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By violating semantic constraints that the control process impose, the semantic attack leads the Industry Control Systems (ICS) into an undesirable state or critical state. The spread of semantic attack has caused huge economic losses and casualties to critical infrastructure. Therefore, detecting semantic attack is referred to an urgent and critical task. However, few existing detecting techniques can achieve satisfactory effects in detecting semantic attack of ICS, due to the high requirements of complete critical state-based semantic behavior features description, joint detection on multivariate type state variables, and validity of field states datasets under semantic attacks. In an effort to deal with above challenges, We label device states databases with temporal characteristics and divide impacts on states of field devices under semantic attacks into three categories, including absent in states set, confused sequences, irregular frequency. On this basis, we establish a behavioral model based on secondary labeling of states-duration evolution graph (BMSLS), then implement an adaptive secure state-based semantic attack detection framework furtherly. Compared with the traditional Auto Regression (AR) algorithm, the newer time series correlation graph model, and other five deep learning algorithms, our proposed framework demonstrates the superior effect on the detection of semantic attack.

Designing a secure authentication scheme for session initial protocol (SIP) over internet protocol (VoIP) networks remains challenging. In this paper, we revisit the protocol of Zhang, Tang and Zhu (2015) and reveal that the protocol is vulnerable to key-compromise impersonation attacks. We then propose a SIP authenticated key agreement protocol (AKAP) using elliptic curve cryptography (ECC). We demonstrate the correctness of the protocol using Burrows-Abadi-Needham (BAN), and its security using the AVISPA simulation tool. We also evaluate its performance against those of Zhang, Tang and Zhu, and others.

Mobile cloud computing (MCC) is an emerging computing model with the growing demands of mobile devices by services diversity using mobile cloud technology. Authentication plays a fundamental role between the smart portable devices located in different places and the mobile cloud computing service providers in MCC. Several authors presented many authentication schemes using different cryptographic approaches for MCC in the past years. However, those schemes have not solved the impersonation attack well. To conquer this suffering and support more security functionalities, we propose an impersonation resistance for a biometric-based authentication scheme using hashing and symmetric parameter function in an MCC environment. The proposed scheme can provide mutual authentication and anonymity using the software AVISPA. The security of the proposed scheme is formally proved in the random oracle model. Regarding functionality and performance analysis, the comparative studies demonstrate that the design has a trade-off between resource consumption and security strengthens for MCC services. Therefore, the proposed scheme is more practical in MCC application.

The minimum dominating set (MDS) of the network is a node subset of smallest size that every node in the network is either in this subset or is adjacent to one or more nodes of this subset. MDS has found wide applications, ranging from network monitoring, routing, to epidemic control, and text processing. However, the majority of existing studies on MDS problem are confined to single networks. In real world, more and more complex systems consist of a set of elements linked up by different types of connections, which are best modeled as multiplex networks with interacting network layers. Though vastly important, the MDS of the multiplex networks has not yet been formally defined and its application and identification remain open issues. In this article, we present the definition of the MDS of the multiplex network and show some of its possible applications. For solving the MDS problem of the multiplex network, we built a spin-glass model and solve it through the belief-propagation (BP) equations under the replica symmetry mean-field theory. As a consequence, we can predict the relative size of the MDS of the multiplex network theoretically and we can propose a BP-guided decimation algorithm to construct an approximate optimal dominating set in practice. Then the algorithm is improved in both accuracy and efficiency by embedding a novel multiplex network-oriented leaf-removal strategy. The effectiveness of the proposed algorithms is finally verified by comparing with other methods on a number of the multiplex network examples.

In general, there are a lot of uncertainties in uncertain information, natural language, and human knowledge. The conclusion can be better deduced by using an approximate reasoning method, while a fuzzy intelligent system can deal with uncertain data and rule evaluation information systems. In order to better explore diffusion and economic growth, this paper constructs a fuzzy intelligent system based on the DSGE model and uses this system to analyze diffusion and economic growth. In order to verify the feasibility of this system, we test the response time and accuracy of the system. In addition, we also use the system to simulate diffusion and economic growth. The results show that with the increase of the task amount, the gap between the actual response time and the expected response time of the fuzzy intelligent system based on the DSGE model increases. When the task quantity is 20, the expected response time is 2.31 and the actual response time is 2.24. When the task quantity is 40, the expected response time is 2.5 and the actual response time is 2.36. The larger the task quantity is, the faster the response time of a fuzzy intelligent system based on the DSEG model is. Therefore, the fuzzy intelligent system based on the DSEG model has good performance and can analyze diffusion and economic growth well.

Nowadays, the valid development and full utilization of renewable energy sources such as wind power and solar energy are considered to be one of the most significant approaches to get rid of the energy dilemma and realize environmental governance. However, although there are more and more renewable power sources connected to power systems, the renewable energy has not been fully used yet. In this context, an optimization model for the regional power system with the pump storage power, wind, hydro, and thermal power is proposed to enhance the economy, environmental protection, energy-saving and stability of the power system. Besides, the fuzzy chance constraint programming and the improved analytic hierarchy process are used to deal with the uncertainty of the wind power and the load demand and the multi-objectives function, respectively. The case study not only verify the effectiveness of the proposed model but also shows that better comprehensive performance of the power system can be achieved through the proposed model when considering the effect of the pump storage power.

Deep learning has recently demonstrated its promising performance for vision-based parking-slot detection. However, very few existing methods explicitly take into account learning the link information of the marking-points, resulting in complex post-processing and erroneous detection. In this paper, we propose an attentional graph neural network based parking-slot detection method, which refers the marking-points in an around-view image as graph-structured data and utilize graph neural network to aggregate the neighboring information between marking-points. Without any manually designed post-processing, the proposed method is end-to-end trainable. Extensive experiments have been conducted on public benchmark dataset, where the proposed method achieves state-of-the-art accuracy. Code is publicly available at \url{https://github.com/Jiaolong/gcn-parking-slot}., Comment: Accepted by RAL

The feedback vertex set (FVS) problem of network aims to find a vertex subset of smallest size that contains at least one vertex of each cycle of the network. The FVS problem has found wide applications, ranging from circuit design, network security and network control and observation etc. However, the majority of existing studies on FVS problem are confined to single networks. In this brief, under the consideration of one kind of control problem of multiplex network, we introduce the concept of FVS of multiplex network. The control problem of multiplex network is naturally mapped into the FVS problem of multiplex network. We then propose two simulated annealing (SA) algorithms to solve the directed FVS problem of multiplex network and undirected FVS problem of multiplex network respectively. The effectiveness of the SA algorithms is finally verified on both artificial multiplex networks and real-world multiplex networks.

Photovoltaic (PV) power generation has developed very rapidly worldwide in the recent years. There is a possibility that the PV power generation will switch from an auxiliary power supply, as of today, to a main power source in many power grids in the future. Naturally, dynamic studies on power grids with a high penetration of PV generators have become increasingly important, and thus have attracted major attentions from both the power industry and the academia. Consequently, dynamic modeling of PV generators has been investigated widely. However, among various proposed models, there is a confusion on the model applicability and a lack of the clarification on the required level of details on the modeling work, which severely limit the real industrial applications of the developed models. This paper reviews the state-of-the-art PV generator dynamic modeling work, with a focus on the modeling principles of PV generator for the power system dynamic studies. The paper presents the detailed modeling process for the recommended PV generator dynamic model, and clarifies the assumptions and simplifications made in the modeling process, thus raises the discussion on the model applicability. Studies that require further attentions on developing the dynamic models of PV generators for power system dynamic studies are identified and presented in the paper. However, this work does not intend to conclude the research work in this important field, instead, it aims to provoke more discussions on developing guidelines on building or selecting the appropriate models to fit into the purpose of the targeted dynamic studies.

The dismantling problem and minimum vertex cover (MVC) problem of network are two fundamental NP-hard problems where the former aims to find a minimal subset of nodes whose removal leaves the network broken in small components of sub-extensive size and the latter is also to find a minimal vertex set which contains at least one incident vertex of every edge. Both of them have a wide range of applications related to circuit design, communication, network security, transportation, epidemic control, molecular biology and economics. In this brief, we first propose a novel belief-propagation equation for the spin glass model of the MVC problem. A belief-propagation-guided decimation (BPD) algorithm is then presented which could construct approximate optimal vertex cover of network. In addition, based on the relationship between the network dismantling problem and the MVC problem, we propose a simple and fast algorithm formed by the MVC and a general node reinsertion strategy, for solving the network dismantling problem. The effectiveness of the proposed algorithms is finally verified by comparing with other well-known algorithms on a number of real world networks.

Label correlations have always been one of the hotspots of multi-label learning. Using label correlations to complete the original label can enrich the information of the label matrix. At the same time, label-specific features give a thought that different labels have inherent characteristics that can be distinguished, and we can use label correlations to enhance the learning process of label-specific features among similar labels. At present, most of the algorithms combine label correlations and label-specific features to improve the multi-label learning effect, but do not consider the impact of label marking errors or defaults in data sets. In fact, the label completion method can further enrich the information of label matrix, and then the joint learning framework of joint label-specific features can effectively improve the robustness of the multi-label learning algorithm. Based on this, this paper proposes a multi-label learning algorithm for joint label completion and label-specific features, and constructs a new multi-label learning algorithm framework by means of joint label completion and label-specific features. Completion matrix and label-specific features are obtained by alternating iteration method, and the label matrix updating the optimization framework fully considers the label correlations. The algorithm in this paper has been demonstrated and trained on several benchmark multi-label data sets by extensive experiments, which verifies the effectiveness of the algorithm.

In order to solve the problem when the target detection of passive bistatic radar is seriously affected by direct signal and multi-path, a clutter cancellation algorithm based on frequency domain analysis is proposed in this paper. By analyzing and processing the frequency domain correlation between the reference signal and the echo signal, the time delay and the amplitude of the direct signal and multipath in the echo signal can be estimated for clutter cancellation. This method can not only reduce the computation greatly, but also eliminate the influence of clutter echo residue on target detection. The effectiveness of the proposed algorithm is verified by simulation and real data processing.

The novel sensing technology airborne passive bistatic radar (PBR) has the problem of being affecting by multipath components in the reference signal. Due to the movement of the receiving platform, different multipath components contain different Doppler frequencies. When the contaminated reference signal is used for space–time adaptive processing (STAP), the power spectrum of the spatial–temporal clutter is broadened. This can cause a series of problems, such as affecting the performance of clutter estimation and suppression, increasing the blind area of target detection, and causing the phenomenon of target self-cancellation. To solve this problem, the authors of this paper propose a novel algorithm based on sparse Bayesian learning (SBL) for direct clutter estimation and multipath clutter suppression. The specific process is as follows. Firstly, the space–time clutter is expressed in the form of covariance matrix vectors. Secondly, the multipath cost is decorrelated in the covariance matrix vectors. Thirdly, the modeling error is reduced by alternating iteration, resulting in a space–time clutter covariance matrix without multipath components. Simulation results showed that this method can effectively estimate and suppress clutter when the reference signal is contaminated.

In recent years, offshore wind power has been vigorously developed. High voltage AC transmission is one of the main grid connection technologies for offshore wind power. Submarine cable has more charging power causing the problem of overvoltage more prominent. To solve the problem of line overvoltage in the AC transmission system of the offshore wind power, this paper proposes a coordinated reactive power control strategy. First, the reactive power generated by wind turbine is considered to reduce the configuration capacity of the reactive power compensation devices. Second, the reactive power compensation is divided into two parts: the land side and the wind power collecting side. Then, with the objective function of minimizing the power loss and the voltage offset, the assignment of the optimal amount of reactive power on both sides is solved. Finally, simulation results prove that the line overvoltage suppression effect of dual-terminal coordinated control strategy proposed in this paper is both economical and effective.

Digital microscopes often require repeated manual focusing to obtain a clear image. Nevertheless, manual focusing easily results in artificial errors, making it difficult to evaluate the image definition, and the focusing process is slow and tedious. This paper presents an automatic focusing method based on Quantum Self-organizing Maps (QSOM) neural network and a new focusing evaluation function. The focusing evaluation function consists of Energy of Gradient (EOG) function and Discrete Wavelet Transform (DWT) function to better evaluates the sharpness of microscopic images at different focusing positions. The obtained data are used as training samples, and QSOM neural network is trained by focusing samples. The trained neural network can accurately predict the position achieve the focus position. Experimental results are given to verify the effectiveness of the proposed method.

In multi-label learning, in order to improve the accuracy of classification, many scholars have considered the relationship between features and features, features and labels or labels and labels, but how to combine the correlation among them is rarely studied. Based on this, this paper proposes a multi-label learning algorithm with kernel extreme learning machine autoencoder. Firstly, the label space is reconstructed by using the non-equilibrium labels completion method in the label space. Then, the non-equilibrium labels space information is added to the input node of the kernel extreme learning machine autoencoder network, and the input features are output as the target. Finally, the kernel extreme learning machine is used for classification. Our method implements the information fusion between features and features, between labels and features, and between labels and labels. Compared with the traditional autoencoder network, the extreme learning machine autoencoder has no iterative process, which reduces the network training time and improves the classification accuracy. The experimental results of the proposed algorithm in the opening benchmark multi-label data sets show that the KELM-AE algorithm has some advantages over other comparative multi-label learning algorithms and the statistical hypothesis testing and stability analysis further illustrate the effectiveness of the proposed algorithm.

Efficient security patch distribution is of essential importance for updating anti-virus software to ensure effective and timely virus detection and cleanup. In this paper, we propose a mixed strategy of patch distribution to combine the advantages of the traditional centralized patch distribution strategy and decentralized patch distribution strategy. A novel network model that contains a central node and a multiplex network composed of patch dissemination network layer and virus propagation network layer is presented, and a competing spreading dynamical process on top of the network model that simulates the interplay between virus propagation and patch dissemination is developed. Such a new framework helps in effectively analyzing the impacts of patches distribution on virus propagation, and developing more realizable schemes for restraining virus propagation. Furthermore, considering the constraints of the capacity of the central node and the bandwidth of network links, an optimal allocation approach of patches is proposed, which could simultaneously optimize multiple dynamical parameters to effectively restrain the virus propagation with a given budget. Accepted version

Large-scale wind and solar power generations have got rapid development in recent years in China and abroad. They always connect to load centres through high-voltage direct current (HVDC) transmission or ultra HVDC (UHVDC) transmission lines. Such systems are so large and it is necessary to make appropriate equivalence according to the purpose of different research. At present, the analysis model of this kind of real system is few, and simplified single-machine infinite-bus system or IEEE example system is adopted most of the time, which may affect the accuracy of analysis results. Here, the UHVDC project from a practical renewable energy base to load centres in China is chosen as an example, the actual structure and parameters are used to establish a typical example system for AC/DC hybrid power grid with high-proportion renewable energy. The new-generation synchronous condenser with large capacity and the supporting synchronous generators are all considered. First, the installed power generation, power grid structure, and load configuration of example system are introduced. Then the load flow distribution and transient stability characteristics are analysed by using DIgSILENT PowerFactory software. Finally, the feasibility of the proposed example system is shown by simulation results.

With the rapid development of power system, wind power becomes an important generating way in AC/DC hybrid power grid. Due to the interference between the wind power and the AC network, the risk of DC system continuous commutation failure will be affected. In order to evaluate this effect reasonably, a method for running out of the fault set is presented in this study. Firstly, a distribution factor is proposed to determine the power line transfer after wind power or power line fault, the line with maximum load rate is chosen as the candidate failure lines; Secondly, the η short-circuit ratio and η effective short-circuit ratio indexes of the DC system is evaluated after every fault; they are used to judge whether the cascading fault of AC system induces the transient overvoltage of the converter bus to lead to wind turbines to take off the power grid net. Thus, a cascading fault sequence of the AC system is formed, and a cascading fault set that causes the transient overvoltage of the converter bus is completed. Finally, the simulation results calculated by power system department-bonneville power administration show that when the commutation failure occurs in the DC system, the system runs out immediately, and the validity of the method is verified.

High share of renewable energy will be one of the basic features of power systems in the future around the world. Compared with photovoltaic (PV) and wind power, concentrating solar power (CSP) equipped with heat storage system has similar regulation characteristics with thermal power units, and it can effectively overcome the intermittent and volatility of PV and wind power generation. Meanwhile, the CSP units can strongly support the voltage, frequency and short-circuit capacity of power systems. So, CSP is a very promising power generation technology for the future power system. The CSP unit has better ramp ability than thermal power unit, and can generally reach a speed between 2% and 5% of rated power per minute. Stated thus, the CSP unit has a good potential to participate in the primary frequency regulation of power grid. At present, there are many researches on the performance of each subsystem of the CSP unit in worldwide, and there are also some theoretical researches on the participation of CSP unit in the dispatching operation of power system. However, there is a lack of research reports on CSP plant’s participation in the primary frequency regulation of the power grid, which is an urgently needed feature for the future power systems with high share of renewable energy. In this paper, a model with primary frequency regulation ability of CSP unit is established in DIgSILENT PowerFactory software. A revised two-area four-machine system is used to investigate the CSP unit’s participation in primary frequency regulation of power system. The simulation results show the good frequency regulation performance of CSP unit, which can effectively support the stable operation of power system with high share of renewable energy in the future.

Integrated energy distribution system (IEDS) could improve the efficiency of energy utilization and minimize environmental pollution based on the multi-energy coupling link. Firstly, the model of distributed energy station is established; secondly, the optimization model for optimal dispatching of IEDS considering AC power flow of distribution system, gas flow of natural gas distribution network and the distributed energy station is established; thirdly, an optimization method for optimal dispatching of IEDS based on the second-order cone relaxation method is proposed. Finally, case studies are performed on IEDS including the modified IEEE 33-node distribution system, the modified 11-node natural gas system and 5 distributed energy stations to verify the effectiveness and efficiency of the proposed method.

A flexible load active management method in optimization operation of distribution networks is proposed to discuss the significant role of active management of flexible load in regulating load curve. The models of translatable load, shiftable load and curtailable load are established and they are divided into price incentive flexible load and contract incentive flexible load firstly. Secondly, a multi-objective optimal dispatch model considering electricity purchasing cost, load peak valley difference and users' acceptance is established for different types of flexible load. Then, considering the proportion of different types of flexible load in different user types, the flexible load response model of residential load, industrial load and commercial load are obtained. Finally, the regulation effects of load curve and dispatch strategies of the overall flexible load are obtained by summarizing the dispatch strategies of different user types.

COVID-19 has infected millions of people, with deaths in more than 200 countries It is therefore essential to understand the dynamic characteristics of the outbreak and to design effective strategies to restrain the large-scale spread of the epidemic In this paper, we present a novel framework to depress the epidemic spreading, by leveraging the decentralized dissemination of information The framework is equivalent to finding a special minimum dominating set for a duplex network which is a general dominating set for one layer and a connected dominating set for another layer Using the spin glass and message passing theory, we present a belief-propagation-guided decimation (BPD) algorithm to construct the special minimum dominating set As a consequence, we could immediately recognize the epidemic as soon as it appeared, and rapidly immunize the whole network at minimum cost © Copyright © 2021 Wang, Zhang, Zhu, Jiang, Wu, Xu and Zhao

Since the coining of the term “Artificial Intelligence” (AI), its definition scope has evolved from an application program with pre-set rules of logic, to a technology that simulates human cognitive function. The goal of AI is to allow computers to imitate human intelligence, perception, thinking, and action, so as to achieve automation and gain analytical insights. To imitate human intelligence, AI applications adopt two methods: rule-based and non-rule-based algorithms. Rule-based AI uses pre-set rules to “learn”, while non-rule-based AI uses machine learning algorithms to “learn” and a trained AI model to “think”. The key driving force for the development of AI is the huge increase of available data and the great improvement of computing power in traditional computers and mobile devices, as well as the continuous development of machine learning algorithms. Commercial banks conduct large numbers of customer capital transactions, and hold large amounts of account management information. The application of AI in commercial banks will effectively improve the retail service level, enhance customer experience, and alleviate difficulties such as “financial disintermediation” and fierce market competition, or become the next profit growth point of commercial banks.

A robo-advisor is an investment advisor based on core artificial intelligence technology. The technology encompasses perceptual intelligence, related to human sensory awareness such as vision and hearing; and cognitive intelligence, linked to human language, understanding and logic, such as learning, reasoning, thinking, and planning. At the current stage of development, perceptual AI mainly comprises image recognition technology, including iris and facial recognition, and unmanned driving as regards vision; and speech synthesis, voiceprint recognition, and speech evaluation as regards hearing. Cognitive AI, which is applied in games, chess, and most notably in natural language processing (NLP), includes semantic analysis, pragmatic analysis, machine translation, text matching, text classification, and dialogue system. Hence, AI technology must correspond to at least one of “eyes, ears and mouth”. Any non-human advisor in the financial industry that does not involve one of these three is simply an application of traditional network engineering technology, and cannot be included under the term robo-advisor.

Telecare medicine information systems (TMISs) provide e-health services such that patients can access medical resources conveniently and doctors can prescribe treatments rapidly. Authentication is an essential security requirement in TMISs. In particular, the growth of password-based remote patient authenticated key exchange combining extended chaotic maps has enhanced the level of secure communications for TMISs. Recently, Lee suggested an improved random-number-based password-authenticated key exchange (PAKE) using extended chaotic maps and synchronized-clock-based PAKE using extended chaotic maps on Guo and Zhang and Xiao et al.’s PAKE. Unfortunately, we found that the nonce-based scheme of Lee is insecure against known session-specific temporary information and server spoofing attacks. To cope with the aforementioned defects, this study aims to provide a new secure PAKE based on extended chaotic maps with more security functionalities for TMISs. Additionally, we show that the proposed scheme for TMISs provides high security along with low communication cost, computational cost, and a variety of security features.

Liu, S. and Zhao, D., 2020. The representation of dynamic factor marine element structure upgrading model based on two kinds of DSGE models. In: Li, L. and Huang, X. (eds.), Sustainable Development in Coastal Regions: A Perspective of Environment, Economy, and Technology. Journal of Coastal Research, Special Issue No. 112, pp. 337-339. Coconut Creek (Florida), ISSN 0749-0208.At present, with the deepening of the implementation of the marine strategy, the transformation and reform of the marine economic structure urgently need to conduct in-depth analysis of the wave driving factors of the marine economy. The dynamic factor analysis of the DSGE model dynamic factor model has important value for the long-term and short-term dynamic adjustment process of the marine factor structure. Based on this, this paper first studies the dynamic factor model representation of DSGE model, then analyzes the upgrading mechanism model based on the spatial-temporal configuration of marine elements, and finally gives the application of dynamic factors based on two kinds of DSGE model in the upgrading model of marine element structure.

As the penetration rate of new energy sources such as wind power in the power grid continues to increase, the power grid generally puts forward requirements for its participating in the primary frequency regulation of power grid. At present, the commonly adopted strategy for wind farms to participate in frequency regulation is that each wind farm should participate in primary frequency regulation of power grid, and frequent adjustment will bring a certain degree of life loss to wind power equipment. On account of this reason, this paper presents a coordinated control strategy for the frequency regulation of wind farms based on a multi-layer hierarchical structure. When the grid frequency fluctuates, the active power of some wind farms with superior performance can be adjusted according to specific circumstances to complete frequency regulation target. The simulation results verify the effectiveness of the strategy and achieve a good frequency regulation effect.

With the increasing complexity of power systems and the widespread penetration of renewable energy sources (RES), real-time situational awareness for power systems is of great significance for operation scheduling. With the impact of RES on power system operation considered, a situational awareness key performance index (KPI) system for power systems with high proportion of RES is proposed in this work, which consists of reserve capacity abundance, ramp resource abundance, center of inertia (COI) frequency deviation, interface power flow margin, synthesized voltage stability, and angle stability margin. Then, the KPIs are synthesized and visualized by decision tree method and radar chart method, respectively, for monitoring the operation states (i.e., normal, alert, and emergency states) of power systems with high proportion of RES. Numerical simulations are conducted in the revised New England 16-machine 68-bus power system and the actual CEPRI-RE power system in the northwest region of China with high proportion of RES. The results show that the proposed KPI-based situational awareness method is able to monitor the real-time state of power systems with high proportion of RES accurately, and can assist power dispatchers to make effective decisions.

Models for simulation are vital for the authenticity of the simulation. Modeling doubly fed induction generation (DFIG) for power system stability analysis has attracted a great deal of research interests in the past few years due to the wide applications of DFIG in wind power generation. The models developed by the wind generator manufacturers are not only complicated, but also proprietary and very specific to their own products. Many simplified generic models have been proposed. Among them, the current-source based model is most widely used for power system stability analysis. The validation work on this model so far has been based on the selected simulation scenarios or limited real measurements. However, there has been no systematic and theoretic analysis on the validity of the current-source based model, which may cause inappropriate applications of this type of model in real engineering practices. This paper presents the conditions for using the current-source based model for power system stability analysis; then examines the model's validity under symmetrical and asymmetrical fault conditions. Our practices for modeling and validating a real wind farm in North China confirm the validity analysis of this work on the current-source based DFIG model.

© 2018 Elsevier Ltd A parallel algorithm is proposed for scalable generation of large-scale tetrahedral meshes. The key innovation is the use of a mesh-simplification based domain decomposition approach. This approach works on a background mesh with both its surface and its interior elements much larger than the final elements desired, and decomposes the domain into subdomains containing no undesirable geometric features in the inter-domain interfaces. In this way, the most time-consuming part of domain decomposition can be efficiently parallelized, and other sequential parts consume reasonably limited computing time since they treat a very coarse background mesh. Meanwhile, the subsequent parallel procedures of mesh generation and improvement are most efficient because they can treat individual subdomains without compromising element quality. Compared with published state-of-the-art parallel algorithms, the developed parallel algorithm can reduce the clock time required by the creation of one billion elements on 512 computer cores from roughly half an hour to less than 4 minutes.

Seeking nodes whose removal can effectively dismantle networks is closely related to the robustness of networks under targeted attacks or malicious software. Modern systems, such as critical infrastructure networks and cyber-physical systems, however, become increasingly dependent on others, which can be encapsulated into the framework of interdependent networks. In this brief, we focus on the optimal dismantling of interdependent networks, and propose a novel algorithm, named inverse explosive percolation (IEP), to find the optimal removal nodes. The IEP proceeds by first identifying the nodes which make the least contribution to the giant mutual connected cluster of interdependent networks. When nodes have the same contribution, the one with the smallest overlapping degree is selected. We apply the IEP algorithm to interdependent networks composed of real-world power grids, Internet networks and artificial networks, and find IEP performs much better than the existing methods in various kinds of networks. Based on its efficiency, IEP can be applied to large-scale systems.

A time delay estimation algorithm based on three-order cumulant with the different symmetry of modulation signal between training sequence and information sequence of AIS (Automatic Identification System) is presented, which aims to overcome the large time delay and strong noise in the receiver side of AIS. Theoretical analysis and simulation results show that this algorithm successfully suppresses the influence of Gaussian noise. Moreover, under non-Gaussian and correlative noise, it can also obtain a fine estimated performance. Simulation results show that this algorithm is superior to the correlative and data-aided time delay estimation ones.

An improved Gaussian Filter (GF) is designed for nonlinear Dynamic Positioning (DP) ships with cross-correlated colored noises and random measurements loss. For the actual nonlinear Dynamic Position System (DPS), the state noises and measurement noises do not satisfy the assumption of Gaussian white noises and the loss of measurements may occur randomly. Therefore, the following circumstances are considered: the state noises and measurement noises are cross-correlated colored noises at the same and adjacent sampling moments; the measurement loss occurs randomly for the data transmission between the sensor units and the estimator units. In order to get the estimator for nonlinear DP ships with cross-correlated colored noises and random measurements loss, a GF framework based on Bayesian theory is proposed, and then the Cubature Mix Kalman Filter based on spherical-radial method is obtained. In the end, the simulation results show that the proposed algorithm has better estimation performance than Unscented Kalman Filter with Measurements Loss and standard Cubature Kalman Filter.

At present, community detection methods are mostly focused on the investigation at unweighted networks. However, real-world networks are always complex, and unweighted networks are not sufficient to reflect the connections among real-world objects. Hence, this paper proposes a community detection algorithm based on a deep sparse autoencoder. First, the second-order neighbors of the nodes are identified, and we can obtain the path weight matrix for the second-order neighbors of the node. We combine the path weight matrix with the weighted adjacent paths of the node to obtain the similarity matrix, which can not only reflect the similarity relationships among connected nodes in the network topology but also the similarity relationships among nodes and second-order neighbors. Then, based on the unsupervised deep learning method, the feature matrix which has a stronger ability to express the features of the network can be obtained by constructing a deep sparse autoencoder. Finally, the K-means algorithm is adopted to cluster the low-dimensional feature matrix and obtain the community structure. The experimental results indicate that compared with 4 typical community detection algorithms, the algorithm proposed here can more accurately identify community structures. Additionally, the results of parameter experiments show that compared with the community structure found by the K-means algorithm, which directly uses the high-dimensional adjacency matrix, the community structure detected by the WCD algorithm in this paper is more accurate.

Presently, a tetrahedral mesher based on the Delaunay triangulation approach may outperform a tetrahedral improver based on local smoothing and flip operations by nearly one order in terms of computing time. Parallelization is a feasible way to speed up the improver and enable it to handle large-scale meshes. In this study, a novel domain decomposition approach is proposed for parallel mesh improvement. It analyses the dual graph of the input mesh to build an inter-domain boundary that avoids small dihedral angles and poorly shaped faces. Consequently, the parallel improver can fit this boundary without compromising the mesh quality. Meanwhile, the new method does not involve any inter-processor communications and therefore runs very efficiently. A parallel pre-processing pipeline that combines the proposed improver and existing parallel surface and volume meshers can prepare a quality mesh containing hundreds of millions of elements in minutes. Experiments are presented to show that the developed system is robust and applicable to models of a complication level experienced in industry.

Alpha-helical transmembrane proteins ( $\alpha \text{TMPs}$ ) belong to an important category of integral membranes. Their structures are highly valuable in relevant research, but costly to solve experimentally. Sequence-based topology prediction provides a practical computational approach to characterize the structure features. Although much progress had been made in the past decade, there is significant room for improvement in predicting the topology structure. Deep learning brings a great opportunity for its capability of mining new features from data. In this work, we propose a novel $\alpha \text{TMP}$ topology prediction method DMCTOP using a Deep Multi-Scale Convolutional Neural Network (DMCNN), which composes of two deep convolutional blocks to extract local and global contextual features. Consequently, the inputs of DMCTOP is simplified to a protein sequence feature and an evolutionary conservation feature. DMCTOP can efficiently and accurately identify all topological types and the N-terminal orientation for an $\alpha \text{TMP}$ sequence. In the testing experiments, the prediction accuracy was calculated according to the whole sequence, the transmembrane segments and the traditional criterion. Our state-of-the-art method achieved the highest prediction accuracy compared to all the previous methods. The standalone tool is available at https://github.com/NENUBioCompute/DMCTOP.

Spatial epidemic spreading, a fundamental dynamical process upon complex networks, attracts huge research interest during the past few decades. To suppress the spreading of epidemic, a couple of effective methods have been proposed, including node vaccination. Under such a scenario, nodes are immunized passively and fail to reveal the mechanisms of active activity. Here, we suggest one novel model of an observer node, which can identify infection through interacting with infected neighbors and inform the other neighbors for vaccination, on multiplex networks, consisting of epidemic spreading layer and information spreading layer. In detail, the epidemic spreading layer supports susceptible-infected-recovered process, while observer nodes will be selected according to several algorithms derived from percolation theory. Numerical simulation results show that the algorithm based on large degree performs better than random placement, while the algorithm based on nodes' degree in the information spreading layer performs the best (i.e., the best suppression efficacy is guaranteed when placing observer nodes based on nodes' degree in the information spreading layer). With the help of state probability transition equation, the above phenomena can be validated accurately. Our work thus may shed new light into understanding control of empirical epidemic control.

Real-time hand gesture recognition in complex environments has many challenges, such as poor real-time performances and robustness to environmental changes. This paper takes the hand gesture control of the unmanned vehicle as the application background, and focuses on the gesture detection and recognition of video streams based on deep learning in the complex environment. In this paper, we detect the hand in a complex environment by training the ssd_mobilenet model, and initialize the tracking with kalman filter. Then, we detect the hand keypoints by following the architecture of Convolutional Pose Machines (CPMs), in order to obtain the belief maps for all keypoints that are used as the train sets of Convolutional Neural Networks (CNNs). Finally, based on the results obtained by our classification, this paper proposes a method of multi-frame recursion to minimize the influences of redundant frames and error frames. In this paper, eight kinds of gestures for controlling vehicle are identified. The experimental results show that our method can successfully realize real-time hand gesture recognition in the video streams. The recognition accuracy can reach 96.7%, and the average recognition speed reaches 12 fps, which basically meets the real-time requirements and successfully applys to mobile terminals such as TX2 for engineering practice.

In recent years, photovoltaic (PV) power generation has developed rapidly at China and abroad. PV power generation is on the trend from auxiliary power supply to main power supply even leading power supply. Due to the fluctuation and intermittent characteristics of PV power generation, and zero inertia, the integration of large-scale grid-connected PV power generation may have a negative impact on the stability of the power system. Modeling grid-connected PV power generation systems is the key factor to investigate the influence between it and other parts of power system. Modeling of PV is also a hot issue that has been studied continuously for many years around the world. GE, WECC, CIGRE, CEPRI and so on have attached great importance to this issue and have published many research results. The existing research at China and abroad is reviewed in this paper, the existing problems are analyzed, and suggestions for future research are given.

In recent years, renewable energy generations, such as wind power generation, have a rapid development in a large scale all over the world. The development trend of renewable energy, from auxiliary power supply to main power supply even leading power supply, is becoming increasing obviously. Due to the fluctuation and intermittence of wind power, the safe and stable operation of power system has been threatened. The modeling of wind power generation system has become a key factor and hotspot issue that influence the interaction between wind plant and power grid, with scholars domestic and overseas all weight in. Hence institutions and organizations, such as GE, IEC, WECC, EPRI, CEPRI, also have attached great importance to this issue, relevant research guideline or report about wind power generation system modeling have been published continuously. The existing research both in domestic and outside is presented first in this paper, then the problems in modeling are analyzed, suggestions and prospects for future research are given at last.

The rapid development of cloud computing has not only brought huge economic benefits, but also brought the issue of computer related crimes. In this paper, a design method of cloud forensics middleware was proposed to obtain credible and complete digital evidence from the cloud in a comprehensive and convenient manner. The design method includes three parts: the remote control side evidence display, the server-side evidence analysis and monitoring management, and client-side memory acquisition and analysis. Compared with the traditional online forensics methods, this method was more in line with the requirements of traditional physical evidence technology, greatly improving the efficiency of the forensic staff and the credibility of the evidence. The method has been verified on Windows 10 (the client) and Centos 7.0 (the server) and was proved to be effective and reliable.

The interaction between renewable energy generation and high-voltage direct current (HVDC) transmission system becomes an essential cause of the subsynchronous oscillation (SSO) problem. In this study, the permanent magnet synchronous generator-based wind farm connected to HVDC transmission system is studied. The susceptivity analysis is carried out to find the main influence factors of the SSO problem. Besides, a parameter design method for subsynchronous damping controller is proposed. The configuration and the performance indicator are discussed, and then the optimisation algorithm is developed based on hybrid genetic algorithm and electromagnetic transient simulation. Finally, the proposed design method is validated by simulation in PSCAD/EMTDC.

In recent years, the collapse of power grid in many countries not only has brought great inconvenience to national life, but also caused huge economic losses. Therefore, it is particularly important to analyze the vulnerability of network structure of power grid. In this paper, US power grid with 4941 nodes and 6594 edges is taken as examples. The network is attacked by deleting some percent nodes according to degree, k-shell value, betweenness centrality, and clustering coefficient, apparently. The largest connected component G, efficiency E, and average distance L are analyzed for measuring vulnerability of US power grid. The simulation results show that, in view of the largest connected component G and efficiency E, Betweenness Centrality-based attack is most destructive to the network structure than other attacks, and the attack based on Aggregation coefficient is the least destructive.

In multi-view and multi-label learning, each example can be represented by multiple data view features and annotated with a set of discrete non-exclusive labels. Missing label learning is an important branch of multi-label learning, which can handle incomplete labels with annotations. Previous work on multi-label learning with missing labels mainly considered data in a single view representation. Based on intuitive understanding, we propose a Two-step Multi-view and Multi-label Missing Label learning optimization solution(TM3L). The first step is to solve the multi-view learning problem by finding the data representation of the common low-dimensional space of all views through subspace learning. While fully considering the complementary information between multiple views, the different degrees of contribution combined with different views are weighted differently. The second step is to solve the multi-label missing label learning problem by using the label matrix completion method in combination with the kernel extreme learning machine classifier. The kernel extreme learning machine can effectively enhance the robustness of the algorithm to missing labels. The experimental results and analysis on multiple benchmark multi-view and multi-label data sets verify the effectiveness of TM3L compared with the state-of-the-art solutions.

In recent decades, many countries have suffered power outages and these accidents were often caused by a small disturbance, but because of the connection structure between the circuits, even a small mistake will cause the power grid cascade to fail and paralyze the network in a large area. In order to find a way to enhance the network robustness, this paper proposes three defense methods based on different ratio: the k-shell value ratio, degree ratio, and residual load ratio. We would compare the quality of the three defense methods by the relative size of the largest connected component after cascading failure. Besides, we also compare the time, the total number of crashed nodes, and the speed of cascading failure progress under three defense methods. From the experimental results, it is found that the defense methods based on the k-shell value ratio and the degree ratio have their own advantages in different situations.

Over the past two decades, with the SCADA systems connected to corporate networks or the Internet, the programmable logic controller (PLC) have suffered a large-scale and catastrophic network attacks for the controlling and monitoring physical industrial and infrastructure processes in the industrial control networks, due to their crucial character and safe characteristic. However, the PLC‘s inferior computing power, restricted storage capacity, “scan-cycle” operating mode, and client’s violent private demand has made it challenging to find forensics framework with the capacity to depress the storage requirement and enhance practicality and robustness strikingly. In an effort to address these challenges, through the establishing the attack model against PLC in a view of the security incident forensics, this paper proposed a PLC security incident forensics framework named PLC-SEIFF. This framework implemented the automatic construction of security constraints rules from PLC control logic STL program, filtering and identifying of irrelevant incident records according by correlation analysis on the basis of multi-sources data.

Network immunization has been widely adopted for restraining epidemic spreading. Majority of the existing results on identifying immunization targets and the measurements of their effectiveness are based purely on network topology. However the topological heuristic strategies neglect important features of the spreading dynamics and consequently may cannot yield reliable results. In this paper, we present a novel network immunization strategy based on explosive percolation and message passing, which considers both the network topology and epidemic dynamic. We compare its performance with the greedy strategy, topological heuristic strategy and random strategy. The results demonstrate the efficiency of our method on a variety of real-world examples.

Multi-labels are more suitable for the ambiguity of the real world. However, missing labels are common in multi-label learning datasets; this results in unbalanced labeling and label diversity, which directly affect the performance of multi-label learning. Therefore, the classification and modeling of imbalanced data in missing multi-label learning are problems that need to be urgently solved Current methods mostly focus on combining sampling techniques with cost-sensitive learning and incorporating label correlation to improve the performance of the classifier, but generally they do not consider label loss caused by label cost. In fact, labeling unknown instances is often affected by the threshold of the discriminant function, especially for the label types near the threshold. Based on our previous research, we believe that information such as data distribution density and label density can be integrated into the label correlation, and that the classification margin can be expanded to effectively solve the labeling quality of labels near the threshold. Therefore, in this paper we propose a non-equilibrium multi-label learning algorithm based on the classification margin and aimed at completing the missing labels. First, the classification margin is proposed, and the label space is expanded by the label density. Then, the information entropy is used to measure the correlation between labels, and the label confidence matrix is constructed. The label confidence matrix is then unbalanced using the positive and negative label density, and the non-equilibrium label confidence matrix is used for label completion to obtain an informative label completion matrix. Finally, the kernel extreme learning machine and the label completion matrix are used for linear prediction. The experimental results show that the proposed algorithm has some advantages over other multi-label learning algorithms.