pwc-archive/papers-with-abstracts huggingface dataset:
Impact of Loss Model Selection on Power Semiconductor Lifetime Prediction in Electric Vehicles
Authors: Hongjian Xia, Yi Zhang, Dao Zhou, Minyou Chen, Wei Lai, Yunhai Wei, Huai Wang
Date: 2022-08-27 00:00:00
Tasks: Model Selection

Power loss estimation is an indispensable procedure to conduct lifetime prediction for power semiconductor device. The previous studies successfully perform steady-state power loss estimation for different applications, but which may be limited for the electric vehicles (EVs) with high dynamics. Based on two EV standard driving cycle profiles, this paper gives a comparative study of power loss estimation models with two different time resolutions, i.e., the output period average and the switching period average. The correspondingly estimated power losses, thermal profiles, and lifetime clearly pointed out that the widely applied power loss model with the output period average is limited for EV applications, in particular for the highly dynamic driving cycle. The difference in the predicted lifetime can be up to 300 times due to the unreasonable choice the loss model, which calls for the industry attention on the differences of the EVs and the importance of loss model selection in lifetime prediction.
Marine Integrated Energy Microgrids
Authors: Chendan Li, Muzaidi Othman, Nor Baizura Ahamad, Marta Molinas
Date: 2022-08-27 00:00:00

Marine sector decarbonization is another important battlefield for meeting the goal of climate action and ensuring the fulfillment of ambitions for a zero-emission society. Driven immediately by the policy incentives such as Energy Efficiency Design Index (EEDI) from International Maritime Organization(IMO), carbon taxation and labeling, a series of innovations centered around marine transportation are emerging from both industry and academia. As an efficient energy system form, the microgrid is playing an increasingly important role as the system constitution form for various marine energy systems. With specific concern about multi-energy integrations, the conventional definition of the microgrid needs also to be extended for an integrated energy system. In this paper, we will first introduce the extended concept of the microgrid as an integrated energy system and its applications in the marine sector, and then present the state of the art for the control, operation, and system integration for it and its clusters. The challenges and opportunities for marine integrated energy microgrids will also be discussed to shed light on future research.
Grid-Forming Loads: Can the loads be in charge of forming the grid in modern power systems?
Authors: Oriol Gomis-Bellmunt, Saman Dadjo Tavakoli, Vinicius A. Lacerda, Eduardo Prieto-Araujo
Date: 2022-08-27 00:00:00
Tasks: Management

Modern power systems are facing the tremendous challenge of integrating vast amounts of variable (non-dispatchable) renewable generation capacity, such as solar photovoltaic or wind power. In this context, the required power system flexibility needs to be allocated in other units that can include energy storage, demand management or providing reserve from renewables curtailing the output power. The present paper proposes the new concept of grid-forming load, which can be considered a totally flexible concept of demand. The concept is not only ensuring the load is supporting the grid stability by adapting the load to the overall system balancing, but also ensures that the load is actually contributing to form the grid and to provide synchronization power to the overall system. In this sense, the new concept allows running a system powered only by renewables operating at maximum power (or operator defined set-point) in grid-following mode, while the overall system control is ubicated in the demand side. This is an important change of paradigm as it considers that all the flexibility, synchronism and stability provision is on the demand side. This concept can applied either to isolated systems or also to future power systems, where millions of loads steer the power system while the renewables are operating at full power. The paper proposes the concept, suggests possible control implementations of the grid-forming load and analyses the concept in four simulation case studies.
Global RTK Positioning in Graphical State Space
Authors: Yihong Ge, Sudan Yan, Shaolin Lü, Cong Li
Date: 2022-08-27 00:00:00
Tasks: Position, Time Series, Time Series Analysis

This paper proposes a new method for RTK post-processing. Different from the traditional forward-backward Kalman filter, in our method, the whole system equation is built on a graphical state space model and solved by factor graph optimization. The position solution provided by the forward Kalman filter is used as the linearization points of the graphical state space model. Constant variables, such as double-difference ambiguity, will exist as constants in the graphical state space model, not as time-series variables. It is shown by experiment results that factor graph optimization with a graphical state space model is more effective than Kalman filter with a traditional discrete-time state space model for RTK post-processing problem.
Research on Multi-Objective Planning of Electric Vehicle Charging Stations Considering the Condition of Urban Traffic Network
Authors: Limeng Wang, Chao Yang, Yi Zhang, Fanjin Bu
Date: 2022-08-27 00:00:00

As an important supporting facility for electric vehicles, the reasonable planning and layout of charging stations are of great significance to the development of electric vehicles. However, the planning and layout of charging stations is affected by various complex factors such as policy economy, charging demand, user charging comfort, and road traffic conditions. How to weigh various factors to construct a reasonable model of charging station location and capacity has become a major difficulty in the field of electric vehicle charging facility planning. Firstly, this paper constructs the location and capacity optimization model of the charging station with the goal of maximizing the revenue of operators and minimizing the user's charging additional cost. At the same time, the road time-consuming index is introduced to quantify the impact of road congestion on the user's charging additional cost, so as to effectively improve the user's satisfaction during charging. Then, aiming at the charging station planning model, a non-dominated sorting genetic algorithm with an elite strategy (NSGA-II) based on chaos initialization and arithmetic crossover operator is proposed. Finally, taking the Haidian District of Beijing as the simulation object, the results show that compared with the situation of urban traffic networks not considered, the model proposed in this paper significantly reduces the cost of lost time of users by 11.4% and the total additional cost of users' charging by 7.6%. It not only ensures the economy of the system, but also effectively improves the charging satisfaction of users, which further verifies the feasibility and effectiveness of the model, and can provide a reference for the planning and layout of charging stations in the future.
Stackelberg game-based optimal scheduling of integrated energy systems considering differences in heat demand across multi-functional areas
Authors: Limeng Wang, Ranran Yang, Yang Qu, Chengzhe Xu
Date: 2022-08-27 00:00:00
Tasks: Management, Scheduling

Demand-side management is very critical in China's energy systems because of its high fossil energy consumption and low system energy efficiency. A building shape factor is introduced in describing the architectural characteristics of different functional areas, which are combined with the characteristics of the energy consumed by users to investigate the features of heating load in different functional areas. A Stackelberg game-based optimal scheduling model is proposed for electro-thermal integrated energy systems, which seeks to maximize the revenue of integrated energy operator (IEO) and minimize the cost of users. Here, IEO and users are the Stackelberg game leader and followers, respectively. The leader uses real-time energy prices to guide loads to participate in demand response, while the followers make energy plans based on price feedback. Using the Karush-Kuhn-Tucker (KKT) condition and the big-M method, the model is transformed into a mixed-integer quadratic programming (MIQP) problem, which is solved by using MATLAB and CPLEX software. The results demonstrate that the proposal manages to balance the interests of IEO and users. Furthermore, the heating loads of public and residential areas can be managed separately based on the differences in energy consumption and building shape characteristics, thereby improving the system operational flexibility and promoting renewable energy consumption.
Optimization of Vehicle Trajectories Considering Uncertainty in Actuated Traffic Signal Timings
Authors: Amr Shafik, Seifeldeen Eteifa, Hesham Rakha
Date: 2022-08-25 00:00:00

This paper introduces a robust optimal green light speed advisory system for fixed and actuated traffic signals when a probability distribution is provided. These distributions represent the domain of possible switching times from the Signal Phasing and Timing (SPaT) messages. The system finds the least-cost vehicle trajectory using a computationally efficient A-star algorithm incorporated in a dynamic programming procedure to minimize the vehicle's total fuel consumption. Constraints are introduced to ensure that vehicles do not, collide with other vehicles, run red lights, or exceed a maximum vehicular jerk for passenger comfort. Results of simulation scenarios are evaluated against comparable trajectories of uninformed drivers to compute fuel consumption savings. The proposed approach produced significant fuel savings compared to the uninformed driver amounting to 37 percent on average for deterministic SPAT and 28 percent for stochastic SPaT. A sensitivity analysis is performed to understand how the degree of uncertainty in SPaT predictions affects the optimal trajectory's fuel consumption. The results present the required levels of confidence in these predictions to achieve most of the possible savings in fuel consumption. Specifically, the proposed system can be within 85 percent of the maximum savings if the timing error is (within 3.3 seconds) at a 95 percent confidence level. They also emphasize the importance of more reliable SPaT predictions the closer the time to green is relative to the time the vehicle is expected to reach the intersection given its current speed.
Slice estimation in diffusion MRI of neonatal and fetal brains in image and spherical harmonics domains using autoencoders
Authors: Hamza Kebiri, Gabriel Girard, Yasser Aleman-Gomez, Thomas Yu, Andras Jakab, Erick Jorge Canales-Rodriguez, Meritxell Bach Cuadra
Date: 2022-08-29 00:00:00
Tasks: Anatomy, Diffusion  MRI

Diffusion MRI (dMRI) of the developing brain can provide valuable insights into the white matter development. However, slice thickness in fetal dMRI is typically high (i.e., 3-5 mm) to freeze the in-plane motion, which reduces the sensitivity of the dMRI signal to the underlying anatomy. In this study, we aim at overcoming this problem by using autoencoders to learn unsupervised efficient representations of brain slices in a latent space, using raw dMRI signals and their spherical harmonics (SH) representation. We first learn and quantitatively validate the autoencoders on the developing Human Connectome Project pre-term newborn data, and further test the method on fetal data. Our results show that the autoencoder in the signal domain better synthesized the raw signal. Interestingly, the fractional anisotropy and, to a lesser extent, the mean diffusivity, are best recovered in missing slices by using the autoencoder trained with SH coefficients. A comparison was performed with the same maps reconstructed using an autoencoder trained with raw signals, as well as conventional interpolation methods of raw signals and SH coefficients. From these results, we conclude that the recovery of missing/corrupted slices should be performed in the signal domain if the raw signal is aimed to be recovered, and in the SH domain if diffusion tensor properties (i.e., fractional anisotropy) are targeted. Notably, the trained autoencoders were able to generalize to fetal dMRI data acquired using a much smaller number of diffusion gradients and a lower b-value, where we qualitatively show the consistency of the estimated diffusion tensor maps.
Latent Signal Models: Learning Compact Representations of Signal Evolution for Improved Time-Resolved, Multi-contrast MRI
Authors: Yamin Arefeen, Junshen Xu, Molin Zhang, Zijing Dong, Fuyixue Wang, Jacob White, Berkin Bilgic, Elfar Adalsteinsson
Date: 2022-08-27 00:00:00
Tasks: Decoder

Purpose: Training auto-encoders on simulated signal evolution and inserting the decoder into the forward model improves reconstructions through more compact, Bloch-equation-based representations of signal in comparison to linear subspaces. Methods: Building on model-based nonlinear and linear subspace techniques that enable reconstruction of signal dynamics, we train auto-encoders on dictionaries of simulated signal evolution to learn more compact, non-linear, latent representations. The proposed Latent Signal Model framework inserts the decoder portion of the auto-encoder into the forward model and directly reconstructs the latent representation. Latent Signal Models essentially serve as a proxy for fast and feasible differentiation through the Bloch-equations used to simulate signal. This work performs experiments in the context of T2-shuffling, gradient echo EPTI, and MPRAGE-shuffling. We compare how efficiently auto-encoders represent signal evolution in comparison to linear subspaces. Simulation and in-vivo experiments then evaluate if reducing degrees of freedom by inserting the decoder into the forward model improves reconstructions in comparison to subspace constraints. Results: An auto-encoder with one real latent variable represents FSE, EPTI, and MPRAGE signal evolution as well as linear subspaces characterized by four basis vectors. In simulated/in-vivo T2-shuffling and in-vivo EPTI experiments, the proposed framework achieves consistent quantitative NRMSE and qualitative improvement over linear approaches. From qualitative evaluation, the proposed approach yields images with reduced blurring and noise amplification in MPRAGE shuffling experiments. Conclusion: Directly solving for non-linear latent representations of signal evolution improves time-resolved MRI reconstructions through reduced degrees of freedom.
An agent-based modeling approach for real-world economic systems: Example and calibration with a Social Accounting Matrix of Spain
Authors: Martin Jaraiz
Date: 2022-08-28 00:00:00
Tasks: Epidemiology

The global economy is one of today's major challenges, with increasing relevance in recent decades. A frequent observation by policy makers is the lack of tools that help at least to understand, if not predict, economic crises. Currently, macroeconomic modeling is dominated by Dynamic Stochastic General Equilibrium (DSGE) models. The limitations of DSGE in coping with the complexity of today's global economy are often recognized and are the subject of intense research to find possible solutions. As an alternative or complement to DSGE, the last two decades have seen the rise of agent-based models (ABM). An attractive feature of ABM is that it can model very complex systems because it is a bottom-up approach that can describe the specific behavior of heterogeneous agents. The main obstacle, however, is the large number of parameters that need to be known or calibrated. To enable the use of ABM with data from the real-world economy, this paper describes an agent-based macroeconomic modeling approach that can read a Social Accounting Matrix (SAM) and deploy from scratch an economic system (labor, activity sectors operating as firms, a central bank, the government, external sectors...) whose structure and activity produce a SAM with values very close to those of the actual SAM snapshot. This approach paves the way for unleashing the expected high performance of ABM models to deal with the complexities of current global macroeconomics, including other layers of interest like ecology, epidemiology, or social networks among others.
Nondistributivity of human logic and violation of response replicability effect in cognitive psychology
Authors: Masanao Ozawa, Andrei Khrennikov
Date: 2022-08-27 00:00:00

The aim of this paper is to promote quantum logic as one of the basic tools for analyzing human reasoning. We compare it with classical (Boolean) logic and highlight the role of violation of the distributive law for conjunction and disjunction. It is well known that nondistributivity is equivalent to incompatibility of logical variables -- the impossibility to assign jointly the two-valued truth values to these variables. A natural question arises as to whether quantum logical nondistributivity in human logic can be tested experimentally. We show that testing the response replicability effect (RRE) in cognitive psychology is equivalent to testing nondistributivity -- under the prevailing conjecture that the mental state update generated by observation is described as orthogonal projection of the mental state vector (the projective update conjecture of Wang and Busemeyer). A simple test of RRE is suggested. In contrast to the previous works in quantum-like modeling, we proceed in the state-dependent framework; in particular, distributivity, compatibility, and RRE are considered in a fixed mental state. In this framework, we improve the previous result on the impossibility to combine question order and response replicability effects by using (von Neumann-L\"uders) projective measurements.
A Consistent ICM-based $χ^2$ Specification Test
Authors: Feiyu Jiang, Emmanuel Selorm Tsyawo
Date: 2022-08-29 00:00:00
Tasks: Computational Efficiency

In spite of the omnibus property of Integrated Conditional Moment (ICM) specification tests, they are not commonly used in empirical practice owing to, e.g., the non-pivotality of the test and the high computational cost of available bootstrap schemes especially in large samples. This paper proposes specification and mean independence tests based on a class of ICM metrics termed the generalized martingale difference divergence (GMDD). The proposed tests exhibit consistency, asymptotic $\chi^2$-distribution under the null hypothesis, and computational efficiency. Moreover, they demonstrate robustness to heteroskedasticity of unknown form and can be adapted to enhance power towards specific alternatives. A power comparison with classical bootstrap-based ICM tests using Bahadur slopes is also provided. Monte Carlo simulations are conducted to showcase the proposed tests' excellent size control and competitive power.
Comparing Stochastic Volatility Specifications for Large Bayesian VARs
Authors: Joshua C. C. Chan
Date: 2022-08-28 00:00:00

Large Bayesian vector autoregressions with various forms of stochastic volatility have become increasingly popular in empirical macroeconomics. One main difficulty for practitioners is to choose the most suitable stochastic volatility specification for their particular application. We develop Bayesian model comparison methods -- based on marginal likelihood estimators that combine conditional Monte Carlo and adaptive importance sampling -- to choose among a variety of stochastic volatility specifications. The proposed methods can also be used to select an appropriate shrinkage prior on the VAR coefficients, which is a critical component for avoiding over-fitting in high-dimensional settings. Using US quarterly data of different dimensions, we find that both the Cholesky stochastic volatility and factor stochastic volatility outperform the common stochastic volatility specification. Their superior performance, however, can mostly be attributed to the more flexible priors that accommodate cross-variable shrinkage.
A Descriptive Method of Firm Size Transition Dynamics Using Markov Chain
Authors: Boyang You, Kerry Papps
Date: 2022-08-27 00:00:00
Tasks: Descriptive

Social employment, which is mostly carried by firms of different types, determines the prosperity and stability of a country. As time passing, the fluctuations of firm employment can reflect the process of creating or destroying jobs. Therefore, it is instructive to investigate the firm employment (size) dynamics. Drawing on the firm-level panel data extracted from the Chinese Industrial Enterprises Database 1998-2013, this paper proposes a Markov-chain-based descriptive approach to clearly demonstrate the firm size transfer dynamics between different size categories. With this method, any firm size transition path in a short time period can be intuitively demonstrated. Furthermore, by utilizing the properties of Markov transfer matrices, the definition of transition trend and the transition entropy are introduced and estimated. As a result, the tendency of firm size transfer between small, medium and large can be exactly revealed, and the uncertainty of size change can be quantified. Generally from the evidence of this paper, it can be inferred that small and medium manufacturing firms in China have greater job creation potentials compared to large firms over this time period.
A restricted eigenvalue condition for unit-root non-stationary data
Authors: Etienne Wijler
Date: 2022-08-27 00:00:00
Tasks: Time Series, Time Series Analysis

In this paper, we develop a restricted eigenvalue condition for unit-root non-stationary data and derive its validity under the assumption of independent Gaussian innovations that may be contemporaneously correlated. The method of proof relies on matrix concentration inequalities and offers sufficient flexibility to enable extensions of our results to alternative time series settings. As an application of this result, we show the consistency of the lasso estimator on ultra high-dimensional cointegrated data in which the number of integrated regressors may grow exponentially in relation to the sample size.
Digital Image Processing Applied To Object Segmentation By Intensity And Motion
Authors: Benjamin Andres Huerfano Zapata
Date: 2022-08-26 00:00:00
Tasks: Semantic Segmentation

The current technological development allows us to carry out tasks that some time ago were unthinkable if not impossible, digital image processing has been one of the major constants of development today, taking into account that its implementation dates from a short time ago, OpenCV [1] is a tool focused on machine vision, In this case implemented in an object-oriented programming platform based on Java language offered by the NetBeans development software, based on the above, a physical platform was proposed and implemented as a closed environment which through the development of an algorithm allowed detection and segmentation of objects by means of the RGB color model; In future works this algorithm will provide the information base for the autonomous robotic platform; this advance opens a wide spectrum for the development of applications and tools in the field of artificial vision.