pwc-archive/papers-with-abstracts huggingface dataset:
Local Exceptionality Detection in Time Series Using Subgroup Discovery
Authors: Dan Hudson, Travis J. Wiltshire, Martin Atzmueller
Date: 2021-08-05 00:00:00
Tasks: Subgroup Discovery, Time Series, Time Series Analysis

In this paper, we present a novel approach for local exceptionality detection on time series data. This method provides the ability to discover interpretable patterns in the data, which can be used to understand and predict the progression of a time series. This being an exploratory approach, the results can be used to generate hypotheses about the relationships between the variables describing a specific process and its dynamics. We detail our approach in a concrete instantiation and exemplary implementation, specifically in the field of teamwork research. Using a real-world dataset of team interactions we include results from an example data analytics application of our proposed approach, showcase novel analysis options, and discuss possible implications of the results from the perspective of teamwork research.
Cross-domain Single-channel Speech Enhancement Model with Bi-projection Fusion Module for Noise-robust ASR
Authors: Fu-An Chao, Jeih-weih Hung, Berlin Chen
Date: 2021-08-26 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Speech Enhancement, speech-recognition, Speech Recognition

In recent decades, many studies have suggested that phase information is crucial for speech enhancement (SE), and time-domain single-channel speech enhancement techniques have shown promise in noise suppression and robust automatic speech recognition (ASR). This paper presents a continuation of the above lines of research and explores two effective SE methods that consider phase information in time domain and frequency domain of speech signals, respectively. Going one step further, we put forward a novel cross-domain speech enhancement model and a bi-projection fusion (BPF) mechanism for noise-robust ASR. To evaluate the effectiveness of our proposed method, we conduct an extensive set of experiments on the publicly-available Aishell-1 Mandarin benchmark speech corpus. The evaluation results confirm the superiority of our proposed method in relation to a few current top-of-the-line time-domain and frequency-domain SE methods in both enhancement and ASR evaluation metrics for the test set of scenarios contaminated with seen and unseen noise, respectively.
Data-driven modeling and control of large-scale dynamical systems in the Loewner framework
Authors: Ion Victor Gosea, Charles Poussot-Vassal, Athanasios C. Antoulas
Date: 2021-08-26 00:00:00

In this contribution, we discuss the modeling and model reduction framework known as the Loewner framework. This is a data-driven approach, applicable to large-scale systems, which was originally developed for applications to linear time-invariant systems. In recent years, this method has been extended to a number of additional more complex scenarios, including linear parametric or nonlinear dynamical systems. We will provide here an overview of the latter two, together with time-domain extensions. Additionally, the application of the Loewner framework is illustrated by a collection of practical test cases. Firstly, for data-driven complexity reduction of the underlying model, and secondly, for dealing with control applications of complex systems (in particular, with feedback controller design).
FPGA-based Implementation of a New Data Frame Correction System for Merging Units
Authors: Mohammad Hashemi, Bijan Alizadeh
Date: 2021-08-26 00:00:00

With today's increasing demand for digital devices in Substation Automation Systems (SAS) based on the IEC61850 standard, the measured data error due to the synchronization problem should be considered as a significant problem in digitalized SAS. Although time tagging and mathematical methods have been proposed to alleviate this problem, they require a massive amount of calculations and elaborations. To develop a solution for both problems of the data error and the massive computation, in this paper, we propose a data frame correction (DFC) system with a new method of data shift computation as a data correction method implemented as a hardware accelerator on FPGA. Compared to the state-of-the-art DFC systems, the results show that the proposed DFC system can achieve data correction with up to 99.6% fewer hardware resources utilization and fulfills 9x calculation speed while maintaining IEC61850 required accuracy in 2.1ms.
Geometric Stochastic Filter with Guaranteed Performance for Autonomous Navigation based on IMU and Feature Sensor Fusion
Authors: Hashim A. Hashim, Mohammed Abouheaf, Mohammad A. Abido
Date: 2021-08-26 00:00:00
Tasks: Autonomous Navigation, Position, Sensor Fusion

This paper concerns the estimation problem of attitude, position, and linear velocity of a rigid-body autonomously navigating with six degrees of freedom (6 DoF). The navigation dynamics are highly nonlinear and are modeled on the matrix Lie group of the extended Special Euclidean Group $\mathbb{SE}_{2}(3)$. A computationally cheap geometric nonlinear stochastic navigation filter is proposed on $\mathbb{SE}_{2}(3)$ with guaranteed transient and steady-state performance. The proposed filter operates based on a fusion of sensor measurements collected by a low-cost inertial measurement unit (IMU) and features (obtained by a vision unit). The closed loop error signals are guaranteed to be almost semi-globally uniformly ultimately bounded in the mean square from almost any initial condition. The equivalent quaternion representation is included in the Appendix. The filter is proposed in continuous form, and its discrete form is tested on a real-world dataset of measurements collected by a quadrotor navigating in three dimensional (3D) space. Keywords: Localization, navigation, position and orientation estimation, stochastic systems, GPS-denied navigation observer, navigation estimator, vision-aided inertial navigation systems (VA-INSs), stochastic differential equation, Gaussian noise, sensor fusion.
Passenger Congestion Alleviation in Large Hub Airport Ground Access System Based on Queueing Theory
Authors: Hu Yiting, Luo Xiling, Bai Dongmei
Date: 2021-08-26 00:00:00

Airport public transport systems are plagued by passenger queue congestion, imposing a substandard travel experience and unexpected delays. To address this issue, this paper proposes a bi-level programming for optimizing queueing network in airport access based on passenger choice behavior. For this purpose, we derive queueing network for airport public transport system, which include the taxi, bus, and subway. Then, we propose a bi-level programming model for optimizing queueing network. The lower level subprogram is designed to correspond to the profit maximization principle for passenger transport mode choice behavior, while the upper level subprogram is designed to minimize the maximum number of passengers waiting to be served. Decision makers consider imposing queue tolls on passengers to incentivize them to change their choice and achieve the goal of avoiding congestion. Finally, we develop the successive weighted averages (MSWA) method to solve the lower subprogram's passenger share rates and the ant lion optimization (ALO) method to solve the bi-level program's queue toll scheme for upper-level objectives. We prove the effectiveness of the proposed method on two situations of simulation, daytime and evening cases. The numerical results highlight that our strategy can alleviate queue congestion for both scenarios and effectively improve evacuation efficiency.
System Identification and Controller Design for Hydraulic Actuator
Authors: Zainab Nisar, Hammad Munawar
Date: 2021-08-26 00:00:00

System Identification of Hydraulic Actuators is critical for analyzing their performance and designing a suitable Control System. Hydraulic actuators are extensively used in many applications, ranging from flight simulators, robotics, orthopaedic surgery, material testing, construction and many other industrial types of machinery. In the aviation industry, hydraulic actuators are currently being used in full flight simulators used for controlling the position and orientation of the motion platform. Every actuator has its own characteristics, therefore, the choice of excitation signals for System Identification must take into account the dynamics of the actuator under consideration. This work proposes the selection of excitation signals based on bandwidth of the hydraulic actuator. Validation of the proposed selection is done by performing system identification, obtaining a mathematical model and comparing it with a nonlinear hydraulic actuator model designed in Simscape. After validation, a nonlinear PID control has been tuned on the identified model and tested on the nonlinear model. Extensive simulations have been run and results show accurate mathematical modelling, as well as precise control has been achieved through the proposed methodology.
Robust AC Transmission Expansion Planning with Load and Renewable Generation Uncertainties
Authors: P. Naga Yasasvi, Abheejeet Mohapatra, Suresh Chandra Srivastava
Date: 2021-08-26 00:00:00

With the integration of Renewable Energy Sources (RESs), the power network must be robust to handle the system's uncertain scenarios. DC Transmission Expansion Planning (TEP) plans are generally not feasible for the AC network. A first bi-level Robust Optimization (RO) solution approach for AC TEP with injection uncertainties in loads and RESs generations is proposed in this paper. It utilizes a well-known convex relaxation and is solved by Benders Decomposition (BD), where the master determines the robust AC TEP plan. A novel dual slave model is developed for the second level of BD, which circumvents the issues in using the Conic Duality Theory (CDT), and aids in the worst-case realization of uncertainties using a novel set of constraints. The developed dual slave is not a mixed-integer problem and is solved using the Primal-Dual Interior Point Method (PDIPM). The proposed work also uses additional linear constraints to reduce BD's computational burden and direct the master towards optimality. Monte-Carlo Simulation (MCS) based verification, via actual nonlinear and non-convex AC Optimal Power Flow (OPF), proves 100% robustness of the TEP plans for all scenarios.
Secure Control of Networked Control Systems Using Dynamic Watermarking
Authors: Dajun Du, Changda Zhang, Xue Li, Minrui Fei, Taicheng Yang, Huiyu Zhou
Date: 2021-08-26 00:00:00

We here investigate secure control of networked control systems developing a new dynamic watermarking (DW) scheme. Firstly, the weaknesses of the conventional DW scheme are revealed, and the tradeoff between the effectiveness of false data injection attack (FDIA) detection and system performance loss is analysed. Secondly, we propose a new DW scheme, and its attack detection capability is interrogated using the additive distortion power of a closed-loop system. Furthermore, the FDIA detection effectiveness of the closed-loop system is analysed using auto/cross covariance of the signals, where the positive correlation between the FDIA detection effectiveness and the watermarking intensity is measured. Thirdly, the tolerance capacity of FDIA against the closed-loop system is investigated, and theoretical analysis shows that the system performance can be recovered from FDIA using our new DW scheme. Finally, experimental results from a networked inverted pendulum system demonstrate the validity of our proposed scheme.
Targeted False Data Injection Attacks Against AC State Estimation Without Network Parameters
Authors: Mingqiu Du, Georgia Pierrou, Xiaozhe Wang, Marthe Kassouf
Date: 2021-08-26 00:00:00
Tasks: State Estimation

State estimation is a data processing algorithm for converting redundant meter measurements and other information into an estimate of the state of a power system. Relying heavily on meter measurements, state estimation has proven to be vulnerable to cyber attacks. In this paper, a novel targeted false data injection attack (FDIA) model against AC state estimation is proposed. Leveraging on the intrinsic load dynamics in ambient conditions and important properties of the Ornstein-Uhlenbeck process, we, from the viewpoint of intruders, design an algorithm to extract power network parameters purely from PMU data, which are further used to construct the FDIA vector. Requiring no network parameters and relying only on limited phasor measurement unit (PMU) data, the proposed FDIA model can target specific states and launch large deviation attacks. Sufficient conditions for the proposed FDIA model are also developed. Various attack vectors and attacking regions are studied in the IEEE 39-bus system, showing that the proposed FDIA method can successfully bypass the bad data detection and launch targeted large deviation attacks with very high probabilities.
Model Predictive Control approach to adaptive messaging intervention for physical activity
Authors: Ibrahim E. Bardakci, Sahar Hojjatinia, Sarah Hojjatinia, Constantino M. Lagoa, David E. Conroy
Date: 2021-08-25 00:00:00
Tasks: Model Predictive Control

In this work, we have developed a framework for synthesizing data driven controllers for a class of uncertain switched systems arising in an application to physical activity interventions. In particular, we present an application of probabilistic model predictive control to design an efficient, tractable, and adaptive intervention using behavioral data sets i.e. physical activity behavior. The models of physical activity for each individual are provided for the design of controllers that maximize the probability of achieving a desired physical activity goal subject to intervention specifications. We have tailored the mixed-integer programming-based approach for evaluating the Model Predictive Control decision at each time step.
Identification of Peer Effects using Panel Data
Authors: Marisa Miraldo, Carol Propper, Christiern Rose
Date: 2021-08-26 00:00:00

We provide new identification results for panel data models with peer effects operating through unobserved individual heterogeneity. The results apply for general network structures governing peer interactions and allow for correlated effects. Identification hinges on a conditional mean restriction requiring exogenous mobility of individuals between groups over time. We apply our method to surgeon-hospital-year data to study take-up of keyhole surgery for cancer, finding a positive effect of the average individual heterogeneity of other surgeons practicing in the same hospital
Ensuring Transport Security; Features of Legal Regulation
Authors: Vitaly Khrustalev, Mattia Masolletti
Date: 2021-08-26 00:00:00

The article analyzes the legal framework regulating the legal provision of transport security in Russia. Special attention is paid to the role of prosecutor's supervision in the field of prevention of crimes in transport.
The Continuity Postulate in Economic Theory: A Deconstruction and an Integration
Authors: Metin Uyanik, M. Ali Khan
Date: 2021-08-26 00:00:00
Tasks: Relation

This paper presents six theorems and ten propositions that can be read as deconstructing and integrating the continuity postulate under the rubric of pioneering work of Eilenberg, Wold, von Neumann-Morgenstern, Herstein-Milnor and Debreu. Its point of departure is the fact that the adjective continuous applied to a function or a binary relation does not acknowledge the many meanings that can be given to the concept it names, and that under a variety of technical mathematical structures, its many meanings can be whittled down to novel and unexpected equivalences that have been missed in the theory of choice. Specifically, it provides a systematic investigation of the two-way relation between restricted and full continuity of a function and a binary relation that, under convex, monotonic and differentiable structures, draws out the behavioral implications of the postulate.
A Quantitative Approach To The Temporal Dependency in Video Coding
Authors: Jingning Han, Paul Wilkins, Yaowu Xu, James Bankoski
Date: 2021-08-26 00:00:00
Tasks: Motion Compensation, Quantization, Video Compression

Motion compensated prediction is central to the efficiency of video compression. Its predictive coding scheme propagates the quantization distortion through the prediction chain and creates a temporal dependency. Prior research typically models the distortion propagation based on the similarity between original pixels under the assumption of high resolution quantization. Its efficacy in the low to medium bit-rate range, where the quantization step size is largely comparable to the magnitude of the residual signals, is questionable. This work proposes a quantitative approach to estimating the temporal dependency. It evaluates the rate and distortion for each coding block using the original and the reconstructed motion compensation reference blocks, respectively. Their difference effectively measures how the quantization error in the reference block impacts the coding efficiency of the current block. A recursive update process is formulated to track such dependency through a group of pictures. The proposed scheme quantifies the temporal dependency more accurately across a wide range of operating bit-rates, which translates into considerable coding performance gains over the existing contenders as demonstrated in the experiments.
Cluster-based Characterization and Modeling for UAV Air-to-Ground Time-Varying Channels
Authors: Zhuangzhuang Cui, Ke Guan, Claude Oestges, César Briso-Rodríguez, Bo Ai, Zhangdui Zhong
Date: 2021-08-26 00:00:00
Tasks: Clustering

With the deep integration between the unmanned aerial vehicle (UAV) and wireless communication, UAV-based air-to-ground (AG) propagation channels need more detailed descriptions and accurate models. In this paper, we aim to perform cluster-based characterization and modeling for AG channels. To our best knowledge, this is the first study that concentrates on the clustering and tracking of multipath components (MPCs) for time-varying AG channels. Based on measurement data at 6.5 GHz with 500 MHz of bandwidth, we first estimate potential MPCs utilizing the space-alternating generalized expectation-maximization (SAGE) algorithm. Then, we cluster the extracted MPCs considering their static and dynamic characteristics by employing K-Power-Means (KPM) algorithm under multipath component distance (MCD) measure. For characterizing time-variant clusters, we exploit a clustering-based tracking (CBT) method, which efficiently quantifies the survival lengths of clusters. Ultimately, we establish a cluster-based channel model, and validations illustrate the accuracy of the proposed model. This work not only promotes a better understanding of AG propagation channels but also provides a general cluster-based AG channel model with certain extensibility.
Comparison of Clustering Methods for Extraction of Uncorrelated Sparse Sources from Data Mixtures
Authors: Malcolm Woolfson
Date: 2021-08-26 00:00:00
Tasks: Clustering

There is an extensive set of methods to determine sparse sources from mixtures where the mixing coefficients are unknown. Each method involves plotting N sets of mixed data against each other in N-dimensional space. In the approach adopted in this paper, N dimensional normalised vectors are produced by joining data points that are adjacent in time. A novel clustering approach is adopted: the two vectors, not necessarily adjacent in time, which are closest to each other are identified and one of these vectors is taken as the principal direction corresponding to one of the sources. It is shown, using a deflation approach, that it is possible to estimate individual sources to within a multiplicative constant. This novel method is compared with two related methods and the standard FastICA algorithm. This new method has comparable performances to three other methods when applied to examples of purely sparse, semi-sparse and non-sparse sources and also when applied to fetal ECG data.
Neural Mode Decomposition based on Fourier neural network and frequency clustering
Authors: Hu Yiting, Wu Zhuangzhi
Date: 2021-08-26 00:00:00
Tasks: Clustering

Since Huang proposed the Empirical Mode Decomposition (EMD) in 1998, mode decomposition has been widely studied, but EMD and relative developed algorithms are still generally lack of adaptability and mathematical theory. This paper propose a new mode decomposition algorithm called Neural Mode Decomposition (NMD) based on Fourier neural network (FNN) and frequency clustering. Firstly, a FNN is constructed to decompose and learn the information of each amplitude modulation frequency component and non-periodic component in the raw data. Secondly, the frequency components obtained by the FNN are clustered into multiple Intrinsic Mode Functions (IMF) with separated spectrum based on the energy of each frequency component learned by FNN. Practical decomposition results on a series of artificial and real data show that NMD algorithm can effectively implement mode decomposition, better reflect the characteristics of raw data than EMD, and has higher adaptability than Variational Mode Decomposition (VMD).
Learning-based Predictive Beamforming for Integrated Sensing and Communication in Vehicular Networks
Authors: Chang Liu, Weijie Yuan, Shuangyang Li, Xuemeng Liu, Husheng Li, Derrick Wing Kwan Ng, Yonghui Li
Date: 2021-08-26 00:00:00
Tasks: Integrated sensing and communication, ISAC

This paper investigates the integrated sensing and communication (ISAC) in vehicle-to-infrastructure (V2I) networks. To realize ISAC, an effective beamforming design is essential which however, highly depends on the availability of accurate channel tracking requiring large training overhead and computational complexity. Motivated by this, we adopt a deep learning (DL) approach to implicitly learn the features of historical channels and directly predict the beamforming matrix to be adopted for the next time slot to maximize the average achievable sum-rate of an ISAC system. The proposed method can bypass the need of explicit channel tracking process and reduce the signaling overhead significantly. To this end, a general sum-rate maximization problem with Cramer-Rao lower bounds (CRLBs)-based sensing constraints is first formulated for the considered ISAC system taking into account the multiple access interference. Then, by exploiting the penalty method, a versatile unsupervised DL-based predictive beamforming design framework is developed to address the formulated design problem. As a realization of the developed framework, a historical channels-based convolutional long short-term memory (LSTM) network (HCL-Net) is devised for predictive beamforming in the ISAC-based V2I network. Specifically, the convolution and LSTM modules are successively adopted in the proposed HCL-Net to exploit the spatial and temporal dependencies of communication channels to further improve the learning performance. Finally, simulation results show that the proposed predictive method not only guarantees the required sensing performance, but also achieves a satisfactory sum-rate that can approach the upper bound obtained by the genie-aided scheme with the perfect instantaneous channel state information available.