pwc-archive/papers-with-abstracts huggingface dataset:
Modular Model Reduction of Interconnected Systems: A Top-Down Approach
Authors: Lars A. L. Janssen, Bart Besselink, Rob H. B. Fey, Nathan van de Wouw
Date: 2023-01-20 00:00:00

Models of complex systems often consist of multiple interconnected subsystem/component models that are developed by multi-disciplinary teams of engineers or scientists. To ensure that such interconnected models can be applied for the purpose of simulation and/or control, a reduced-order model for the interconnected dynamics is needed. In the scope of this paper, we pursue this goal by subsystem reduction to warrant modularity of the reduction approach. Clearly, by reducing the complexity of the subsystem models, not only the accuracy of the subsystem models is affected, but, consequently, also the accuracy of the interconnected model. It is typically difficult to predict a priori how the interconnected model accuracy is affected precisely by the subsystem reduction. In this work, we address this challenge by introducing a top-down approach which enables the translation of given accuracy requirements of the interconnected system model to accuracy requirements at subsystem model level, by using mathematical tools from robust performance analysis. This allows for the independent reduction of subsystem models while guaranteeing the desired accuracy of the interconnected model. In addition, we show how this top-down approach can be used to significantly reduce the interconnected model in an illustrative structural dynamics case study.
Integrated Planning of Multi-energy Grids: Concepts and Challenges
Authors: Marwan Mostafa, Daniela Vorwerk, Johannes Heise, Alex Povel, Natalia Sanina, Davood Babazadeh, Christian Töbermann, Arne Speerforck, Christian Becker, Detlef Schulz
Date: 2023-01-20 00:00:00
Tasks: Novel Concepts

In order to meet ever-stricter climate targets and achieve the eventual decarbonization of the energy supply of German industrial metropolises, the focus is on gradually phasing out nuclear power, then coal and gas combined with the increased use of renewable energy sources and employing hydrogen as a clean energy carrier. While complete electrification of the energy supply of households and the transportation sector may be the ultimate goal, a transitional phase is necessary as such massive as well as rapid expansion of the electrical distribution grid is infeasible. Additionally, German industries have expressed their plans to use hydrogen as their primary strategy in meeting carbon targets. This poses challenges to the existing electrical, gas, and heating distribution grids. It becomes necessary to integrate the planning and developing procedures for these grids to maximize efficiencies and guarantee security of supply during the transition. The aim of this paper is thus to highlight those challenges and present novel concepts for the integrated planning of the three grids as one multi-energy grid.
On the Uplink SINR Meta Distribution of UAV-assisted Wireless Networks
Authors: Yujie Qin, Mustafa A. Kishk, Mohamed-Slim Alouini
Date: 2023-01-20 00:00:00

This letter studies the signal-to-interference-plus-noise (SINR) meta distribution of uplink transmission of UAV-enabled wireless networks with inversion power control. Within a framework of stochastic geometry, the Matern cluster process (MCP) is used to model the locations of users and UAVs. Conditional success probability and moments are derived to compute the exact expression and moment matching approximation of SINR meta distribution (beta approximation). Specifically, the effect of the power control compensation factor and UAV altitude are studied. Our numerical results show that UAV altitude has a higher impact on the system reliability than transmit power at low values of SINR threshold since users benefit more from establishing line-of-sight (LoS) links with UAVs.
An LMI Framework for Contraction-based Nonlinear Control Design by Derivatives of Gaussian Process Regression
Authors: Yu Kawano, Kenji Kashima
Date: 2023-01-20 00:00:00
Tasks: GPR, regression

Contraction theory formulates the analysis of nonlinear systems in terms of Jacobian matrices. Although this provides the potential to develop a linear matrix inequality (LMI) framework for nonlinear control design, conditions are imposed not on controllers but on their partial derivatives, which makes control design challenging. In this paper, we illustrate this so-called integrability problem can be solved by a non-standard use of Gaussian process regression (GPR) for parameterizing controllers and then establish an LMI framework of contraction-based control design for nonlinear discrete-time systems, as an easy-to-implement tool. Later on, we consider the case where the drift vector fields are unknown and employ GPR for functional fitting as its standard use. GPR describes learning errors in terms of probability, and thus we further discuss how to incorporate stochastic learning errors into the proposed LMI framework.
Variable Sampling MPC via Differentiable Time-Warping Function
Authors: Zehui Lu, Shaoshuai Mou
Date: 2023-01-20 00:00:00

Designing control inputs for a system that involves dynamical responses in multiple timescales is nontrivial. This paper proposes a parameterized time-warping function to enable a non-uniformly sampling along a prediction horizon given some parameters. The horizon should capture the responses under faster dynamics in the near future and preview the impact from slower dynamics in the distant future. Then a variable sampling MPC (VS-MPC) strategy is proposed to jointly determine optimal control and sampling parameters at each timestamp. VS-MPC adapts how it samples along the horizon and determines optimal control accordingly at each timestamp without offline tuning or trial and error. A numerical example of a wind farm battery energy storage system is also provided to demonstrate that VS-MPC outperforms the uniform sampling MPC.
A Novel Feeder-level Microgrid Unit Commitment Algorithm Considering Cold-load Pickup, Phase Balancing, and Reconfiguration
Authors: Rongxing Hu, Ashwin Shirsat, Valliappan Muthukaruppan, Si Zhang, Yiyan Li, Lidong Song, Bei Xu, Victor Paduani, Ning Lu, Mesut Baran, Wenyuan Tang
Date: 2023-01-19 00:00:00
Tasks: Scheduling

This paper presents a novel 2-stage microgrid unit commitment (Microgrid-UC) algorithm considering cold-load pickup (CLPU) effects, three-phase load balancing requirements, and feasible reconfiguration options. Microgrid-UC schedules the operation of switches, generators, battery energy storage systems, and demand response resources to supply 3-phase unbalanced loads in an islanded microgrid for multiple days. A performance-based CLPU model is developed to estimate additional energy needs of CLPU so that CLPU can be formulated into the traditional 2-stage UC scheduling process. A per-phase demand response budget term is added to the 1st stage UC objective function to meet 3-phase load unbalance limits. To reduce computational complexity in the 1st stage UC, we replace the spanning tree method with a feasible reconfiguration topology list method. The proposed algorithm is developed on a modified IEEE 123-bus system and tested on the real-time simulation testbed using actual load and PV data. Simulation results show that Microgrid-UC successfully accounts for CLPU, phase imbalance, and feeder reconfiguration requirements.
Degradation of crude oil and pure hydrocarbon fractions by some wild bacterial and fungal species
Authors: Srwa A. Mohammed, Taha J. Omar, Ayad H. Hasan
Date: 2023-01-20 00:00:00

The use of biodegradation as a method for cleaning up soil that has been contaminated by spilt petroleum can be an effective strategy. So, this study investigated the existence of the wild microorganism in soil contaminated with oil and study their ability to degrade petroleum in vitro. Nineteen samples were collected from various locations near Taq Taq (TTOPCO) natural seeps in the Kurdistan Region of Iraq. Morphological, cultural, biochemical tests and molecular identification were used to identify the microbial communities, in addition, spore texture and the colour of the fungal isolates were investigated on the fungal isolates. Out of the19 samples, 17 indigenous bacterial strains and 5 fungal strains were successfully isolated. From the absorption spectrophotometry, Bacillus anthracis, Bacillus cereus, Achromobacter sp. and Pseudomonas aeruginosa for the bacterial isolates grew well on a minimal salt medium supplemented with 1% crude oil. Results showed that these isolates mentioned above had a strong ability to degrade crude oil by reducing the colour of 2,6-dichlorophenol indophenol (DCPIP) from deep blue to colourless. However, for the fractions of hydrocarbon, the bacterial isolates failed and did not affect the colour of any of the fractions. The results for fungi showed that Aspergillus lentulus and Rhizopus arrhizus had a strong ability to degrade both crude oil and fraction F1 by reducing the colour of DCPIP. Each fungal isolates also had a great tolerance to different concentrations of crude oil when grown on solid MSM. This study showed these microorganisms have a strong ability to degrade crude oil and can be used to clean up soil and the environment.
Environmentally-Extended Input-Output analyses efficiently sketch large-scale environmental transition plans -- illustration by Canada's road industry
Authors: Anne de Bortoli, Maxime Agez
Date: 2023-01-19 00:00:00

Industries struggle to build robust environmental transition plans as they lack the tools to quantify their ecological responsibility over their value chain. Companies mostly turn to sole greenhouse gas (GHG) emissions reporting or time-intensive Life Cycle Assessment (LCA), while Environmentally-Extended Input-Output (EEIO) analysis is more efficient on a wider scale. We illustrate EEIO analysis usefulness to sketch transition plans on the example of Canada s road industry - estimation of national environmental contributions, most important environmental issues, main potential transition levers of the sector, and metrics prioritization for green purchase plans). To do so, openIO-Canada, a new Canadian EEIO database, coupled with IMPACT World plus v1.30-1.48 characterization method, provides a multicriteria environmental diagnosis of Canada s economy. The road industry generates a limited impact (0.5-1.8 percent) but must reduce the environmental burden from material purchases - mainly concrete and asphalt products - through green purchase plans and eco-design and invest in new machinery powered with cleaner energies such as low-carbon electricity or bioenergies. EEIO analysis also captures impacts often neglected in process-based pavement LCAs - amortization of capital goods, staff consumptions, and services - and shows some substantial impacts advocating for enlarging system boundaries in standard LCA. Yet, pavement construction and maintenance only explain 5 percent of the life cycle carbon footprint of Canada s road network, against 95 percent for the roads usage. Thereby, a carbon-neutral pathway for the road industry must first focus on reducing vehicle consumption and wear through better design and maintenance of roads (...)
Haves and Have-Nots: A Theory of Economic Sufficientarianism
Authors: Christopher P. Chambers, Siming Ye
Date: 2023-01-20 00:00:00

We introduce a generalization of the concept of sufficientarianism, intended to rank allocations involving multiple consumption goods. In ranking allocations of goods for a fixed society of agents, sufficientarianism posits that allocations are compared according to the number of individuals whose consumption is deemed sufficient. We base our analysis on a novel ethical concept, which we term sufficientarian judgment. Sufficientarian judgment asserts that if in starting from an allocation in which all agents have identical consumption, a change in one agent's consumption hurts society, then there is no change in any other agent's consumption which could subsequently benefit society. Sufficientarianism is shown to be equivalent to sufficientarian judgment, symmetry, and separability. We investigate our axioms in an abstract environment, and in specific economic environments. Finally, we argue formally that sufficientarian judgment is closely related to the leximin principle.
Learning Frequency-Specific Quantization Scaling in VVC for Standard-Compliant Task-driven Image Coding
Authors: Kristian Fischer, Fabian Brand, Christian Herglotz, André Kaup
Date: 2023-01-20 00:00:00
Tasks: Quantization

Today, visual data is often analyzed by a neural network without any human being involved, which demands for specialized codecs. For standard-compliant codec adaptations towards certain information sinks, HEVC or VVC provide the possibility of frequency-specific quantization with scaling lists. This is a well-known method for the human visual system, where scaling lists are derived from psycho-visual models. In this work, we employ scaling lists when performing VVC intra coding for neural networks as information sink. To this end, we propose a novel data-driven method to obtain optimal scaling lists for arbitrary neural networks. Experiments with Mask R-CNN as information sink reveal that coding the Cityscapes dataset with the proposed scaling lists result in peak bitrate savings of 8.9 % over VVC with constant quantization. By that, our approach also outperforms scaling lists optimized for the human visual system. The generated scaling lists can be found under https://github.com/FAU-LMS/VCM_scaling_lists.
Deep Learning for scalp High Frequency Oscillations Identification
Authors: Gaëlle Milon-Harnois, Nisrine Jrad, Daniel Schang, Patrick van Bogaert, Pierre Chauvet
Date: 2023-01-20 00:00:00
Tasks: Binary Classification, Deep Learning, EEG, Electroencephalogram (EEG), Vocal Bursts Intensity Prediction

Since last 2 decades, High Frequency Oscillations (HFOs) are studied as a promising biomarker to localize the epileptogenic zone of patients with refractory focal epilepsy. As HFOs visual detection is time consuming and subjective, automatization of HFO detection is required. Most HFO detectors were developed on invasive electroencephalograms (iEEG) whereas scalp electroencephalograms (EEG) are used in clinical routine. In order HFO detection can benefit to more patients, scalp HFO detectors has to be developed. However, HFOs identification in scalp EEG is more challenging than in iEEG since scalp HFOs are of lower rate, lower amplitude and more likely to be corrupted by several sources of artifacts than iEEG HFOs. The main goal of this study is to explore the ability of deep learning architecture to identify scalp HFOs from the remaining EEG signal. Hence, a binary classification Convolutional Neural Network (CNN) is learned to analyze High Density Electroencephalograms (HD-EEG). EEG signals are first mapped into a 2D time-frequency image, several color definitions are then used as an input for the CNN. Experimental results show that deep learning allows simple end-to-end learning of preprocessing, feature extraction and classification modules while reaching competitive performance.
Electrically Small Multimodal 3D Beamforming MIMO Antenna for PHY-Layer Security
Authors: Abel Zandamela, Nicola Marchetti, Adam Narbudowicz
Date: 2023-01-20 00:00:00

This work proposes an electrically small 3D beamforming antenna for PHYsical Layer (PHY-layer) security. The antenna comprises two layers of stacked patch structures and is a five-mode five-port MIMO system operating around 1.85 GHz with electrical size ${ka=0.98}$ and radiation efficiency of up to ${55\%}$. By studying the properties of the excited modes, phase and amplitude control allow for unidirectional beam scanning towards any direction around the elevation and azimuth planes. PHY-layer security is investigated using the directional modulation (DM) technique, which transmits unscrambled baseband constellation symbols to a pre-specified secure direction while simultaneously spatially distorting the same constellations in all other directions. Bit Error Rate (BER) calculations reveal very low values of ${2\times10^{-5}}$ for the desired direction of the legitimate receiver, with BER${<10^{-2}}$ beamwidths of ${55^{\circ}}$ and ${58^{\circ}}$ for the azimuth and elevation planes, respectively.
Variable-Wise Diagonal Preconditioning for Primal-Dual Splitting: Design and Applications
Authors: Kazuki Naganuma, Shunsuke Ono
Date: 2023-01-20 00:00:00
Tasks: Hyperspectral Unmixing

This paper proposes a method for designing diagonal preconditioners for a preconditioned primal-dual splitting method (P-PDS), an efficient algorithm that solves nonsmooth convex optimization problems. To speed up the convergence of P-PDS, a design method has been proposed to automatically determine appropriate preconditioners from the problem structure. However, the existing method has two limitations. One is that it directly accesses all elements of matrices representing linear operators involved in a given problem, which is inconvenient for handling linear operators implemented as procedures rather than matrices. The other is that it takes an element-wise preconditioning approach, which turns certain types of proximity operators into analytically intractable forms. To overcome these limitations, we establish an Operator norm-based design method of Variable-wise Diagonal Preconditioning (OVDP). First, OVDP constructs diagonal preconditioners using only (upper bounds) of the operator norms of linear operators, thus eliminating the need for their explicit matrix representations. Furthermore, since OVDP takes a variable-wise preconditioning approach, it keeps any proximity operator analytically computable. We also prove that our preconditioners satisfy the convergence condition of P-PDS. Finally, we demonstrate the effectiveness and usefulness of OVDP through applications to mixed noise removal of hyperspectral images, hyperspectral unmixing, and graph signal recovery.
A Radar Kit for Hands-On Distance-Learning
Authors: Markus Gardill, Tushar Tandon
Date: 2023-01-20 00:00:00

We present an approach to experimental radar systems education based on a combination of commercial low-cost hardware with modern open-source software technologies. Following a discussion of the general top-level architecture of flexible, software-defined radar systems, we introduce the specific selection of subsystems, their capabilities, and current system limitations. Compared to existing approaches to practical radar education, a more top-level modular design with a greater focus on performance and flexibility of baseband processing is selected while reducing the complexity of circuit and subsystem assembly and total system cost. We present example measurements obtained from the radar kit. The radar kit allows for bringing a radar lab to the students instead of students into the labs. It enables practical hands-on radar education also in distance-only-learning scenarios.
Schrödinger Spectrum based Continuous Cuff-less Blood Pressure Estimation using Clinically Relevant Features from PPG Signal and its Second Derivative
Authors: Aayushman Ghosh, Sayan Sarkar, Jayant Kalra
Date: 2023-01-20 00:00:00
Tasks: Blood pressure estimation

The presented study aims to estimate blood pressure (BP) using photoplethysmogram (PPG) signals while employing multiple machine learning models. The study proposes a novel algorithm for signal reconstruction, which utilizes the semi-classical signal analysis (SCSA) technique. The proposed algorithm optimises the semi-classical constant and eliminates the trade-off between complexity and accuracy in reconstruction. The reconstructed signals' spectral features are extracted and incorporated with clinically relevant PPG and its second derivative's (SDPPG) morphological features. The developed method was assessed using a publicly available virtual in-silico dataset with more than 4000 subjects, and the Multi-Parameter Intelligent Monitoring in Intensive Care Units dataset. Results showed that the method attained a mean absolute error of 5.37 and 2.96 mmHg for systolic and diastolic BP, respectively, using the CatBoost supervisory algorithm. This approach met the standards set by the Advancement of Medical Instrumentation, and achieved Grade A for all BP categories in the British Hypertension Society protocol. The proposed framework performs well even when applied to a combined database of the MIMIC-III and the Queensland dataset. This study also evaluates the proposed method's performance in a non-clinical setting with noisy and deformed PPG signals, to validate the efficacy of the SCSA method. The noise stress tests showed that the algorithm maintained its key feature detection, signal reconstruction capability, and estimation accuracy up to a 10 dB SNR ratio. It is believed that the proposed cuff-less BP estimation technique has the potential to perform well on resource-constrained settings due to its straightforward implementation approach.
Robust Symbol Level Precoding for Overlay Cognitive Radio Networks
Authors: Lu Liu, Christos Masouros, A. Lee Swindlehurst
Date: 2023-01-20 00:00:00
Tasks: Philosophy, Quantization

This paper focuses on designing robust symbol-level precoding (SLP) in an overlay cognitive radio (CR) network, where the primary and secondary networks transmit signals concurrently. When the primary base station (PBS) shares data and perfect channel state information (CSI) with the cognitive base station (CBS), we derive an SLP approach that minimizes the CR transmission power and satisfies symbol-wise Safety Margin (SM) constraints of both primary users (PUs) and cognitive users (CUs). The resulting optimization has a quadratic objective and linear inequality (LI) constraints, which can be solved by standard convex methods. For the case of imperfect CSI from the PBS, we propose robust SLP schemes. First, with a norm-bounded CSI error model to approximate the uncertain channels, we adopt a max-min philosophy to conservatively achieve robust SLP constraints. Second, we use the additive quantization noise model (AQNM) to describe the quantized PBS CSI and employ a stochastic constraint to formulate the problem. Both robust approaches also result in a quadratic objective with LI constraints. Simulation results show that, rather than simply trying to eliminate the network's cross-interference, the proposed robust SLP schemes enable the primary and secondary networks to aid each other in meeting their quality of service constraints.
Resource Optimization for Semantic-Aware Networks with Task Offloading
Authors: Zelin Ji, Zhijin Qin, Xiaoming Tao, Han Zhu
Date: 2023-01-20 00:00:00
Tasks: Edge-computing, Management, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning

The limited capabilities of user equipment restrict the local implementation of computation-intensive applications. Edge computing, especially the edge intelligence system, enables local users to offload the computation tasks to the edge servers to reduce the computational energy consumption of user equipment and accelerate fast task execution. However, the limited bandwidth of upstream channels may increase the task transmission latency and affect the computation offloading performance. To overcome the challenge arising from scarce wireless communication resources, we propose a semantic-aware multi-modal task offloading system that facilitates the extraction and offloading of semantic task information to edge servers. To cope with the different tasks with multi-modal data, a unified quality of experience (QoE) criterion is designed. Furthermore, a proximal policy optimization-based multi-agent reinforcement learning algorithm (MAPPO) is proposed to coordinate the resource management for wireless communications and computation in a distributed and low computational complexity manner. Simulation results verify that the proposed MAPPO algorithm outperforms other reinforcement learning algorithms and fixed schemes in terms of task execution speed and the overall system QoE.