. This task is often found in many applications related to data mining, deep learning, etc. To solve these problems, traditional approaches and methods require that the entire data sample be submitted in batch form.
 Objective. The aim of the work is to propose a method of fuzzy probabilistic data clustering using evolutionary optimization of cat swarm, that would be devoid of the drawbacks of traditional data clustering approaches.
 Method. The procedure of fuzzy probabilistic data clustering using evolutionary algorithms, for faster determination of sample extrema, cluster centroids and adaptive functions, allowing not to spend machine resources for storing intermediate calculations and do not require additional time to solve the problem of data clustering, regardless of the dimension and the method of presentation for processing.
 Results. The proposed data clustering algorithm based on evolutionary optimization is simple in numerical implementation, is devoid of the drawbacks inherent in traditional fuzzy clustering methods and can work with a large size of input information processed online in real time.
 Conclusions. The results of the experiment allow to recommend the developed method for solving the problems of automatic clustering and classification of big data, as quickly as possible to find the extrema of the sample, regardless of the method of submitting the data for processing. The proposed method of online probabilistic fuzzy data clustering based on evolutionary optimization of cat swarm is intended for use in hybrid computational intelligence systems, neuro-fuzzy systems, in training artificial neural networks, in clustering and classification problems.

A Generalized Identification of Joint Structural State and Unknown Inputs Using Data Fusion MKF-UI

The classical Kalman filter (KF) can estimate the structural state online in real time. However, the classical KF presupposes that external excitations are known. The existing methods of Kalman filter with unknown inputs (KF-UI) have limitations that require observing the acceleration response at the excitation point or assuming the unknown force. To surmount the above defects, an innovative modal Kalman filter with unknown inputs (MKF-UI) is proposed in this paper. Modal transformation and modal truncation are used to reduce the dimensionality of the structural state, and the accelerations at the excitation positions do not need to observe. Besides, the proposed MKF-UI does not require the assumption of unknown external excitation. Therefore, the proposed approach is suitable for the generalized identification of dynamic structural states and unknown loadings. The effectiveness and feasibility of the proposed identification approach are ascertained by some numerical simulation examples.

Learning whole-image descriptors for real-time loop detection and kidnap recovery under large viewpoint difference

We present a real-time stereo visual-inertial-SLAM system which is able to recover from complicated kidnap scenarios and failures online in realtime. We propose to learn the whole-image-descriptor in a weakly supervised manner based on NetVLAD and decoupled convolutions. We analyze the training difficulties in using standard loss formulations and propose an allpairloss and show its effect through extensive experiments. Compared to standard NetVLAD, our network takes an order of magnitude fewer computations and model parameters, as a result runs about three times faster. We evaluate the representation power of our descriptor on standard datasets with precision–recall. Unlike previous loop detection methods which have been evaluated only on fronto-parallel revisits, we evaluate the performance of our method with competing methods on scenarios involving large viewpoint difference. Finally, we present the fully functional system with relative computation and handling of multiple world co-ordinate system which is able to reduce odometry drift, recover from complicated kidnap scenarios and random odometry failures. We open source our fully functional system as an add-on for the popular VINS-Fusion.

E-Classroom Interactional Competencies: Mediating and Assisting Language Learning During Synchronous Online Lessons

Interaction is seen by many English language teachers and scholars as an essential part of face-to-face English language classrooms. Teachers require specific competencies to effectively use interaction as a tool for mediating and assisting learning. These can be referred to as classroom interactional competence (CIC). However, the situation created by the ongoing global COVID-19 pandemic which began in early 2020, and the recent advancement in technologies have led to teachers conducting synchronous online lessons through video-conferencing software. The online environment is distinctly different from the face-to-face classroom and teachers require new and additional skills to effectively utilise interaction online in real time. This exploratory study used an online mixed-method survey of 75 university level English language teachers who had engaged in synchronous online teaching due to COVID-19, to explore the competencies that teachers need to use interaction as a tool to mediate and assist language learning in synchronous online lessons. Teachers were found to require three competencies, in addition to their CIC – technological competencies, online environment management competencies, and online teacher interactional competencies – which together constitute e-CIC. The findings provide greater insights into the needs of teachers required to teach synchronously online and will be of interest to teachers and teacher educators.

Optical Flow Monocular Visual-Inertial Odometry with Online Photometric Calibration

Kanade-Lucas-Tomasi (KLT) optical flow algorithm based on the brightness constancy assumption is widely used in visual simultaneous localization and mapping (SLAM) and visual odometry (VO). However, the automatic adjustment of camera exposure time, the attenuation factor of sensor irradiance caused by vignetting, and the nonlinear camera response function will cause the same feature point to have different brightness values on different image frames, thus breaking this assumption. Hence, we propose a gain-adaptive KLT optical flow algorithm with online photometric calibration, and on this basis, design a monocular visual-inertial odometry which is insensitive to brightness changes. This method can calibrate the photometric parameters online in real time, meet the assumption of constant brightness in practical applications, and make the algorithm more robust and accurate in the case of dynamic changes in brightness. Experimental results on the TUM Mono and EuRoC datasets show that the proposed algorithm can reliably calibrate the photometric parameters of any video sequence and perform well in the environment with varying brightness.

CuSO4 chromogenic tracing for migration and crystallization of salt solution in porous materials

The migration and crystallization of CuSO4 solution characterized by color change property on the surface of glass of hydrophobic property, capillaries and porous resin materials were observed online in real time. The research results indicated that under the condition of stable environment, crystals generated by CuSO4 droplets were relatively complete, and crystals would also easily be accumulated both inside and outside of the capillaries, in particularly, the crystal morphology inside the capillaries altered along with the change of solution state. Meanwhile, there were parabolic migration and crystallization of the salt solution in the porous material samples, and the samples with saturated salt solution had a wide range of crystal areas when the salt solution had been completely crystallized in the porous samples, while the crystal belt in the interior samples was produced with less salt solution. In this paper, the parabola with opposite direction in the process of salt solution migration and crystallization, as well as the distribution of whitespace and crystal belt were found for the first time.

Multiple Object Tracking for Occluded Particles

The precise detection and tracking of multiple particles under a microscope are of significance in the research of the individual and cluster behavior of dynamic bacteria and subcellular structures. However, the existing detection algorithms cannot separate occluded particles from each other, and most of the tracking algorithms aimed to address the occlusion involve several uncertainties. In this paper, a two-step detection algorithm based on the threshold segmentation and morphological open operation has been developed for identify non-fluorescent labeled particles under microscope, which could separate micro-contact targets. Moreover, we have proposed a novel correlation algorithm that can exploit the strengths of the global shortest path algorithm and Hungarian algorithm, which updating online in real time and considering the occlusion among particles. The proposed approach could achieve the temporal optimal match and spatial optimal solution by utilizing the multi-frame information. Moreover, the proposed method could realize the tracking of occluded particles tracking, and outperform the single global shortest path algorithm and Hungarian algorithm. The proposed method was successfully applied to six real image sequences with the maximum number of particles per frame ranging from 23 to 55, as well as a synthetic and fluorescent labeled sequence. The results of the contrast experiments demonstrated that the proposed algorithm is practical and can realize real-time tracking.

A 2D-FM model-based robust iterative learning model predictive control for batch processes

The work deals with composite iterative learning model predictive control (CILMPC) for uncertain batch processes via a two dimensional Fornasini–Marchesini (2D-FM) model. A novel equivalent error system is first presented which is composed of state error and tracking error. Then an iterative learning predictive updating law is constructed by 2D state feedback control and the ‘worst’ case linear quadratic function is also designed. Besides, the update controller considering the input and output constraint will be optimized using the worst-case objective function along the infinite moving horizon. The solvable conditions that can be optimized online in real time are constructed using linear matrix inequalities (LMIs). The stability of the proposed control scheme can be achieved with the feasibility of the optimization problem. Compared with robust traditional MPC using one-dimensional models, the presented control approach can guarantee more degrees of tuning to achieve faster convergence of tracking error, which is of more significance since uncertainties exist inevitably in industrial batch processes. Finally, an injection molding process and a three-tank are introduced as two cases to demonstrate the feasibility and superiority of the proposed MPC strategy.

An intrusion detection algorithm based on data streams mining and cognitive computing

With the emergence of large-volume and high-speed streaming data, traditional techniques for mining closed frequent itemsets has become inefficient. Online mining of closed frequent itemsets over streaming data is one of the most important issues in data streams minging. In view of the low efficiency of traditional closed frequent item data mining, a combined data structure based on the principle of cognitive computing is proposed, that is, combining the effective bit first with the extended dictionary frequent item list to form a mixed data structure that can identify the closed frequent information in data streams. At the same time, a variety of pruning strategies based on cognitive computing are proposed to avoid the generation of a large number of intermediate itemsets and to remove the non closed frequent term sets from the Hash Table of Closed Itemsets (CIHT). Closed Frequent Itemset Deletion and Search Strategy (CFIDWSS) is used to effectively add or remove the closed frequent itemsets, so as to greatly reduce the search space and improve the user response speed. The proposed algorithm solves the problem of low efficiency of data streams mining of closed frequent items. On the basis of the above algorithms, this paper proposes a new intrusion detection model. Through the mining of normal or abnormal patterns of data stream information, the corresponding database of network access pattern is established. Then the database is used to detect the intrusion online in real time and improve the detection accuracy of the system. Theoretical and experimental results show that the proposed algorithm and intrusion detection system have good performance.

A scalable online algorithm for passive seismic tomography in underground mines

Understanding and monitoring the seismic responses of rock masses to massive mining are crucial for safety and economic viability of ever larger and deeper underground operations. Seismic monitoring can be used to detect stress variations and hazardous instabilities, but its effectiveness requires accurate estimations of the nonhomogeneous propagation velocity of microseismic waves. While predetermined velocity models are not accurate enough and might bias hypocenter localization, using active-source seismic tomography methods to estimate the velocity field provides limited spatial coverage. Thus, passive seismic tomography using first-arrival traveltimes of mining-induced microseisms (of unknown hypocenters) constitutes a promising tool. However, available methods solving this high-dimensional statistical inverse problem do not scale well with the data set size and cannot easily refine or update estimations with new data. We have developed a novel passive seismic tomography method able to dynamically learn the nonhomogeneous velocity field from a streaming of noisy first-arrival times, online (in real time) or from catalogs. We have developed a new Bayesian approach that avoids linearizing the forward problem and allows for general 3D velocity models. This is combined with the use of the stochastic gradient descent (SGD) method, which underlies much of the recent progress in machine learning and provides increasing accuracy at a cost scaling linearly with the data set size. Moreover, we introduce an adaptive variant of SGD based on raypath density, which significantly improves the speed of the algorithm, and we implement a parallel version of our method enabling its systematic use in real applications. These include the design of optimal sensor locations, the dynamic update of velocity estimates in production conditions, and the real-time determination of hypocenters and their uncertainty. Our method’s reach and effectiveness are illustrated with simulated seismic data on 3D checkerboards, using synthetic and real acquisition geometries, and on a dense 2D velocity grid.

Study on Control Strategy of Microbus Power System Based on Branch-and-Bound Algorithm

With the development of information technology, the scale of data center infrastructure has become larger and larger, which has always brought many challenges, such as unbalanced power load, high energy consumption, small scalable space and so on. In recent years, a distributed power supply structure, namely microbus power supply, has emerged, which is different from the traditional power distribution mode in computer room. It can adjust the power load distribution online in real time. This paper analyses the advantages of this micro bus structure power supply mode, and optimizes the cabinet power collection strategy based on branch and bound algorithm. By modeling and simulating the power consumption of the computer room in a real data center, the experimental results show that the proposed algorithm can greatly reduce the unbalance rate of the three-phase power supply equipment in the upper end of the computer room. Compared with the traditional power supply mode, it can reduce the maximum power imbalance by 49.88%.

Efficient swing control of an overhead crane with simultaneous payload hoisting and external disturbances

Abstract The effects of time varying parameters and external disturbances on a flexible system may badly degrade the control performance resulting in excessive induced vibrations/oscillations. For an overhead crane, the oscillation can be even worse when both factors occur simultaneously. This paper proposes a novel swing control approach for an underactuated overhead crane having payload hoisting and external disturbance simultaneously. The proposed scheme is designed based on a predictive unity magnitude shaper and an adaptive feedback control which efficiently suppress payload swing to handle both effects. Furthermore, the control parameters can be updated online in real time to progressively suppress the payload swing. To evaluate the effectiveness of the proposed method, experiments are carried out with a simultaneous payload hoisting and external disturbances including a non-zero initial condition, persistent disturbance (wind) and instant disturbance. The developed controller achieves higher robustness under all testing conditions with significant swing reductions of at least 45% and 69% in the overall and residual swing responses, respectively over a comparative control method. It is envisaged that the proposed method can be very beneficial as an anti-swing controller for various cranes under the influence of disturbance and hoisting simultaneously.

State of charge estimation for lithium-ion batteries based on adaptive dual Kalman filter

Accurate estimation of the battery state of charge (SOC) is of great significance for enhancing its service life and safety. In this study, based on the fractional-order equivalent circuit model of lithium-ion battery, the SOC estimation methods using dual Kalman filter (DKF) and dual extended Kalman filter (DEKF) are simulated and compared, in terms of model accuracy and SOC estimation accuracy. Then, combining the advantages of the DKF and DEKF algorithms, an SOC estimation algorithm based on adaptive double Kalman filter is proposed. This algorithm uses the recursive least squares (RLS) method to update the battery model parameters online in real time, and employs the DKF algorithm to filter the SOC twice to reduce the interferences from the battery model error and the current measurement error. In the experimental studies, the measured SOC values are compared with the estimated SOC values produced by the proposed algorithm. The comparison results show that SOC estimation error of the proposed algorithm is within the range of ±0.01 under most test conditions, and it can automatically correct SOC to true value in the presence of system errors. Thus, the validity, accuracy, robustness and adaptability of the proposed algorithm under different operation conditions are verified.

An active and dynamic credit reporting system for SMEs in China

SMEs in China always face financing constraints and hardly obtain bank loans under unsound financing system due to the information asymmetry, while thousands of SMEs have contributed greatly to Chinese economic development in the last decades. Credit reporting has been verified to be an effective way to lower information asymmetry. However, existed credit reporting systems for SMEs can not meet the development of SMEs and provide enough information to the financial institutions in China. This paper introduces an active and dynamic credit reporting framework based on Big data and Blockchain for SMEs. The framework is composed of five modules, including credit data acquisition, authentication, evaluation, reporting, and interaction. And it features in capturing diversified data online, conducting evaluation and analysis in real time, generating online credit reports for users automatically, and providing an effective way for different entities to interact. A case study from a real credit evaluation company is also proposed finally to show the proposed framework.

Two‐level area‐load modelling for OPF of power system using reinforcement learning

Load modelling is essential to the planning and operation of a power system. This study proposes a two-level hierarchical framework of real-time area-load modelling for optimal power flow (OPF). The upper-level problem is a parameter identification for an area-load model improved via using a weighting strategy decayed with time, whereas the lower-level optimisation is a dynamic OPF considering N − 1 static security constraints. In the framework, the error of the lower-level optimisation is added into the upper-level model, which guides a search direction for the load modelling toward minimising the error between the online measurement and equivalent model output as much as possible. An improved method is proposed based on function optimisation by reinforcement learning to identify the parameters of the area-load model online in real time. Simulation studies verify the effectiveness of the proposed framework, algorithm and improved strategies.

Control-oriented model of dielectrophoresis and electrorotation for arbitrarily shaped objects.

The most popular modeling approach for dielectrophoresis (DEP) is the effective multipole (EM) method. It approximates the polarization-induced charge distribution in an object of interest by a set of multipolar moments. The Coulombic interaction of these moments with the external polarizing electric field then gives the DEP force and torque acting on the object. The multipolar moments for objects placed in arbitrary harmonic electric fields are, however, known only for spherical objects. This shape restriction significantly limits the use of the EM method. We present an approach for online (in real time) computation of multipolar moments for objects of arbitrary shapes having even arbitrary internal composition (inhomogeneous objects, more different materials, etc.). We exploit orthonormality of spherical harmonics to extract the multipolar moments from a numerical simulation of the polarized object. This can be done in advance (offline) for a set of external electric fields forming a basis so that the superposition principle can then be used for online operation. DEP force and torque can thus be computed in fractions of a second, which is needed, for example, in model-based control applications. We validate the proposed model against reference numerical solutions obtained using Maxwell stress tensor. We also analyze the importance of the higher-order multipolar moments using a sample case of a Tetris-shaped micro-object placed inside a quadrupolar microelectrode array and exposed to electrorotation. The implementation of the model in Matlab and Comsol is offered for free download.

Optimal Containment Control of Unknown Heterogeneous Systems With Active Leaders

This brief presents a partially model-free solution to the distributed containment control of multiagent systems using off-policy reinforcement learning (RL). The followers are assumed to be heterogeneous with different dynamics, and the leaders are assumed to be active in the sense that their control inputs can be nonzero. Optimality is explicitly imposed in solving the containment problem to not only drive the agents’ states into a convex hull of the leaders’ states but also minimize their transient responses. Inhomogeneous algebraic Riccati equations (AREs) are derived to solve the optimal containment control with active leaders. The resulting control protocol for each agent depends on its own state and an estimation of an interior point inside the convex hull spanned by the leaders. This estimation is provided by designing a distributed observer for a trajectory inside the convex hull of active leaders. Only the knowledge of the leaders’ dynamics is required by the observer. An off-policy RL algorithm is developed to solve the inhomogeneous AREs online in real time without requiring any knowledge of the followers’ dynamics. Finally, a simulation example is presented to show the effectiveness of the presented algorithm.

Validation of Inner Magnetosphere Particle Transport and Acceleration Model (IMPTAM) With Long‐Term GOES MAGED Measurements of keV Electron Fluxes at Geostationary Orbit

AbstractSurface charging by keV (kiloelectron Volt) electrons can pose a serious risk for satellites. There is a need for physical models with the correct and validated dynamical behavior. The 18.5‐month (2013–2015) output from the continuous operation online in real time as a nowcast of the Inner Magnetosphere Particle Transport and Acceleration Model (IMPTAM) is compared to the GOES 13 MAGnetospheric Electron Detector (MAGED) data for 40, 75, and 150 keV energies. The observed and modeled electron fluxes were organized by Magnetic Local Time (MLT) and IMPTAM driving parameters; the observed Interplanetary Magnetic Field (IMF) BZ, BY, and |B|; the solar wind speed VSW; the dynamic pressure PSW; and Kp and SYM‐H indices. The peaks for modeled fluxes are shifted toward midnight, but the ratio between the observed and modeled fluxes at around 06 MLT is close to 1. All the statistical patterns exhibit very similar features with the largest differences of about 1 order of magnitude at 18–24 MLT. Based on binary event analysis, 20–78% of threshold crossings are reproduced, but Heidke skill scores are low. The modeled fluxes are off by a factor of 2 in terms of the median symmetric accuracy. The direction of the error varies with energy: overprediction by 50% for 40 keV, overprediction by 2 for 75 keV, and underprediction by 18% for 150 keV. The revealed discrepancies are due to the boundary conditions developed for ions but used for electrons, absence of substorm effects, representations of electric and magnetic fields which can result in not enough adiabatic acceleration, and simple models for electron lifetimes.