Miracle-dz/UniFAR-FTUs huggingface dataset:
LQR Control of Input LC filter of a Tram with On-board Energy Storage System

The main traction power converter of a trams is connected to DC trolley wire by input LC filter. The tram input LC filter is almost undamped resonant circuit, which is often loaded by the motor constant torque command. Sources of the input LC filter oscillation may be present on the vehicle, typically drive control influence. The source of LC filter excitation except the tram drive could be the tram’s trolley voltage caused by fluctuations in power consumption and harmonics from surrounding vehicles, and temporary loss of catenary voltage caused by crossings of insulated sections or icing on catenary. The next source could be the tram ESS, which is connected in parallel to the tram dc-link capacitor. The dynamic ESS control, which is necessary for majority of power management techniques, can also destabilize the LC filter. On the other hand, the proper ESS control could be used for LC-filter stabilization. The LQR based stabilization of LC filter using ESS is presented in this paper.
Mixture of noises and sampling of non-log-concave posterior distributions

This work considers a challenging radio-astronomy inverse problem of physical parameter inference from multispectral observations. The forward model underlying this problem is a computationally expensive numerical simulation. In addition, the observation model mixes different sources of noise yielding a non-concave log-likelihood function. To overcome these issues, we introduce a likelihood approximation with controlled error. Given the absence of ground truth, parameter inference is conducted with a Markov chain Monte Carlo (MCMC) algorithm to provide credibility intervals along with point estimates. To this aim, we propose a new sampler that addresses the numerical challenges induced by the observation model, in particular the non-log-concavity of the posterior distribution. The efficiency of the proposed method is demonstrated on synthetic yet realistic astrophysical data. We believe that the proposed approach is very general and can be adapted to many similar difficult inverse problems.
Consumer demand profile management through Demand side load management: a review

Demand side management is an intervention by the consumer or utility, to effect changes in demand profile for the grid or a cluster of consumers. The load profile management is usually done to bring down the peak demand for the period (usually a daily peak demand). This is done through voluntary participation of consumer. Demand side management is an important tool available to consumers to take advantage of differential tariffs in force now-a-days. The utilities are equally or more benefited by the DSM implementations for its advantages including improvement in overall efficiency, reliability and reducing denial of service. This work presents the basics concepts of DSM and discusses select DSM implementations, reported by various authors. This work discusses various architectures useful in DSM implementations.
A Lightweight Fluid Model for Mobile Ad hoc Distributed Systems

Emerging edge applications introduced new computing time-variant topologies with mobile nodes connected via ad hoc networks. Such topologies are fundamentally different from cloud infrastructures due to the lack of hierarchy and clear network function separation. Sometimes, nodes that are sources are also destinations and routers, hence, creating dynamic flow patterns traversing the network. The latter, thus, can change the average performance of a distributed system, such as throughput and latency. This work introduces an analytical model based on fluid quantities to study data flows of distributed systems in mobile ad hoc networks. Using an approach based on a network of queues with evenly distributed bandwidth over concurrent flows, this lightweight model enables fast, coarse-grained analysis of different distributed systems configurations. They enable the analysis of different topologies, mobility and data flow models with a small footprint. The model was implemented, validated and evaluated with stress workloads to confirm its accuracy.
Maximization of L1-norm Using Jacobi Rotations

In recent years, we observe a growing interest in approaches to principal component analysis (PCA) based on L1-norm maximization. Unfortunately, existing L1-PCA algorithms are either computationally expensive or inaccurate. In this paper, we propose to use Jacobi-based rotational framework for solving L1-norm maximization problem. Under this framework, two new suboptimal algorithms are developed: the first one based on exhaustive angle search, and the second one based on a differentiable approximation of the absolute value function. Experimental studies show that the proposed approaches provide high accuracy as compared to the existing suboptimal algorithms. They are also considerably faster than currently the most accurate method based on bit-flipping. Simulation results show that both approaches can be used to perform independent component analysis (ICA) under whitening assumption achieving better robustness to outliers than other methods.
Multistable State of Six Dimensional Memristor HR Neuron Model and Its Hardware Implementation

This paper proposes a new six dimensional memristor HR neuron model, the model is an improvement to the existing memristor HR model. It uses a cosine memristor and a sine memristor in neurons, respectively for the coupling and electromagnetic induction coupling. The model has no equilibrium point. Through the phase diagram and the bifurcation diagram, the Lyapunov index spectrum analyses its dynamic behavior, finding an improved memristor HR neuron model can show the boosting behavior, and changing the initial value of memristor can produce firing pattern booster to different discrete levels. The dissipation of the proposed model is studied. These patterns are homogeneous with extreme stability. Finally, the simulation results could be captured through the stm32 implementation and the oscilloscope. The results agree with the theoretical analysis.
Massive MIMO with Advanced Multiuser Detector with Improved Shark Smell Optimization

Massive MIMO systems employ hundreds of antennas to service a number of wireless broadband terminals at the same time. Massive MIMO systems need for simplified, maybe linear techniques for efficient beamforming as well as decoding. To address the signaling overhead complexity, effective signal processing methods must be applied on both ends. When massive MIMO networks could be successfully deployed, there are several fundamental problems that must be resolved. This paper intends to introduce an advanced model for detecting the Gaussian channel matrix. Here, the major objective is to attain minimal Bit Error Rate (BER) between the transmitted and received bits, for which an optimization concept is integrated. Accordingly, the Gaussian channel matrix is optimally tuned, such that the BER among transmitted and received bits should be minimal. For optimization purpose, an Opposition Learning Insisted shark smell optimization (OLISSO) is implemented in this work. Finally, the outcomes of the presented scheme are evaluated over the other extant schemes with respect to varied measures. Moreover, error analysis was performed with respect to MAE, MSE and RMSE, respectively.
Experimental Validation of Wideband SBL Models for DOA Estimation

Sparse Bayesian learning (SBL) has been successful in direction of arrival (DOA) estimation due to its robustness and high resolution using a few snapshots. Most wideband SBL algorithms make the simplifying assumption that distinct sources have the same power spectrum across frequency bands. However, this assumption may not be true in practice (for example speech signals). We analyze three wideband signal models and compare variants of wideband SBL (called SBL1, SBL2, and SBL3) with different assumptions on source signal power spectrum. The localization performance of SBL algorithms is compared with wideband processing of conventional beamforming (CBF) and multiple signal classification (MUSIC). The experimental validation is presented using simulated data and experimental LOCATA data. This comparative study shows that SBL3 which simultaneously enforces sparsity and models frequency-dependent signal spectrum shows superior performance in most scenarios.
AC Battery: Modular Layout with Cell-level Degradation Control

This paper proposes a three-phase AC battery based on the modular multilevel converter (M2C). The AC battery concept allows plug-and-play combinatorial integration of diverse battery cells with different characteristics such as nominal voltage, state of charge $(SoC)$, and degradation levels. The resulting modular and reconfigurable battery pack can cost-effectively cover various applications, from electrified vehicles to stationary storage. To this end, in each sub-module (SM) of the M2C, battery cells (or battery modules composed of one or more battery cells) are connected to a single capacitor, thus enabling a cell-to-cell battery integration and control. In this scenario, the traditional battery management system (BMS) can be replaced by control schemes at the converter level, aiming to equalise critical parameters associated with battery cells (or battery modules). In this context, the degradation level of battery cells integrated by the proposed AC battery is a critical parameter to be considered. Indeed, it is likely that the integrated battery cells by the AC battery have different degradation levels. Therefore, methods for managing their degradation state are required to equalise the lifespan of battery cells within the AC battery. Based on that, this paper proposes a consensus-based distributed control scheme to manage the degradation process of battery cells (or battery modules composed of one or more battery cells) within the AC battery. Simulation results corroborate the effectiveness of the proposal.
Robust State of Charge Estimation via KMPE Loss based Extreme Machine Learning

Since energy storage system (ESS) usually works under complex environments that are susceptible to noise or other random fluctuations, it is crucial to develop robust state of charge (SOC) estimation methods in battery management system for effectively management of the ESS. The mean square error (MSE) criterion based original extreme learning machine (ELM) model can only perform well under Gaussian measurement noise. To enhance the estimation accuracy of ELM under non-Gaussian measurement noise, a new robust ELM is utilized to achieve accurate estimation of SOC in this paper, in which the kernel mean p-power error (KMPE) loss with wider performance surface and flexibility is used to replace the MSE in conventional ELM. Some experiments are conducted to test the effectiveness of the proposed approach for SOC estimation under various work conditions with complex non-Gaussian measurement noises.
The Road to Widely Deploying Processing-in-Memory: Challenges and Opportunities

Processing-in-memory (PIM) refers to a computing paradigm where some or all of the computation for an ap-plication is moved closer to where the data resides (e.g., in main memory). While PIM has been the subject of ongoing research since the 1970s [8], [11], [17], [19], [26], 2[8], [29], [33], it has experienced a resurgence in the last decade due to (1) the pressing need to reduce the energy and latency overheads associated with data movement between the CPU and memory in conventional systems [6], [18], and (2) recent innovations in memory technologies that can enable PIM integration (e.g., [13]–[16], [20], [21], [24], [31]). Recently-released products and prototypes, ranging from programmable near-memory pro-cessing units [7], [36] to custom near-bank accelerators for machine learning [22], [23], [30] and analog compute support within memory arrays [9], [27], have demonstrated the viability of manufacturing PIM architectures.
Joint Localization and Synchronization of Distributed Camera-Attached Microphone Arrays for Indoor Scene Analysis

This paper describes an automatic calibration method that localizes and synchronizes distributed camera-attached microphone arrays (e.g., Microsoft Azure Kinect) used for audiovisual indoor scene analysis. Operating multiple audio-visual sensors as a large-scale array is a key to resolving object occlusions and sound overlaps by integrating audio-visual information obtained from multiple angles. A naive solution to the calibration problem is to synchronize microphone arrays after localizing them using only visual information. This cascading approach, however, would suffer from the error propagation problem. We thus propose a principled statistical method that fully uses audio-visual information at once. Our method only asks a user to make handclaps and jointly estimates the sensor positions and time offsets and the time-varying source position with the GraphSLAM algorithm based on a unified state-space model associating all the latent calibration targets with the audio-visual observations. The experiment using real recordings shows the stable behavior of the proposed method.
Conditional Variational Graph Autoencoder for Air Quality Forecasting

To control air pollution and mitigate its negative effect on health, it is of the utmost importance to have accurate real-time forecasting models. Existing deep-learning-based air quality forecasting models typically deploy temporal and-less often-spatial modules. Yet, data scarcity emerges as a real issue in this domain, a problem that can be solved by capturing the data distribution. In this work, we address data scarcity by proposing a novel conditional variational graph autoencoder. Our model is able to forecast air pollution by efficiently encoding the spatio-temporal correlations of the known data. Additionally, we leverage dynamic context data such as weather or satellite images to condition the model's behaviour. We formulate the problem as a context-aware graph-based matrix completion task and utilize street-level data from mobile stations. Experiments on real-world air quality datasets show the improved performance of our model with respect to state-of-the-art approaches.
Stochastic Simulations of Storage and Inventory Systems

This article aims to present the applications of Levy processes for the stochastic modeling of storage resources. Two cases were considered. In the first one, the volume of supplies to the storehouse is described by a random process (Levy process), while issuing the products is described by a deterministic and linear function. The second case is reversed: the delivery to the storehouse is described by a linear function (variable: time), while issuing the goods is described by a Levy process. For both cases the form of the stock level process and examples of its trajectories, when the net supply is a Levy process, are given. We investigated the following net supply processes: gamma process, α-stable Levy process with α = 0.5, Cauchy process, Wiener process.
WarpedAdS3,dS3, and flows fromN=(0,2)SCFTs

We present the general form of all timelike supersymmetric solutions to 3D U(1)^3 gauged supergravity, a known consistent truncation of string theory. We uncover a rich vacuum structure, including an infinite class of new timelike-warped AdS_3 (Goedel) and timelike-warped dS_3 critical points. We outline the construction of supersymmetric flows, driven by irrelevant scalar operators in the SCFT, which interpolate between critical points. For flows from AdS_3 to Goedel, the natural candidate for the central charge decreases along the flow. Flows to timelike-warped dS_3 exhibit topology change.
Safety Warning of Lithium-Ion Battery Energy Storage Cabin by Image Recogonition

Lithium-ion battery will emit gas-liquid escapes from the safety valve when it gets in an accident. The escapes contains a large amount of visible white vaporized electrolyte and some colorless gas. Effective identification of the white vaporized electrolyte and an early warning can greatly reduce the risk of fire, even an explosion in the energy storage power stations. In this paper, an early warning method of lithium-ion battery fire is proposed, which is based on gas-liquid escape image recognition. Firstly, an image recogonition algorithm based on the YOLOv3 is proposed. The original Darknet53 feature extraction network in the algorithm is replaced with a lightweight ReXNet feature extraction network, considering the safety requirements of fast and accurate identification of the storage. In addition, the K-means clustering algorithm is used to obtain appropriate initialized anchor boxes to speed up the convergence of the model. Finally, the multi-scale feature fusion is combined with the path aggregation network to improve the detection accuracy of the model, so that the model can achieve good recognition of both large and small targets. The results show that the method shows a good effect in identifying the vaporized electrolyte of the actual lithium-ion battery storage. The model prediction speed tested on the GTX1650 graphics card can reach 65 frames per second, and the average accuracy is 84.35%. It basically meets the needs of practical applications. The research in this paper can further improve the safety of lithium-ion battery energy storage power stations and promote the healthy development of electrochemical energy storage.
Estimating road traffic flows in macroscopic Markov model

Traffic flows gain more and more attention in transportation engineering. One possible means of understanding the traffic flow in a city is to gather sequences of traffic position data, called link flows, measured by vehicle-mounted sensors, which are increasingly available by various providers for municipalities. Link flows can be used for planning of operation and maintenance, and for forecasting of future traffic events. In this paper, we investigate how the microscopic Markov traffic model can be used to predict traffic congestion on the roads between different nodes or regions of a city. The proposed model is evaluated in a numerical study by using real traffic data recorded in the city of Porto. The results show that the model developed for simulation is of limited use for predicting the traffic between different areas of a city.
Ageing Mitigation and Loss Control in Reconfigurable Batteries in Series-Level Setups

This paper presents a novel control method that reduces the low-frequency ripple of dynamically reconfigurable battery technology to reduce cell ageing and loss. It furthermore shifts the residual ripple to higher frequencies where the lower impedance reduces heating and the dielectric capacitance of electrodes and electrolyte shunt the current around the electrochemical reactions.
SafeWalk: a Simulation Tool Kit for Exploring Software Requirements in a Safety-Critical Product Line

SafeWalk is a tool kit designed for simulation of a safety-critical product line of astronaut jetpacks. It provides (1) a Unity-based simulation development environment and (2) software artifacts inspired by a real jetpack used by astronauts during spacewalks. SafeWalk has been developed to be a readily extensible and real-time configurable product line for use in research and education. It provides a rich environment conducive to empirical research into safety-critical requirements and to visualization of safety-related human-cyberphysical interactions.
High-Dimensional Data Learning Based on Tensorial-Singular Space of Tensor Train Cores

Tensors are multidimensional data structures used to represent many real world data. In the context of supervised learning, Support Vector Machines (SVMs) are known to be very efficient for different classification tasks. In this work, we propose a kernel metric for SVM to deal with non linear classification problems. First, we use the Tensor Train Decomposition (TTD) to decompose a tensor into TT-cores of order three and two matrices. In order to mitigate the problem of non-uniqueness of TTD, we propose a kernel based on the tensorial singular subspaces spanned by TT-cores. The TT-based kernel function proposed is based on the tools of t-Algebra of 3-rd order tensors. We also show that it is possible to use different kernel functions on each TT-core. Numerical experiments on real-world datasets show the competitivity of our approach compared to existing methods and the superiority of our method when dealing with few-sample of high-dimensional inputs.
Gaussian fluctuations in the two-dimensional BCS-BEC crossover: finite temperature properties

The role of fluctuations is enhanced in lower dimensionality systems: in a two dimensions off- diagonal long-range order is destroyed by the fluctuations at any finite temperature, drastically modifying the critical properties with respect to the three-dimensional counterpart. Recently two-dimensional systems of interacting fermions have been the subject of Montecarlo studies and experimental investigations, in particular an ultracold gas of attractive fermions with a widely tunable interaction due to a Feshbach resonance has been realized and the Berezinskii- Kosterlitz-Thouless transition has been observed. The present work deals with the theoretical description of an ultracold Fermi gas: we discuss the role of Gaussian fluctuations of the order parameter in the equation of state, in particular we take into account the first sound velocity, showing that the inclusion of order parameter fluctuations is needed in order to get the correct composite-boson limit in the strong-coupling regime. The theory is also compared with experimental data. Finally we focus on the superfluid density in the weak-coupling, intermediate and strong-coupling regimes at finite temperature, through which the Berezinskii-Kosterlitz-Thouless critical temperature is obtained.
Evoking sensation in the phantom hand of amputees using invasive stimulation of peripheral nerves

Close to 3 million people suffer from upper limb amputation worldwide - the condition that dramatically de-creases the quality of life. Despite the progress in prosthetic technologies and neuroprosthetics, a fully functional bidirectional neural interface has not been developed yet for the upper limb, which prevents an amputee from fully integrating with the prosthesis - both mechanically and neurally - and hence implementation of such an interface is in high demand. A practically relevant bidirectional prosthesis of the upper limb would rely on stimulation of somatosensory pathways as the way to generate tactile and proprioceptive sensations. Moreover, such stimulation could serve to suppress phantom-limb pain, which is important because the majority of amputees suffer such pain. In this study we investigated the dynamic of tactile sense restoration and phantom limb pain suppression using stimulation of peripheral nerves with implanted electrodes. For quantitative estimation of the results, we used a survey along with images where a patient could draw a sensation field on the phantom limb. We conducted three sessions of mapping during which we matched stimulation sites with areas on the hand and levels of chronic pain. The expansion of sensation fields without dramatic increase of stimulation amplitudes and chronic pain relief during stimulation sessions were observed for all subjects.