pwc-archive/papers-with-abstracts huggingface dataset:
A New OFDM System for IIR Channels
Authors: Xiang-Gen Xia
Date: 2022-12-09 00:00:00

In this paper, we propose a new OFDM system for an IIR channel with the form of $B(z)/A(z)$ for two polynomials $A(z)$ and $B(z)$. Different from the conventional OFDM transmission over an FIR channel, a guard interval of an OFDM symbol is added such that the corresponding part at receiver is the cyclic prefix (CP) of the received OFDM symbol. The guard interval and CP lengths are the same and not smaller than the orders of polynomials $A(z)$ and $B(z)$. The OFDM symbol without the guard interval is the same as the conventional OFDM symbol without the CP. At the receiver, the IIR channel is then converted to $N$ intersymbol interference (ISI) free subchannels, where $N$ is the number of subcarriers of an OFDM symbol.
Reconfigurable Intelligent Surfaces for 6G -- Applications, Challenges and Solutions
Authors: Yajun Zhao, Xin Lv
Date: 2022-12-03 00:00:00

It is expected that scholars will continuously strengthen the depth and breadth of theoretical research on RIS, and provide a higher theoretical upper bound for the engineering application of RIS. While making breakthroughs in academic research, it has also made rapid progress in engineering application research and industrialization promotion. This paper will provide an overview of RIS engineering applications, and make a systematic and in-depth analysis of the challenges and candidate solutions of RIS engineering applications. Future trends and challenges are also provided.
Cortical origins of MacKay-type visual illusions: A case for the non-linearity
Authors: Cyprien Tamekue, Dario Prandi, Yacine Chitour
Date: 2022-12-09 00:00:00

To study the interaction between retinal stimulation by redundant geometrical patterns and the cortical response in the primary visual cortex (V1), we focus on the MacKay effect (Nature, 1957) and Billock and Tsou's experiments (PNAS, 2007). We use a controllability approach to describe these phenomena starting from a classical biological model of neuronal field equations with a non-linear response function. The external input containing a localised control function is interpreted as a cortical representation of the static visual stimuli used in these experiments. We prove that while the MacKay effect is essentially a linear phenomenon (i.e., the nonlinear nature of the activation does not play any role in its reproduction), the phenomena reported by Billock and Tsou are wholly nonlinear and depend strongly on the shape of the nonlinearity used to model the response function.
Hyperbolic Audio Source Separation
Authors: Darius Petermann, Gordon Wichern, Aswin Subramanian, Jonathan Le Roux
Date: 2022-12-09 00:00:00
Tasks: Audio Source Separation

We introduce a framework for audio source separation using embeddings on a hyperbolic manifold that compactly represent the hierarchical relationship between sound sources and time-frequency features. Inspired by recent successes modeling hierarchical relationships in text and images with hyperbolic embeddings, our algorithm obtains a hyperbolic embedding for each time-frequency bin of a mixture signal and estimates masks using hyperbolic softmax layers. On a synthetic dataset containing mixtures of multiple people talking and musical instruments playing, our hyperbolic model performed comparably to a Euclidean baseline in terms of source to distortion ratio, with stronger performance at low embedding dimensions. Furthermore, we find that time-frequency regions containing multiple overlapping sources are embedded towards the center (i.e., the most uncertain region) of the hyperbolic space, and we can use this certainty estimate to efficiently trade-off between artifact introduction and interference reduction when isolating individual sounds.
High Quality Audio Coding with MDCTNet
Authors: Grant Davidson, Mark Vinton, Per Ekstrand, Cong Zhou, Lars Villemoes, Lie Lu
Date: 2022-12-08 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We propose a neural audio generative model, MDCTNet, operating in the perceptually weighted domain of an adaptive modified discrete cosine transform (MDCT). The architecture of the model captures correlations in both time and frequency directions with recurrent layers (RNNs). An audio coding system is obtained by training MDCTNet on a diverse set of fullband monophonic audio signals at 48 kHz sampling, conditioned by a perceptual audio encoder. In a subjective listening test with ten excerpts chosen to be balanced across content types, yet stressful for both codecs, the mean performance of the proposed system for 24 kb/s variable bitrate (VBR) is similar to that of Opus at twice the bitrate.
A Data-driven Cognitive Salience Model for Objective Perceptual Audio Quality Assessment
Authors: Pablo M. Delgado, Jürgen Herre
Date: 2022-12-08 00:00:00
Tasks: Audio Quality Assessment

Objective audio quality measurement systems often use perceptual models to predict the subjective quality scores of processed signals, as reported in listening tests. Most systems map different metrics of perceived degradation into a single quality score predicting subjective quality. This requires a quality mapping stage that is informed by real listening test data using statistical learning (i.e., a data-driven approach) with distortion metrics as input features. However, the amount of reliable training data is limited in practice, and usually not sufficient for a comprehensive training of large learning models. Models of cognitive effects in objective systems can, however, improve the learning model. Specifically, considering the salience of certain distortion types, they provide additional features to the mapping stage that improve the learning process, especially for limited amounts of training data. We propose a novel data-driven salience model that informs the quality mapping stage by explicitly estimating the cognitive/degradation metric interactions using a salience measure. Systems incorporating the novel salience model are shown to outperform equivalent systems that only use statistical learning to combine cognitive and degradation metrics, as well as other well-known measurement systems, for a representative validation dataset.
Simulating the Power Electronics-Dominated Grid using Schwarz-Schur Complement based Hybrid Domain Decomposition Algorithm
Authors: Fatemeh Kalantari, Jian Shi, Harish Krishnamoorthy
Date: 2022-12-09 00:00:00

This paper proposes a novel two-stage hybrid domain decomposition algorithm to speed up the dynamic simulations and the analysis of power systems that can be computationally demanding due to the high penetration of renewables. On the first level of the decomposition, a Schwarz-based strategy is used to decouple the original problem into various subsystems through boundary variable relaxation, while on the second level, each decoupled subsystem is further decomposed into subdomains that are solved independently using the Schur-complement approach. Convergence is checked in both stages to ensure that the parallelized implementation of the subsystems can produce identical results to the original problem. The proposed approach is tested on an IEEE 9 bus system in which one synchronous generator is replaced with a solar PV farm through a grid-forming inverter (GFM) with an admittance control method to evaluate its effectiveness and applicability for large-scale and very-large-scale implementations. Since conventional dual-loop GFMs are not stable when connecting to a stronger grid with a small grid inductance, a virtual inductance method is adopted to increase the equivalent inductance connecting the grid to enhance stability.
Self-sustaining Ultra-wideband Positioning System for Event-driven Indoor Localization
Authors: Philipp Mayer, Michele Magno, Luca Benini
Date: 2022-12-09 00:00:00
Tasks: Indoor Localization, Motion Detection, Position, TAG

Smart and unobtrusive mobile sensor nodes that accurately track their own position have the potential to augment data collection with location-based functions. To attain this vision of unobtrusiveness, the sensor nodes must have a compact form factor and operate over long periods without battery recharging or replacement. This paper presents a self-sustaining and accurate ultra-wideband-based indoor location system with conservative infrastructure overhead. An event-driven sensing approach allows for balancing the limited energy harvested in indoor conditions with the power consumption of ultra-wideband transceivers. The presented tag-centralized concept, which combines heterogeneous system design with embedded processing, minimizes idle consumption without sacrificing functionality. Despite modest infrastructure requirements, high localization accuracy is achieved with error-correcting double-sided two-way ranging and embedded optimal multilateration. Experimental results demonstrate the benefits of the proposed system: the node achieves a quiescent current of $47~nA$ and operates at $1.2~\mu A$ while performing energy harvesting and motion detection. The energy consumption for position updates, with an accuracy of $40~cm$ (2D) in realistic non-line-of-sight conditions, is $10.84~mJ$. In an asset tracking case study within a $200~m^2$ multi-room office space, the achieved accuracy level allows for identifying 36 different desk and storage locations with an accuracy of over $95~{\%}$. The system`s long-time self-sustainability has been analyzed over $700~days$ in multiple indoor lighting situations.
A Modified Sequence-to-point HVAC Load Disaggregation Algorithm
Authors: Kai Ye, Hyeonjin Kim, Yi Hu, Ning Lu, Di wu, PJ Rehm
Date: 2022-12-09 00:00:00

This paper presents a modified sequence-to-point (S2P) algorithm for disaggregating the heat, ventilation, and air conditioning (HVAC) load from the total building electricity consumption. The original S2P model is convolutional neural network (CNN) based, which uses load profiles as inputs. We propose three modifications. First, the input convolution layer is changed from 1D to 2D so that normalized temperature profiles are also used as inputs to the S2P model. Second, a drop-out layer is added to improve adaptability and generalizability so that the model trained in one area can be transferred to other geographical areas without labelled HVAC data. Third, a fine-tuning process is proposed for areas with a small amount of labelled HVAC data so that the pre-trained S2P model can be fine-tuned to achieve higher disaggregation accuracy (i.e., better transferability) in other areas. The model is first trained and tested using smart meter and sub-metered HVAC data collected in Austin, Texas. Then, the trained model is tested on two other areas: Boulder, Colorado and San Diego, California. Simulation results show that the proposed modified S2P algorithm outperforms the original S2P model and the support-vector machine based approach in accuracy, adaptability, and transferability.
Towards a Formal Specification Framework for Manufacturing Execution Systems
Authors: Maria Witsch, Birgit Vogel-Heuser
Date: 2022-12-09 00:00:00

Manufacturing Execution Systems (MES) optimize production and business processes at the same time. However, the engineering and specification of MES is a challenging, interdisciplinary process. Especially IT and production experts with different views and background have to cooperate. For successful and efficient MES software projects, misunderstandings in the specification process have to be avoided. Therefore, textual specifications need to be complemented by unambiguous graphical models, reducing the complexity by integrating interdisciplinary views and domain specific terms based on different background knowledge. Today's modeling notations focus on the detailed modeling of a certain domain specific problem area. They do not support interdisciplinary discussion adequately. To bridge this gap a novel MES Modeling Language (MES-ML) integrating all necessary views important for MES and pointing out their interdependencies has been developed. Due to its formal basis, comparable and consistent MES-models can be created for specification, standardization, testing, and documentation of MES software. In this paper, the authors present the formal basis of the modeling language and its core notation. The application of MES-ML is demonstrated taking a yogurt production as an example. Finally, the authors give some evaluation results that underline the effectiveness and efficiency of this new modeling approach with reference to four applications in industrial MES-projects in the domain of discrete and hybrid manufacturing.
Improving transferability between different engineering stages in the development of automated material flow modules
Authors: Daniel Regulin, Thomas Aicher, Birgit Vogel-Heuser
Date: 2022-12-09 00:00:00

For improving flexibility and robustness of the engineering of automated production systems (aPS) in case of extending, reducing or modifying parts, several approaches propose an encapsulation and clustering of related functions, e.g. from the electrical, mechanical or software engineering, based on a modular architecture. Considering the development of these modules, there are different stages, e.g. module planning or functional engineering, which have to be completed. A reference model that addresses the different stages for the engineering of aPS is proposed by AutomationML. Due to these different stages and the integration of several engineering disciplines, e.g. mechanical, electrical/electronic or software engineering, information not limited to one discipline are stored redundantly increasing the effort to transfer information and the risk of inconsistency. Although, data formats for the storage and exchange of plant engineering information exist, e.g. AutomationML, fixed domain specific structures and relations of the information, e.g. for automated material flow systems (aMFS), are missing. This paper presents the integration of a meta model into the development of modules for aMFS to improve the transferability and consistency of information between the different engineering stages and the increasing level of detail from the coarse-grained plant planning to the fine-grained functional engineering.
Introduction of an Assistance System to Support Domain Experts in Programming Low-code to Leverage Industry 5.0
Authors: Eva-Maria Neumann, Birgit Vogel-Heuser, Fabian Haben, Marius Krueger, Timotheus Wieringa
Date: 2022-12-09 00:00:00

The rapid technological leaps of Industry 4.0 increase the pressure and demands on humans working in automation, which is one of the main motivators of Industry 5.0. In particular, automation software development for mechatronic systems becomes increasingly challenging, as both domain knowledge and programming skills are required for high-quality, maintainable software. Especially for small companies from automation and robotics without dedicated software engineering departments, domain-specific low-code platforms become indispensable that enable domain experts to develop code intuitively using visual programming languages, e.g., for tasks such as retrofitting mobile machines. However, for extensive functionalities, visual programs may become overwhelming due to the scaling-up problem. In addition, the ever-shortening time-to-market increases the time pressure on programmers. Thus, an assistance system concept is introduced that can be implemented by low-code platform suppliers based on combining data mining and static code analysis. Domain experts are supported in developing low-code by targeted recommendations, metric-based complexity measurement, and reducing complexity by encapsulating functionalities. The concept is implemented for the industrial low-code platform HAWE eDesign to program hydraulic components in mobile machines, and its benefits are confirmed in a user study and an industrial expert workshop.
Reinforcement Learning and Mixed-Integer Programming for Power Plant Scheduling in Low Carbon Systems: Comparison and Hybridisation
Authors: Cormac O'Malley, Patrick de Mars, Luis Badesa, Goran Strbac
Date: 2022-12-09 00:00:00
Tasks: Reinforcement Learning (RL), Scheduling

Decarbonisation is driving dramatic growth in renewable power generation. This increases uncertainty in the load to be served by power plants and makes their efficient scheduling, known as the unit commitment (UC) problem, more difficult. UC is solved in practice by mixed-integer programming (MIP) methods; however, there is growing interest in emerging data-driven methods including reinforcement learning (RL). In this paper, we extensively test two MIP (deterministic and stochastic) and two RL (model-free and with lookahead) scheduling methods over a large set of test days and problem sizes, for the first time comparing the state-of-the-art of these two approaches on a level playing field. We find that deterministic and stochastic MIP consistently produce lower-cost UC schedules than RL, exhibiting better reliability and scalability with problem size. Average operating costs of RL are more than 2 times larger than stochastic MIP for a 50-generator test case, while the cost is 13 times larger in the worst instance. However, the key strength of RL is the ability to produce solutions practically instantly, irrespective of problem size. We leverage this advantage to produce various initial solutions for warm starting concurrent stochastic MIP solves. By producing several near-optimal solutions simultaneously and then evaluating them using Monte Carlo methods, the differences between the true cost function and the discrete approximation required to formulate the MIP are exploited. The resulting hybrid technique outperforms both the RL and MIP methods individually, reducing total operating costs by 0.3% on average.
Model-based control algorithms for the quadruple tank system: An experimental comparison
Authors: Anders H. D. Andersen, Tobias K. S. Ritschel, Steen Hørsholt, Jakob Kjøbsted Huusom, John Bagterp Jørgensen
Date: 2022-12-09 00:00:00
Tasks: Model Predictive Control

We compare the performance of proportional-integral-derivative (PID) control, linear model predictive control (LMPC), and nonlinear model predictive control (NMPC) for a physical setup of the quadruple tank system (QTS). We estimate the parameters in a continuous-discrete time stochastic nonlinear model for the QTS using a prediction-error-method based on the measured process data and a maximum likelihood (ML) criterion. In the NMPC algorithm, we use this identified continuous-discrete time stochastic nonlinear model. The LMPC algorithm is based on a linearization of this nonlinear model. We tune the PID controller using Skogestad's IMC tuning rules using a transfer function representation of the linearized model. Norms of the observed tracking errors and the rate of change of the manipulated variables are used to compare the performance of the control algorithms. The LMPC and NMPC perform better than the PID controller for a predefined time-varying setpoint trajectory. The LMPC and NMPC algorithms have similar performance.
Model-based training of manual procedures in automated production systems
Authors: Frieder Loch, Gennadiy D. Koltun, Victoria Karaseva, Dorothea Pantfoerder, Birgit Vogel-Heuser
Date: 2022-12-09 00:00:00

Maintenance engineers deal with increasingly complex automated production systems (aPSs). Such systems are characterized by an increasing computerization or the addition of robots that collaborate with human workers. The effects of changing or replacing components of such systems are difficult to assess since there are complex interdependencies between process parameters and the state of the components. This paper proposes a model-based training system that visualizes these interdependencies using domain-independent SysML models. The training system consists of a virtual training system for initial training and an online support system for assistance during maintenance or changeover procedures. Both systems use structural SysML models to visualize the state of the machine at a certain step of a procedure. An evaluation of the system in a changeover procedure against a paper-based manual showed promising results regarding effectiveness, usability and attractiveness.
Optimal Sizing and Pricing of Renewable Power to Ammonia Systems Considering the Limited Flexibility of Ammonia Synthesis
Authors: Zhipeng Yu, Jin Lin, Feng Liu, Jiarong Li, Yuxuan Zhao, Yonghua Song, Yanhua Song, Xinzhen Zhang
Date: 2022-12-09 00:00:00

Converting renewable energy into ammonia has been recognized as a promising way to realize ``green hydrogen substitution" in the chemical industry. However, renewable power to ammonia (RePtA) requires an essential investment in facilities to provide a buffer against the strong volatility of renewable energy and the limited flexibility of ammonia synthesis, which involves the three main stakeholders, namely, power, hydrogen, and ammonia. Therefore, the sizing and pricing of RePtA play a core role in balancing the interest demands of investors. This paper proposes an optimal sizing and pricing method for RePtA system planning. First, power to ammonia (P2A) is modeled as a flexible load, especially considering the limited flexibility of ammonia synthesis, which has been verified using real dynamic regulation data. Second, the multi-investor economic (MIE) model is established considering both external and internal trading modes. Then, a two-stage decomposed sizing and pricing method is proposed to solve the problem caused by the strong coupling of planning, operation, and trading, and information gap decision theory (IGDT) method is utilized to handle the uncertainty of renewable generation. Finally, real data from a real-life system in Inner Mongolia are utilized to verify the proposed approach. The results show that the system proposed has a yield of 8.15%.
Wasserstein Distributionally Robust Control of Partially Observable Linear Stochastic Systems
Authors: Astghik Hakobyan, Insoon Yang
Date: 2022-12-09 00:00:00

Distributionally robust control (DRC) aims to effectively manage distributional ambiguity in stochastic systems. While most existing works address inaccurate distributional information in fully observable settings, we consider a partially observable DRC problem for discrete-time linear systems using the Wasserstein metric. For a tractable solution, we propose a novel approximation method exploiting the Gelbrich bound of Wasserstein distance. Using techniques from modern distributionally robust optimization, we derive a closed-form expression for the optimal control policy and a tractable semidefinite programming problem for the worst-case distribution policy in both finite-horizon and infinite-horizon average-cost settings. The proposed method features several salient theoretical properties, such as a guaranteed cost property and a probabilistic out-of-sample performance guarantee, demonstrating the distributional robustness of our controller. Furthermore, the resulting controller is shown to ensure the closed-loop stability of the mean-state system. The empirical performance of our method is tested through numerical experiments on a power system frequency control problem.