pwc-archive/papers-with-abstracts huggingface dataset:
A fault detection scheme for PV panels in large scale PV stations with complex installation conditions
Authors: Qianni Cao, Chen Shen, Mengshuo Jia
Date: 2021-05-19 00:00:00
Tasks: Fault Detection, Fault Diagnosis

Faults in photovoltaic (PV) systems can seriously affect the efficiency, energy yield as well as the security of the entire PV plant, if not detected and corrected quickly. Therefore, fault diagnosis of PV arrays is indispensable for improving the reliability, efficiency, productivity and safety of PV power stations. Instead of conventional thresholding methods and artificial intelligent (AI) machine learning approaches, an innovative Gaussian Mixture Model (GMM) based fault detection approach is proposed in this work. This approach combines the superiority of GMM in modeling stochastic power outputs of PV modules and the flexibility and simplicity of Sandia PV Array Performance Model (SPAM) to accurately detect under-performing modules. Firstly, GMM is proposed to represent the probabilistic distribution functions (PDF) of different PV modules' power outputs, and the parameter sets of which are obtained by Expectation Maximization algorithm. Secondly, a simplified explicit expression for output power of PV modules, which highlights the influences of tilt and azimuth angles, is deduced based on the SPAM. Then, an orientation independent vector C is developed to eliminate the probability distribution differences of power outputs caused by varying azimuth angles and tilt angles. Jensen-Shannon (JS) divergence, which captures the differences between probability density of C of each PV module, are generated and used as fault indicators. Simulation data acquired from the original SPAM are used to assess the performance of the proposed approach. Results show that the proposed approach successfully detects faults in PV systems. This work is especially suitable for the PV modules that have different installation parameters such as azimuth angles and tilt angles, and it does not require the use of irradiance or temperature sensors.
Designing a Pseudo-Random Bit Generator with a Novel 5D-Hyperchaotic System
Authors: Ngoc T. Nguyen, Toan Q. Bui, Ghyslain Gagnon, Pascal Giard, Georges Kaddoum
Date: 2021-05-19 00:00:00

Dynamic and non-linear systems are emerging as potential candidates for random bit generation. In this context, chaotic systems, which are both dynamic and stochastic, are particularly suitable. This paper introduces a new continuous chaotic system along with its corresponding implementation, which targets field-programmable gate array (FPGA). This chaotic system has five dimensions, which exhibit complex chaotic dynamics, thus enabling the utilization of chaotic signals in cryptography. A mathematical analysis is presented to demonstrate the dynamic characteristics of the proposed hyperchaotic system. A novel digital implementation of the proposed system is presented. Moreover, a data scrambling circuit is implemented to eliminate the bias effect and increase the randomness of the bitstream generated from the chaotic signals. We show that the proposed random bit generator has high randomness. The generated bits successfully pass well-known statistical randomness test-suites, i.e., NIST SP800-22, Diehard, and TestU01. The ready-to-use random bit generator is deployed on a Xilinx Zynq-7000 SoC ZC702 Evaluation Kit. Experimental results show that the proposed random bit generator can achieve a maximum throughput of 6.78 Gbps, which is over 3.6 times greater than state-of-the-art designs while requiring under 4% of the resources available on the targeted FPGA.
Mahalanobis distance-based robust approaches against false data injection attacks on dynamic power state estimation
Authors: Jing Lin, Kaiqi Xiong
Date: 2021-05-19 00:00:00
Tasks: State Estimation

Many researchers have studied false data injection (FDI) attacks in power state estimation, but existing state estimation approaches are still highly vulnerable to FDI attacks. In this paper, we investigate the problem of the above three FDI attacks against dynamic power state estimation (DSE). Although the three attacks were discovered in SSE several years ago, none of them has been well addressed in static power state systems. In this research, we propose two robust defense approaches against the above three efficient FDI attacks on DSE. Compared to existing approaches, our proposed approaches have three major differences and significant strengths: (1) they defend against the three FDI attacks on dynamic power state estimation rather than static power state estimation, (2) they give a robust estimator that can accurately extract a subset of attack-free sensors for power state estimation, and (3) they adopt the little-known Mahalanobis distance in the consistency check of power sensor measurements, which is different from the Euclidean distance used in all the existing studies on power state estimation. We mathematically prove that the Mahalanobis distance is not only useful but also much better than the Euclidean distance in the consistency check of power sensor measurements. Our time complexity analysis shows that the two proposed robust defense approaches are efficient. Moreover, in order to demonstrate the effectiveness of the proposed approaches, we compare them with the three well-known approaches: the least square approach, the Imhotep-SMT approach, and the MEE-UKF approach. Our extensive experiments show that the proposed approaches further reduce the estimation error by two orders of magnitude and four orders of magnitude compared to the Imhotep-SMT approach and the least square approach, respectively. Moreover, our approach is more stable than the MEE-UKF approach.
A real-time distributed post-disaster restoration planning strategy for distribution networks
Authors: Jianfeng Fu, Alfredo Nunez, Bart De Schutter
Date: 2021-05-18 00:00:00
Tasks: Decision Making, Model Predictive Control

After disasters, distribution networks have to be restored by repair, reconfiguration, and power dispatch. During the restoration process, changes can occur in real time that deviate from the situations considered in pre-designed planning strategies. That may result in the pre-designed plan to become far from optimal or even unimplementable. This paper proposes a centralized-distributed bi-level optimization method to solve the real-time restoration planning problem. The first level determines integer variables related to routing of the crews and the status of the switches using a genetic algorithm (GA), while the second level determines the dispatch of active/reactive power by using distributed model predictive control (DMPC). A novel Aitken- DMPC solver is proposed to accelerate convergence and to make the method suitable for real-time decision making. A case study based on the IEEE 123-bus system is considered, and the acceleration performance of the proposed Aitken-DMPC solver is evaluated and compared with the standard DMPC method.
Strontium-releasing mesoporous bioactive glasses with anti-adhesive zwitterionic surface as advanced biomaterials for bone tissue regeneration
Authors: Carlotta Pontremoli, Isabel Izquierdo-Barba, Giorgia Montalbano, Maria Vallet-Regi, Chiara Vitale-Brovarone, Sonia Fiorilli
Date: 2021-05-19 00:00:00

Hypothesis The treatment of bone fractures still represents a challenging clinical issue when complications due to impaired bone remodelling (i.e. osteoporosis) or infections occur. These clinical needs still require a radical improvement of the existing therapeutic approach through the design of advanced biomaterials combining the ability to promote bone regeneration with anti-fouling/anti-adhesive properties able to minimise unspecific biomolecules adsorption and bacterial adhesion. Strontium-containing mesoporous bioactive glasses (Sr-MBG), able to exert a pro-osteogenic effect by releasing Sr2+ ions, have been successfully functionalised to provide mixed-charge surface groups with low-fouling abilities. Experiments Sr-MBG have been post-synthesis modified by co-grafting hydrolysable short chain silanes containing amino (aminopropylsilanetriol) and carboxylate (carboxyethylsilanetriol) moieties to achieve a zwitterionic zero-charge surface and then characterised in terms of textural-structural properties, bioactivity, cytotoxicity, pro-osteogenic and low-fouling capabilities. Findings After zwitterionization the in vitro bioactivity is maintained, as well as the ability to release Sr2+ ions capable to induce a mineralization process. Irrespective of their size, Sr-MBG particles did not exhibit any cytotoxicity in pre-osteoblastic MC3T3-E1 up to the concentration of 75 ug/mL. Finally, the zwitterionic Sr-MBGs show a significant reduction of serum protein adhesion with respect to pristine ones. These results open promising future expectations in the design of nanosystems combining pro-osteogenic and anti-adhesive properties.
How Lévy flights triggered by presence of defectors affect evolution of cooperation in spatial games
Authors: Genki Ichinose, Daiki Miyagawa, Erika Chiba, Hiroki Sayama
Date: 2021-05-19 00:00:00
Tasks: Sensitivity

Cooperation among individuals has been key to sustaining societies. However, natural selection favors defection over cooperation. Cooperation can be favored when the mobility of individuals allows cooperators to form a cluster (or group). Mobility patterns of animals sometimes follow a L\'evy flight. A L\'evy flight is a kind of random walk but it is composed of many small movements with a few big movements. The role of L\'evy flights for cooperation has been studied by Antonioni and Tomassini. They showed that L\'evy flights promoted cooperation combined with conditional movements triggered by neighboring defectors. However, the optimal condition for neighboring defectors and how the condition changes by the intensity of L\'evy flights are still unclear. Here, we developed an agent-based model in a square lattice where agents perform L\'evy flights depending on the fraction of neighboring defectors. We systematically studied the relationships among three factors for cooperation: sensitivity to defectors, the intensity of L\'evy flights, and population density. Results of evolutionary simulations showed that moderate sensitivity most promoted cooperation. Then, we found that the shortest movements were best for cooperation when the sensitivity to defectors was high. In contrast, when the sensitivity was low, longer movements were best for cooperation. Thus, L\'evy flights, the balance between short and long jumps, promoted cooperation in any sensitivity, which was confirmed by evolutionary simulations. Finally, as the population density became larger, higher sensitivity was more beneficial for cooperation to evolve. Our study highlights that L\'evy flights are an optimal searching strategy not only for foraging but also for constructing cooperative relationships with others.
Integrability and Identification in Multinomial Choice Models
Authors: Debopam Bhattacharya
Date: 2019-02-28 00:00:00
Tasks: counterfactual

McFadden's random-utility model of multinomial choice has long been the workhorse of applied research. We establish shape-restrictions under which multinomial choice-probability functions can be rationalized via random-utility models with nonparametric unobserved heterogeneity and general income-effects. When combined with an additional restriction, the above conditions are equivalent to the canonical Additive Random Utility Model. The sufficiency-proof is constructive, and facilitates nonparametric identification of preference-distributions without requiring identification-at-infinity type arguments. A corollary shows that Slutsky-symmetry, a key condition for previous rationalizability results, is equivalent to absence of income-effects. Our results imply theory-consistent nonparametric bounds for choice-probabilities on counterfactual budget-sets. They also apply to widely used random-coefficient models, upon conditioning on observable choice characteristics. The theory of partial differential equations plays a key role in our analysis.
Trading-off Bias and Variance in Stratified Experiments and in Matching Studies, Under a Boundedness Condition on the Magnitude of the Treatment Effect
Authors: Clément de Chaisemartin
Date: 2021-05-18 00:00:00

I consider estimation of the average treatment effect (ATE), in a population composed of $S$ groups or units, when one has unbiased estimators of each group's conditional average treatment effect (CATE). These conditions are met in stratified experiments and in matching studies. I assume that each CATE is bounded in absolute value by $B$ standard deviations of the outcome, for some known $B$. This restriction may be appealing: outcomes are often standardized in applied work, so researchers can use available literature to determine a plausible value for $B$. I derive, across all linear combinations of the CATEs' estimators, the minimax estimator of the ATE. In two stratified experiments, my estimator has twice lower worst-case mean-squared-error than the commonly-used strata-fixed effects estimator. In a matching study with limited overlap, my estimator achieves 56\% of the precision gains of a commonly-used trimming estimator, and has an 11 times smaller worst-case mean-squared-error.
Online Labour Index 2020: New ways to measure the world's remote freelancing market
Authors: Fabian Stephany, Otto Kässi, Uma Rani, Vili Lehdonvirta
Date: 2021-05-19 00:00:00

The Online Labour Index (OLI) was launched in 2016 to measure the global utilisation of online freelance work at scale. Five years after its creation, the OLI has become a point of reference for scholars and policy experts investigating the online gig economy. As the market for online freelancing work matures, a high volume of data and new analytical tools allow us to revisit half a decade of online freelance monitoring and extend the index's scope to more dimensions of the global online freelancing market. In addition to measuring the utilisation of online labour across countries and occupations by tracking the number of projects and tasks posted on major English-language platforms, the new Online Labour Index 2020 (OLI 2020) also tracks Spanish- and Russian-language platforms, reveals changes over time in the geography of labour supply, and estimates female participation in the online gig economy. The rising popularity of software and tech work and the concentration of freelancers on the Indian subcontinent are examples of the insights that the OLI 2020 provides. The OLI 2020 delivers a more detailed picture of the world of online freelancing via an interactive online visualisation updated daily. It provides easy access to downloadable open data for policymakers, labour market researchers, and the general public (www.onlinelabourobservatory.org).
A Simple Model of Monetary Policy under Phillips-Curve Causal Disagreements
Authors: Ran Spiegler
Date: 2021-05-19 00:00:00

I study a static textbook model of monetary policy and relax the conventional assumption that the private sector has rational expectations. Instead, the private sector forms inflation forecasts according to a misspecified subjective model that disagrees with the central bank's (true) model over the causal underpinnings of the Phillips Curve. Following the AI/Statistics literature on Bayesian Networks, I represent the private sector's model by a direct acyclic graph (DAG). I show that when the private sector's model reverses the direction of causality between inflation and output, the central bank's optimal policy can exhibit an attenuation effect that is sensitive to the noisiness of the true inflation-output equations.
Anabolic Persuasion
Authors: Kfir Eliaz, Ran Spiegler
Date: 2021-05-18 00:00:00

We present a model of optimal training of a rational, sluggish agent. A trainer commits to a discrete-time, finite-state Markov process that governs the evolution of training intensity. Subsequently, the agent monitors the state and adjusts his capacity at every period. Adjustments are incremental: the agent's capacity can only change by one unit at a time. The trainer's objective is to maximize the agent's capacity - evaluated according to its lowest value under the invariant distribution - subject to an upper bound on average training intensity. We characterize the trainer's optimal policy, and show how stochastic, time-varying training intensity can dramatically increase the long-run capacity of a rational, sluggish agent. We relate our theoretical findings to "periodization" training techniques in exercise physiology.
On training targets for noise-robust voice activity detection
Authors: Sebastian Braun, Ivan Tashev
Date: 2021-02-15 00:00:00
Tasks: Action Detection, Activity Detection

The task of voice activity detection (VAD) is an often required module in various speech processing, analysis and classification tasks. While state-of-the-art neural network based VADs can achieve great results, they often exceed computational budgets and real-time operating requirements. In this work, we propose a computationally efficient real-time VAD network that achieves state-of-the-art results on several public real recording datasets. We investigate different training targets for the VAD and show that using the segmental voice-to-noise ratio (VNR) is a better and more noise-robust training target than the clean speech level based VAD. We also show that multi-target training improves the performance further.
Deep Neural Network Assisted Second-Order Perturbation-Based Nonlinearity Compensation
Authors: O. S. Sunish Kumar, Lutz Lampe, Shenghang Luo, Mrinmoy Jana, Jeebak Mitra, Chuandong Li
Date: 2021-05-19 00:00:00

We propose a fiber nonlinearity post-compensation technique using the DNN and the second-order perturbation theory. We achieve 1 dB Q-factor improvement for a 32 Gbaud PDM-64-QAM at 1200 km compared to the linear dispersion compensation.
Integrated Communication and Navigation for Ultra-Dense LEO Satellite Networks: Vision, Challenges and Solutions
Authors: Yu Wang, Hejia Luo, Ying Chen, Jun Wang, Rong Li, Bin Wang
Date: 2021-05-19 00:00:00

Next generation beyond 5G networks are expected to provide both Terabits per second data rate communication services and centimeter-level accuracy localization services in an efficient, seamless and cost-effective manner. However, most of the current communication and localization systems are separately designed, leading to an under-utilization of radio resources and network performance degradation. In this paper, we propose an integrated communication and navigation (ICAN) framework to fully unleash the potential of ultra-dense LEO satellite networks for optimal provisioning of differentiated services. The specific benefits, feasibility analysis and challenges for ICAN enabled satellite system are explicitly discussed. In particular, a novel beam hopping based ICAN satellite system solution is devised to adaptively tune the network beam layout for dual functional communication and positioning purposes. Furthermore, a thorough experimental platform is built following the Third Generation Partnership Project (3GPP) defined non-terrestrial network simulation parameters to validate the performance gain of the ICAN satellite system
Origins of ECG and Evolution of Automated DSP Techniques: A Review
Authors: Neha Arora, Biswajit Mishra
Date: 2021-05-19 00:00:00

Over the years researchers have studied the evolution of Electrocardiogram (ECG) and the complex classification of cardiovascular diseases. This review focuses on the evolution of the ECG, and covers the most recent signal processing schemes with milestones over last 150 years in a systematic manner. Development phases of ECG, ECG leads, portable ECG monitors, Signal Processing Schemes and the Complex Transformations are discussed. It also provides recommendations for the inclusion of certain important points based on the review.
Conformational variability of loops in the SARS-CoV-2 spike protein
Authors: Samuel W. K. Wong, Zongjun Liu
Date: 2021-05-18 00:00:00

The SARS-CoV-2 spike (S) protein facilitates viral infection, and has been the focus of many structure determination efforts. Its flexible loop regions are known to be involved in protein binding and may adopt multiple conformations. This paper identifies the S protein loops and studies their conformational variability based on the available Protein Data Bank (PDB) structures. While most loops had essentially one stable conformation, 17 of 44 loop regions were observed to be structurally variable with multiple substantively distinct conformations based on a cluster analysis. Loop modeling methods were then applied to the S protein loop targets, and the prediction accuracies discussed in relation to the characteristics of the conformational clusters identified. Loops with multiple conformations were found to be challenging to model based on a single structural template.
Effect of In-vitro Heat Stress Challenge on the function of Blood Mononuclear Cells from Dairy Cattle ranked as High, Average and Low Immune Responders
Authors: Shannon L. Cartwright, Marnie McKechnie, Julie Schmied, Alexandra M. Livernois, Bonnie A. Mallard
Date: 2021-05-18 00:00:00

The warming climate is causing livestock to experience heat stress at an increasing frequency. Holstein cows are particularly susceptible to heat stress because of their high metabolic rate. Heat stress negatively affects immune function, particularly with respect to the cell-mediated immune response, which leads to increased susceptibility to disease. Cattle identified as having enhanced immune response have lower incidence of disease. Therefore, the objective of this study was to evaluate the impact of in vitro heat challenge on blood mononuclear cells from dairy cattle, that had previously been ranked for immune response, in terms of heat shock protein 70 concentration, nitric oxide production, and cell proliferation. Bovine blood mononuclear cells, from Holstein dairy cattle previously ranked for immune response based on their estimated breeding values, were subjected to three heat treatments: thermoneutral, heat stress 1 and heat stress 2. Cells of each treatment were evaluated for heat shock protein 70, cell proliferation and nitric oxide production. Blood mononuclear cells from dairy cattle classified as high immune responders, based on their estimated breeding values for antibody and cell-mediated responses, produced a significantly greater concentration of heat shock protein 70 under most heat stress treatments compared to average and low responders, and greater cell-proliferation across all treatments. Similarly, a trend was observed where high responders displayed greater nitric oxide production compared to average and low responders across heat treatments. Overall, these results suggest that blood mononuclear cells from high immune responder dairy cows are more thermotolerant compared to average and low immune responders