pwc-archive/papers-with-abstracts huggingface dataset:
Development of Charging, Discharging Scheduling Algorithm for Economical and Energy Efficient Operation of Multi EV Charging Station
Authors: Hojun Jin, Sangkeum Lee, Sarvar Hussain Nengroo, Dongsoo Har
Date: 2022-05-09 00:00:00
Tasks: Management, Scheduling

As the number of electric vehicles (EVs) significantly increases, the excessive charging demand of parked EVs in the charging station may incur an instability problem to the electricity network during peak hours. For the charging station to take a microgrid (MG) structure, an economical and energy-efficient power management scheme is required for the power provision of EVs while considering the local load demand of the MG. For these purposes, this study presents the power management scheme of interdependent MG and EV fleets aided by a novel EV charg-ing/discharging scheduling algorithm. In this algorithm, the maximum amount of discharging power from parked EVs is determined based on the difference between local load demand and photovoltaic (PV) power production to alleviate imbalances occurred between them. For the power management of the MG with charging/discharging scheduling of parked EVs in the PV-based charging station, multi-objective optimization is performed to minimize the operating cost and grid dependency. In addition, the proposed scheme maximizes the utilization of EV charging/discharging while satisfying the charging requirements of parked EVs. Moreover, a more economical and energy-efficient PV-based charging station is established using the future trends of local load demand and PV power production predicted by a gated recurrent unit (GRU) network. With the proposed EV charging/discharging scheduling algorithm, the operating cost of PV-based charging station is decreased by 167.71% and 28.85% compared with the EV charg-ing scheduling algorithm and the conventional EV charging/discharging scheduling algorithm, respectively. It is obvious that the economical and energy-efficient operation of PV-based charging station can be accomplished by applying the power management scheme with the proposed EV charging/discharging scheduling strategy.
Damage Maximization for Combat Network with Limited Costs
Authors: Jintao Yu, Bing Xiao, Yuzhu Cui
Date: 2022-05-09 00:00:00

Maximizing the damage by attacking specific nodes of the combat network can efficiently disrupt enemies' defense capability, protect our critical units, and enhance the resistance to the destruction of system-of-system~(SOS). However, the modeling of the combat network damage is not practical enough. In this paper, we report a more realistic model to study the combat network damage maximization problems. By analyzing realistic situations, the cost of damage is redefined based on the network topology and the functional characteristics of nodes. The damage effect is also updated according to the combat network topology and operational capability. Hence, a cost-limited damage maximization model for the combat network is constructed. In addition, to obtain optimal solutions, an improved genetic algorithm~(IPGA) based on prior information is proposed. As a result, our method has a significant advantage in the feasibility and effectiveness compared with other algorithms in experiments. The attack pattern of the combat network and the convergence and complexity of the proposed algorithm are further explored. The improved model and algorithm, as well as the mined attack patterns, can provide support for military decisions.
Robustness of double-layer group-dependent combat network with cascading failure
Authors: Jintao Yu, Bing Xiao, Yuzhu Cui
Date: 2022-05-09 00:00:00

The networked combat system-of-system (CSOS) is the trend of combat development with the innovation of technology. The achievement of combat effectiveness requires CSOS to have a good ability to deal with external interference. Here we report a modeling method of CSOS from the perspective of complex networks and explore the robustness of the combat network based on this. Firstly, a more realistic double-layer heterogeneous dependent combat network model is established. Then, the conditional group dependency situation is considered to design failure rules for dependent failure, and the coupling relation between the double-layer subnets is analyzed for cascading failure. Based on this, the initial load and capacity of the node are defined, respectively, as well as the load redistribution strategy and the status judgment rules for the cascading failure model. Simulation experiments are carried out by changing the attack modes and different parameters, and the results show that the robustness of the combat network can be effectively improved by improving the tolerance limit of one-way dependency of the functional net, the node capacity of the functional subnet and the tolerance of the overload state. The conclusions of this paper can provide a useful reference for network structure optimization and network security protection in the military field.
A Contraction-constrained Model Predictive Control for Nonlinear Processes using Disturbance Forecasts
Authors: Ryan Mccloy, Lai Wei, Jie Bao
Date: 2022-05-09 00:00:00
Tasks: Model Predictive Control

Model predictive control (MPC) has become the most widely used advanced control method in process industry. In many cases, forecasts of the disturbances are available, e.g., predicted renewable power generation based on weather forecast. While the predictions of disturbances may not be accurate, utilizing the information can significantly improve the control performance in response to the disturbances. By exploiting process and disturbance models, future system behaviour can be predicted and used to optimise control actions via minimisation of an economical cost function which incorporates these predictions. However, stability guarantee of the resulting closed-loop system is often difficult in this approach when the processes are nonlinear. Proposed in the following article is a contraction-constrained predictive controller which optimises process economy whilst ensuring stabilisation to operating targets subject to disturbance measurements and forecasts.
From Discovery to Production: Challenges and Novel Methodologies for Next Generation Biomanufacturing
Authors: Wei Xie, Giulia Pedrielli
Date: 2022-05-08 00:00:00
Tasks: Decision Making

The increasingly pressing demand of novel drugs (e.g., gene therapies for personalized cancer care, ever evolving vaccines) with unprecedented levels of personalization, has put a remarkable pressure on the traditionally long time required by the pharma R&D and manufacturing to go from design to production of new products. The revolution has already brought important changes in the technologies used within the industry. In fact, practitioners are increasingly moving away from the classical paradigm of large-scale batch production to continuous biomanufacturing with flexible and modular design, which is further supported by the recent technology advance in single-use equipment. In contrast to long design processes, low product variability (one-fits-all), and highly rigid systems, modern pharma players are answering the question: can we bring design and process control up to the speed that novel production technologies give us to quickly set up a flexible production run? In this tutorial, we present key challenges and potential solutions from the world of operations research that can support answering such question. We first present technical challenges and novel methods for the design of next generation drugs, followed by the process modeling and control approaches to successfully and efficiently manufacture them.
A co-design method of online learning SMC law via an input-mappping strategy
Authors: Yaru Yu, Dewei Li, Dongya Zhao, Yugeng Xi
Date: 2022-05-07 00:00:00

The research on sliding mode control strategy is generally based on the robust approach. The larger parameter space consideration will inevitably sacrifice part of the performance. Recently, the data-driven sliding mode control method attracts much attention and shows excellent benefits in the fact that data is introduced to compensate the controller. Nevertheless, most of the research on data-driven sliding mode control relied on identification techniques, which limits its online applications due to the special requirements of the data. In this paper, an input-mapping technique is inserted into the design framework of sliding mode control to compensate for the influence generated by the unknown dynamic of the system. The major novelty of the proposed input-mapping sliding mode control strategy lies in that the sliding mode surface and the sliding mode controller are co-designed through online learning from historical input-output data to minimize an objective function. Then, the convergence rate of the system is improved significantly based on the method designed in this work. Finally, some simulations are provided to show the effectiveness and superiority of the proposed methods.
A Probabilistic Framework for Power System Large-Disturbance Global Instability Risk Assessment in the Presence of Renewable Wind Generation
Authors: Umair Shahzad
Date: 2022-05-07 00:00:00

The increasing demand of large scale wind integration in the conventional power system brings a lot of challenges. One of them is the stability of the power system when subjected to a large disturbance, such as a fault. This paper proposes a probabilistic risk-based framework for computing a global instability index, incorporating angle, voltage, and frequency stability, for a large disturbance. Moreover, the impact of high wind penetration on this index is also observed. Case studies and associated simulations are conducted on the IEEE 39-bus test system using DIgSILENT PowerFactory software. The results show that higher penetration of wind generation enhances the global stability of the power system. Moreover, the impact of changing system generation and load is studied on the global instability index.
A Hybrid Trim Strategy for Coaxial Compound Helicopter
Authors: Yuan Su, Zeyuan Wang, Yihua Cao
Date: 2022-05-07 00:00:00

Interest in the coaxial compound helicopter (CCH) has been increasing in the civil aviation and engineering community for its high-speed and high-maneuverability features, and is likely to continue to do so for the foreseeable future. Since the control in CCH is totally different from the conventional helicopter, the redundant control strategy design is one of the biggest challenges. In this study, the CCH model based on XH-59A is built to investigate the impact of the propeller and the elevator on the flight performance. Four trim strategies with different objectives are proposed and then compared to find the optimal control allocation. A heuristic descent search method is applied to search the optimal velocity at which the propeller and the elevator are engaged. A significant improvement of power required at medium and high speed with acceptable rotor airloads increment was found by using the Hybrid Trim strategy in the speed range of 0-100m/s, with regard to a pre-configured pitch angle schedule. The corresponding control variables obtained locate in a reasonable control range, with a maximum power reduced of 13% at 100m/s, which showcases the potential of the Hybrid Trim strategy.
I Can Read Your Mind: Control Mechanism Secrecy of Networked Dynamical Systems under Inference Attacks
Authors: Jianping He, Yushan Li, Lin Cai, Xinping Guan
Date: 2022-05-07 00:00:00
Tasks: Inference Attack

Recent years have witnessed the fast advance of security research for networked dynamical system (NDS). Considering the latest inference attacks that enable stealthy and precise attacks into NDSs with observation-based learning, this article focuses on a new security aspect, i.e., how to protect control mechanism secrets from inference attacks, including state information, interaction structure and control laws. We call this security property as control mechanism secrecy, which provides protection of the vulnerabilities in the control process and fills the defense gap that traditional cyber security cannot handle. Since the knowledge of control mechanism defines the capabilities to implement attacks, ensuring control mechanism secrecy needs to go beyond the conventional data privacy to cover both transmissible data and intrinsic models in NDSs. The prime goal of this article is to summarize recent results of both inference attacks on control mechanism secrets and countermeasures. We first introduce the basic inference attack methods on the state and structure of NDSs, respectively, along with their inference performance bounds. Then, the corresponding countermeasures and performance metrics are given to illustrate how to preserve the control mechanism secrecy. Necessary conditions are derived to guide the secrecy design. Finally, thorough discussions on the control laws and open issues are presented, beckoning future investigation on reliable countermeasure design and tradeoffs between the secrecy and control performance.
Reinforcement Learning Approach to Estimation in Linear Systems
Authors: Minyue Fu
Date: 2022-05-06 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), State Estimation

This paper addresses two important estimation problems for linear systems, namely system identification and model-free state estimation. Our focus is on ARMAX models with unknown parameters. We first provide a reinforcement learning algorithm for system identification with guaranteed consistency. This algorithm is then used to provide a novel solution to model-free state estimation. These results are then applied to solving the model-free LQG control problem in the reinforcement learning setting.
Energy-efficient Connected Cruise Control with Lean Penetration of Connected Vehicles
Authors: Minghao Shen, Chaozhe R. He, Tamas Molnar, A. Harvey Bell, Gabor Orosz
Date: 2022-05-06 00:00:00

This paper focuses on energy-efficient longitudinal controller design for a connected automated truck that travels in mixed traffic consisting of connected and non-connected vehicles. The truck has access to information about connected vehicles beyond line of sight using vehicle-to-vehicle (V2V) communication. A novel connected cruise control design is proposed which incorporates additional delays into the control law when responding to distant connected vehicles to account for the finite propagation of traffic waves. The speeds of non-connected vehicles are modeled as stochastic processes. A fundamental theorem is proven which links the spectral properties of the motion signals to the average energy consumption. This enables us to tune controller parameters and maximize energy efficiency. Simulations with synthetic data and real traffic data are used to demonstrate the energy efficiency of the control design. It is demonstrated that even with lean penetration of connected vehicles, our controller can bring significant energy savings.
Power Control of Grid-Forming Converters Based on Full-State Feedback
Authors: Meng Chen, Dao Zhou, Frede Blaabjerg
Date: 2022-05-06 00:00:00

The active and reactive power controllers of grid-forming converters are traditionally designed separately, which relies on the assumption of loop decoupling. This paper proposes a full-state feedback control for the power loops of grid-forming converters. First, the power loops are modeled considering their natural coupling, which, therefore, can apply to all kinds of line impedance, i.e., resistive, inductive, or complex. Then a full-state feedback control design is used. By this way, the eigenvalues of the system can be arbitrarily placed to any positions in the timescale of power loops. Therefore, the parameters can be directly chosen by the predefined specifications. A step-by-step parameters design procedure is also given in this paper. Experimental results verify the proposed method.
Robust phase retrieval with complexity-guidance for coherent X-ray imaging
Authors: Mansi Butola, Sunaina Rajora, Kedar Khare
Date: 2022-05-09 00:00:00
Tasks: Retrieval

Reconstruction of a stable and reliable solution from noisy incomplete Fourier intensity data recorded in a coherent X-ray imaging (CXI) experiment is a challenging problem. The Relaxed Averaged Alternating Reflections (RAAR) algorithm that is concluded with a number of Error Reduction (ER) iterations is a popular choice. The RAAR-ER algorithm is usually employed for several hundreds of times starting with independent random guesses to obtain trial solutions that are then averaged to obtain the phase retrieval transfer function (PRTF). In this paper, we examine the phase retrieval solution obtained using the RAAR-ER methodology from perspective of the complexity parameter that was introduced by us in recent works. We observe that a single run of the RAAR-ER algorithm produces a solution with higher complexity compared to what is expected based on the complexity parameter as manifested by spurious high frequency grainy artifacts in the solution that do not seem to go away completely even after a number of trial solutions are averaged. We then describe a CG-RAAR (Complexity Guided RAAR) phase retrieval method that can effectively address this inconsistency problem and provides artifact-free solutions. The CG-RAAR methodology is first illustrated with simulated unblocked noisy Fourier intensity data and later applied to centrally-blocked noisy cyanobacterium data which is available from the CXIDB database. Our simulation and experimental results using CG-RAAR suggest two important improvements over the popular RAAR-ER algorithm. The CG-RAAR solutions after the averaging procedure is more reliable in the sense that it contains smallest features consistent with the resolution estimated by the PRTF curve. Secondly, since the single run of the CG-RAAR solution does not have grainy artifacts, the number of trial solutions needed for the averaging process is reduced.
An age-structured epidemic model with vaccination
Authors: Ruiyang Zhou, Fengying Wei
Date: 2022-05-08 00:00:00
Tasks: model, Sensitivity

In this article, we construct an age-structured model for COVID-19 with vaccination and analyze it from multiple perspectives. We derive the unique disease-free equilibrium point and the basic reproduction number $ \mathscr{R}_0 $, then we show that the disease-free equilibrium is locally asymptotically stable when $ \mathscr{R}_0 < 1 $, while is unstable when $ \mathscr{R}_0 > 1 $. We also work out endemic equilibrium points and reveal the stability. We use sensitivity analysis to explore how parameters influence $ \mathscr{R}_0 $. Sensitivity analysis helps us develop more targeted strategies to control epidemics. Finally, this model is used to discuss the cases in Shijiazhuang, Hebei Province at the beginning of 2021. We compare reported cases with the simulation to evaluate the measures taken by Shijiazhuang government. Our study shows how age structure, vaccination and drastic containment measures can affect the epidemic.
A unified test for regression discontinuity designs
Authors: Koki Fusejima, Takuya Ishihara, Masayuki Sawada
Date: 2022-05-09 00:00:00
Tasks: Diagnostic, regression, valid

Diagnostic tests for regression discontinuity design face a size-control problem. We document a massive over-rejection of the diagnostic restriction among empirical studies in the top five economics journals. At least one diagnostic test was rejected for 19 out of 59 studies, whereas less than 5% of the collected 787 tests rejected the null hypotheses. In other words, one-third of the studies rejected at least one of their diagnostic tests, whereas their underlying identifying restrictions appear plausible. Multiple testing causes this problem because the median number of tests per study was as high as 12. Therefore, we offer unified tests to overcome the size-control problem. Our procedure is based on the new joint asymptotic normality of local polynomial mean and density estimates. In simulation studies, our unified tests outperformed the Bonferroni correction. We implement the procedure as an R package rdtest with two empirical examples in its vignettes.
Policy Choice in Time Series by Empirical Welfare Maximization
Authors: Toru Kitagawa, Weining Wang, Mengshan Xu
Date: 2022-05-08 00:00:00
Tasks: Time Series, Time Series Analysis

This paper develops a novel method for policy choice in a dynamic setting where the available data is a multi-variate time series. Building on the statistical treatment choice framework, we propose Time-series Empirical Welfare Maximization (T-EWM) methods to estimate an optimal policy rule by maximizing an empirical welfare criterion constructed using nonparametric potential outcome time series. We characterize conditions under which T-EWM consistently learns a policy choice that is optimal in terms of conditional welfare given the time-series history. We derive a nonasymptotic upper bound for conditional welfare regret. To illustrate the implementation and uses of T-EWM, we perform simulation studies and apply the method to estimate optimal restriction rules against Covid-19.
Dynamic demand for differentiated products with fixed-effects unobserved heterogeneity
Authors: Victor Aguirregabiria
Date: 2022-05-08 00:00:00

This paper studies identification and estimation of a dynamic discrete choice model of demand for differentiated product using consumer-level panel data with few purchase events per consumer (i.e., short panel). Consumers are forward-looking and their preferences incorporate two sources of dynamics: last choice dependence due to habits and switching costs, and duration dependence due to inventory, depreciation, or learning. A key distinguishing feature of the model is that consumer unobserved heterogeneity has a Fixed Effects (FE) structure -- that is, its probability distribution conditional on the initial values of endogenous state variables is unrestricted. I apply and extend recent results to establish the identification of all the structural parameters as long as the dataset includes four or more purchase events per household. The parameters can be estimated using a sufficient statistic - conditional maximum likelihood (CML) method. An attractive feature of CML in this model is that the sufficient statistic controls for the forward-looking value of the consumer's decision problem such that the method does not require solving dynamic programming problems or calculating expected present values.
Identification and Estimation of Dynamic Games with Unknown Information Structure
Authors: Konan Hara, Yuki Ito, Paul Koh
Date: 2022-05-07 00:00:00
Tasks: counterfactual

This paper studies the identification and estimation of dynamic games when the underlying information structure is unknown to the researcher. To tractably characterize the set of Markov perfect equilibrium predictions while maintaining weak assumptions on players' information, we introduce \textit{Markov correlated equilibrium}, a dynamic analog of Bayes correlated equilibrium. The set of Markov correlated equilibrium predictions coincides with the set of Markov perfect equilibrium predictions that can arise when the players can observe more signals than assumed by the analyst. Using Markov correlated equilibrium as the solution concept, we propose tractable computational strategies for informationally robust estimation, inference, and counterfactual analysis that deal with the non-convexities arising in dynamic environments. We use our method to analyze the dynamic competition between Starbucks and Dunkin' in the US and the role of informational assumptions.