pwc-archive/papers-with-abstracts huggingface dataset:
A Novel Composite Resilience Indicator for Decentralized Infrastructure Systems (CRI-DS)
Authors: Lamis Amer, Murat Erkoc, Esber Andiroglu, Nurcin Celik
Date: 2022-07-17 00:00:00
Tasks: Decision Making

Resilience is a key driver for planning adaptation strategies to mitigate risks due to both natural and anthropogenic hazards. The effectiveness of a resilience-driven decision-making strategy for adapting systems against stressors depends on how resilience is mapped to decision variables. This requires a functional resilience metric, without which it would not be possible to identify the priority needs for improvement, assess changes, or show improvement in post-adaptation resilience. In this paper, we aim to contribute to existing methodologies by proposing a novel model-based resilience assessment strategy while building on the notion of composite resilience indicators. Our proposed indicator is functional, reproducible, and is mapped to adaptation decisions. We propose a framework for integrating the developed resilience functional form into an adaptation decision-making model. To illustrate our approach, we use a case study on assessing resilience of On-Site Wastewater Treatment and Disposal Systems (OSTDS) exposed to risks due to sea-level rise.
Non-Parametric Neuro-Adaptive Formation Control
Authors: Christos K. Verginis, Zhe Xu, Ufuk Topcu
Date: 2022-07-17 00:00:00

We develop a learning-based algorithm for the distributed formation control of networked multi-agent systems governed by unknown, nonlinear dynamics. Most existing algorithms either assume certain parametric forms for the unknown dynamic terms or resort to unnecessarily large control inputs in order to provide theoretical guarantees. The proposed algorithm avoids these drawbacks by integrating neural network-based learning with adaptive control in a two-step procedure. In the first step of the algorithm, each agent learns a controller, represented as a neural network, using training data that correspond to a collection of formation tasks and agent parameters. These parameters and tasks are derived by varying the nominal agent parameters and a user-defined formation task to be achieved, respectively. In the second step of the algorithm, each agent incorporates the trained neural network into an online and adaptive control policy in such a way that the behavior of the multi-agent closed-loop system satisfies the user-defined formation task. Both the learning phase and the adaptive control policy are distributed, in the sense that each agent computes its own actions using only local information from its neighboring agents. The proposed algorithm does not use any a priori information on the agents' unknown dynamic terms or any approximation schemes. We provide formal theoretical guarantees on the achievement of the formation task.
Finite Time Privacy Preserving Quantized Average Consensus with Transmission Stopping
Authors: Apostolos I. Rikos, Christoforos N. Hadjicostis, Karl H. Johansson
Date: 2022-07-17 00:00:00
Tasks: Privacy Preserving

Due to their flexibility, battery powered or energy-harvesting wireless networks are employed in diverse applications. Securing data transmissions between wireless devises is of critical importance in order to avoid privacy-sensitive user data leakage. In this paper, we focus on the scenario where some nodes are curious (but not malicious) and try to identify the initial states of one (or multiple) other nodes, while some nodes aim to preserve the privacy of their initial states from the curious nodes. We present a privacy preserving finite transmission event-triggered quantized average consensus algorithm. Its operation is suitable for battery-powered or energy-harvesting wireless network since it guarantees (i) efficient (quantized) communication, and (ii) transmission ceasing (which allows preservation of available energy). Furthermore, we present topological conditions under which the proposed algorithm allows nodes to preserve their privacy. We conclude with a comparison of our algorithm against other algorithms in the existing literature.
Optimal Database Allocation in Finite Time with Efficient Communication and Transmission Stopping over Dynamic Networks
Authors: Apostolos I. Rikos, Christoforos N. Hadjicostis, Karl H. Johansson
Date: 2022-07-17 00:00:00
Tasks: Quantization

In this paper, we focus on the problem of data sharing over a wireless computer network (i.e., a wireless grid). Given a set of available data, we present a distributed algorithm which operates over a dynamically changing network, and allows each node to calculate the optimal allocation of data in a finite number of time steps. We show that our proposed algorithm (i) converges to the optimal solution in finite time with very high probability, and (ii) once the optimal solution is reached, each node is able to cease transmissions without needing knowledge of a global parameter such as the network diameter. Furthermore, our algorithm (i) operates exclusively with quantized values (i.e., each node processes and transmits quantized information), (ii) relies on event-driven updates, and (iii) calculates the optimal solution in the form of a quantized fraction which avoids errors due to quantization. Finally, we demonstrate the operation, performance, and potential advantages of our algorithm over random dynamic networks.
Distributed Finite Time k-means Clustering with Quantized Communucation and Transmission Stopping
Authors: Apostolos I. Rikos, Gabriele Oliva, Christoforos N. Hadjicostis, Karl H. Johansson
Date: 2022-07-17 00:00:00
Tasks: Clustering

In this paper, we present a distributed algorithm which implements the $k$-means algorithm in a distributed fashion for multi-agent systems with directed communication links. The goal of $k$-means is to partition the network's agents in mutually exclusive sets (groups) such that agents in the same set have (and possibly share) similar information and are able to calculate a representative value for their group.During the operation of our distributed algorithm, each node (i) transmits quantized values in an event-driven fashion, and (ii) exhibits distributed stopping capabilities. Transmitting quantized values leads to more efficient usage of the available bandwidth and reduces the communication bottleneck. Also, in order to preserve available resources, nodes are able to distributively determine whether they can terminate the operation of the proposed algorithm. We characterize the properties of the proposed distributed algorithm and show that its execution (on any static and strongly connected digraph) will partition all agents to mutually exclusive clusters in finite time. We conclude with examples that illustrate the operation, performance, and potential advantages of the proposed algorithm.
Locational Aspect of Fast Frequency Reserves in Low-Inertia Systems -- Control Performance Analysis
Authors: Georgios Misyris, Deepak Ramasubramanian, Parag Mitra, Vikas Singhvi
Date: 2022-07-17 00:00:00

This paper evaluates the frequency performance of an AC system when primary frequency response is provided by inverter-based resources located at remote-areas. Due to potentially larger wave propagation constants over longer lines, fast active power response from inverter based resources may have a negative impact on the system frequency response. Within this context, this paper presents a control performance analysis is presented in order to identify limitations for improving the frequency stability when inverter-based resources in remote locations use local frequency measurements. Our results suggest that there exists a trafeoff between disturbance rejection and stability robustness when allocating primary frequency control. In particular, fast frequency control can have a negative impact on the damping ratio of poorly damped electromechanical modes.
Mapping Disruption Sources in the Power Grid and Implications for Resilience
Authors: Maureen S. Golan, Javad Mohammadi
Date: 2022-07-17 00:00:00

Developing models and metrics that can address resilience against disruptions is vital to ensure power grid reliability and that adequate recovery and adaptation mechanisms are in place. In this paper, we propose a novel disruption mapping approach and apply it to the publicly available U.S. Department of Energy DOE-417 Electric Emergency and Disturbance Report to holistically analyze the origin of anomalous events and their propagation through the cyber, physical and human domains. We show that capturing the disruption process onset has implications for quantifying, mitigating, and reporting power grid resilience.
Stability analysis and stabilization of systems with hyperexponential rates
Authors: Konstantin Zimenko, Denis Efimov, Andrey Polyakov
Date: 2022-07-16 00:00:00

Hyperexponential stability is investigated for dynamical systems with the use of both, explicit and implicit, Lyapunov function methods. A nonlinear hyperexponential control is designed for stabilizing linear systems. The tuning procedure is formalized in LMI form. Through numeric experiments, it is observed that the proposed hyperexponential control is less sensitive with respect to noises and discretization errors than its finite-time analog. It also demonstrates better performance in the presence of delays as well. Theoretical results are supported by numerical simulations.
Power Hardware-In-the-Loop Testing of a Peer-to-Peer Energy Trading Framework
Authors: Thomas Perrau, Maksim Stojkovic, Gregor Verbic
Date: 2022-07-16 00:00:00
Tasks: energy trading

This paper demonstrates the value of power hardware-in-the-loop (PHIL) testing for the study of peer-to-peer (P2P) energy trading. P2P has emerged as a promising candidate for coordinating large numbers of distributed energy resources (DER) that pose a risk to network operations if left unmanaged. The existing literature has so far relied on pure software simulations to study DER and distribution networks within this context. This requires the development of simplified models for complex components due to the computational limitations involved. Issues that arise through the operation of physical hardware in real-world applications are therefore neglected. We present PHIL testing as a solution to this problem by exhibiting its ability to capture the complex behaviors of physical DER devices. A high-fidelity PHIL test environment is introduced that combines key hardware elements with a simulated network model to study a P2P trading scenario. The initial findings reveal several underlying challenges of coordinating DER that are not typically discussed in prior works.
A Study of Long-term Energy-mix Optimization Model: A Case Study in Japan
Authors: Shintaro Negishi
Date: 2022-07-16 00:00:00
Tasks: Decision Making

There is a strong need to reduce greenhouse gas emissions to deal with climate change. In the power sector, changing the power generation method in the medium and long term is needed to reduce greenhouse gas emissions. This paper proposes a long-term energy-mix optimization mod-el to obtain the process of carbon neutrality in the power system. The proposed model models power supply and demand at an hourly granularity and determines the generation capacity that minimizes the long-term energy supply cost. Compared with the models proposed in previous studies, the proposed model can determine the installed capacity to maintain the balance of power supply and demand by adding the capacity of regulation reserve required by fluctuations in the output of variable renewable energy as a constraint condition. A Japan energy mix calculation is reported as a case study of the proposed model. This model can clarify the roadmap to achieving each country's emission reduction target and support the government's decision-making.
Assessing the impact of cyber attacks manipulating distributed energy resources on power system operation
Authors: Philipp Linnartz, Alexander Winkens, Andreas Ulbig
Date: 2022-07-16 00:00:00

Successful cyber attacks on power systems cause severe disruptions. One possible manipulation strategy is the utilization of distributed energy resources (DERs) to disturb power system operation. In addition to the impact on bulk power system frequency, local cascading effects caused by DER control and protection can increase the severity of this strategy. To investigate these effects, manipulation scenarios including the disconnection as well as the manipulation of active (P) and reactive power (Q) setpoints of DERs are derived. The impact is analyzed using time-domain simulations and quantified using assessment criteria such as voltage band violation and plant protection triggering. Though DER disconnection leads to high amounts of lost P injection the manipulation of Q setpoints offers potential to disconnect additional DERs through local cascading effects. To mitigate the impact of the manipulation scenarios automated tap changer operation as well as a limitation of remotely accessible Q is suitable.
NOCT: Nonlinear Observability with Constraints and Time Offset
Authors: Jianzhu Huai, Yukai Lin, Yujia Zhang
Date: 2022-07-16 00:00:00
Tasks: Sensor Fusion

Nonlinear systems of affine control inputs overarch many sensor fusion instances. Analyzing whether a state variable in such a nonlinear system can be estimated (i.e., observability) informs better estimator design. Among the research on local observability of nonlinear systems, approaches based on differential geometry have attracted much attention for the solid theoretic foundation and suitability to automated deduction. Such approaches usually work with a system model of unconstrained control inputs and assume that the control inputs and observation outputs are timestamped by the same clock. To our knowledge, it has not been shown how to conduct the observability analysis with additional constraints enforced on the system's observations or control inputs. To this end, we propose procedures to convert a system model of affine control inputs with linear constraints into a constraint-free standard model which is apt to be analyzed by the classic observability analysis procedure. Then, the whole analysis procedure is illustrated by applying to the well-studied visual inertial odometry (VIO) system which estimates the camera-IMU relative pose and time offset. The findings about unobservable variables under degenerate motion concur with those obtained with linearized VIO systems in other studies, whereas the findings about observability of time offset extend those in previous studies. These findings are further validated by simulation.
Distributed Safe Learning and Planning for Multi-robot Systems
Authors: Zhenyuan Yuan, Minghui Zhu
Date: 2022-07-16 00:00:00
Tasks: Active Learning, Collision Avoidance, Model Predictive Control, Motion Planning, Navigate, Uncertainty Quantification

This paper considers the problem of online multi-robot motion planning with general nonlinear dynamics subject to unknown external disturbances. We propose dSLAP, a distributed safe learning and planning framework that allows the robots to safely navigate through the environments by coupling online learning and motion planning. Gaussian process regression is used to online learn the disturbances with uncertainty quantification. The planning algorithm ensures collision avoidance against the learning uncertainty and utilizes set-valued analysis to achieve fast adaptation in response to the newly learned models. A set-valued model predictive control problem is then formulated and solved to return a control policy that balances between actively exploring the unknown disturbances and reaching goal regions. Sufficient conditions are established to guarantee the safety of the robots in the absence of backup policy. Monte Carlo simulations are conducted for evaluation.
Information Theoretic Data Injection Attacks with Sparsity Constraints
Authors: Xiuzhen Ye, Iñaki Esnaola, Samir M. Perlaza, Robert F. Harrison
Date: 2020-07-06 00:00:00
Tasks: State Estimation

Information theoretic sparse attacks that minimize simultaneously the information obtained by the operator and the probability of detection are studied in a Bayesian state estimation setting. The attack construction is formulated as an optimization problem that aims to minimize the mutual information between the state variables and the observations while guaranteeing the stealth of the attack. Stealth is described in terms of the Kullback-Leibler (KL) divergence between the distributions of the observations under attack and without attack. To overcome the difficulty posed by the combinatorial nature of a sparse attack construction, the attack case in which only one sensor is compromised is analytically solved first. The insight generated in this case is then used to propose a greedy algorithm that constructs random sparse attacks. The performance of the proposed attack is evaluated in the IEEE 30 Bus Test Case.
Spatial and temporal correlations in neural networks with structured connectivity
Authors: Yan-Liang Shi, Roxana Zeraati, Anna Levina, Tatiana A. Engel
Date: 2022-07-16 00:00:00

Correlated fluctuations in the activity of neural populations reflect the network's dynamics and connectivity. The temporal and spatial dimensions of neural correlations are interdependent. However, prior theoretical work mainly analyzed correlations in either spatial or temporal domains, oblivious to their interplay. We show that the network dynamics and connectivity jointly define the spatiotemporal profile of neural correlations. We derive analytical expressions for pairwise correlations in networks of binary units with spatially arranged connectivity in one and two dimensions. We find that spatial interactions among units generate multiple timescales in auto- and cross-correlations. Each timescale is associated with fluctuations at a particular spatial frequency, making a hierarchical contribution to the correlations. External inputs can modulate the correlation timescales when spatial interactions are nonlinear, and the modulation effect depends on the operating regime of network dynamics. These theoretical results open new ways to relate connectivity and dynamics in cortical networks via measurements of spatiotemporal neural correlations.
Balancing the trade-off between cost and reliability for wireless sensor networks: a multi-objective optimized deployment method
Authors: Long Chen, Yingying Xu, Fangyi Xu, Qian Hu, Zhenzhou Tang
Date: 2022-07-19 00:00:00
Tasks: Multiobjective Optimization

The deployment of the sensor nodes (SNs) always plays a decisive role in the system performance of wireless sensor networks (WSNs). In this work, we propose an optimal deployment method for practical heterogeneous WSNs which gives a deep insight into the trade-off between the reliability and deployment cost. Specifically, this work aims to provide the optimal deployment of SNs to maximize the coverage degree and connection degree, and meanwhile minimize the overall deployment cost. In addition, this work fully considers the heterogeneity of SNs (i.e. differentiated sensing range and deployment cost) and three-dimensional (3-D) deployment scenarios. This is a multi-objective optimization problem, non-convex, multimodal and NP-hard. To solve it, we develop a novel swarm-based multi-objective optimization algorithm, known as the competitive multi-objective marine predators algorithm (CMOMPA) whose performance is verified by comprehensive comparative experiments with ten other stateof-the-art multi-objective optimization algorithms. The computational results demonstrate that CMOMPA is superior to others in terms of convergence and accuracy and shows excellent performance on multimodal multiobjective optimization problems. Sufficient simulations are also conducted to evaluate the effectiveness of the CMOMPA based optimal SNs deployment method. The results show that the optimized deployment can balance the trade-off among deployment cost, sensing reliability and network reliability. The source code is available on https://github.com/iNet-WZU/CMOMPA.
Dark Matter and Dark Energy
Authors: Varun Sahni
Date: 2004-03-13 00:00:00
Tasks: Diagnostic

I briefly review our current understanding of dark matter and dark energy. The first part of this paper focusses on issues pertaining to dark matter including observational evidence for its existence, current constraints and the `abundance of substructure' and `cuspy core' issues which arise in CDM. I also briefly describe MOND. The second part of this review focusses on dark energy. In this part I discuss the significance of the cosmological constant problem which leads to a predicted value of the cosmological constant which is almost $10^{123}$ times larger than the observed value $\la/8\pi G \simeq 10^{-47}$GeV$^4$. Setting $\la$ to this small value ensures that the acceleration of the universe is a fairly recent phenomenon giving rise to the `cosmic coincidence' conundrum according to which we live during a special epoch when the density in matter and $\la$ are almost equal. Anthropic arguments are briefly discussed but more emphasis is placed upon dynamical dark energy models in which the equation of state is time dependent. These include Quintessence, Braneworld models, Chaplygin gas and Phantom energy. Model independent methods to determine the cosmic equation of state and the Statefinder diagnostic are also discussed. The Statefinder has the attractive property $\atridot/a H^3 = 1 $ for LCDM, which is helpful for differentiating between LCDM and rival dark energy models. The review ends with a brief discussion of the fate of the universe in dark energy models.
Dual-space Compressed Sensing
Authors: Xudong Lv, Ashok Ajoy
Date: 2022-07-15 00:00:00
Tasks: compressed sensing, Edge Detection, Image Reconstruction

Compressed sensing (CS) is a powerful method routinely employed to accelerate image acquisition. It is particularly suited to situations when the image under consideration is sparse but can be sampled in a basis where it is non-sparse. Here we propose an alternate CS regime in situations where the image can be sampled in two incoherent spaces simultaneously, with a special focus on image sampling in Fourier reciprocal spaces (e.g. real-space and k-space). Information is fed-forward from one space to the other, allowing new opportunities to efficiently solve the optimization problem at the heart of CS image reconstruction. We show that considerable gains in imaging acceleration are then possible over conventional CS. The technique provides enhanced robustness to noise, and is well suited to edge-detection problems. We envision applications for imaging collections of nanodiamond (ND) particles targeting specific regions in a volume of interest, exploiting the ability of lattice defects (NV centers) to allow ND particles to be imaged in reciprocal spaces simultaneously via optical fluorescence and 13C magnetic resonance imaging (MRI) respectively. Broadly this work suggests the potential to interface CS principles with hybrid sampling strategies to yield speedup in signal acquisition in many practical settings.
Warp and Weft Wiring method for rapid, modifiable, self-aligned, and bonding-free fabrication of multi electrodes microfluidic sensors
Authors: Ali Olyanasab, Zahra Meskar, Mohsen Annabestani, Ali Mousavi Shaegh, Mehdi Fardmanesh
Date: 2022-07-14 00:00:00
Tasks: Management

The need for rapid fabrication of microfluidic devices has become increasingly critical as microfluidics become part of biomedical sensors. Using Warp and Weft Wiring (WWW) of copper wires, this paper presents a novel low-cost method for rapid, self-aligned, bonding-free, and modifiable fabrication of multi-electrodes microfluidic sensors. All the proposed features are promising and highly recommended for the development of Point-of-Care Tests (POCTs), while most of the conventional methods have low chances of coming out of the research labs and play no role in POCTs development. To have an experimental proof of concept, the proposed chip was fabricated and then tested with two sets of experiments that showed the potential applications of water quality management, hygiene, biomedical impedance measurement, cell analysis, flow cytometry, etc.