Miracle-dz/UniFAR-FTUs huggingface dataset:
Constrained Attitude Control of Uncertain Spacecraft With Appointed-Time Control Performance

This article studies the appointed-time attitude tracking control of the spacecraft on the special orthogonal group, with the attitude forbidden zone, the parameter uncertainties, and the external disturbances. A novel projection function is proposed, such that the normalized boresight vector of the sensitive instrument is mapped to a reduced dimensional vector in the Euclidean space. If the reduced dimensional vector is uniformly bounded, the constraint on the attitude forbidden zone will be satisfied at all the time. By virtue of the designed reduced dimensional vector and the associated auxiliary vectors, a set of vector-based error functions, the appointed-time performance constraints, and the according switching law is carefully constructed. The proposed vector-based adaptive control scheme ensures that the spacecraft attitude can satisfy the attitude constraint and appointed-time control performance simultaneously, in the presence of parameter uncertainties and external disturbances. Simulation results show the effectiveness of the designed control scheme.
ECG Arrhythmia Classification for Comparing Pre-Trained Deep Learning Models

In order to perform the diverse tasks of deep learning, different models of neural networks have been proposed, and for several years now many deep neural networks have been developed for a variety of uses. In general, the labor of training these networks is the most challenging part of this methodology, and is by far the most time consuming, both in terms of effort required to configure the process and computational complexity required to perform it. To relieve this process, pre-trained models have been made available that already learned to extract powerful and informative features from big sets of images, thus reducing training time for particular problems, but, given the large number of options, it would consume a lot of time testing each of them. In this study, we tested the performance of nineteen pre-trained networks that are available for MATLAB and compared their performance for classification of ECG signals from MIT-BIH arrhythmia dataset. We report the main characteristics of these networks, training times, and performance for this classification job to serve as reference for their use by other researchers.
Visual Coherence for Cross-Virtuality Analytics

The novel research field of Cross-Virtuality Analytics, where users move or interconnect stages of the Reality-Virtuality continuum to analyse data, can profit in multiple ways from the well researched area of visual coherence. In this position paper, we present initial ideas how visual coherence approaches and the data gathered by them can be used to improve Cross-Virtuality in general, what novel transition techniques could be developed and how the visual analysis process can profit from these approaches.
Eye Fundus Image Processing Using Fuzzy Logic

In this work, an improvement is made to eye fundus images to highlight the characteristics associated either with diabetic retinopathy and glaucoma pathologies, considering observations collected from specialized medical personnel, which are intended to facilitate diagnosis. Migration from the RGB (Red-Green-Blue) color space to the CIE L*a*b* space, defined by the Commission International d’Eclairage (CIE), is proposed for image processing. Given this migration, the intensity levels of pixel illumination are analyzed and modified by employing two fuzzy inference systems (FIS) to improve the image’s contrast and highlight the details associated with these pathologies. The results section presents the relevant data obtained in this proposal. With them, it is demonstrated that applying fuzzy logic in processing eye fundus images is a viable option to support medical personnel in elaborating a diagnosis, considering that fuzzy schemes had been scarcely used for this type of image.
DDoS Attacks on Smart Manufacturing Systems: A Cross-Domain Taxonomy and Attack Vectors

Denial of Service is a significant availability threat in Industrial Cyber-Physical systems and smart manufacturing is not an exception. The types, methods, and duration of these attacks have been evolving rapidly and their number has increased dramatically, reaching a new record in history. In particular, digitisation of the manufacturing process and increased connectivity have created a battleground between product quality of service and threats associated with cross-domains and multi-vector attacks that affect the manufacturing system performance. The existing research on cyber-threats related to smart manufacturing system does not consider the comprehensive landscape of denial of service attacks. In this study, we classify well-accepted (distributed) denial of service attacks according to a proposed taxonomy, focusing on both the multi-vector attacks and cross-domain attacks. Utilising the taxonomy, more than fifty different denial of service attacks on smart manufacturing system were classified in terms of Endpoint and Network (distributed) denial of service attacks. As an example, a Cyber-Physical Conveyor System was used to examine the proposed taxonomy.
Multi-Scope Feature Extraction for Point Cloud Completion

Point cloud completion aims to predict a complete geometric shape based on a partial point cloud. Recent methods often adopt an encoder-decoder framework, where the encoder extracts global features from the partial points and the decoder utilizes a folding-based model to reform multiple 2D grids to 3D surfaces. To effectively explore local features in the partial points, we propose a multi-scope feature extraction method in the encoder, where multiple k-nearest neighbors are considered in the edge convolution. Furthermore, we integrate the original partial point cloud in the decoder to maintain the given geometric shape information. Finally, we refine those coarse points from the decoder by both the merging and sampling operations to output the final completed point cloud. Extensive experiments verify the effectiveness of the proposed approach where both the multi-scope feature extraction and the integration of partial point cloud improve the performance. Overall, our method achieves better performance than the existing methods in both the Earth Mover's Distance (EMD) and the F-score.
Chemical Bath Deposition of Cu2O Thin Films on FTO Substrates: Effect of Sequential Deposition

Sequential deposition of Cu2O on FTO substrates was performed by chemical bath deposition to increase the thickness of the films. The variation of structural, optical, morphological, and electrochemical properties was studied as the number of deposits increased. The increase of thickness promotes an increase in the crystallite size and the lattice constant a, as well as a shift in the region of the absorption of light towards lower energies, reducing the band gap and the transmittance slightly. Also, it was estimated a change in the preferential orientation from the plane (111) to (200), caused a decrease in the structural disorder (Urbach energy) as a consequence of reducing the conductivity. On the other hand, during the electrochemical characterization a p-type behavior was observed in all the films, and as the thickness increased a shift towards more positive flat band potential values and a decrease in the carrier concentration was observed, which is the result of the decrease in structural disorder. The photocurrent measurements showed that the best performances were by the thinnest and the thickest films because some are the most conductive and the others absorb more light.
Design and Real-Time Validation of Higher Order Sliding Mode Observer-Based Integral Sliding Mode MPPT Control for a DC Microgrid

In a photovoltaic (PV) system-based microgrid, maximum power point tracking (MPPT) control plays a crucial role to improve the efficiency and stability. Since the past few years, one of the key control schemes to enhance the effectiveness of the microgrid is the observer-based MPPT control. This article proposes a higher order sliding mode observer (HOSMO)-based integral sliding mode control (ISMC) for MPPT control to ensure an efficient operation of a closed-loop dc microgrid. The proposed MPPT control is mainly focused on obtaining a chatter-free output voltage and stabilized output power from the PV-system-based microgrid and further ensure insensitivity to uncertainties and reduction in steady-state error. ISMC is applied to carry out finite-time stabilization throughout the entire response of the system. To justify the efficacy of the proposed approach, various test scenarios are simulated in real-time, and the performance is investigated through extensive comparative results. The MATLAB simulations and real-time simulation results achieved with OPAL-RT are compared. The superior performance of the proposed approach is observed in terms of high efficiency, good accuracy, and robust performance under varying meteorological conditions.
Effect of the oxidation degree on the bandgap of graphene oxides by Tour method

In this work graphene oxides (GOs) were synthesized by the tour method. The oxidation degree of GOs was varied using different dosages of KMnO4 during the synthesis (0.5, 3, and 6 g). By X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS), we quantitatively determined the oxidation degree of the materials (4.1, 49.5, and 53.9%). In addition, bandgaps ranging from 0.8 to 2.18 eV were calculated, thus verifying the dependence of the bandgap on the oxidation degree. With this work, we demonstrate that by the tour method it is possible to synthesize and control the oxidation degree in the GOs, in such a way that we can change the nature of the material from semimetallic to semiconducting with the modification of its bandgap. The study of our GOs was complemented with FT-IR spectroscopy and Raman spectroscopy.
Diagnosis of Lung Cancer Based on CT Scans Using Convolutional Neural Networks

Lung cancer is one of the deadliest cancers and has the highest mortality rate among all tumors. Early detection and treatment are critical to the cure of lung cancer. This paper uses the deep learning model to make the diagnosis based on Computed Tomography images. Specifically, the VGG16 is trained to classify lung Computed Tomography into benign, normal, and malignant. The dataset was collected in Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases. This model gives high accuracy up to 87.93%. Besides, this paper evaluates the robustness of this model by the adversarial attack method. Moreover, we visualize the output features for some layers in both the origin and attacked models, analyzing how the attacks affect the model.
Series-Parallel Active Damping to Improve the Robustness of Inverter in Weak Grid

Since weak grid introduces non-negligible inductive line impedance, the parameters of grid connected inverter will change, which may lead to system collapse in serious cases. This paper proposes to add series-parallel active damping at Point of Common Coupling (PCC) that equivalents resistance characteristics in specific frequency band to suppress harmonic components. Compared with independent parallel active damping, series-parallel active damping introduces extra positive real part in correcting the equivalent output impedance of inverter. It can completely decouple the grid connected inverter and grid impedance within the control bandwidth. This method ensures the high modularity and passivity of output impedance and suppresses harmonics in a wide range. Finally, the effectiveness of the proposed strategy is verified by simulation and experimental results.
Discovering a Class of Workflow Nets with Reduced Exceeding Language

This paper deals with the automated discovery of workflow nets (WFN) from logs of event traces that represent the observed behaviour of a discrete event process. The aim is to obtain a WFN N that represents closely the language of the log λ, whose surplus language of the model ℒ(N)\λ is low. The approach pursues to build a subclass of block-structured WFN composed of block subnets with cycles discovered from subsets of λ. This paper focuses on the discovery of blocks from subsets of traces that have the same event alphabet. In the proposed technique, a block is formed by two subnets using the same event labels: a) N<</inf>, a WFN state-machine derived from the causal relations between events, and b) NC, a WFN marked-graph that allows firing once all the different transitions. Then, N is obtained by the synchronous composition of N<</inf> and NC (N<||NC). The algorithms derived from the method, which have polynomial time complexity, build highly precise models.
A Lightweight FPGA-based IDS-ECU Architecture for Automotive CAN

Recent years have seen an exponential rise in complex software-driven functionality in vehicles, leading to a rising number of electronic control units (ECUs), network capabilities, and interfaces. These expanded capabilities also bring-in new planes of vulnerabilities making intrusion detection and management a critical capability; however, this can often result in more ECUs and network elements due to the high computational overheads. In this paper, we present a consolidated ECU architecture incorporating an Intrusion Detection System (IDS) for Automotive Controller Area Network (CAN) along with traditional ECU functionality on an off-the-shelf hybrid FPGA device, with near-zero overhead for the ECU functionality. We propose two quantised multi-layer perceptrons (QMLP's) as isolated IDSs for detecting a range of attack vectors including Denial-of-Service, Fuzzing and Spoofing, which are accelerated using off-the-shelf deep-learning processing unit (DPU) IP block from Xilinx, operating fully transparently to the software on the ECU. The proposed models achieve the state-of-the-art classification accuracy for all the attacks, while we observed a 15x reduction in power consumption when compared against the GPU-based implementation of the same models quantised using Nvidia libraries. We also achieved a 2.3x speed up in per-message processing latency (at 0.24 ms from the arrival of a CAN message) to meet the strict end-to-end latency on critical CAN nodes and a 2.6x reduction in power consumption for inference when compared to the state-of-the-art IDS models on embedded IDS and loosely coupled IDS accelerators (GPUs) discussed in the literature.
Goofspiel — the game of pure strategy

The game of pure strategy, sometimes called Goofspiel or Gops (see [2] and [3]), is played by two players, using a normal deck of cards, as follows. The 13 clubs are first taken out of the deck and of the remaining 39 cards the 13 hearts are given to Player I, the 13 diamonds to Player II, and the 13 spades are placed face down in the center. The spades are shuffled and one is turned face up. At this point, the two players choose one of their cards and then simultaneously discard it. The one who discards the higher card (ace being low, king high) wins from the other an amount equal to the value of the upturned spade (ace = 1, king = 13). If both players discard the same card, then neither wins. The three cards are then thrown away, a new spade upturned and the game continues. After 13 plays, there are no remaining cards and the game ends.
A Betweenness Centrality Based Clustering in VANETs

A vehicular communication system in smart cities is critical. For vehicular ad hoc networks (VANETs), scalability and routing reliability are important challenges. Clustering can help to overcome these challenges. The network may seem smaller and more stable with a clustered architecture; however, because of the rapid mobility of the vehicles, the clustering can be unstable. The network's stability depends upon the suitable cluster head (CH) selection and maintaining a CH over a prolonged period is not easy. A new Betweenness Centrality Based Clustering approach (BCBC) is introduced which aims to increase the clustering stability by using the betweenness centrality method. Using this technique, the most significant vehicle in a cluster is chosen to serve as the CH. The results show the good performance of our proposed in terms of CH lifetime and cluster member lifetime at different transmission ranges, as well as its superiority over the most used techniques in the literature.
A Head Motion Recognition Approach for Alertness Detection

Alertness detection is an important component of systems used to detect driver fatigue in order to prevent road accidents. Various approaches have been proposed in literature, including the use of sensors that track physiological markers and camera based systems that rely on computer vision techniques to track facial markers. In this paper, we adopt liveness detection to determine alertness. Liveness detection is a method used in biometrics to determine if a face, or fingerprint, is from a live person. The results show that the approach is robust to different lighting conditions.
Combined Nonlinear Control System for Non-Affine Multi-Loop Plant with Control and State Delays

The paper considers the synthesis of a combined nonlinear controller of the control multiply-connected system for an input-non-affine plant with delay at the input signal and state. Dynamics of the plant changes in stages. The plant under consideration operates in conditions of a parametric, functional and structural uncertainties, in the presence of a permanent external noise and measurement only the controlled variable (output signal). The structure of a multiply connected control system includes an implicit reference model, predictor-compensator and correction filters. The authors use the hyperstability criterion and method for constructing L-dissipative dynamic systems to synthesize the nonlinear algorithms for combined control system. The structural synthesis is performed by defining the special estimates which guarantee the validity of the V.M. Popov's integral inequality. Operability of the developed hyperstable control system with structural perturbation is ensured with the help of the special L-dissipativity conditions. At the final section of the article with the help of simulation the authors show the quality of the obtained nonlinear control system. The results of the system simulation show the fulfillment of the control goal with a sufficiently high quality. The obtained results may be useful for construction the control systems for technical plants which dynamics can be described as non-affine MIMO-system. For example, the robot-manipulators and some plants of heat power engineering has the mathematical description like multi-connected systems.
3DBrushVR: From Virtual Reality Primitives to Complex Manifold Objects

SurfaceBrush and Brush2Model are two systems which enable users to create 3D objects intuitively using a hand-held controller in virtual reality (VR). These state-of-the-art methods either start modeling from dense collections of stroke ribbons drawn by professional artists, or from the most basic point skeletons, line skeletons, and polygon skeletons. Thus, it is very challenging for novices and amateurs to design complex models efficiently. We propose 3D-BrushVR, a novel VR modeling tool that uses volume skeleton-based convolution surfaces. It enables the user to draw with arbitrarily shaped brushes and generate 3D manifold objects by fusing the brushed primitives. Unlike existing VR drawing and modeling tools, our approach can directly take some common but complex objects as primitives, and assemble them using implicit surfaces, thus providing a more flexible and powerful modeling ability. To achieve real-time performance, we introduce a new GPU-based method to calculate the volume fields of the resulting convolution surfaces. We also introduce several specially designed time-varying shaders to render the designed model for a better and more appealing modeling experience. We demonstrate the usability and modeling ability of our 3DBrushVR interface by comparing it with the state-of-the-art methods in an observational study. Experimental results further validate the effectiveness and flexibility of our approach.
How Elderly Learners Participate in MOOC? An Empirical Examination of Instructional Design and Practice Based on MOOC

With the continuous in-depth study of the elderly in the international community, the development potential of the elderly is gradually being discovered by us. At the same time, with the continuous development of Chinese society, in recent years, researchers have shown an increased interest in the issue of the allocation of education resources for the elderly. In this essay, we attempt to build a relevant model by analyzing the characteristics of distance learning in the MOOC for the elderly on the basis of existing research. On this basis, taking the “Digital Photography Fundamentals” MOOC distance learning for the elderly as an example, using the questionnaire method, combined with the relevant data of the MOOC platform to discuss the influence of this model on the participation, satisfaction, goal achievement and learning experience of the elderly in course learning. After that, the interview method was used to collect relevant opinions and suggestions of the elderly on the course model and provide support for the collected questionnaire and platform data. Finally, sort out the research results, and put forward corresponding strategies for the design and practice of the MOOC distance learning model for the elderly from the three levels of teachers, elderly learners, and course.
Parameter analysis and introduction of new features based on LSTM stock price prediction model

This paper constructs a stock price prediction model based on LSTM neural network, introduces two parameters: memory days and prediction days, and analyzes the model's specific performance in the parameter space. Based on the MAPE evaluation index, it is found that the fitting effect deteriorates with the increase of memory days and prediction days and optimizes with the increase of the number of neurons. In addition, this paper introduces the commonly used indicators in stock market analysis, expands the original model's features, and compares the impact of training and analyzing the new features on the model. All features are screened and analyzed based on the random forest model according to their importance. Finally, the model is used to predict and analyze the same class of stocks.
A Conceptual Replication and Extension of Triangulation by Walking for Measuring Perceived Distance Through a Wall

Triangulation by walking is a method that has been used to measure perceived distance, where observers walk a triangular path. This method has been used at action space distances of approximately 1.5 to 30 meters. In this work, a conceptual replication of these triangulation by walking methods are discussed and evaluated for use in measuring the perceived distance of an object seen through a window set into a wall. The motivation for this work is to use triangulation by walking to study how perceived distance operates when augmented reality (AR) is used to visualize objects located behind opaque surfaces, in an AR application termed “x-ray vision. ” This paper reports on experiences replicating an implementation of triangulation by walking as reported by Fukusima, Da Silva, and Loomis (1997). Their method was conceptually replicated in both outdoor and indoor settings, and the method was further extended to measure perceived distances of objects seen through a wall. These extensions are discussed in some detail, focusing on the modifications to the triangulation by walking method as well as the ramifications of these changes. Problems arising from using triangular geometry in calculations of perceived target locations are also introduced, and an alternate method is proposed that works to diminish the problematic effects.