Miracle-dz/UniFAR-FTUs huggingface dataset:
Scale-Aware Multispectral Fusion of RGB and NIR Images Based on Alternating Guidance

In low light condition, color (RGB) images captured by increasing the camera ISO contain much noise and detail loss. However, near infrared (NIR) images are robust to noise and have clear textures without color. In this paper, we propose scale-aware multispectral fusion of RGB and NIR images based on alternating guidance. Low light RGB images provide large-scale image structure and color information, while NIR images have fine details lost in RGB images. Since they are complementary, we adopt alternating guidance for the fusion of them using weighted least squares (WLS). First, we perform the first guidance to denoise the RGB image and obtain base layer. Then, we conduct the second guidance for scale-aware detail transfer of the NIR image and yield detail layer. Finally, we combine the base and detail layers to generate a fusion image. We maximize the multispectral advantage of RGB and NIR images for fusion based on alternating guidance. Experimental results show that the proposed method achieves good performance in noise reduction, detail transfer and color reproduction, and is superior to the state-of-the-art ones in terms of quantitative measurement and computational efficiency.
A Multi-label Classification Model for Type Recognition of Single-Phase-to-Ground Fault Based on KNN-Bayesian Method

In non-solidly earthed distribution networks, single-phase-to-ground fault (SPGF) greatly threats the safety of person and equipment. Though the existing fault line selection and location techniques have made great contributions in reducing the harm of SPGF, certain amount of power loss still exists in SPGF because of low efficiency in detecting and repairing faults of current techniques. Accurate type classification of SPGF could help reveal the fault nature from different views and further improve the fault repair efficiency. In this paper, a multi-label classification model for recognizing types of SPGF is proposed, In the proposed model, SPGF are classified respectively, and corresponding symptom features are determined according to the following five aspects: time-domain continuity, time-domain stability, volt-ampere characteristics of transition impedance, transition impedance size and fault point medium. In addition, a multi-label classification architecture for SPGF is constructed with an 8-dimension feature space and a 14-label fault type space. Finally, a KNN-Bayesian method is designed to solve the multi-label classification problem. The feasibility and advantages of the proposed model and methods are verified by the field data and the comparison with KNN method.
Bearing Fault Diagnosis Based on Extreme Machine Learning Optimized by Differential Evolution

In order to improve the accuracy of bearing fault diagnosis, a novel method based on multi-masking empirical mode decomposition (MMEMD) and extreme machine learning optimized by differential evolution algorithm (DE_ELM) is proposed. MMEMD is a new improved empirical mode decomposition (EMD), which can overcome the defect of mode mixing and improve the feature effectiveness. Differential evolution algorithm is used for determining the parameters of extreme machine learning (ELM) to improve the classification accuracy. In implementation of the proposed method, firstly, bearing signals are decomposed into different intrinsic mode functions (IMF) and sample entropy of each IMF is calculated as the fault feature. Then, the training set is input to the DE_ELM and the fault classification model is obtained. Finally, the testing set is input to the model for fault diagnosis. The proposed method examined by the bearing fault diagnosis experiment. The results show that the method can reliably identify the different faults and has a high fault diagnosis accuracy.
Small Scale Multivariate Testing of Dynamic Wireless Charging

This project presents a testing methodology to complete multivariate analysis on dynamic wireless charging systems through an adjustable and semi-modular laboratory experimentation setup. The test setup proposed allows for variation of operational frequency, input power, coil alignments, operational airgap, receiver movement speed and coil geometries. The test apparatus facilitates easy adjustment of all system variables. Initial results obtained were utilised to evaluate the future applications for dynamic wireless charging. The test setup proved an excellent foundation for future studies and improvements to its design are suggested. The maximum efficiency obtained from the setup was 58%. The technology of dynamic wireless charging is feasible for niche applications, though it is not suitable for widespread adoption.
Research on SCESS-DFIG DC Bus Voltage Fluctuation Suppression Strategy for Frequency Inertia Regulation of Power Grid

The doubly fed induction generator with super capacitor is helpful to enhance the system inertia. At the same time, its structural characteristics are more suitable for the power grid topology without communication lines. However, the change of its topology also brings some problems. In this article, when SCESS-DFIG is participating in frequency regulation, DC bus voltage fluctuation caused by power imbalance of multiple converters is studied. The dynamic power response of super capacitor with DC-DC converter is a step load for grid side converter. The load of DC bus is nonlinear and random, so the balance and stability of the original system will change accordingly. Because of the complex topology and system composition of the doubly fed induction generator with super capacitor, the conventional feed-forward compensation control strategy can not be used to improve its dynamic response speed. Therefore, this article designs a control strategy which is applied for DC bus voltage fluctuation of the doubly fed induction generator with super capacitor. The theory proposed in this article is verified by simulation model and experiment.
Control for Multiplicative Noise Systems With Packet Losses and Measurement Delays

This paper mainly considers the control problems for discrete-time multiplicative noise systems with packet losses and measurement delays. The main contributions are two-fold. Firstly, based on Pontryagin’s maximum principle, the optimal output feedback controller is obtained. Secondly, a necessary and sufficient stabilization condition for multiplicative noise systems is derived in terms of the coupled algebraic Riccati equations. Moreover, it has been proved that the stabilization condition only depends on the eigenvalue of the system matrix and the probability of packet losses which is not related to measurement delays.
Octave-Spanning Dispersive Slot Waveguide Based Chip-Level Ultrashort Pulse Stretcher

A silicon nitride horizontal slot waveguide with silicon dioxide cladding is designed to achieve flat negative dispersion (< −600 ps/(<inline-formula> <tex-math notation="LaTeX">$\text {nm}\cdot \text {km}$ </tex-math></inline-formula>)) over an octave-spanning bandwidth (782 to 2100 nm). Simulations show that, when a 10-fs pulse with 1-W peak power is input into a 5-cm long waveguide, the pulse width becomes 448 times wider and its peak power is reduced to <inline-formula> <tex-math notation="LaTeX">$2.23\times 10^{-3}$ </tex-math></inline-formula> W. After passing through another ideal positive dispersion element, we find that the output pulse peak power can be recompressed close to 1 W in the case of different input pulse widths. Moreover, for the input pulse with a much higher peak power, the pulse width of the output pulse can be compressed to a much narrower level because of the spectral broadening introduced by optical nonlinearity. A 100-fs input pulse with 10-kW peak power can be compressed to 12.5 fs with a peak power of 63 kW. Considering the time-domain broadening and the peak power reduction, such a slot waveguide with a flat negative dispersion could be used as a time stretcher for 10-fs level pulse in on-chip chirped pulse amplification (CPA) system.
Modeling Photovoltaic Generation Uncertainties for Monte Carlo Method based Probabilistic Load Flow Analysis of Distribution Network

Distributed generation such as photovoltaic is more encouraged by government policies to exploit the renewable energy resource and for sustainable energy production. The integration of photovoltaic generation to the distribution network introduces large uncertainties, the inclusion of these uncertainties in load flow analysis used for planning, operation, and expansion of the power system networks is done by using the probabilistic load flow studies approach. With this, the risk identification and its mitigation associated with the power network can be easily performed. In this work, the modeling of photovoltaic generation based on historical data that is obtained by power system measurements is presented. The distribution of photovoltaic generation obtained is complex and does not match any standard distributions. A numerical method is used to generate the samples that are used for Monte Carlo simulation to include photovoltaic generation with its original characteristics and uncertainties in probabilistic analysis. The analysis is done by integrating a 1000 kW photovoltaic generation system in IEEE 13 Node Test Feeder, which is highly loaded and unbalanced. As a function of a photovoltaic generation uncertainty, statistical distribution of variables such as currents and voltage of each network elements are calculated. Using them, the network health and the power loss associated with the network are determined.
TASC: Efficient Task Assignment in Spatial Crowdsourcing with Workers Privacy Protection

Crowdsourcing has open not just opportunities but also options for people to work together without boundaries it is the future of collaboration. Task assignments are the main function of any crowdsourcing platform. Many recent studies focused on the workers privacy without taking into consideration the overall task matching utility score. In this paper, we adapt the batch matching method to maximizing the overall task assignment in spatial crowdsourcing while maintaining the crowd workers privacy using the DCentroid scheme. Then, theoretically evaluates the performance of the proposed task assignment scheme to show the effectiveness of this method. This paper introduces (TASC) a task assignment approach with worker location privacy protection in Spatial Crowdsourcing (SC). The research theoretically analyzes TASC framework and guarantees the crowd workers privacy while minimizing the task waiting time, worker travel distance, and system overhead.
Disturbance Rejection Control Using a Novel Velocity Fusion Estimation Method for Levitation Control Systems

Magnetic levitation has been applied to maglev trains and magnetic suspension wind tunnel. However, there are some problems with the existing levitation control. For example, it is difficult to extract smooth velocity signals from the gap sensor with noise. The classical differentiator is susceptible to noise, which makes the levitation system sometimes vibrate. The accuracy and stability of levitation control need to be improved. The velocity fusion estimation method (VFE) is proposed to extract the velocity signal from the gap and acceleration sensor, which is theoretically derived to prove that it reduced the noise of the velocity signal. Then disturbance rejection control(DRC) is proposed that add VFE into classical levitation control. Because of the high-quality velocity signal and accelerometer’s fast responsiveness, it makes DRC have many advantages. The advantages of using the proposed control structure are that it improved the control accuracy of the target gap and resisted disturbance effectively. Air-gap fluctuations in levitation systems can be reduced in transient pulse disturbance test, white noise disturbance test, and external force disturbance test when it applies the proposed control method. The effectiveness and advantages of the proposed control method are verified by the simulation and experiments.
Corticomuscular Co-Activation Based Hybrid Brain-Computer Interface for Motor Recovery Monitoring

The effect of corticomuscular coactivation based hybrid brain-computer interface (h-BCI) on post-stroke neurorehabilitation has not been explored yet. A major challenge in this area is to find an appropriate corticomuscular feature which can not only drive an h-BCI but also serve as a biomarker for motor recovery monitoring. Our previous study established the feasibility of a new method of measuring corticomuscular co-activation called correlation of band-limited power time-courses (CBPT) of EEG and EMG signals, outperforming the traditional EEG-EMG coherence in terms of accurately controlling a robotic hand exoskeleton device by the stroke patients. In this paper, we have evaluated the neurophysiological significance of CBPT for motor recovery monitoring by conducting a 5-week long longitudinal pilot trial on 4 chronic hemiparetic stroke patients. Results show that the CBPT variations correlated significantly ( ${p}$ -value< 0.05) with the dynamic changes in motor outcome measures during the therapy for all the patients. As the bandpower based biomarkers are popular in literature, a comparison with such biomarkers has also been made to cross-verify whether the changes in CBPT are indeed neurophysiological. Thus the study concludes that CBPT can serve as a biomarker for motor recovery monitoring while serving as a corticomuscular co-activation feature for h-BCI based neurorehabilitation. Despite an observed significant positive change between pre- and post-intervention motor outcomes, the question of the clinical effectiveness of CBPT is subject to further controlled trial on a larger cohort.
Deep Learning Architecture for Group Activity Recognition using Description of Local Motions*

Nowadays, the recognition of group activities is a significant problem, specially in video surveillance. It is increasingly important to have vision architectures that automatically allow timely recognition of group activities and predictions about them in order to make decisions. This paper proposes a computer vision architecture able to learn and recognise group activities using the movements of it in the scene. It is based on the Activity Description Vector (ADV), a descriptor able to represent the trajectory information of an image sequence as a collection of the local movements that occur in specific regions of the scene. The proposal evolves this descriptor towards the generation of images able to be the input queue of a two-stream convolutional neural network capable of robustly classifying group activities. Hence, this proposal, besides the use of trajectory analysis that allows a simple high level understanding of complex groups activities, takes advantage of the deep learning characteristics providing a robust architecture for multi-class recognition. The architecture has been evaluated and compared to other approaches using BEHAVE and INRIA dataset sequences obtaining great performance in the recognition of group activities.
Graph based Cooperative Localization for Connected and Semi-Autonomous Vehicles

Cooperative Real-time Localization is expected to play a crucial role in various applications in the field of Connected and Semi-Autonomous vehicles (CAVs), such as collision avoidance/warning, cooperative adaptive cruise control, etc. Future 5G wireless systems are expected to enable cost-effective Vehicle-to-Everything (V2X) systems, allowing CAVs to share the measured data with other entities of the network. Typical measurement models usually deployed for this problem, are absolute position from Global Positioning System (GPS), relative distance to neighboring vehicles and relative angle or azimuth angle, extracted from Light Detection and Ranging (LIDAR) or Radio Detection and Ranging (RADAR) sensors. In this paper, we provide a cooperative localization approach that performs multi modal-fusion between the interconnected vehicles, by representing a fleet of connected cars as an undirected graph, encoding each vehicle position relative to its neighboring vehicles. This method is based on the so called Laplacian Processing, a Graph Signal Processing tool, that allows to capture intrinsic geometry of the undirected graph of vehicles rather than their absolute position on global coordinate system, significantly outperforming current state of the art approaches, in terms of localization mean square and maximum absolute error and computational complexity.
A Two-Stage Pulsed Power Supply for Low-DC-Voltage and Low-Frequency Pulsed-Current Loads

For a power supply of a low-voltage, low frequency, and pulsed current load such as the transmit and receive module (T/R module) of the solid-state radar, its output power is pulsed and input power is required to be constant. To balance the power difference and maintain a constant output voltage, the value of the storage capacitance is usually very large. In this article, a novel two-stage pulsed power supply is proposed. The intermedia bus capacitance is used as the storage capacitor to balance the power. An interleaved half-bridge converter with a wide voltage gain is adopted, so that the voltage of the storage capacitance can vary in a wide range, which greatly reduces the value of the storage capacitance. To suppress the fluctuation of the input current, a voltage feedforward control and a current preregulation control are proposed. Furthermore, the efficiency of the pulsed power supply is discussed. The comparative study shows an efficiency advantage of the two-stage scheme. Finally, a prototype with the output peak power of 2 kW, the average power of 325 W, and the pulse repetition frequency of 150–300 Hz is assembled and tested to verify the validity of the proposed two-stage pulsed power supply and its control methods.
Contribution to System Frequency Stability and Resilience from PV Plants: A Closed-loop System Identification Approach

The rapid uptake of renewable energy resources and displacement of synchronous generators may pose threats to system frequency stability and resilience. Starting from the August 2018 separation event in Australia, this work models and discusses how utility-scale PV plants could contribute to frequency stability and resilience of islanded areas following separation events. In this regard, a converter-based dynamic equivalent of aggregated PV power plants is proposed which takes into account possible practical issues such as measurement and coordination delays. The unknown parameters of the proposed model are identified through a novel closed-loop identification process based on least-square minimization. Also, a simplified model is constructed to reproduce the system frequency during the event under study, thereby capturing continuous impact of PV response on the frequency. The proposed aggregated model can considerably reduce the complexity of frequency stability analysis as well as its processing time while capturing with good fidelity the frequency response from PV farms.
A New Coordinated Control Strategy of Flywheel Energy Storage System Participating in Primary Frequency Regulation of Power Grid

This paper proposes a new coordinated control strategy for conventional thermal generators with the application of flywheel energy storage system (FESS) to participate in power grid primary frequency regulation (PFR). Through probability density analysis of power grid frequency distribution characteristics, this paper finds that small frequency fluctuation data accounts for more than 80% of total frequency data and proposes that frequency regulation assignments under small disturbances should be undertaken by FESS alone which can significantly reduce generator governor operation times. Comprehensively analyzing power grid frequency regulation requirement and FESS state of charge (SOC) recovery requirement, this paper designs the control rules of FESS output power and then proposes the detailed control strategies in four different working modes. As for frequency regulation indexes, the number of governor operations representing generator wear and tear is included as a new index to help evaluate the effectiveness of the proposed strategy. Finally, Matlab/Simulink is used to simulate a typical PFR system with FESS and the simulation results show that the proposed control strategy can effectively improve the frequency quality, maintain a good SOC level, reduce the number of governor operations thereby has good regulation effects.
Automatic Generation of Smart Human-Machine Interfaces

These last years have seen a rapid growth of applications able to interact with humans in a sophisticated way: mood interpretation and adaptation is the next research frontier and it gets closer day by day. This paper introduces a model-driven process able to automate the creation of modern Human-Machine Interfaces. In particular, by proposing a probabilistic extension of Dynamic State Machines, this paper explores the usage of such a formalism in the automatic generation of applications able to understand the mood of the user and to react at the best. The approach is applied to an example case study.
Functional Replicas of Proprietary Three-Axis Attitude Sensors via LSTM Neural Networks

In this paper, machine-learning-based tools are utilized to learn a model of the functionality of a commercial chip with built-in signal processing and sensor fusion algorithms. Specifically, a fully integrated 9-axis Inertial Measurement Unit (IMU) with embedded algorithms providing the three-axis attitude and corresponding quaternion by fusing all the sensors is considered. Traditionally, extended Kalman filters are used for fusing IMU sensors; however, subtle algorithmic fixes (e.g., magnetic and angular alignment calibration for all sensors, Kalman filter tuning, temperature drift compensation, dynamic magnetic effects) need to be deployed to attain precise attitude (especially heading). A recurrent neural network (RNN) was trained using the chip to substitute for the built-in algorithms of the IMU Chip to output the approximate attitude given the 9-axis sensor data. We show the efficacy of our approach by mounting two IMUs on a board and utilize one IMU, which has its own internal algorithms, to train a machine learning system to fuse the raw data from the sensors on the second IMU to generate comparable accuracy (and in some cases, even outperform the original IMU).
Distributed Voltage Optimization based on the Auxiliary Problem Principle in Active Distribution Systems

Voltage optimization is a key management issue in active distribution systems that can be tackled by acting on the reactive powers provided by distributed generators. Due to the large number of nodes, distributed algorithms are preferable to solve the optimization problem with limited measurements and information exchange. Starting from a centralized formulation of the voltage optimization problem and adopting a network partitioning in voltage control zones, the problem is reformulated in a decentralized form. Then, the Auxiliary Problem Principle is applied to obtain a distributed iterative algorithm that solves the decentralized problem with limited data exchange. The effectiveness of the distributed algorithm is tested on a 24-nodes LV distribution grid with six DGs.