pwc-archive/papers-with-abstracts huggingface dataset:
Community Time-Activity Trajectory Modelling based on Markov Chain Simulation and Dirichlet Regression
Authors: Chen Xia, Yuqing Hu, Jianli Chen
Date: 2022-12-13 00:00:00
Tasks: regression, Trajectory Modeling

Accurate modeling of human time-activity trajectory is essential to support community resilience and emergency response strategies such as daily energy planning and urban seismic vulnerability assessment. However, existing modeling of time-activity trajectory is only driven by socio-demographic information with identical activity trajectories shared among the same group of people and neglects the influence of the environment. To further improve human time-activity trajectory modeling, this paper constructs community time-activity trajectory and analyzes how social-demographic and built environment influence people s activity trajectory based on Markov Chains and Dirichlet Regression. We use the New York area as a case study and gather data from American Time Use Survey, Policy Map, and the New York City Energy & Water Performance Map to evaluate the proposed method. To validate the regression model, Box s M Test and T-test are performed with 80% data training the model and the left 20% as the test sample. The modeling results align well with the actual human behavior trajectories, demonstrating the effectiveness of the proposed method. It also shows that both social-demographic and built environment factors will significantly impact a community's time-activity trajectory. Specifically, 1) Diversity and median age both have a significant influence on the proportion of time people assign to education activity. 2) Transportation condition affects people s activity trajectory in the way that longer commute time decreases the proportion of biological activity (eg. sleeping and eating) and increases people s working time. 3) Residential density affects almost all activities with a significant p-value for all biological needs, household management, working, education, and personal preference.
Two-level Robust State Estimation for Multi-Area Power Systems Under Bounded Uncertainties
Authors: Shiva Moshtagh, Mehdi Rahmani
Date: 2022-12-13 00:00:00
Tasks: State Estimation

This paper introduces a two-level robust approach to estimate the unknown states of a large-scale power system while the measurements and network parameters are subjected to uncertainties. The bounded data uncertainty (BDU) considered in the power network is a structured uncertainty which is inevitable in practical systems due to error in transmission lines, inaccurate modelling, unmodeled dynamics, parameter variations, and other various reasons. In the proposed approach, the corresponding network is first decomposed into smaller subsystems (areas), and then a two-level algorithm is presented for state estimation. In this algorithm, at the first level, each area uses a weighted least squares (WLS) technique to estimate its own states based on a robust hybrid estimation utilizing phasor measurement units (PMUs), and at the second level, the central coordinator processes all the results from the subareas and gives a robust estimation of the entire system. The simulation results for IEEE 30-bus test system verifies the accuracy and performance of the proposed multi-area robust estimator.
Mist and Edge Computing Cyber-Physical Human-Centered Systems for Industry 5.0: A Cost-Effective IoT Thermal Imaging Safety System
Authors: P. Fraga-Lamas, D. Barros, S. I. Lopes, T. M. Fernández-Caramés
Date: 2022-12-13 00:00:00
Tasks: Edge-computing

While many companies worldwide are still striving to adjust to Industry 4.0 principles, the transition to Industry 5.0 is already underway. Under such a paradigm, Cyber-Physical Human-centered Systems (CPHSs) have emerged to leverage operator capabilities in order to meet the goals of complex manufacturing systems towards human-centricity, resilience and sustainability. This article first describes the essential concepts for the development of Industry 5.0 CPHSs and then analyzes the latest CPHSs, identifying their main design requirements and key implementation components. Moreover, the major challenges for the development of such CPHSs are outlined. Next, to illustrate the previously described concepts, a real-world Industry 5.0 CPHS is presented. Such a CPHS enables increased operator safety and operation tracking in manufacturing processes that rely on collaborative robots and heavy machinery. Specifically, the proposed use case consists of a workshop where a smarter use of resources is required, and human proximity detection determines when machinery should be working or not in order to avoid incidents or accidents involving such machinery. The proposed CPHS makes use of a hybrid edge computing architecture with smart mist computing nodes that processes thermal images and reacts to prevent industrial safety issues. The performed experiments show that, in the selected real-world scenario, the developed CPHS algorithms are able to detect human presence with low-power devices (with a Raspberry Pi 3B) in a fast and accurate way (in less than 10 ms with a 97.04% accuracy), thus being an effective solution that can be integrated into many Industry 5.0 applications. Finally, this article provides specific guidelines that will help future developers and managers to overcome the challenges that will arise when deploying the next generation of CPHSs for smart and sustainable manufacturing.
Gershgorin Disc-based Voltage Stability Regions for DER Siting and Control in Distribution Grids
Authors: Jaimie Swartz, Elizabeth Ratnam, Abhishek Bhardwaj, Alexandra von Meier
Date: 2022-12-12 00:00:00

As a consequence of the transition to distributed and renewable energy systems, some distribution system operators are increasingly concerned about power quality, including steady-state voltage volatility. In this paper, we study the control of real and reactive power injections by inverter-based distributed energy resources to regulate voltage magnitudes and phase angles measured by sensors positioned across 3-phase unbalanced distribution grids. Our proposed controllers are agnostic to the location and type of communications supporting energy resource operations. To design the controllers, we apply the Gershgorin Disc Theorem and determine analytic stability regions in terms of renewable energy operating parameters and grid impedances. The stability regions yield direct relationships between renewable energy system siting and the convergence of voltage phasors to references. Beyond defining stability regions, we compute ranges of stable operating parameters for renewable energy systems that promote operational flexibility by including customer economics. By means of a case study on the IEEE 123-node test circuit, we observe our approach to coordinating renewable energy systems achieves non-oscillatory voltage regulation and reduces the duration of voltage violations by 26%.
What is the Best Way to Optimally Parameterize the MPC Cost Function for Vehicle Guidance?
Authors: David Stenger, Robert Ritschel, Felix Krabbes, Rick Voßwinkel, Hendrik Richter
Date: 2022-12-11 00:00:00
Tasks: Autonomous Vehicles, Bayesian Optimization, Model Predictive Control

Model predictive control (MPC) is a promising approach for the lateral and longitudinal control of autonomous vehicles. However, the parameterization of the MPC with respect to high-level requirements such as passenger comfort as well as lateral and longitudinal tracking is a challenging task. Numerous tuning parameters as well as conflicting requirements need to be considered. This contribution formulates the MPC tuning task as a multi-objective optimization problem. Solving it is challenging for two reasons: First, MPC-parameterizations are evaluated on an computationally expensive simulation environment. As a result, the used optimization algorithm needs to be as sampleefficient as possible. Second, for some poor parameterizations the simulation cannot be completed and therefore useful objective function values are not available (learning with crash constraints). In this contribution, we compare the sample efficiency of multi-objective particle swarm optimization (MOPSO), a genetic algorithm (NSGA-II) and multiple versions of Bayesian optimization (BO). We extend BO, by introducing an adaptive batch size to limit the computational overhead and by a method on how to deal with crash constraints. Results show, that BO works best for a small budget, NSGA-II is best for medium budgets and for large budgets none of the evaluated optimizers is superior to random search. Both proposed BO extensions are shown to be beneficial.
Model-driven Engineering of Manufacturing Automation Software Projects -- A SysML-based Approach
Authors: Birgit Vogel-Heuser, Daniel Schuetz, Timo Frank, Christoph Legat
Date: 2022-12-09 00:00:00

This paper comprises a SysML-based approach to support the model-driven engineering (MDE) of Manufacturing Automation Software Projects (MASP). The Systems Modeling Language (SysML) is adapted to define the SysML-AT (SysML for automation), a specialized language profile that covers (non-)functional requirements, corresponding software applications and properties of proprietary hardware components. Furthermore, SysML-AT supports an automated software generation for run-time environments conforming to IEC 61131-3. A prototypical tool support was realized for adapted SysML Parametric Diagrams (PD) inside an industrial automation software development tool. Coupling the model editor and online data from the provided run-time environment enables direct debugging inside the model. The approach was evaluated by several case studies and additional usability experiments. With the latter, the suitability of the MDE approach for future users was proven.
Land use/land cover dynamics on vulnerable regions in Uruguay approached by a method combining Maximum Entropy and Population Dynamics
Authors: Johny Arteaga, Jhonny Agudelo, Alejandro Brazeiro, Hugo Fort
Date: 2022-12-13 00:00:00
Tasks: Time Series, Time Series Analysis

We present an exploratory population dynamics approach, described by Lotka-Volterra (LV) generalized equations, to explain/predict the dynamics and competition between land use/land cover (LULC) classes over vulnerable regions in Uruguay. We use the Mapbiomas-Pampa dataset composed by 20 annual LULC maps from 2000-2019. From these LULC maps we extract the main LULC classes, their spatial distribution and the time series of areas covered for each class. The interaction coefficients between species are inferred through the pairwise maximum entropy (PME) method from the spatial covariance matrices for different training periods. The main finding is that this LVPME method globally outperforms the more traditional Markov chains approach at predicting the trajectories of areas of LULC classes.
The Role of Mandated Mental Health Treatment in the Criminal Justice System
Authors: Rachel Nesbit
Date: 2022-12-13 00:00:00

Mental health disorders are particularly prevalent among those in the criminal justice system and may be a contributing factor in recidivism. Using North Carolina court cases from 1994 to 2009, this paper evaluates how mandated mental health treatment as a term of probation impacts the likelihood that individuals return to the criminal justice system. I use random variation in judge assignment to compare those who were required to seek weekly mental health counseling to those who were not. The main findings are that being assigned to seek mental health treatment decreases the likelihood of three-year recidivism by about 12 percentage points, or 36 percent. This effect persists over time, and is similar among various types of individuals on probation. In addition, I show that mental health treatment operates distinctly from drug addiction interventions in a multiple-treatment framework. I provide evidence that mental health treatment's longer-term effectiveness is strongest among more financially-advantaged probationers, consistent with this setting, in which the cost of mandated treatment is shouldered by offenders. Finally, conservative calculations result in a 5:1 benefit-to-cost ratio which suggests that the treatment-induced decrease in future crime would be more than sufficient to offset the costs of treatment.
Identifying the regional drivers of influenza-like illness in Nova Scotia with dominance analysis
Authors: Yigit Aydede, Jan Ditzen
Date: 2022-12-13 00:00:00

The spread of viral pathogens is inherently a spatial process. While the temporal aspects of viral spread at the epidemiological level have been increasingly well characterized, the spatial aspects of viral spread are still understudied due to a striking absence of theoretical expectations of how spatial dynamics may impact the temporal dynamics of viral populations. Characterizing the spatial transmission and understanding the factors driving it are important for anticipating local timing of disease incidence and for guiding more informed control strategies. Using a unique data set from Nova Scotia, the objective of this study is to apply a new novel method that recovers a spatial network of the influenza-like viral spread where the regions in their dominance are identified and ranked. We, then, focus on identifying regional predictors of those dominant regions.
The Effect of Financial Resources on Fertility: Evidence from Administrative Data on Lottery Winners
Authors: Yung-Yu Tsai, Hsing-Wen Han, Kuang-Ta Lo, Tzu-Ting Yang
Date: 2022-12-12 00:00:00

This paper utilizes wealth shocks from winning lottery prizes to examine the causal effect of financial resources on fertility. We employ extensive panels of administrative data encompassing over 0.4 million lottery winners in Taiwan and implement a triple-differences design. Our analyses reveal that a substantial lottery win can significantly increase fertility, the implied wealth elasticity of which is around 0.06. Moreover, the primary channel through which fertility increases is by prompting first births among previously childless individuals. Finally, our analysis reveals that approximately 25% of the total fertility effect stems from increased marriage rates following a lottery win.
IT companies: the specifics of social networks valuation
Authors: K. V. Yupatova, O. A. Malafeyev, V. S. Lipatnikov, V. Y. Bezrukikh
Date: 2022-12-12 00:00:00

The study discusses the main features which affect the IT companies valuation on the example of social networks. The relevance of the chosen topic is due to the fact that people live in the information age now, and information technologies surround us everywhere. Because of this, social networks have become very popular. They assist people to communicate with each other despite of the time and distance. Social networks are also companies that operate in order to generate income therefore their owners need to know how promising and profitable their business is. The social networks differ from traditional companies in this case the purpose of the research is determining the features of social networks that affect the accuracy and adequacy of the results of company valuation. The paper reviews the definitions of information technology, social networks, history, types of social networks, distinguishing features based on domestic and foreign literature. There are analyzed methods of assessing the value of Internet companies, their characteristics and methods of application. There is the six social networks evaluation was assessed in the practical part of the study: Facebook, Twitter, Pinterest, Snapchat, Sina Weibo and Vkontakte on the basis of the literature studied and the methods for evaluating the Internet companies which recommended in it, including the method of discounting the cash flow of the company as part of the income approach and the multiplier method as part of a comparative approach. Based on the analysis, the features that affect the social networks valuation are identified.
Towards deep generation of guided wave representations for composite materials
Authors: Mahindra Rautela, J. Senthilnath, Armin Huber, S. Gopalakrishnan
Date: 2022-12-13 00:00:00
Tasks: Decoder, Property Prediction

Laminated composite materials are widely used in most fields of engineering. Wave propagation analysis plays an essential role in understanding the short-duration transient response of composite structures. The forward physics-based models are utilized to map from elastic properties space to wave propagation behavior in a laminated composite material. Due to the high-frequency, multi-modal, and dispersive nature of the guided waves, the physics-based simulations are computationally demanding. It makes property prediction, generation, and material design problems more challenging. In this work, a forward physics-based simulator such as the stiffness matrix method is utilized to collect group velocities of guided waves for a set of composite materials. A variational autoencoder (VAE)-based deep generative model is proposed for the generation of new and realistic polar group velocity representations. It is observed that the deep generator is able to reconstruct unseen representations with very low mean square reconstruction error. Global Monte Carlo and directional equally-spaced samplers are used to sample the continuous, complete and organized low-dimensional latent space of VAE. The sampled point is fed into the trained decoder to generate new polar representations. The network has shown exceptional generation capabilities. It is also seen that the latent space forms a conceptual space where different directions and regions show inherent patterns related to the generated representations and their corresponding material properties.
Learning-Based Conditional Image Coder Using Color Separation
Authors: Panqi Jia, Ahmet Burakhan Koyuncu, Georgii Gaikov, Alexander Karabutov, Elena Alshina, Andre Kaup
Date: 2022-12-12 00:00:00
Tasks: Image Compression

Recently, image compression codecs based on Neural Networks(NN) outperformed the state-of-art classic ones such as BPG, an image format based on HEVC intra. However, the typical NN codec has high complexity, and it has limited options for parallel data processing. In this work, we propose a conditional separation principle that aims to improve parallelization and lower the computational requirements of an NN codec. We present a Conditional Color Separation (CCS) codec which follows this principle. The color components of an image are split into primary and non-primary ones. The processing of each component is done separately, by jointly trained networks. Our approach allows parallel processing of each component, flexibility to select different channel numbers, and an overall complexity reduction. The CCS codec uses over 40% less memory, has 2x faster encoding and 22% faster decoding speed, with only 4% BD-rate loss in RGB PSNR compared to our baseline model over BPG.
A Robust Data-Driven Fault Diagnosis scheme based on Recursive Dempster-Shafer Combination Rule *
Authors: Cartocci N., M. R. Napolitano, G. Costante, F. Crocetti, P. Valigi, M. L. Fravolini
Date: 2022-12-13 00:00:00
Tasks: Fault Diagnosis

In-flight sensor fault diagnosis and recursive combination of residual signals via the Dempster-Shafer (DS) theory have been considered in this study. In particular, a novel evidence-based combination rule of residual errors as a function of a reliability measure derived from streaming data is proposed for the purpose of online robust sensors fault diagnosis. The proposed information fusion mechanism is divided into three steps. In the first step, the classic DS probability mass combination rule is applied; then, the difference between the previous posterior mass and the current prior mass associated with fault events is computed. Finally, the increment of the posterior mass of a fault event is weighted as a function of a reliability coefficient that depends on the norm of control activity. A Sensor Fault Isolation scheme based on the proposed combination rule has been worked out and compared with well-known state-of-the-art recursive combination rules. A quantitative analysis has been performed exploiting multi-flight data of a P92 Tecnam aircraft. The proposed approach showed to be effective, particularly in reducing the false alarms rate.
PCA Methods and Evidence Based Filtering for Robust Aircraft Sensor Fault Diagnosis
Authors: N. Cartocci, G. Costante, M. R. Napolitano, P. Valigi, F. Crocetti, M. L. Fravolini
Date: 2022-12-13 00:00:00
Tasks: Decision Making, Diagnostic, Fault Diagnosis

In this paper PCA and D-PCA techniques are applied for the design of a Data Driven diagnostic Fault Isolation (FI) and Fault Estimation (FE) scheme for 18 primary sensors of a semi-autonomous aircraft. Specifically, Contributions-based, and Reconstruction-based Contributions approaches have been considered. To improve FI performance an inference mechanism derived from evidence-based decision making theory has been proposed. A detailed FI and FE study is presented for the True Airspeed sensor based on experimental data. Evidence Based Filtering (EBF) showed to be very effective particularly in reducing false alarms.
A Data Quality Assessment Framework for AI-enabled Wireless Communication
Authors: Hanning Tang, Liusha Yang, Rui Zhou, Jing Liang, Hong Wei, Xuan Wang, Qingjiang Shi, Zhi-Quan Luo
Date: 2022-12-13 00:00:00
Tasks: Diversity

Using artificial intelligent (AI) to re-design and enhance the current wireless communication system is a promising pathway for the future sixth-generation (6G) wireless network. The performance of AI-enabled wireless communication depends heavily on the quality of wireless air-interface data. Although there are various approaches to data quality assessment (DQA) for different applications, none has been designed for wireless air-interface data. In this paper, we propose a DQA framework to measure the quality of wireless air-interface data from three aspects: similarity, diversity, and completeness. The similarity measures how close the considered datasets are in terms of their statistical distributions; the diversity measures how well-rounded a dataset is, while the completeness measures to what degree the considered dataset satisfies the required performance metrics in an application scenario. The proposed framework can be applied to various types of wireless air-interface data, such as channel state information (CSI), signal-to-interference-plus-noise ratio (SINR), reference signal received power (RSRP), etc. For simplicity, the validity of our proposed DQA framework is corroborated by applying it to CSI data and using similarity and diversity metrics to improve CSI compression and recovery in Massive MIMO systems.
Stochastic Modeling of Biofilm Formation with Bacterial Quorum Sensing
Authors: Fatih Gulec, Andrew W. Eckford
Date: 2022-12-12 00:00:00

Bacteria generally live in complicated structures called biofilms, consisting of communicating bacterial colonies and extracellular polymeric substance (EPS). Since biofilms are related to detrimental effects such as infection or antibiotic resistance in different settings, it is essential to model their formation. In this paper, a stochastic model is proposed for biofilm formation, using bacterial quorum sensing (QS). In this model, the biological processes in the biofilm formation are modeled as a chemical reaction network which includes bacterial reproduction, productions of autoinducer and EPS, and their diffusion. The modified explicit tau-leap simulation algorithm is adapted based on the two-state QS mechanism. Our approach is validated by using the experimental results of $\textit{Pseudomonas putida}$ IsoF bacteria for autoinducer and bacteria concentration. It is also shown that the percentage of EPS in the biofilm increases significantly after the state change in QS, while it decreases before QS is activated. The presented work shows how the biofilm growth can be modeled realistically by using the QS mechanism in stochastic simulations of chemical reactions.