pwc-archive/papers-with-abstracts huggingface dataset:
Audio-visual child-adult speaker classification in dyadic interactions
Authors: Anfeng Xu, Kevin Huang, Tiantian Feng, Helen Tager-Flusberg, Shrikanth Narayanan
Date: 2023-10-03 00:00:00
Tasks: Active Speaker Detection, Classification, Diagnostic

Interactions involving children span a wide range of important domains from learning to clinical diagnostic and therapeutic contexts. Automated analyses of such interactions are motivated by the need to seek accurate insights and offer scale and robustness across diverse and wide-ranging conditions. Identifying the speech segments belonging to the child is a critical step in such modeling. Conventional child-adult speaker classification typically relies on audio modeling approaches, overlooking visual signals that convey speech articulation information, such as lip motion. Building on the foundation of an audio-only child-adult speaker classification pipeline, we propose incorporating visual cues through active speaker detection and visual processing models. Our framework involves video pre-processing, utterance-level child-adult speaker detection, and late fusion of modality-specific predictions. We demonstrate from extensive experiments that a visually aided classification pipeline enhances the accuracy and robustness of the classification. We show relative improvements of 2.38% and 3.97% in F1 macro score when one face and two faces are visible, respectively.
Scaling Up Music Information Retrieval Training with Semi-Supervised Learning
Authors: Yun-Ning Hung, Ju-Chiang Wang, Minz Won, Duc Le
Date: 2023-10-02 00:00:00
Tasks: Information Retrieval, Music Information Retrieval, Retrieval

In the era of data-driven Music Information Retrieval (MIR), the scarcity of labeled data has been one of the major concerns to the success of an MIR task. In this work, we leverage the semi-supervised teacher-student training approach to improve MIR tasks. For training, we scale up the unlabeled music data to 240k hours, which is much larger than any public MIR datasets. We iteratively create and refine the pseudo-labels in the noisy teacher-student training process. Knowledge expansion is also explored to iteratively scale up the model sizes from as small as less than 3M to almost 100M parameters. We study the performance correlation between data size and model size in the experiments. By scaling up both model size and training data, our models achieve state-of-the-art results on several MIR tasks compared to models that are either trained in a supervised manner or based on a self-supervised pretrained model. To our knowledge, this is the first attempt to study the effects of scaling up both model and training data for a variety of MIR tasks.
Time-Variant Overlap-Add in Partitions
Authors: Hagen Jaeger, Uwe Simmer, Jörg Bitzer, Matthias Blau
Date: 2023-09-30 00:00:00

Virtual and augmented realities are increasingly popular tools in many domains such as architecture, production, training and education, (psycho)therapy, gaming, and others. For a convincing rendering of sound in virtual and augmented environments, audio signals must be convolved in real-time with impulse responses that change from one moment in time to another. Key requirements for the implementation of such time-variant real-time convolution algorithms are short latencies, moderate computational cost and memory footprint, and no perceptible switching artifacts. In this engineering report, we introduce a partitioned convolution algorithm that is able to quickly switch between impulse responses without introducing perceptible artifacts, while maintaining a constant computational load and low memory usage. Implementations in several popular programming languages are freely available via GitHub.
A Novel U-Net Architecture for Denoising of Real-world Noise Corrupted Phonocardiogram Signal
Authors: Ayan Mukherjee, Rohan Banerjee, Avik Ghose
Date: 2023-09-30 00:00:00
Tasks: Denoising

The bio-acoustic information contained within heart sound signals are utilized by physicians world-wide for auscultation purpose. However, the heart sounds are inherently susceptible to noise contamination. Various sources of noises like lung sound, coughing, sneezing, and other background noises are involved in such contamination. Such corruption of the heart sound signal often leads to inconclusive or false diagnosis. To address this issue, we have proposed a novel U-Net based deep neural network architecture for denoising of phonocardiogram (PCG) signal in this paper. For the design, development and validation of the proposed architecture, a novel approach of synthesizing real-world noise corrupted PCG signals have been proposed. For the purpose, an open-access real-world noise sample dataset and an open-access PCG dataset has been utilized. The performance of the proposed denoising methodology has been evaluated on the synthesized noisy PCG dataset. The performance of the proposed algorithm has been compared with existing state-of-the-art (SoA) denoising algorithms qualitatively and quantitatively. The proposed denoising technique has shown improvement in performance as comparison to the SoAs.
Probabilistic Load Forecasting of Distribution Power Systems based on Empirical Copulas
Authors: Pål Forr Austnes, Celia García-Pareja, Fabio Nobile, Mario Paolone
Date: 2023-10-05 00:00:00
Tasks: Load Forecasting, quantile regression

Accurate and reliable electricity load forecasts are becoming increasingly important as the share of intermittent resources in the system increases. Distribution System Operators (DSOs) are called to accurately forecast their production and consumption to place optimal bids in the day-ahead market. Forecasts must account for the volatility of weather-parameters that impacts both the production and consumption of electricity. If DSO-loads are small or lower-granularity forecasts are needed, parametric statistical methods may fail to provide reliable performance since they rely on a priori statistical distributions of the variables to forecast. In this paper, we introduce a Probabilistic Load Forecast (PLF) method based on Empirical Copulas (ECs). The model is datadriven, does not need a priori assumption on parametric distribution for variables, nor the dependence structure (copula). It employs a kernel density estimate of the underlying distribution using beta kernels that have bounded support on the unit hypercube. The method naturally supports variables with widely different distributions, such as weather data (including forecasted ones) and historic electricity consumption, and produces a conditional probability distribution for every time step in the forecast, which allows inferring the quantiles of interest. The proposed non-parametric approach differs significantly from previous forecasting methods based on copulas, which typically uses copulas to model hierarchical dependence. The bandwidth of the beta kernel density estimators is optimized using Integrated Square Error (ISE). We present results from an open dataset and showcase the strength of the model with respect to Quantile Regression (QR) using standard probabilistic evaluation metrics.
Interpreting the Value of Flexibility in AC Security-Constrained Transmission Expansion Planning via a Cooperative Game Framework
Authors: Andrey Churkin, Wangwei Kong, Mohammad Iman Alizadeh, Florin Capitanescu, Pierluigi Mancarella, Eduardo A. Martínez Ceseña
Date: 2023-10-05 00:00:00
Tasks: Decision Making

Security-constrained transmission expansion planning (SCTEP) is an inherently complex problem that requires simultaneously solving multiple contingency states of the system (usually corresponding to N-1 security criterion). Existing studies focus on effectively finding optimal solutions; however, single optimal solutions are not sufficient to interpret the value of flexibility (e.g., from energy storage systems) and support system planners in well-informed decision making. In view of planning uncertainties, it is necessary to estimate the contributions of flexibility to various objectives and prioritise the most effective investments. In this regard, this work introduces a SCTEP tool that enables interpreting the value of flexibility in terms of contributions to avoided load curtailment and total expected system cost reduction. Inspired by cooperative game theory, the tool ranks the contributions of flexibility providers and compares them against traditional line reinforcements. This information can be used by system planners to prioritise investments with higher contributions and synergistic capabilities.
Impact of Artificial Intelligence on Electrical and Electronics Engineering Productivity in the Construction Industry
Authors: Nwosu Obinnaya Chikezie Victor
Date: 2023-10-05 00:00:00

Artificial intelligence (AI) can revolutionize the development industry, primarily electrical and electronics engineering. By automating recurring duties, AI can grow productivity and efficiency in creating. For instance, AI can research constructing designs, discover capability troubles, and generate answers, reducing the effort and time required for manual analysis. AI also can be used to optimize electricity consumption in buildings, which is a critical difficulty in the construction enterprise. Via machines gaining knowledge of algorithms to investigate electricity usage patterns, AI can discover areas wherein power may be stored and offer guidelines for enhancements. This can result in significant value financial savings and reduced carbon emissions. Moreover, AI may be used to improve the protection of creation websites. By studying statistics from sensors and cameras, AI can locate capacity dangers and alert workers to take suitable action. This could help save you from injuries and accidents on production sites, lowering the chance for workers and enhancing overall safety in the enterprise. The impact of AI on electric and electronics engineering productivity inside the creation industry is enormous. AI can transform how we layout, build, and function buildings by automating ordinary duties, optimising electricity intake, and enhancing safety. However, ensuring that AI is used ethically and responsibly and that the advantages are shared fairly throughout the enterprise is essential.
Liquid Cooling System for a High Power, Medium Frequency, and Medium Voltage Isolated Power Converter
Authors: Hooman Taghavi, Ahmad El Shafei, Adel Nasiri
Date: 2023-10-05 00:00:00

Power electronics systems, widely used in various applications such as industrial automation, electric cars, and renewable energy, have the primary function of converting and controlling electrical power to the desired type of load. Despite their reliability and efficiency, power losses in these systems generate significant heat that must be dissipated to maintain performance and prevent damage. Cooling systems play a crucial role in ensuring safe operating temperatures for system components. Air and liquid cooling are the leading technologies used in the power electronics world. Air cooling is simple and cost-effective but is limited by ambient temperature and component thermal resistance. While more efficient, liquid cooling requires more maintenance and has higher upfront costs. Water-cooling systems have become famous for regulating thermal loads as they can effectively remove heat from localized high-temperature areas, such as the challenging hotspots in power electronics systems. In addition to designing a cooling system for a power electronic system, this study investigated the impact of three major parameters; cold plate material, channel shape/size, and coolant inlet velocity. The research examined and analyzed these factors and their trade-off analysis to obtain cooling system design and optimization insights. This study might improve power electronics system performance, reliability, and durability by improving heat dissipation and thermal management.
Inverter Pulse Width Modulation Control Techniques for Electric Motor Drive Systems
Authors: Prerit Pramod
Date: 2023-10-05 00:00:00

This paper provides a simple introduction to pulse width modulation control techniques used for the control of power converters in the context of electric motor drive systems. A summary of each technique is presented along with analytical models that provide intuitive insight and enable their rapid implementation for practical purposes.
Razumikhin-type ISS Lyapunov function and small gain theorem for discrete time time-delay systems with application to a biased min-consensus protocol
Authors: Yuanqiu Mo, Wenwu Yu, Huazhou Hou, Soura Dasgupta
Date: 2023-10-05 00:00:00

This paper considers small gain theorems for the global asymptotic and exponential input-to-state stability for discrete time time-delay systems using Razumikhin-type Lyapunov function. Among other things, unlike the existing literature, it provides both necessary and sufficient conditions for exponential input-to-state stability in terms of the Razumikhin-type Lyapunov function and the small gain theorem. Previous necessary ad sufficient conditions were with the more computationally onerous, Krasovskii-type Lyapunov functions. The result finds application in the robust stability analysis of a graph-based distributed algorithm, namely, the biased min-consensus protocol, which can be used to compute the length of the shortest path from each node to its nearest source in a graph. We consider the biased min-consensus protocol under perturbations that are common in communication networks, including noise, delay and asynchronous communication. By converting such a perturbed protocol into a discrete time time-delay nonlinear system, we prove its exponential input-to-state stability under perturbations using our Razumikhin-type Lyapunov-based small gain theorem. Simulations are provided to verify the theoretical results.
Vehicle Cabin Climate MPC Parameter Tuning Using Constrained Contextual Bayesian Optimization (C-CMES)
Authors: David Stenger, Tim Reuscher, Heike Vallery, Dirk Abel
Date: 2023-10-05 00:00:00
Tasks: Bayesian Optimization

Climate-controlled cabins have for decades been standard in vehicles. Model Predictive Controllers (MPCs) have shown promising results in achieving temperature tracking in vehicle cabins and may improve upon model-free control performance. However, for the multi-zone climate control case, proper controller tuning is challenging, as externally, e.g., passenger-triggered changes in compressor setting and thus mass flow lead to degraded control performance. This paper presents a tuning method to automatically determine robust MPC parameters, as a function of the blower mass flow. Constrained contextual Bayesian optimization (BO) is used to derive policies minimizing a high-level cost function subject to constraints in a defined scenario. The proposed method leverages random disturbances and model-plant mismatch within the training episodes to generate controller parameters achieving robust disturbance rejection. The method contains a postprocessing step to achieve smooth policies that can be utilized in real-world applications. First, simulation results show that the mass flow-dependent policy outperforms a constant parametrization, while achieving the desired closed-loop behavior. Second, the robust tuning method greatly reduces worst-case overshoot and produces consistent closed-loop behavior under varying operating conditions.
Power Optimization in Multi-IRS Aided Delay-Constrained IoVT Systems
Authors: Baolin Chong, Hancheng Lu, Langtian Qin, Chenwu Zhang, Jiasen Li, Chang Wen Chen
Date: 2023-10-05 00:00:00

With the advancement of video sensors in the Internet of Things, Internet of Video Things (IoVT) systems, capable of delivering abundant and diverse information, have been increasingly deployed for various applications. However, the extensive transmission of video data in IoVT poses challenges in terms of delay and power consumption. Intelligent reconfigurable surface (IRS), as an emerging technology, can enhance communication quality and consequently improve system performance by reconfiguring wireless propagation environments. Inspired by this, we propose a multi-IRS aided IoVT system that leverages IRS to enhance communication quality, thereby reducing power consumption while satisfying delay requirements. To fully leverage the benefits of IRS, we jointly optimize power control for IoVT devices and passive beamforming for IRS to minimize long-term total power consumption under delay constraints. To solve this problem, we first utilize Lyapunov optimization to decouple the long-term optimization problem into each time slot. Subsequently, an alternating optimization algorithm employing optimal solution-seeking and fractional programming is proposed to effectively solve the optimization problems at each time slot. Simulation results demonstrate that the proposed algorithm significantly outperforms benchmark algorithms in terms of long-term total power consumption. Moreover, a trade-off between the number of IRS elements and system performance is also proved.
Synthesis of Data-Driven Nonlinear State Observers using Lipschitz-Bounded Neural Networks
Authors: Wentao Tang
Date: 2023-10-04 00:00:00

This paper focuses on the model-free synthesis of state observers for nonlinear autonomous systems without knowing the governing equations. Specifically, the Kazantzis-Kravaris/Luenberger (KKL) observer structure is leveraged, where the outputs are fed into a linear time-invariant (LTI) system to obtain the observer states, which can be viewed as the states nonlinearly transformed by an immersion mapping, and a neural network is used to approximate the inverse of the nonlinear immersion and estimate the states. In view of the possible existence of noises in output measurements, this work proposes to impose an upper bound on the Lipschitz constant of the neural network for robust and safe observation. A relation that bounds the generalization loss of state observation according to the Lipschitz constant, as well as the $H_2$-norm of the LTI part in the KKL observer, is established, thus reducing the model-free observer synthesis problem to that of Lipschitz-bounded neural network training, for which a direct parameterization technique is used. The proposed approach is demonstrated on a chaotic Lorenz system.
Generalized Stochastic Dynamic Aggregative Game for Demand-Side Management in Microgrids with Shared Battery
Authors: Shahram Yadollahi, Hamed Kebriaei, Sadegh Soudjani
Date: 2023-10-04 00:00:00
Tasks: Management

In this paper, we focus on modeling and analysis of demand-side management in a microgrid where agents utilize grid energy and a shared battery charged by renewable energy sources. We model the problem as a generalized stochastic dynamic aggregative game with chance constraints that capture the effects of uncertainties in the renewable generation and agents' demands. Computing the solution of the game is a complex task due to probabilistic and coupling constraints among the agents through the state of charge of the shared battery. We investigate the Nash equilibrium of this game under uncertainty considering both the uniqueness of the solution and the effect of uncertainty on the solution. Simulation results demonstrate that the presented stochastic method is superior to deterministic methods.
Proximal Policy Optimization-Based Reinforcement Learning Approach for DC-DC Boost Converter Control: A Comparative Evaluation Against Traditional Control Techniques
Authors: Utsab Saha, Atik Jawad, Shakib Shahria, A. B. M Harun-Ur Rashid
Date: 2023-10-04 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This article proposes a proximal policy optimization (PPO)-based reinforcement learning (RL) approach for DC-DC boost converter control that is compared with traditional control methods. The performance of the PPO algorithm is evaluated using MATLAB Simulink co-simulation, and the results demonstrate that the most efficient approach for achieving short settling time and stability is to combine the PPO algorithm with a reinforcement learning-based control method. The simulation results show that the control method based on RL with the PPO algorithm pro vides step response characteristics that outperform traditional control approaches, thereby enhancing DC-DC boost converter control. This research also highlights the inherent capability of the reinforcement learning method to enhance the performance of boost converter control.
Algorithm to find new identifiable reparametrizations of parametric rational ODE models
Authors: Nicolette Meshkat, Alexey Ovchinnikov, Thomas Scanlon
Date: 2023-10-04 00:00:00

Structural identifiability concerns the question of which unknown parameters of a model can be recovered from (perfect) input-output data. If all of the parameters of a model can be recovered from data, the model is said to be identifiable. However, in many models, there are parameters that can take on an infinite number of values but yield the same input-output data. In this case, those parameters and the model are called unidentifiable. The question is then what to do with an unidentifiable model. One can try to add more input-output data or decrease the number of unknown parameters, if experimentally feasible, or try to find a reparametrization to make the model identifiable. In this paper, we take the latter approach. While existing approaches to find identifiable reparametrizations were limited to scaling reparametrizations or were not guaranteed to find a globally identifiable reparametrization even if it exists, we significantly broaden the class of models for which we can find a globally identifiable model with the same input-output behavior as the original one. We also prove that, for linear models, a globally identifiable reparametrization always exists and show that, for a certain class of linear compartmental models, with and without inputs, an explicit reparametrization formula exists. We illustrate our method on several examples and provide detailed analysis in supplementary material on github.
Low-carbon optimal dispatch of integrated energy system considering demand response under the tiered carbon trading mechanism
Authors: Limeng Wang, Xuemeng Liu, Yang Li, Duo Chang, Xing Ren
Date: 2023-10-04 00:00:00
Tasks: Scheduling

In the operation of the integrated energy system (IES), considering further reducing carbon emissions, improving its energy utilization rate, and optimizing and improving the overall operation of IES, an optimal dispatching strategy of integrated energy system considering demand response under the stepped carbon trading mechanism is proposed. Firstly, from the perspective of demand response (DR), considering the synergistic complementarity and flexible conversion ability of multiple energy sources, the lateral time-shifting and vertical complementary alternative strategies of electricity-gas-heat are introduced and the DR model is constructed. Secondly, from the perspective of life cycle assessment, the initial quota model of carbon emission allowances is elaborated and revised. Then introduce a tiered carbon trading mechanism, which has a certain degree of constraint on the carbon emissions of IES. Finally, the sum of energy purchase cost, carbon emission transaction cost, equipment maintenance cost and demand response cost is minimized, and a low-carbon optimal scheduling model is constructed under the consideration of safety constraints. This model transforms the original problem into a mixed integer linear problem using Matlab software, and optimizes the model using the CPLEX solver. The example results show that considering the carbon trading cost and demand response under the tiered carbon trading mechanism, the total operating cost of IES is reduced by 5.69% and the carbon emission is reduced by 17.06%, which significantly improves the reliability, economy and low carbon performance of IES.