Search

China is in the stage of rapid development of new urbanization, cities are more vulnerable when facing various risks, and the construction of urban resilience (UR) makes cities more secure. At the same time, the allocation of digital factors has a vital impact on the construction of UR under energy constraints. Based on the panel data of cities in China from 2011 to 2020, this paper studies the influence and mechanism of digital factor allocation (DFA) on UR under energy constraints through panel fixed effect model (FE), mediation effect model, and panel threshold model. The main empirical results include: (1) The misallocation of digital factors has a negative impact on UR. In the case of differences in the division of urban agglomeration, the impact of digital factor misallocation (DFM) on UR will also be different. (2) The misallocation of digital factors has an impact on UR through the intermediary effects of capital allocation efficiency, green innovation and industrial structure upgrading. (3) There is a nonlinear relationship between the DFM and UR. [ABSTRACT FROM AUTHOR]

Global climate change has caused governments worldwide to take actions to improve their energy efficiency. This paper investigates how China's Top-1000 program, a command-and-control type of energy-saving mandate, has affected the operational choices of firms, and in turn, their profitability. We apply the propensity score matching method to find "identical twins" for the participants in the Top- 1000 program, then conduct a difference-in-differences analysis on the matched sample. Our findings suggest that the profitability of the enterprises targeted for energy savings decreased by one-third, mainly due to increased production costs. The targeted enterprises tended to increase their fixed assets per capita, which was associated with improvements in energy efficiency. Furthermore, compared to similar untargeted enterprises, there was a significant slowdown in the production growths of the targeted enterprises, raising concerns about carbon leakage due to increased production by less efficient producers. [ABSTRACT FROM AUTHOR]

This paper addresses the distributed resilient filtering for discrete-time large-scale systems (LSSs) with energy constraints, where their information are collected by sensor networks with a same topology structure. As a typical model of information physics systems, LSSs have an inherent merit of modeling wide area power systems, automation processes and so forth. In this paper, two kinds of channels are employed to implement the information transmission in order to extend the service time of sensor nodes powered by energy-limited batteries. Specifically, the one has the merit of high reliability by sacrificing energy cost and the other reduces the energy cost but could result in packet loss. Furthermore, a communication scheduling matrix is introduced to govern the information transmission in these two kind of channels. In this scenario, a novel distributed filter is designed by fusing the compensated neighboring estimation. Then, two matrix-valued functions are derived to obtain the bounds of the covariance matrices of one-step prediction errors and the filtering errors. In what follows, the desired gain matrices are analytically designed to minimize the provided bounds with the help of the gradient-based approach and the mathematical induction. Furthermore, the effect on filtering performance from packet loss is profoundly discussed and it is claimed that the filtering performance becomes better when the probability of packet loss decreases. Finally, a simulation example on wide area power systems is exploited to check the usefulness of the designed distributed filter. [ABSTRACT FROM AUTHOR]

This paper studies the issue of developing the optimal deception attacks on the multiple channels in cyber–physical systems, where the attackers are limited by energy constraints. To fully utilize the eavesdropped data, by linearly combining the innovations from the different channels, a fusion attack model is proposed under the stealthiness condition. According to the statistical characteristics of the correlated stochastic variables and the orthogonality principle, the state estimation error is quantified and analyzed by deriving the iteration of the error covariance matrices of the remote estimators under the proposed attack framework. Moreover, by analyzing the correlations of the decision variables in the objective function, it is shown that the attack parameters and energy allocation strategy can be derived by two steps without loss of optimality, such that the optimal attack scheme is acquired by solving a multivariate semi-definite programming (SDP) problem and a linear 0–1 programming problem respectively. Finally, simulation examples are provided to illustrate the effectiveness of the proposed method. • A novel energy constrained fusion attack model is proposed as the linear combination of transmitted innovations. • Under the proposed attack framework, the remote state estimation errors are analyzed. • By analyzing the structure of the objective function, the optimal attack scheme is found by solving a multivariate SDP problem and a linear 0–1 programming problem. [ABSTRACT FROM AUTHOR]

This paper is concerned with the leader-follower consensus of multiagent systems with wireless communications with the main objective of reducing the power consumption. First, by assuming that the sampling period jumps from one to another only from a given set, a new stochastic sampling approach is introduced to reduce the sampling frequency of each agent. Then, only 1-D of the sampled data is selected, and transmitted to its neighboring agents. Finally, each agent is scheduled to communicate with others intermittently. A unified Markovian system model is proposed to capture the above stochastic sampling, measurement selection scheme and intermittent transmission process, and such a novel protocol can significantly reduce the power consumption. Based on the Lyapunov stability theory and the Markovian jump system approach, the distributed consensus-based controller gain is obtained by solving an optimization problem. The advantage of the proposed consensus protocol is verified by two simulation examples. The simulation results explicitly show our result is more energy-efficient than that of existing one. [ABSTRACT FROM PUBLISHER]

It has been shown that the heating, ventilation, and air conditioning (HVAC) systems of commercial buildings can offer ancillary services to power systems without loss of comfort. In this paper, we propose a new control framework for reliable scheduling and provision of frequency reserves by aggregations of commercial buildings. The framework incorporates energy-constrained frequency signals, which are adopted by several transmission system operators for loads and storage devices. We use a hierarchical approach with three levels: 1) reserve capacities are allocated among buildings (e.g., on a daily basis) using techniques from robust optimization; 2) a robust model predictive controller optimizes the HVAC system consumption typically every 30 minutes; and 3) a feedback controller adjusts the consumption to provide reserves in real time. We demonstrate how the framework can be used to estimate the reserve capacities in simulations with typical Swiss office buildings and different reserve product characteristics. Our results show that an aggregation of approximately 100 buildings suffices to meet the 5-MW minimum bid size of the Swiss reserve market. [ABSTRACT FROM PUBLISHER]

This paper addresses a scheduling problem with a cumulative continuous resource and energy constraints. Given a set of non-preemptive tasks, each task requires a continuously-divisible resource. The instantaneous resource usage of a task is limited by a minimum and maximum resource requirement. Its processing has to lie within a time-window and the total energy received obtained by integrating a function f of the instantaneous resource usage over the processing interval must reach a required value (where f is a non-decreasing, continuous function). The problem is to find a feasible schedule of the tasks, which satisfies all the constraints. This problem, which is a generalization of the well-known cumulative scheduling problem, is NP-complete. For the case where all functions f are linear, we exhibit structural properties of the feasible solutions and we present a Mixed Integer Linear Program (MILP) based on an event-based formulation. We also adapt the famous 'left-shift/right-shift' satisfiability test (energetic reasoning) and the associated time-window adjustments to our problem. To achieve this test, we present three different ways for computing the relevant intervals. Finally, we present a hybrid branch-and-bound method to solve the problem, which performs, at each node, the satisfiability test and time-window adjustments and, when the domains of all start and end times are small enough, the remaining solution space is searched via the event-based MILP. [ABSTRACT FROM AUTHOR]

This paper is concerned with the weighted observation fusion Kalman filtering problem for a class of multi-sensor fusion systems with random measurement delays and energy constraints. To reduce the energy consumptions, each sensor intermittently sends information to the fusion center in a random way. By using the full rank decomposition approach, the observation fusion equation is derived. Without resorting to the augmentation technique, optimal weighted observation fusion Kalman filter (WOFKF) is given, and it is proved that the performance of the WOFKF is equivalent to that of the centralized fusion Kalman filter. Simulations show the effectiveness of the proposed fusion methods. [ABSTRACT FROM AUTHOR]

In this paper, we present new algorithms and analysis for the linear inverse sensor placement and scheduling problems over multiple time instances with power and communications constraints. The proposed algorithms, which deal directly with minimizing the mean squared error (MSE), are based on the convex relaxation approach to address the binary optimization scheduling problems that are formulated in sensor network scenarios. We propose to balance the energy and communications demands of operating a network of sensors over time while we still guarantee a minimum level of estimation accuracy. We measure this accuracy by the MSE for which we provide average case and lower bounds analyses that hold in general, irrespective of the scheduling algorithm used. We show experimentally how the proposed algorithms perform against state-of-the-art methods previously described in the literature. [ABSTRACT FROM AUTHOR]

Mobile robots can be used in many applications, such as carpet cleaning, search and rescue, and exploration. Many studies have been devoted to the control, sensing, and communication of robots. However, the deployment of robots has not been fully addressed. The deployment problem is to determine the number of groups unloaded by a carrier, the number of robots in each group, and the initial locations of those robots. This paper investigates robot deployment for coverage tasks. Both timing and energy constraints are considered; the robots carry limited energy and need to finish the tasks before deadlines. We build power models for mobile robots and calculate the robots' power consumption at different speeds. A speed-management method is proposed to decide the traveling speeds to maximize the traveling distance under both energy and timing constraints. Our method uses rectangle scanlines as the coverage routes, and solves the deployment problem using fewer robots. Finally, we provide an approach to consider areas with random obstacles. Compared with two simple heuristics, our solution uses 36% fewer robots for open areas and 32% fewer robots for areas with obstacles. [ABSTRACT FROM AUTHOR]

In this paper, we study algorithmic problems for quantitative models that are motivated by the applications in modeling embedded systems. We consider two-player games played on a weighted graph with mean-payoff objective and with energy constraints. We present a new pseudopolynomial algorithm for solving such games, improving the best known worst-case complexity for pseudopolynomial mean-payoff algorithms. Our algorithm can also be combined with the procedure by Andersson and Vorobyov to obtain a randomized algorithm with currently the best expected time complexity. The proposed solution relies on a simple fixpoint iteration to solve the log-space equivalent problem of deciding the winner of energy games. Our results imply also that energy games and mean-payoff games can be reduced to safety games in pseudopolynomial time. [ABSTRACT FROM AUTHOR]

We consider a cumulative scheduling problem where a task duration and resource consumption are not fixed. The consumption profile of the task, which can vary continuously over time, is a decision variable of the problem to be determined and a task is completed as soon as the integration over its time window of a non-decreasing and continuous processing rate function of the consumption profile has reached a predefined amount of energy. The goal is to find a feasible schedule, which is an NP-hard problem. For the case where functions are concave and piecewise linear, we present two propagation algorithms. The first one is the adaptation to concave functions of the variant of the energetic reasoning previously established for linear functions. Furthermore, a full characterization of the relevant intervals for time-window adjustments is provided. The second algorithm combines a flow-based checker with time-bound adjustments derived from the time-table disjunctive reasoning for the cumulative constraint. Complementarity of the algorithms is assessed via their integration in a hybrid branch-and-bound and computational experiments on small-size instances. [ABSTRACT FROM AUTHOR]

This study presents a closed‐loop coordination mode and model between generation maintenance scheduling (GMS) and long‐term security‐constrained unit commitment (SCUC) considering energy constraints and N − 1 contingencies that aims at improving the security and economy of power system operations. Given the calculation complexity, this study employs constraint transformation techniques and an efficient approach termed the relaxation induced method, which is based on the solution of the relaxed mixed‐integer programming (MIP) model. The proposed approach can quickly obtain a near‐optimal solution. If the near‐optimal solution is not acceptable for system operators, it can be used to warm‐start the solution of the original MIP problem. The modified IEEE 30‐bus test system and a provincial power system in China are employed to demonstrate the effectiveness of the proposed model and algorithm. [ABSTRACT FROM AUTHOR]

Recently, the security problem of wireless networked control systems (WNCSs) has become critical. In this article, we focus on strategies designing problem for sensor and denial-of-service (DoS) attacker in WNCS based on linear quadratic Gaussian (LQG) cost. In this scenario, a sensor measures the output of the system and transmits its local state estimate to the remote estimator via an unreliable channel which may suffer interference from an intelligent DoS attacker. In each step, the sensor demands to determine the power to transmit its packet data, at the same time, the attacker needs to determine the interference power to degrade the performance of the WNCS. To analyze this interactive decision-making process, we construct a two-player zero-sum stochastic game and propose an improved LQG cost considering energy-efficient factors. Then, for continuous power-level setting, we discuss the existence and the structure of equilibrium strategies of the sensor–attacker game. For a discrete power set, an algorithm is developed to solve equilibrium strategies for both players. Finally, we present an example to illustrate our results. [ABSTRACT FROM AUTHOR]

Energy constraints of sensor nodes in wireless sensor networks (WSNs) is a major challenge and minimising the overall data transmitted across a network using data aggregation, distributed source coding, and compressive sensing have been proposed as mechanisms for energy saving. Similarly, use of mobile nodes capable of relocating within the network has been widely explored for energy saving. In this study, the authors propose a novel method for using miniature aerial vehicles for data collection instead of actively sensing from a deployed network. The proposed mechanism is referred as data collection fly (DCFly). It is suitable for data collection from WSNs deployed in harsh‐undulating terrain with the base station located far from the sensing region. The DCFly is compared with data collection based on multi‐hop data aggregation and data collection with mobile sinks. The numerical results justify that the proposed data collection mechanism is effective and efficient for use in WSNs. [ABSTRACT FROM AUTHOR]

In wireless multi-hop packet radio networks (MPRNs) that employ geographic transmissions, sleep schedules or node activation techniques may be used to power off some nodes to conserve energy. We consider the problem of selecting which nodes should power on to listen to a scheduled transmission when the channel suffers from random fading. We choose the objective of maximizing the expected value of the distance covered in a single transmission between a transmitter and the farthest receiver that successfully receives the packet, under a constraint on the expected number of receivers that turn on. Since there is a tradeoff between the distance of a node from the transmitter and the probability that the node receives the message correctly, we propose to use node-activation based on link-distance (NA-BOLD). We investigate optimal and sub-optimal NA-BOLD schemes and compare their performance with that of schemes that use a constant sleep schedule for every node within some radius of the transmitter. Our results show that the proposed NA-BOLD schemes achieve significantly larger transmission distances than conventional schemes. [ABSTRACT FROM AUTHOR]

This study proposes a set of novel random access protocols combining Packet Repetition (PR) schemes, such as Contention Resolution Diversity Slotted Aloha (CRDSA) and Irregular Repetition SA (IRSA), with Non Orthogonal Multiple Access (NOMA). Differently from previous NOMA/CRDSA and NOMA/IRSA proposals, this work analytically derives the energy levels considering two realistic elements: the residual interference due to imperfect Interference Cancellation (IC), and the presence of requirements on the power spent for the transmission. More precisely, the energy-limited scenario is based on the relationship between the average available energy and the selected code modulation pair, thus being of specific interest for the implementation of the Internet of Things (IoT) technology in forthcoming fifth-generation (5G) systems. Moreover, a theoretical model based on the density evolution method is developed and numerically validated by extensive simulations to evaluate the limiting throughput and to explore the actual performance of different NOMA/PR schemes in energy-constrained scenarios. [ABSTRACT FROM AUTHOR]

We performed a food provisioning experiment in a population of Pied Flycatchers Ficedula hypoleuca breeding at high altitude in central Spain to test if food availability before and during laying determines clutch size. Food was provided to one of two pairs with the same date of initiation of nest-building (15 dyads of subsequently reproducing pairs were thus created). Food provisioning began on the day of initiation of nest-building and ended on the day after the last egg was laid. Although laying date was unaffected by the experiment, clutch size in the experimental treatment was significantly larger. This result could indicate that food availability at laying (1) proximately constrained clutch size or (2) that females evaluated future conditions for incubating eggs and feeding nestlings based on food availability at laying. Reproductive success (proportion of eggs that resulted in fledged young) was significantly reduced in the experimental treatment. This effect suggest that supplemented females were tricked by the experiment into laying more eggs than the number of eggs they were able to incubate with success and the number of nestlings they were able to feed, a source of error in clutch size adjustment which could be common in non-experimental situations. [ABSTRACT FROM AUTHOR]

The carrying capacity of the environment for a population is one of the key concepts in ecology and it is incorporated in the growth term of reaction-diffusion equations describing populations in space. Analysis of reaction-diffusion models of populations in heterogeneous space have shown that, when the maximum growth rate and carrying capacity in a logistic growth function vary in space, conditions exist for which the total population size at equilibrium (i) exceeds the total population that which would occur in the absence of diffusion and (ii) exceeds that which would occur if the system were homogeneous and the total carrying capacity, computed as the integral over the local carrying capacities, was the same in the heterogeneous and homogeneous cases. We review here work over the past few years that has explained these apparently counter-intuitive results in terms of the way input of energy or another limiting resource (e.g., a nutrient) varies across the system. We report on both mathematical analysis and laboratory experiments confirming that total population size in a heterogeneous system with diffusion can exceed that in the system without diffusion. We further report, however, that when the resource of the population in question is explicitly modeled as a coupled variable, as in a reaction-diffusion chemostat model rather than a model with logistic growth, the total population in the heterogeneous system with diffusion cannot exceed the total population size in the corresponding homogeneous system in which the total carrying capacities are the same. [ABSTRACT FROM AUTHOR]