pwc-archive/papers-with-abstracts huggingface dataset:
Optimal Topology Transition
Authors: Tong Han, David J. Hill, Yue Song
Date: 2022-08-22 00:00:00

Network topology has significant impacts on operational performance of power systems. While extensive research efforts have been devoted to optimization of network topology for improving various system performances, the problem of how to transition from the initial topology to the desired optimal topology requires study. To address this problem, we propose the concept of optimal topology transition (OTT). This aims to find the topology transition trajectory from an initial topology to a desired terminal topology, which optimizes certain transition performance and satisfies operational constraints. The OTT problem is further formulated as a mixed-integer program under certain assumptions. Next, we propose the formulation of transition-embedded topology optimization that is capable of optimizing network topology and its transition trajectory simultaneously. Considering the time complexity of directly solving the mixed-integer programs, an efficient problem-specific solution algorithm is developed. Finally, numerical studies demonstrate the effectiveness of the proposed OTT and transition-embedded topology optimization models, as well as the superiority of the obtained optimal transition trajectories compared to ad hoc transition trajectories.
Data-Driven Control of Distributed Event-Triggered Network Systems
Authors: Xin Wang, Jian Sun, Gang Wang, Frank Allgöwer, Jie Chen
Date: 2022-08-22 00:00:00

The present paper deals with data-driven event-triggered control of a class of unknown discrete-time interconnected systems (a.k.a. network systems). To this end, we start by putting forth a novel distributed event-triggering transmission strategy based on periodic sampling, under which a model-based stability criterion for the closed-loop network system is derived, by leveraging a discrete-time looped-functional approach. Marrying the model-based criterion with a data-driven system representation recently developed in the literature, a purely data-driven stability criterion expressed in the form of linear matrix inequalities (LMIs) is established. Meanwhile, the data-driven stability criterion suggests a means for co-designing the event-triggering coefficient matrix and the feedback control gain matrix using only some offline collected state-input data. Finally, numerical results corroborate the efficacy of the proposed distributed data-driven ETS in cutting off data transmissions and the co-design procedure.
Algorithms of Real-Time Navigation and Control of Autonomous Unmanned Vehicles
Authors: Yang Zhang
Date: 2022-08-22 00:00:00
Tasks: Computational Efficiency, Self-Driving Cars, Trajectory Planning

The rapid development of robotics has benefited by more and more people putting their attention to it. With the demand for robots is growing for the purpose of fulfilling tasks instead of humans, how to control the robot better is becoming a hot topic. For obstacle avoidance, we proposed algorithms for both 2D planar environments and 3D space environments. The example cases we raise are those that need to be addressed but have always been ignored. In addition, we put efforts into trajectory planning for robots. The two scenarios we set are self-driving cars on the road and reconnaissance and surveillance of drones. For future expectations, there are some possible directions. How to combine traditional navigation algorithms and high-tech algorithms together so as to fulfill the tasks perfectly while the computational efficiency is not too high is a worthy topic. In addition, extending the obstacle avoidance algorithms to more competitive situations. Moreover, cooperation among multi robots are worth attention by researchers. All in all, there is still a long way to go for the development of navigation and control of mobile robots. Despite this, we believe we do not need to wait for too long time to see the revolution of robots.
Abstraction-Free Control Synthesis to Satisfy Temporal Logic Constraints under Sensor Faults and Attacks
Authors: Luyao Niu, Zhouchi Li, Andrew Clark
Date: 2022-08-22 00:00:00
Tasks: State Estimation

We study the problem of synthesizing a controller to satisfy a complex task in the presence of sensor faults and attacks. We model the task using Gaussian distribution temporal logic (GDTL), and propose a solution approach that does not rely on computing any finite abstraction to model the system. We decompose the GDTL specification into a sequence of reach-avoid sub-tasks. We develop a class of fault-tolerant finite time convergence control barrier functions (CBFs) to guarantee that a dynamical system reaches a set within finite time almost surely in the presence of malicious attacks. We use the fault-tolerant finite time convergence CBFs to guarantee the satisfaction of `reach' property. We ensure `avoid' part in each sub-task using fault-tolerant zeroing CBFs. These fault-tolerant CBFs formulate a set of linear constraints on the control input for each sub-task. We prove that if the error incurred by system state estimation is bounded by a certain threshold, then our synthesized controller fulfills each reach-avoid sub-task almost surely for any possible sensor fault and attack, and thus the GDTL specification is satisfied with probability one. We demonstrate our proposed approach using a numerical study on the coordination of two wheeled mobile robots.
Observer-based Leader-following Consensus for Positive Multi-agent Systems Over Time-varying Graphs
Authors: Ruonan Li, Yichen Zhang, Yutao Tang, Shurong Li
Date: 2022-08-22 00:00:00

This paper addresses the leader-following consensus problem for discrete-time positive multi-agent systems over time-varying graphs. We assume that the followers may have mutually different positive dynamics which can also be different from the leader. Compared with most existing positive consensus works for homogeneous multi-agent systems, the formulated problem is more general and challenging due to the interplay between the positivity requirement and high-order heterogeneous dynamics. To solve the problem, we present an extended version of existing observer-based design for positive multi-agent systems. By virtue of the common quadratic Lyapunov function technique, we show the followers will maintain their state variables in the positive orthant and finally achieve an output consensus specified by the leader. A numerical example is used to verify the efficacy of our algorithms.
Towards Unifying Resilience and Sustainability for Transportation Infrastructure Systems: Conceptual Framework, Critical Indicators, and Research Needs
Authors: H M Imran Kays, Arif Mohaimin Sadri
Date: 2022-08-22 00:00:00

Sustainability aspects of transportation infrastructure systems primarily focus on system performance based on environmental, social, and economic impacts. In contrast, resilience aspects demonstrate the ability to withstand external shocks i.e. robustness as well as to recover from the loss of functionality due to such disruptions i.e. rapidity. Therefore, sustainability and resilience are two key aspects which should be given adequate attention during the planning, design, construction, operations, and maintenance phases of any civil infrastructure system. As both concepts are equally important to sustain an infrastructure for a longer duration, their concurrent assessments within a unified framework are highly desirable. While there has been a recent focus towards solving this dilemma, review of existing studies revealed the lack of such unifying frameworks that can quantify sustainability and resilience indicators to simultaneously assess system performance. Moreover, a single decision or performance indicator could reinforce one and undermine another. As such, this study proposed a forward-looking unification framework, where the sustainability and resilience of transportation infrastructure systems can be analyzed simultaneously. In this regard, the proposed unifying framework is explained using seven critical indicators including emission, speed, temperature, energy consumption, delay, mobility, and accessibility. This study also investigates the interdependencies, relationships, and tradeoffs between sustainability and resilience based on these indices. While some indices would help the system to attain both at the same time, they are compromised in some cases. Finally, the study identifies immediate research needs as well as the ones in the long-term.
A Two-phase On-line Joint Scheduling for Welfare Maximization of Charging Station
Authors: Qilong Huang, Qing-Shan Jia, Xiang Wu, Shengyuan Xu, Xiaohong Guan
Date: 2022-08-22 00:00:00
Tasks: Model Predictive Control, Scheduling

The large adoption of EVs brings practical interest to the operation optimization of the charging station. The joint scheduling of pricing and charging control will achieve a win-win situation both for the charging station and EV drivers, thus enhancing the operational capability of the station. We consider this important problem in this paper and make the following contributions. First, a joint scheduling model of pricing and charging control is developed to maximize the expected social welfare of the charging station considering the Quality of Service and the price fluctuation sensitivity of EV drivers. It is formulated as a Markov decision process with variance criterion to capture uncertainties during operation. Second, a two-phase on-line policy learning algorithm is proposed to solve this joint scheduling problem. In the first phase, it implements event-based policy iteration to find the optimal pricing scheme, while in the second phase, it implements scenario-based model predictive control for smart charging under the updated pricing scheme. Third, by leveraging the performance difference theory, the optimality of the proposed algorithm is theoretically analyzed. Numerical experiments for a charging station with distributed generation and energy storage demonstrate the effectiveness of the proposed method and the improved social welfare of the charging station.
Spatially Selective Active Noise Control Systems
Authors: Tong Xiao, Buye Xu, Chuming Zhao
Date: 2022-08-22 00:00:00

Active noise control (ANC) systems are commonly designed to achieve maximal sound reduction regardless of the incident direction of the sound. When desired sound is present, the state-of-the-art methods add a separate system to reconstruct it. This can result in distortion and latency. In this work, we propose a multi-channel ANC system that only reduces sound from undesired directions, and the system truly preserves the desired sound instead of reproducing it. The proposed algorithm imposes a spatial constraint on the hybrid ANC cost function to achieve spatial selectivity. Based on a six-channel microphone array on a pair of augmented eyeglasses, results show that the system minimized only noise coming from undesired directions. The control performance could be maintained even when the array was heavily perturbed. The proposed algorithm was also compared with the existing methods in the literature. Not only did the proposed system provide better noise reduction, but it also required much less effort. The binaural localization cues did not need to be reconstructed since the system preserved the physical sound wave from the desired source.
Preemptive Scheduling of EV Charging for Providing Demand Response Services
Authors: Shiping Shao, Farshad Harirchi, Devang Dave, Abhishek Gupta
Date: 2022-08-21 00:00:00
Tasks: Scheduling

We develop a new algorithm for scheduling the charging process of a large number of electric vehicles (EVs) over a finite horizon. We assume that EVs arrive at the charging stations with different charge levels and different flexibility windows. The arrival process is assumed to have a known distribution and that the charging process of EVs can be preemptive. We pose the scheduling problem as a dynamic program with constraints. We show that the resulting formulation leads to a monotone dynamic program with Lipschitz continuous value functions that are robust against perturbation of system parameters. We propose a simulation based fitted value iteration algorithm to determine the value function approximately, and derive the sample complexity for computing the approximately optimal solution.
High-Performance Transmission Mechanism Design of Multi-Stream Carrier Aggregation for 5G Non-Standalone Network
Authors: Jun Yu, Shunqing Zhang, Jiayun Sun, Shugong Xu, Shan Cao
Date: 2022-08-21 00:00:00

Multi-stream carrier aggregation is a key technology to expand bandwidth and improve the throughput of the fifth-generation wireless communication systems. However, due to the diversified propagation properties of different frequency bands, the traffic migration task is much more challenging, especially in hybrid sub-6 GHz and millimeter wave bands scenario. Existing schemes either neglected to consider the transmission rate difference between multi-stream carrier, or only consider simple low mobility scenario. In this paper, we propose a low-complexity traffic splitting algorithm based on fuzzy proportional integral derivative control mechanism. The proposed algorithm only relies on the local radio link control buffer information of sub-6 GHz and mmWave bands, while frequent feedback from user equipment (UE) side is minimized. As shown in the numerical examples, the proposed traffic splitting mechanism can achieve more than 90% link resource utilization ratio for different UE transmission requirements with different mobilities, which corresponds to 10% improvement if compared with conventional baselines.
Co-Optimizing Distributed Energy Resources in Linear Complexity under Net Energy Metering
Authors: Ahmed S. Alahmed, Lang Tong, Qing Zhao
Date: 2022-08-21 00:00:00
Tasks: energy management, Management, Scheduling

The co-optimization of behind-the-meter distributed energy resources is considered for prosumers under the net energy metering tariff. The distributed energy resources considered include renewable generations, flexible demands, and battery energy storage systems. An energy management system co-optimizes the consumptions and battery storage based on locally available stochastic renewables by solving a stochastic dynamic program that maximizes the expected operation surplus. To circumvent the exponential complexity of the dynamic program solution, we propose a closed-form and linear computation complexity co-optimization algorithm based on a relaxation-projection approach to a constrained stochastic dynamic program. Sufficient conditions for optimality for the proposed solution are obtained. Numerical studies demonstrate orders of magnitude reduction of computation costs and significantly reduced optimality gap.
Study on the Concept and Development of a Mobile Incubator
Authors: Fehmi Can Ay, Nesim Bilici, Rahmetullah Varol, Atasangu Yilmaz, Ufuk Gorkem Kirabali, Abdurrahim Yilmaz, Huseyin Uvet
Date: 2022-08-20 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

Creating the best possible conditions is essential for proper cell growth. Incubators, a type of biotechnological instrument, are used to simulate this condition and maintain the cells within them. The processes involved in creating a mobile incubator, which are essential for monitoring a cell culture's physiological parameters, are outlined in this article. The goal is to keep image-taking during cell development from compromising data accuracy. The cell culture is prone to contamination once it has been removed from the incubation environment for further monitoring. The proposed approach allows for on-the-go monitoring of the cell culture. Moreover, it enables constant monitoring.
Rigid Base Biasing in Molecular Dynamics enables enhanced sampling of DNA conformations
Authors: Aderik Voorspoels, Jocelyne Vreede, Enrico Carlon
Date: 2022-08-22 00:00:00

All-atom simulations have become increasingly popular to study conformational and dynamical properties of nucleic acids as they are accurate and provide high spatial and time resolutions. This high resolution however comes at a heavy computational cost and within the time scales of simulations nucleic acids weakly fluctuate around their ideal structure exploring a limited set of conformations. We introduce the RBB-NA algorithm which is capable of controlling rigid base parameters in all-atom simulations of Nucleic Acids. With suitable biasing potentials this algorithm can "force" a DNA or RNA molecule to assume specific values of the six rotational (tilt, roll, twist, buckle, propeller, opening) and/or the six translational parameters (shift, slide, rise, shear, stretch, stagger). The algorithm enables the use of advanced sampling techniques to probe the structure and dynamics of locally strongly deformed Nucleic Acids. We illustrate its performance showing some examples in which DNA is strongly twisted, bent or locally buckled. In these examples RBB-NA reproduces well the unconstrained simulations data and other known features of DNA mechanics, but it also allows one to explore the anharmonic behavior characterizing the mechanics of nucleic acids in the high deformation regime.
Electrostatic complementarity at the interface drives transient protein-protein interactions
Authors: Greta Grassmann, Lorenzo Di Rienzo, Giorgio Gosti, Marco Leonetti, Giancarlo Ruocco, Mattia Miotto, Edoardo Milanetti
Date: 2022-08-21 00:00:00
Tasks: Specificity

Understanding the molecular mechanisms driving the binding between bio-molecules is a crucial challenge in molecular biology. In this respect, characteristics like the preferentially hydrophobic composition of the binding interfaces, the role of van der Waals interactions (short range forces), and the consequent shape complementarity between the interacting molecular surfaces are well established. However, no consensus has yet been reached on how and how much electrostatic participates in the various stages of protein-protein interactions. Here, we perform extensive analyses on a large dataset of protein complexes for which both experimental binding affinity and pH data were available. We found that (i) although different classes of dimers do not present marked differences in the amino acid composition and charges disposition in the binding region, (ii) homodimers with identical binding region show higher electrostatic compatibility with respect to both homodimers with non-identical binding region and heterodimers. The level of electrostatic compatibility also varies with the pH of the complex, reaching the lowest values for low pH. Interestingly, (iii) shape and electrostatic complementarity behave oppositely when one stratifies the complexes by their binding affinity. Conversely, complexes with low values of binding affinity exploit Coulombic complementarity to acquire specificity, suggesting that electrostatic complementarity may play a greater role in transient (or less stable) complexes. In light of these results, (iv) we provide a fast and efficient method to measure electrostatic complementarity without the need of knowing the complex structure. Expanding the electrostatic potential on a basis of 2D orthogonal polynomials, we can discriminate between transient and permanent protein complexes with an AUC of the ROC of 0.8.
Graph neural networks for materials science and chemistry
Authors: Patrick Reiser, Marlen Neubert, André Eberhard, Luca Torresi, Chen Zhou, Chen Shao, Houssam Metni, Clint van Hoesel, Henrik Schopmans, Timo Sommer, Pascal Friederich
Date: 2022-08-05 00:00:00

Machine learning plays an increasingly important role in many areas of chemistry and materials science, e.g. to predict materials properties, to accelerate simulations, to design new materials, and to predict synthesis routes of new materials. Graph neural networks (GNNs) are one of the fastest growing classes of machine learning models. They are of particular relevance for chemistry and materials science, as they directly work on a graph or structural representation of molecules and materials and therefore have full access to all relevant information required to characterize materials. In this review article, we provide an overview of the basic principles of GNNs, widely used datasets, and state-of-the-art architectures, followed by a discussion of a wide range of recent applications of GNNs in chemistry and materials science, and concluding with a road-map for the further development and application of GNNs.
Optimal Pre-Analysis Plans: Statistical Decisions Subject to Implementability
Authors: Maximilian Kasy, Jann Spiess
Date: 2022-08-20 00:00:00
Tasks: valid

What is the purpose of pre-analysis plans, and how should they be designed? We model the interaction between an agent who analyzes data and a principal who makes a decision based on agent reports. The agent could be the manufacturer of a new drug, and the principal a regulator deciding whether the drug is approved. Or the agent could be a researcher submitting a research paper, and the principal an editor deciding whether it is published. The agent decides which statistics to report to the principal. The principal cannot verify whether the analyst reported selectively. Absent a pre-analysis message, if there are conflicts of interest, then many desirable decision rules cannot be implemented. Allowing the agent to send a message before seeing the data increases the set of decision rules that can be implemented, and allows the principal to leverage agent expertise. The optimal mechanisms that we characterize require pre-analysis plans. Applying these results to hypothesis testing, we show that optimal rejection rules pre-register a valid test, and make worst-case assumptions about unreported statistics. Optimal tests can be found as a solution to a linear-programming problem.
On mechanism design with expressive preferences: an aspect of the social choice of Brexit
Authors: Anindya Bhattacharya, Debapriya Sen
Date: 2022-08-21 00:00:00

We study some problems of collective choice when individuals can have expressive preferences, that is, where a decision-maker may care not only about the material benefit from choosing an action but also about some intrinsic morality of the action or whether the action conforms to some identity-marker of the decision-maker. We construct a simple framework for analyzing mechanism design problems with such preferences and present some results focussing on the phenomenon we call "Brexit anomaly". The main findings are that while deterministic mechanisms are quite susceptible to Brexit anomaly, even with stringent domain restriction, random mechanisms assure more positive results.