pwc-archive/papers-with-abstracts huggingface dataset:
Weakly Supervised Caveline Detection For AUV Navigation Inside Underwater Caves
Authors: Boxiao Yu, Reagan Tibbetts, Titon Barua, Ailani Morales, Ioannis Rekleitis, Md Jahidul Islam
Date: 2023-03-07 00:00:00
Tasks: Management

Underwater caves are challenging environments that are crucial for water resource management, and for our understanding of hydro-geology and history. Mapping underwater caves is a time-consuming, labor-intensive, and hazardous operation. For autonomous cave mapping by underwater robots, the major challenge lies in vision-based estimation in the complete absence of ambient light, which results in constantly moving shadows due to the motion of the camera-light setup. Thus, detecting and following the caveline as navigation guidance is paramount for robots in autonomous cave mapping missions. In this paper, we present a computationally light caveline detection model based on a novel Vision Transformer (ViT)-based learning pipeline. We address the problem of scarce annotated training data by a weakly supervised formulation where the learning is reinforced through a series of noisy predictions from intermediate sub-optimal models. We validate the utility and effectiveness of such weak supervision for caveline detection and tracking in three different cave locations: USA, Mexico, and Spain. Experimental results demonstrate that our proposed model, CL-ViT, balances the robustness-efficiency trade-off, ensuring good generalization performance while offering 10+ FPS on single-board (Jetson TX2) devices.
TS-SEP: Joint Diarization and Separation Conditioned on Estimated Speaker Embeddings
Authors: Christoph Boeddeker, Aswin Shanmugam Subramanian, Gordon Wichern, Reinhold Haeb-Umbach, Jonathan Le Roux
Date: 2023-03-07 00:00:00
Tasks: Action Detection, Activity Detection, Speech Recognition

Since diarization and source separation of meeting data are closely related tasks, we here propose an approach to perform the two objectives jointly. It builds upon the target-speaker voice activity detection (TS-VAD) diarization approach, which assumes that initial speaker embeddings are available. We replace the final combined speaker activity estimation network of TS-VAD with a network that produces speaker activity estimates at a time-frequency resolution. Those act as masks for source extraction, either via masking or via beamforming. The technique can be applied both for single-channel and multi-channel input and, in both cases, achieves a new state-of-the-art word error rate (WER) on the LibriCSS meeting data recognition task. We further compute speaker-aware and speaker-agnostic WERs to isolate the contribution of diarization errors to the overall WER performance.
Kernel interpolation of acoustic transfer functions with adaptive kernel for directed and residual reverberations
Authors: Juliano G. C. Ribeiro, Shoichi Koyama, Hiroshi Saruwatari
Date: 2023-03-07 00:00:00
Tasks: regression

An interpolation method for region-to-region acoustic transfer functions (ATFs) based on kernel ridge regression with an adaptive kernel is proposed. Most current ATF interpolation methods do not incorporate the acoustic properties for which measurements are performed. Our proposed method is based on a separate adaptation of directional weighting functions to directed and residual reverberations, which are used for adapting kernel functions. Thus, the proposed method can not only impose constraints on fundamental acoustic properties, but can also adapt to the acoustic environment. Numerical experimental results indicated that our proposed method outperforms the current methods in terms of interpolation accuracy, especially at high frequencies.
AST-SED: An Effective Sound Event Detection Method Based on Audio Spectrogram Transformer
Authors: Kang Li, Yan Song, Li-Rong Dai, Ian McLoughlin, Xin Fang, Lin Liu
Date: 2023-03-07 00:00:00
Tasks: Audio Tagging, Decoder, Event Detection, Sound Event Detection

In this paper, we propose an effective sound event detection (SED) method based on the audio spectrogram transformer (AST) model, pretrained on the large-scale AudioSet for audio tagging (AT) task, termed AST-SED. Pretrained AST models have recently shown promise on DCASE2022 challenge task4 where they help mitigate a lack of sufficient real annotated data. However, mainly due to differences between the AT and SED tasks, it is suboptimal to directly utilize outputs from a pretrained AST model. Hence the proposed AST-SED adopts an encoder-decoder architecture to enable effective and efficient fine-tuning without needing to redesign or retrain the AST model. Specifically, the Frequency-wise Transformer Encoder (FTE) consists of transformers with self attention along the frequency axis to address multiple overlapped audio events issue in a single clip. The Local Gated Recurrent Units Decoder (LGD) consists of nearest-neighbor interpolation (NNI) and Bidirectional Gated Recurrent Units (Bi-GRU) to compensate for temporal resolution loss in the pretrained AST model output. Experimental results on DCASE2022 task4 development set have demonstrated the superiority of the proposed AST-SED with FTE-LGD architecture. Specifically, the Event-Based F1-score (EB-F1) of 59.60% and Polyphonic Sound detection Score scenario1 (PSDS1) score of 0.5140 significantly outperform CRNN and other pretrained AST-based systems.
Quantum Power Electronics: From Theory to Implementation
Authors: Meysam Gheisarnejad, Mohammad-Hassan Khooban
Date: 2023-03-08 00:00:00
Tasks: Deep Reinforcement Learning

While impressive progress has been already achieved in wide-bandgap (WBG) semicon-ductors such as 4H-SiC and GaN technologies, the lack of intelligent methodologies to control the gate drives prevented to the exploit the maximum potential of semiconductor chips, from obtaining the desired devices operations. Thus, a potent ongoing trend is to design a fast gate driver switching scheme to upgrade the performance of electronic equipment at the system level. To address this issue, this work proposes a novel intelligent scheme for the control of gate driver switching using the concept of quantum computation in machine learning. In particular, the quantum principle is incorporated into deep reinforcement learning (DRL) to address the hardware limitations of con-ventional computers and the growing amount of data sets. Taking potential benefits of quantum theory, the DRL algorithm influenced by quantum specifications (referred to as QDRL) will not only ameliorate the performance of the native algorithm on traditional computers, but also enhance the progress of relevant research fields like quantum computing and machine learning. To test the prac-ticability and usefulness of QDRL, a dc/dc parallel boost converters feeding constant power loads (CPLs) is chosen as the case study, and several Power Hard-ware-in-the-loop (PHiL) experiments and comparative analysis are given.
Data-Driven Robust Backward Reachable Sets for Set-Theoretic Model Predictive Control
Authors: Mehran Attar, Walter Lucia
Date: 2023-03-08 00:00:00
Tasks: Model Predictive Control

In this paper, we propose a novel approach for computing robust backward reachable sets from noisy data for unknown constrained linear systems subject to bounded disturbances. In particular, we develop an algorithm for obtaining zonotopic inner approximations that can be used for control purposes. It is shown that such sets, if built on an extended space including states and inputs, can be used to embed the system's one-step evolution in the computed extended regions. Such a result is then exploited to build a set-theoretic model predictive controller that, offline, builds a recursive family of robust data-driven reachable sets, and online, computes recursively admissible control actions without explicitly resorting to either a model of the system or the available data.The validity of the proposed data-driven solution is verified by means of a numerical simulation and its performance is contrasted with the model-based counterpart.
An FPGA-Based Semi-Automated Traffic Control System Using Verilog HDL
Authors: Anik Mallik, Sanjoy Kundu, Md. Ashikur Rahman
Date: 2023-03-08 00:00:00

Traffic Congestion is one of the severe problems in heavily populated countries like Bangladesh where Automated Traffic Control System needs to be implemented. An FPGA-based Semi-automated system is introduced in this paper including a completely new feature "Safe State" to avoid sudden unwanted collision. Here we used sequential encoding which has made the program much simpler and so that easy to control and modify. The experimental result showed the automated change in traffic lights according to the specified timing sequences which would be able to conduct maximum possible transitions of vehicles occur at different directions simultaneously without facing any accident.
An Optimal Energy Management Algorithm Considering Regenerative Braking and Renewable Energy for EV Charging in Railway Stations
Authors: Georgia Pierrou, Yannick Zwirner, Gabriela Hug
Date: 2023-03-08 00:00:00
Tasks: energy management, Management

This paper proposes a novel optimal Energy Management System (EMS) algorithm for Electric Vehicle (EV) charging in smart electric railway stations with renewable generation. As opposed to previous railway EMS methods, the proposed EMS coordinates the combined Regenerative Braking Energy (RBE), renewable generation, electric railway demand and EV charging demand at the EV parking lot of the railway station. Numerical results using a scenario-based approach on an actual railway station in Chur, Switzerland demonstrate that the proposed algorithm can effectively minimize the expected daily operating cost for the train station over an entire year.
Robust data-driven control design for linear systems subject to input saturations
Authors: Alexandre Seuret, Sophie Tarbouriech
Date: 2023-03-08 00:00:00

This paper deals with the problem of providing a data-driven solution to the local stabilization of linear systems subject to input saturation. After presenting a model-based solution to this well-studied problem, a systematic method to transform model-driven into data-driven LMI conditions is presented. This technical solution is demonstrated to be equivalent to the recent advanced results on LMI formulations based on S-procedure or Peterson Lemmas. However, the advantage of the proposed method relies on its simplicity and its potential to be applicable to a wide class of problems of stabilization of (non)linear discrete-time systems. The method is then illustrated on an academic example.
Balancing DSO interests and PV system economics with alternative tariffs
Authors: Alejandro Pena-Bello, Robin Junod, Christophe Ballif, Nicolas Wyrsch
Date: 2023-03-08 00:00:00

Distributed rooftop photovoltaics (PV) is one of the pillars of the energy transition. However, the massive integration of distributed PV systems challenges the existing grid, with high amounts of PV injection possibly leading to over-voltage and reverse power flow, with line and transformer overloading, among other issues. Moreover, the increase in PV self-consumption and consequently the reduction of imported electricity poses a problem in recovering Transmission System Operators (TSOs) and Distribution System Operators (DSOs) grid costs, that until now have been directly linked to the amount of electricity consumed due to the volumetric nature of traditional tariffs. To investigate whether alternative tariffs could mitigate PV impacts at the distribution level without hampering PV development, we assess five electricity tariffs that could help the DSOs to recover the costs of maintaining the distribution grid. Additionally, we evaluate how such tariffs may affect private investment in storage and their impact on three types of low-voltage networks (i.e., urban, semi-urban, and rural). We found that tariffs with a capacity-based component promote further adoption of PV and storage. At the same time, they allow the DSOs to recover the grid cost without incurring relevant economic differences for the customer. However, all assessed tariffs were found to have a limited role in mitigating PV impacts at the distribution level.
An Extended Model for Ecological Robustness to Capture Power System Resilience
Authors: Hao Huang, Katherine R. Davis, H. Vincent Poor
Date: 2023-03-07 00:00:00

The long-term resilient property of ecosystems has been quantified as ecological robustness (RECO) in terms of the energy transfer over food webs. The RECO of resilient ecosystems favors a balance of food webs' network efficiency and redundancy. By integrating RECO with power system constraints, the authors are able to optimize power systems' inherent resilience as ecosystems through network design and system operation. A previous model used on real power flows and aggregated redundant components for a rigorous mapping between ecosystems and power systems. However, the reactive power flows also determine power systems resilience; and the power components' redundancy is part of the global network redundancy. These characteristics should be considered for RECO-oriented evaluation and optimization for power systems. Thus, this paper extends the model for quantifying RECO in power systems using real, reactive, and apparent power flows with the consideration of redundant placement of generators. Recalling the performance of RECO-oriented optimal power flows under N-x contingencies, the analyses suggest reactive power flows and redundant components should be included for RECO to capture power systems' inherent resilience.
Learning to Influence Vehicles' Routing in Mixed-Autonomy Networks by Dynamically Controlling the Headway of Autonomous Cars
Authors: Xiaoyu Ma, Negar Mehr
Date: 2023-03-07 00:00:00

It is known that autonomous cars can increase road capacities by maintaining a smaller headway through vehicle platooning. Recent works have shown that these capacity increases can influence vehicles' route choices in unexpected ways similar to the well-known Braess's paradox, such that the network congestion might increase. In this paper, we propose that in mixed-autonomy networks, i.e., networks where roads are shared between human-driven and autonomous cars, the headway of autonomous cars can be directly controlled to influence vehicles' routing and reduce congestion. We argue that the headway of autonomous cars -- and consequently the capacity of link segments -- is not just a fixed design choice; but rather, it can be leveraged as an {infrastructure control} strategy to {dynamically} regulate capacities. Imagine that similar to variable speed limits which regulate the maximum speed of vehicles on a road segment, a control policy regulates the headway of autonomous cars along each road segment. We seek to influence vehicles' route choices by directly controlling the headway of autonomous cars to prevent Braess-like unexpected outcomes and increase network efficiency. We model the dynamics of mixed-autonomy traffic networks while accounting for the vehicles' route choice dynamics. We train an RL policy that learns to regulate the headway of autonomous cars such that the total travel time in the network is minimized. We will show empirically that our trained policy can not only prevent Braess-like inefficiencies but also decrease total travel time.
Modular Adaptive Safety-Critical Control
Authors: Max Cohen, Calin Belta
Date: 2023-03-07 00:00:00
Tasks: parameter estimation

This paper presents an adaptive control approach for uncertain nonlinear systems subject to safety constraints that allows for modularity in the selection of the parameter estimation algorithm. Such modularity is achieved by unifying the concepts of input-to-state stability (ISS) and input-to-state safety (ISSf) via control Lyapunov functions (CLFs) and control barrier functions (CBFs), respectively. In particular, we propose a class of exponential ISS-CLFs and ISSf high order CBFs that can be combined with a general class of parameter estimation algorithms akin to those found in the literature on concurrent learning adaptive control. We demonstrate that the unification of ISS and ISSf in an adaptive control setting allows for maintaining a single set of parameter estimates for both the CLF and CBF that can be generated by a class of update laws satisfying a few general properties. The modularity of our approach is demonstrated via numerical examples by comparing performance in terms of stability and safety across different parameter estimation algorithms.
Discovery of Perception Performance Limiting Triggering Conditions in Automated Driving
Authors: Ahmad Adee, Roman Gansch, Peter Liggesmeyer, Claudius Glaeser, Florian Drews
Date: 2023-03-07 00:00:00

Highly automated driving (HAD) vehicles are complex systems operating in an open context. Performance limitations originating from sensing and understanding the open context under triggering conditions may result in unsafe behavior, thus, need to be identified and modeled. This aspect of safety is also discussed in standardization activities such as ISO 21448, safety of the intended functionality (SOTIF). Although SOTIF provides a non-exhaustive list of scenario factors to identify and analyze performance limitations under triggering conditions, no concrete methodology is yet provided to identify novel triggering conditions. We propose a methodology to identify and model novel triggering conditions in a scene in order to assess SOTIF using Bayesian network (BN) and p-value hypothesis testing. The experts provide the initial BN structure while the conditional belief tables (CBTs) are learned using dataset. P-value hypothesis testing is used to identify the relevant subset of scenes. These scenes are then analyzed by experts who provide potential triggering conditions present in the scenes. The novel triggering conditions are modeled in the BN and retested. As a case study, we provide p-value hypothesis testing of BN of LIDAR using real world data.
Systematic Modeling Approach for Environmental Perception Limitations in Automated Driving
Authors: Ahmad Adee, Roman Gansch, Peter Liggesmeyer
Date: 2023-03-07 00:00:00

Highly automated driving (HAD) vehicles are complex systems operating in an open context. Complexity of these systems as well as limitations and insufficiencies in sensing and understanding the open context may result in unsafe and uncertain behavior. The safety critical nature of the HAD vehicles demands to model limitations, insufficiencies and triggering conditions to argue safe behavior. Standardization activities such as ISO/PAS 21448 provide guidelines on the safety of the intended functionality (SOTIF) and focus on the performance limitations and triggering conditions. Although, SOTIF provides a non-exhaustive list of scenario factors that may serve as a starting point to identify and analyze performance limitations and triggering conditions, yet no concrete methodology is provided to model these factors. We propose a novel methodology to model triggering conditions and performance limitations in a scene to assess SOTIF. We utilize Bayesian network (BN) in this regard. The experts provide the BN structure and conditional belief tables are learned using the maximum likelihood estimator. We provide performance limitation maps (PLMs) and conditional performance limitation maps (CPLMs), given a scene. As a case study, we provide PLMs and CPLMs of LIDAR in a defined scene using real world data.
Simultaneous Recursive Identification of Parameters and Switching Manifolds Identification of Discrete-Time Switched Linear Systems
Authors: Zengjie Zhang, Yingwei Du, Tong Liu, Fangzhou Liu, Martin Buss
Date: 2023-03-07 00:00:00

A novel procedure for the online identification of a class of discrete-time switched linear systems, which simultaneously estimates the parameters and switching manifolds of the systems, is proposed in this paper. Firstly, to estimate the parameters of the subsystems, a discrete-time concurrent learning-based recursive parameter estimator is designed to guarantee the exponential convergence of the estimation errors to zero. Secondly, as an assistant procedure of the identification framework, an online switching detection method is proposed by making use of the history stacks produced by the concurrent learning estimators. Thirdly, techniques of incremental support vector machine are applied to develop the recursive algorithm to estimate the system switching manifolds, with its stability proven by a Lynapunov-based method. At the end of the paper, the stability and precision of the proposed identification methods are confirmed by the numerical simulation of a 2-order switched linear system. Compared to the traditional offline identification methods, the proposed online identification framework possesses superior efficiency with respect to large amounts of data, while the limitations and outlook of this framework are also discussed within the conclusion.
The potential of retrofitting existing coal power plants: a case study for operation with green iron
Authors: Johannes Janicka, Paulo Debiagi, Arne Scholtissek, Andreas Dreizler, Bernd Epple, Reiner Pawellek, Alexander Maltsev, Christian Hasse
Date: 2023-03-07 00:00:00

Storing electrical energy for long periods and transporting it over long distances is an essential task of the necessary transition to a CO$_2$-free energy economy. An oxidation-reduction cycle based on iron and its oxides represents a very promising technology in this regard. The present work assesses the potential of converting an existing modern coal-fired power plant to operation with iron. For this purpose, a systematic retrofit study is carried out, employing a model that balances all material and energy fluxes in a state-of-the-art coal-fired power plant. Particular attention is given to components of the burner system and the system$'$s heat exchanger. The analysis provides evidence that main components such as the steam generator and steam cycle can be reused with moderate modifications. Major modifications are related to the larger amounts of solids produced during iron combustion, for instance in the particle feeding and removal systems. Since the high particle densities and lower demand for auxiliary systems improve the heat transfer, the net efficiencies of iron operation can be one to two percentage points better than coal operation, depending on operating conditions. This new insight can significantly accelerate the introduction of this innovative technology by guiding future research and the development of the retrofit option.