pwc-archive/papers-with-abstracts huggingface dataset:
A New Era of Mobility: Exploring Digital Twin Applications in Autonomous Vehicular Systems
Authors: S M Mostaq Hossain, Sohag Kumar Saha, Shampa Banik, Trapa Banik
Date: 2023-05-09 00:00:00
Tasks: Autonomous Vehicles

Digital Twins (DTs) are virtual representations of physical objects or processes that can collect information from the real environment to represent, validate, and replicate the physical twin's present and future behavior. The DTs are becoming increasingly prevalent in a variety of fields, including manufacturing, automobiles, medicine, smart cities, and other related areas. In this paper, we presented a systematic reviews on DTs in the autonomous vehicular industry. We addressed DTs and their essential characteristics, emphasized on accurate data collection, real-time analytics, and efficient simulation capabilities, while highlighting their role in enhancing performance and reliability. Next, we explored the technical challenges and central technologies of DTs. We illustrated the comparison analysis of different methodologies that have been used for autonomous vehicles in smart cities. Finally, we addressed the application challenges and limitations of DTs in the autonomous vehicular industry.
Facial Expression Recognition at the Edge: CPU vs GPU vs VPU vs TPU
Authors: Mohammadreza Mohammadi, Heath Smith, Lareb Khan, Ramtin Zand
Date: 2023-05-17 00:00:00
Tasks: CPU, Facial Expression Recognition, Facial Expression Recognition (FER), GPU

Facial Expression Recognition (FER) plays an important role in human-computer interactions and is used in a wide range of applications. Convolutional Neural Networks (CNN) have shown promise in their ability to classify human facial expressions, however, large CNNs are not well-suited to be implemented on resource- and energy-constrained IoT devices. In this work, we present a hierarchical framework for developing and optimizing hardware-aware CNNs tuned for deployment at the edge. We perform a comprehensive analysis across various edge AI accelerators including NVIDIA Jetson Nano, Intel Neural Compute Stick, and Coral TPU. Using the proposed strategy, we achieved a peak accuracy of 99.49% when testing on the CK+ facial expression recognition dataset. Additionally, we achieved a minimum inference latency of 0.39 milliseconds and a minimum power consumption of 0.52 Watts.
Emergency Response Person Localization and Vital Sign Estimation Using a Semi-Autonomous Robot Mounted SFCW Radar
Authors: Christian A. Schroth, Christian Eckrich, Ibrahim Kakouche, Stefan Fabian, Oskar von Stryk, Abdelhak M. Zoubir, Michael Muma
Date: 2023-05-25 00:00:00
Tasks: Human Detection

The large number and scale of natural and man-made disasters have led to an urgent demand for technologies that enhance the safety and efficiency of search and rescue teams. Semi-autonomous rescue robots are beneficial, especially when searching inaccessible terrains, or dangerous environments, such as collapsed infrastructures. For search and rescue missions in degraded visual conditions or non-line of sight scenarios, radar-based approaches may contribute to acquire valuable, and otherwise unavailable information. This article presents a complete signal processing chain for radar-based multi-person detection, 2D-MUSIC localization and breathing frequency estimation. The proposed method shows promising results on a challenging emergency response dataset that we collected using a semi-autonomous robot equipped with a commercially available through-wall radar system. The dataset is composed of 62 scenarios of various difficulty levels with up to five persons captured in different postures, angles and ranges including wooden and stone obstacles that block the radar line of sight. Ground truth data for reference locations, respiration, electrocardiogram, and acceleration signals are included. The full emergency response benchmark data set as well as all codes to reproduce our results, are publicly available at https://doi.org/10.21227/4bzd-jm32.
Weakly-Supervised Speech Pre-training: A Case Study on Target Speech Recognition
Authors: Wangyou Zhang, Yanmin Qian
Date: 2023-05-25 00:00:00
Tasks: Denoising, Self-Supervised Learning, speech-recognition, Speech Recognition

Self-supervised learning (SSL) based speech pre-training has attracted much attention for its capability of extracting rich representations learned from massive unlabeled data. On the other hand, the use of weakly-supervised data is less explored for speech pre-training. To fill this gap, we propose a weakly-supervised speech pre-training method based on speaker-aware speech data. It adopts a similar training procedure to the widely-used masked speech prediction based SSL framework, while incorporating additional target-speaker enrollment information as an auxiliary input. In this way, the learned representation is steered towards the target speaker even in the presence of highly overlapping interference, allowing potential applications to tasks such as target speech recognition. Our experiments on Libri2Mix and WSJ0-2mix datasets show that the proposed model achieves significantly better ASR performance compared to WavLM, the state-of-the-art SSL model with denoising capability.
Transfer Learning for Personality Perception via Speech Emotion Recognition
Authors: Yuanchao Li, Peter Bell, Catherine Lai
Date: 2023-05-25 00:00:00
Tasks: Emotion Recognition, Speech Emotion Recognition, Transfer Learning

Holistic perception of affective attributes is an important human perceptual ability. However, this ability is far from being realized in current affective computing, as not all of the attributes are well studied and their interrelationships are poorly understood. In this work, we investigate the relationship between two affective attributes: personality and emotion, from a transfer learning perspective. Specifically, we transfer Transformer-based and wav2vec2-based emotion recognition models to perceive personality from speech across corpora. Compared with previous studies, our results show that transferring emotion recognition is effective for personality perception. Moreoever, this allows for better use and exploration of small personality corpora. We also provide novel findings on the relationship between personality and emotion that will aid future research on holistic affect recognition.
Spoofing Attacker Also Benefits from Self-Supervised Pretrained Model
Authors: Aoi Ito, Shota Horiguchi
Date: 2023-05-24 00:00:00
Tasks: Self-Supervised Learning

Large-scale pretrained models using self-supervised learning have reportedly improved the performance of speech anti-spoofing. However, the attacker side may also make use of such models. Also, since it is very expensive to train such models from scratch, pretrained models on the Internet are often used, but the attacker and defender may possibly use the same pretrained model. This paper investigates whether the improvement in anti-spoofing with pretrained models holds under the condition that the models are available to attackers. As the attacker, we train a model that enhances spoofed utterances so that the speaker embedding extractor based on the pretrained models cannot distinguish between bona fide and spoofed utterances. Experimental results show that the gains the anti-spoofing models obtained by using the pretrained models almost disappear if the attacker also makes use of the pretrained models.
Bounded Projection Matrix Approximation with Applications to Community Detection
Authors: Zheng Zhai, Hengchao Chen, Qiang Sun
Date: 2023-05-21 00:00:00
Tasks: Clustering, Community Detection

Community detection is an important problem in unsupervised learning. This paper proposes to solve a projection matrix approximation problem with an additional entrywise bounded constraint. Algorithmically, we introduce a new differentiable convex penalty and derive an alternating direction method of multipliers (ADMM) algorithm. Theoretically, we establish the convergence properties of the proposed algorithm. Numerical experiments demonstrate the superiority of our algorithm over its competitors, such as the semi-definite relaxation method and spectral clustering.
Online Influence Maximization under Decreasing Cascade Model
Authors: Fang Kong, Jize Xie, Baoxiang Wang, Tao Yao, Shuai Li
Date: 2023-05-19 00:00:00
Tasks: model

We study online influence maximization (OIM) under a new model of decreasing cascade (DC). This model is a generalization of the independent cascade (IC) model by considering the common phenomenon of market saturation. In DC, the chance of an influence attempt being successful reduces with previous failures. The effect is neglected by previous OIM works under IC and linear threshold models. We propose the DC-UCB algorithm to solve this problem, which achieves a regret bound of the same order as the state-of-the-art works on the IC model. Extensive experiments on both synthetic and real datasets show the effectiveness of our algorithm.
A Modular System Architecture for an Offshore Off-grid Platform for Climate neutral Power-to-X Production in H2Mare
Authors: Pascal Häbig, Daniel Dittler, Maximilian Fey, Timo Müller, Nikola Mößner, Nasser Jazdi, Michael Weyrich, Kai Hufendiek
Date: 2023-05-25 00:00:00

Power-to-X (PtX) products constitute a promising solution component in the defossilisation of hard-to-abate sectors. Where direct electrification is not possible, they can find application as chemical feedstock, easy to transport energy carriers and storage. In this contribution, a modular system architecture for a highly automated operation of an offshore off-grid PtX production platform consisting of five layers (asset, control, communication, digital, application) is presented. The assumed advantage of such an off-grid platform is the good scalability worldwide as well as easy exploitation of renewable potentials in any location. Using the example of a power-to-methanol process, a simplified prototype is outlined.
Value of Information Analysis for rationalising information gathering in building energy analysis
Authors: Max Langtry, Chaoqun Zhuang, Rebecca Ward, Nikolas Makasis, Monika J. Kreitmair, Zack Xuereb Conti, Domenic Di Francesco, Ruchi Choudhary
Date: 2023-05-25 00:00:00
Tasks: Decision Making, Scheduling

The use of monitored data to improve the accuracy of building energy models and operation of energy systems is ubiquitous, with topics such as building monitoring and Digital Twinning attracting substantial research attention. However, little attention has been paid to quantifying the value of the data collected against its cost. This paper argues that without a principled method for determining the value of data, its collection cannot be prioritised. It demonstrates the use of Value of Information analysis (VoI), which is a Bayesian Decision Analysis framework, to provide such a methodology for quantifying the value of data collection in the context of building energy modelling and analysis. Three energy decision-making examples are presented: ventilation scheduling, heat pump maintenance scheduling, and ground source heat pump design. These examples illustrate the use of VoI to support decision-making on data collection.
Development of high accurate family-use digital refractometer based on CMOS
Authors: zhenxing Wang, Zhenyuan Jia
Date: 2023-05-25 00:00:00
Tasks: Management

This study aims to develop a low-cost refractometer for measuring the sucrose content of fruit juice, which is an important factor affecting human health. While laboratory-grade refractometers are expensive and unsuitable for personal use, existing low-cost commercial options lack stability and accuracy. To address this gap, we propose a refractometer that replaces the expensive CCD sensor and light source with a conventional LED and a reasonably priced CMOS sensor. By analyzing the output waveform pattern of the CMOS sensor, we achieve high precision with a personal-use-appropriate accuracy of 0.1%. We tested the proposed refractometer by conducting 100 repeated measurements on various fruit juice samples, and the results demonstrate its reliability and consistency. Running on a 48 MHz ARM processor, the algorithm can acquire data within 0.2 seconds. Our low-cost refractometer is suitable for personal health management and small-scale production, providing an affordable and reliable method for measuring sucrose concentration in fruit juice. It improves upon the existing low-cost options by offering better stability and accuracy. This accessible tool has potential applications in optimizing the sucrose content of fruit juice for better health and quality control.
Robust asymptotic observer of motion states with nonlinear friction
Authors: Michael Ruderman
Date: 2023-05-25 00:00:00
Tasks: Friction, Robust Design, State Estimation

This paper revisits the previously proposed linear asymptotic observer of the motion state variables with nonlinear friction and provides a robust design suitable for both, transient presliding and steady-state sliding phases of the relative motion. The class of motion systems with the only measurable output displacement is considered. The reduced-order Luenberger type observer is designed based on the obtained simplified state-space representation with a time-varying system matrix. The resulted observation error dynamics proves to be robust and appropriate for all variations of the system matrix, which are due to the nonlinear spatially varying friction. A specially designed tribological setup to accurately monitor the relative motion between two contacting friction surfaces is used to collect the experimental data of the deceleration trajectories when excited by a series of impulses. The performance of the state estimation using the proposed observer is shown based on the collected experimental data.
Frequency domain parametric estimation of fractional order impedance models for Li-ion batteries
Authors: Freja Vandeputte, Noël Hallemans, Jishnu Ayyangatu Kuzhiyil, Nessa Fereshteh Saniee, Widanalage Dhammika Widanage, John Lataire
Date: 2023-05-25 00:00:00

The impedance of a Li-ion battery contains information about its state of charge (SOC), state of health (SOH) and remaining useful life (RUL). Commonly, electrochemical impedance spectroscopy (EIS) is used as a nonparametric data-driven technique for estimating this impedance from current and voltage measurements. In this article, however, we propose a consistent parametric estimation method based on a fractional order equivalent circuit model (ECM) of the battery impedance. Contrary to the nonparametric impedance estimate, which is only defined at the discrete set of excited frequencies, the parametric estimate can be evaluated in every frequency of the frequency band of interest. Moreover, we are not limited to a single sine or multisine excitation signal. Instead, any persistently exciting signal, like for example a noise excitation signal, will suffice. The parametric estimation method is first validated on simulations and then applied to measurements of commercial Samsung 48X cells. For now, only batteries in rest, i.e. at a constant SOC after relaxation, are considered.
Deterministic policy gradient based optimal control with probabilistic constraints
Authors: Arunava Naha, Subhrakanti Dey
Date: 2023-05-25 00:00:00
Tasks: Model Predictive Control, reinforcement-learning, Reinforcement Learning (RL)

This paper studies a deep deterministic policy gradient (DDPG) based actor critic (AC) reinforcement learning (RL) technique to control a linear discrete-time system with a quadratic control cost while ensuring a constraint on the probability of potentially risky or undesirable events. The proposed methodology can be applied to both known and unknown system models with minor adjustments to the reward structure (negative cost). The problem is formulated by considering the average expected quadratic cost of the states and inputs over an infinite time horizon. Risky or undesirable events are represented as functions of the states at the next time step exceeding a user-defined limit. Two strategies are employed to manage the probabilistic constraint in scenarios of known and unknown system models. In the case of a known system model, the probabilistic constraint is replaced with an upper bound, such as the Chernoff bound. For unknown system models, the expected value of the indicator function of the occurrence of the risky or undesirable event is used. We have adopted a deterministic policy gradient (DPG) based AC method to derive a parameterised optimal policy. Extensive numerical simulations are performed using a second- and a fourth-order system, and the proposed method is compared with the standard risk-neutral linear quadratic regulator (LQR) and a chance-constrained model predictive control (MPC) method. The results demonstrate the effectiveness of the proposed approach in both known and unknown system model scenarios.
Semi-global Exponential Stability for Dual Quaternion Based Rigid-Body Tracking Control
Authors: Vrushabh Zinage, S P Arjun Ram, Maruthi R. Akella, Efstathios Bakolas
Date: 2023-05-24 00:00:00
Tasks: Collision Avoidance

Semi-Global Exponential Stability (SGES) is proved for the combined attitude and position rigid body motion tracking problem, which was previously only known to be asymptotically stable. Dual quaternions are used to jointly represent the rotational and translation tracking error dynamics of the rigid body. A novel nonlinear feedback tracking controller is proposed and a Lyapunov based analysis is provided to prove the semi-global exponential stability of the closed-loop dynamics. Our analysis does not place any restrictions on the reference trajectory or the feedback gains. This stronger SGES result aids in further analyzing the robustness of the rigid body system by establishing Input-to-State Stability (ISS) in the presence of time-varying additive and bounded external disturbances. Motivated by the fact that in many aerospace applications, stringent adherence to safety constraints such as approach path and input constraints is critical for overall mission success, we present a framework for safe control of spacecraft that combines the proposed feedback controller with Control Barrier Functions. Numerical simulations are provided to verify the SGES and ISS results and also showcase the efficacy of the proposed nonlinear feedback controller in several non-trivial scenarios including the Mars Cube One (MarCO) mission, Apollo transposition and docking problem, Starship flip maneuver, collision avoidance of spherical robots, and the rendezvous of SpaceX Dragon 2 with the International Space Station.
Sensitivity-Informed Parameter Selection for Improved Soil Moisture Estimation from Remote Sensing Data
Authors: Bernard T. Agyeman, Erfan Orouskhani, Mohamed Naouri, Willemijn Appels, Maik Wolleben, Jinfeng Liu, Sirish L. Shah
Date: 2023-05-24 00:00:00
Tasks: Management, parameter estimation, Scheduling, Sensitivity, Soil moisture estimation

Improving the accuracy of soil moisture estimation is required for advancing irrigation scheduling and water conservation efforts. Central to this task are soil hydraulic parameters, which govern moisture dynamics but are rarely known precisely and must therefore be inferred from observational data. In large-scale agricultural fields, estimating the complete set of these parameters is often impractical due to the sparse and noisy nature of available measurements. To address this challenge, this work develops a framework that uses sensitivity analysis and orthogonal projection to identify parameters that are both reliably estimable from available data. These parameters, together with the spatial distribution of soil moisture, are jointly estimated by assimilating observational data into a cylindrical-coordinate version of the Richards equation using an extended Kalman filter. The soil moisture measurements are obtained from microwave remote sensors mounted on center pivot irrigation systems - an emerging and practical technology for capturing field-scale variability. Numerical simulations and field experiments conducted on a large-scale site in Lethbridge, Alberta, Canada, demonstrate that the proposed method improves soil moisture estimation accuracy by 24-43% and enhances predictive model performance by 50%. Furthermore, the estimated parameters - particularly saturated hydraulic conductivity - exhibit good agreement with experimental measurements.
Adaptive observer of state variables of a nonlinear time varying system with unknown constant parameters
Authors: Olga Kozachek, Alexey Bobtsov, Nikolay Nikolaev
Date: 2023-05-24 00:00:00
Tasks: parameter estimation, regression

The paper proposes an adaptive observer of the state vector of a nonlinear time varying system based on measurements of the output variable. The problem is solved under the assumption that the control matrix (vector) and the nonlinear component of the equation of state of the system contain unknown constant parameters. When developing an adaptive observer, the GPEBO (generalized parameter estimation based observer) method was used, also known as a generalized observer based on parameter estimation, which was proposed in [1]. During the synthesis of the observer, a preliminary parametrization of the original nonlinear system is carried out. Then the resulting system is reduced to a linear regression model. At the next stage, unknown constant regression parameters are estimated using the least squares method with the forgetting factor [2, 3]. The article suggests the development of the result proposed by the authors in [4]. In [4], a linear non-stationary system containing unknown parameters in a control matrix (vector) was considered. This result is an extension of the result obtained in [4] for the case when the equation of state of the system contains a partially unknown nonlinearity.
Synthesis of an adaptive observer of state variables for a linear stationary object in the presence of measurement noise
Authors: Alexey Bobtsov, Vladimir Virobyev, Nikolay Nikolaev, Anton Pyrkin, Romeo Ortega
Date: 2023-05-24 00:00:00

The article is devoted to the problem of synthesis of observers of state variables for linear stationary objects operating under conditions of noise or disturbances in the measurement channel. The paper considers a fully observable linear stationary system with known parameters. It is assumed that the state variables are not measured, and the measured output variable contains a small amplitude (in general, modulo less than one) additive noise or disturbance. It is also assumed that there is no a priori information about the disturbance or noise in the measurement channel (for example, frequency spectrum, covariance, etc.). It is well known that a large number of methods of observer synthesis have been obtained for this type of objects, including the Kalman filter, which has proven itself in practice. Under the condition of complete observability and the presence of some a priori information about the process (which is typical for the case when a disturbance in the measurement channel can be represented as white noise), approaches based on Kalman filtering demonstrate the highest quality of convergence of estimates of state variables to true values. Without taking into account the numerous results obtained using the application of the Kalman filter, an alternative idea of constructing an observer of state variables is considered in this paper. The alternative of the new approach is primarily due to the fact that there is no need to use the usual approaches based on the Luenberger observer. The paper proposes an approach based on the evaluation of unknown parameters (in this case, an unknown vector of initial conditions of the variables of the object state) of a linear regression model.