pwc-archive/papers-with-abstracts huggingface dataset:
A Deep Reinforcement Learning-based Sliding Mode Control Design for Partially-known Nonlinear Systems
Authors: Sahand Mosharafian, Shirin Afzali, Yajie Bao, Javad Mohammadpour Velni
Date: 2022-05-06 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning (RL)

Presence of model uncertainties creates challenges for model-based control design, and complexity of the control design is further exacerbated when coping with nonlinear systems. This paper presents a sliding mode control (SMC) design approach for nonlinear systems with partially known dynamics by blending data-driven and model-based approaches. First, an SMC is designed for the available (nominal) model of the nonlinear system. The closed-loop state trajectory of the available model is used to build the desired trajectory for the partially known nonlinear system states. Next, a deep policy gradient method is used to cope with unknown parts of the system dynamics and adjust the sliding mode control output to achieve a desired state trajectory. The performance (and viability) of the proposed design approach is finally examined through numerical examples.
An Incremental Negative Sequence Admittance Method for Fault Detection in Inverter-Based Microgrids
Authors: Kwasi Opoku, Subash Pokharel, Aleksandar Dimitrovski
Date: 2022-05-05 00:00:00
Tasks: Fault Detection

Superimposed sequence quantities have been relied on for years to provide pure fault components for various applications in protection schemes. In more recent times, they have been employed in various solutions to the challenges introduced by integration of distributed generation in distribution systems. To improve the overall reliability of protection schemes deployed in such active distribution networks, this paper evaluates a new approach for detecting unbalanced faults using negative sequence quantities. A proposed superimposed negative sequence admittance element is validated using simulation tests on a modelled community microgrid. The performance of the method is tested for various fault scenarios in MATLAB/Simulink.
Modelling Pre-fatigue, Low-velocity Impact and Fatigue behaviours of Composite Helicopter Tail Structures under Multipoint Coordinated Loading Spectrum
Authors: Zheng-Qiang Cheng, Wei Tan, Jun-Jiang Xiong
Date: 2022-05-05 00:00:00

This paper aims to numerically study the pre-fatigue, low-velocity impact (LVI) and fatigue progressive damage behaviours of a full-scale composite helicopter tail structure under multipoint coordinated loading spectrum. First, a fatigue progressive damage model (PDM) incorporating multiaxial fatigue residual strength degradation rule, fatigue failure criteria based on fatigue residual strength concept and sudden stiffness degradation rule was proposed. Then, an LVI progressive damage model for plain-weave (PW) and unidirectional (UD) composites was developed. Moreover, a full-process analysis algorithm with a reasonable damage transfer strategy for pre-fatigue, LVI and fatigue progressive damage analysis was proposed. Finally, a highly computational efficient and accurate full-scale global-local finite element (FE) model of helicopter tail structure was built to predict strain distribution under two flight working conditions, to predict LVI damage under impact loading, and to assess fatigue damage behaviours under multipoint coordinated loading spectrum. The numerical predictions agree well with test results from this work and literature data, indicating that the developed pre-fatigue, LVI, fatigue PDMs and algorithms, as well as the global-local FE modelling based on shell-to-solid coupling, can effectively analyse the impact damage tolerance of full-scale aircraft structures.
Exponentially Stable Adaptive Optimal Control of Uncertain LTI Systems
Authors: Anton Glushchenko, Konstantin Lastochkin
Date: 2022-05-05 00:00:00

A novel method of an adaptive linear quadratic (LQ) regulation of uncertain continuous linear time-invariant systems is proposed. Such an approach is based on the direct self-tuning regulators design framework and the exponentially stable adaptive control technique developed earlier by the authors. Unlike the known solutions, a procedure is proposed to obtain a non-overparametrized regression equation (RE) with respect to the unknown controller parameters from an initial RE of the LQ-based reference tracking control system. On the basis of such result, an adaptive law is proposed, which under mild regressor finite excitation condition provides monotonous convergence of the LQ-controller parameters to an adjustable set of their true values, which bound is defined only by the machine precision. Using the Lyapunov-based analysis, it is proved that the mentioned law guarantees the exponential stability of the closed-loop adaptive optimal control system. The simulation examples are provided to validate the theoretical contributions.
Ultra-sensitive Flexible Sponge-Sensor Array for Muscle Activities Detection and Human Limb Motion Recognition
Authors: Jiao Suo, Yifan Liu, Clio Cheng, Keer Wang, Meng Chen, Ho-Yin Chan, Roy Vellaisamy, Ning Xi, Vivian W. Q. Lou, Wen Jung Li
Date: 2022-04-30 00:00:00
Tasks: Action Detection, Activity Detection

Human limb motion tracking and recognition plays an important role in medical rehabilitation training, lower limb assistance, prosthetics design for amputees, feedback control for assistive robots, etc. Lightweight wearable sensors, including inertial sensors, surface electromyography sensors, and flexible strain/pressure, are promising to become the next-generation human motion capture devices. Herein, we present a wireless wearable device consisting of a sixteen-channel flexible sponge-based pressure sensor array to recognize various human lower limb motions by detecting contours on the human skin caused by calf gastrocnemius muscle actions. Each sensing element is a round porous structure of thin carbon nanotube/polydimethylsiloxane nanocomposites with a diameter of 4 mm and thickness of about 400 {\mu}m. Ten human subjects were recruited to perform ten different lower limb motions while wearing the developed device. The motion classification result with the support vector machine method shows a macro-recall of about 97.3% for all ten motions tested. This work demonstrates a portable wearable muscle activity detection device with a lower limb motion recognition application, which can be potentially used in assistive robot control, healthcare, sports monitoring, etc.
Distinctive ionic transport of freshly excised human epileptogenic brain tissue
Authors: David Emin, Aria Fallah, Noriko Salamon, Gary Mathern, Massoud Akhtari
Date: 2022-05-05 00:00:00

Epileptogenic lesions have higher concentrations of sodium than does normal brain tissue. Such lesions are palpably recognized by a surgeon and then excised in order to eliminate epileptic seizures with their associated abnormal electrical behavior. Here we study the frequency-dependent electrical conductivities of lesion-laden tissues excised from the brains of epilepsy patients. The low-frequency (< 1000 Hz) conductivity of biological tissue primarily probes extracellular solvated sodium-cations traveling parallel to membranes within regions bounded by blockages. This conductivity rises monotonically toward saturation as the frequency surpasses the rate with which diffusing solvated sodium cations encounter blockages. We find that saturation occurs at dramatically higher frequencies in excised brain tissue containing epileptogenic lesions than it does in normal brain tissue. By contrast, such an effect is not reported for tumors embedded in other excised biological tissue. All told, epileptogenic lesions generate frequency-dependent conductivities that differ qualitatively from those of both normal brain tissues and tumors.
A 2D-programmable and Scalable Reconfigurable Intelligent Surface Remotely Controlled via Digital Infrared Code
Authors: Andrey Sayanskiy, Andrey Belov, Ruslan Yafasov, Andrey Lyulyakin, Alexander Sherstobitov, Stanislav Glybovski, Vladimir Lyashev
Date: 2022-04-28 00:00:00

Reconfigurable Intelligent Surfaces (RISs) are promising and relatively low-cost tools for improving signal propagation in wireless communications. An RIS assists a base station in optimizing the channel and maximizing its capacity by dynamically manipulating with reflected field. Typically, RISs are based on dynamically reconfigurable reflectarrays, i.e. two-dimensional arrays of passive patch antennas, individually switchable between two or more reflection phases. Different communication scenarios and environments require RISs to provide a different spatial resolution of reflected field patterns, which depends on the aperture dimensions and the number of patches. Here we demonstrate a 1-bit RIS for 5-GHz Wi-Fi band made by assembling together multiple independently operating building blocks all powered by the same DC source. Each block contains four separately phase-switchable patch antennas with varactor diodes and a common microcontroller extracting digital control commands from modulated infrared light illuminating the entire RIS. Such distributed light-sensitive controllers grant the possibility of scaling the aperture by adding or removing blocks without re-designing any control circuitry. Moreover, in the proposed RIS a full 2D phase encoding capability is achieved along with a robust remote infrared control.
Multiple-access relay stations for long-haul fiber-optic radio frequency transfer
Authors: Qi Li, Liang Hu, Jinbo Zhang, Jianping Chen, Guiling Wu
Date: 2022-05-05 00:00:00
Tasks: Position

We report on the realization of a long-haul radio frequency (RF) transfer scheme by using multiple-access relay stations (MARSs). The proposed scheme with independent link noise compensation for each fiber sub-link effectively solves the limitation of compensation bandwidth for long-haul transfer. The MARS can have the capability to share the same modulated optical signal for the front and rear fiber sub-links, simplifying the configuration at the repeater station and enabling the transfer system to have the multiple-access capability. At the same time, we for the first time theoretically model the effect of the MARS position on the fractional frequency instability of the fiber-optic RF transfer, demonstrating that the MARS position has little effect on system's performance when the ratio of the front and rear fiber sub-links is around $1:1$. We experimentally demonstrate a 1 GHz signal transfer by using one MARS connecting 260 and 280 km fiber links with the fractional frequency instabilities of less than $5.9\times10^{-14}$ at 1 s and $8.5\times10^{-17}$ at 10,000 s at the remote site and of $5.6\times10^{-14}$ and $6.6\times10^{-17}$ at the integration times of 1 s and 10,000 s at the MARS. The proposed scalable technique can arbitrarily add the same MARSs in the fiber link, which has great potential in realizing ultra-long-haul RF transfer.
Multi-core fiber enabled fading noise suppression in φ-OFDR based quantitative distributed vibration sensing
Authors: Yuxiang Feng, Weilin Xie, Yinxia Meng, Jiang Yang, Qiang Yang, Yan Ren, Tianwai Bo, Zhongwei Tan, Wei Wei, Yi Dong
Date: 2022-05-03 00:00:00

Coherent fading has been regarded as a critical issue in phase-sensitive optical frequency domain reflectometry ({\phi}-OFDR) based distributed fiber-optic sensing. Here, we report on an approach for fading noise suppression in {\phi}-OFDR with multi-core fiber. By exploiting the independent nature of the randomness in the distribution of reflective index in each of the cores, the drastic phase fluctuations due to the fading phenomina can be effectively alleviated by applying weighted vectorial averaging for the Rayleigh backscattering traces from each of the cores with distinct fading distributions. With the consistent linear response with respect to external excitation of interest for each of the cores, demonstration for the propsoed {\phi}-OFDR with a commercial seven-core fiber has achieved highly sensitive quantitative distributed vibration sensing with about 2.2 nm length precision and 2 cm sensing resolution along the 500 m fiber, corresponding to a range resolution factor as high as about about 4E-5. Featuring long distance, high sensitivity, high resolution, and fading robustness, this approach has shown promising potentials in various sensing techniques for a wide range of practical scenarios.
Mutation induced infection waves in diseases like COVID-19
Authors: Fabian Jan Schwarzendahl, Jens Grauer, Benno Liebchen, Hartmut Löwen
Date: 2022-05-06 00:00:00

After more than 6 million deaths worldwide, the ongoing vaccination to conquer the COVID-19 disease is now competing with the emergence of increasingly contagious mutations, repeatedly supplanting earlier strains. Following the near-absence of historical examples of the long-time evolution of infectious diseases under similar circumstances, models are crucial to exemplify possible scenarios. Accordingly, in the present work we systematically generalize the popular susceptible-infected-recovered model to account for mutations leading to repeatedly occurring new strains, which we coarse grain based on tools from statistical mechanics to derive a model predicting the most likely outcomes. The model predicts that mutations can induce a super-exponential growth of infection numbers at early times, which self-amplify to giant infection waves which are caused by a positive feedback loop between infection numbers and mutations and lead to a simultaneous infection of the majority of the population. At later stage -- if vaccination progresses too slowly -- mutations can interrupt an ongoing decrease of infection numbers and can cause infection revivals which occur as single waves or even as whole wave trains featuring alternative periods of decreasing and increasing infection numbers. This panorama of possible mutation-induced scenarios should be tested in more detailed models to explore their concrete significance for specific infectious diseases. Further, our results might be useful for discussions regarding the importance of a release of vaccine-patents to reduce the risk of mutation-induced infection revivals but also to coordinate the release of measures following a downwards trend of infection numbers.
Leverage, Influence, and the Jackknife in Clustered Regression Models: Reliable Inference Using summclust
Authors: James G. MacKinnon, Morten Ørregaard Nielsen, Matthew D. Webb
Date: 2022-05-06 00:00:00
Tasks: Diagnostic, regression

We introduce a new Stata package called summclust that summarizes the cluster structure of the dataset for linear regression models with clustered disturbances. The key unit of observation for such a model is the cluster. We therefore propose cluster-level measures of leverage, partial leverage, and influence and show how to compute them quickly in most cases. The measures of leverage and partial leverage can be used as diagnostic tools to identify datasets and regression designs in which cluster-robust inference is likely to be challenging. The measures of influence can provide valuable information about how the results depend on the data in the various clusters. We also show how to calculate two jackknife variance matrix estimators efficiently as a byproduct of our other computations. These estimators, which are already available in Stata, are generally more conservative than conventional variance matrix estimators. The summclust package computes all the quantities that we discuss.
Cluster-Robust Inference: A Guide to Empirical Practice
Authors: James G. MacKinnon, Morten Ørregaard Nielsen, Matthew D. Webb
Date: 2022-05-06 00:00:00

Methods for cluster-robust inference are routinely used in economics and many other disciplines. However, it is only recently that theoretical foundations for the use of these methods in many empirically relevant situations have been developed. In this paper, we use these theoretical results to provide a guide to empirical practice. We do not attempt to present a comprehensive survey of the (very large) literature. Instead, we bridge theory and practice by providing a thorough guide on what to do and why, based on recently available econometric theory and simulation evidence. To practice what we preach, we include an empirical analysis of the effects of the minimum wage on labor supply of teenagers using individual data.
Estimation and Inference by Stochastic Optimization
Authors: Jean-Jacques Forneron
Date: 2022-05-06 00:00:00
Tasks: Stochastic Optimization

In non-linear estimations, it is common to assess sampling uncertainty by bootstrap inference. For complex models, this can be computationally intensive. This paper combines optimization with resampling: turning stochastic optimization into a fast resampling device. Two methods are introduced: a resampled Newton-Raphson (rNR) and a resampled quasi-Newton (rqN) algorithm. Both produce draws that can be used to compute consistent estimates, confidence intervals, and standard errors in a single run. The draws are generated by a gradient and Hessian (or an approximation) computed from batches of data that are resampled at each iteration. The proposed methods transition quickly from optimization to resampling when the objective is smooth and strictly convex. Simulated and empirical applications illustrate the properties of the methods on large scale and computationally intensive problems. Comparisons with frequentist and Bayesian methods highlight the features of the algorithms.
Comment on Jackson and Sonnenschein (2007) "Overcoming Incentive Constraints by Linking Decisions"
Authors: Ian Ball, Matt O. Jackson, Deniz Kattwinkel
Date: 2022-05-06 00:00:00

We correct a bound in the definition of approximate truthfulness used in the body of the paper of Jackson and Sonnenschein (2007). The proof of their main theorem uses a different permutation-based definition, implicitly claiming that the permutation-version implies the bound-based version. We show that this claim holds only if the bound is loosened. The new bound is still strong enough to guarantee that the fraction of lies vanishes as the number of problems grows, so the theorem is correct as stated once the bound is loosened.
Human Tracking with mmWave Radars: a Deep Learning Approach with Uncertainty Estimation
Authors: Jacopo Pegoraro, Michele Rossi
Date: 2022-05-06 00:00:00
Tasks: Position

mmWave radars have recently gathered significant attention as a means to track human movement within indoor environments. Widely adopted Kalman filter tracking methods experience performance degradation when the underlying movement is highly non-linear or presents long-term temporal dependencies. As a solution, in this article we design a convolutional-recurrent Neural Network (NN) that learns to accurately estimate the position and the velocity of the monitored subjects from high dimensional radar data. The NN is trained as a probabilistic model, utilizing a Gaussian negative log-likelihood loss function, obtaining explicit uncertainty estimates at its output, in the form of time-varying error covariance matrices. A thorough experimental assessment is conducted using a 77 GHz FMCW radar. The proposed architecture, besides allowing one to gauge the uncertainty in the tracking process, also leads to greatly improved performance against the best approaches from the literature, i.e., Kalman filtering, lowering the average error against the ground truth from 32.8 to 7.59 cm and from 56.8 to 14 cm/s in terms of position and velocity tracking, respectively.
SPARCS: A Sparse Recovery Approach for Integrated Communication and Human Sensing in mmWave Systems
Authors: Jacopo Pegoraro, Jesus Omar Lacruz, Michele Rossi, Joerg Widmer
Date: 2022-05-06 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

A well established method to detect and classify human movements using Millimeter-Wave ( mmWave) devices is the time-frequency analysis of the small-scale Doppler effect (termed micro-Doppler) of the different body parts, which requires a regularly spaced and dense sampling of the Channel Impulse Response ( CIR). This is currently done in the literature either using special-purpose radar sensors, or interrupting communications to transmit dedicated sensing waveforms, entailing high overhead and channel utilization. In this work we present SPARCS, an integrated human sensing and communication solution for mmWave systems. SPARCS is the first method that reconstructs high quality signatures of human movement from irregular and sparse CIR samples, such as the ones obtained during communication traffic patterns. To accomplish this, we formulate the micro-Doppler extraction as a sparse recovery problem, which is critical to enable a smooth integration between communication and sensing. Moreover, if needed, our system can seamlessly inject short CIR estimation fields into the channel whenever communication traffic is absent or insufficient for the micro-Doppler extraction. SPARCS effectively leverages the intrinsic sparsity of the mmWave channel, thus drastically reducing the sensing overhead with respect to available approaches. We implemented SPARCS on an IEEE 802.11ay Software Defined Radio (SDR) platform working in the 60 GHz band, collecting standard-compliant CIR traces matching the traffic patterns of real WiFi access points. Our results show that the micro-Doppler signatures obtained by SPARCS enable a typical downstream application such as human activity recognition with more than 7 times lower overhead with respect to existing methods, while achieving better recognition performance.
Integration of NOMA with Reflecting Intelligent Surfaces: A Multi-cell Optimization with SIC Decoding Errors
Authors: Wali Ullah Khan, Eva Lagunas, Asad Mahmood, Zain Ali, Muhammad Asif Symeon Chatzinotas, Björn Ottersten
Date: 2022-05-06 00:00:00

Reflecting intelligent surfaces (RIS) has gained significant attention due to its high energy and spectral efficiency in next-generation wireless networks. By using low-cost passive reflecting elements, RIS can smartly reconfigure the signal propagation to extend the wireless communication coverage. This letter proposes a new optimization scheme in a multi-cell RIS-NOMA network to enhance the spectral efficiency under signal decoding errors. In particular, the power budget of the base station and the transmit power of NOMA users while the reflection matrix of RIS is simultaneously optimized in each cell. Due to objective function and quality of service constraints, the joint problem is formulated as non-convex, which is very complex and challenging to obtain the optimal global solution. To make the problem tractable, we first decouple it into two sub-problems and transform it by inner approximation and successive convex approximation techniques. Then we adopt a standard optimization method for power allocation and DC programming for the reflection matrix. Numerical results demonstrate the benefits of the proposed optimization scheme in the multi-cell RIS-NOMA network.
Hybrid Beamforming Design for Millimeter Wave Multiuser MIMO Systems with Dynamic Subarrays
Authors: Gengshan Wang, Zhijia Yang, Tierui Gong
Date: 2022-05-06 00:00:00

In this letter, we investigate the millimeter wave (mmWave) downlink multiuser multiple-input multiple-output (MU-MIMO) system, adopting the dynamic subarray architecture at the base station and considering the multi-stream communication for each user. Aiming at maximizing the system spectral efficiency, we propose a novel hybrid beamforming design. First, assuming no inter-user interference (IUI), we easily get the optimal fully-digital beamformers and combiners using the singular value decomposition of each user channel and the waterfilling algorithm. Then, based on the obtained fullydigital beamformers, we propose a Kuhn-Munkres algorithmassisted dynamic hybrid beamforming design, which guarantees that each radio-frequency chain is connected to at least one antenna. Finally, we propose to further project each obtained digital beamformer onto the null space of all the other equivalent user channels to cancel the IUI. Numerical results verify the superiority of our proposed hybrid beamforming design.