pwc-archive/papers-with-abstracts huggingface dataset:
Pathway to Future Symbiotic Creativity
Authors: Yike Guo, Qifeng Liu, Jie Chen, Wei Xue, Jie Fu, Henrik Jensen, Fernando Rosas, Jeffrey Shaw, Xing Wu, Jiji Zhang, Jianliang Xu
Date: 2022-08-18 00:00:00
Tasks: Philosophy

This report presents a comprehensive view of our vision on the development path of the human-machine symbiotic art creation. We propose a classification of the creative system with a hierarchy of 5 classes, showing the pathway of creativity evolving from a mimic-human artist (Turing Artists) to a Machine artist in its own right. We begin with an overview of the limitations of the Turing Artists then focus on the top two-level systems, Machine Artists, emphasizing machine-human communication in art creation. In art creation, it is necessary for machines to understand humans' mental states, including desires, appreciation, and emotions, humans also need to understand machines' creative capabilities and limitations. The rapid development of immersive environment and further evolution into the new concept of metaverse enable symbiotic art creation through unprecedented flexibility of bi-directional communication between artists and art manifestation environments. By examining the latest sensor and XR technologies, we illustrate the novel way for art data collection to constitute the base of a new form of human-machine bidirectional communication and understanding in art creation. Based on such communication and understanding mechanisms, we propose a novel framework for building future Machine artists, which comes with the philosophy that a human-compatible AI system should be based on the "human-in-the-loop" principle rather than the traditional "end-to-end" dogma. By proposing a new form of inverse reinforcement learning model, we outline the platform design of machine artists, demonstrate its functions and showcase some examples of technologies we have developed. We also provide a systematic exposition of the ecosystem for AI-based symbiotic art form and community with an economic model built on NFT technology. Ethical issues for the development of machine artists are also discussed.
Optimized Precoding for MU-MIMO With Fronthaul Quantization
Authors: Yasaman Khorsandmanesh, Emil Björnson, Joakim Jaldén
Date: 2022-09-05 00:00:00
Tasks: Quantization

One of the first widespread uses of multi-user multiple-input multiple-output (MU-MIMO) is in 5G networks, where each base station has an advanced antenna system (AAS) that is connected to the baseband unit (BBU) with a capacity-constrained fronthaul. In the AAS configuration, multiple passive antenna elements and radio units are integrated into a single box. This paper considers precoded downlink transmission over a single-cell MU-MIMO system. We study optimized linear precoding for AAS with a limited-capacity fronthaul, which requires the precoding matrix to be quantized. We propose a new precoding design that is aware of the fronthaul quantization and minimizes the mean-squared error at the receiver side. We compute the precoding matrix using a sphere decoding (SD) approach. We also propose a heuristic low-complexity approach to quantized precoding. This heuristic is computationally efficient enough for massive MIMO systems. The numerical results show that our proposed precoding significantly outperforms quantization-unaware precoding and other previous approaches in terms of the sum rate. The performance loss for our heuristic method compared to quantization-aware precoding is insignificant considering the complexity reduction, which makes the heuristic method feasible for real-time applications. We consider both perfect and imperfect channel state information.
Augmented Deep Unfolding for Downlink Beamforming in Multi-cell Massive MIMO With Limited Feedback
Authors: Yifan Ma, Xianghao Yu, Jun Zhang, S. H. Song, Khaled B. Letaief
Date: 2022-09-03 00:00:00
Tasks: Quantization

In limited feedback multi-user multiple-input multiple-output (MU-MIMO) cellular networks, users send quantized information about the channel conditions to the associated base station (BS) for downlink beamforming. However, channel quantization and beamforming have been treated as two separate tasks conventionally, which makes it difficult to achieve global system optimality. In this paper, we propose an augmented deep unfolding (ADU) approach that jointly optimizes the beamforming scheme at the BSs and the channel quantization scheme at the users. In particular, the classic WMMSE beamformer is unrolled and a deep neural network (DNN) is leveraged to pre-process its input to enhance the performance. The variational information bottleneck technique is adopted to further improve the performance when the feedback capacity is strictly restricted. Simulation results demonstrate that the proposed ADU method outperforms all the benchmark schemes in terms of the system average rate.
Learning to Predict on Octree for Scalable Point Cloud Geometry Coding
Authors: Yixiang Mao, Yueyu Hu, Yao Wang
Date: 2022-09-06 00:00:00
Tasks: Decoder, Denoising

Octree-based point cloud representation and compression have been adopted by the MPEG G-PCC standard. However, it only uses handcrafted methods to predict the probability that a leaf node is non-empty, which is then used for entropy coding. We propose a novel approach for predicting such probabilities for geometry coding, which applies a denoising neural network to a "noisy" context cube that includes both neighboring decoded voxels as well as uncoded voxels. We further propose a convolution-based model to upsample the decoded point cloud at a coarse resolution on the decoder side. Integration of the two approaches significantly improves the rate-distortion performance for geometry coding compared to the original G-PCC standard and other baseline methods for dense point clouds. The proposed octree-based entropy coding approach is naturally scalable, which is desirable for dynamic rate adaptation in point cloud streaming systems.
Sound Event Localization and Detection for Real Spatial Sound Scenes: Event-Independent Network and Data Augmentation Chains
Authors: Jinbo Hu, Yin Cao, Ming Wu, Qiuqiang Kong, Feiran Yang, Mark D. Plumbley, Jun Yang
Date: 2022-09-05 00:00:00
Tasks: Data Augmentation, Direction of Arrival Estimation, Event Detection, Room Impulse Response (RIR), Sound Event Detection, Sound Event Localization and Detection

Sound event localization and detection (SELD) is a joint task of sound event detection and direction-of-arrival estimation. In DCASE 2022 Task 3, types of data transform from computationally generated spatial recordings to recordings of real-sound scenes. Our system submitted to the DCASE 2022 Task 3 is based on our previous proposed Event-Independent Network V2 (EINV2) with a novel data augmentation method. Our method employs EINV2 with a track-wise output format, permutation-invariant training, and a soft parameter-sharing strategy, to detect different sound events of the same class but in different locations. The Conformer structure is used for extending EINV2 to learn local and global features. A data augmentation method, which contains several data augmentation chains composed of stochastic combinations of several different data augmentation operations, is utilized to generalize the model. To mitigate the lack of real-scene recordings in the development dataset and the presence of sound events being unbalanced, we exploit FSD50K, AudioSet, and TAU Spatial Room Impulse Response Database (TAU-SRIR DB) to generate simulated datasets for training. We present results on the validation set of Sony-TAu Realistic Spatial Soundscapes 2022 (STARSS22) in detail. Experimental results indicate that the ability to generalize to different environments and unbalanced performance among different classes are two main challenges. We evaluate our proposed method in Task 3 of the DCASE 2022 challenge and obtain the second rank in the teams ranking. Source code is released.
Priority Based Synchronization for Faster Learning in Games
Authors: Abbasali Koochakzadeh, Yasin Yazıcıoğlu
Date: 2022-09-06 00:00:00

Learning in games has been widely used to solve many cooperative multi-agent problems such as coverage control, consensus, self-reconfiguration or vehicle-target assignment. One standard approach in this domain is to formulate the problem as a potential game and to use an algorithm such as log-linear learning to achieve the stochastic stability of globally optimal configurations. Standard versions of such learning algorithms are asynchronous, i.e., only one agent updates its action at each round of the learning process. To enable faster learning, we propose a synchronization strategy based on decentralized random prioritization of agents, which allows multiple agents to change their actions simultaneously when they do not affect each other's utility or feasible actions. We show that the proposed approach can be integrated into any standard asynchronous learning algorithm to improve the convergence speed while maintaining the limiting behavior (e.g., stochastically stable configurations). We support our theoretical results with simulations in a coverage control scenario.
Fast Adaptive Regression-based Model Predictive Control
Authors: Eslam Mostafa, Hussein A. Aly, Ahmed Elliethy
Date: 2022-09-06 00:00:00
Tasks: model, Model Predictive Control, regression

Model predictive control (MPC) is an optimal control method that predicts the future states of the system being controlled and estimates the optimal control inputs that drive the predicted states to the required reference. The computations of the MPC are performed at pre-determined sample instances over a finite time horizon. The number of sample instances and the horizon length determine the performance of the MPC and its computational cost. A long horizon with a large sample count allows the MPC to better estimate the inputs when the states have rapid changes over time, which results in better performance but at the expense of high computational cost. However, this long horizon is not always necessary, especially for slowly-varying states. In this case, a short horizon with less sample count is preferable as the same MPC performance can be obtained but at a fraction of the computational cost. In this paper, we propose an adaptive regression-based MPC that predicts the best minimum horizon length and the sample count from several features extracted from the time-varying changes of the states. The proposed technique builds a synthetic dataset using the system model and utilizes the dataset to train a support vector regressor that performs the prediction. The proposed technique is experimentally compared with several state-of-the-art techniques on both linear and non-linear models. The proposed technique shows a superior reduction in computational time with a reduction of about 35-65\% compared with the other techniques without introducing a noticeable loss in performance.
Ecosystem for Demand-side Flexibility Revisited: The Danish Solution
Authors: Peter Alexander Vistar Gade, Trygve Skjøtskift, Henrik W. Bindner, Jalal Kazempour
Date: 2022-09-06 00:00:00

Denmark has recently set a legislation called Market Model 3.0 to make the ecosystem for demand-side flexibility more attractive to stakeholders involved. The main change is to relax the previous mandate that required each aggregator to be associated with a retailer and a balance responsible party. We explain the rationale behind such a change and its implications, particularly on the pre-qualification of demand-side portfolios providing ancillary services.
Compatibility checking of multiple control barrier functions for input constrained systems
Authors: Xiao Tan, Dimos V. Dimarogonas
Date: 2022-09-06 00:00:00

State and input constraints are ubiquitous in control system design. One recently developed tool to deal with these constraints is control barrier functions (CBF) which transform state constraints into conditions in the input space. CBF-based controller design thus incorporates both the CBF conditions and input constraints in a quadratic program. However, the CBF-based controller is well-defined only if the CBF conditions are compatible. In the case of perturbed systems, robust compatibility is of relevance. In this work, we propose an algorithmic solution to verify or falsify the (robust) compatibility of given CBFs a priori. Leveraging the Lipschitz properties of the CBF conditions, a grid sampling and refinement method with theoretical analysis and guarantees is proposed.
The Twin-in-the-Loop approach for vehicle dynamics control
Authors: Federico Dettù, Simone Formentin, Sergio Matteo Savaresi
Date: 2022-09-06 00:00:00

In vehicle dynamics control, engineering a suitable regulator is a long and costly process. The starting point is usually the design of a nominal controller based on a simple control-oriented model and its testing on a full-fledged simulator. Then, many driving hours are required during the End-of-Line (EoL) tuning phase to calibrate the controller for the physical vehicle. Given the recent technological advances, in this paper we consider the pioneering perspective where the simulator can be run on-board in the electronic control unit, so as to calculate the nominal control action in real-time. In this way, it can be shown that, in the EoL phase, we only need to tune a simple compensator of the mismatch between the expected and the measured outputs. The resulting approach not only exploits the already available simulator and nominal controller and significantly simplifies the design process, but also outperforms the state-of-the-art in terms of tracking accuracy and robustness within a challenging active braking control case study.
Reinforcement learning-based optimised control for tracking of nonlinear systems with adversarial attacks
Authors: Farshad Rahimi, Sepideh Ziaei
Date: 2022-09-05 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper introduces a reinforcement learning-based tracking control approach for a class of nonlinear systems using neural networks. In this approach, adversarial attacks were considered both in the actuator and on the outputs. This approach incorporates a simultaneous tracking and optimization process. It is necessary to be able to solve the Hamilton-Jacobi-Bellman equation (HJB) in order to obtain optimal control input, but this is difficult due to the strong nonlinearity terms in the equation. In order to find the solution to the HJB equation, we used a reinforcement learning approach. In this online adaptive learning approach, three neural networks are simultaneously adapted: the critic neural network, the actor neural network, and the adversary neural network. Ultimately, simulation results are presented to demonstrate the effectiveness of the introduced method on a manipulator.
Cooperative Predictive Cruise Control: A Bargaining Game Approach
Authors: Miguel F. Arevalo-Castiblanco, Jaime Pachon, Duvan Tellez-Castro, Eduardo Mojica-Nava
Date: 2022-09-05 00:00:00
Tasks: Decision Making

This paper considers a cooperative cruise control problem from a predictive control perspective. Online decision-making is used to be executed during the driving process based on the information obtained from the network. We formalize a synchronization problem approach from a predictive control theory using bargaining games to find an operating agreement between the vehicles. Finally, we test these results in an emulation environment in a hardware-in-the-loop system.
Granular Compensation, Information, and Carbon Pricing Promote DER Deployment
Authors: Hafiz Anwar Ullah Khan, Burçin Ünel, Yury Dvorkin
Date: 2022-09-05 00:00:00

The socially efficient deployment of Distributed Energy Resources (DERs), e.g., rooftop solar, depends on the underlying retail electricity policies. Current debates on DER policies, including Net Energy Metering (NEM) variants, center around developing value-reflective compensation policies that can expedite DER deployment while preventing potential cost shifts between DER adopters and non-adopters. However, these debates mostly ignore the temporally- and spatially- granular value of DERs, market failures (e.g., information asymmetry among DER stakeholders) and externalities (e.g., carbon-dioxide emissions). In this paper, we develop a game-theoretic approach with information asymmetry to examine efficiency implications of adopting granular DER compensation policies, e.g., value stacks and distributional locational marginal price, instead of NEM with flat retail rates. We show that granular compensation policies result in more efficient market outcomes than under NEM, even in the presence of information asymmetry, thus avoiding the need for interventions. Combined with granular DER compensation, carbon pricing provides the most accurate price signal to DER investors/aggregators, and leads to the highest social welfare.
Evaluation of the Region of Attractions of Higher Dimensional Hyperbolic Systems using the Extended Dynamic Mode Decomposition
Authors: Camilo Garcia-Tenorio, Duvan Tellez-Castro, Eduardo Mojica-Nava, Alain Vande Wouwer
Date: 2022-09-05 00:00:00

This paper proposes an original methodology to compute the regions of attraction in hyperbolic and polynomial nonlinear dynamical systems using the eigenfunctions of the discrete-time approximation of the Koopman operator given by the extended dynamic mode decomposition algorithm. The proposed method relies on the spectral decomposition of the Koopman operator to build eigenfunctions that capture the boundary of the region of attraction. The algorithm relies solely on data that can be collected in experimental studies and does not require a mathematical model of the system. Two examples of dynamical systems, a population model and a higher dimensional chemical reaction system, allows demonstrating the reliability of the results.
A negative imaginary approach to hybrid integrator-gain system control
Authors: Kanghong Shi, Nastaran Nikooienejad, Ian R. Petersen, S. O. Reza Moheimani
Date: 2022-09-05 00:00:00

In this paper, we show that a hybrid integrator-gain system (HIGS) is a nonlinear negative imaginary (NNI) system. We prove that the positive feedback interconnection of a linear negative imaginary (NI) system and a HIGS is asymptotically stable. We apply the HIGS to a MEMS nanopositioner, as an example of a linear NI system, in a single-input single-output framework. We analyze the stability and the performance of the closed-loop interconnection in both time and frequency domains through simulations and demonstrate the applicability of HIGS as an NNI controller to a linear NI system.
Recursive Gaussian Process over graphs for Integrating Multi-timescale Measurements in Low-Observable Distribution Systems
Authors: Shweta Dahale, Balasubramaniam Natarajan
Date: 2022-09-04 00:00:00
Tasks: Matrix Completion, State Estimation, Time Series, Time Series Analysis

The transition to a smarter grid is empowered by enhanced sensor deployments and smart metering infrastructure in the distribution system. Measurements from these sensors and meters can be used for many applications, including distribution system state estimation (DSSE). However, these measurements are typically sampled at different rates and could be intermittent due to losses during the aggregation process. These multi time-scale measurements should be reconciled in real-time to perform accurate grid monitoring. This paper tackles this problem by formulating a recursive multi-task Gaussian process (RGP-G) approach that sequentially aggregates sensor measurements. Specifically, we formulate a recursive multi-task GP with and without network connectivity information to reconcile the multi time-scale measurements in distribution systems. The proposed framework is capable of aggregating the multi-time scale measurements batch-wise or in real-time. Following the aggregation of the multi time-scale measurements, the spatial states of the consistent time-series are estimated using matrix completion based DSSE approach. Simulation results on IEEE 37 and IEEE 123 bus test systems illustrate the efficiency of the proposed methods from the standpoint of both multi time-scale data aggregation and DSSE.
Extension of a Linear Controller Scheme to Non-Linear Systems and its Application on Inverted Pendulum
Authors: Justin Jacob, Navin Khaneja
Date: 2022-09-04 00:00:00

This paper presents the control and stabilization of the rotary inverted pendulum based on a general controller scheme. The proposed scheme has its foundation in classical control theory, and the importance of an integrator in disturbance rejection is emphasized. The system's dynamics are obtained by the Euler Lagrange method and are approximated for small-angle as balancing the pendulum is the objective. Experimental results demonstrate that the proposed control scheme can achieve the stabilization of a non-linear system. Also, the boundedness and convergence of the non-linear system with the controller subjected to the initial condition are validated.
Flux Linkage Based Evaluation Method for Voltage Inertia and Voltage Recovery Capability Under Large Disturbances
Authors: Yinhong Lin, Huaichang Ge, Bin Wang, Qinglai Guo, Hongbin Sun
Date: 2022-09-04 00:00:00

High-voltage direct current (HVDC) transmission applications and the growth of the dynamic load in large-scale receiving-end grids lead to a higher risk of short-term voltage instability. An effective way to address this problem is to improve the system's dynamic voltage support capability by changing the operation status of dynamic var devices, and the dynamic var reserve (DVR) is commonly used. Due to the time delay in synchronous machine excitation systems, the dynamic var reserved at steady state cannot be exploited immediately under large disturbances. In addition, some reactive power is produced immediately through electromagnetic induction. The voltage support effect of the two capabilities is analyzed based on the flux linkage and an approximate simulation of the fault impact. Then two novel indexes for evaluating the voltage inertia and voltage recovery capability are proposed, which are related to the voltage nadir and voltage recovery speed. The indexes' physical meanings are revealed by comparison with the frequency response, and the potential applications in planning and optimal reactive power dispatch (ORPD) are introduced. Numerical simulations based on the IEEE 39-bus system verify that the indexes can quantify the voltage support capabilities, and the minimum voltage support requirements are obtained to maintain systems' security.
An interpretative and adaptive MPC for nonlinear systems
Authors: Liang Wu
Date: 2022-09-04 00:00:00
Tasks: C++ code, Model Predictive Control

Model predictive control (MPC) for nonlinear systems suffers a trade-off between the model accuracy and real-time computational burden. One widely used approximation method is the successive linearization MPC (SL-MPC) with EKF method, in which the EKF algorithm is to handle unmeasured disturbances and unavailable full states information. Inspired by this, an interpretative and adaptive MPC (IA-MPC) method, is presented in this paper. In our IA-MPC method, a linear state-space model is firstly obtained by performing the linearization of a first-principle-based model at the initial point, and then this linear state-space model is transformed into an equivalent ARX model. This interpretative ARX model is then updated online by the EKF algorithm, which is modified as a decoupled one without matrix-inverse operator. The corresponding ARX-based MPC problem are solved by our previous construction-free, matrix-free and library-free CDAL-ARX algorithm. This simple library-free C-code implementation would significantly reduce the difficulty in deploying nonlinear MPC on embedded platforms. The performance of the IA-MPC method is tested against the nonlinear MPC with EKF and SL-MPC with EKF method in four typical nonlinear benchmark examples, which show the effectiveness of our IA-MPC method.