pwc-archive/papers-with-abstracts huggingface dataset:
Resource Allocation for Secure Ultra-Reliable Low-Latency-Communication in IoT Applications
Authors: Solmaz Sorkhi Asbaghi, Mahmood Mohassel Feghhi, Javad Musevi niya
Date: 2023-12-16 00:00:00

The Internet of Things (IoT) has a significant demand in society due to its features, and it is constantly improving. In the context of wireless technology, Ultra-reliable and low-latency communication (URLLC) is one of the essential and challenging services in fifth-generation (5G) networks and beyond. The research on URLLC is still in its early stages due to its conflicting requirements, regarding high reliability and low latency. In this paper, we study the performance of secure short-packet communications and resource allocation in IoT systems. To this end, we investigate a health center automation, where the goal of the access point is to send critical messages to devices without eavesdropping. In this context, our goal is to maximize the weighted sum throughput and minimize the total transmit power, respectively. The problems of maximizing the weighted sum throughput, and minimizing the total transmit power are non-convex and hard to solve. To overcome this challenge, we use efficient mathematical techniques, such as the block coordinate descent (BCD) method and gradient ascent algorithm; we also use estimation methods such as Ralston, Heun, and forward-backward, in the derivative part of the gradient ascent algorithm. The simulation results show the performance advantages of the BCD algorithm and the gradient ascent in the short packet transmission scheme, also the simulation results show the superiority of the proposed methods in most cases.
Swarm of Robotic Aerial Base Stations for mmWave Multi-Hop Backhauling
Authors: Yuan Liao, Vasilis Friderikos, Halim Yanikomeroglu
Date: 2023-12-16 00:00:00

Robotic aerial base stations (RABSs) that are able to anchor at tall urban landforms are expected to bring further flexibility to millimeter-wave (mmWave) multi-hop backhaul networks in highly dense urban environments. In this paper, a swarm of RABSs are deployed to construct a dynamic mmWave backhaul network according to the traffic spatial distribution, and relocate their positions in subsequent time epochs according to the traffic temporal dynamic. The overall energy efficiency of the proposed framework is maximized by determining the RABS deployment, relocation and route formation under the channel capacity and hop constraints. The problem is formulated as a mixed-integer linear fractional programming (MILFP) and a two-stage method is developed to overcome the computational complexity. A wide set of numerical investigations reveal that compared to fixed small cells, only half as many RABSs are required to cover the same volume of traffic demand.
A potential missile guidance law based-on chaos
Authors: Dhrubajyoti Mandal
Date: 2023-12-15 00:00:00

An important field of research in defense-related technology is the design of guidance laws. A guided missile is generally challenging to intercept if its trajectory becomes unpredictable. In this short communication, we have discussed a possible application of the chaos theory in developing an advanced guided missile, where the guidance law is based upon a robust chaotic map. This type of guided missile may be almost impossible to intercept by existing missile defense systems due to its unpredictable trajectory.
Integration of Second-Order Bandstop Filter Into a Dual-Polarized 5G Millimeter-Wave Magneto-Electric Dipole Antenna
Authors: Jiangcheng Chen, Markus Berg, Kimmo Rasilainen, Zeeshan Siddiqui, Marko E. Leinonen, Aarno Pärssinen
Date: 2023-12-15 00:00:00

This communication proposes a dual-wideband differentially fed dual-polarized magnetoelectric (ME) dipole with second-order bandstop filtering for millimeter-wave (mm-Wave) applications at 24.25-29.5 GHz and 37-43.5 GHz. Without disturbing the complementary antenna operation, two resonator types (hairpin and coupled {\lambda}/4 open-/short-circuited stub resonators), are embedded into the wideband ME dipole to create two transmission poles and two zeros for sharp band-edge selectivity. This allows independent manipulation of the transmission poles and zeros and a compact ME dipole size. Across the operating band, the symmetric filtering antenna design has more than 31.6 dB of port-to-port isolation. Measured results show symmetrical E- and H-plane radiation patterns and cross-polarization levels lower than -25.1 dB. The measured gains of the single element and a 2x2 array are 8.3 dBi and 12.5 dBi, respectively. Also, the band rejection reaches 23.7 dB and 21.8 dB for single element and array, respectively.
Stable compensators in parallel to stabilize arbitrary proper rational SISO plants
Authors: Abdul Hannan Faruqi, Anindya Chatterjee
Date: 2023-12-15 00:00:00

We consider stabilization of linear time-invariant (LTI) and single input single output (SISO) plants in the frequency domain from a fresh perspective. Compensators that are themselves stable are sometimes preferred because they make starting the system easier. Such starting remains easy if there is a stable compensator in parallel with the plant rather than in a feedback loop. In such an arrangement, we explain why it is possible to stabilize all plants whose transfer functions are proper rational functions of the Laplace variable $s$. In our proposed architecture we have (i) an optional compensator $C_s(s)$ in series with the plant $P(s)$, (ii) a necessary compensator $C_p(s)$ in parallel with $C_s(s)P(s)$, along with (iii) a feedback gain $K$ for the combined new plant $C_s(s)P(s)+C_p(s)$. We show that stabilization with stable $C_s(s)$ and $C_p(s)$ is always possible. In our proposed solution the closed-loop plant is biproper and has all its zeros in the left half plane, so there is a $K_0$ such that the plant is stable for $K>K_0$. We are not aware of prior use of parallel compensators with such a goal. Our proposed architecture works even for plants that are impossible to stabilize with stable compensators in the usual single-loop feedback architecture. Several examples are provided.
Full-Envelope Flight Control for Compound Vertical Takeoff and Landing Aircraft
Authors: Jean-Marie Kai
Date: 2023-12-15 00:00:00

This paper presents a flight control design for compound Vertical Takeoff and Landing (VTOL) vehicles. With their multitude of degrees of controllability as well as the significant variations in their flight characteristics, VTOL vehicles present challenges when it comes to designing their flight control system, especially for the transition phase where the vehicle transitions between near-hovering and high-speed wing-borne flights. This work extends previous research on the design of unified and generic control laws that can be applied to a broad class of vehicles such as hovering vehicles and fixed-wing aircraft. This paper exploits this unifying property and presents an extension for the case of compound VTOL vehicles. The proposed control approach consists of nonlinear geometric control laws that are continuously applicable over the entire flight envelope, excluding the use of switching policies between different control algorithms. A transition strategy consisting of a sequence of high-level setpoints is associated with the flight control laws, it is defined with respect to flight envelope limitations and is applied in this work to a commercially available compound unmanned aerial vehicle. The control algorithms are implemented on a Pixhawk controller, they are evaluated via Hardware-In-the-Loop simulations and finally validated in a flight experiment.
Inter-domain Resource Collaboration in Satellite Networks: An Intelligent Scheduling Approach Towards Hybrid Missions
Authors: Chenxi Bao, Di Zhou, Min Sheng, Yan Shi, Jiandong Li
Date: 2023-12-15 00:00:00
Tasks: Scheduling

Since the next-generation satellite network consisting of various service function domains, such as communication, observation, navigation, etc., is moving towards large-scale, using single-domain resources is difficult to provide satisfied and timely service guarantees for the rapidly increasing mission demands of each domain. Breaking the barriers of independence of resources in each domain, and realizing the cross-domain transmission of missions to efficiently collaborate inter-domain resources is a promising solution. However, the hybrid scheduling of different missions and the continuous increase in the number of service domains have strengthened the differences and dynamics of mission demands, making it challenging for an efficient cross-domain mission scheduling (CMS). To this end, this paper first accurately characterizes the communication resource state of inter-satellite in real-time exploiting the sparse resource representation scheme, and systematically characterizes the differentiation of mission demands by conducting the mission priority model. Based on the information of resources and missions, we construct the top- and bottom-layer mission scheduling models of reward association exploiting the correlation of intra- and inter-domain mission scheduling and formulate the Markov decision process-based hierarchical CMS problem. Further, to achieve higher adaptability and autonomy of CMS and efficiently mitigate the impact of network scale, a hierarchical intelligent CMS algorithm is developed to dynamically adjust and efficiently match the CMS policy according to different mission demands. Simulation results demonstrate that the proposed algorithm has significant performance gain compared with independent domains and the existing CMS algorithms, and can still guarantee high service performance under different network scales.
Collision Course for Objects Moving on a Spherical Manifold
Authors: Animesh Chakravarthy, Debasish Ghose
Date: 2023-12-15 00:00:00

In this paper, we address the problem of predicting collision for objects moving on the surface of a spherical manifold. Toward this end, we develop the notion of a collision triangle on such manifolds. We use this to determine analytical conditions governing the speed ratios and direcions of motion of objects that lead to collisions on the sphere. We first develop these conditions for point objects and subsequently extend this to the case of circular patch shaped objects moving on the sphere.
Frequency-domain Gaussian Process Models for $H_\infty$ Uncertainties
Authors: Alex Devonport, Peter Seiler, Murat Arcak
Date: 2023-12-15 00:00:00
Tasks: Gaussian Processes

Complex-valued Gaussian processes are commonly used in Bayesian frequency-domain system identification as prior models for regression. If each realization of such a process were an $H_\infty$ function with probability one, then the same model could be used for probabilistic robust control, allowing for robustly safe learning. We investigate sufficient conditions for a general complex-domain Gaussian process to have this property. For the special case of processes whose Hermitian covariance is stationary, we provide an explicit parameterization of the covariance structure in terms of a summable sequence of nonnegative numbers. We then establish how an $H_\infty$ Gaussian process can serve as a prior for Bayesian system identification and as a probabilistic uncertainty model for probabilistic robust control. In particular, we compute formulas for refining the uncertainty model by conditioning on frequency-domain data and for upper-bounding the probability that the realizations of the process satisfy a given integral quadratic constraint.
Multi-Objective Complementary Control
Authors: Jiapeng Xu, Xiang Chen, Ying Tan, Kemin Zhou
Date: 2023-12-15 00:00:00

This paper proposes a novel multi-objective control framework for linear time-invariant systems in which performance and robustness can be achieved in a complementary way instead of a trade-off. In particular, a state-space solution is first established for a new stabilizing control structure consisting of two independently designed controllers coordinated with a Youla-type operator ${\bm Q}$. It is then shown by performance analysis that these two independently designed controllers operate in a naturally complementary way for a tracking control system, due to the coordination function of ${\bm Q}$ driven by the residual signal of a Luenberger observer. Moreover, it is pointed out that ${\bm Q}$ could be further optimized with an additional gain factor to achieve improved performance, through a data-driven methodology for a measured cost function.
LQG Information Design
Authors: Masaki Miyashita, Takashi Ui
Date: 2023-12-15 00:00:00

This paper addresses information design in a workhorse model of network games, where agents have linear best responses, the information designer optimizes a quadratic objective, and the payoff state follows a multivariate Gaussian distribution. We formulate the problem as semidefinite programming (SDP) and utilize the duality principle to characterize an optimal information structure. A Gaussian information structure is shown to be optimal among all information structures. A necessary and sufficient condition for optimality is that the induced equilibrium strategy profile and the state jointly satisfy a linear constraint derived from complementary slackness conditions. Consequently, the true state is typically revealed to the entire population of agents, even though individual agents remain only partially informed. In symmetric network games, an optimal information structure inherits the same degree of symmetry, which facilitates its computation.
Measurement-based/Model-less Estimation of Voltage Sensitivity Coefficients by Feedforward and LSTM Neural Networks in Power Distribution Grids
Authors: Robin Henry, Rahul Gupta
Date: 2023-12-14 00:00:00
Tasks: regression, Sensitivity

The increasing adoption of measurement units in electrical power distribution grids has enabled the deployment of data-driven and measurement-based control schemes. Such schemes rely on measurement-based estimated models, where the models are first estimated using raw measurements and then used in the control problem. This work focuses on measurement-based estimation of the voltage sensitivity coefficients which can be used for voltage control. In the existing literature, these coefficients are estimated using regression-based methods, which do not perform well in the case of high measurement noise. This work proposes tackling this problem by using neural network (NN)-based estimation of the voltage sensitivity coefficients which is robust against measurement noise. In particular, we propose using Feedforward and Long-Short Term Memory (LSTM) neural networks. The trained NNs take measurements of nodal voltage magnitudes and active and reactive powers and output the vector of voltage magnitude sensitivity coefficients. The performance of the proposed scheme is compared against the regression-based method for a CIGRE benchmark network.
Quantifying the Uncertainty of Sensitivity Coefficients Computed from Uncertain Compound Admittance Matrix and Noisy Grid Measurements
Authors: Rahul Gupta
Date: 2023-12-14 00:00:00
Tasks: Sensitivity

The power-flow sensitivity coefficients (PFSCs) are widely used in the power system for expressing linearized dependencies between the controlled (i.e., the nodal voltages, lines currents) and control variables (e.g., active and reactive power injections, transformer tap positions, etc.). The PFSCs are often computed by knowing the compound admittance matrix of a given network and the grid states. However, when the branch parameters (or admittance matrix) are inaccurate or known with limited accuracy, the computed PFSCs can not be relied upon. Uncertain PFSCs, when used in control, can lead to infeasible control set-points. In this context, this paper presents a method to quantify the uncertainty of the PFSCs from uncertain branch parameters and noisy grid-state measurements that can be used for formulating safe control schemes. We derive an analytical expression using the error-propagation principle. The developed tool is numerically validated using Monte Carlo simulations.
Model-Free Change Point Detection for Mixing Processes
Authors: Hao Chen, Abhishek Gupta, Yin Sun, Ness Shroff
Date: 2023-12-14 00:00:00
Tasks: Change Point Detection, model

This paper considers the change point detection problem under dependent samples. In particular, we provide performance guarantees for the MMD-CUSUM test under exponentially $\alpha$, $\beta$, and fast $\phi$-mixing processes, which significantly expands its utility beyond the i.i.d. and Markovian cases used in previous studies. We obtain lower bounds for average-run-length (ARL) and upper bounds for average-detection-delay (ADD) in terms of the threshold parameter. We show that the MMD-CUSUM test enjoys the same level of performance as the i.i.d. case under fast $\phi$-mixing processes. The MMD-CUSUM test also achieves strong performance under exponentially $\alpha$/$\beta$-mixing processes, which are significantly more relaxed than existing results. The MMD-CUSUM test statistic adapts to different settings without modifications, rendering it a completely data-driven, dependence-agnostic change point detection scheme. Numerical simulations are provided at the end to evaluate our findings.
Fault Diagnosis and Prognosis Capabilities for Wind Turbine Hydraulic Pitch Systems
Authors: Alessio Dallabona, Mogens Blanke, Henrik C. Pedersen, Dimitrios Papageorgiou
Date: 2023-12-14 00:00:00
Tasks: Fault Diagnosis, Prognosis

Wind energy is the leading non-hydro renewable technology. Increasing reliability is a key factor in reducing the downtime of high-power wind turbines installed in remote off-shore places, where maintenance is costly and less reactive. Defects in the pitch system are responsible for up to 20% of a wind turbine downtime.Thus, monitoring such defects is essential for avoiding it. This paper presents a generic assessment of the diagnosis capabilities in hydraulic pitch systems, which are used in high-power wind turbines. A mathematical model of the non-linear system dynamics is presented along with a description of the most frequent faults that occur. Structural analysis is used to assess which defects can be detected in the pitch system. The structural properties are furthermore explored to investigate the possibility of reducing the amount of sensors without compromising the fault diagnosis capabilities. Robustness to model uncertainty is finally addressed and generic principles for estimating the detectable magnitude of wear and tear are presented.
Robust Errant Beam Prognostics with Conditional Modeling for Particle Accelerators
Authors: Kishansingh Rajput, Malachi Schram, Willem Blokland, Yasir Alanazi, Pradeep Ramuhalli, Alexander Zhukov, Charles Peters, Ricardo Vilalta
Date: 2023-11-22 00:00:00
Tasks: Anomaly Detection

Particle accelerators are complex and comprise thousands of components, with many pieces of equipment running at their peak power. Consequently, particle accelerators can fault and abort operations for numerous reasons. These faults impact the availability of particle accelerators during scheduled run-time and hamper the efficiency and the overall science output. To avoid these faults, we apply anomaly detection techniques to predict any unusual behavior and perform preemptive actions to improve the total availability of particle accelerators. Semi-supervised Machine Learning (ML) based anomaly detection approaches such as autoencoders and variational autoencoders are often used for such tasks. However, supervised ML techniques such as Siamese Neural Network (SNN) models can outperform unsupervised or semi-supervised approaches for anomaly detection by leveraging the label information. One of the challenges specific to anomaly detection for particle accelerators is the data's variability due to system configuration changes. To address this challenge, we employ Conditional Siamese Neural Network (CSNN) models and Conditional Variational Auto Encoder (CVAE) models to predict errant beam pulses at the Spallation Neutron Source (SNS) under different system configuration conditions and compare their performance. We demonstrate that CSNN outperforms CVAE in our application.
OceanBench: The Sea Surface Height Edition
Authors: J. Emmanuel Johnson, Quentin Febvre, Anastasia Gorbunova, Sammy Metref, Maxime Ballarotta, Julien Le Sommer, Ronan Fablet
Date: 2023-09-27 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Benchmarking, Sensor Fusion, Transfer Learning

The ocean profoundly influences human activities and plays a critical role in climate regulation. Our understanding has improved over the last decades with the advent of satellite remote sensing data, allowing us to capture essential quantities over the globe, e.g., sea surface height (SSH). However, ocean satellite data presents challenges for information extraction due to their sparsity and irregular sampling, signal complexity, and noise. Machine learning (ML) techniques have demonstrated their capabilities in dealing with large-scale, complex signals. Therefore we see an opportunity for ML models to harness the information contained in ocean satellite data. However, data representation and relevant evaluation metrics can be the defining factors when determining the success of applied ML. The processing steps from the raw observation data to a ML-ready state and from model outputs to interpretable quantities require domain expertise, which can be a significant barrier to entry for ML researchers. OceanBench is a unifying framework that provides standardized processing steps that comply with domain-expert standards. It provides plug-and-play data and pre-configured pipelines for ML researchers to benchmark their models and a transparent configurable framework for researchers to customize and extend the pipeline for their tasks. In this work, we demonstrate the OceanBench framework through a first edition dedicated to SSH interpolation challenges. We provide datasets and ML-ready benchmarking pipelines for the long-standing problem of interpolating observations from simulated ocean satellite data, multi-modal and multi-sensor fusion issues, and transfer-learning to real ocean satellite observations. The OceanBench framework is available at github.com/jejjohnson/oceanbench and the dataset registry is available at github.com/quentinf00/oceanbench-data-registry.
Some Finite-Sample Results on the Hausman Test
Authors: Jinyong Hahn, Zhipeng Liao, Nan Liu, Shuyang Sheng
Date: 2023-12-16 00:00:00

This paper shows that the endogeneity test using the control function approach in linear instrumental variable models is a variant of the Hausman test. Moreover, we find that the test statistics used in these tests can be numerically ordered, indicating their relative power properties in finite samples.
The Dynamic Triple Gamma Prior as a Shrinkage Process Prior for Time-Varying Parameter Models
Authors: Peter Knaus, Sylvia Frühwirth-Schnatter
Date: 2023-12-16 00:00:00
Tasks: Time Series, Time Series Analysis

Many existing shrinkage approaches for time-varying parameter (TVP) models assume constant innovation variances across time points, inducing sparsity by shrinking these variances toward zero. However, this assumption falls short when states exhibit large jumps or structural changes, as often seen in empirical time series analysis. To address this, we propose the dynamic triple gamma prior -- a stochastic process that induces time-dependent shrinkage by modeling dependence among innovations while retaining a well-known triple gamma marginal distribution. This framework encompasses various special and limiting cases, including the horseshoe shrinkage prior, making it highly flexible. We derive key properties of the dynamic triple gamma that highlight its dynamic shrinkage behavior and develop an efficient Markov chain Monte Carlo algorithm for posterior sampling. The proposed approach is evaluated through sparse covariance modeling and forecasting of the returns of the EURO STOXX 50 index, demonstrating favorable forecasting performance.