Miracle-dz/UniFAR-FTUs huggingface dataset:
Fault Detection Method of Luojia1-01 Satellite Attitude Control System Based on Supervised Local Linear Embedding

This paper is aimed at the telemetry data of Luojia1-01 satellite attitude control system, and carries out the research on high-dimensional data feature extraction and fault detection based on supervised local linear embedding (SLLE). Because the general linear feature extraction method can not mine the feature information of nonlinear high-dimensional telemetry data, a data feature extraction and fault detection method based on local linear embedding is designed. Combined with the statistics SPE and $T^{2}$ , the low-dimensional feature information is obtained for data statistics and monitoring faults. Due to the local linear embedded manifold learning method for the traditional batch processing mode is difficult to update and improve the database online, and the supervised local linear embedding method is introduced. The online sample feature extraction and fault detection schemes are designed, and the database is updated by updating the weight matrix through online samples. Finally, the effectiveness of the method is verified by Luojia1-01 satellite telemetry data. The results show that the fault detection method based on SLLE reduces the false alarm rate (FAR) by approximately 3% and the missing alarm rate (MAR) by approximately 10% compared with local linear embedding (LLE). This method effectively improves the detection capability of the anomalous state of Luojia1-01 satellite and has certain engineering application value.
Wideband Four-Way Filtering Power Divider Using Simple Three-Line Coupled Structures

Two quarter-wavelength three-line coupled structures (TLCSs) are employed in the design of filtering power divider (FPD) to achieve a wide fractional bandwidth and good out-of-band rejection. The center frequency of the FPD passband can be adjusted flexibly by changing the length of the TLCS while the fractional bandwidth will remain unchanged. The frequency selectivity and passband bandwidth of the FPD can be easily controlled with the variations of the TLCS width and gap, respectively. Furthermore, the proposed FPD topology can be extended to design a four-way FPD through adding two conventional Wilkinson power dividers. This four-way FPD is fabricated with the center frequencies of 2.42 GHz and 3-dB fractional bandwidths of over 100%. The simulated and measured results are compared with good agreement.
Beyond Clippy's Counsel: Word Processor Feature Underuse among the Digital Generation

The current article provides a new perspective on appropriation by investigating what causes features of popular applications, rather than the applications as whole, to not be used by young, technologically competent audiences. Determinants of underuse are conceptualized from appropriation literature. In line with the active user paradox, we propose that feature underuse stems from conscious choices as well as knowledge and experience gaps users are not aware of. Support for determinants’ influences was sought by observing and interviewing 30 students as they performed tasks in Microsoft Word, an evolving application with a burgeoning feature set that many members of its target audience are often obliged to use. Features’ convenience, complexity, and performance predicted underuse, though it was also related to conscious processes such as users’ perceptions of ease of use and usefulness. Conclusions are drawn on the granularity of appropriation, and that the active user paradox can also explain why individual features of a larger program are underused.
Time Domain Analysis of Ultra-long Cable over Lossy Ground under the High-power Electromagnetic Environment

The time response analysis of the ultra-long cable over lossy half ground under the high-power electromagnetic environment is discussed. Because of the actual cable is relatively long and the computing resources are limited, the simulation calculation of ultra-long cable is usually difficult. In this paper, the induced current of ultra-long cable over lossy ground under the high-power electromagnetic environment is calculated by using time-domain integral equation (TDIE) method combined with one-dimensional periodic structure theory. The variation of the induced current on the ultra-long cable under different conditions are analyzed.
CLAP: Compact Labeling Scheme for Attribute-Based IoT Policy control

In order to create services using IoT devices, the underlying network infrastructure must support large number of such devices with different underlying protocols, and diverse requirements from the service applications (privacy, reliability and QoS guarantee, etc.). Many of these requirements can be realized by implementing an in-network packet forwarding policy in the infrastructure supporting direct device-to-device communications. However, with large number of devices deployed in the IoT network, the number of rules required for policy enforcement grows very rapidly, and it becomes an infrastructural challenge to installing and managing the rules in switches/routers. We argue that attaching service and role-based labels to address IoT devices can significantly reduce the number of rules by using wild-cards. We formulate a scheme that can produce the optimum length labels for representing the service attributes of the communicating IoT devices. Due to non-convex nature of the optimization, we develop two heuristic solutions for the label generating scheme. Through evaluation using a simulated but practical IoT network environment with large number of devices, we demonstrate the benefits of the scheme that can reduce the number of rules by several orders of multitude.
Annotating 3D Models and Their Parts via Deep Feature Embedding

Need to organize 3D shape data has prompted studies on comparison and retrieval of 3D shape models. Being able to query 3D shape models by words, in addition to 3D model examples and 2D sketches, would be quite beneficial. This paper proposes a method to associate whole 3D models (e.g., automobile) as well as their parts (e.g., tire, body, engine) with word labels so that the 3D model can be queried by words. The associations between 3D shapes and words are learned from a dataset of 3D models whose whole model and segmented parts are labeled with words. Feature vectors of these words (distributed representation) and feature vectors of whole and partial geometries of 3D models are embedded, by Word Shape embedding Network (WSN) into a common feature embedding space. As the word feature vectors are learned by Word2Vec trained on Wikipedia corpus, the common embedding space can be queried by a wide variety of words that are not included in the labeled 3D model dataset. Experimental evaluation has shown that, with the proposed algorithm, 3D shape can be queried by labels of either whole or part shape, or labels that are semantically close but not included in the original 3D model dataset.
Insider Threat Detection using an Artificial Immune system Algorithm

Insider threats result from legitimate users abusing their privileges, causing tremendous damage or losses. Malicious insiders can be the main threats to an organization. This paper presents an anomaly detection system for detecting insider threat activities in an organization using an ensemble that consists of negative selection algorithms (NSA). The proposed system classifies a selected user activity into either of two classes: "normal" or "malicious. " The effectiveness of our proposed detection system is evaluated using case studies from the computer emergency response team (CERT) synthetic insider threat dataset. Our results show that the proposed method is very effective in detecting insider threats.
Stability Comparison of Paralleled String Inverters and Central Inverters in PV System

Mega-scale solar projects are turning to string inverters nowadays. Multi-parallel string inverters are employed as the interface to the utility grid, giving rise to potential unstable problems. Aiming at this issue, the relationship between number of inverters, output power and grid impedance is analyzed. Based on this, according to different topology, control structures and filter parameters, four types of Standard Equivalent Model (SEM) for multi-inverters system are established. By analyzing the stability margin of the four SEMs and the sensitivity of their control parameters, the difference of stability between the string inverters and the central inverters is compared. Finally, the established SEM and the comparison results of the string and central inverters are verified by simulation and experimental results.
Spin-Dependent Tunneling in Semiconductor Structures Without an Inversion Center

The scientific article is devoted to a theoretical study of the tunneling of electrons through semiconductor barriers. It is shown that the tunneling process itself in such systems is spin-dependent and the transparency of the structures depends on the orientation of the electron spins even for symmetric barriers in the absence of an external magnetic field. The microscopic nature of this effect consists in the spin-orbit splitting of electronic states in the barrier, which is cubic in the wave vector. The actuality of the article is considering the theory of spin-dependent tunneling for single barriers and for resonant two-barrier structures. It is shown that the dependence of the tunnel transparency of the structures on the mutual orientation of the spin and the electron wave vector can be used for spin injection and for the electrical detection of the spin state of electrons. The influence of the spin-orbit interaction on the tunneling passage of charge carriers through non-centrosymmetric semiconductor barriers can manifest itself during the injection of free charge carriers from ferromagnets into a semiconductor through a potential barrier. The scientific novelty lies in the fact that, in the study of tunneling magnetoresistance in the structure of Fe/GaAs/Au, an anisotropic dependence of resistance on the magnetization of the Fe layer in the interface plane was found. Model calculation of the injection of electrons from Fe to Au through a semiconductor barrier GaAs taking into account the effect, enabled to explain the anisotropy observed experimentally.
Logarithmic Quantization based Symbolic Abstractions for Nonlinear Control Systems

This paper studies symbolic abstractions for nonlinear control systems using logarithmic quantization. With a logarithmic quantizer, we approximate the state and input sets, and then construct a novel discrete abstraction for nonlinear control systems. A feedback refinement relation between the constructed discrete abstraction and the original system is established. Using the constructed discrete abstraction, the safety controller synthesis problem is studied. With the discrete abstraction and the abstract specification, the existence of a safety controller is investigated, and the algorithm is proposed to compute the abstract controller. Finally, a numerical example is given to illustrate the obtained results.
Crane load position control using Lyapunov-based pendulum damping and nonlinear MPC position control

A crane control system is presented where the crane load can track a position trajectory with ultimately bounded pendulum motions of the load. This is done with an inner loop using a Lyapunov-based damping controller which is designed to damp out the pendulum motions of the load, and an outer loop where a nonlinear MPC (model predictive controller) controls the position of the load. The Lyapunov-based damping controller is designed to be exponentially stable, which means that the pendulum motion will be ultimately bounded in the presence of a bounded perturbation. Then this is achieved by constraining the control action of the nonlinear MPC so that the perturbations to the Lyapunov-based damping controller are sufficiently bounded. The suspension point of the load, which is the tip of the crane, can then be controlled to track a time-varying desired trajectory in the plane with ultimately bounded pendulum motions. The control system is validated in simulations in a simulation study.
Analysis of Waveform Parameters for Multi-segments of Wire Grids of Bounded Wave Simulator

In this paper, the different combinations of the numbers of wire grids in a bounded wave simulator are discussed. The wire grids are divided into five sections. These sections are provided with the same projection lengths and different numbers of wire grids. By comparing the simulated results of the all-plate model and the model of bounded wave simulator with 26 wires, the proposed model of bounded wave simulator with 26, 26, 14, 26, 18 wires for five sections, it is shown that the results of the proposed model are in more agreement to the all-plate model .
Design and Implementation of Robust Generalized Dynamic Inversion Control for Stabilizing Rotary Inverted Pendulum

This paper will illustrate the application of constraint based Robust Generalized Dynamic Inversion (RGDI) control for balancing the Rotary Inverted Pendulum system. The proposed control law is established by hybridizing the equivalent control (conventional GDI) and the robust control. The equivalent control is constructed by formulating the dynamical constraints, based on the attitude deviation functions of the rotary arm and the vertical pendulum. The constraint dynamics are then inverted using dynamically scaled Moore-Penrose Generalized Inverse to solve for the equivalent control. For the robust term, sliding mode control is utilized, that will provide robustness against parametric uncertainties, system nonlinearities, such that semi-global practically stable angular position tracking is guaranteed. The controller's capability is verified through computer simulation and experimental studies on the Quanser's SRV02 Rotary Inverted Pendulum system.
Novel Ultra-wideband Wide-angle Scanning Phased Array

In this paper, a novel ultra-wideband phased array antenna element is designed by improving the traditional wide-angle scanning array of Planar Ultra-wideband Modular Antenna (PUMA). By using the asymmetric dipole arms and a new structure to eliminate the common-mode resonance, the bandwidth of the antenna is broadened and the scanning angle of the antenna is greatly improved. Simulation results show that the proposed antenna can scan up to ±60° in the E plane and ±45° in the H plane at a 5:1 bandwidth.
A velocity-thresholds power splitting optimization for hybrid electric vehicles

The various operating scenarios of hybrid electric vehicles (HEVs) require the design of suitable power splitting strategies for the hybrid drivetrain. A compromise between low complexity and high efficiency is always an issue for the power management implementation. In this paper the parallel HEV is assumed to operate under a power splitting strategy based on vehicle velocity and battery state of charge thresholds. The off-line minimization of a cost function related to the vehicle electrical and fuel consumptions determines the velocity-based quantization levels and the corresponding ratios of the power splitting between the actuators. The effectiveness of the proposed approach is verified through numerical simulations of the new European driving cycle and the worldwide harmonized light vehicle test procedure.
Quasi Z-Source based Full Bridge Isolated DC/DC Topology as A Basic Module for DC-Series PV Converter Connected to HVDC Grid

DC series-parallel photovoltaic (PV) plants have many advantages. As the most important equipment in the system, DC-Series DC/DC converter has some special technical requirements compared to other applications, especially, the wide input and output voltage regulation range requirement presents a unique challenge to the DC/DC converter design. The paper proposes a Quasi Z-Source based Full Bridge Isolated DC/DC Converter (QZS-FBIC) as a basic module, which cascaded through input parallel and output series (IPOS) to improve power capacity and output voltage adjustment range, so as to meet with the needs of the DC series-parallel PV generation system. The proposed converter can operate both in boost and buck mode. Besides some normal operating modules, several redundant modules are added, and all of these modules putting in and cutting out control to enlarge the range of the output voltage of the converter. A 20kV/500kW DC/DC converter model and 60kV/l.5MW system model are built in Matlab / Simulink to test the proposed topology and control strategies.
On The Equivalence between Hybrid and Full Digital Beamforming in mmWave communications

Recently, Millimeter Wave (mmWave) systems have emerged as a potential solution for the spectrum scarcity problem experienced by current wireless technologies. However, practically implementing such systems is challenging as they suffer from high hardware complexity and power consumption. Analog beamforming is considered as one suitable approach for practical implementation of such systems, relaxing the hardware and power consumption requirements compared to full digital beamforming solution. Moreover, hybrid beamforming solutions emerged as an attractive solution that can capture the trade-off between digital and analog ones. In this paper we mathematically show that in pure Line of Sight (LoS) channels, using hybrid beamforming with Zero Forcing (ZF) at the baseband can achieve equivalent Spectral Efficiency (SE) compared to the full digital ZF precoding, with lower hardware complexity, and lower power consumption. Moreover, we validate the equivalence in SE performance between hybrid ZF and digital ZF by simulation results.
On the evaluation of application level delays in public LoRaWAN networks

The Internet of Things paradigm aims to connect a very large number of smart objects located all around the world for increasing the knowledge about the environment. Low-power wireless communications are compulsory to reduce the smart object invasiveness and ensure reasonable duration of autonomous node. For this reason, several Low-Power Wide-Area Network (LPWAN) technologies have been proposed in the recent past, because of their large range and low consumption. Most notably, the LoRaWAN solution emerged as the most accepted LPWAN and several research works appeared, discussing its advantages and limitations. However, very few, if any, discuss timing performance in a real scenario, considering not only the wireless frontend but also the wired backend. In this work, the authors implement a real use case based on a public LoRaWAN infrastructure and propose metrics to formally evaluate the delays at the application level (i.e. considering the whole infrastructure as a black box). The result is an average delay in the range 400-700 ms, and the proof that the delay is directly dependent on both the LoRaWAN backend and the connection speed of information endpoints.
MD2.1 - High-speed Visible Light Communication Based on Machine Learning (Invited)

We summarize the latest research progresses applying machine learning in VLC systems. 2.7Gbit/s CAP64 VLC transmission based on a high-power 1-Watt phosphorescent white LED over 1.2m-free-space is demonstrated. As far as we know, this is the highest data rate using a high-power phosphorescent white LED.
Three-Phase Interleaved LLC Converter with Capacitive Current Balancing and Reduced Switch Voltage Stress

Interleaved converters are a typical solution to enhance a current capacity and reduce current ripples of input or output ports. Conventional interleaved LLC converters need additional switches and feedback control loop, or some passive components, for active current balancing, increasing the circuit complexity and cost. This paper proposes a novel three-phase interleaved LLC converter with an automatic capacitive current balancing capability. Two flying capacitors contribute to not only the automatic capacitive current balancing but also reduced switch voltage stresses and asymmetric resonant operations to reduce RMS currents. The operation analysis and capacitive current balancing capability are discussed. The experimental verification using a l-kW prototype demonstrated that the automatic capacitive current balancing could be achieved despite the significant mismatch in transformers' parameters.
An Online Rapid Mesh Segmentation Method Based on an Online Sequential Extreme Learning Machine

The existing mesh segmentation methods currently require long training times and have high computational complexity. Consequently, many of these methods cannot meet the rapid requirements of digital geometry processing in the Web environment. This paper proposes an online rapid mesh segmentation method based on an online sequential extreme learning machine (OS-ELM). In the training stage, the OS-ELM is trained by analyzing the mapping relationship between the shape descriptors of the mesh and the Gaussian curvature threshold. We reduce the dimensionality of the shape descriptor vector via principal component analysis (PCA) and extract the Gaussian curvature threshold of the mesh as the sample label using statistics. In the segmentation stage, the Gaussian curvature threshold is quickly extracted to realize the online rapid mesh segmentation via the OS-ELM. Simultaneously, the OS-ELM is updated to realize online incremental learning based on a small number of training samples. Our method is verified using the meshes provided from ShapeNetCore. The experimental results indicate that segmentation results similar to manual segmentation can be rapidly generated online using our method.
Designing a Low-Cost IoT Sensing Platform for VOC Material Classification

Improvements in small sized sensors allow us to easily detect the presence of Volatile Organic Compounds (VOCs) in the air using easy-to-deploy Internet of Things (IoT)-scale devices. However, classifying what VOC exists in the environment still remains as a complex task. Knowing what VOCs are in the air can help us remove the main cause that vents VOC materials in order to maintain clean air quality. In this work, we present VOCkit, an IoT sensor kit for non-chemical experts to easily detect and classify different types of VOCs. VOCkit combines miniature chemically-designed fluorometric sensors for recognizing VOCs with an embedded imaging system for classification. Exposing the fluorometric sensors with various VOCs, result in photophysical property change of each fluorescent compound, which composes the sensors, and the synergistic combination of the changes create unique individual fluorescent color patterns respectively to the VOC material. The fluorescent color change pattern is captured using a camera and the images are processed with machine learning algorithms on the embedded platform for VOC classification. Using 500 fluorometric sensor images collected for five different commonly contactable VOCs, we show the feasibility of performing VOC classification on small-sized IoT devices. For the VOC types of our interest, our results show a classification accuracy of 97%, implying the potential applicability of VOCkit for real-world usage.