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Accurate sag source location and precise sag type recognition are both essential to verifying the responsible party for the sag and taking countermeasures to improve power quality. In this paper, an attention-based independently recurrent neural network (IndRNN) for sag source location and sag type recognition in sparsely monitored power system is proposed. Specially, the given inputs are voltage waveforms collected by limited meters in sparsely monitored power system, and the desired outputs simultaneously contain the following information: the located lines where sag occurs; the corresponding sag types, including motor starting, transformer energizing and short circuit; and the fault phase for short circuit. In essence, the responsibility of the proposed method is to automatically establish a nonlinear function that relates the given inputs to the desired outputs with categorization labels as few as possible. A favorable feature of the proposed method is that it can be realized without system parameters or models. The proposed method is validated by IEEE 30-bus system and a real 134-bus system. Experimental results demonstrate that the accuracy of sag source location is higher than 99% for all lines, and the accuracy of sag type recognition is also higher than 99% for various sag sources including motor starting, transformer energizing and 7 different types of short circuits. Furthermore, a comparison among different monitor placements for the proposed method is conducted, which illustrates that the observability of power networks should be ensured to achieve satisfactory performance.

The control of dynamical networks is a topic of great value with applications across multiple disciplines. An efficient control scheme based on the connection adaption strategy (CAS) has been proposed by Zhou et al. who revealed that the controllability is restricted by the topological distance between the units and the controller. In this paper, based on the framework of the CAS, we propose a distributed control scheme by partitioning the network space into grids. With this partition approach, the distance between units and controller could be much reduced, resulting in a significant enhancement of the controllability of the dynamical networks. In addition, we introduce an effective length to measure the time-varying topological distance between the mobile units and the controller. The introduced effective length could well reflect the controllability of the dynamical networks with mobile units.

As one of the key techniques determining the overall system performances, efficient and reliable algorithms for improving the classification accuracy of motor imagery (MI) based electroencephalography (EEG) signals are highly desired for the development of brain-computer interface (BCI) systems. In this study, we propose, for the first time to the best of our knowledge, a novel data adaptive empirical wavelet transform (EWT) based signal decomposition method for improving the classification accuracy of MI based EEG signals. Specifically, to reduce the system complexity and execution time, the proposed method selects 18 electrodes out of 118 to analyze the non-stationary and nonlinear EEG signal behaviors. Meanwhile, the method adopts the Welch power spectral density (PSD) analysis method for single mode selection out of the total 10 for each channel, and the Hilbert transform (HT) method for both instantaneous amplitude (IA) and instantaneous frequency (IF) signal components extraction for each selected mode. With seven commonly used machine-learning classifiers adopted, extensive experiments were conducted with the benchmark dataset IVa from BCI competition III to evaluate the performance of the proposed method. Results show that with the IA and IF component features being tested using the least-square support vector machine (LS-SVM) classifier, the EWT method achieves an average classification accuracy of 95.2% and 94.6% respectively, which is higher as compared with the existing methods. While for every participant, a classification accuracy of at least 80% could be achieved by employing a single feature only. Results also show that a combination of EWT and higher order statistics features, which contain both kurtosis and skewness of the extracted instantaneous components, help achieve a higher success rate. The better performances of EWT over those of the existing methods demonstrate the effectiveness and great potential of EWT for BCI system applications.

The robustness and computational load are the key challenges in motor imagery (MI) based on electroencephalography (EEG) signals to decode for the development of practical brain-computer interface (BCI) systems. In this study, we propose a robust and simple automated multivariate empirical wavelet transform (MEWT) algorithm for the decoding of different MI tasks. The main contributions of this study are four-fold. First, the multiscale principal component analysis method is utilized in the preprocessing module to obtain robustness against noise. Second, a novel automated channel selection strategy is proposed and then is further verified with comprehensive comparisons among three different strategies for decoding channel combination selection. Third, a sub-band alignment method by utilizing MEWT is adopted to obtain joint instantaneous amplitude and frequency components for the first time in MI applications. Four, a robust correlation-based feature selection strategy is applied to largely reduce the system complexity and computational load. Extensive experiments for subject-specific and subject independent cases are conducted with the three-benchmark datasets from BCI competition III to evaluate the performances of the proposed method by employing typical machine-learning classifiers. For subject-specific case, experimental results show that an average sensitivity, specificity and classification accuracy of 98% was achieved by employing multilayer perceptron neural networks, logistic model tree and least-square support vector machine (LS-SVM) classifiers, respectively for three datasets, resulting in an improvement of upto 23.50% in classification accuracy as compared with other existing method. While an average sensitivity, specificity and classification accuracy of 93%, 92.1% and 91.4% was achieved for subject independent case by employing LS-SVM classifier for all datasets with an increase of up to 18.14% relative to other existing methods. Results also show that our proposed algorithm provides a classification accuracy of 100% for subjects with small training size in subject-specific case, and for subject independent case by employing a single source subject. Such satisfactory results demonstrate the great potential of the proposed MEWT algorithm for practical MI EEG signals classification.

Differential signals play significant roles in control practices, but they are prone to noise corruption. A noise-tolerant time-optimal system-based tracking differentiator (TD) was first proposed by Jingqing Han, which is constructed in the form of state feedback control for a discrete-time and double-integral system. However, its performances of signal-tracking filtering and differentiation extraction are sensitive to the sampling period. To relax the sensitivity on the sampling period, a time-criterion-based feedback control algorithm is proposed to construct the TD. The control algorithm is derived by comparing the time that the initial state is driven to the switching curve or the origin with any given sampling period. The impact of the algorithm parameter setting on filtering is analyzed in the frequency domain. Meanwhile, a compensation scheme is introduced to deal with the trade-off between the filtering ability and phase delay. The simulation results show that the proposed TD has smaller static errors in signal-tracking filtering, and better differentiation acquisition compared with others. The experiments conducted on STM32F405 reveal that the proposed TD uses the shortest execution time among others for processing the same input signals.

This study investigates the observer-based sliding mode load frequency control for multiarea interconnected power systems under deception attack. By introducing the observer and combining it with the system state equation, the expression of the system error is obtained. A sliding mode surface is proposed to make sure the state of the systems to be stable. Then, the state equation of the system under sliding mode control is derived. The asymptotic stability of the whole system is proved by using the linear matrix inequality (LMI) technique and Lyapunov stability theory. Furthermore, a sliding mode control law is proposed to ensure that the attacked power system can reach a stable position. Numerical simulation results are presented to support the correctness of the results.

In this paper, a novel multiobjective lightning flash algorithm (MOLFA) is proposed to solve the multiobjective optimization problem. The charge population state of the lightning flash algorithm is defined, and we prove that the charge population state sequence is a Markov chain. Since the convergence analysis of MOLFA is to investigate whether a Pareto optimal solution can be reached when the optimal charge population state is obtained, the development of a charge population state is analyzed to achieve the goal of this paper. Based on the martingale theory, the MOLFA convergence analysis is carried out in terms of the supermartingale convergence theorem, which shows that the MOLFA can reach the global optimum with probability one. Finally, the effectiveness of the proposed MOLFA is verified by a numerical simulation example.

For the future development of integrated energy systems with high penetration of renewable energy, an integrated community energy systems (ICES) dispatch model is proposed including various renewable energy sources and energy conversion units. Energy coupling matrices of ICES based on traditional energy hub (EH) models are constructed. Uncertainties of long-term forecast data of renewable energy sources and internal loads are depicted by multi-interval uncertainty sets (MIUS). To cope with the impacts caused by uncertainties of renewable energy sources and internal loads, the whole dispatch process is divided into two stages. Considering various constraints of ICES, we solved the dispatch model through the improved particle swarm optimization (IPSO) algorithm in the first stage. The optimal evolutionary dispatch is then proposed in the second stage to overcome the evolution and errors of short-term forecast data and obtain the optimal dispatch plan. The effectiveness of the proposed dispatch method is demonstrated using an example considering dramatic uncertainties. Compared with the traditional methods, the proposed dispatch method effectively reduces system operating costs and improves the environmental benefits, which helps to achieve a win-win situation for both energy companies and users.

The connection adaption strategy (CAS) has been proposed for the synchronization of networked mobile chaotic agents, which is considered to be a simpler scheme compared to commonly used coupling adaption strategies. However, this strategy only provides a limited range of feasible coupling strength allowing a success control. In this paper, we develop the CAS by introducing a jump process to resolve this problem. We show that the proposed approach systematically outperforms the original CAS in the whole range of the mobility and the range of feasible coupling strength is extensively expanded. In addition, we show that motion of the agents could be classified into three different regimes. The dynamical features of these motion regimes are analyzed and relevant measures are provided to characterize the controllability of the network in each regime.

The topology of a supply chain network affects the impacts of disruptions in it. We formulate a network-based measure of the impact of a disruption loss in a supply chain propagating downstream from an originating node. The measure takes into account the loss profile of the originating node, the structure of the supply network, and the resilience of the network components. We obtain an analytical expression for the impact measure under a beta-distributed initial loss (generalizable to any continuous distribution supported on the interval 0,1), under a breakthrough scenario (in which a fraction of the initial production loss reaches a focal company downstream as opposed to containment upstream or at the originating point). Furthermore, we obtain a closed-form solution for a supply chain network with a k-ary tree topology; a numerical study is performed for a scale-free network and a random network. Our proposed approach enables the evaluation of potential losses for a focal company considering its supply chain network structure, which may help the company to plan or redesign a robust and resilient network in response to different types of disruptions.

Individual decision to accept a new idea or product is often driven by both self-adoption and others' persuasion, which has been simulated using a double threshold model [Huang et al., Scientific Reports 6, 23766 (2016)]. We extend the study to consider the case with limited persuasion. That is, a set of individuals is chosen from the population to be equipped with persuasion capabilities, who may succeed in persuading their friends to take the new entity when certain conditions are satisfied. Network node centrality is adopted to characterize each node's influence, based on which three heuristic strategies are applied to pick out persuaders. We compare these strategies for persuader selection on both homogeneous and heterogeneous networks. Two regimes of the underline networks are identified in which the system exhibits distinct behaviors: when networks are sufficiently sparse, selecting persuader nodes in descending order of node centrality achieves the best performance; when networks are sufficiently dense, however, selecting nodes with medium centralities to serve as the persuaders performs the best. Under respective optimal strategies for different types of networks, we further probe which centrality measure is most suitable for persuader selection. It turns out that for the first regime, degree centrality offers the best measure for picking out persuaders from homogeneous networks; while in heterogeneous networks, betweenness centrality takes its place. In the second regime, there is no significant difference caused by centrality measures in persuader selection for homogeneous network; while for heterogeneous networks, closeness centrality offers the best measure.

With rapid advancements in artificial intelligence and mobile robots, some of the tedious yet simple jobs in modern libraries, like book accessing and returning (BAR) operations that had been fulfilled manually before, could be undertaken by robots. Due to the limited accuracies of the existing positioning and navigation (P&N) technologies and the operational errors accumulated within the robot P&N process, however, most of the current robots are not able to fulfill such high-precision operations. To address these practical issues, we propose, for the first time (to the best of our knowledge), to combine the binocular vision and Quick Response (QR) code identification techniques together to improve the robot P&N accuracies, and then construct an autonomous library robot for high-precision BAR operations. Specifically, the binocular vision system is used for dynamic digital map construction and autonomous P&N, as well as obstacle identification and avoiding functions, while the QR code identification technique is responsible for both robot operational error elimination and robotic arm BAR operation determination. Both simulations and experiments are conducted to verify the effectiveness of the proposed technique combination, as well as the constructed robot. Results show that such a technique combination is effective and robust, and could help to significantly improve the P&N and BAR operation accuracies, while reducing the BAR operation time. The implemented autonomous robot is fully-autonomous and cost-effective, and may find applications far beyond libraries with only sophisticated technologies employed.

BACKGROUND: It is believed that combined interventions may be more effective than individual interventions in mitigating epidemic. However there is a lack of quantitative studies on performance of the combination of individual interventions under different temporal settings. METHODOLOGY/PRINCIPAL FINDINGS: To better understand the problem, we develop an individual-based simulation model running on top of contact networks based on real-life contact data in Singapore. We model and evaluate the spread of influenza epidemic with intervention strategies of workforce shift and its combination with school closure, and examine the impacts of temporal factors, namely the trigger threshold and the duration of an intervention. By comparing simulation results for intervention scenarios with different temporal factors, we find that combined interventions do not always outperform individual interventions and are more effective only when the duration is longer than 6 weeks or school closure is triggered at the 5% threshold; combined interventions may be more effective if school closure starts first when the duration is less than 4 weeks or workforce shift starts first when the duration is longer than 4 weeks. CONCLUSIONS/SIGNIFICANCE: We therefore conclude that identifying the appropriate timing configuration is crucial for achieving optimal or near optimal performance in mitigating the spread of influenza epidemic. The results of this study are useful to policy makers in deliberating and planning individual and combined interventions.

Finding the solution for driving a complex network at the minimum energy cost with a given number of controllers, known as the minimum-cost control problem, is critically important but remains largely open. We propose a projected gradient method to tackle this problem, which works efficiently in both synthetic and real-life networks. The study is then extended to the case where each controller can only be connected to a single network node to have the lowest connection complexity. We obtain the interesting insight that such connections basically avoid high-degree nodes of the network, which is in resonance with recent observations on controllability of complex networks. Our results provide the first technical path to enabling minimum-cost control of complex networks, and contribute new insights to locating the key nodes from a minimum-cost control perspective.