pwc-archive/papers-with-abstracts huggingface dataset:
SAMPLE-HD: Simultaneous Action and Motion Planning Learning Environment
Authors: Michal Nazarczuk, Tony Ng, Krystian Mikolajczyk
Date: 2022-06-01 00:00:00
Tasks: Motion Planning, Question Answering, Scene Understanding, Visual Navigation

Humans exhibit incredibly high levels of multi-modal understanding - combining visual cues with read, or heard knowledge comes easy to us and allows for very accurate interaction with the surrounding environment. Various simulation environments focus on providing data for tasks related to scene understanding, question answering, space exploration, visual navigation. In this work, we are providing a solution to encompass both, visual and behavioural aspects of simulation in a new environment for learning interactive reasoning in manipulation setup. SAMPLE-HD environment allows to generate various scenes composed of small household objects, to procedurally generate language instructions for manipulation, and to generate ground truth paths serving as training data.
Active Inference for Robotic Manipulation
Authors: Tim Schneider, Boris Belousov, Hany Abdulsamad, Jan Peters
Date: 2022-06-01 00:00:00

Robotic manipulation stands as a largely unsolved problem despite significant advances in robotics and machine learning in the last decades. One of the central challenges of manipulation is partial observability, as the agent usually does not know all physical properties of the environment and the objects it is manipulating in advance. A recently emerging theory that deals with partial observability in an explicit manner is Active Inference. It does so by driving the agent to act in a way that is not only goal-directed but also informative about the environment. In this work, we apply Active Inference to a hard-to-explore simulated robotic manipulation tasks, in which the agent has to balance a ball into a target zone. Since the reward of this task is sparse, in order to explore this environment, the agent has to learn to balance the ball without any extrinsic feedback, purely driven by its own curiosity. We show that the information-seeking behavior induced by Active Inference allows the agent to explore these challenging, sparse environments systematically. Finally, we conclude that using an information-seeking objective is beneficial in sparse environments and allows the agent to solve tasks in which methods that do not exhibit directed exploration fail.
Roadmap to Autonomous Surgery -- A Framework to Surgical Autonomy
Authors: Amritpal Singh
Date: 2022-05-26 00:00:00

Robotic surgery has increased the domain of surgeries possible. Several examples of partial surgical automation have been seen in the past decade. We break down the path of automation tasks into features required and provide a checklist that can help reach higher levels of surgical automation. Finally, we discuss the current challenges and advances required to make this happen.
FabKG: A Knowledge graph of Manufacturing Science domain utilizing structured and unconventional unstructured knowledge source
Authors: Aman Kumar, Akshay G Bharadwaj, Binil Starly, Collin Lynch
Date: 2022-05-24 00:00:00
Proceeding: NAACL (SUKI) 2022 7
Tasks: Knowledge Graphs, NER, Question Answering, Transfer Learning

As the demands for large-scale information processing have grown, knowledge graph-based approaches have gained prominence for representing general and domain knowledge. The development of such general representations is essential, particularly in domains such as manufacturing which intelligent processes and adaptive education can enhance. Despite the continuous accumulation of text in these domains, the lack of structured data has created information extraction and knowledge transfer barriers. In this paper, we report on work towards developing robust knowledge graphs based upon entity and relation data for both commercial and educational uses. To create the FabKG (Manufacturing knowledge graph), we have utilized textbook index words, research paper keywords, FabNER (manufacturing NER), to extract a sub knowledge base contained within Wikidata. Moreover, we propose a novel crowdsourcing method for KG creation by leveraging student notes, which contain invaluable information but are not captured as meaningful information, excluding their use in personal preparation for learning and written exams. We have created a knowledge graph containing 65000+ triples using all data sources. We have also shown the use case of domain-specific question answering and expression/formula-based question answering for educational purposes.
Semi-supervised Time Domain Target Speaker Extraction with Attention
Authors: Zhepei Wang, Ritwik Giri, Shrikant Venkataramani, Umut Isik, Jean-Marc Valin, Paris Smaragdis, Mike Goodwin, Arvindh Krishnaswamy
Date: 2022-06-18 00:00:00
Tasks: Target Speaker Extraction

In this work, we propose Exformer, a time-domain architecture for target speaker extraction. It consists of a pre-trained speaker embedder network and a separator network based on transformer encoder blocks. We study multiple methods to combine speaker information with the input mixture, and the resulting Exformer architecture obtains superior extraction performance compared to prior time-domain networks. Furthermore, we investigate a two-stage procedure to train the model using mixtures without reference signals upon a pre-trained supervised model. Experimental results show that the proposed semi-supervised learning procedure improves the performance of the supervised baselines.
Audio-video fusion strategies for active speaker detection in meetings
Authors: Lionel Pibre, Francisco Madrigal, Cyrille Equoy, Frédéric Lerasle, Thomas Pellegrini, Julien Pinquier, Isabelle Ferrané
Date: 2022-06-09 00:00:00
Tasks: Active Speaker Detection, Management, Optical Flow Estimation, speaker-diarization, Speaker Diarization

Meetings are a common activity in professional contexts, and it remains challenging to endow vocal assistants with advanced functionalities to facilitate meeting management. In this context, a task like active speaker detection can provide useful insights to model interaction between meeting participants. Motivated by our application context related to advanced meeting assistant, we want to combine audio and visual information to achieve the best possible performance. In this paper, we propose two different types of fusion for the detection of the active speaker, combining two visual modalities and an audio modality through neural networks. For comparison purpose, classical unsupervised approaches for audio feature extraction are also used. We expect visual data centered on the face of each participant to be very appropriate for detecting voice activity, based on the detection of lip and facial gestures. Thus, our baseline system uses visual data and we chose a 3D Convolutional Neural Network architecture, which is effective for simultaneously encoding appearance and movement. To improve this system, we supplemented the visual information by processing the audio stream with a CNN or an unsupervised speaker diarization system. We have further improved this system by adding visual modality information using motion through optical flow. We evaluated our proposal with a public and state-of-the-art benchmark: the AMI corpus. We analysed the contribution of each system to the merger carried out in order to determine if a given participant is currently speaking. We also discussed the results we obtained. Besides, we have shown that, for our application context, adding motion information greatly improves performance. Finally, we have shown that attention-based fusion improves performance while reducing the standard deviation.
Overcoming High Frequency Limitations of Current-Mode Control Using a Control Conditioning Approach -- Part II: Implementation and Hardware
Authors: Xiaofan Cui, Al-Thaddeus Avestruz
Date: 2022-06-21 00:00:00

This article is the second part of a paper series about interference in extremum (i.e., peak or valley) current-mode control, which applies to both fixed and variable switching frequency power converters. Specifically, this part presents three control conditioning methods that mitigate the adverse effect of interference. These methods are new ways to use: (i) slope compensation; (ii) low-pass filtering; and (iii) the phenomenon of comparator-overdrive-delay, for control conditioning. The stability criterion, closed-loop dynamics, and transient performance are derived with mathematical rigor for each method. The design tradeoffs are illustrated, discussed, and compared. The effectiveness of all three methods are demonstrated and validated in hardware using a power converter operating at multi-MHz switching frequencies.
Overcoming High Frequency Limitations of Current-Mode Control Using a Control Conditioning Approach -- Part I: Modeling and Analysis
Authors: Xiaofan Cui, Al-Thaddeus Avestruz
Date: 2022-06-21 00:00:00

Current-mode control is one of the most popular controller strategies for power converters. With the advent of wide bandgap devices including GaN and SiC, higher switching frequencies have become more viable at higher power because of lower switching losses. However, the advantage of higher switching frequency for faster, higher bandwidth control is squandered because of current sensor interference. We present a framework for characterizing and analyzing this interference as uncertainties to the controller model. These uncertainties introduce additional dynamics and nonlinearity that can result in instability and poor transient performance of the current control loop. In this paper, we provide a model framework based on a new control conditioning approach that guarantees global stability and a strategy for optimizing transient performance. In Part II of this paper series, we present the analysis, design, and hardware validation of three effective solutions.
A Proximal-Point Lagrangian Based Parallelizable Nonconvex Solver for Bilinear Model Predictive Control
Authors: Yingzhao Lian, Yuning Jiang, Daniel F. Opila, Colin N. Jones
Date: 2022-06-21 00:00:00
Tasks: Model Predictive Control

Nonlinear model predictive control has been widely adopted to manipulate bilinear systems with dynamics that include products of the inputs and the states. These systems are ubiquitous in chemical processes, mechanical systems, and quantum physics, to name a few. Running a bilinear MPC controller in real time requires solving a non-convex optimization problem within a limited sampling time. This paper proposes a novel parallel proximal-point Lagrangian based bilinear MPC solver via an interlacing horizon-splitting scheme. The resulting algorithm converts the non-convex MPC control problem into a set of parallelizable small-scale multi-parametric quadratic programs (mpQPs) and an equality-constrained linear-quadratic regulator problem. As a result, the solutions of mpQPs can be pre-computed offline to enable efficient online computation. The proposed algorithm is validated on a simulation of an HVAC system control. It is deployed on a TI LaunchPad XL F28379D microcontroller to execute speed control on a field-controlled DC motor, where the MPC updates at 10 ms and solves the problem in 1.764 ms on average and at most 2.088 ms.
Steady-state nonlinearity of open-loop reset systems
Authors: Marcin B. Kaczmarek, Xinxin Zhang, S. Hassan HosseinNia
Date: 2022-06-21 00:00:00

In this paper, we introduce a new representation for open-loop reset systems. We show that at steady-state a reset integrator can be modelled as a parallel interconnection of the base-linear system and piece-wise constant nonlinearity. For sinusoidal input signals, this nonlinearity takes a form of a square wave. Subsequently, we show how the behaviour of a general open-loop reset system is related to the nonlinearity of a reset integrator. The proposed approach simplifies the analysis of reset elements in the frequency domain and provides new insights into the behaviour of reset control systems.
Digital twin of a MWh-scale grid battery system for efficiency and degradation analysis
Authors: Jorn M. Reniers, David A. Howey
Date: 2022-06-21 00:00:00
Tasks: Management

Large-scale grid-connected lithium-ion batteries are increasingly being deployed to support renewable energy roll-out on the power grid. These battery systems consist of thousands of individual cells and various ancillary systems for monitoring and control. Although many studies have focused on the behaviour of single lithium-ion cells, the impact of system design choices and ancillary system controls on long-term degradation and efficiency of system, containing thousands of cells, has rarely been considered in detail. Here, we simulate a 1 MWh grid battery system consisting of 18900 individual cells, each represented by a separate electrochemical model, as well as the thermal management system and power electronic converters. Simulations of the impact of cell-to-cell variability, thermal effects, and degradation effects were run for up to 10000 cycles and 10 years. It is shown that electrical contact resistances and cell-to-cell variations in initial capacity and resistance have a smaller effect on performance than previously thought. Instead, the variation in degradation rate of individual cells dominates the system behaviour over the lifetime. The importance of careful thermal management system control is demonstrated, with proportional control improving overall efficiency by 5 %-pts over on-off methods, also increasing the total usable energy of the battery by 5 %-pts after 10 years.
Optimization simulation of reflow welding based on prediction of regional center temperature field
Authors: Yuan Sui, Fan-yang Bu, Zi-long Shao, Wei Yan
Date: 2022-06-21 00:00:00
Tasks: Prediction

Before reflow soldering of integrated electronic products, the numerical simulation of temperature control curve of reflow furnace is crucial for selecting proper parameters and improving the overall efficiency of reflow soldering process and product quality. According to the heat conduction law and the specific heat capacity formula, the first-order ordinary differential equation of the central temperature curve of the welding area with respect to the temperature distribution function in the furnace on the conveyor belt displacement is obtained. For the gap with small temperature difference, the sigmoid function is used to obtain a smooth interval temperature transition curve; For the gap with large temperature difference, the linear combination of exponential function and primary function is used to approach the actual concave function, so as to obtain the complete temperature distribution function in the furnace. The welding parameters are obtained by solving the ordinary differential equation, and a set of optimal process parameters consistent with the process boundary are obtained by calculating the mean square error between the predicted temperature field and the real temperature distribution. At the same time, a set of reflow optimization strategies are designed for speed interval prediction strategy, minimum parameter interval prediction strategy, and the most symmetrical parameter interval prediction of solder paste melting reflow area. The simulation results show that the temperature field prediction results obtained by this method are highly consistent with the actual sensor data, and have strong correlation. This method can help to select appropriate process parameters, optimize the production process, reduce equipment commissioning practice and optimize the solder joint quality of production products.
Electrochemical Parameter Identification for Lithium-ion Battery Sources in Self-Sustained Transportation Energy Systems
Authors: Yuxuan Gu, Jianxiao Wang, Yuanbo Chen, Kedi Zheng, Zhongwei Deng, Qixin Chen
Date: 2022-06-21 00:00:00
Tasks: Sensitivity

Lithium-ion battery (LIB) sources have played an essential role in self-sustained transportation energy systems and have been widely deployed in the last few years. To realize reliable battery maintenance, identifying its electrochemical parameters is necessary. However, the battery model contains many parameters while the measurable states are only the current and voltage, inducing the identification inherently an ill-conditioned problem. A parameter identification approach is proposed, including the experiment, model, and algorithm. Electrochemical parameters are first grouped manually based on the physical properties and assigned to two sequenced tests for identification. The two tests named the quasi-static test and the dynamic test, are compressed on time for practical implementation. Proper optimization models and a sensitivity-oriented stepwise (SSO) optimization algorithm are developed to search for the optimal parameters efficiently. Typically, the Sobol method is applied to conduct the sensitivity analysis. Based on the sensitivity indexes, the SSO algorithm can decouple the mixed impacts of different parameters during the identification. For validation, numerical experiments on a typical NCM811 battery at different life stages are conducted. The proposed approach saves about half the time finding the proper parameter value. The identification accuracy of crucial parameters related to battery degradation can exceed 95\%. Case study results indicate that the identified parameters can not only improve the accuracy of the battery model but also be used as the indicator of the battery SOH.
Assessment of the Center of Inertia and Regional Inertia with Load Contribution via a Fully Data-Driven Method
Authors: Lucas Lugnani, Mario Paternina, Daniel Dotta
Date: 2022-06-20 00:00:00

This paper proposes a new comprehensive and fully data-driven methodology to estimate the center of inertia (COI) and the regional inertia, considering the displacement of the COI due to disturbances and load inertial contributions. The strategy uses the typicality-based data analysis (TDA) technique to detect the right pilot-bus that represents the COI. In the TDA, a compound of correlation and cosine similarities is implemented to approximate the actual distribution of the data and find the point (bus) closest to the mean which is elected as the pilot-bus. Then, the frequency response at the pilot-bus and the active power deviations are embedded into an autoregressive moving average exogenous input (ARMAX)-based approach to determine the regional inertia represented by an equivalent machine, whose inertia constant corresponds to the inertial contribution in the Region. % the This is done in order to perform the inertial estimation of the region using the frequency response of the pilot-bus and the active power of the region interconnections to model the region as and equivalent machine whose inertial constant is estimated using ARMAX identification technique. The methodology is tested using the IEEE 68-bus benchmark test system and an adapted version with aggregated dynamical loads, corroborating the method effectiveness.
An interval-valued recursive estimation framework for linearly parameterized systems
Authors: Laurent Bako, Seydi Ndiaye, Eric Blanco
Date: 2022-06-20 00:00:00

This paper proposes a recursive interval-valued estimation framework for identifying the parameters of linearly parameterized systems which may be slowly time-varying. It is assumed that the model error (which may consist in measurement noise or model mismatch or both) is unknown but lies at each time instant in a known interval. In this context, the proposed method relies on bounding the error generated by a given reference point-valued recursive estimator, for example, the well-known recursive least squares algorithm. We discuss the trade-off between computational complexity and tightness of the estimated parametric interval.
Model-Free Optimal Control of Inverter for Dynamic Voltage Support
Authors: Yifei Guo, Bikash C. Pal, Rabih A. Jabr
Date: 2022-06-20 00:00:00

Inverter-based resources (IBRs) are required to provide dynamic voltage support (DVS) during voltage dips to enhance the low-voltage ride-through capability. In this paper, we develop a model-free control method to achieve the optimal DVS (ODVS) without relying on the knowledge of grid parameters. Delving into the optimum trajectory of the ODVS problem, it is found that either the current constraint and the maximum active power constraint of IBRs are binding or one of the constraints is binding. This inspires us to search for the optimum in a closed-loop way by a perturb-and-observe (P&O)-based optimum seeking (OS) controller with either the power factor angle or the reactive current being the manipulated (perturbed) variable. The system is guaranteed to converge asymptotically to the optimum provided the stepsize sequence is diminishing and non-summable. The proposed model-free optimal control is finally implemented within a single-stage photovoltaic (PV) system, where dynamic simulations demonstrate the optimal and fast DVS performance
Sparse Representations of Dynamical Networks: A Coprime Factorization Approach
Authors: Şerban Sabău, Andrei Sperilă, Cristian Oară, Ali Jadbabaie
Date: 2022-06-20 00:00:00

We study a class of dynamical networks modeled by linear and time-invariant systems which are described by state-space realizations. For these networks, we investigate the relations between various types of factorizations which preserve the structure of their component subsystems' interconnection. In doing so, we provide tractable means of shifting between different types of sparsity-preserving representations and we show how to employ these factorizations to obtain distributed implementations for stabilizing and possibly stable controllers. By formulating all these results for both discrete- and continuous-time systems, we develop specialized distributed implementations that, up to this point, were only available for networks modeled as discrete-time systems.
Work-loop techniques for optimising nonlinear forced oscillators
Authors: Arion Pons, Tsevi Beatus
Date: 2022-06-20 00:00:00

Linear and nonlinear resonant states can be restrictive: they exist at particular discrete states in frequency and/or elasticity, under particular (e.g., simple-harmonic) waveforms. In forced oscillators, this restrictiveness is an obstacle to system design and control modulation: altering the system elasticity, or modulating the response, would both appear to necessarily incur a penalty to efficiency. In this work, we describe an approach for bypassing this obstacle. Using novel work-loop techniques, we prove and illustrate how certain classes of resonant optimisation problem lead to non-unique solutions. In a structural optimisation context, several categories of energetically-optimal elasticity are non-unique. In an optimal control context, several categories of energetically-optimal frequency are non-unique. For these classes of non-unique optimum, we can derive simple bounds defining the optimal region. These novel theoretical results have practical implications for the design and control of a range of biomimetic propulsion systems, including flapping-wing micro-air-vehicles: using these results, we can generate efficient forms of wingbeat modulation for flight control.
Consensus ADMM-Based Distributed Simultaneous Imaging & Communication
Authors: Nishant Mehrotra, Ashutosh Sabharwal, César A. Uribe
Date: 2022-06-20 00:00:00

This paper takes the first steps toward enabling wireless networks to perform both imaging and communication in a distributed manner. We propose Distributed Simultaneous Imaging and Symbol Detection (DSISD), a provably convergent distributed simultaneous imaging and communication scheme based on the alternating direction method of multipliers. We show that DSISD achieves similar imaging and communication performance as centralized schemes, with order-wise reduction in computational complexity. We evaluate the performance of DSISD via 2.4 GHz Wi-Fi simulations.