pwc-archive/papers-with-abstracts huggingface dataset:
How do the resting EEG preprocessing states affect the outcomes of postprocessing?
Authors: Shiang Hu, Jie Ruan, Juan Hou, Pedro Antonio Valdes-Sosa, Zhao Lv
Date: 2023-10-22 00:00:00
Tasks: EEG, Functional Connectivity

Plenty of artifact removal tools and pipelines have been developed to correct the EEG recordings and discover the values below the waveforms. Without visual inspection from the experts, it is susceptible to derive improper preprocessing states, like the insufficient preprocessed EEG (IPE), and the excessive preprocessed EEG (EPE). However, little is known about the impacts of IPE or EPE on the postprocessing in the frequency, spatial and temporal domains, particularly as to the spectra and the functional connectivity (FC) analysis. Here, the clean EEG (CE) was synthesized as the ground truth based on the New-York head model and the multivariate autoregressive model. Later, the IPE and the EPE were simulated by injecting the Gaussian noise and losing the brain activities, respectively. Then, the impacts on postprocessing were quantified by the deviation caused by the IPE or EPE from the CE as to the 4 temporal statistics, the multichannel power, the cross spectra, the dispersion of source imaging, and the properties of scalp EEG network. Lastly, the association analysis was performed between the PaLOSi metric and the varying trends of postprocessing with the evolution of preprocessing states. This study shed light on how the postprocessing outcomes are affected by the preprocessing states and PaLOSi may be a potential effective quality metric.
Towards Subject Agnostic Affective Emotion Recognition
Authors: Amit Kumar Jaiswal, Haiming Liu, Prayag Tiwari
Date: 2023-10-20 00:00:00
Tasks: Domain Adaptation, EEG, Emotion Recognition, Meta-Learning

This paper focuses on affective emotion recognition, aiming to perform in the subject-agnostic paradigm based on EEG signals. However, EEG signals manifest subject instability in subject-agnostic affective Brain-computer interfaces (aBCIs), which led to the problem of distributional shift. Furthermore, this problem is alleviated by approaches such as domain generalisation and domain adaptation. Typically, methods based on domain adaptation confer comparatively better results than the domain generalisation methods but demand more computational resources given new subjects. We propose a novel framework, meta-learning based augmented domain adaptation for subject-agnostic aBCIs. Our domain adaptation approach is augmented through meta-learning, which consists of a recurrent neural network, a classifier, and a distributional shift controller based on a sum-decomposable function. Also, we present that a neural network explicating a sum-decomposable function can effectively estimate the divergence between varied domains. The network setting for augmented domain adaptation follows meta-learning and adversarial learning, where the controller promptly adapts to new domains employing the target data via a few self-adaptation steps in the test phase. Our proposed approach is shown to be effective in experiments on a public aBICs dataset and achieves similar performance to state-of-the-art domain adaptation methods while avoiding the use of additional computational resources.
FOLEY-VAE: Generación de efectos de audio para cine con inteligencia artificial
Authors: Mateo Cámara, José Luis Blanco
Date: 2023-10-24 00:00:00

In this research, we present an interface based on Variational Autoencoders trained with a wide range of natural sounds for the innovative creation of Foley effects. The model can transfer new sound features to prerecorded audio or microphone-captured speech in real time. In addition, it allows interactive modification of latent variables, facilitating precise and customized artistic adjustments. Taking as a starting point our previous study on Variational Autoencoders presented at this same congress last year, we analyzed an existing implementation: RAVE [1]. This model has been specifically trained for audio effects production. Various audio effects have been successfully generated, ranging from electromagnetic, science fiction, and water sounds, among others published with this work. This innovative approach has been the basis for the artistic creation of the first Spanish short film with sound effects assisted by artificial intelligence. This milestone illustrates palpably the transformative potential of this technology in the film industry, opening the door to new possibilities for sound creation and the improvement of artistic quality in film productions.
GESI: Gammachirp Envelope Similarity Index for Predicting Intelligibility of Simulated Hearing Loss Sounds
Authors: Ayako Yamamoto, Toshio Irino, Fuki Miyazaki, Honoka Tamaru
Date: 2023-10-23 00:00:00

We propose an objective intelligibility measure (OIM), called the Gammachirp Envelope Similarity Index (GESI), which can predict the speech intelligibility (SI) of simulated hearing loss (HL) sounds for normal hearing (NH) listeners. GESI is an intrusive method that computes the SI metric using the gammachirp filterbank (GCFB), the modulation filterbank, and the extended cosine similarity measure. The unique features of GESI are that i) it reflects the hearing impaired (HI) listener's HL that appears in the audiogram and is caused by active and passive cochlear dysfunction, ii) it provides a single goodness metric, as in the widely used STOI and ESTOI, that can be used immediately to evaluate SE algorithms, and iii) it provides a simple control parameter to accept the level asymmetry of the reference and test sounds and to deal with individual listening conditions and environments. We evaluated GESI and the conventional OIMs, STOI, ESTOI, MBSTOI, and HASPI versions 1 and 2 by using four SI experiments on words of male and female speech sounds in both laboratory and remote environments. GESI was shown to outperform the other OIMs in the evaluations. GESI could be used to improve SE algorithms in assistive listening devices for individual HI listeners.
An Iterative Approach to Data-Driven Inference for Decoding Oscillator Network Structures
Authors: Shicheng Li, Bharat Singhal, Jr-Shin Li
Date: 2023-10-24 00:00:00

In complex networks, interactions between multiple agents give rise to an array of intricate global dynamics, ranging from synchronization to cluster formations. Decoding the connectivity structure as well as the types of interactions from measurement data is the first step toward understanding these intriguing behaviors. In this paper, we present a bilinear optimization framework to infer both the connectivity and interaction functions of oscillator networks with the identical class of coupling functions. We then propose an iterative algorithm to solve the resulting bilinear problem and illustrate its convergence. We validate our approach on both simulated and noisy experimental datasets, where we demonstrate its effectiveness compared with existing approaches.
Comparison of Unscented Kalman Filter Design for Agricultural Anaerobic Digestion Model
Authors: Simon Hellmann, Terrance Wilms, Stefan Streif, Sören Weinrich
Date: 2023-10-24 00:00:00
Tasks: State Estimation

Dynamic operation of biological processes, such as anaerobic digestion (AD), requires reliable process monitoring to guarantee stable operating conditions at all times. Unscented Kalman filters (UKF) are an established tool for nonlinear state estimation, and there exist numerous variants of UKF implementations, treating state constraints, improvements of numerical performance and different noise cases. So far, however, a unified comparison of proposed methods emphasizing the algorithmic details is lacking. The present study thus examines multiple unconstrained and constrained UKF variants, addresses aspects crucial for direct implementation and applies them to a simplified AD model. The constrained UKF considering additive noise delivered the most accurate state estimations. The long run time of the underlying optimization could be vastly reduced through pre-calculated gradients and Hessian of the associated cost function, as well as by reformulation of the cost function as a quadratic program. However, unconstrained UKF variants showed lower run times at competitive estimation accuracy. This study provides useful advice to practitioners working with nonlinear Kalman filters by paying close attention to algorithmic details and modifications crucial for successful implementation.
A Behavioral Perspective on Models of Linear Dynamical Networks with Manifest Variables
Authors: Shengling Shi, Zhiyong Sun, Bart De Schutter
Date: 2023-10-24 00:00:00

Networks of dynamical systems play an important role in various domains and have motivated many studies on the control and analysis of linear dynamical networks. For linear network models considered in these studies, it is typically pre-determined what signal channels are inputs and what are outputs. These models do not capture the practical need to incorporate different experimental situations, where different selections of input and output channels are applied to the same network. Moreover, a unified view of different network models is lacking. This work makes an initial step towards addressing the above issues by taking a behavioral perspective, where input and output channels are not pre-determined. The focus of this work is on behavioral network models with only external variables. By exploiting the concept of hypergraphs, novel dual graphical representations, called system graphs and signal graphs, are introduced for behavioral networks. Moreover, connections between behavioral network models and structural vector autoregressive models are established. In addition to their connections in graphical representations, it is shown that the regularity of interconnections is an essential assumption when choosing a structural vector autoregressive model.
Modeling and Contribution of Flexible Heating Systems for Transmission Grid Congestion Management
Authors: David Kröger, Milijana Teodosic, Christian Rehtanz
Date: 2023-10-24 00:00:00
Tasks: Management

The large-scale integration of flexible heating systems in the European electricity market leads to a substantial increase of transportation requirements and consecutively grid congestions in the continental transmission grid. Novel model formulations for the grid-aware operation of both individual small-scale heat pumps and large-scale power-to-heat (PtH) units located in district heating networks are presented. The functionality of the models and the contribution of flexible heating systems for transmission grid congestion management is evaluated by running simulations for the target year 2035 for the German transmission grid. The findings show a decrease in annual conventional redispatch volumes and renewable energy sources (RES) curtailment resulting in cost savings of approximately 6 % through the integration of flexible heating systems in the grid congestion management scheme. The analysis suggests that especially large-scale PtH units in combination with thermal energy storages can contribute significantly to the alleviation of grid congestion and foster RES integration.
Fair Beam Allocations through Reconfigurable Intelligent Surfaces
Authors: Rujing Xiong, Ke Yin, Tiebin Mi, Jialong Lu, Kai Wan, Robert Caiming Qiu
Date: 2023-10-24 00:00:00

A fair beam allocation framework through reconfigurable intelligent surfaces (RISs) is proposed, incorporating the Max-min criterion. This framework focuses on designing explicit beamforming functionalities through optimization. Firstly, realistic models, grounded in geometrical optics, are introduced to characterize the input/output behaviors of RISs, effectively bridging the gap between the requirements on explicit beamforming operations and their practical implementations. Then, a highly efficient algorithm is developed for Max-min optimizations involving quadratic forms. Leveraging the Moreau-Yosida approximation, we successfully reformulate the original problem and propose iterations to attain the optimal solution. A comprehensive analysis of the algorithm's convergence is provided. Importantly, this approach exhibits excellent extensibility, making it readily applicable to address a broader class of Max-min optimization problems. Finally, numerical and prototype experiments are conducted to validate the effectiveness of the framework. With the proposed beam allocation framework and algorithm, we clarify that several crucial redistribution functionalities of RISs, such as explicit beam-splitting, fair beam allocation, and wide-beam generation, can be effectively implemented. These explicit beamforming functionalities have not been thoroughly examined previously.
Attitude Takeover Control for Noncooperative Space Targets Based on Gaussian Processes with Online Model Learning
Authors: YuHan Liu, Pengyu Wang, Chang-Hun Lee, Roland Tóth
Date: 2023-10-24 00:00:00
Tasks: Gaussian Processes

One major challenge for autonomous attitude takeover control for on-orbit servicing of spacecraft is that an accurate dynamic motion model of the combined vehicles is highly nonlinear, complex and often costly to identify online, which makes traditional model-based control impractical for this task. To address this issue, a recursive online sparse Gaussian Process (GP)-based learning strategy for attitude takeover control of noncooperative targets with maneuverability is proposed, where the unknown dynamics are online compensated based on the learnt GP model in a semi-feedforward manner. The method enables the continuous use of on-orbit data to successively improve the learnt model during online operation and has reduced computational load compared to standard GP regression. Next to the GP-based feedforward, a feedback controller is proposed that varies its gains based on the predicted model confidence, ensuring robustness of the overall scheme. Moreover, rigorous theoretical proofs of Lyapunov stability and boundedness guarantees of the proposed method-driven closed-loop system are provided in the probabilistic sense. A simulation study based on a high-fidelity simulator is used to show the effectiveness of the proposed strategy and demonstrate its high performance.
Negative Imaginary Control Using Hybrid Integrator-Gain Systems: Application to MEMS Nanopositioner
Authors: Kanghong Shi, Nastaran Nikooienejad, Ian R. Petersen, S. O. Reza Moheimani
Date: 2023-10-24 00:00:00

In this paper, we propose a new approach to address the control problem for negative imaginary (NI) systems by using hybrid integrator-gain systems (HIGS). We investigate the single HIGS of its original form and its two variations, including a multi-HIGS and the serial cascade of two HIGS. A single HIGS is shown to be a nonlinear negative imaginary system, and so is the multi-HIGS and the cascade of two HIGS. We show that these three types of HIGS can be used as controllers to asymptotically stabilize linear NI systems. The results of this paper are then illustrated in a real-world experiment where a 2-DOF microelectromechanical system nanopositioner is stabilized by a multi-HIGS.
Numerical Derivative-based Flexible Integration Algorithm for Power Electronic Systems Simulation Considering Nonlinear Components
Authors: Han Xu, Bochen Shi, Zhujun Yu, Jialin Zheng, Zhengming Zhao
Date: 2023-10-24 00:00:00
Tasks: Computational Efficiency

Simulation is an efficient tool in the design and control of power electronic systems. However, quick and accurate simulation of them is still challenging, especially when the system contains a large number of switches and state variables. Conventional general-purpose integration algorithms assume nonlinearity within systems but face inefficiency in handling the piecewise characteristics of power electronic switches. While some specialized algorithms can adapt to the piecewise characteristics, most of these methods require systems to be piecewise linear. In this article, a numerical derivative-based flexible integration algorithm is proposed. This algorithm can adapt to the piecewise characteristic caused by switches and have no difficulty when nonlinear non-switching components are present in the circuit. This algorithm consists of a recursive numerical scheme that obtains high-order time derivatives of nonlinear components and a decoupling strategy that further increases computational efficiency. The proposed method is applied to solve a motor derive system and a large-scale power conversion system (PCS) to verify its accuracy and efficiency by comparing experimental waveforms and simulated results given by commercial software. Our proposed method demonstrates several-fold acceleration compared to multiple commonly used algorithms in Simulink.
Multi-split configuration design for fluid-based thermal management systems
Authors: Saeid Bayat, Nastaran Shahmansouri, Satya RT Peddada, Alexander Tessier, Adrian Butscher, James T Allison
Date: 2023-10-24 00:00:00
Tasks: Management

High power density systems require efficient cooling to maintain their thermal performance. Despite this, as systems get larger and more complex, human practice and insight may not suffice to determine the desired thermal management system designs. To this end, a framework for automatic architecture exploration is presented in this article for a class of single-phase, multi-split cooling systems. For this class of systems, heat generation devices are clustered based on their spatial information, and flow-split are added only when required and at the location of heat devices. To generate different architectures, candidate architectures are represented as graphs. From these graphs, dynamic physics models are created automatically using a graph-based thermal modeling framework. Then, an optimal fluid flow distribution problem is solved by addressing temperature constraints in the presence of exogenous heat loads to achieve optimal performance. The focus in this work is on the design of general multi-split heat management systems. The architectures discussed here can be used for various applications in the domain of configuration design. The multi-split algorithm can produce configurations where splitting can occur at any of the vertices. The results presented include 3 categories of cases and are discussed in detail.
Nested Control Co-design of a Spar Buoy Horizontal-axis Floating Offshore Wind Turbine
Authors: Saeid Bayat, Yong Hoon Lee, James T. Allison
Date: 2023-10-24 00:00:00
Tasks: Computational Efficiency

Floating offshore wind turbine (FOWT) systems involve several coupled physical analysis disciplines, including aeroelasticity, multi-body structural dynamics, hydrodynamics, and controls. Conventionally, physical structure (plant) and control design decisions are treated as two separate problems, and generally, control design is performed after the plant design is complete. However, this sequential design approach cannot fully capitalize upon the synergy between plant and control design decisions. These conventional design practices produce suboptimal designs, especially in cases with strong coupling between plant and control design decisions. Control co-design (CCD) is a holistic design approach that accounts fully for plant-control design coupling by optimizing these decisions simultaneously. CCD is especially advantageous for system design problems with complex interactions between physics disciplines, which is the case for FOWT systems. This paper presents and demonstrates a nested CCD approach using open-loop optimal control (OLOC) for a simplified reduced-order model that simulates FOWT dynamic behavior. This simplified model is helpful for optimization studies due to its computational efficiency, but is still sufficiently rich enough to capture important multidisciplinary physics couplings and plant-control design coupling associated with a horizontal-axis FOWT system with a spar buoy floating platform. The CCD result shows an improvement in the objective function, annual energy production (AEP), compared to the baseline design by more than eleven percent. Optimization studies at this fidelity level can provide system design engineers with insights into design directions that leverage design coupling to improve performance. These studies also provide a template for future more detailed turbine CCD optimization studies that utilize higher fidelity models and design representations.
Improved Dual-Output Step-Down Soft-Switching Current-Fed Push-Pull DC-DC Converter
Authors: Minoo Mohebbifar, Mohammad Panahazari, Omid Mirzapour
Date: 2023-10-24 00:00:00

Multi-port DC-DC converters are gaining more significance in modern power system environments by enabling the connection of multiple renewable energy sources, so the efficient operation of these converters is paramount. Soft switching methods increase efficiency in DC-DC converters and increase the reliability and lifespan of devices by relieving stress on components. This paper proposes a method for soft-switching of a dual-output step-down current-fed full-bridge push-pull DC-DC converter. The converter enables two independent outputs to supply different loads. The topology achieves zero-current switching on the primary side and zero-voltage switching on the secondary side, eliminating the need for active-clamp circuits and passive snubbers to absorb surge voltage. This reduces switching losses and lower voltage and current stresses on power electronic devices. The paper thoroughly investigates the proposed converter's operation principle, control strategy, and characteristics. Equations for the voltage and current of all components are derived, and the conditions for achieving soft switching are calculated. Simulation results in EMTDC/PSCAD software validate the accuracy of the proposed method.
A Review of Economic Incentives for Efficient Operation of Flexible Transmission
Authors: Xinyang Rui, Omid Mirzapour, Brittany Pruneau, Mostafa Sahraei-Ardakani
Date: 2023-10-24 00:00:00

The growing penetration of renewable energy requires upgrades to the transmission network to ensure the deliverability of renewable generation. As an efficient alternative to transmission expansion, flexible transmission technologies, whose benefits have been widely studied, can alleviate transmission system congestion and enhance renewable energy integration. However, under the current market structure, investments for these technologies only receive a regulated rate of return, providing little to no incentive for efficient operation. Additionally, a regulated rate of return creates an incentive for building more transmission lines rather than efficient utilization of the existing system. Therefore, investments in flexible transmission technologies remain rather limited. To facilitate the deployment of flexible transmission, improve system efficiency, and accommodate renewable energy integration, a proper incentive structure for flexible transmission technologies, compatible with the current market design, is vital. This paper reviews the current market-based mechanisms for various flexible transmission technologies, including impedance control, dynamic line rating, and transmission switching. This review pinpoints current challenges of the market-based operation of flexible transmission and provides insights for future endeavors in designing efficient price signals for flexible transmission operation.