Miracle-dz/UniFAR-FTUs huggingface dataset:
An Ontology Model for Formative Assessments

The ontology model can help students “own” their learning process by giving them personalized feedback from formative assessments. However, there is no comprehensive ontology model of the necessary data. Therefore, this study aims to construct an ontology model of formative assessment using heutagogy approach. The first part of the research is a review of nine review articles, which is followed by design research. Formative assessments of the heutagogy approach are traditional (objective and essay tests) and outcome-based (such as developmental portfolios, the analytic rubric, and the perception rubric). Both assessments could provide personalized feedback, but they differ in response start, learning domain, speed of assessment results, focus, and execution time. Using a heutagogy approach, the design research results in an ontology schema, which is made up of learning resources and formative assessments. Our ontology model is the basis for the personalized feedback of an ontology-based recommender system. Moreover, this model can be used with other recommendation techniques.
An Open Source Grid Observer for the Analysis of Power Flexibilities in Low Voltage Distribution Grid Simulations

The existence of voltage range violations represents a challenge in low voltage grids with a high penetration of decentralized generation, storage capacities or electro-mobility. If grid expansion is to be avoided in the future, an extensive information exchange between the system’s components is required to provide an optimal energy supply and guarantee operating requirements. This can be studied in details by modelling the energy system components and the communication between them, which is one of the aims of the research project "Future Energy Laboratories". Highly integrated application scenarios for sample districts in Germany are simulated and thoroughly analysed during the project. For the detection and correction of voltage range violations at individual buses of a grid, an Open Source Grid Observer tool was developed and tested as a flexibility feature in a quasi-dynamic energy system simulation of a residential district. It allows a wide range of compatibility between various simulation scenarios, as it gives recommendations for power control at considered buses according to predefined flexibilities, such as the type of power (active or reactive), flexibility components (e.g. generation and storage) and injection increment. The corrections were calculated using two methods of the voltage sensitivity analysis theory. A comparison between the methods’ robustness, accuracy and calculation speed is presented, being important performance factors to be considered in this kind of simulations. The functionality of the tool is demonstrated in scenarios with temporary peak loads from electric vehicle charging or a high share of distributed photovoltaic power generation.
Hyperspectral Image Classification Based on Multi-Level Spectral-Spatial Transformer Network

Deep learning methods have been widely used in hyperspectral image classification (HSIC). In recent years, Convolutional Neural Network (CNN) has become a mainstream model of deep learning for HSIC. Although the CNN-based method has made great progress, it still faces a series of challenges such as insufficient use of long-distance information, limited receiving domain, and high computational overhead. In order to overcome these issues, this paper proposes a multi-level spectral-spatial transformer network (MSTNet) for HSIC through the image-based classification framework. The proposed network learns feature representation through a transformer encoder, and integrates multi-level features through a decoder to generate classification results. Finally, the experimental results on two real hyperspectral data sets verified the superiority of the method.
Hyperspectral Unmixing Using Convolutional Autoencoder For Metal Detection In Lithium-Ion Battery Recycling Applications

Recent advancements in hyperspectral imaging systems have opened up possibilities for identifying and distinguishing materials based on their spectral characteristics, as every material has its unique spectral signature. In our work, we present a novel approach for detecting and distinguishing copper and aluminum foils present in shredded lithium-ion batteries (LIBs) using convolutional autoencoder for hyperspectral unmixing. In hyperspectral applications, unmixing is a key procedure for estimating spectral signatures of pure materials (endmembers) as well as the corresponding fractional spatial extent (abundances) of endmembers in mixed pixels of hyperspectral images (HSIs). We perform hyperspectral unmixing on a real hyperspectral dataset using a convolutional autoencoder with sparse regularization. We evaluate the performance of the autoencoder framework using VNIR (visible and near-infrared) HSI data acquired with the Specim FX10 hyperspectral sensor. Our experimental unmixing results demonstrate that convolutional autoencoder showed a significant improvement in unmixing performance compared with competing unmixing methods. To the best of our knowledge, this work is the first to implement hyperspectral unmixing using autoencoder in LIB recycling, which is highly significant for automated sorting of valuable metals in LIB recycling industrial applications.
Analysis of Lightning Transients in 2 × 25 kV 50 Hz Railway Traction System using EMTP

Lightning strike can generate intense surge voltage or current on traction power systems. Such transients can damage the electrical insulation of equipment and cause operational problems. To ensure a reliable operation of railway transportation, it is necessary to investigate the effects of lightning transients on the system and to design effective protection measures. The present study focuses on the 2×25 kV 50 Hz italian railway system. The system is modeled according to the Multiconductor Transmission Line (MTL) approach. Numerical simulations were carried out through EMTP. They aim at analyzing the distribution of direct lightning current and transient voltage on pole and catenary of the railway system. Surge arresters are modelled in order to mitigate the effects. Their response is discussed.
Multisource Remote Sensing Data Classification Based on A Dual Attention Fusion Network

Joint classification of multisource remote sensing data is a very meaningful yet challenging task. Especially when the actual scene composition is complex, spectral confusing categories overlapping and different appearances of the same object will bring great challenges for classification. In this paper, a novel Dual Attention Fusion Network (DAFNet) is proposed to solve the above problems. Firstly, a spectral attention block is designed to highlight or suppress the channel map, so as to better distinguish the spectral confusing categories. At the same time, we introduce a spatial attention block that integrates the features of all locations by weighted sum, to ignore the effect of spatial differences. Experimental results on real dataset show that the proposed method can effectively improve the classification results compared to other competitive works.
Generalized Multiscale Inversion for Heterogeneous Problems

In this work, we propose a generalized multiscale inversion algorithm for heterogeneous problems that aims at solving an inverse problem on a computational coarse grid. Previous inversion techniques for multiscale problems seek a coarse-grid media properties, e.g., permeability and conductivity, and by doing so, they assume that there exists a homogenized representation of the underlying fine-scale permeability field on a coarse grid. Generally such assumptions do not hold for highly heterogeneous fields, e.g., fracture media or channelized fields, where the width of channels are very small compared to the coarse-grid sizes. In these cases, grid refinement can lead to many degrees of freedom, and thus unattractive to apply. The proposed algorithm is based on the Generalized Multiscale Finite Element Method (GMsFEM), which uses local spectral problems to identify non-localized features, i.e., channels (high-conductivity inclusions that connect the boundaries of the coarse-grid block). The inclusion of these features in the coarse space enables one to achieve a good accuracy. The approach is valid under the assumption that the solution can be well represented in a reduced-dimensional space by multiscale basis functions. In practice, these basis functions are non-observable as we do not identify the fine-scale features of the permeability field. Our inversion algorithm finds the discretization parameters of the resulting system. By doing so, we identify the appropriate coarse-grid parameters representing the permeability field instead of fine-grid permeability field. We illustrate the approach by numerical results for fractured media.
Threshold Voltage Behavior and Short-Circuit Capability of p-Gate GaN HEMTs Depending on Drain- and Gate-Voltage Stress

In this study, the impact and correlation of drain- and gate-voltage stress on the threshold voltage of commercially available p-gate GaN HEMTs are investigated. This is based on single-pulse measurements acquired with a custom pulse setup, thus the transient behavior can be determined and subsequently translated into dc characteristics. As a novelty, measurements of the threshold voltage for short-circuit stress of up to 600 V are shown. As a result, a constant threshold voltage shift is observed for on-state drain-voltage stress, while off-state drain-voltage and gate-voltage stress lead to a threshold voltage instability. Finally, drain stress and gate stress appears to superimpose, consequently already application-relevant gate-driving conditions show a drastic impact of the short-circuit capability.
Accurate calculations of Stationary Distributions and Mean First Passage Times in Markov Renewal Processes and Markov Chains

Abstract This article describes an accurate procedure for computing the mean first passage times of a finite irreducible Markov chain and a Markov renewal process. The method is a refinement to the Kohlas, Zeit fur Oper Res, 30, 197–207, (1986) procedure. The technique is numerically stable in that it doesn’t involve subtractions. Algebraic expressions for the special cases of one, two, three and four states are derived.Aconsequence of the procedure is that the stationary distribution of the embedded Markov chain does not need to be derived in advance but can be found accurately from the derived mean first passage times. MatLab is utilized to carry out the computations, using some test problems from the literature.
Design of Active Magnetic Bearing for Industrial Size Compressor System using Kalman Filter Based Optimal Controller

Anti-surge controller has a very important role in industrial size compressor system to avoid the occurrence of a surge phenomenon. This anti-surge controller can be constructed using a component known as Active Magnetic Bearing (AMB), which can also provide a better efficiency for the overall compressor system compared the conventional bearing. The AMB consists of two components, a thrust magnetic bearing (TMB), and a radial magnetic bearing (RMB). The discussion presented in this paper will mainly focus on the design of the proposed RMB controller system, which is essential in AMB based anti-surge controller, for the industrial size compressor system. The RMB controller system consists of 3 main components, LQR controller, Kalman filter, and automatic compressor trip system. The RMB controller system helps to stabilize the rotor radial position and protect the compressor system when the rotor radial displacement is too far from its nominal value. The simulation result shows that the proposed system has a good performance to stabilize the rotor radial position. The simulation result also shows that the automatic compressor trip system can properly generate an appropriate output command when the rotor displacement is outside the permitted value.
A Review of Research on Human Behavior Recognition Methods Based on Deep Learning

As an important research hotspot in the field of computer vision, human behavior recognition has important research significance and wide application prospects in many fields such as intelligent surveillance, smart home, and virtual reality, and has attracted much attention from scholars at home and abroad. With the great success of deep learning in image classification, the use of deep learning in human behavior recognition has gradually become a development trend, but there are still some difficulties and challenges. This paper firstly introduces the human behavior recognition methods based on deep learning, secondly, organizes the human behavior dataset, compares the performance of each behavior recognition algorithm and the specific applications, and finally, analyzes the challenges faced by behavior recognition and the outlook for the future.
Counting of Pine Wood Nematode Based on VDNet Convolutional Neural Network

Pine wood nematode disease is a devastating forest disease caused by pine wood nematode, which seriously destroys the construction of ecological environment. In order to strengthen the supervision of forest epidemic and realize the detection and counting of pine wood nematodes, we propose a deep CNN counting model based on VGG-16 network and dilated convolution (DC), which is called VDNet network. The model is divided into two parts: front-end network and back-end network. The front-end uses the first 13 layers of VGG-16 to extract shallow features, and the back-end uses six layers of DC to extract more advanced features; Dilated convolution can expand the receptive field while maintaining the same resolution, replace the pooling operation, avoid further shrinking the output image, and generate a high-quality distribution density map of pine wood nematode trees; Finally, a traditional convolution layer is used to output the density map. The number of pine wilt trees in the input image is obtained by integrating the pixels of the density map, and theoretical analysis and experimental verification are carried out. The results show that the average absolute error of VDNet network is 2.78 and the mean square error is 3.65. The average accuracy is 86%.
Dynamic Modeling and Simulation of Solar Water Pumping With Hybrid Storage System

Due to the energy crisis and surge in the price of energy, using renewable energy sources has become a trend these days. Ironically, in many Middle East countries like Iran, they still use diesel generators to power their water pumps while they have a good potential for solar energy. In the authors' previous work, an optimum solar water pump with a hybrid storage system was proposed, and its feature were presented. Like any other feasible project, it is necessary to simulate the system to examine its stability and analyze system in details. To do so, in this paper, an exact system component model is implemented on Simulink, and the result of dynamic analysis are provided. It is shown that the water pump works properly in different circumstances, and it is a trustable system to substitute with traditional water pumping systems.
Coulomb Blockade Oscillations in a Double-Dot System

The conductance of two conducting dots coupled by very small capacitances is calculated. It shows remarkable structure as a function of a gate-induced potential Vg. At temperatures just below the Coulomb blockade energy, the conductance is found to oscillate periodically with Vg. As the temperature is reduced, however, the periodic oscillations give way to quasi-random conductance peaks. This behavior is a result of the incommensurate energy scales required to add charges to the two dots.
On Subcritically Stein Fillable 5-manifolds

Abstract We make some elementary observations concerning subcritically Stein fillable contact structures on 5-manifolds. Specifically, we determine the diffeomorphism type of such contact manifolds in the case where the fundamental group is finite cyclic, and we show that on the 5-sphere, the standard contact structure is the unique subcritically fillable one. More generally, it is shown that subcritically fillable contact structures on simply connected 5-manifolds are determined by their underlying almost contact structure. Along the way, we discuss the homotopy classification of almost contact structures.
Laser-field-induced modifications of electron-transfer processes in ion-atom collisions

Electron transfer in the presence of a laser field is explored for the prototype collision system ${\mathrm{He}}^{2+}\text{\ensuremath{-}}\mathrm{H}(1s)$ on the basis of a quantum-mechanical calculation. At collision energies ${E}_{P}\ensuremath{\geqslant}2\phantom{\rule{0.3em}{0ex}}\mathrm{keV}∕\mathrm{amu}$ the capture probabilities are found to be conisderably enhanced or suppressed depending on the exact synchronization of the time-dependent projectile and laser interactions. At lower collision energies the total capture cross section is strongly enhanced irrespective of the relative phase between both fields. This enhancement should be directly observable in future experiments.
Absolute oscillator strengths for lines of neutral cobalt between 2276 A and 9357 A and a redetermination of the solar cobalt abundance

Absolute oscillator strengths of neutral cobalt have been determined from hook measurements for 159 transitions and emission intensity measurements for 314 transitions between 2276 A and 9357 A. Ninety-five of these transitions were subjected to the procedure developed by Cardon, Smith, and Whaling which fits combined absorption and emission data to a set of consistent, optimum, relative oscillator strengths and upper level lifetimes. These relative values were normalized to the radiative lifetimes of Figger et al. and of Marek and Vogt obtained by pulsed laser fluorescence. Absolute oscillator strengths for 362 transitions and 36 lifetimes were determined. Typical uncertainties in the reported absolute oscillator strengths are +- 15-25% (2/3 confidence level). Equivalent widths were obtained for nineteen solar cobalt lines with the McMath solar telescope at Kitt Peak National Observatory. These widths were used to redetermine the solar cobalt abundance, assuming the photospheric model of Holweger and a microturbulence velocity of 1.0 km s/sup -1/. The adopted solar cobalt abundance is the mean value log +12 = 4.92 +- 0.08 ( +- 19%), from the 19 cobalt transitions. This value is in excellent agreement with the solar values of Ross and Aller, of Biemont, and of Holweger and that ofmore » Cameron for carbonaceous chondrites. « less
Ropelength Criticality

The ropelength problem asks for the minimum-length configuration of a knotted tube embedded with fixed diameter. The core curve of such a tube is called a tight knot, and its length is a knot invariant measuring complexity. In terms of the core curve, the thickness constraint has two parts: an upper bound on curvature and a self-contact condition. We give a set of necessary and sufficient conditions for criticality with respect to this constraint, based on a version of the Kuhn–Tucker theorem that we established in previous work. The key technical difficulty is to compute the derivative of thickness under a smooth perturbation. This is accomplished by writing thickness as the minimum of a C1-compact family of smooth functions in order to apply a theorem of Clarke. We give a number of applications, including a classification of critical curves with no self-contacts (constrained by curvature alone), a characterization of helical segments in tight links, and an explicit but surprisingly complicated description of tight clasps.
Robust adaptive sliding-mode fault-tolerant control with L 2 -gain performance for flexible spacecraft using redundant reaction wheels

A robust adaptive fault-tolerant control approach for attitude tracking of flexible spacecraft is proposed for use in situations when there are reaction wheels/actuator failures, external disturbances and time-varying inertia-parameter uncertainties. More specifically, a robust controller based on sliding-mode control scheme is first designed to ensure that the equilibrium point in the closed-loop system is uniform ultimate bounded stability, incorporating constraints on actuator failures, whose failure time instants, patterns and values are unknown, as motivated from a practical spacecraft control application. Then, this controller is redesigned such that an assumption on a bound required of the unknown and time-varying inertia matrix is released by using online estimation for this bound. The prescribed robust performance is also evaluated by L2-gain less than a given small level for the penalty output signal. Complete stability and performance analysis are presented, and illustrative simulation results of an application to flexible spacecraft show that the high precise attitudes control and vibration suppression are successfully achieved using various scenarios of control effect failures.
Propulsion of targets with different confinement geometries in water by Nd: YAG laser at 1064nm

Laser propulsion in air or vacuum has been developed as a thruster technology for the attitude control of micro class satellites. Laser propulsion in water can be used as a technology for propelling underwater platform or controlling microfluid device. Laser propulsion effects in water are much better in air due to the force from laser-induced bubble in water. The target geometries will influence the propulsion effects in air. In order to investigate the influence of target geometries on laser propulsion in water, targets with/without conical cavity and hemispherical cavity are designed in this paper. The momentum IT gained by targets and the momentum coupling coefficient Cm are investigated experimentally by high-speed photography method. It shows that the propulsion effects are better if there is a cavity on the laser irradiated surface of the target, and a hemispherical cavity works better than a conical cavity. In addition, IT increases with the laser energy, but the increasing trend slows gradually, and Cm increases with the laser energy first, and then levels off for all four targets. These results are both due to the laser plasma shielding. In conclusion, we need design suitable target geometries and use optimal laser energy to get the best propulsion effect for controlling microfluid device or micro class satellites.
Numerical approximations of the Cahn-Hilliard and Allen-Cahn Equations with general nonlinear potential using the Invariant Energy Quadratization approach

In this paper, we carry out stability and error analyses for two first-order, semi-discrete time stepping schemes, which are based on the newly developed Invariant Energy Quadratization approach, for solving the well-known Cahn-Hilliard and Allen-Cahn equations with general nonlinear bulk potentials. Some reasonable sufficient conditions about boundedness and continuity of the nonlinear functional are given in order to obtain optimal error estimates. These conditions are naturally satisfied by two commonly used nonlinear potentials including the double-well potential and regularized logarithmic Flory-Huggins potential. The well-posedness, unconditional energy stabilities and optimal error estimates of the numerical schemes are proved rigorously.