pwc-archive/papers-with-abstracts huggingface dataset:
MEDIC: Zero-shot Music Editing with Disentangled Inversion Control
Authors: Huadai Liu, Jialei Wang, Xiangtai Li, Rongjie Huang, Yang Liu, Jiayang Xu, Zhou Zhao
Date: 2024-07-18 00:00:00
Tasks: Audio Generation

Text-guided diffusion models make a paradigm shift in audio generation, facilitating the adaptability of source audio to conform to specific textual prompts. Recent works introduce inversion techniques, like DDIM inversion, to zero-shot editing, exploiting pretrained diffusion models for audio modification. Nonetheless, our investigation exposes that DDIM inversion suffers from an accumulation of errors across each diffusion step, undermining its efficacy. Moreover, existing editing methods fail to achieve effective complex non-rigid music editing while maintaining essential content preservation and high editing fidelity. To counteract these issues, we introduce the Disentangled Inversion technique to disentangle the diffusion process into triple branches, rectifying the deviated path of the source branch caused by DDIM inversion. In addition, we propose the Harmonized Attention Control framework, which unifies the mutual self-attention control and cross-attention control with an intermediate Harmonic Branch to progressively achieve the desired harmonic and melodic information in the target music. Collectively, these innovations comprise the Disentangled Inversion Control (DIC) framework, enabling accurate music editing while safeguarding content integrity. To benchmark audio editing efficacy, we introduce ZoME-Bench, a comprehensive music editing benchmark hosting 1,100 samples spread across ten distinct editing categories. This facilitates both zero-shot and instruction-based music editing tasks. Our method achieves unparalleled performance in edit fidelity and essential content preservation, outperforming contemporary state-of-the-art inversion techniques.
PCQ: Emotion Recognition in Speech via Progressive Channel Querying
Authors: Xincheng Wang, Liejun Wang, Yinfeng Yu, Xinxin Jiao
Date: 2024-07-17 00:00:00
Tasks: Emotion Recognition, Speech Emotion Recognition

In human-computer interaction (HCI), Speech Emotion Recognition (SER) is a key technology for understanding human intentions and emotions. Traditional SER methods struggle to effectively capture the long-term temporal correla-tions and dynamic variations in complex emotional expressions. To overcome these limitations, we introduce the PCQ method, a pioneering approach for SER via \textbf{P}rogressive \textbf{C}hannel \textbf{Q}uerying. This method can drill down layer by layer in the channel dimension through the channel query technique to achieve dynamic modeling of long-term contextual information of emotions. This mul-ti-level analysis gives the PCQ method an edge in capturing the nuances of hu-man emotions. Experimental results show that our model improves the weighted average (WA) accuracy by 3.98\% and 3.45\% and the unweighted av-erage (UA) accuracy by 5.67\% and 5.83\% on the IEMOCAP and EMODB emotion recognition datasets, respectively, significantly exceeding the baseline levels.
Managing Risk using Rolling Forecasts in Energy-Limited and Stochastic Energy Systems
Authors: Thomas Mortimer, Robert Mieth
Date: 2024-07-18 00:00:00
Tasks: Decision Making, Sequential Decision Making

We study risk-aware linear policy approximations for the optimal operation of an energy system with stochastic wind power, storage, and limited fuel. The resulting problem is a sequential decision-making problem with rolling forecasts. In addition to a risk-neutral objective, this paper formulates two risk-aware objectives that control the conditional value-at-risk of system cost and the buffered probability of exceeding a predefined threshold of unserved load. The resulting policy uses a parameter-modified cost function approximation that reduces the computational load compared to the direct inclusion of those risk measures in the problem objective. We demonstrate our method on a numerical case study.
Scalar Field Mapping with Adaptive High-Intensity Region Avoidance
Authors: Muzaffar Qureshi, Tochukwu Elijah Ogri, Zachary I. Bell, Rushikesh Kamalapurkar
Date: 2024-07-18 00:00:00

This research is motivated by a scenario where a group of UAVs is assigned to map an unknown scalar field, with the imperative of maintaining a safe distance from the sources of the field to evade detection or damage. The location of the sources is unknown a priori, so the UAVs rely on measurements of the field intensity to gauge safety. The UAVs estimate the unknown scalar field using Gaussian process (GP) regression and use the estimate to generate a map of high-intensity regions using Hough transform (HT), updated online based on the field measurements. A convergence analysis shows the boundedness of the error between the actual scalar field and the learned scalar field. The effectiveness of the method is evaluated through simulations, showcasing its ability to accurately learn scalar fields with multiple high-intensity regions while reducing the number of measurements taken inside the high-intensity regions.
Evaluating the Impact of Data Availability on Machine Learning-augmented MPC for a Building Energy Management System
Authors: Jens Engel, Thomas Schmitt, Tobias Rodemann, Jürgen Adamy
Date: 2024-07-18 00:00:00
Tasks: energy management, Management, Model Predictive Control

A major challenge in the development of Model Predictive Control (MPC)-based energy management systems (EMSs) for buildings is the availability of an accurate model. One approach to address this is to augment an existing gray-box model with data-driven residual estimators. The efficacy of such estimators, and hence the performance of the EMS, relies on the availability of sufficient and suitable training data. In this work, we evaluate how different data availability scenarios affect estimator and controller performance. To do this, we perform software-in-the-loop (SiL) simulation with a physics-based digital twin using real measurement data. Simulation results show that acceptable estimation and control performance can already be achieved with limited available data, and we confirm that leveraging historical data for pretraining boosts efficacy.
Predictive control for nonlinear stochastic systems: Closed-loop guarantees with unbounded noise
Authors: Johannes Köhler, Melanie N. Zeilinger
Date: 2024-07-18 00:00:00
Tasks: Computational Efficiency, Friction, Model Predictive Control

We present a stochastic model predictive control framework for nonlinear systems subject to unbounded process noise with closed-loop guarantees. First, we provide a conceptual shrinking-horizon framework that utilizes general probabilistic reachable sets and minimizes the expected cost. Then, we provide a tractable receding-horizon formulation that uses a nominal state to minimize a deterministic quadratic cost and satisfy tightened constraints. Our theoretical analysis demonstrates recursive feasibility, satisfaction of chance constraints, and bounds on the expected cost for the resulting closed-loop system. We provide a constructive design for probabilistic reachable sets of nonlinear continuously differentiable systems using stochastic contraction metrics and an assumed bound on the covariance matrices. Numerical simulations highlight the computational efficiency and theoretical guarantees of the proposed method. Overall, this paper provides a framework for computationally tractable stochastic predictive control with closed-loop guarantees for nonlinear systems with unbounded noise.
Geometric Data Fusion for Collaborative Attitude Estimation
Authors: Yixiao Ge, Behzad Zamani, Pieter van Goor, Jochen Trumpf, Robert Mahony
Date: 2024-07-18 00:00:00

In this paper, we consider the collaborative attitude estimation problem for a multi-agent system. The agents are equipped with sensors that provide directional measurements and relative attitude measurements. We present a bottom-up approach where each agent runs an extended Kalman filter (EKF) locally using directional measurements and augments this with relative attitude measurements provided by neighbouring agents. The covariance estimates of the relative attitude measurements are geometrically corrected to compensate for relative attitude between the agent that makes the measurement and the agent that uses the measurement before being fused with the local estimate using the convex combination ellipsoid (CCE) method to avoid data incest. Simulations are undertaken to numerically evaluate the performance of the proposed algorithm.
Trajectory Planning Using Tire Thermodynamics for Automated Drifting
Authors: Takao Kobayashi, Trey P. Weber, J. Christian Gerdes
Date: 2024-07-17 00:00:00
Tasks: Friction, Motion Planning, Trajectory Planning

Automated vehicles need to estimate tire-road friction information, as it plays a key role in safe trajectory planning and vehicle dynamics control. Notably, friction is not solely dependent on road surface conditions, but also varies significantly depending on the tire temperature. However, tire parameters such as the friction coefficient have been conventionally treated as constant values in automated vehicle motion planning. This paper develops a simple thermodynamic model that captures tire friction temperature variation. To verify the model, it is implemented into trajectory planning for automated drifting - a challenging application that requires leveraging an unstable, drifting equilibrium at the friction limits. The proposed method which captures the hidden tire dynamics provides a dynamically feasible trajectory, leading to more precise tracking during experiments with an LQR (Linear Quadratic Regulator) controller.
Effects of dynamic power electronic load models on power systems analysis using ZIP-E loads
Authors: Gabriel E. Colon-Reyes, Reid Dye, Claire Tomlin, Duncan Callaway
Date: 2024-07-17 00:00:00

Power grids are seeing more devices connected at the load level in the form of power electronics: e.g., data centers, electric vehicle chargers, and battery storage facilities. Therefore it is necessary to perform power system analyses with load models that capture these loads' behavior, which has historically not been done. To this end, we propose ZIP-E loads, a composite load model that has a ZIP load with a dynamic power electronic, or E, load model. We perform small signal and transient analysis of the IEEE WSCC 9 Bus test case with ZIP and ZIP-E load models. For small signals, we conclude that ZIP loads destabalize networks significantly faster than corresponding ZIP-E loads. In stable cases, transient results showed significantly larger oscillations for ZIP loads. Further, we find that a higher network loading condition is correlated with a higher sensitivity to load model choice. These results suggests that the constant power portion of the ZIP load has a large destabilizing effect and can generally overestimate instability, and that attention should be drawn to load model choice if operating near a stability boundary.
Distilling Tiny and Ultra-fast Deep Neural Networks for Autonomous Navigation on Nano-UAVs
Authors: Lorenzo Lamberti, Lorenzo Bellone, Luka Macan, Enrico Natalizio, Francesco Conti, Daniele Palossi, Luca Benini
Date: 2024-07-17 00:00:00
Tasks: Autonomous Navigation, Collision Avoidance

Nano-sized unmanned aerial vehicles (UAVs) are ideal candidates for flying Internet-of-Things smart sensors to collect information in narrow spaces. This requires ultra-fast navigation under very tight memory/computation constraints. The PULP-Dronet convolutional neural network (CNN) enables autonomous navigation running aboard a nano-UAV at 19 frame/s, at the cost of a large memory footprint of 320 kB -- and with drone control in complex scenarios hindered by the disjoint training of collision avoidance and steering capabilities. In this work, we distill a novel family of CNNs with better capabilities than PULP-Dronet, but memory footprint reduced by up to 168x (down to 2.9 kB), achieving an inference rate of up to 139 frame/s; we collect a new open-source unified collision/steering 66 k images dataset for more robust navigation; and we perform a thorough in-field analysis of both PULP-Dronet and our tiny CNNs running on a commercially available nano-UAV. Our tiniest CNN, called Tiny-PULP-Dronet v3, navigates with a 100% success rate a challenging and never-seen-before path, composed of a narrow obstacle-populated corridor and a 180{\deg} turn, at a maximum target speed of 0.5 m/s. In the same scenario, the SoA PULP-Dronet consistently fails despite having 168x more parameters.
Shackling Uncertainty using Mixed Criticality in Monte-Carlo Tree Search
Authors: Franco Cordeiro, Samuel Tardieu, Laurent Pautet
Date: 2024-07-17 00:00:00

In the world of embedded systems, optimizing actions with the uncertain costs of multiple resources is a complex challenge. Existing methods include plan building based on Monte Carlo Tree Search (MCTS), an approach that thrives in multiple online planning scenarios. However, these methods often overlook uncertainty in worst-case cost estimations. A system can fail to operate before achieving a critical objective when actual costs exceed optimistic worst-case estimates. Conversely, a system based on pessimistic worst-case estimates would lead to resource over-provisioning even for less critical objectives. To solve similar issues, the Mixed Criticality (MC) approach has been developed in the real-time systems community. In this paper, we propose to extend the MCTS heuristic in three directions. Firstly, we reformulate the concept of MC to account for uncertain worst-case costs. High-criticality tasks must be executed regardless of their uncertain costs. Low-criticality tasks are either executed in low-criticality mode utilizing resources up-to their optimistic worst-case estimates, or executed in high-criticality mode by degrading them, or discarded when resources are scarce. Secondly, although the MC approach was originally developed for real-time systems, focusing primarily on worst-case execution time as the only uncertain resource, our approach extends the concept of resources to deal with several resources at once, such as the time and energy required to perform an action. Finally, we propose an extension of MCTS with MC concepts, which we refer to as $(MC)^2TS$, to efficiently adjust resource allocation to uncertain costs according to the criticality of actions. We demonstrate our approach in an active perception scenario. Our evaluation shows $(MC)^2TS$ outperforms the traditional MCTS regardless of whether the worst case estimates are optimistic or pessimistic.
Record 202.3 Tb/s Transmission over Field-Deployed Fibre using 15.6 THz S+C+L-Bands
Authors: Jiaqian Yang, Eric Sillekens, Benjamin J. Puttnam, Ronit Sohanpal, Mindaugas Jarmolovičius, Romulo Aparecido, Henrique Buglia, Ruben S. Luis, Ralf Stolte, Polina Bayvel, Robert I. Killey
Date: 2024-07-17 00:00:00

Ultra-wideband, field-deployed metropolitan fibre transmission is experimentally demonstrated, measuring a record 202.3 Tb/s GMI and 189.5 Tb/s after decoding with 20.9 dBm launch power and lumped amplification only. An experimentally-optimised 5 dB pre-tilt over the 15.6 THz optical bandwidth was applied to overcome ISRS.
Experimental validation of the closed-form GN model accounting for distributed Raman amplification in an S+C+L-band hybrid amplified long-haul transmission system
Authors: Jiaqian Yang, Henrique Buglia, Eric Sillekens, Mingming Tan, Pratim Hazarika, Dini Pratiwi, Ronit Sohanpal, Mindaugas Jarmolovičius, Romulo Aparecido, Ralf Stolte, Wladek Forysiak, Polina Bayvel, Robert I. Killey
Date: 2024-07-17 00:00:00

The accuracy of a recently-developed closed-form GN nonlinear interference model is evaluated in experimental 1065 km S+C+L band WDM transmission with backward Raman pumping. The model accurately estimates the nonlinear interference and ASE with total SNR error of less than 0.6 dB.
AlphaPEM: an open-source dynamic 1D physics-based PEM fuel cell model for embedded applications
Authors: Raphaël Gass, Zhongliang Li, Samir Jemeï, Rachid Outbib, Daniel Hissel
Date: 2024-07-17 00:00:00

The urgency of the energy transition requires improving the performance and longevity of hydrogen technologies. AlphaPEM is a dynamic one-dimensional (1D) physics-based PEM fuel cell system simulator, programmed in Python and experimentally validated. It offers a good balance between accuracy and execution speed. The modular architecture allows for addition of new features, and it has a user-friendly graphical interface. An automatic calibration method is proposed to match the model to the studied machine. The software provides information on the internal states of the system in response to any current density and can produce polarization and EIS curves. AlphaPEM facilitates the use of a model in embedded conditions, allowing real-time modification of the fuel cell's operating conditions.
Probabilistic Reachability Analysis of Stochastic Control Systems
Authors: Saber Jafarpour, Zishun Liu, Yongxin Chen
Date: 2024-07-17 00:00:00

We address the reachability problem for continuous-time stochastic dynamic systems. Our objective is to present a unified framework that characterizes the reachable set of a dynamic system in the presence of both stochastic disturbances and deterministic inputs. To achieve this, we devise a strategy that effectively decouples the effects of deterministic inputs and stochastic disturbances on the reachable sets of the system. For the deterministic part, many existing methods can capture the deterministic reachability. As for the stochastic disturbances, we introduce a novel technique that probabilistically bounds the difference between a stochastic trajectory and its deterministic counterpart. The key to our approach is introducing a novel energy function termed the Averaged Moment Generating Function that yields a high probability bound for this difference. This bound is tight and exact for linear stochastic dynamics and applicable to a large class of nonlinear stochastic dynamics. By combining our innovative technique with existing methods for deterministic reachability analysis, we can compute estimations of reachable sets that surpass those obtained with current approaches for stochastic reachability analysis. We validate the effectiveness of our framework through various numerical experiments. Beyond its immediate applications in reachability analysis, our methodology is poised to have profound implications in the broader analysis and control of stochastic systems. It opens avenues for enhanced understanding and manipulation of complex stochastic dynamics, presenting opportunities for advancements in related fields.
A Safe and Data-efficient Model-based Reinforcement Learning System for HVAC Control
Authors: Xianzhong Ding, Zhiyu An, Arya Rathee, Wan Du
Date: 2024-07-16 00:00:00
Tasks: Model-based Reinforcement Learning

Model-Based Reinforcement Learning (MBRL) has been widely studied for Heating, Ventilation, and Air Conditioning (HVAC) control in buildings. One of the critical challenges is the large amount of data required to effectively train neural networks for modeling building dynamics. This paper presents CLUE, an MBRL system for HVAC control in buildings. CLUE optimizes HVAC operations by integrating a Gaussian Process (GP) model to model building dynamics with uncertainty awareness. CLUE utilizes GP to predict state transitions as Gaussian distributions, effectively capturing prediction uncertainty and enhancing decision-making under sparse data conditions. Our approach employs a meta-kernel learning technique to efficiently set GP kernel hyperparameters using domain knowledge from diverse buildings. This drastically reduces the data requirements typically associated with GP models in HVAC applications. Additionally, CLUE incorporates these uncertainty estimates into a Model Predictive Path Integral (MPPI) algorithm, enabling the selection of safe, energy-efficient control actions. This uncertainty-aware control strategy evaluates and selects action trajectories based on their predicted impact on energy consumption and human comfort, optimizing operations even under uncertain conditions. Extensive simulations in a five-zone office building demonstrate that CLUE reduces the required training data from hundreds of days to just seven while maintaining robust control performance. It reduces comfort violations by an average of 12.07% compared to existing MBRL methods, without compromising on energy efficiency.
Pareto local search for a multi-objective demand response problem in residential areas with heat pumps and electric vehicles
Authors: Thomas Dengiz, Andrea Raith, Max Kleinebrahm, Jonathan Vogl, Wolf Fichtner
Date: 2024-07-16 00:00:00
Tasks: Evolutionary Algorithms, Heuristic Search, reinforcement-learning, Reinforcement Learning

In future energy systems characterized by significant shares of fluctuating renewable energy sources, there is a need for a fundamental change in electricity consumption. The energy system requires the ability to adapt to the intermittent electricity generation of renewable energy sources. This can be achieved by integrating flexible electrical loads, such as electric heating devices and electric vehicles, in combination with efficient control methods. In this paper, we introduce the Pareto local search method PALSS with heuristic search operations to solve the multi-objective optimization problem of a residential area with different types of flexible loads. PALSS shifts the flexible electricity load with the objective of minimizing the electricity cost and peak load while maintaining the inhabitants' comfort in favorable ranges. Further, we include reinforcement learning into the heuristic search operations in the approach RELAPALSS and use the dichotomous method for obtaining all Pareto-optimal solutions of the multi-objective optimization problem with conflicting goals. The methods are evaluated in simulations with different configurations of the residential area. The results show that PALSS and RELAPALSS strongly outperform the two multi-objective evolutionary algorithms NSGA-II and SPEA-II from the literature and the conventional control approach. The inclusion of reinforcement learning in RELAPALSS leads to additional improvements. Our study reveals the need for multi-objective optimization methods to utilize renewable energy sources in residential areas.
Imitation learning with artificial neural networks for demand response with a heuristic control approach for heat pumps
Authors: Thomas Dengiz, Max Kleinebrahm
Date: 2024-07-16 00:00:00
Tasks: Imitation Learning

The flexibility of electrical heating devices can help address the issues arising from the growing presence of unpredictable renewable energy sources in the energy system. In particular, heat pumps offer an effective solution by employing smart control methods that adjust the heat pump's power output in reaction to demand response signals. This paper combines imitation learning based on an artificial neural network with an intelligent control approach for heat pumps. We train the model using the output data of an optimization problem to determine the optimal operation schedule of a heat pump. The objective is to minimize the electricity cost with a time-variable electricity tariff while keeping the building temperature within acceptable boundaries. We evaluate our developed novel method, PSC-ANN, on various multi-family buildings with differing insulation levels that utilize an underfloor heating system as thermal storage. The results show that PSC-ANN outperforms a positively evaluated intelligent control approach from the literature and a conventional control approach. Further, our experiments reveal that a trained imitation learning model for a specific building is also applicable to other similar buildings without the need to train it again with new data. Our developed approach also reduces the execution time compared to optimally solving the corresponding optimization problem. PSC-ANN can be integrated into multiple buildings, enabling them to better utilize renewable energy sources by adjusting their electricity consumption in response to volatile external signals.