pwc-archive/papers-with-abstracts huggingface dataset:
TeCANet: Temporal-Contextual Attention Network for Environment-Aware Speech Dereverberation
Authors: Helin Wang, Bo Wu, LianWu Chen, Meng Yu, Jianwei Yu, Yong Xu, Shi-Xiong Zhang, Chao Weng, Dan Su, Dong Yu
Date: 2021-03-31 00:00:00
Tasks: Room Impulse Response (RIR), Speech Dereverberation

In this paper, we exploit the effective way to leverage contextual information to improve the speech dereverberation performance in real-world reverberant environments. We propose a temporal-contextual attention approach on the deep neural network (DNN) for environment-aware speech dereverberation, which can adaptively attend to the contextual information. More specifically, a FullBand based Temporal Attention approach (FTA) is proposed, which models the correlations between the fullband information of the context frames. In addition, considering the difference between the attenuation of high frequency bands and low frequency bands (high frequency bands attenuate faster than low frequency bands) in the room impulse response (RIR), we also propose a SubBand based Temporal Attention approach (STA). In order to guide the network to be more aware of the reverberant environments, we jointly optimize the dereverberation network and the reverberation time (RT60) estimator in a multi-task manner. Our experimental results indicate that the proposed method outperforms our previously proposed reverberation-time-aware DNN and the learned attention weights are fully physical consistent. We also report a preliminary yet promising dereverberation and recognition experiment on real test data.
Generalized State-Feedback Controller Synthesis for Underactuated Systems through Bayesian Optimization
Authors: Miguel A. Solis, Sinnu Susan Thomas
Date: 2021-03-31 00:00:00
Tasks: Bayesian Optimization

Underactuated systems pose the challenge of being able to control a plant whose degrees of freedom are not necessarily directly linked to an actuator or where such a relationship is not straightforward. Rotary inverted pendulum is an example of such systems, which on its simplest representation consists of a pendulum whose vertical angle should be taken up to the upward unstable position based on the impulse given from another bar and an appropriate control strategy, bar that is controlled by an electrical motor. This problem is often tackled by linear control theory with state-feedback controllers that is frequently obtained by means of designing a feedback gain meeting some constraints. This article reports a Bayesian Optimization approach for designing a generalized state-feedback controller, that involves more parameters than a simple state-feedback control law, but with the benefit of achieving lower control effort in terms of the signal amplitude. The source code is made publicly available to facilitate further research.
Transactive Resilience in Renewable Microgrids: A Contract-Theoretic Approach
Authors: Juntao Chen, Yunhan Huang, Quanyan Zhu
Date: 2021-03-31 00:00:00
Tasks: energy trading

Renewable energy-based microgrids play a critical role in future smart grids. Due to the uncertainties of renewable generations, the microgrids face potential risk of load shedding during operation. To address this problem, we propose a contract-based approach to enhance the resilience of microgrids. Specifically, in the framework, the microgrids who may not be self-efficient to meet their local demands can purchase the needed power from their connected microgrids by signing a contract that specifies the power price in advance. We leverage a principal-agent model to capture the energy trading relationships between the microgrids through a resilience as a service (RaaS) paradigm. By focusing on the incentive compatible and individual rational constraints of the service requester, the service provider designs the optimal contracts for the transactive resilience that yields the largest payoff despite the incomplete information. We characterize analytical solutions of the optimal contracts for several scenarios where the service requester has various options on its hidden actions. Numerical simulations are used to illustrate and corroborate the obtained results.
Static extraction of memory access profiles for multi-core interference analysis of real-time tasks
Authors: Thomas Carle, Hugues Cassé
Date: 2021-03-31 00:00:00

We present a static analysis framework for real-time task systems running on multi-core processors. Our method analyzes tasks in isolation at the binary level and generates worst-case timing and memory access profiles. These profiles can then be combined to perform an interference analysis at the task system level, as part of a multi-core Worst-Case Response Time (WCRT) analysis. In this paper we introduce a formal description of the models and algorithmic building blocks composing our framework. We also discuss how the memory access profiles generated by our method could be used to feed existing state-of-the-art WCRT frameworks. To the best of our knowledge, it is the first time that a method is documented on how to produce sound, safe and precise inputs for interference analysis methods.
Capturing Power System Dynamics by Physics-Informed Neural Networks and Optimization
Authors: Georgios S. Misyris, Jochen Stiasny, Spyros Chatzivasileiadis
Date: 2021-03-31 00:00:00

This paper proposes a tractable framework to determine key characteristics of non-linear dynamic systems by converting physics-informed neural networks to a mixed integer linear program. Our focus is on power system applications. Traditional methods in power systems require the use of a large number of simulations and other heuristics to determine parameters such as the critical clearing time, i.e. the maximum allowable time within which a disturbance must be cleared before the system moves to instability. The work proposed in this paper uses physics-informed neural networks to capture the power system dynamic behavior and, through an exact transformation, converts them to a tractable optimization problem which can be used to determine critical system indices. By converting neural networks to mixed integer linear programs, our framework also allows to adjust the conservativeness of the neural network output with respect to the existing stability boundaries. We demonstrate the performance of our methods on the non-linear dynamics of converter-based generation in response to voltage disturbances.
Multicriteria design and experimental verification of hybrid renewable energy systems. Application to electric vehicle charging stations
Authors: Paula Bastida-Molina, Elias Hurtado-Perez, Maria Cristina Moros Gomez, Carlos Vargas-Salgado
Date: 2021-03-31 00:00:00

The installation of electric vehicle charging stations (EVCS) will be essential to promote the acceptance by the users of electric vehicles (EVs). However, if EVCS are exclusively supplied by the grid, negative impacts on its stability together with possible CO2 emission increases could be produced. Introduction of hybrid renewable energy systems (HRES) for EVCS can cope with both drawbacks by reducing the load on the grid and generating clean electricity. This paper develops a methodology based on a weighted multicriteria process to design the most suitable configuration for HRES in EVCS. This methodology determines the local renewable resources and the EVCS electricity demand. Then, taking into account environmental, economic and technical aspects, it deduces the most adequate HRES design for the EVCS. Besides, an experimental stage to validate the design deduced from the multicriteria process is included. Therefore, the final design for the HRES in EVCS is supported not only by a complete numerical evaluation, but also by an experimental verification of the demand being fully covered. Methodology application to Valencia (Spain) proves that an off-grid HRES with solar PV, wind resources and batteries support would be the most suitable configuration for the system. This solution was also experimentally verified.
A Transactive Energy Market Framework Considering Network Constraints and Fairness
Authors: Fredmar Asarias, Michael Angelo Pedrasa
Date: 2021-03-31 00:00:00
Tasks: Fairness

The continuous penetration of distributed energy resources (DER) in the electric power grid is driving a new paradigm shift towards transactive energy system (TES), an active and more sustainable system characterized by distributed generation and energy exchanges among consumers and producers in the network. This transition, however, comes with challenges such as dealing with the nonlinear and non-convex power flows of the system, determining an optimal transaction price to maximize overall system welfare, and ensuring fairness for all participants. In this paper, we propose a three-stage transactive energy framework that aims to address these challenges. In the first stage, the cost without trading is calculated which will serve as the reference in the profit maximization problem in the next stage. DER dispatch, power flows and initial transaction payments/incentives of the participants will then be determined in the second stage. A benefit allocation algorithm is applied in the third control stage to determine the optimal transaction price and final payments/incentives that will ensure fairness for trading participants. The proposed framework was tested in an IEEE 33-bus system and results show that fair benefits are given for all participants during trading and the system operates within the network and economic constraints.
Learning Robustness with Bounded Failure: An Iterative MPC Approach
Authors: Monimoy Bujarbaruah, Akhil Shetty, Kameshwar Poolla, Francesco Borrelli
Date: 2019-11-22 00:00:00

We propose an approach to design a Model Predictive Controller (MPC) for constrained Linear Time Invariant systems performing an iterative task. The system is subject to an additive disturbance, and the goal is to learn to satisfy state and input constraints robustly. Using disturbance measurements after each iteration, we construct Confidence Support sets, which contain the true support of the disturbance distribution with a given probability. As more data is collected, the Confidence Supports converge to the true support of the disturbance. This enables design of an MPC controller that avoids conservative estimate of the disturbance support, while simultaneously bounding the probability of constraint violation. The efficacy of the proposed approach is then demonstrated with a detailed numerical example.
Islanded Microgrid Restoration Studies with Graph-Based Analysis
Authors: Ogbonnaya Bassey, Karen L. Butler-Purry
Date: 2021-03-30 00:00:00

The need to restore and keep the grid running or fast restoration during emergencies such as extreme weather conditions is quite apparent given the reliance of other infrastructure on electricity. One promising approach to electricity restoration is the use of locally available energy resources to restore the system to form isolated microgrids. In this paper, we present a black start restoration method that forms islanded microgrids after a blackout. The master DGs in the formed microgrids are coordinated to work together through droop control. Several constraints, including incentive-based demand response (DR) with direct load control (DLC) and distributed generator (DG) operation constraints, were formulated and linearized to realize a mixed-integer linear programming (MILP) restoration model. To improve compactness and to ensure that the model is neither under-sized nor over-sized, a pre-processing graph analysis approach was introduced which helps to characterize the least number of restoration steps needed to optimally restore the microgrid. Studies were performed on a modified IEEE 123 node test feeder to evaluate the effects of demand response, non-dispatchable DGs, and choice of restoration steps on the quality of the restoration solution.
Energy Optimization in Massive MIMO UAV-Aided MEC-Enabled Vehicular Networks
Authors: Emmanouel T. Michailidis, Nikolaos I. Miridakis, Angelos Michalas, Emmanouil Skondras, Dimitrios J. Vergados, Dimitrios D. Vergados
Date: 2021-02-07 00:00:00
Tasks: Edge-computing, Scheduling

This paper presents a novel unmanned aerial vehicle (UAV) aided mobile edge computing (MEC) architecture for vehicular networks. It is considered that the vehicles should complete latency critical computation intensive tasks either locally with on-board computation units or by offloading part of their tasks to road side units (RSUs) with collocated MEC servers. In this direction, a hovering UAV can serve as an aerial RSU (ARSU) for task processing or act as an aerial relay and further offload the computation tasks to a ground RSU (GRSU). In order to significantly reduce the delay during data offloading and downloading, this architecture relies on the benefits of massive multiple input multiple output (MIMO). Therefore, it is considered that the vehicles, the ARSU, and the GRSU employ large scale antennas. A three dimensional (3D) geometrical representation of the MEC enabled network is introduced and an optimization method is proposed that minimizes the weighted total energy consumption (WTEC) of the vehicles and ARSU subject to transmit power allocation, task allocation, and timeslot scheduling. The numerical results verify the theoretical derivations, emphasize on the effectiveness of the massive MIMO transmission, and provide useful engineering insights.
Toward Reliable Designs of Data-Driven Reinforcement Learning Tracking Control for Euler-Lagrange Systems
Authors: Zhikai Yao, Jennie Si, Ruofan Wu, Jianyong Yao
Date: 2020-12-31 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper addresses reinforcement learning based, direct signal tracking control with an objective of developing mathematically suitable and practically useful design approaches. Specifically, we aim to provide reliable and easy to implement designs in order to reach reproducible neural network-based solutions. Our proposed new design takes advantage of two control design frameworks: a reinforcement learning based, data-driven approach to provide the needed adaptation and (sub)optimality, and a backstepping based approach to provide closed-loop system stability framework. We develop this work based on an established direct heuristic dynamic programming (dHDP) learning paradigm to perform online learning and adaptation and a backstepping design for a class of important nonlinear dynamics described as Euler-Lagrange systems. We provide a theoretical guarantee for the stability of the overall dynamic system, weight convergence of the approximating nonlinear neural networks, and the Bellman (sub)optimality of the resulted control policy. We use simulations to demonstrate significantly improved design performance of the proposed approach over the original dHDP.
The Intrinsic Communication in Power Systems: A New Perspective to Understand Synchronization Stability
Authors: Yitong Li, Timothy C. Green, Yunjie Gu
Date: 2021-03-30 00:00:00

Synchronization is an essential element in three-phase ac electric power systems. The large-scale integration of converter-interfaced resources leads to the power grid transformation from voltage-source-dominated to voltage-current-source-composite, which also raises new challenges to model and analyze the system synchronization. In this article, we present the intrinsic analogy of a power system to a communication system, which is here called power-communication isomorphism. Based on this isomorphism, we revisit power system synchronization stability from a communication perspective and thereby establish a theory that unifies the synchronization dynamics of heterogeneous power apparatuses. In particular, the proposed theory is used to interpret and analyze, for example, the different power control speeds of power apparatuses; the role of network line dynamics in synchronization analysis; and the synchronization capability of current-source-dominated (e.g., grid-following-converter-dominated) grids. The findings are verified on the IEEE 68-bus test system.
好奇，国家总理吃过的北京大学食堂到底长啥样？都有哪些特色菜？超清视频【字幕】
Authors: 科学博士
Date: 2021-02-28 00:00:00

好奇，国家总理吃过的北京大学食堂到底长啥样？都有哪些特色菜？超清视频
#科学博士​ #北京大学​ #食堂​ #美食​ #特色菜​ #国家总理​
本视频记得打开CC字幕。

★推荐影片★
北大未名湖畔的小猫咪居然不怕人好萌呀，赏美景撸猫咪，么么哒！超清视频
https://youtu.be/NkdHdWJ9-SI​

走进北京大学图书馆，亚洲最大，知识的圣殿，多少人梦寐以求想在这里看书
https://youtu.be/jErniaRkv9k​

真的吗？北大未名湖结冰后人可以在湖面行走？北京大学
https://youtu.be/fT9JIlQjJBQ
北大未名湖镇湖之宝| 圆明园历史文物翻尾石鱼，近距离观赏【字幕】
Authors: 科学博士
Date: 2021-03-24 00:00:00

北大未名湖镇湖之宝| 圆明园历史文物翻尾石鱼，近距离观赏
#科学博士​ #北大未名湖​ #翻尾石鱼​ #圆明园文物​ #镇湖之宝​ #北京大学​
本视频记得打开CC字幕。

★推荐影片★
好奇，国家总理吃过的北京大学食堂到底长啥样？都有哪些特色菜？超清视频
https://youtu.be/Vf2JfClMges​

北大未名湖畔的小猫咪居然不怕人好萌呀，赏美景撸猫咪，么么哒！超清视频
https://youtu.be/NkdHdWJ9-SI​

走进北京大学图书馆，亚洲最大，知识的圣殿，多少人梦寐以求想在这里看书
https://youtu.be/jErniaRkv9k​

真的吗？北大未名湖结冰后人可以在湖面行走？北京大学
https://youtu.be/fT9JIlQjJBQ​

北大校园内的宫廷小红楼，这些古建筑文物里面到底是啥样？北京大学
https://youtu.be/WDcbmpfcQGc
Dimension reduction of open-high-low-close data in candlestick chart based on pseudo-PCA
Authors: Wenyang Huang, Huiwen Wang, Shanshan Wang
Date: 2021-03-31 00:00:00
Tasks: Dimensionality Reduction

The (open-high-low-close) OHLC data is the most common data form in the field of finance and the investigate object of various technical analysis. With increasing features of OHLC data being collected, the issue of extracting their useful information in a comprehensible way for visualization and easy interpretation must be resolved. The inherent constraints of OHLC data also pose a challenge for this issue. This paper proposes a novel approach to characterize the features of OHLC data in a dataset and then performs dimension reduction, which integrates the feature information extraction method and principal component analysis. We refer to it as the pseudo-PCA method. Specifically, we first propose a new way to represent the OHLC data, which will free the inherent constraints and provide convenience for further analysis. Moreover, there is a one-to-one match between the original OHLC data and its feature-based representations, which means that the analysis of the feature-based data can be reversed to the original OHLC data. Next, we develop the pseudo-PCA procedure for OHLC data, which can effectively identify important information and perform dimension reduction. Finally, the effectiveness and interpretability of the proposed method are investigated through finite simulations and the spot data of China's agricultural product market.
Deep Noise Suppression With Non-Intrusive PESQNet Supervision Enabling the Use of Real Training Data
Authors: Ziyi Xu, Maximilian Strake, Tim Fingscheidt
Date: 2021-03-31 00:00:00
Tasks: Denoising, Speech Enhancement

Data-driven speech enhancement employing deep neural networks (DNNs) can provide state-of-the-art performance even in the presence of non-stationary noise. During the training process, most of the speech enhancement neural networks are trained in a fully supervised way with losses requiring noisy speech to be synthesized by clean speech and additive noise. However, in a real implementation, only the noisy speech mixture is available, which leads to the question, how such data could be advantageously employed in training. In this work, we propose an end-to-end non-intrusive PESQNet DNN which estimates perceptual evaluation of speech quality (PESQ) scores, allowing a reference-free loss for real data. As a further novelty, we combine the PESQNet loss with denoising and dereverberation loss terms, and train a complex mask-based fully convolutional recurrent neural network (FCRN) in a "weakly" supervised way, each training cycle employing some synthetic data, some real data, and again synthetic data to keep the PESQNet up-to-date. In a subjective listening test, our proposed framework outperforms the Interspeech 2021 Deep Noise Suppression (DNS) Challenge baseline overall by 0.09 MOS points and in particular by 0.45 background noise MOS points.
RIS-Assisted UAV for Timely Data Collection in IoT Networks
Authors: Ahmed Al-Hilo, Moataz Samir, Mohamed Elhattab, Chadi Assi, Sanaa Sharafeddine
Date: 2021-03-31 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, Edge-computing, Scheduling, Trajectory Planning

Intelligent Transportation Systems are thriving thanks to a wide range of technological advances, namely 5G communications, Internet of Things, artificial intelligence and edge computing. Central to this is the wide deployment of smart sensing devices and accordingly the large amount of harvested information to be processed for timely decision making. Robust network access is, hence, essential for offloading the collected data before a set deadline, beyond which the data loses its value. In environments where direct communication can be impaired by, for instance, blockages such as in urban cities, unmanned aerial vehicles (UAVs) can be considered as an alternative for providing and enhancing connectivity, particularly when IoT devices (IoTD) are constrained with their resources. Also, to conserve energy, IoTDs are assumed to alternate between their active and passive modes. This paper, therefore, considers a time-constrained data gathering problem from a network of sensing devices and with assistance from a UAV. A Reconfigurable Intelligent Surface (RIS) is deployed to further improve both the connectivity and energy efficiency of the UAV, particularly when multiple devices are served concurrently and experience different channel impairments. This integrated problem brings challenges related to the configuration of the phase shift elements of the RIS, the scheduling of IoTDs transmissions as well as the trajectory of the UAV. First, the problem is formulated with the objective of maximizing the total number of served devices each during its activation period. Owing to its complexity and the incomplete knowledge about the environment, we leverage deep reinforcement learning in our solution; the UAV trajectory planning is modeled as a Markov Decision Process, and Proximal Policy Optimization is invoked to solve it. Next, the RIS configuration is then handled via Block Coordinate Descent.
Space-Time Video Regularity and Visual Fidelity: Compression, Resolution and Frame Rate Adaptation
Authors: Dae Yeol Lee, Hyunsuk Ko, Jongho Kim, Alan C. Bovik
Date: 2021-03-31 00:00:00

In order to be able to deliver today's voluminous amount of video contents through limited bandwidth channels in a perceptually optimal way, it is important to consider perceptual trade-offs of compression and space-time downsampling protocols. In this direction, we have studied and developed new models of natural video statistics (NVS), which are useful because high-quality videos contain statistical regularities that are disturbed by distortions. Specifically, we model the statistics of divisively normalized difference between neighboring frames that are relatively displaced. In an extensive empirical study, we found that those paths of space-time displaced frame differences that provide maximal regularity against our NVS model generally align best with motion trajectories. Motivated by this, we build a new video quality prediction engine that extracts NVS features from displaced frame differences, and combines them in a learned regressor that can accurately predict perceptual quality. As a stringent test of the new model, we apply it to the difficult problem of predicting the quality of videos subjected not only to compression, but also to downsampling in space and/or time. We show that the new quality model achieves state-of-the-art (SOTA) prediction performance compared on the new ETRI-LIVE Space-Time Subsampled Video Quality (STSVQ) database, which is dedicated to this problem. Downsampling protocols are of high interest to the streaming video industry, given rapid increases in frame resolutions and frame rates.