pwc-archive/papers-with-abstracts huggingface dataset:
Optimal Detector Placement in Networked Control Systems under Cyber-attacks with Applications to Power Networks
Authors: Anh Tung Nguyen, Sribalaji C. Anand, André M. H. Teixeira, Alexander Medvedev
Date: 2023-04-12 00:00:00

This paper proposes a game-theoretic method to address the problem of optimal detector placement in a networked control system under cyber-attacks. The networked control system is composed of interconnected agents where each agent is regulated by its local controller over unprotected communication, which leaves the system vulnerable to malicious cyber-attacks. To guarantee a given local performance, the defender optimally selects a single agent on which to place a detector at its local controller with the purpose of detecting cyber-attacks. On the other hand, an adversary optimally chooses a single agent on which to conduct a cyber-attack on its input with the aim of maximally worsening the local performance while remaining stealthy to the defender. First, we present a necessary and sufficient condition to ensure that the maximal attack impact on the local performance is bounded, which restricts the possible actions of the defender to a subset of available agents. Then, by considering the maximal attack impact on the local performance as a game payoff, we cast the problem of finding optimal actions of the defender and the adversary as a zero-sum game. Finally, with the possible action sets of the defender and the adversary, an algorithm is devoted to determining the Nash equilibria of the zero-sum game that yield the optimal detector placement. The proposed method is illustrated on an IEEE benchmark for power systems.
A Persistent-Excitation-Free Method for System Disturbance Estimation Using Concurrent Learning
Authors: Zengjie Zhang, Fangzhou Liu, Tong Liu, Jianbin Qiu, Martin Buss
Date: 2023-04-12 00:00:00

Observer-based methods are widely used to estimate the disturbances of different dynamic systems. However, a drawback of the conventional disturbance observers is that they all assume persistent excitation (PE) of the systems. As a result, they may lead to poor estimation precision when PE is not ensured, for instance, when the disturbance gain of the system is close to the singularity. In this paper, we propose a novel disturbance observer based on concurrent learning (CL) with time-variant history stacks, which ensures high estimation precision even in PE-free cases. The disturbance observer is designed in both continuous and discrete time. The estimation errors of the proposed method are proved to converge to a bounded set using the Lyapunov method. A history-sample-selection procedure is proposed to reduce the estimation error caused by the accumulation of old history samples. A simulation study on epidemic control shows that the proposed method produces higher estimation precision than the conventional disturbance observer when PE is not satisfied. This justifies the correctness of the proposed CL-based disturbance observer and verifies its applicability to solving practical problems.
Multisensor fusion-based digital twin in additive manufacturing for in-situ quality monitoring and defect correction
Authors: Lequn Chen, Xiling Yao, Kui Liu, Chaolin Tan, Seung Ki Moon
Date: 2023-04-12 00:00:00

Early detection and correction of defects are critical in additive manufacturing (AM) to avoid build failures. In this paper, we present a multisensor fusion-based digital twin for in-situ quality monitoring and defect correction in a robotic laser direct energy deposition process. Multisensor fusion sources consist of an acoustic sensor, an infrared thermal camera, a coaxial vision camera, and a laser line scanner. The key novelty and contribution of this work are to develop a spatiotemporal data fusion method that synchronizes and registers the multisensor features within the part's 3D volume. The fused dataset can be used to predict location-specific quality using machine learning. On-the-fly identification of regions requiring material addition or removal is feasible. Robot toolpath and auto-tuned process parameters are generated for defecting correction. In contrast to traditional single-sensor-based monitoring, multisensor fusion allows for a more in-depth understanding of underlying process physics, such as pore formation and laser-material interactions. The proposed methods pave the way for self-adaptation AM with higher efficiency, less waste, and cleaner production.
Using Demand Response to Improve Power System Small-Signal Stability
Authors: Mengqi Yao, Sandip Roy, Johanna L. Mathieu
Date: 2023-04-12 00:00:00

With the increase of uncertain and intermittent renewable energy supply on the grid, the power system has become more vulnerable to instability. In this paper, we develop a demand response strategy to improve power system small-signal stability. We pose the problem as an optimization problem wherein the total demand-responsive load is held constant at each time instance but shifted between different buses to improve small-signal stability, which is measured by small-signal stability metrics that are functions of subsets of the system's eigenvalues, such as the smallest damping ratio. To solve the problem, we use iterative linear programming and generalized eigenvalue sensitivities. We demonstrate the approach via a case study that uses the IEEE 14-bus system. Our results show that shifting the load between buses, can improve a small-signal stability margin. We explore the use of models of different fidelity and find that it is important to include models of the automatic voltage regulators and power system stabilizers. In addition, we show that load shifting can achieve similar improvements to generation shifting and better improvement than simply tuning power system stabilizers.
Encrypted Price-based Market Mechanism for Optimal Load Frequency Control
Authors: Jihoon Suh, Takashi Tanaka
Date: 2023-04-11 00:00:00

The global trend of energy deregulation has led to the market mechanism replacing some functionality of load frequency control (LFC). Accordingly, information exchange among participating generators and the market operator plays a crucial role in optimizing social utility. However, privacy has been an equally pressing concern in such settings. This conflict between individuals' privacy and social utility has been a long-standing challenge in market mechanism literature as well as in Cyber-Physical Systems (CPSs). In this paper, we propose a novel encrypted market architecture that leverages a hybrid encryption method and two-party computation protocols, enabling the secure synthesis and implementation of an optimal price-based market mechanism. This work spotlights the importance of secure and efficient outsourcing of controller synthesis, which is a critical element within the proposed framework. A two-area LFC model is used to conduct a case study.
State estimation of a carbon capture process through POD model reduction and neural network approximation
Authors: Siyu Liu, Xunyuan Yin, Jinfeng Liu
Date: 2023-04-11 00:00:00
Tasks: Computational Efficiency, State Estimation

This paper presents an efficient approach for state estimation of post-combustion CO2 capture plants (PCCPs) by using reduced-order neural network models. The method involves extracting lower-dimensional feature vectors from high-dimensional operational data of the PCCP and constructing a reduced-order process model using proper orthogonal decomposition (POD). Multi-layer perceptron (MLP) neural networks capture the dominant dynamics of the process and train the network parameters with low-dimensional data obtained from open-loop simulations. The proposed POD-MLP model can be used as the basis for estimating the states of PCCPs at a significantly decreased computational cost. For state estimation, a reduced-order extended Kalman filtering (EKF) scheme based on the POD-MLP model is developed. Our simulations demonstrate that the proposed POD-MLP modeling approach reduces computational complexity compared to the POD-only model for nonlinear systems. Additionally, the POD-MLP-EKF algorithm can accurately reconstruct the full state information of PCCPs while significantly improving computational efficiency compared to the EKF based on the original PCCP model.
Structured IQC Synthesis of Robust $\mathcal{H}_2$ Controllers in the Frequency Domain
Authors: Maximilian Schütte, Annika Eichler, Herbert Werner
Date: 2023-04-11 00:00:00

The problem of robust controller synthesis for plants affected by structured uncertainty, captured by integral quadratic constraints, is discussed. The solution is optimized towards a worst-case white noise rejection specification, which is a generalization of the standard $\mathcal{H}_2$-norm to the robust setting including possibly non-LTI uncertainty. Arbitrary structural constraints can be imposed on the control solution, making this method suitable for distributed systems. The nonsmooth optimization algorithm used to solve the robust synthesis problem operates directly in the frequency domain, eliminating scalability issues for complex systems and providing local optimality certificates. The method is evaluated using a literature example and a real-world system using a novel implementation of a robust $\mathcal{H}_2$-performance bound.
Statistical measures of complexity applied to ecological networks
Authors: Claudia Huaylla, Marcelo N Kuperman, Lucas A. Garibaldi
Date: 2023-04-11 00:00:00

Networks are a convenient way to represent many interactions among different entities as they provide an efficient and clear methodology to evaluate and organize relevant data. While there are many features for characterizing networks there is a quantity that seems rather elusive: Complexity. The quantification of the complexity of networks is nowadays a fundamental problem. Here, we present a novel tool for identifying the complexity of ecological networks. We compare the behavior of two relevant indices of complexity: K-complexity and Single value decomposition (SVD) entropy. For that, we use real data and null models. Both null models consist of randomized networks built by swapping a controlled number of links of the original ones. We analyze 23 plant-pollinator and 19 host-parasite networks as case studies. Our results show interesting features in the behavior for the K-complexity and SVD entropy with clear differences between pollinator-plant and host-parasite networks, especially when the degree distribution is not preserved. Although SVD entropy has been widely used to characterize network complexity, our analyses show that K-complexity is a more reliable tool. Additionally, we show that degree distribution and density are important drivers of network complexity and should be accounted for in future studies.
Learning to exploit z-Spatial Diversity for Coherent Nonlinear Optical Fiber Communication
Authors: Sebastian Jung, Tim Uhlemann, Alexander Span, Maximilian Bauhofer, Stephan ten Brink
Date: 2023-04-12 00:00:00
Tasks: Diversity

Higher-order solitons inherently possess a spatial periodicity along the propagation axis. The pulse expands and compresses in both, frequency and time domain. This property is exploited for a bandwidth-limited receiver by sampling the optical signal at two different distances. Numerical simulations show that when pure solions are transmitted and the second (i.e., further propagated) signal is also processed, a significant gain in terms of required receiver bandwidth is obtained. Since all pulses propagating in a nonlinear optical fiber exhibit solitonic behavior given sufficient input power and propagation distance, the above concept can also be applied to spectrally efficient Nyquist pulse shaping and higher symbol rates. Transmitter and receiver are trainable structures as part of an autoencoder, aiming to learn a suitable predistortion and post-equalization using both signals to increase the spectral efficiency.
Visibility graph analysis of the grains and oilseeds indices
Authors: Hao-Ran Liu, Ming-Xia Li, Wei-Xing Zhou
Date: 2023-04-12 00:00:00
Tasks: Clustering

The Grains and Oilseeds Index (GOI) and its sub-indices of wheat, maize, soyabeans, rice, and barley are daily price indexes reflect the price changes of the global spot markets of staple agro-food crops. In this paper, we carry out a visibility graph (VG) analysis of the GOI and its five sub-indices. Our findings reveal that the degree distributions of the VGs, except for rice, exhibit exponentially truncated power-law tails, while the rice VG conforms to a power-law tail. The average clustering coefficients of the six VGs are quite large ($>0.5$) and exhibit a nice power-law relation with respect to the average degrees of the VGs. For each VG, the clustering coefficients of nodes are inversely proportional to their degrees for large degrees and are correlated to their degrees as a power law for small degrees. All the six VGs exhibit small-world characteristics. The degree-degree correlation coefficients shows that the VGs for maize and soyabeans indices exhibit weak assortative mixing patterns, while the other four VGs are weakly disassortative. The average nearest neighbor degree functions have similar patterns, and each function shows a more complex mixing pattern which decreases for small degrees, increases for mediate degrees, and decreases again for large degrees.
Payroll Tax Incidence: Evidence from Unemployment Insurance
Authors: Audrey Guo
Date: 2023-04-12 00:00:00

Economic models assume that payroll tax burdens fall fully on workers, but where does tax incidence fall when taxes are firm-specific and time-varying? Unemployment insurance in the United States has the key feature of varying both across employers and over time, creating the potential for labor demand responses if tax costs cannot be fully passed on to worker wages. Using state policy changes and matched employer-employee job spells from the LEHD, I study how employment and earnings respond to payroll tax increases for highly exposed employers. I find significant drops in employment growth driven by lower hiring, and minimal evidence of pass-through to earnings. The negative employment effects are strongest for young and low-earning workers.
Comment on Matsushima, Miyazaki, and Yagi (2010) "Role of Linking Mechanisms in Multitask Agency with Hidden Information"
Authors: Ian Ball, Deniz Kattwinkel
Date: 2023-04-12 00:00:00

We correct a gap in the proof of Theorem 2 in Matsushima et al. (2010).
A Note on Cursed Sequential Equilibrium and Sequential Cursed Equilibrium
Authors: Meng-Jhang Fong, Po-Hsuan Lin, Thomas R. Palfrey
Date: 2023-04-11 00:00:00

In this short note, we compare the cursed sequential equilibrium (CSE) by Fong et al. (2023) and the sequential cursed equilibrium (SCE) by Cohen and Li (2023). We identify eight main differences between CSE and SCE with respect to the following features: (1) the family of applicable games, (2) the number of free parameters, (3) the belief updating process, (4) the treatment of public histories, (5) effects in games of complete information, (6) violations of subgame perfection and sequential rationality, (7) re-labeling of actions, and (8) effects in one-stage simultaneous-move games.
Regularizing Contrastive Predictive Coding for Speech Applications
Authors: Saurabhchand Bhati, Jesús Villalba, Piotr Żelasko, Laureano Moro-Velazquez, Najim Dehak
Date: 2023-04-12 00:00:00
Tasks: Acoustic Unit Discovery, Automatic Speech Recognition, Data Augmentation, speech-recognition, Speech Recognition

Self-supervised methods such as Contrastive predictive Coding (CPC) have greatly improved the quality of the unsupervised representations. These representations significantly reduce the amount of labeled data needed for downstream task performance, such as automatic speech recognition. CPC learns representations by learning to predict future frames given current frames. Based on the observation that the acoustic information, e.g., phones, changes slower than the feature extraction rate in CPC, we propose regularization techniques that impose slowness constraints on the features. Here we propose two regularization techniques: Self-expressing constraint and Left-or-Right regularization. We evaluate the proposed model on ABX and linear phone classification tasks, acoustic unit discovery, and automatic speech recognition. The regularized CPC trained on 100 hours of unlabeled data matches the performance of the baseline CPC trained on 360 hours of unlabeled data. We also show that our regularization techniques are complementary to data augmentation and can further boost the system's performance. In monolingual, cross-lingual, or multilingual settings, with/without data augmentation, regardless of the amount of data used for training, our regularized models outperformed the baseline CPC models on the ABX task.
Speech Reconstruction from Silent Tongue and Lip Articulation By Pseudo Target Generation and Domain Adversarial Training
Authors: Rui-Chen Zheng, Yang Ai, Zhen-Hua Ling
Date: 2023-04-12 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

This paper studies the task of speech reconstruction from ultrasound tongue images and optical lip videos recorded in a silent speaking mode, where people only activate their intra-oral and extra-oral articulators without producing sound. This task falls under the umbrella of articulatory-to-acoustic conversion, and may also be refered to as a silent speech interface. We propose to employ a method built on pseudo target generation and domain adversarial training with an iterative training strategy to improve the intelligibility and naturalness of the speech recovered from silent tongue and lip articulation. Experiments show that our proposed method significantly improves the intelligibility and naturalness of the reconstructed speech in silent speaking mode compared to the baseline TaLNet model. When using an automatic speech recognition (ASR) model to measure intelligibility, the word error rate (WER) of our proposed method decreases by over 15% compared to the baseline. In addition, our proposed method also outperforms the baseline on the intelligibility of the speech reconstructed in vocalized articulating mode, reducing the WER by approximately 10%.
Automatic Aortic Valve Pathology Detection from 3-Chamber Cine MRI with Spatio-Temporal Attention Maps
Authors: Y. On, K. Vimalesvaran, C. Galazis, S. Zaman, J. Howard, N. Linton, N. Peters, G. Cole, A. A. Bharath, M. Varela
Date: 2023-04-12 00:00:00
Tasks: 3D Classification, Classification

The assessment of aortic valve pathology using magnetic resonance imaging (MRI) typically relies on blood velocity estimates acquired using phase contrast (PC) MRI. However, abnormalities in blood flow through the aortic valve often manifest by the dephasing of blood signal in gated balanced steady-state free precession (bSSFP) scans (Cine MRI). We propose a 3D classification neural network (NN) to automatically identify aortic valve pathology (aortic regurgitation, aortic stenosis, mixed valve disease) from Cine MR images. We train and test our approach on a retrospective clinical dataset from three UK hospitals, using single-slice 3-chamber cine MRI from N = 576 patients. Our classification model accurately predicts the presence of aortic valve pathology (AVD) with an accuracy of 0.85 +/- 0.03 and can also correctly discriminate the type of AVD pathology (accuracy: 0.75 +/- 0.03). Gradient-weighted class activation mapping (Grad-CAM) confirms that the blood pool voxels close to the aortic root contribute the most to the classification. Our approach can be used to improve the diagnosis of AVD and optimise clinical CMR protocols for accurate and efficient AVD detection.
Scalable Multi-task Semantic Communication System with Feature Importance Ranking
Authors: Jiangjing Hu, Fengyu Wang, Wenjun Xu, Hui Gao, Ping Zhang
Date: 2023-04-12 00:00:00
Tasks: Feature Importance, Semantic Communication

Semantic communications are expected to be an innovative solution to the emerging intelligent applications in the era of connected intelligence. In this paper, a novel scalable multitask semantic communication system with feature importance ranking (SMSC-FIR) is explored. Firstly, the multi-task correlations are investigated by a joint semantic encoder to extract relevant features. Then, a new scalable coding method is proposed based on feature importance ranking, which dynamically adjusts the coding rate and guarantees that important features for semantic tasks are transmitted with higher priority. Simulation results show that SMSC-FIR achieves performance gain w.r.t. individual intelligent tasks, especially in the low SNR regime.
Low-Complexity Memoryless Linearizer for Analog-to-Digital Interfaces
Authors: Deijany Rodriguez Linares, Håkan Johansson
Date: 2023-04-12 00:00:00

This paper introduces a low-complexity memoryless linearizer for suppression of distortion in analog-to-digital interfaces. It is inspired by neural networks, but has a substantially lower complexity than the neural-network schemes that have appeared earlier in the literature in this context. The paper demonstrates that the proposed linearizer can outperform the conventional parallel memoryless Hammerstein linearizer even when the nonlinearities have been generated through a memoryless polynomial model. Further, a design procedure is proposed in which the linearizer parameters are obtained through matrix inversion. Thereby, the costly and time consuming numerical optimization that is traditionally used when training neural networks is eliminated. Moreover, the design and evaluation incorporate a large set of multi-tone signals covering the first Nyquist band. Simulations show signal-to-noise-and-distortion ratio (SNDR) improvements of some 25 dB for multi-tone signals that correspond to the quadrature parts of OFDM signals with QPSK modulation.
Frame Error Rate Prediction for Non-Stationary Wireless Vehicular Communication Links
Authors: Anja Dakić, Benjamin Rainer, Markus Hofer, Thomas Zemen
Date: 2023-04-12 00:00:00

Wireless vehicular communication will increase the safety of road users. The reliability of vehicular communication links is of high importance as links with low reliability may diminish the advantage of having situational traffic information. The goal of our investigation is to obtain a reliable coverage area for non-stationary vehicular scenarios. Therefore we propose a deep neural network (DNN) for predicting the expected frame error rate (FER). The DNN is trained in a supervised fashion, where a time-limited sequence of channel frequency responses has been labeled with its corresponding FER values assuming an underlying wireless communication system, i.e. IEEE 802.11p. For generating the training dataset we use a geometry-based stochastic channel model (GSCM). We obtain the ground truth FER by emulating the time-varying frequency responses using a hardware-in-the-loop setup. Our GSCM provides the propagation path parameters which we use to fix the statistics of the fading process at one point in space for an arbitrary amount of time, enabling accurate FER estimation. Using this dataset we achieve an accuracy of 85% of the DNN. We use the trained model to predict the FER for measured time-varying channel transfer functions obtained during a measurement campaign. We compare the predicted output of the DNN to the measured FER on the road and obtain a prediction accuracy of 78%.