pwc-archive/papers-with-abstracts huggingface dataset:
A Real-time Fire Segmentation Method Based on A Deep Learning Approach
Authors: Mengna Li, Youmin Zhang, Lingxia Mu, Jing Xin, Ziquan Yu, Shangbin Jiao, Han Liu, Guo Xie, Yi Yingmin
Date: 2022-07-22 00:00:00
Proceeding: IFAC-PapersOnLine 2022 7
Tasks: Decoder, Real-Time Semantic Segmentation, Segmentation

As a kind of the forest “fault”, fire is highly destructive and difficult to rescue. Fire segmentation is helpful for firefighters to understand the fire scale and formulate a reasonable fire-fighting plan. Therefore, this paper proposes a real-time fire segmentation method based on deep learning. This method is an improved version of deeplbav3+, which is an encoder-decoder structure network. Encoder network is composed of deep convolutional neural network and atrous spatial pyramid pooling. Different from deeplabv3+, in order to improve the segmentation speed, this paper uses the lightweight network mobilenetv3 to build a new deep convolutional neural network and does not use atrous convolution, but it will affect the segmentation accuracy. Therefore, in order to compensate for the loss of segmentation accuracy, on the basis of the original decoder network, this paper adds two different shallow features to make the network contain rich fire feature information. Experimental results show that the comprehensive performance of this method is better than the original deeplabv3+, especially the segmentation speed of the network is greatly improved, which is about 59 FPS.
Cooperative Multi-Cell Massive Access with Temporally Correlated Activity
Authors: Weifeng Zhu, Meixia Tao, Xiaojun Yuan, Fan Xu, Yunfeng Guan
Date: 2023-04-19 00:00:00
Tasks: Action Detection, Activity Detection, Bayesian Inference, compressed sensing

This paper investigates the problem of activity detection and channel estimation in cooperative multi-cell massive access systems with temporally correlated activity, where all access points (APs) are connected to a central unit via fronthaul links. We propose to perform user-centric AP cooperation for computation burden alleviation and introduce a generalized sliding-window detection strategy for fully exploiting the temporal correlation in activity. By establishing the probabilistic model associated with the factor graph representation, we propose a scalable Dynamic Compressed Sensing-based Multiple Measurement Vector Generalized Approximate Message Passing (DCS-MMV-GAMP) algorithm from the perspective of Bayesian inference. Therein, the activity likelihood is refined by performing standard message passing among the activities in the spatial-temporal domain and GAMP is employed for efficient channel estimation. Furthermore, we develop two schemes of quantize-and-forward (QF) and detect-and-forward (DF) based on DCS-MMV-GAMP for the finite-fronthaul-capacity scenario, which are extensively evaluated under various system limits. Numerical results verify the significant superiority of the proposed approach over the benchmarks. Moreover, it is revealed that QF can usually realize superior performance when the antenna number is small, whereas DF shifts to be preferable with limited fronthaul capacity if the large-scale antenna arrays are equipped.
An Information-Theoretic Analysis of Discrete-Time Control and Filtering Limitations by the I-MMSE Relationships
Authors: Neng Wan, Dapeng Li, Naira Hovakimyan, Petros G. Voulgaris
Date: 2023-04-18 00:00:00

Fundamental limitations or performance trade-offs/limits are important properties and constraints of both control and filtering systems. Among various trade-off metrics, total information rate that characterizes the sensitivity trade-offs and time-averaged performance of control and filtering systems was conventionally studied by using the differential entropy rate and Kolmogorov-Bode formula. In this paper, by extending the famous I-MMSE (mutual information -- minimum mean-square error) relationships to the discrete-time additive white Gaussian channels with and without feedback, a new paradigm is introduced to estimate and analyze total information rate as a control and filtering trade-off metric. Under this framework, we explore the trade-off properties of total information rate for a variety of the discrete-time control and filtering systems, e.g., LTI, LTV, and nonlinear, and propose an alternative approach to investigate total information rate via optimal estimation.
Dynamic Chunk Convolution for Unified Streaming and Non-Streaming Conformer ASR
Authors: Xilai Li, Goeric Huybrechts, Srikanth Ronanki, Jeff Farris, Sravan Bodapati
Date: 2023-04-18 00:00:00
Tasks: speech-recognition, Speech Recognition

Recently, there has been an increasing interest in unifying streaming and non-streaming speech recognition models to reduce development, training and deployment cost. The best-known approaches rely on either window-based or dynamic chunk-based attention strategy and causal convolutions to minimize the degradation due to streaming. However, the performance gap still remains relatively large between non-streaming and a full-contextual model trained independently. To address this, we propose a dynamic chunk-based convolution replacing the causal convolution in a hybrid Connectionist Temporal Classification (CTC)-Attention Conformer architecture. Additionally, we demonstrate further improvements through initialization of weights from a full-contextual model and parallelization of the convolution and self-attention modules. We evaluate our models on the open-source Voxpopuli, LibriSpeech and in-house conversational datasets. Overall, our proposed model reduces the degradation of the streaming mode over the non-streaming full-contextual model from 41.7% and 45.7% to 16.7% and 26.2% on the LibriSpeech test-clean and test-other datasets respectively, while improving by a relative 15.5% WER over the previous state-of-the-art unified model.
Coded Speech Quality Measurement by a Non-Intrusive PESQ-DNN
Authors: Ziyi Xu, Ziyue Zhao, Tim Fingscheidt
Date: 2023-04-18 00:00:00

Wideband codecs such as AMR-WB or EVS are widely used in (mobile) speech communication. Evaluation of coded speech quality is often performed subjectively by an absolute category rating (ACR) listening test. However, the ACR test is impractical for online monitoring of speech communication networks. Perceptual evaluation of speech quality (PESQ) is one of the widely used metrics instrumentally predicting the results of an ACR test. However, the PESQ algorithm requires an original reference signal, which is usually unavailable in network monitoring, thus limiting its applicability. NISQA is a new non-intrusive neural-network-based speech quality measure, focusing on super-wideband speech signals. In this work, however, we aim at predicting the well-known PESQ metric using a non-intrusive PESQ-DNN model. We illustrate the potential of this model by predicting the PESQ scores of wideband-coded speech obtained from AMR-WB or EVS codecs operating at different bitrates in noisy, tandeming, and error-prone transmission conditions. We compare our methods with the state-of-the-art network topologies of QualityNet, WaweNet, and DNSMOS -- all applied to PESQ prediction -- by measuring the mean absolute error (MAE) and the linear correlation coefficient (LCC). The proposed PESQ-DNN offers the best total MAE and LCC of 0.11 and 0.92, respectively, in conditions without frame loss, and still is best when including frame loss. Note that our model could be similarly used to non-intrusively predict POLQA or other (intrusive) metrics. Upon article acceptance, code will be provided at GitHub.
Neural Speech Enhancement with Very Low Algorithmic Latency and Complexity via Integrated Full- and Sub-Band Modeling
Authors: Zhong-Qiu Wang, Samuele Cornell, Shukjae Choi, Younglo Lee, Byeong-Yeol Kim, Shinji Watanabe
Date: 2023-04-18 00:00:00
Tasks: Speech Enhancement

We propose FSB-LSTM, a novel long short-term memory (LSTM) based architecture that integrates full- and sub-band (FSB) modeling, for single- and multi-channel speech enhancement in the short-time Fourier transform (STFT) domain. The model maintains an information highway to flow an over-complete input representation through multiple FSB-LSTM modules. Each FSB-LSTM module consists of a full-band block to model spectro-temporal patterns at all frequencies and a sub-band block to model patterns within each sub-band, where each of the two blocks takes a down-sampled representation as input and returns an up-sampled discriminative representation to be added to the block input via a residual connection. The model is designed to have a low algorithmic complexity, a small run-time buffer and a very low algorithmic latency, at the same time producing a strong enhancement performance on a noisy-reverberant speech enhancement task even if the hop size is as low as $2$ ms.
How Tiny Can Analog Filterbank Features Be Made for Ultra-low-power On-device Keyword Spotting?
Authors: Subhajit Ray, Xinghua Sun, Nolan Tremelling, Maria Gordiyenko, Peter Kinget
Date: 2023-04-17 00:00:00
Tasks: Keyword Spotting

Analog feature extraction is a power-efficient and re-emerging signal processing paradigm for implementing the front-end feature extractor in on device keyword-spotting systems. Despite its power efficiency and re-emergence, there is little consensus on what values the architectural parameters of its critical block, the analog filterbank, should be set to, even though they strongly influence power consumption. Towards building consensus and approaching fundamental power consumption limits, we find via simulation that through careful selection of its architectural parameters, the power of a typical state-of-the-art analog filterbank could be reduced by 33.6x, while sacrificing only 1.8% in downstream 10-word keyword spotting accuracy through a back-end neural network.
Generalized Analytical Estimation of Sensitivity Matrices in Unbalanced Distribution Networks
Authors: Salish Maharjan, Rui Cheng, Zhaoyu Wang
Date: 2023-04-19 00:00:00
Tasks: Sensitivity

Fast and accurate estimation of sensitivity matrices is significant for the enhancement of distribution system modeling and automation. Analytical estimations have mainly focused on voltage magnitude sensitivity to active/reactive power injections for unbalance networks with Wye-connected loads and neglecting DERs' smart inverter functionality. Hence, this paper enhances the scope of analytical estimation of sensitivity matrices for unbalanced networks with 1-phase, 2-phase, and 3-phase Delta/Wye-connected loads, DERs with smart inverter functionality, and substation/line step-voltage regulators (SVR). A composite bus model comprising of DER, Delta- and Wye-connected load is proposed to represent a generic distribution bus, which can be simplified to load, PV, or voltage-controlled bus as required. The proposed matrix-based analytical method consolidates voltage magnitude and angle sensitivity to active/reactive power injection and tap-position of all SVRs into a single algorithm. Extensive case studies on IEEE networks show the accuracy and wide scope of the proposed algorithm compared to the existing benchmark method.
State estimation of an electrochemical lithium-ion battery model: improved observer performance by hybrid redesign
Authors: E. Petri, T. Reynaudo, R. Postoyan, D. Astolfi, D. Nesic, S. Rael
Date: 2023-04-19 00:00:00
Tasks: Management, State Estimation

Effective management and just-in-time maintenance of lithium-ion batteries require the knowledge of unmeasured (internal) variables that need to be estimated. Observers are thus designed for this purpose using a mathematical model of the battery internal dynamics. It appears that it is often difficult to tune the observers to obtain good estimation performances both in terms of convergence speed and accuracy, while these are essential in practice. In this context, we demonstrate how a recently developed hybrid multi-observer can be used to improve the performance of a given observer designed for an electrochemical model of a lihium-ion battery. Simulation results, obtained with standard parameters values, show the estimation performance improvement using the proposed method.
Learning controllers from data via kernel-based interpolation
Authors: Zhongjie Hu, Claudio De Persis, Pietro Tesi
Date: 2023-04-19 00:00:00

We propose a data-driven control design method for nonlinear systems that builds on kernel-based interpolation. Under some assumptions on the system dynamics, kernel-based functions are built from data and a model of the system, along with deterministic model error bounds, is determined. Then, we derive a controller design method that aims at stabilizing the closed-loop system by cancelling out the system nonlinearities. The proposed method can be implemented using semidefinite programming and returns positively invariant sets for the closed-loop system.
Network Algebraization and Port Relationship for Power-Electronic-Dominated Power Systems
Authors: Rui Ma, Xiaowen Yang, Meng Zhan
Date: 2023-04-19 00:00:00

Different from the quasi-static network in the traditional power system, the dynamic network in the power-electronic-dominated power system should be considered due to rapid response of converters' controls. In this paper, a nonlinear differential-algebraic model framework is established with algebraic equations for dynamic electrical networks and differential equations for the (source) nodes, by generalizing the Kron reduction. The internal and terminal voltages of source nodes including converters are chosen as ports of nodes and networks. Correspondingly, the impact of dynamic network becomes clear, namely, it serves as a voltage divider and generates the terminal voltage based on the internal voltage of the sources instantaneously, even when the dynamics of inductance are included. With this simplest model, the roles of both nodes and the network become apparent.Simulations verify the proposed model framework in the modified 9-bus system.
Model-Free Control Design Procedure Applied to Lateral Vehicle Control
Authors: M. Moreno-Gonzalez, J. Villagra
Date: 2023-04-18 00:00:00

Model-Free Control has proven its performance in a wide variety of systems. Although its adequate tuning can be achieved using the knowledge of the system and optimization-based approaches, there is not yet a systematic design procedure for this kind of control scheme. In this paper, a non-iterative Three Term Controller tuning procedure is adapted and extended to fit Model-free controllers' structure. This procedure is successfully applied to design the lateral control of an automated car with realistic performance requirements.
Optimal Energy Rationing for Prepaid Electricity Customers
Authors: Maitreyee Marathe, Line A. Roald
Date: 2023-04-18 00:00:00
Tasks: energy management, Management

For a large (and recently increasing) number of households, affordability is a major hurdle in accessing sufficient electricity and avoiding service disconnections. For such households, in-home energy rationing, i.e. the need to actively prioritize how to use a limited amount of electricity, is an everyday reality. In this paper, we consider a particularly vulnerable group of customers, namely prepaid electricity customers, who are required to pay for their electricity a-priori. With this group of customers in mind, we propose an optimization-based energy management framework to effectively use a limited budget and avoid the disruptions and fees associated with disconnections. The framework considers forecasts of future use and knowledge of appliance power ratings to help customers prioritize and limit use of low-priority loads, with the goal of extending access to their critical loads. Importantly, the proposed management system has minimal requirements in terms of in-home hardware and remote communication, lending itself well to adoption across different regions and utility programs. Our case study demonstrates that by considering both current and future electricity consumption and more effectively managing access to low-priority loads, the proposed framework increases the value provided to customers and avoids disconnections.
Fast or Cheap: Time and Energy Optimal Control of Ship-to-Shore Cranes
Authors: Filipe Marques Barbosa, Anton Kullberg, Johan Löfberg
Date: 2023-04-18 00:00:00

This paper addresses the trade-off between time- and energy-efficiency for the problem of loading and unloading a ship. Container height constraints and energy consumption and regeneration are dealt with. We build upon a previous work that introduced a coordinate system suitable to deal with container avoidance constraints and incorporate the energy related modeling. In addition to changing the coordinate system, standard epigraph reformulations result in an optimal control problem with improved numerical properties. The trade-off is dealt with through the use of weighting of the total time and energy consumption in the cost function. An illustrative example is provided, demonstrating that the energy consumption can be substantially reduced while retaining approximately the same loading time.
Towards Real-World Indoor Smart Electromagnetic Environments -- A Large-Scale Experimental Demonstration
Authors: Arianna Benoni, Federico Capra, Marco Salucci, Andrea Massa
Date: 2023-04-18 00:00:00

To the best of the authors' knowledge, this work presents the first large-scale indoor experimental assessment of an implementation of the emerging Smart ElectroMagnetic Environment (SEME) paradigm, which is based on the deployment of static-passive EM skins (SP-EMSs) to enhance the coverage in a 5 [GHz] Wi-Fi network. Unlike standard (laboratory-based) validations reported in the state-of-the-art (SoA) literature, the scenario at hand mimics a realistic indoor environment to replicate as close as possible the user experience when using commodity devices. Representative results from the experimental field trials are re-ported to confirm the performance predictions arising from the numerical studies and the tolerance analyses carried out with a commercial ray-tracing (RT) tool. Besides experimentally validating the SEME idea, this study is also aimed at (roughly) quantifying the economic advantage of a SEME implementation, relying on simple-manufacturing/low-cost field manipulating devices without any additional biasing circuitry, with respect to standard approaches that imply the densification of the active radiating sources.
Complexity reduction for resilient state estimation of uniformly observable nonlinear systems
Authors: Junsoo Kim, Jin Gyu Lee, Henrik Sandberg, Karl H. Johansson
Date: 2023-04-18 00:00:00
Tasks: State Estimation

A resilient state estimation scheme for uniformly observable nonlinear systems, based on a method for local identification of sensor attacks, is presented. The estimation problem is combinatorial in nature, and so many methods require substantial computational and storage resources as the number of sensors increases. To reduce the complexity, the proposed method performs the attack identification with local subsets of the measurements, not with the set of all measurements. A condition for nonlinear attack identification is introduced as a relaxed version of existing redundant observability condition. It is shown that an attack identification can be performed even when the state cannot be recovered from the measurements. As a result, although a portion of measurements are compromised, they can be locally identified and excluded from the state estimation, and thus the true state can be recovered. Simulation results demonstrate the effectiveness of the proposed scheme.
Distributed Search Planning in 3-D Environments With a Dynamically Varying Number of Agents
Authors: Savvas Papaioannou, Panayiotis Kolios, Theocharis Theocharides, Christos G. Panayiotou, Marios M. Polycarpou
Date: 2023-04-18 00:00:00
Tasks: Model Predictive Control

In this work, a novel distributed search-planning framework is proposed, where a dynamically varying team of autonomous agents cooperate in order to search multiple objects of interest in three-dimension (3-D). It is assumed that the agents can enter and exit the mission space at any point in time, and as a result the number of agents that actively participate in the mission varies over time. The proposed distributed search-planning framework takes into account the agent dynamical and sensing model, and the dynamically varying number of agents, and utilizes model predictive control (MPC) to generate cooperative search trajectories over a finite rolling planning horizon. This enables the agents to adapt their decisions on-line while considering the plans of their peers, maximizing their search planning performance, and reducing the duplication of work.
Guidance of the resonance energy flow in the mechanism of coupled magnetic pendulums
Authors: Valery N. Pilipchuk, Krystian Polczyński, Maksymilian Bednarek, Jan Awrejcewicz
Date: 2023-04-18 00:00:00
Tasks: Descriptive

This paper presents a methodology of controlling the resonance energy exchange in mechanical system consisting of two weakly coupled magnetic pendulums interacting with the magnetic field generated by coils placed underneath. It is shown that properly guided magnetic fields can effectively change mechanical potentials in a way that the energy flow between the oscillators takes the desired direction. Studies were considered by using a specific set of descriptive functions characterizing the total excitation level, its distribution between the pendulums, and the phase shift. The developed control strategies are based on the observation that, in the case of antiphase oscillation, the energy is moving from the pendulum subjected to the repelling magnetic field, to the oscillator under the attracting field. In contrast, during the inphase oscillations, the energy flow is reversed. Therefore, closed-loop controller requires only the information about phase shift, which is easily estimated from dynamic state signals through the coherency index. Advantage of suggested control strategy is that the temporal rate of inputs is dictated by the speed of beating, which is relatively slow compared to the carrying oscillations.
AoI-Delay Tradeoff in Mobile Edge Caching: A Mixed-Order Drift-Plus-Penalty Algorithm
Authors: Ran Li, Chuan Huang, Xiaoqi Qin, Lei Yang
Date: 2023-04-18 00:00:00
Tasks: Decision Making, Scheduling, Sequential Decision Making

Mobile edge caching (MEC) is a promising technique to improve the quality of service (QoS) for mobile users (MU) by bringing data to the network edge. However, optimizing the crucial QoS aspects of message freshness and service promptness, measured by age of information (AoI) and service delay, respectively, entails a tradeoff due to their competition for shared edge resources. This paper investigates this tradeoff by formulating their weighted sum minimization as a sequential decision-making problem, incorporating high-dimensional, discrete-valued, and linearly constrained design variables. First, to assess the feasibility of the considered problem, we characterize the corresponding achievable region by deriving its superset with the rate stability theorem and its subset with a novel stochastic policy, and develop a sufficient condition for the existence of solutions. Next, to efficiently solve this problem, we propose a mixed-order drift-plus-penalty algorithm by jointly considering the linear and quadratic Lyapunov drifts and then optimizing them with dynamic programming (DP). Finally, by leveraging the Lyapunov optimization technique, we demonstrate that the proposed algorithm achieves an $O(1/V)$ versus $O(V)$ tradeoff for the average AoI and average service delay.