Miracle-dz/UniFAR-FTUs huggingface dataset:
Masked-RPCA: Moving Object Detection With an Overlaying Model

Moving object detection in a given video sequence is a pivotal step in many computer vision applications such as video surveillance. Robust Principal Component Analysis (RPCA) performs low-rank and sparse decomposition to accomplish such a task when the background is stationary and the foreground is dynamic and relatively small. A fundamental issue with the RPCA is the assumption that the low-rank and sparse components are added at each pixel, whereas in reality, the moving foreground is overlaid on the background. We propose the masked decomposition (i.e. an overlaying model) where each element either belongs to the low-rank or the sparse component, decided by a mask. We introduce the Masked-RPCA (MRPCA) algorithm to recover the mask (hence the sparse object) and the low-rank components simultaneously, via a non-convex formulation. An adapted version of the Douglas–Rachford splitting algorithm is utilized to solve the proposed formulation. Our experiments using real-world video sequences show consistently better performance for both cases of static and dynamic background videos compared to RPCA and its variants based on the additive model. Additionally, we show that utilizing non-convex priors in our formulation leads to improved results without any added complexity compared to a relaxed formulation using convex surrogates and methods based on the additive model.
A Novel Fault Location Method for Distribution Networks Based on Transient Energy Relative Entropy of FDM Transform

Aimed at the problem that the transient zero sequence current of each point in distribution networks is characterized by nonlinearity and non-stationarity when a single phase to ground (SPG) fault occurs, a fault diagnosis methodology based on Fourier decomposition method (FDM) and multi-scale transient energy relative entropy is proposed. First, the zero sequence current after a SPG fault is expanded based on the generalized Fourier expansion method, and a series of Fourier intrinsic band functions (FIBFs) which are adaptive, local, orthogonal and uncorrelated are obtained. Subsequently, the Fourier-Hilbert spectrum (FHS) based on FIBFs is utilized to define the instantaneous energy (IE) density of the fault. After that, the difference between the signals on both sides of various sections is represented by energy relative entropy. Through calculation and comparison, the fault section can be accurately determined. This methodology is suitable for different transition resistances, fault angles and noise interference. Finally, numerical results verify the effectiveness of the proposed methodology.
The Real Time Motion Pattern Recognition of Lower Limb Based on sEMG signals

In this work, we proposed a Back-propagation neural network (BPNN) method based on surface electromyo-graphy(sEMG) signals for recognition of movement in human locomotion. The method only needs six sEMG signals to help detect hemiplegia patient walking. We use experiment first verify the availability of the sEMG data acquisition system. Then quantify the system with a gold standard optoelectronic system Vicon-nexus (VN). The Hip and Knee Joint Angles has a good correlation coefficient and mean squared error of volunteers locomotion. In contrast to the existing methods, the proposed model not only avoids the complex sensor system but also ensures the accuracy of recognition. Meanwhile, the aim of this paper is to build the regression model which relates the multichannel sEMG signals to human lower limb joint angles in order to develop a more natural study for the patients.
A Deep Reinforcement Learning Based Switch Controller Mapping Strategy in Software Defined Network

Software defined network (SDN) is a promising technology which can reduce network management complexity through the decoupling of the control plane and data plane. Due to large number of switches in the data plane, distributed and multiple controllers are necessary in the control plane for managing the switches. The switch controller mapping strategy for identifying the mapping relationships between the switch and controller is crucial in order to optimize the network performance. Considering the dynamics of the network behavior, it is quite important and challenging to develop models to reflect the network topology dynamics and to propose method for solving the long-term network performance optimization. Inspired by the recent advances in Artificial Intelligence (AI), in this paper, we propose a Deep Reinforcement Learning (DRL) based strategy for solving the switch controller mapping problem. A DRL based mapping strategy is proposed, in which Markov Decision Process (MDP) formulation is devised and Deep $Q$ -network (DQN) is proposed to achieve the maximization of long-term system performance by leveraging network latency, load balancing and system stability. Extensive simulations show that the DQN based algorithm can achieve the best system stability results while maintaining moderate switch controller latency and system equilibrium performance comparing with the optimization which only considers current system performance for switch controller mapping decision, and the optimization approaches which generate mapping decisions purely based on latency or load balancing separately.
Trajectory Prediction based on Constraints of Vehicle Kinematics and Social Interaction†

Trajectory prediction for vehicles is a popular subject since it is beneficial for efficient and secure trajectory planning. In structured traffic scenarios, the behaviour and motion of vehicles are heavily dependent on the social interaction constraints, such as road geometry and surrounding vehicles, and the kinematics model constraints, such as continuous heading and maximum acceleration. To take these factors into account, we analyse the particular characteristics of driving vehicles and propose a model that predicts the possible and feasible trajectory for host vehicle in 3 seconds. In this model, the trajectory of host vehicle takes the center-line as reference, imitates the leader vehicle and focuses on the social vehicles through attention concentration mechanism (ACM) with spatial and temporal information encoded in a fusion hidden state. Furthermore, in order to make the trajectory feasible for vehicle dynamics and kinematics, we introduce a prediction diagnosis method to check the continuous heading and maximum acceleration condition, pruning and adjusting the prediction candidates. Experiments on released public datasets show that this framework can well evaluate the traffic interactions and forecast the trajectory more accurately than common networks.
Investigating Co-Activation between Medial Prefrontal and Primary Motor Cortical Spike Trains during Task Learning

The medial prefrontal cortex (mPFC) and primary motor cortex (M1) are both actively involved in the reward guided learning. However, how the information is conveyed in spike trains between these two regions has not been investigated. Spike prediction models have been developed to predict the spike trains between two cortical areas, for example, CA3 to CA1 in hippocampus, as well as premotor to M1. In this paper, we investigate the co-activation between mPFC and M1 by comparing three spike prediction models with different nonlinear capacity. Our data was collected from the mPFC and M1 of a rat when it was learning a two-lever discrimination task. The mPFC spike trains are served as the input of models and M1 spike trains as the desired output. We compare the spike prediction performances of three models, including generalized linear model (GLM), second order GLM, and staged point-process model. Our results show that all three model outputs have similar discrete-time rescaling Kolmogorov-Smirnov test results and similar correlation coefficient (0.42 on average across neurons). The paired t-tests across neurons also show the lack of significant difference. The preliminary results indicate possible linear co-activation between the M1 and mPFC during the learning stage.
Part-Based Lipreading for Audio-Visual Speech Recognition

Lipreading is an important component of audio-visual speech recognition. However, lips are usually modeled as a whole in lipreading, which ignores that each part of lip focuses on different characteristics of mouth and the overall model can not fit each part perfectly. Besides, features based on the whole lip usually vary a lot according to different speakers, which leads that the training databases usually need to contain as much speakers as possible. In this paper, A part-based lipreading (PBL) method is proposed to deal with the mismatch between an overall lip model and the separate parts of lips, also the excessive dependence of models on the speakers in training set. PBL models lips partly and predicts jointly. It employs a uniform partition strategy on convolutional features and generates several part-level sub-results for final prediction. Experiments are performed on a large publicly available dataset (LRW) and part of it (p-LRW, 65 words), in order to simulate the progressive instructions in the working scene of robots. Word accuracy of PBL reaches 82.8% on LRW and 88.9% on p-LRW. Finally, an end-to-end audio-visual speech recognition system using PBL is established and achieves 98.3% word accuracy on LRW.
Cellular Automata in path planning navigation control applied in surveillance task using the e-Puck architecture

Surveillance is a canonical task for robotics, so it is one of the most popular activities for a wide range of problems for robots. The problem’s complexity increases proportionally with the number of rooms because the robot must act avoiding obstacles, saving time and energy. The proposed algorithms employ a combination of different search techniques to control a robot during a surveillance task. Our inspiration came from the possibility to use a discrete model employing a cellular automata combined with different heuristics approaches. This improvement of the best path selection was performed due to A* search optimization. Then, for solving collision problems a cellular automata rule is used in the expansion of obstacles, for a collision-free path. Finally, the surveillance task was implemented in a simulator called V-REP using e-Puck architecture. Simulation and experimental results indicate that this surveillance task could be implemented in low-cost architectures in a simplified way. The novelty consists of implementing these techniques in a realistic robot simulator, and also of a systematic analysis of each heuristic function used in contrast to the data structure optimization algorithm proposed herein.
Preparation and characterization of thermally stable poly(amide-urea)s functionalized with anthraquinone chromophore

Two new diamines containing bulky anthraquinone pendant units were prepared via reactions of 1- and 2-aminoanthraquinone with 3,5-dinitrobenzoylchloride and a subsequent reduction of their nitro groups. A novel series of highly organosoluble poly(amide-urea)s were synthesized from the reactions of the prepared diamines with various commercially available diisocyanates via a step-growth addition reaction process in N-methyl-2-pyrrolidone (NMP). The effects of two factors (time and temperature) on the reaction were studied to optimize the conditions for the preparation of high molecular weight polymers. All poly(amide-urea)s were characterized by Fourier Transform Infrared (FTIR) and 1H-nuclear magnetic resonance (NMR) spectroscopies and elemental analysis. The resulting poly(amide-urea)s had inherent viscosities in the range of 0.54–0.73 dl/g. They exhibited excellent solubility in polar solvents. The temperature for 10% weight loss of the polymers in air was all above 285°C, their residues were more than 36% at 700°C in air, and their Tg values were in the range of 148–190°C. According to the wide-angle X-ray diffraction (WAXD), the polymers were almost amorphous. The optical properties of poly(amide-urea)s measured by ultraviolet–visible (UV–Vis) spectroscopy showed absorption maxima at 303–429 nm. Copyright © 2008 John Wiley & Sons, Ltd.
Distribution System Fault Detection through Feature-Summarizing Matrix: A Case Study

For power distribution systems, a fast and accurate fault detection method is imperative to guarantee power supply equipment and personal safety. To this end, a novel feature-summarizing matrix based fault detection method is proposed in this paper. Taking into account some well-known tricky conditions leading to imperceptive faults, specifically, that are long-transmission, non-linear loading, noises, and high impedance, the proposed method centers on a feature-summarizing matrix which is devised towards the tricky factors, and based on voltages RMS information delivered by Hilbert transform, and harmonics information by discrete Fourier transform, and a special detection method by Hilbert-Huang transform. A typical 10.5kV radial distribution network with three feeders is emulated with the APT software, for demonstrating the performance of the proposed fault detection method in comparison with several classical methods.
Path Planning for Continuous-curvature Avoidance using Hierarchical Four Parameter Logistic Curves

Robots are widely used as unmanned vehicles in smart factories. In order to operate the robot in a known environment, the robot must be able to plan the path. The planned path must enable the robot to reach a destination from the starting point and the robot must avoid all obstacles during movement.This study uses a four-parameter logic curve path planning method that uses a closed formula solution and a curve with minimal design parameters to quickly generate an ideal path. The characteristics of the curve are used to derive the S and half-S curves as the solution path. The shortest path is used as the selection reference target to select parameters B and C for the four-parameter logic curve. For the S path, the best solution is chosen. The half -S curve is limited by the elastic end-point heading angle, which gives a unique set of parameter solutions. The generated path does not allow the robot to completely avoid obstacles so a hierarchical half-S curve path planning mechanism is used. A via point is generated at the collision point using the gradient vector for the edge of the obstacle. The planner continues to perform half-S path planning until the path planning ends.
Effect of Electric Field Uniformity on Streamer and Breakdown Characteristics in a Gas-to-Liquid Oil under Positive Lightning Impulse

This paper presents an experimental study on the streamer characteristics of a gas-to-liquid (GTL) oil in a semi-uniform field under positive lightning impulse. To realize the semi-uniform field, a plane-needle-plane electrode geometry was applied. The needle-plane geometry, non-uniform field, with the same oil gap distance was also employed for comparison. The streamers propagate farther when the applied voltage increases in both fields, however compared to the nonuniform field, the streamers in semi-uniform field are constrained along the axial direction with less side branches. Additionally, the streamer initiation voltage and breakdown voltage are higher in semi-uniform field while the streamer propagation is facilitated compared to the non-uniform field. Meanwhile, two more electrode geometries with different field uniformities were used to measure the streamer initiation voltages and breakdown voltages in the GTL oil. The results were also compared with conventional mineral oil, and GTL oil shows similar streamer initiation voltages and breakdown voltages as the mineral oil in the present investigation, which provides additional reference for the application of GTL oil in power transformers.
An Optimal Integrated Approach Considering Distribution System Reconfiguration and Protection Coordination

Distribution system reconfiguration (DSR) studies are gaining increased attention. DSR deals with minimizing the power loss by optimally reconfiguring the power system lines. However, the original protection coordination may not be valid for the reconfigured system. This paper presents an integrated approach that considers both distribution system reconfiguration and protection coordination. The DSR problem is formulated as a mixed integer nonlinear programming (MINLP) problem to determine the optimal configuration. Meanwhile, the protection coordination problem is a nonlinear programming (NLP) problem to determine the Directional Overcurrent Relay (DOCR) settings. The methodology is tested on the 119-bus radial distribution test systems, considering different load and generation scenarios. The results show that the proposed protection scheme can ensure proper protection for reconfigurable distribution systems.
A heterogeneous tactical cloud architecture for emergency services search and rescue operations

With an increased adoption of uninhabited platforms in tactical environments such as search and rescue operations the proliferation of tactical assets equipped with advanced computing, sensing and networking capabilities becomes complex to manage. Edge/tactical cloud architectures provide the necessary automation processes for managing and scaling services and applications deployed across a larger number of platforms. However, size weight and power restrictions on tactical platforms leads to a heterogeneity in deployed infrastructure for both compute capabilities and available network bearers which raises complexities that need to be addressed by the underlying cloud infrastructure. In this paper we propose and demonstrate a heterogeneous cloud architecture used for a search and rescue training operation conducted by Surf Life Saving South Australia in collaboration with the Defence Science and Technology Group.
Modeling of Superconducting Conductor on Round Core (CORC) Cables: 2D and 3D

One of the High Temperature Superconducting (HTS) cables can be made by the Conductor on Round Core (CORC) structure, and it has been regarded as the reasonable option for many superconducting applications, e.g. power transmission cable and high field magnet. This article shows the simulation of superconducting CORC performed by the finite-element method (FEM) using H-formulation model, which was on the commercial platform of COMSOL. We used the 2-dimension (2D) and 3-dimension (3D) models for the superconducting CORC, and their outcomings were analyzed and compared. There were 2 design parameters in this study: (i) the gap angle, between each HTS tape, and (ii) the degree of twist: generally defined called pitch. The 2D CORC model's calculation speed was faster than 3D CORC model, but the 3D CORC model was able to simulate the loss in a realistic range.
Sorting cells from fluorescent markers organization in confocal microscopy: 3D versus 2D images

We consider the problem of sorting cells from fluorescent markers spatial organization via single cell microscopy. This problem is important in bioimaging since this organization can reflect the healthy or pathological state of cells, the chromatin chain configurations or the spatial organization of DNA during main cell cycle. In this work, we address the question of the detection of spatial organization differences according to fluorescent markers density or distribution using textural features. We compare the performances by simulating 3D images and 2D images of the focal plane with various size of the point spread function of the confocal microscope, that is determined by varying the numerical aperture N A of the used objective lens.
Impact Assessment of Real Time Demand Control on Active AC/DC Hybrid Distribution Networks

Large integration of DC distributed energy resources and DC electrical loads into the existing AC distribution network are made possible with the deployment of power electronics converters. This transforms the conventional AC network to an AC/DC hybrid distribution network (HDN). However, intermittent renewable energy, uncertain market energy price and vulnerable load demand of consumers causes serious real time power imbalance situation. To address the above deficiency, this paper assesses the impact of real time demand control on AC/DC HDN by designing a multi-objective, non-linear, convex, real time energy management algorithm based on Lyapunov optimization technique, which works only by knowing the updated present data of the uncertain parameters rather than their probabilistic estimated values. Efficacy of the proposed methodology is evaluated by simulating case studies on modified IEEE 33 bus AC/DC HDN test case.
Study on Development Characteristics of Cavity Discharge in Composite Insulation of Superconducting Cable

Superconducting cable is a promising application in future power transmission systems due to the advantage of low power loss, compact dimensions and large current capacity. Cavity discharge is a common defect during cable manufacturing and transportation, which might cause severe damage to superconducting cable. Therefore, cavity discharge characteristics in superconducting cables should be further studied before large scale industrial applications. In this paper, experimental platform was proposed and pulse current method was employed to study the development characteristics of cavity discharge of composite insulation immersed in liquid nitrogen. In the experiment part, a constant temperature and pressure experimental platform was designed and cavity defect specimen were made to simulate the actual operating environment superconducting cable's insulation. Step-up voltage method was used to for specimen testing and discharge signal was collected on each voltage step until breakdown. Accordingly, $Q-\varphi-n$ three-dimensional graphs of cavity discharge were drawn and typical characteristic parameters were extracted from graphs in the analysis. Results is shown as follows: With the development of cavity discharge of composite insulation at the liquid nitrogen temperature, average amplitude and show a trend of slowly increasing at the beginning stage after initial, decreasing at the medium stage, and increasing dramatically at the before breakdown. Also, discharge phase is gradually widened to 0 ° and 180 ° while discharge initials around 90° in positive half cycle and 270 ° in negative half circle. According to the discharge features, the entire process of cavity discharge in composite insulation of superconducting cable can be categorized into 4 stages by clustering analysis. The study of cavity discharge characteristics might provide a better vision on the understanding and diagnosis of superconducting cable's health statue.
A Low Complexity Long Short-Term Memory Based Voice Activity Detection

Voice Activity Detection (VAD) plays an important role in audio processing, but it is also a common challenge when a voice signal is corrupted with strong and transient noise. In this paper, an accurate and causal VAD module using a long short-term memory (LSTM) deep neural network is proposed. A set of features including Gammatone cepstral coefficients (GTCC) and selected spectral features are used. The low complex structure allows it can be easily implemented in speech processing algorithms and applications. With carefully pre-processing and labeling the collected training data in the classes of speech or non-speech and training on the LSTM net, experiments show the proposed VAD is able to distinguish speech from different types of noisy background effectively. Its robustness against changes including varying frame length, moving speech sources and speaking in different languages, are further investigated.