pwc-archive/papers-with-abstracts huggingface dataset:
Self-Supervised Feature Learning of 1D Convolutional Neural Networks with Contrastive Loss for Eating Detection Using an In-Ear Microphone
Authors: Vasileios Papapanagiotou, Christos Diou, Anastasios Delopoulos
Date: 2021-08-02 00:00:00
Tasks: image-classification, Image Classification

The importance of automated and objective monitoring of dietary behavior is becoming increasingly accepted. The advancements in sensor technology along with recent achievements in machine-learning--based signal-processing algorithms have enabled the development of dietary monitoring solutions that yield highly accurate results. A common bottleneck for developing and training machine learning algorithms is obtaining labeled data for training supervised algorithms, and in particular ground truth annotations. Manual ground truth annotation is laborious, cumbersome, can sometimes introduce errors, and is sometimes impossible in free-living data collection. As a result, there is a need to decrease the labeled data required for training. Additionally, unlabeled data, gathered in-the-wild from existing wearables (such as Bluetooth earbuds) can be used to train and fine-tune eating-detection models. In this work, we focus on training a feature extractor for audio signals captured by an in-ear microphone for the task of eating detection in a self-supervised way. We base our approach on the SimCLR method for image classification, proposed by Chen et al. from the domain of computer vision. Results are promising as our self-supervised method achieves similar results to supervised training alternatives, and its overall effectiveness is comparable to current state-of-the-art methods. Code is available at https://github.com/mug-auth/ssl-chewing .
A Survey on Audio Synthesis and Audio-Visual Multimodal Processing
Authors: Zhaofeng Shi
Date: 2021-08-01 00:00:00
Tasks: Audio Synthesis, Music Generation, Survey, text-to-speech, Text to Speech

With the development of deep learning and artificial intelligence, audio synthesis has a pivotal role in the area of machine learning and shows strong applicability in the industry. Meanwhile, significant efforts have been dedicated by researchers to handle multimodal tasks at present such as audio-visual multimodal processing. In this paper, we conduct a survey on audio synthesis and audio-visual multimodal processing, which helps understand current research and future trends. This review focuses on text to speech(TTS), music generation and some tasks that combine visual and acoustic information. The corresponding technical methods are comprehensively classified and introduced, and their future development trends are prospected. This survey can provide some guidance for researchers who are interested in the areas like audio synthesis and audio-visual multimodal processing.
Aircraft turnaround time estimation in early design phases: simulation tools development and application to the case of box-wing architecture
Authors: Marco Picchi Scardaoni, Fabio Magnacca, Andrea Massai, Vittorio Cipolla
Date: 2021-08-02 00:00:00

This work deals with the problem of estimating the turnaround time in the early stages of aircraft design. The turnaround time has a significant impact in terms of marketability and value creation potential of an aircraft and, for this reason, it should be considered as an important driver of fuselage and cabin design decisions. Estimating the turnaround time during the early stages of aircraft design is therefore an essential task. This task becomes even more decisive when designers explore unconventional aircraft architectures or, in general, are still evaluating the fuselage design and its internal layout. In particular, it is of paramount importance to properly estimate the boarding and deboarding times, which contribute for up the 40% to the overall turnaround time. For this purpose, a tool, called SimBaD, has been developed and validated with publicly available data for existing aircraft of different classes. In order to demonstrate SimBaD capability of evaluating the influence of fuselage and cabin features on the turnaround time, its application to an unconventional box-wing aircraft architecture, known as PrandtlPlane, is presented as case study. Finally, considering standard scenarios provided by aircraft manufacturers, a comparison between the turnaround time of the PrandtlPlane and the turnaround time of a conventional competitor aircraft is presented.
Control Design of Dynamic Virtual Power Plants: An Adaptive Divide-and-Conquer Approach
Authors: Verena Häberle, Michael W. Fisher, Eduardo Prieto-Araujo, Florian Dörfler
Date: 2021-08-02 00:00:00

In this paper, we present a novel control approach for dynamic virtual power plants (DVPPs). In particular, we consider a group of heterogeneous distributed energy resources (DERs) which collectively provide desired dynamic ancillary services such as fast frequency and voltage control. Our control approach relies on an adaptive divide-and-conquer strategy: first, we disaggregate the desired frequency and voltage control specifications of the aggregate DVPP via adaptive dynamic participation matrices (ADPMs) to obtain the desired local behavior for each device. Second, we design local linear parameter-varying (LPV) $\mathcal{H}_\infty$ controllers to optimally match this local behaviors. In the process, the control design also incorporates the physical and engineered limits of each DVPP device. Furthermore, our adaptive control design can properly respond to fluctuating device capacities, and thus include weather-driven DERs into the DVPP setup. Finally, we demonstrate the effectiveness of our control strategy in a case study based on the IEEE nine-bus system.
Complexity of the LTI system trajectory boundedness problem
Authors: Guillaume O. Berger, Raphaël M. Jungers
Date: 2021-08-02 00:00:00

We study the algorithmic complexity of the problem of deciding whether a Linear Time Invariant dynamical system with rational coefficients has bounded trajectories. Despite its ubiquitous and elementary nature in Systems and Control, it turns out that this question is quite intricate, and, to the best of our knowledge, unsolved in the literature. We show that classical tools, such as Gaussian Elimination, the Routh--Hurwitz Criterion, and the Euclidean Algorithm for GCD of polynomials indeed allow for an algorithm that is polynomial in the bit size of the instance. However, all these tools have to be implemented with care, and in a non-standard way, which relies on an advanced analysis.
Quantitatively Nonblocking Supervisory Control of Discrete-Event Systems
Authors: Renyuan Zhang, Jiahao Wang, Zenghui Wang, Kai Cai
Date: 2021-08-02 00:00:00

In this paper, we propose two new nonblocking properties of automata as quantitative measures of maximal distances to marker states. The first property, called {\em quantitative nonblockingness}, captures the practical requirement that at least one of the marker states (representing e.g. task completion) be reachable within a prescribed number of steps from any non-marker state and following any trajectory of the system. The second property, called {\em heterogeneously quantitative nonblockingness}, distinguishes individual marker states and requires that each marker state be reached within a given bounded number of steps from any other state and following any trajectory of the system. Accordingly, we formulate two new problems of quantitatively nonblocking supervisory control and heterogeneously quantitatively nonblocking supervisory control, and characterize their solvabilities in terms of new concepts of quantitative language completability and heterogeneously quantitative language completability, respectively. It is proved that there exists the unique supremal (heterogeneously) quantitatively completable sublanguage of a given language, and we develop effective algorithms to compute the supremal sublanguages. Finally, combining with the algorithm of computing the supremal controllable sublanguage, we design algorithms to compute the maximally permissive solutions to the formulated (heterogeneously) quantitatively nonblocking supervisory control problems.
Zonotope-based Controller Synthesis for LTL Specifications
Authors: Wei Ren, Julien Calbert, Raphael Jungers
Date: 2021-08-02 00:00:00

This paper studies the controller synthesis problem for Linear Temporal Logic (LTL) specifications using (constrained) zonotope techniques. First, we implement (constrained) zonotope techniques to partition the state space and further to verify whether the LTL specification can be satisfied. Once the LTL specification can be satisfied, the next step is to design a controller to guarantee the satisfaction of the LTL specification for dynamic systems. Based on the verification of the LTL specification, an abstraction-based control design approach is proposed in this paper: a novel abstraction construction is developed first, then finite local abstract controllers are designed to achieve the LTL specification, and finally the designed abstract controllers are combined and refined as the controller for the original system. The proposed control strategy is illustrated via a numerical example from autonomous robots.
Nonlinear Controller Design with Prediction Horizon Time Reduction Applied to Unstable CSTR System
Authors: Chinmay Rajhans, Sowmya Gupta
Date: 2021-08-02 00:00:00
Tasks: Model Predictive Control

Ensuring nominal asymptotic stability of the Nonlinear Model Predictive Control controller is not trivial. Stabilizing ingredients such as terminal penalty term and terminal region are crucial in establishing the asymptotic stability. Current work presents alternate approaches namely arbitrary controller based approach and linear quadratic regulator based approach, which provide larger degrees of freedom for enlarging the terminal region as against conservative approaches from the literature. Efficacy of the proposed approaches is demonstrated using benchmark two state continuous stirrer tank reactor system around an unstable operating point. Terminal regions obtained using the arbitrary controller based approach and linear quadratic regulator based approach are approximately 45 and 412 times larger by area measure when compared to the largest terminal region obtained using the approach from the literature. As a result, there is significant reduction in the prediction and control horizon time.
Distributed Channel Access for Control Over Unknown Memoryless Communication Channels
Authors: Tahmoores Farjam, Henk Wymeersch, Themistoklis Charalambous
Date: 2021-03-10 00:00:00

We consider the distributed channel access problem for a system consisting of multiple control subsystems that close their loop over a shared wireless network. We propose a distributed method for providing deterministic channel access without requiring explicit information exchange between the subsystems. This is achieved by utilizing timers for prioritizing channel access with respect to a local cost which we derive by transforming the control objective cost to a form that allows its local computation. This property is then exploited for developing our distributed deterministic channel access scheme. A framework to verify the stability of the system under the resulting scheme is then proposed. Next, we consider a practical scenario in which the channel statistics are unknown. We propose learning algorithms for learning the parameters of imperfect communication links for estimating the channel quality and, hence, define the local cost as a function of this estimation and control performance. We establish that our learning approach results in collision-free channel access. The behavior of the overall system is exemplified via a proof-of-concept illustrative example, and the efficacy of this mechanism is evaluated for large-scale networks via simulations.
Quantum Scheduling for Millimeter-Wave Observation Satellite Constellation
Authors: Joongheon Kim, Yunseok Kwak, Soyi Jung, Jae-Hyun Kim
Date: 2021-08-02 00:00:00
Tasks: Scheduling

In beyond 5G and 6G network scenarios, the use of satellites has been actively discussed for extending target monitoring areas, even for extreme circumstances, where the monitoring functionalities can be realized due to the usage of millimeter-wave wireless links. This paper designs an efficient scheduling algorithm which minimizes overlapping monitoring areas among observation satellite constellation. In order to achieve this objective, a quantum optimization based algorithm is used because the overlapping can be mathematically modelled via a max-weight independent set (MWIS) problem which is one of well-known NP-hard problems.
Frequency support Scheme based on parametrized power curve for de-loaded Wind Turbine under various wind speed
Authors: Cheng Zhong, Yueming Lv, Huayi Li, Jikai Chen, Yang Li
Date: 2021-08-02 00:00:00

With increased wind power penetration in modern power systems, wind plants are required to provide frequency support similar to conventional plants. However, for the existing frequency regulation scheme of wind turbines, the control gains in the auxiliary frequency controller are difficult to set because of the compromise of the frequency regulation performance and the stable operation of wind turbines, especially when the wind speed remains variable. This paper proposes a novel frequency regulation scheme (FRS) for de-loaded wind turbines. Instead of an auxiliary frequency controller, frequency support is provided by modifying the parametrized power versus rotor speed curve, including the inertia power versus rotor speed curve and the droop power versus rotor speed curve. The advantage of the proposed scheme is that it does not contain any control gains and generally adapts to different wind speeds. Further, the proposed scheme can work for the whole section of wind speed without wind speed measurement information. The compared simulation results demonstrate the scheme improves the system frequency response while ensuring the stable operation of doubly-fed induction generators (DFIGs)-based variable-speed wind turbines (VSWTs) under various wind conditions. Furthermore, the scheme prevents rotor speed overdeceleration even when the wind speed decreases during frequency regulation control.
Fuzzy-based Higher Adaptive Order Sliding Mode Observers
Authors: Mohamed Camil Belhadjoudja
Date: 2021-08-01 00:00:00

In this article, I introduce the notion of Fuzzy-based Higher adaptive order sliding mode observers through the example of the super-twisting adaptive order sliding mode observer. I begin by presenting the super-twisting second order sliding mode observer for systems in triangular form. Then I replace the sign functions with fuzzy inference systems in order to eliminate the chattering effect. I then show that this variant of the super-twisting second order sliding mode observer may not converge depending on the input, so I replace the square root with an adaptive power $\gamma$. The efficiency of the proposed method is illustrated by simulations made on MATLAB/Simulink.
Green Hydrogen Plant: Optimal control strategies for integrated hydrogen storage and power generation with wind energy
Authors: Arjen T. Veenstra, Albert H. Schrotenboer, Michiel A. J. Uit het Broek, Evrim Ursavas
Date: 2021-08-01 00:00:00

The intermittent nature of renewable energy resources such as wind and solar causes the energy supply to be less predictable leading to possible mismatches in the power network. To this end, hydrogen production and storage can provide a solution by increasing flexibility within the system. Stored hydrogen can either be converted back to electricity or it can be used as feed-stock for industry, heating for built environment, and as fuel for vehicles. This research examines the optimal strategies for operating integrated energy systems consisting of renewable energy production and hydrogen storage. Using Markov decision process theory, we construct optimal policies for day-to-day decisions on how much energy to store as hydrogen, or buy from or sell to the electricity market, and on how much hydrogen to sell for use as gas. We pay special emphasis to practical settings, such as contractually binding power purchase agreements, varying electricity prices, different distribution channels, green hydrogen offtake agreements, and hydrogen market price uncertainties. Extensive experiments and analysis are performed in the context of Northern Netherlands where Europe's first Hydrogen Valley is being formed. Results show that substantial gains in operational revenues of up to 51\% are possible by introducing hydrogen storage units and competitive hydrogen market-prices. This amounts to a \euro 126,000 increase in revenues per turbine per year for a 4.5 MW wind turbine. Moreover, our results indicate that hydrogen offtake agreements will be crucial in keeping the energy transition on track.
Practical Adoption of Cloud Computing in Power Systems- Drivers, Challenges, Guidance, and Real-world Use Cases
Authors: Song Zhang, Amritanshu Pandey, Xiaochuan Luo, Maggy Powell, Ranjan Banerji, Lei Fan, Abhineet Parchure, Edgardo Luzcando
Date: 2021-07-31 00:00:00
Tasks: Cloud Computing

Motivated by The Federal Energy Regulatory Commission's (FERC) recent direction and ever-growing interest in cloud adoption by power utilities, a Task Force was established to assist power system practitioners with secure, reliable and cost-effective adoption of cloud technology to meet various business needs. This paper summarizes the business drivers, challenges, guidance, and best practices for cloud adoption in power systems from the Task Force's perspective, after extensive review and deliberation by its members, including grid operators, utility companies, software vendors, and cloud providers. The paper begins by enumerating various business drivers for cloud adoption in the power industry. It follows with the discussion of the challenges and risks of migrating power grid utility workloads to the cloud. Next, for each corresponding challenge or risk, the paper provides appropriate guidance. Notably, the guidance is directed toward power industry professionals who are considering cloud solutions and are yet hesitant about the practical execution. Finally, to tie all the sections together, the paper documents various real-world use cases of cloud technology in the power system domain, which both the power industry practitioners and software vendors can look toward to design and select their own future cloud solutions. We hope that the information in this paper will serve as helpful guidance for the development of NERC guidelines and standards relevant to cloud adoption in the industry.
Performance assessment and tuning of PID control using TLBO: the single-loop case and PI/P cascade case
Authors: Wei zhang, He Dong, Yunlang Xu, Xiaoping Li
Date: 2021-07-31 00:00:00
Tasks: Stochastic Optimization

Proportional-integral-derivative (PID) control, the most common control strategy in the industry, always suffers from health problems resulting from external disturbances, improper tuning, etc. Therefore, there have been many studies on control performance assessment (CPA) and optimal tuning. Minimum output variance (MOV) is used as a benchmark for CPA of PID, but it is difficult to be found due to the associated non-convex optimization problem. For the optimal tuning, many different objective functions have been proposed, but few consider the stochastic disturbance rejection. In this paper, a multi-objective function simultaneously considering integral of absolute error (IAE) and MOV is proposed to optimize PID for better disturbance rejection. The non-convex problem and multi-objective problem are solved by teaching-learning-based optimization (TLBO). This stochastic optimization algorithm can guarantee a tighter lower bound for MOV due to the excellent capability of local optima avoidance and needs less calculation time due to the low complexity. Furthermore, CPA and the tuning method are extended to the PI/P cascade case. The results of several numerical examples of CPA problems show that TLBO can generate better MOV than existing methods within one second on most examples. The simulation results of the tuning method applied to two temperature control systems reveal that the weight of the multi-objective function can compromise other performance criteria such as overshoot and settling time to improve the disturbance rejection. It also indicates that the tuning method can be utilized to multi-stage PID control strategy to resolve the contradiction between disturbance rejection and other performance criteria.
Dynamic Virtual Power Plant: A New Concept for Grid Integration of Renewable Energy Sources
Authors: B. Marinescu, O. Gomis-Bellmunt, F. Dörfler, H. Schulte, L. Sigrist
Date: 2021-07-31 00:00:00

The notion of Virtual Power Plant (VPP) has been used many times in last years in power systems and for several reasons. As a general trend, the behavior of a classic synchronous generator is to be emulated for a class of conventional grid components like, e.g., renewable generators or/and power electronic units. Most of the times production of these units is of interest, as it is the case for the new AGC scheme of Spain which, from this point of view, looks like a VPP. However, dynamic aspects are of high importance, especially for increasing the actual rate of penetration of Renewable Energy Sources (RES). Indeed, to go above the actual rate of RES penetration, one should deal with full participation of RES to grid services. For that, we propose here a new concept called Dynamic VPP (DVPP) which fully integrates the dynamic aspects at all levels: locally (for each RES generator), globally (for grid ancillary services and interaction with other neighbor elements of the grid) and economically (for internal optimal dispatch and participation to electricity markets). A DVPP is a set of RES along with a set of control and operation procedures. This means methodologies for: choosing the participating RES, optimal and continuous operation as a whole (especially in case of loss of natural resources - e.g., wind, sun - on a part of the DVPP), regulation (in the dynamic sense) to ensure local objectives for each generator, participation to ancillary services of the DVPP as a unit and to diminish negative effects of interaction with neighbor dynamics elements of the power system, integration in both actual power systems scenarios (with mixed classic and power electronics based generation) and future ones with high degree of RES penetration.
Interruption flows for reliability evaluation of power distribution networks
Authors: Fabio Luiz Usberti, Celso Cavellucci, Christiano Lyra
Date: 2021-07-30 00:00:00

Energy networks should strive for reliability. How can it be assessed, measured, and improved? What are the best trade-offs between investments and their worth? The flow-based framework for the reliability assessment of energy networks proposed in this paper addresses these questions with a focus on power distribution networks. The framework introduces the concept of iflows, or interruption flows, which translate the analytical reliability evaluation into solving a series of node balance equations computable in linear time. The iflows permeate the network, providing relevant information to support linear formulations of reliability optimization problems. Numerical examples showcase the evaluation process obtained through iflows in illustrative distribution networks with distributed generation. A visual representation of the reliability state provides insights into the most critical regions of the network. A case study of the optimal allocation of switches in power distribution systems is described. Computational experiments were conducted using a benchmark of distribution networks, having up to 881 nodes. The results confirm the effectiveness of the approach in terms of providing high-quality information and optimal trade-offs to aid reliability decisions for energy networks.