pwc-archive/papers-with-abstracts huggingface dataset:
Iterative Learning Control with Mismatch Compensation for Residual Vibration Suppression in Delta Robots
Authors: Mingkun Wu, Alisa Rupenyan, Burkhard Corves
Date: 2024-11-12 00:00:00

Unwanted vibrations stemming from the energy-optimized design of Delta robots pose a challenge in their operation, especially with respect to precise reference tracking. To improve tracking accuracy, this paper proposes an adaptive mismatch-compensated iterative learning controller based on input shaping techniques. We establish a dynamic model considering the electromechanical rigid-flexible coupling of the Delta robot, which integrates the permanent magnet synchronous motor. Using this model, we design an optimization-based input shaper, considering the natural frequency of the robot, which varies with the configuration. We proposed an iterative learning controller for the delta robot to improve tracking accuracy. Our iterative learning controller incorporates model mismatch where the mismatch approximated by a fuzzy logic structure. The convergence property of the proposed controller is proved using a Barrier Composite Energy Function, providing a guarantee that the tracking errors along the iteration axis converge to zero. Moreover, adaptive parameter update laws are designed to ensure convergence. Finally, we perform a series of high-fidelity simulations of the Delta robot using Simscape to demonstrate the effectiveness of the proposed control strategy.
Singularity-Avoidance Control of Robotic Systems with Model Mismatch and Actuator Constraints
Authors: Mingkun Wu, Alisa Rupenyan, Burkhard Corves
Date: 2024-11-12 00:00:00

Singularities, manifesting as special configuration states, deteriorate robot performance and may even lead to a loss of control over the system. This paper addresses the kinematic singularity concerns in robotic systems with model mismatch and actuator constraints through control barrier functions (CBFs). We propose a learning-based control strategy to prevent robots entering singularity regions. More precisely, we leverage Gaussian process (GP) regression to learn the unknown model mismatch, where the prediction error is restricted by a deterministic bound. Moreover, we offer the criteria for parameter selection to ensure the feasibility of CBFs subject to actuator constraints. The proposed approach is validated by high-fidelity simulations on a 2 degrees-of-freedom (DoFs) planar robot.
RRT* Based Optimal Trajectory Generation with Linear Temporal Logic Specifications under Kinodynamic Constraints
Authors: Saksham Gautam, Ratnangshu Das, Pushpak Jagtap
Date: 2024-11-09 00:00:00
Tasks: Motion Planning

In this paper, we present a novel RRT*-based strategy for generating kinodynamically feasible paths that satisfy temporal logic specifications. Our approach integrates a robustness metric for Linear Temporal Logics (LTL) with the system's motion constraints, ensuring that the resulting trajectories are both optimal and executable. We introduce a cost function that recursively computes the robustness of temporal logic specifications while penalizing time and control effort, striking a balance between path feasibility and logical correctness. We validate our approach with simulations and real-world experiments in complex environments, demonstrating its effectiveness in producing robust and practical motion plans. This work represents a significant step towards expanding the applicability of motion planning algorithms to more complex, real-world scenarios.
SoundSil-DS: Deep Denoising and Segmentation of Sound-field Images with Silhouettes
Authors: Risako Tanigawa, Kenji Ishikawa, Noboru Harada, Yasuhiro Oikawa
Date: 2024-11-12 00:00:00
Tasks: Denoising, Object

Development of optical technology has enabled imaging of two-dimensional (2D) sound fields. This acousto-optic sensing enables understanding of the interaction between sound and objects such as reflection and diffraction. Moreover, it is expected to be used an advanced measurement technology for sonars in self-driving vehicles and assistive robots. However, the low sound-pressure sensitivity of the acousto-optic sensing results in high intensity of noise on images. Therefore, denoising is an essential task to visualize and analyze the sound fields. In addition to denoising, segmentation of sound and object silhouette is also required to analyze interactions between them. In this paper, we propose sound-field-images-with-object-silhouette denoising and segmentation (SoundSil-DS) that jointly perform denoising and segmentation for sound fields and object silhouettes on a visualized image. We developed a new model based on the current state-of-the-art denoising network. We also created a dataset to train and evaluate the proposed method through acoustic simulation. The proposed method was evaluated using both simulated and measured data. We confirmed that our method can applied to experimentally measured data. These results suggest that the proposed method may improve the post-processing for sound fields, such as physical model-based three-dimensional reconstruction since it can remove unwanted noise and separate sound fields and other object silhouettes. Our code is available at https://github.com/nttcslab/soundsil-ds.
AEROMamba: An efficient architecture for audio super-resolution using generative adversarial networks and state space models
Authors: Wallace Abreu, Luiz Wagner Pereira Biscainho
Date: 2024-11-11 00:00:00
Tasks: Audio Super-Resolution, GPU, Mamba, State Space Models, Super-Resolution

Audio super-resolution aims to enhance low-resolution signals by creating high-frequency content. In this work, we modify the architecture of AERO (a state-of-the-art system for this task) for music super-resolution. SPecifically, we replace its original Attention and LSTM layers with Mamba, a State Space Model (SSM), across all network layers. Mamba is capable of effectively substituting the mentioned modules, as it offers a mechanism similar to that of Attention while also functioning as a recurrent network. With the proposed AEROMamba, training requires 2-4x less GPU memory, since Mamba exploits the convolutional formulation and leverages GPU memory hierarchy. Additionally, during inference, Mamba operates in constant memory due to recurrence, avoiding memory growth associated with Attention. This results in a 14x speed improvement using 5x less GPU. Subjective listening tests (0 to 100 scale) show that the proposed model surpasses the AERO model. In the MUSDB dataset, degraded signals scored 38.22, while AERO and AEROMamba scored 60.03 and 66.74, respectively. For the PianoEval dataset, scores were 72.92 for degraded signals, 76.89 for AERO, and 84.41 for AEROMamba.
DCF-DS: Deep Cascade Fusion of Diarization and Separation for Speech Recognition under Realistic Single-Channel Conditions
Authors: Shu-Tong Niu, Jun Du, Ruo-Yu Wang, Gao-Bin Yang, Tian Gao, Jia Pan, Yu Hu
Date: 2024-11-11 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speaker-diarization, Speaker Diarization, Speech Enhancement, speech-recognition, Speech Recognition, Speech Separation

We propose a single-channel Deep Cascade Fusion of Diarization and Separation (DCF-DS) framework for back-end automatic speech recognition (ASR), combining neural speaker diarization (NSD) and speech separation (SS). First, we sequentially integrate the NSD and SS modules within a joint training framework, enabling the separation module to leverage speaker time boundaries from the diarization module effectively. Then, to complement DCF-DS training, we introduce a window-level decoding scheme that allows the DCF-DS framework to handle the sparse data convergence instability (SDCI) problem. We also explore using an NSD system trained on real datasets to provide more accurate speaker boundaries. Additionally, we incorporate an optional multi-input multi-output speech enhancement module (MIMO-SE) within the DCF-DS framework, which offers further performance gains. Finally, we enhance diarization results by re-clustering DCF-DS outputs, improving ASR accuracy. By incorporating the DCF-DS method, we achieved first place in the realistic single-channel track of the CHiME-8 NOTSOFAR-1 challenge. We also perform the evaluation on the open LibriCSS dataset, achieving a new state-of-the-art single-channel speech recognition performance.
Diff-MSTC: A Mixing Style Transfer Prototype for Cubase
Authors: Soumya Sai Vanka, Lennart Hannink, Jean-Baptiste Rolland, George Fazekas
Date: 2024-11-10 00:00:00
Tasks: Deep Learning, Style Transfer

In our demo, participants are invited to explore the Diff-MSTC prototype, which integrates the Diff-MST model into Steinberg's digital audio workstation (DAW), Cubase. Diff-MST, a deep learning model for mixing style transfer, forecasts mixing console parameters for tracks using a reference song. The system processes up to 20 raw tracks along with a reference song to predict mixing console parameters that can be used to create an initial mix. Users have the option to manually adjust these parameters further for greater control. In contrast to earlier deep learning systems that are limited to research ideas, Diff-MSTC is a first-of-its-kind prototype integrated into a DAW. This integration facilitates mixing decisions on multitracks and lets users input context through a reference song, followed by fine-tuning of audio effects in a traditional manner.
PSELDNets: Pre-trained Neural Networks on Large-scale Synthetic Datasets for Sound Event Localization and Detection
Authors: Jinbo Hu, Yin Cao, Ming Wu, Fang Kang, Feiran Yang, Wenwu Wang, Mark D. Plumbley, Jun Yang
Date: 2024-11-10 00:00:00
Tasks: Direction of Arrival Estimation, Sound Event Localization and Detection

Sound event localization and detection (SELD) has seen substantial advancements through learning-based methods. These systems, typically trained from scratch on specific datasets, have shown considerable generalization capabilities. Recently, deep neural networks trained on large-scale datasets have achieved remarkable success in the sound event classification (SEC) field, prompting an open question of whether these advancements can be extended to develop general-purpose SELD models. In this paper, leveraging the power of pre-trained SEC models, we propose pre-trained SELD networks (PSELDNets) on large-scale synthetic datasets. These synthetic datasets, generated by convolving sound events with simulated spatial room impulse responses (SRIRs), contain 1,167 hours of audio clips with an ontology of 170 sound classes. These PSELDNets are transferred to downstream SELD tasks. When we adapt PSELDNets to specific scenarios, particularly in low-resource data cases, we introduce a data-efficient fine-tuning method, AdapterBit. PSELDNets are evaluated on a synthetic-test-set using collected SRIRs from TAU Spatial Room Impulse Response Database (TAU-SRIR DB) and achieve satisfactory performance. We also conduct our experiments to validate the transferability of PSELDNets to three publicly available datasets and our own collected audio recordings. Results demonstrate that PSELDNets surpass state-of-the-art systems across all publicly available datasets. Given the need for direction-of-arrival estimation, SELD generally relies on sufficient multi-channel audio clips. However, incorporating the AdapterBit, PSELDNets show more efficient adaptability to various tasks using minimal multi-channel or even just monophonic audio clips, outperforming the traditional fine-tuning approaches.
GitChameleon: Unmasking the Version-Switching Capabilities of Code Generation Models
Authors: Nizar Islah, Justine Gehring, Diganta Misra, Eilif Muller, Irina Rish, Terry Yue Zhuo, Massimo Caccia
Date: 2024-11-05 00:00:00
Tasks: Code Completion, Code Generation

The rapid evolution of software libraries presents a significant challenge for code generation models, which must adapt to frequent version updates while maintaining compatibility with previous versions. Existing code completion benchmarks often overlook this dynamic aspect, and the one that does consider it relies on static code prediction tasks without execution-based evaluation, offering a limited perspective on a model's practical usability. To address this gap, we introduce \textbf{\GitChameleon{}}, a novel, manually curated dataset comprising 116 Python code completion problems, each conditioned on specific library versions and accompanied by executable unit tests. \GitChameleon{} is designed to rigorously assess the ability of modern large language models (LLMs) to generate version-specific code that is not only syntactically correct but also functionally accurate upon execution. Our comprehensive evaluations reveal that state-of-the-art LLMs struggle with this task; for instance, \textbf{GPT-4o} achieves a pass@10 of only 39.9\% (43.7\% when provided with error feedback), highlighting the complexity of the problem and the limitations of current models. By providing an execution-based benchmark that emphasizes the dynamic nature of code libraries, \GitChameleon{} serves as a critical tool to advance the development of more adaptable and reliable code generation models. For facilitation for further exploration of version-conditioned code generation, we make our code repository publicly accessible at \url{https://github.com/NizarIslah/GitChameleon}.
Degradation mode estimation using reconstructed open circuit voltage curves from multi-year home storage field data
Authors: Jan Figgener, Jakob Bors, Matthias Kuipers, Felix Hildenbrand, Mark Junker, Lucas Koltermann, Philipp Woerner, Marc Mennekes, David Haberschusz, Kai-Philipp Kairies, Dirk Uwe Sauer
Date: 2024-11-12 00:00:00

A battery's open circuit voltage (OCV) curve can be seen as its electrochemical signature. Its shape and age-related shift provide information on aging processes and material composition on both electrodes. However, most OCV analyses have to be conducted in laboratories or specified field tests to ensure suitable data quality. Here, we present a method that reconstructs the OCV curve continuously over the lifetime of a battery using the operational data of home storage field measurements over eight years. We show that low-dynamic operational phases, such as the overnight household supply with electricity, are suitable for recreating quasi OCV curves. We apply incremental capacity analysis and differential voltage analysis and show that known features of interest from laboratory measurements can be tracked to determine degradation modes in field operation. The dominant degradation mode observed for the home storage systems under evaluation is the loss of lithium inventory, while the loss of active material might be present in some cases. We apply the method to lithium nickel manganese cobalt oxide (NMC), a blend of lithium manganese oxide (LMO) and NMC, and lithium iron phosphate (LFP) batteries. Field capacity tests validate the method.
A Symmetry-Preserving Reduced-Order Observer
Authors: Jeremy W. Hopwood, Craig A. Woolsey
Date: 2024-11-12 00:00:00

A symmetry-preserving, reduced-order state observer is presented for the unmeasured part of a system's state, where the nonlinear system dynamics exhibit symmetry under the action of a Lie group. The proposed observer takes advantage of this symmetry through the use of a moving frame that constructs invariant mappings of the measurements. Sufficient conditions for the observer to be asymptotically stable are developed by studying the stability of an invariant error system. As an illustrative example, the observer is applied to the problem of rigid-body velocity estimation, which demonstrate how exploiting the symmetry of the system can simplify the stabilization of the estimation error dynamics.
Control-Oriented Models Inform Synthetic Biology Strategies in CAR T Cell Immunotherapy
Authors: Raffaele Romagnoli
Date: 2024-11-12 00:00:00

Chimeric antigen receptor (CAR) T cell therapy is revolutionizing the treatment of blood cancers. Mathematical models that can predict the effectiveness of immunotherapies such as CAR T are of increasing interest due to their ability to reduce the number of experiments performed and to guide the theoretical development of new therapeutic strategies. {Following this rationale, we propose the use of control-oriented models to guide the augmentation of CAR T therapy with synthetic gene circuitry. Here we present an initial investigation where we adapt a previously developed CAR T model for control-oriented purposes. We then explore the impact of realistic alternative activation methods as control inputs to ensure effective tumor clearance.
ElectricityEmissions.jl: A Framework for the Comparison of Carbon Intensity Signals
Authors: Joe Gorka, Noah Rhodes, Line Roald
Date: 2024-11-10 00:00:00

An increasing number of individuals, companies and organizations are interested in computing and minimizing the carbon emissions associated with their real-time electricity consumption. To achieve this, they require a carbon signal, i.e. a metric that defines the real-time carbon intensity of their electricity supply. Unfortunately, in a grid with multiple generation sources and multiple consumers, the physics of the system do not provide an unambiguous way to trace electricity from source to sink. As a result, there are a multitude of proposed carbon signals, each of which has a distinct set of properties and method of calculation. It remains unclear which signal best quantifies the carbon footprint of electricity. This paper seeks to inform the discussion about which carbon signal is better or more suitable for two important use cases, namely carbon-informed load shifting and carbon accounting. We do this by developing a new software package ElectricityEmissions$.$jl, that computes several established and newly proposed carbon emission metrics for standard electric grid test cases. We also demonstrate how the package can be used to investigate the effects of using these metrics to guide load shifting. Our results affirm previous research, which showed that the choice of carbon emission metric has significant impact on shifting results and associated carbon emission reductions. In addition, we demonstrate the impact of load shifting on both the consumers that perform the shifting and consumers that do not. Disconcertingly, we observe that shifting according to common metrics such as average carbon emissions can reduce the amount of emissions allocated to the consumer doing the shifting, while increasing the total emissions of the power system.
Stochastic MPC for Finite Gaussian Mixture Disturbances with Guarantees
Authors: Maico H. W. Engelaar, Micha P. P. Swaanen, Mircea Lazar, Sofie Haesaert
Date: 2024-11-12 00:00:00
Tasks: Model Predictive Control, valid

This paper presents a stochastic model predictive control (SMPC) algorithm for linear systems subject to additive Gaussian mixture disturbances, with the goal of satisfying chance constraints. We focus on a special case where each Gaussian mixture component has a similar variance. To solve the SMPC problem, we formulate a branch model predictive control (BMPC) problem on simplified dynamics and leverage stochastic simulation relations (SSR). Our contribution is an extension of the SMPC literature to accommodate Gaussian mixture disturbances while retaining recursive feasibility and closed-loop guarantees. We illustrate the retention of guarantees with a case study of vehicle control on an ill-maintained road.
Efficient Creation of Behavior Models with Variable Modeling Depths Used in Digital Twins
Authors: Valentin Stegmaier, Walter Schaaf, Nasser Jazdi, Michael Weyrich
Date: 2024-11-12 00:00:00

Behavior models form an integral component of Digital Twins. The specific characteristics of these models may vary depending on the use case. One of these key characteristics is the modeling depth. Behavior models with a lower modeling depth depict the behavior of the asset in an abstract way, while those with a higher modeling depth depict the behavior in detail. Even if very detailed behavior models are flexible and realistic, they also require a lot of resources such as computing power, simulation time and memory requirements. In some applications, however, only limited resources are available. The automated creation of Digital Twins is of crucial importance for their widespread use. Although there are methods for the automated creation of behavior models for Digital Twins with a specific modeling depth, there is currently no method for the automated creation of behavior models with varying modeling depths. This article presents such an approach and demonstrates its advantages using two industrial use cases. It is demonstrated that the automatically created behavior models of lower modeling depth yield results that are almost identical to those of models with a higher modeling depth, but with significantly reduced computing time and required memory. This enables the efficient use of behavior models in a variety of use cases, regardless of the availability of resources.
Skipped Adjacency Pulse Width Modulation: Zero Voltage Switching over Full Duty Cycle Range for Hybrid Flying Capacitor Multi-Level Converters without Dynamic Level Changing
Authors: Inhwi Hwang
Date: 2024-11-10 00:00:00

This paper proposes a method to achieve zero voltage switching (ZVS) across the full duty cycle range in hybrid flying capacitor multilevel (FCML) converters, eliminating the need for dynamic level changing and active re-balancing. Utilizing skipped adjacency pulse width modulation (SAPWM), this approach avoids the nearest pole voltage level, thereby increasing volt-seconds within specific duty cycle range. The method uses a modified PWM scheme, which preserves effective pole voltage by changing duty reference and employing digital logic processing. Simulation results verify the proposed method achieving full-range ZVS. This SAPWM technique is compatible with hybrid FCML converters with various levels, offering enhanced efficiency and reduced switching losses.
Safety Filter Design for Articulated Frame Steering Vehicles In the Presence of Actuator Dynamics Using High-Order Control Barrier Functions
Authors: Naeim Ebrahimi Toulkani, Reza Ghabcheloo
Date: 2024-11-12 00:00:00

Articulated Frame Steering (AFS) vehicles are widely used in heavy-duty industries, where they often operate near operators and laborers. Therefore, designing safe controllers for AFS vehicles is essential. In this paper, we develop a Quadratic Program (QP)-based safety filter that ensures feasibility for AFS vehicles with affine actuator dynamics. To achieve this, we first derive the general equations of motion for AFS vehicles, incorporating affine actuator dynamics. We then introduce a novel High-Order Control Barrier Function (HOCBF) candidate with equal relative degrees for both system controls. Finally, we design a Parametric Adaptive HOCBF (PACBF) and an always-feasible, QP-based safety filter. Numerical simulations of AFS vehicle kinematics demonstrate the effectiveness of our approach.