pwc-archive/papers-with-abstracts huggingface dataset:
Effect of noise on output-only modal identification of beams
Authors: Gholamreza Jahangiri, Seyed Rasoul Nabavian, Mohammad Reza Davoodi, Bahram Navayi Neya, Seyedamin Mostafavian
Date: 2020-08-19 00:00:00
Tasks: Structural Health Monitoring

In most cases, structural health monitoring depends on the determination of the modal parameters of the first few modes of a structure. The data that are used to identify these modes by output-only methods include the structure response together with noise. The presence of noise in the structure response affects the modal parameters. The current study was undertaken to examine the possibility and accuracy of identifying the modal parameters of beams in the presence of noise. For this purpose, the modal parameters of the different modes of single-span beams were obtained using output data with different signal-to-noise ratios. The acceleration signals were obtained by transient analysis and then different powers of noise were generated and added to the signals. The modal parameters of the beams were obtained using output-only methods. Parameters having signal-to-noise ratios of greater than 13.98 dB for all modes considered were identified. At a signal-to-noise ratio of -6.02 to 13.98 dB (higher noise level), it was not possible to identify the modal parameters of the first mode of beams, but the parameters of the higher modes were identified with good accuracy.
The Smart Mask: Active Closed-Loop Protection against Airborne Pathogens
Authors: Naren Vikram Raj Masna, Rohan Reddy Kalavakonda, Reiner Dizon, Anamika Bhuniaroy, Soumyajit Mandal, Swarup Bhunia
Date: 2020-08-21 00:00:00

Face masks provide effective, easy-to-use, and low-cost protection against airborne pathogens or infectious agents, including SARS-CoV-2. There is a wide variety of face masks available on the market for various applications, but they are all passive in nature, i.e., simply act as air filters for the nasal passage and/or mouth. In this paper, we present a new "active mask" paradigm, in which the wearable device is equipped with smart sensors and actuators to both detect the presence of airborne pathogens in real time and take appropriate action to mitigate the threat. The proposed approach is based on a closed-loop control system that senses airborne particles of different sizes close to the mask and then makes intelligent decisions to reduce their concentrations. This paper presents a specific implementation of this concept in which the on-board controller determines ambient air quality via a commercial particulate matter sensor, and if necessary activates a piezoelectric actuator that generates a mist spray to load these particles, thus causing them to fall to the ground. The proposed system communicates with the user via a smart phone application that provides various alerts, including notification of the need to recharge and/or decontaminate the mask prior to reuse. The application also enables a user to override the on-board control system and manually control the mist generator if necessary. Experimental results from a functional prototype demonstrate significant reduction in airborne particulate counts near the mask when the active protection system is enabled.
Designing adaptive robust extended Kalman filter based on Lyapunov-based controller for robotics manipulators
Date: 2020-08-15 00:00:00
Tasks: Position

In this paper, a position and velocity estimation method for robotic
manipulators which are affected by constant bounded disturbances is considered.
The tracking control problem is formulated as a disturbance rejection problem,
with all the unknown parameters and dynamic uncertainties lumped into
disturbance. Using adaptive robust extended Kalman filter(AREKF) the movement
and velocity of each joint is predicted to use in discontinuous Lyapunov-based
controller structure. The parameters of the error dynamics have been validated
off-line by real data. Computer simulation results given for a two degree of
freedom manipulator demonstrate the efficacy of the improved Kalman Filter by
comparing the performance of EKF and improved AREKF. Although it is shown that
accurate trajectory tracking can be achieved by using the proposed controller.
An Analysis of Sampling Effect on the Absolute Stability of Discrete-time Bilateral Teleoperation Systems
Date: 2020-08-15 00:00:00

Absolute stability of discrete-time teleoperation systems can be jeopardized
by choosing inappropriate sampling time architecture. A modified structure is
presented for the bilateral teleoperation system including continuous-time
slave robot, master robot, human operator, and the environment with
sampled-data PD-like + dissipation controllers which make the system absolute
stable in the presence of the time delay and sampling rates in the
communication network. The output position and force signals are quantized with
uniform sampling periods. Input-delay approach is used in this paper to convert
the sampled-data system to a continuous-time counterpart. The main contribution
of this paper is calculating a lower bound on the maximum sampling period as a
stability condition. Also, the presented method imposes upper bounds on the
damping of robots and notifies the sampling time importance on the transparency
and stability of the system. Both simulation and experimental results are
performed to show the validity of the proposed conditions and verify the
effectiveness of the sampling scheme.
Modeling Communication Networks in a Real-Time Simulation Environment for Evaluating Controls of Shipboard Power Systems
Authors: Colin Ogilvie, Juan Ospina, Charalambos Konstantinou, Tuyen Vu, Mark Stanovich, Karl Schoder, Mischa Steurer
Date: 2020-08-15 00:00:00

Interest by the U.S. Navy in the development and deployment of advanced controls in future shipboard platforms has motivated the development of the Controls Evaluation Framework (CEF) for use in investigating dynamics present in complex automated systems. This paper reports on the implementation and investigation of a communication network component within the CEF. This implementation is designed to augment the CEF's available feature set, permitting the exploration of various communication conditions on advanced control performance. Results obtained from controller hardware-in-the-loop testing are presented and analyzed to demonstrate performance characteristics pertaining to the implemented module.
Transferring Inter-Class Correlation
Authors: Wen Hui, Wu Yue, Yang Chenming, Li Jingjing, Zhu Yue, Jiang Xu, Duan Hancong
Date: 2020-08-11 00:00:00
Tasks: Open-Ended Question Answering

The Teacher-Student (T-S) framework is widely utilized in the classification
tasks, through which the performance of one neural network (the student) can be
improved by transferring knowledge from another trained neural network (the
teacher). Since the transferring knowledge is related to the network capacities
and structures between the teacher and the student, how to define efficient
knowledge remains an open question. To address this issue, we design a novel
transferring knowledge, the Self-Attention based Inter-Class Correlation (ICC)
map in the output layer, and propose our T-S framework, Inter-Class Correlation
Transfer (ICCT).
On-line Capacity Estimation for Lithium-ion Battery Cells via an Electrochemical Model-based Adaptive Interconnected Observer
Authors: Anirudh Allam, Simona Onori
Date: 2020-08-24 00:00:00
Tasks: Capacity Estimation, Management

Battery aging is a natural process that contributes to capacity and power fade, resulting in a gradual performance degradation over time and usage. State of Charge (SOC) and State of Health (SOH) monitoring of an aging battery poses a challenging task to the Battery Management System (BMS) due to the lack of direct measurements. Estimation algorithms based on an electrochemical model that take into account the impact of aging on physical battery parameters can provide accurate information on lithium concentration and cell capacity over a battery's usable lifespan. A temperature-dependent electrochemical model, the Enhanced Single Particle Model (ESPM), forms the basis for the synthesis of an adaptive interconnected observer that exploits the relationship between capacity and power fade, due to the growth of Solid Electrolyte Interphase layer (SEI), to enable combined estimation of states (lithium concentration in both electrodes and cell capacity) and aging-sensitive transport parameters (anode diffusion coefficient and SEI layer ionic conductivity). The practical stability conditions for the adaptive observer are derived using Lyapunov's theory. Validation results against experimental data show a bounded capacity estimation error within 2% of its true value. Further, effectiveness of capacity estimation is tested for two cells at different stages of aging. Robustness of capacity estimates under measurement noise and sensor bias are studied.
Low-Complexity Robust Beamforming Design for IRS-Aided MISO Systems with Imperfect Channels
Authors: Yasaman Omid, Seyyed MohammadMahdi Shahabi, Cunhua Pan, Yansha Deng, Arumugam Nallanathan
Date: 2020-08-24 00:00:00

In this paper, large-scale intelligent reflecting sur-face (IRS)-assisted multiple-input single-output (MISO) system is considered in the presence of channel uncertainty. To maximize the average sum rate of the system by jointly optimizing the active beamforming at the BS and the passive phase shifts at the IRS, while satisfying the power constraints, a novel robust beamforming design is proposed by using the penalty dual decomposition (PDD) algorithm. By applying the upper bound maximization/minimization (BSUM) method, in each iteration of the algorithm, the optimal solution for each variable can be obtained with closed-form expression. Simulation results show that the proposed scheme achieves high performance with very low computational complexity.
Optimal Scheduling of Anticipated COVID-19 Vaccination: A Case Study of New York State
Date: 2020-08-24 00:00:00
Tasks: Benchmarking, Scheduling

This study aims to determine an optimal control strategy for vaccine
scheduling in COVID-19 pandemic treatment by converting widely acknowledged
infectious disease model named SEIR into an optimal control problem. The
problem is augmented by adding medication and vaccine limitations to match
real-world situations. Two version of the problem is formulated to minimize the
number of infected individuals at the same provide the optimal vaccine possible
to reduce the susceptible population to a considerably lower state. Optimal
control problems are solved using RBF-Galerkin method. These problems are
tested with a benchmarking dataset to determine required parameters. After this
step, problems are tested with recent data for New York State, USA. The results
regarding the proposed optimal control problem provides a set of evidences from
which an optimal strategy for vaccine scheduling can be chosen, when the
vaccine for COVID-19 will be available.
Enhanced Normalized Mutual Information for Localization in Noisy Environments
Authors: Samuel Todd Flanagan, Drupad K. Khublani, J. -F. Chamberland, Siddharth Agarwal, Ankit Vora
Date: 2020-08-24 00:00:00
Tasks: Autonomous Vehicles

Fine localization is a crucial task for autonomous vehicles. Although many algorithms have been explored in the literature for this specific task, the goal of getting accurate results from commodity sensors remains a challenge. As autonomous vehicles make the transition from expensive prototypes to production items, the need for inexpensive, yet reliable solutions is increasing rapidly. This article considers scenarios where images are captured with inexpensive cameras and localization takes place using pre-loaded fine maps of local roads as side information. The techniques proposed herein extend schemes based on normalized mutual information by leveraging the likelihood of shades rather than exact sensor readings for localization in noisy environments. This algorithmic enhancement, rooted in statistical signal processing, offers substantial gains in performance. Numerical simulations are used to highlight the benefits of the proposed techniques in representative application scenarios. Analysis of a Ford image set is performed to validate the core findings of this work.
Genes Preferring Non-AUG Start Codons in Bacteria
Authors: Anne Gvozdjak, Manoj P. Samanta
Date: 2020-08-25 00:00:00
Tasks: Translation

Here we investigate translational regulation in bacteria by analyzing the distribution of start codons in fully assembled genomes. We report 36 genes (infC, rpoC, rnpA, etc.) showing a preference for non-AUG start codons in evolutionarily diverse phyla ("non-AUG genes"). Most of the non-AUG genes are functionally associated with translation, transcription or replication. In E. coli, the percentage of essential genes among these 36 is significantly higher than among all genes. Furthermore, the functional distribution of these genes suggests that non-AUG start codons may be used to reduce gene expression during starvation conditions, possibly through translational autoregulation or IF3-mediated regulation.
Aphasic Speech Recognition using a Mixture of Speech Intelligibility Experts
Date: 2020-08-25 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Mixture-of-Experts, Robust Speech Recognition, speech-recognition, Speech Recognition

Robust speech recognition is a key prerequisite for semantic feature
extraction in automatic aphasic speech analysis. However, standard
one-size-fits-all automatic speech recognition models perform poorly when
applied to aphasic speech. One reason for this is the wide range of speech
intelligibility due to different levels of severity (i.e., higher severity
lends itself to less intelligible speech). To address this, we propose a novel
acoustic model based on a mixture of experts (MoE), which handles the varying
intelligibility stages present in aphasic speech by explicitly defining
severity-based experts. At test time, the contribution of each expert is
decided by estimating speech intelligibility with a speech intelligibility
detector (SID). We show that our proposed approach significantly reduces phone
error rates across all severity stages in aphasic speech compared to a baseline
approach that does not incorporate severity information into the modeling
process.
Multiview Variational Deep Learning with Application to Scalable Indoor Localization
Authors: Minseuk Kim, Changjun Kim, Dongsoo Han, June-Koo Kevin Rhee
Date: 2020-08-25 00:00:00
Tasks: Deep Learning, Indoor Localization, Variational Inference

Radio channel state information (CSI) measured with many receivers is a good resource for localizing a transmit device with machine learning with a discriminative model. However, CSI localization is nontrivial when the radio map is complicated, such as in building corridors. This paper introduces a view-selective deep learning (VSDL) system for indoor localization using CSI of WiFi. The multiview training with CSI obtained from multiple groups of access points (APs) generates latent features on supervised variational deep network. This information is then applied to an additional network for dominant view classification to enhance the regression accuracy of localization. As non-informative latent features from multiple views are rejected, we can achieve a localization accuracy of 0.77 m, which outperforms by 30 $\%$ the best known accuracy in practical applications in a real building environment. To the best of our knowledge, this is the first approach to apply variational inference and to construct a scalable system for radio localization. Furthermore, our work investigates a methodology for supervised learning with multiview data where informative and non-informative views coexist.
Probabilistic Voltage Sensitivity based Preemptive Voltage Monitoring in Unbalanced Distribution Networks
Date: 2020-08-25 00:00:00
Tasks: Management, Sensitivity

With increasing penetration of renewable energy and active consumers, control
and management of power distribution networks has become challenging. Renewable
energy sources can cause random voltage fluctuations as their output power
depends on weather conditions. Conventional voltage control schemes such as tap
changers and capacitor banks lack the foresight required to quickly alleviate
voltage violations. Thus, there is an urgent need for effective approaches for
predicting and mitigating voltage violations as a result of random fluctuations
in power injections. This work proposes a novel voltage monitoring approach
based on low-complexity, data-driven probabilistic voltage sensitivity
analysis. The usefulness of this work is not only in predicting voltage
violations in unbalanced distribution grids, but also in opening up the door
for optimal voltage control. Using system data and forecasts, the proposed
approach predicts the distribution of system node voltages which is then used
to to identify nodes that may violate the nominal operational limits with high
probability. The method is tested on the IEEE 37 node distribution system
considering integrated distributed solar energy sources. The method is
validated against the classic load flow based method and offers over 95\%
accuracy in predicting voltage violations.
"Near" Weighted Utilitarian Characterizations of Pareto Optima
Authors: Yeon-Koo Che, Jinwoo Kim, Fuhito Kojima, Christopher Thomas Ryan
Date: 2020-08-25 00:00:00

We characterize Pareto optimality via "near" weighted utilitarian welfare maximization. One characterization sequentially maximizes utilitarian welfare functions using a finite sequence of nonnegative and eventually positive welfare weights. The other maximizes a utilitarian welfare function with a certain class of positive hyperreal weights. The social welfare ordering represented by these "near" weighted utilitarian welfare criteria is characterized by the standard axioms for weighted utilitarianism under a suitable weakening of the continuity axiom.
Loop-shaping for reset control systems -- A higher-order sinusoidal-input describing functions approach
Date: 2020-08-25 00:00:00
Tasks: Sensitivity

The ever-growing demands on speed and precision from the precision motion
industry have pushed control requirements to reach the limitations of linear
control theory. Nonlinear controllers like reset provide a viable alternative
since they can be easily integrated into the existing linear controller
structure and designed using industry-preferred loop-shaping techniques.
However, currently, loop-shaping is achieved using the describing function (DF)
and performance analysed using linear control sensitivity functions not
applicable for reset control systems, resulting in a significant deviation
between expected and practical results. We overcome this major bottleneck to
the wider adaptation of reset control with two contributions in this paper.
First, we present the extension of frequency-domain tools for reset controllers
in the form of higher-order sinusoidal-input describing functions (HOSIDFs)
providing greater insight into their behaviour. Second, we propose a novel
method which uses the DF and HOSIDFs of the open-loop reset control system for
the estimation of the closed-loop sensitivity functions, establishing for the
first time - the relation between open-loop and closed-loop behaviour of reset
control systems in the frequency domain. The accuracy of the proposed solution
is verified in both simulation and practice on a precision positioning stage
and these results are further analysed to obtain insights into the tuning
considerations for reset controllers.
The Impact of Sodomy Law Repeals on Crime
Date: 2020-08-25 00:00:00

We exploit variation in the timing of decriminalization of same-sex sexual
intercourse across U.S. states to estimate the impact of these law changes on
crime through difference-in-difference and event-study models. We provide the
first evidence that sodomy law repeals led to a decline in the number of
arrests for disorderly conduct, prostitution, and other sex offenses.
Furthermore, we show that these repeals led to a reduction in arrests for drug
and alcohol consumption.
Platoon--assisted Vehicular Cloud in VANET: Vision and Challenges
Authors: Meysam Nasimi, Mohammad Asif Habibi, Hans D. Schotten
Date: 2020-08-25 00:00:00
Tasks: Autonomous Vehicles

Intelligent connected vehicles equipped with wireless sensors, intelligent control system, and communication devices are expected to commercially launch and emerge on road in short-term. These smart vehicles are able to partially/fully drive themselves; collect data from sensors, make and execute decisions based on that data; communicate with other vehicles, pedestrians, and nodes installed on the road; and provide infotainment and value-added services, such as broadband transmission of ultra-high definition video, files/apps downloading and uploading, online gaming, access to social media, audio/video conference streaming (office-in-car), live TV streaming, etc.; and so on. In addition, it is also possible for autonomous vehicles to form a "platoon" on road; maintaining close proximity in order to reduce the consumption of fuel and/or emission of gas, decrease costs, increase safety, and enhance the efficiency of the legacy transportation system. These emerging vehicular applications demand a large amount of computing and communication capacity to excel in their compute-intensive and latency-sensitive tasks. Based on these facts, the authors of this paper presented a visionary concept -- "platoon-assisted vehicular cloud" -- that exploits underutilized resources in platoons to augment vehicular cloud aiming to provide cost-effective and on-demand computing resources. Moreover, the authors presented three potential scenarios and explained the exploitation of platoon resources and roadside infrastructure to facilitate new applications. Besides system design, the paper did also summarize a number of open research challenges with the purpose of motivating new advances and potential solutions to this field.
Reconfigurable Intelligent Surfaces based Cognitive Radio Networks
Authors: Abubakar U. Makarfi, Rupak Kharel, Khaled M. Rabie, Omprakash Kaiwartya, Xingwang Li, Dinh-Thuan Do
Date: 2020-08-25 00:00:00
Tasks: Management

Over the last decade, cognitive radios (CRs) have emerged as a technology for improving spectrum efficiency through dynamic spectrum access techniques. More recently, as research interest is shifting beyond 5G communications, new technologies such as reconfigurable intelligent surfaces (RISs) have emerged as enablers of smart radio environments, to further improve signal coverage and spectrum management capabilities. Based on the promise of CRs and RISs, this paper seeks to investigate the concept of adopting both concepts within a network as a means of maximizing the potential benefits available. The paper considers two separate models of RIS-based networks and analyzes several performance metrics associated with the CR secondary user. Monte Carlo simulations are presented to validate the derived expressions. The results indicate the effects of key parameters of the system and the clear improvement of the CR network, in the presence of a RIS-enhanced primary network.