Novel cyber-physical collaborative detection and localization method against dynamic load altering attacks in smart energy grids

Owing to the integration of energy digitization and artificial intelligence technology,smart energy grids can realize the stable,efficient and clean operation of power systems.However,the emergence of cyber-physical attacks,such as dynamic load-altering attacks(DLAAs)has introduced great challenges to the security of smart energy grids.Thus,this study developed a novel cyber-physical collaborative security framework for DLAAs in smart energy grids.The proposed framework integrates attack prediction in the cyber layer with the detection and localization of attacks in the physical layer.First,a data-driven method was proposed to predict the DLAA sequence in the cyber layer.By designing a double radial basis function network,the influence of disturbances on attack prediction can be eliminated.Based on the prediction results,an unknown input observer-based detection and localization method was further developed for the physical layer.In addition,an adaptive threshold was designed to replace the traditional precomputed threshold and improve the detection performance of the DLAAs.Consequently,through the collaborative work of the cyber-physics layer,injected DLAAs were effectively detected and located.Compared with existing methodologies,the simulation results on IEEE 14-bus and 118-bus power systems verified the superiority of the proposed cyber-physical collaborative detection and localization against DLAAs.

Leader-following consensus protocols for formation control of multi-agent network

Two protocols are presented,which can make agents reach consensus while achieving and preserving the desired formation in fixed topology with and without communication timedelay for multi-agent network.First,the protocol without considering the communication time-delay is presented,and by using Lyapunov stability theory,the sufficient condition of stability for this multi-agent system is presented.Further,considering the communication time-delay,the effectiveness of the protocol based on Lyapunov-Krasovskii function is demonstrated.The main contribution of the proposed protocols is that,as well as the velocity consensus is considered,the formation control is concerned for multi-agent systems described as the second-order equations.Finally,numerical examples are presented to illustrate the effectiveness of the proposed protocols.

Privacy Preserving Solution for the Asynchronous Localization of Underwater Sensor Networks

Location estimation of underwater sensor networks(USNs)has become a critical technology,due to its fundamental role in the sensing,communication and control of ocean volume.However,the asynchronous clock,security attack and mobility characteristics of underwater environment make localization much more challenging as compared with terrestrial sensor networks.This paper is concerned with a privacy-preserving asynchronous localization issue for USNs.Particularly,a hybrid network architecture that includes surface buoys,anchor nodes,active sensor nodes and ordinary sensor nodes is constructed.Then,an asynchronous localization protocol is provided,through which two privacy-preserving localization algorithms are designed to estimate the locations of active and ordinary sensor nodes.It is worth mentioning that,the proposed localization algorithms reveal disguised positions to the network,while they do not adopt any homomorphic encryption technique.More importantly,they can eliminate the effect of asynchronous clock,i.e.,clock skew and offset.The performance analyses for the privacy-preserving asynchronous localization algorithms are also presented.Finally,simulation and experiment results reveal that the proposed localization approach can avoid the leakage of position information,while the location accuracy can be significantly enhanced as compared with the other works.

Communication-Aware Formation Control of AUVs With Model Uncertainty and Fading Channel via Integral Reinforcement Learning

Most formation approaches of autonomous underwater vehicles(AUVs)focus on the control techniques,ignoring the influence of underwater channel.This paper is concerned with a communication-aware formation issue for AUVs,subject to model uncertainty and fading channel.An integral reinforcement learning(IRL)based estimator is designed to calculate the probabilistic channel parameters,wherein the multivariate probabilistic collocation method with orthogonal fractional factorial design(M-PCM-OFFD)is employed to evaluate the uncertain channel measurements.With the estimated signal-to-noise ratio(SNR),we employ the IRL and M-PCM-OFFD to develop a saturated formation controller for AUVs,dealing with uncertain dynamics and current parameters.For the proposed formation approach,an integrated optimization solution is presented to make a balance between formation stability and communication efficiency.Main innovations lie in three aspects:1)Construct an integrated communication and control optimization framework;2)Design an IRL-based channel prediction estimator;3)Develop an IRL-based formation controller with M-PCM-OFFD.Finally,simulation results show that the formation approach can avoid local optimum estimation,improve the channel efficiency,and relax the dependence of AUV model parameters.

Energy-Efficient Underwater Data Collection:A Q-Learning Based Approach

Underwater data collection is an importance part in the process of network monitoring,network management and intrusion detection.However,the limited-energy of nodes is a major challenge to collect underwater data.The solution of this problem are not only in the hands of network topology but in the hands of path of autonomous underwater vehicle(AUV).With the problem in hand,an energy-efficient data collection scheme is designed for mobile underwater network.Especially,the data collection scheme is divided into two phases,i.e.,routing algorithm design for sensor nodes and path planing for AUV.With consideration of limited-energy and network robustness,Q-learning based dynamic routing algorithm is designed in the first phase to optimize the routing selection of nodes,through which a potential-game based optimal rigid graph method is proposed to balance the trade-off between the energy consumption and the network robustness.With the collected data,Q-learning based path planning strategy is proposed for AUV in the second phase to find the desired path to gather the data from data collector,then a mode-free tracking controller is developed to track the desired path accurately.Finally,the performance analysis and simulation results reveal that the proposed approach can guarantee energy-efficient and improve network stability.

Delay-dependent exponential stability criteria for stochastic systems with polytopic-type uncertainties

This paper considers the problem of delay-dependent exponential stability in mean square for stochastic systems with polytopic-type uncertainties and time-varying delay. Applying the descriptor model transformation and introducing free weighting matrices, a new type of Lyapunov-Krasovskii functional is constructed based on linear matrix inequalities （LMIs）, and some new delay-dependent criteria are obtained. These criteria include the delay-independent/rate- dependent and delay-dependent/rate-independent exponential stability criteria. These new criteria are less conservative than existing ones. Numerical examples demonstrate that these new criteria are effective and are an improvement over existing ones.

Consensus of second-order nonlinear multi-agent systems via sliding mode observer and controller

This paper investigates the consensus problem of second-order nonlinear multi-agent systems (MASs) via the sliding mode control (SMC) approach. The velocity of each agent is assumed to be unmeasurable. A second-order sliding mode observer is designed to estimate the velocity. Then a distributed discontinuous control law based on first-order SMC is presented to solve the consensus problem. Moreover, to overcome the chatting problem, two controllers based on the boundary layer method and the super-twisting algorithm respectively are presented. It is shown that the MASs will achieve consensus under some given conditions. Some examples are provided to demonstrate the effectiveness of the proposed control laws.

Dynamic Coverage with Wireless Sensor and Actor Networks in Underwater Environment

This paper studies the problem of dynamic coverage with wireless sensor and actor networks (WSANs) in underwater environment. Different from most existing works, the WSANs consist of two kinds of nodes, i.e., sensor nodes (SNs) which cannot move autonomously and actor nodes (ANs) which can move autonomously according to the performance requirement. The problem of how to coordinate two kinds of nodes to facilitate dynamic coverage in underwater environment is challenging due to their heterogeneous capabilities. To reduce redundancy of communication links and improve connectivity between ANs and SNs in underwater WSANs, a min-weighted rigid graph based topology optimization scheme is first developed, such that the underwater communication energy consumption can be saved. With the optimized topology, a dynamic coverage strategy is proposed to improve the coverage among SNs and ANs for underwater WSAN where underwater fluid motions are considered. Furthermore, it is proved that the network coverage area is connected by using the min-weighted rigid graph. Finally, simulation results are presented to show the effectiveness of the main results. © 2014 Chinese Association of Automation.

Model-Free Formation Control of Autonomous Underwater Vehicles:A Broad Learning-Based Solution

Dear Editor,We develop a broad learning-based algorithm to enforce the formation control of AUVs.Compared with the deep learning(DL)based formation solutions,our solution employs the broad learning system(BLS)to remodel the learning framework without a retraining process.

Bilateral Teleoperation of Multiple Agents with Formation Control

This paper studies the formation problem for multislave teleoperation system over general communication networks,where multiple mobile slave agents are coupled with a single master robot. The forward and backward network transmission time delays are assumed to be asymmetric and time-varying.Due to the quantization in the network, a dynamic quantization strategy is provided to quantize the output signals of the master robot and slave agents before transmitting. Then, a novel masterslave protocol is designed to achieve the formation task under variable time delays and quantization. Additionally, the sufficient conditions for stability are presented to show that the formation protocol can stabilize the master-slave system under variable time delays and quantization. Finally, simulation are performed to show effectiveness of the main results.

Reprogramming tumor-associated macrophages and inhibiting tumor neovascularization by targeting MANF-HSF1-HSP70-1 pathway:An effective treatment for hepatocellular carcinoma

In advanced hepatocellular carcinoma(HCC)tissues,M2-like tumor-associated macrophages(TAMs)are in the majority and promotes HCC progression.Contrary to the pro-tumor effect of M2-like TAMs,M1-like TAMs account for a small proportion and have anti-tumor effects.Since TAMs can switch from one type to another,reprogramming TAMs may be an important treatment for HCC therapy.However,the mechanisms of phenotypic switch and reprogramming TAMs are still obscure.In this study,we analyzed differential genes in normal macrophages and TAMs,and found that loss of MANF in TAMs accompanied by high levels of downstream genes negatively regulated by MANF.MANF reprogrammed TAMs into M1 phenotype.Meanwhile,loss of MANF promoted HCC progression in HCC patients and mice HCC model,especially tumor neovascularization.Additionally,macrophages with MANF supplement suppressed HCC progression in mice,suggesting MANF supplement in macrophage was an effective treatment for HCC.Mechanistically,MANF enhanced the HSF1-HSP70-1 interaction,restricted HSF1 in the cytoplasm of macrophages,and decreased both mRNA and protein levels of HSP70-1,which in turn led to reprogramming TAMs,and suppressing neovascularization of HCC.Our study contributes to the exploration the mechanism of TAMs reprogramming,which may provide insights for future therapeutic exploitation of HCC neovascularization.

Detection and isolation of false data injection attack via adaptive Kalman filter bank

Due to the integration of cyber–physical systems,smart grids have faced the new security risks caused by false data injection attacks(FDIAs).FDIAs can bypass the traditional bad data detection techniques by falsifying the process of state estimation.For this reason,this paper studies the detection and isolation problem of FDIAs based on the adaptive Kalman filter bank(AKFB)in smart grids.Taking the covert characteristics of FDIAs into account,a novel detection method is proposed based on the designed AKF.Moreover,the adaptive threshold is proposed to solve the detection delay caused by a priori threshold in the current detection methods.Considering the case of multiple attacked sensor nodes,the AKFB-based isolation method is developed.To reduce the number of isolation iterations,a logical decision matrix scheme is designed.Finally,the effectiveness of the proposed detection and isolation method is demonstrated on an IEEE 22-bus smart grids.

Active resilient defense control against false data injection attacks in smart grids

The emerging of false data injection attacks(FDIAs)can fool the traditional detection methods by injecting false data,which has brought huge risks to the security of smart grids.For this reason,a resilient active defense control scheme based on interval observer detection is proposed in this paper to protect smart grids.The proposed active defense highlights the integration of detection and defense against FDIAs in smart girds.First,a dynamic physical grid model under FDIAs is modeled,in which model uncertainty and parameter uncertainty are taken into account.Then,an interval observer-based detection method against FDIAs is proposed,where a detection criteria using interval residual is put forward.Corresponding to the detection results,the resilient defense controller is triggered to defense the FDIAs if the system states are affected by FDIAs.Linear matrix inequality(LMI)approach is applied to design the resilient controller with H_(∞)performance.The system with the resilient defense controller can be robust to FDIAs and the gain of the resilient controller has a certain gain margin.Our active resilient defense approach can be built in real time and show accurate and quick respond to the injected FDIAs.The effectiveness of the proposed defense scheme is verified by the simulation results on an IEEE 30-bus grid system.

A learnable self-supervised task for unsupervised domain adaptation on point cloud classification and segmentation

1 Introduction Deep neural networks have exhibited excellent performance in supervised tasks on point clouds,such as classification,segmentation[1]and registration[2].In supervised learning schemes,manual labeling of massive point clouds is needed for model training.However,point clouds captured from different scenarios exist inevitable distribution discrepancy,and model trained from one domain always generalize badly in another domain.To reduce the doamin distribution discrepancy,many studies[3–6]have emerged for point cloud unsupervised domain adaptation(UDA)by learning domain-invariant features,where[5]proposed using adaptive nodes to align the local features between the source and the target domains[3,4],and[6]proposed utilizing self-supervised tasks to help capture highly transferable feature representations.