Self-Triggered Set Stabilization of Boolean Control Networks and Its Applications

Set stabilization is one of the essential problems in engineering systems, and self-triggered control(STC) can save the storage space for interactive information, and can be successfully applied in networked control systems with limited communication resources. In this study, the set stabilization problem and STC design of Boolean control networks are investigated via the semi-tensor product technique. On the one hand, the largest control invariant subset is calculated in terms of the strongly connected components of the state transition graph, by which a graph-theoretical condition for set stabilization is derived. On the other hand, a characteristic function is exploited to determine the triggering mechanism and feasible controls. Based on this, the minimum-time and minimum-triggering open-loop, state-feedback and output-feedback STCs for set stabilization are designed,respectively. As classic applications of self-triggered set stabilization, self-triggered synchronization, self-triggered output tracking and self-triggered output regulation are discussed as well. Additionally, several practical examples are given to illustrate the effectiveness of theoretical results.

The Effect of Key Nodes on theMalware Dynamics in the Industrial Control Network

As industrialization and informatization becomemore deeply intertwined,industrial control networks have entered an era of intelligence.The connection between industrial control networks and the external internet is becoming increasingly close,which leads to frequent security accidents.This paper proposes a model for the industrial control network.It includes a malware containment strategy that integrates intrusion detection,quarantine,and monitoring.Basedonthismodel,the role of keynodes in the spreadofmalware is studied,a comparisonexperiment is conducted to validate the impact of the containment strategy.In addition,the dynamic behavior of the model is analyzed,the basic reproduction number is computed,and the disease-free and endemic equilibrium of the model is also obtained by the basic reproduction number.Moreover,through simulation experiments,the effectiveness of the containment strategy is validated,the influence of the relevant parameters is analyzed,and the containment strategy is optimized.In otherwords,selective immunity to key nodes can effectively suppress the spread ofmalware andmaintain the stability of industrial control systems.The earlier the immunization of key nodes,the better.Once the time exceeds the threshold,immunizing key nodes is almost ineffective.The analysis provides a better way to contain the malware in the industrial control network.

Cluster DetectionMethod of Endogenous Security Abnormal Attack Behavior in Air Traffic Control Network

In order to enhance the accuracy of Air Traffic Control(ATC)cybersecurity attack detection,in this paper,a new clustering detection method is designed for air traffic control network security attacks.The feature set for ATC cybersecurity attacks is constructed by setting the feature states,adding recursive features,and determining the feature criticality.The expected information gain and entropy of the feature data are computed to determine the information gain of the feature data and reduce the interference of similar feature data.An autoencoder is introduced into the AI(artificial intelligence)algorithm to encode and decode the characteristics of ATC network security attack behavior to reduce the dimensionality of the ATC network security attack behavior data.Based on the above processing,an unsupervised learning algorithm for clustering detection of ATC network security attacks is designed.First,determine the distance between the clustering clusters of ATC network security attack behavior characteristics,calculate the clustering threshold,and construct the initial clustering center.Then,the new average value of all feature objects in each cluster is recalculated as the new cluster center.Second,it traverses all objects in a cluster of ATC network security attack behavior feature data.Finally,the cluster detection of ATC network security attack behavior is completed by the computation of objective functions.The experiment took three groups of experimental attack behavior data sets as the test object,and took the detection rate,false detection rate and recall rate as the test indicators,and selected three similar methods for comparative test.The experimental results show that the detection rate of this method is about 98%,the false positive rate is below 1%,and the recall rate is above 97%.Research shows that this method can improve the detection performance of security attacks in air traffic control network.

The Deformation Analysis of the 3D Alignment Control Network Based on the Multiple Congruence Models

In the construction and maintenance of particle accelerators,all the accelerator elements should be installed in the same coordinate system,only in this way could the devices in the actual world be consistent with the design drawings.However,with the occurrence of the movements of the reinforced concrete cover plates at short notice or building deformations in the long term,the control points upon the engineering structure will be displaced,and the fitness between the subnetwork and the global control network may be irresponsible.Therefore,it is necessary to evaluate the deformations of the 3D alignment control network.Different from the extant investigations,in this paper,to characterize the deformations of the control network,all of the congruent models between the points measured in different epochs have been identified,and the congruence model with the most control points is considered as the primary or fundamental model,the remaining models are recognized as the additional ones.Furthermore,the discrepancies between the primary S-transformation parameters and the additional S-transformation parameters can reflect the relative movements of the additional congruence models.Both the iterative GCT method and the iterative combinatorial theory are proposed to detect multiple congruence models in the control network.Considering the actual work of the alignment,it is essential to identify the competitive models in the monitoring network,which can provide us a hint that,even the fitness between the subnetwork and the global control network is good,there are still deformations which may be ignored.The numerical experiments show that the suggested approaches can describe the deformation of the 3D alignment control network roundly.

Preliminary Design of Control Network for HT-7U Tokamak Cryogenic System

In the course of the cryoplant modernization, a control network will be set up in order to facilitate the control, the supervision, the centralized data acquisition and the alarm handling of the cryogenic system for HT-7U tokamak. The paper introduces the preliminary design of control network based on the Controller Link Network for HT-7U tokamak cryogenic system. The multi-layer structure mentioned in the paper is the mainstream of automatic control. The control philosophy, the structure of the network and the components for control are also presented.

Antidepressants combined with psychodrama improve the coping style and cognitive control network in patients with childhood trauma-associated major depressive disorder

BACKGROUND The use of antidepressant therapy alone has a limited efficacy in patients with childhood trauma-associated major depressive disorder(MDD).However,the effectiveness of antidepressant treatment combined with psychodrama in these patients is unclear.AIM To evaluate the effectiveness of antidepressant treatment combined with psychodrama.METHODS Patients with childhood trauma-associated MDD treated with antidepressants were randomly assigned to either the psychodrama intervention(observation group)or the general health education intervention(control group)and received combination treatment for 6 mo.The observation group received general health education given by the investigator together with the“semi-structured group intervention model”of Yi Shu psychodrama.A total of 46 patients were recruited,including 29 cases in the observation group and 17 cases in the control group.Symptoms of depression and anxiety as well as coping style and resting-state functional magnetic resonance imaging were assessed before and after the intervention.RESULTS Symptoms of depression and anxiety,measured by the Hamilton Depression Scale,Beck Depression Inventory,and Beck Anxiety Inventory,were reduced after the intervention in both groups of patients.The coping style of the observation group improved significantly in contrast to the control group,which did not.In addition,an interaction between treatment and time in the right superior parietal gyrus node was found.Furthermore,functional connectivity between the right superior parietal gyrus and left inferior frontal gyrus in the observation group increased after the intervention,while in the control group the connectivity decreased.CONCLUSION This study supports the use of combined treatment with antidepressants and psychodrama to improve the coping style of patients with childhood trauma-associated MDD.Functional connectivity between the superior parietal gyrus and inferior frontal gyrus was increased after this combined treatment.We speculate that psychodrama enhances the internal connectivity of the cognitive control network and corrects the negative attention bias of patients with childhood trauma-associated MDD.Elucidating the neurobiological features of patients with childhood trauma-associated MDD is important for the development of methods that can assist in early diagnosis and intervention.

A New Intrusion Detection Algorithm AE-3WD for Industrial Control Network

In this paper,we propose a intrusion detection algorithm based on auto-encoder and three-way decisions(AE-3WD)for industrial control networks,aiming at the security problem of industrial control network.The ideology of deep learning is similar to the idea of intrusion detection.Deep learning is a kind of intelligent algorithm and has the ability of automatically learning.It uses self-learning to enhance the experience and dynamic classification capabilities.We use deep learning to improve the intrusion detection rate and reduce the false alarm rate through learning,a denoising AutoEncoder and three-way decisions intrusion detection method AE-3WD is proposed to improve intrusion detection accuracy.In the processing,deep learning AutoEncoder is used to extract the features of high-dimensional data by combining the coefficient penalty and reconstruction loss function of the encode layer during the training mode.A multi-feature space can be constructed by multiple feature extractions from AutoEncoder,and then a decision for intrusion behavior or normal behavior is made by three-way decisions.NSL-KDD data sets are used to the experiments.The experiment results prove that our proposed method can extract meaningful features and effectively improve the performance of intrusion detection.

Stabilization of multiple independent linear systems with control networks

The problem of stabilizing multiple independent linear systems sharing one common network cable is presented and solved. Both the quantization and time sequencing are studied in the field of control over networks by providing the formulated stabilizing sufficient condition which illustrates the relationship between the system instability, quantization and time sequencing,and the data rate is also presented in terms of the quantization and time sequencing. A numerical example is given to illustrate the result.

A Method for Surveying Control Network Optimization Based on Reliability Properties

调查控制网络优化设计与标准有关，例如精确，可靠性，敏感和费用，并且这些标准与对方仔细相关。为调查控制网络模拟优化设计的一个新方法被建议。这个方法基于观察价值的内部可靠性索引。为调查控制网络优化图案，分析方法和模拟方法有二个方法，并且两个都随广阔计算取决于计算程序。

Applications of Graph Theory to Gross Error Detection for GPS Geodetic Control Networks

This paper describes a broad perspective of the application of graph theory to establishment of GPS control networks whereby the GPS network is considered as a connected and directed graph with three components.In this algorithm the gross error detection is undertaken through loops of different spanning trees using the "Loop Law" in which the individual components Δ X, Δ Y and Δ Z sum up to zero.If the sum of the respective vector components ∑X,∑Y and ∑Z in a loop is not zero and if the error is beyond the tolerable limit (ε>w),it indicates the existence of gross errors in one of the baselines in the loop and therefore the baseline must be removed or re_observed.After successful screening of errors by graph theory,network adjustment can be carried out.In this paper,the GPS data from the control network established as reference system for the HP Dam at Baishan county in Liaoning province is presented to illustrate the algorithm.

Chinese Surveying and Control Network for Earth-Orbit Satellites and Deep Space Detection

The relationship between the surveying and contro l network(CSN) for earth-orbit satellite and spatial geodesy, and the relationshi p between the CSN for deep space celestial bodies and detectors, and deep space detection are briefly summarized, and so are the basic technical needs of the de ep space surveying and control network(DSN). Then, the techniques, the constitue nts and the distributing of Chinese satellite CSN (CSCSN) and other radio observ ing establishments in China are introduced. Lastly, with the primary CSCSN and o ther observing establishments, some projects for China to rebuild a more perfect CSCSN, and to establish a DSN are analyzed and stated.

INFORMATION DESIGN OF UNIVERSAL ADJUSTMENT PROGRAM OF PLANE CONTROL NETWORKS

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Developing Environment of Hierarchical and Distributed Real-Time Fire-Control Network

This paper introduces a hierarchical real-time environment for developing ship-bornefire-control system. Advanced computer networks are used to simulate the system with the requiredengagement scenario, including own-ship and parameters, and data processing and transmission,mission calculation, graphical supervision and gunnery ballistics outputting. The simulation systemis able to receive instruction from, or send information to the command-control center. Furthermore,the system can also be used to compare various designed schemes and analyze the accuracy andeffectiveness of the system.

Hierarchical hybrid control network design based on LON and master-slave RS-422/485 protocol

Aiming at the weaknesses of LON bus, combining the coexistence of fieldbus and DCS (Distribu ted Control Systems) in control networks, the authors introduce a hierarchical hybrid control network design based on LON and master slave RS 422/485 protocol. This design adopts LON as the trunk, master slave RS 422/485 control networks are connected to LON as special subnets by dedicated gateways. It is an implementation method for isomerous control network integration. Data management is ranked according to real time requirements for different network data. The core components, such as control network nodes, router and gateway, are detailed in the paper. The design utilizes both communication advantage of LonWorks technology and the more powerful control ability of universal MCUs or PLCs, thus it greatly increases system response speed and performance cost ratio.

Context-Aware System Modeling Based on Boolean Control Network

Boolean control network consists of a set of Boolean variables whose state is determined by other variables in the network. Boolean network is used for modeling complex system. In this paper, we have presented a model of a context-aware system used in smart home based on Boolean control networks. This modeling describes the relationship between the context elements (person, time, location, and activity) and services (Morning Call, Sleeping, Guarding, Entertainment, and normal), which is effective to logical inference. We apply semi tensor matrix product to describe the dynamic of the system. This matrix form of expression is a convenient and reasonable way to design logic control system.

Quantization and Event-Triggered Policy Design for Encrypted Networked Control

This paper proposes a novel event-driven encrypted control framework for linear networked control systems(NCSs),which relies on two modified uniform quantization policies,the Paillier cryptosystem,and an event-triggered strategy.Due to the fact that only integers can work in the Pailler cryptosystem,both the real-valued control gain and system state need to be first quantized before encryption.This is dramatically different from the existing quantized control methods,where only the quantization of a single value,e.g.,the control input or the system state,is considered.To handle this issue,static and dynamic quantization policies are presented,which achieve the desired integer conversions and guarantee asymptotic convergence of the quantized system state to the equilibrium.Then,the quantized system state is encrypted and sent to the controller when the triggering condition,specified by a state-based event-triggered strategy,is satisfied.By doing so,not only the security and confidentiality of data transmitted over the communication network are protected,but also the ciphertext expansion phenomenon can be relieved.Additionally,by tactfully designing the quantization sensitivities and triggering error,the proposed event-driven encrypted control framework ensures the asymptotic stability of the overall closedloop system.Finally,a simulation example of the secure motion control for an inverted pendulum cart system is presented to evaluate the effectiveness of the theoretical results.

Observability of Periodically Switched Boolean Control Networks

In this paper,observability is studied for periodically switched Boolean control networks(PSBCNs),which are managed with periodic switching signal and consist of some Boolean control networks.Firstly,via semi-tensor product of matrices,PSBCNs are expressed as algebraic forms.Secondly,a parallel system is constructed by combining two same PSBCNs,based on which,the observability problem of the original PSBCN can be transformed into the set reachability problem of this parallel system.Then,two necessary and sufficient conditions are obtained to detect reachability of parallel systems and observability of PSBCNs.In addition,the proposed conditions are extended to the case of state constraints.Finally,a practical example and a numerical example are provided to illustrate the results.

Asurvey onob vabilit of Boolean control networks

Observability is a fundamental property of a partially observed dynamical system,which means whether one can use an input sequence and the corresponding output sequence to determine the initial state.Observability provides bases for many related problems,such as state estimation,identification,disturbance decoupling,controller synthesis,etc.Until now,fundamental improvement has been obtained in observability of Boolean control networks(BCNs)mainly based on two methods-Edward F.Moore's partition and our observability graph or their equivalent representations found later based on the semitensor product(STP)of matrices(where the STP was proposed by Daizhan Cheng),including necessary and sufficient conditions for different types of observability,extensions to probabilistic Boolean networks(PBNs)and singular BCNs,even to nondeterministic finite-transition systems(NFTSs);and the development(with the help of the STP of matrices)in related topics,such as com-putation of smallest invariant dual subspaces of BNs containing a set of Boolean functions,multiple-experiment observability verification/decomposition in BCNs,disturbance decoupling in BCNs,etc.This paper provides a thorough survey for these topics.The contents of the paper are guided by the above two methods.First,we show that Moore's partition-based method closely relates the following problems:computation of smallest invariant dual subspaces of BNs,multiple-experiment observ-ability verification/decomposition in BCNs,and disturbance decoupling in BCNs.However,this method does not apply to other types of observability or nondeterministic systems.Second,we show that based on our observability graph,four different types of observability have been verified in BCNs,verification results have also been extended to PBNs,singular BCNs,and NFTSs.In addition,Moore's partition also shows similarities between BCNs and linear time-invariant(LTI)control systems,e.g.,smallest invariant dual subspaces of BNs containing a set of Boolean functions in BCNs vs unobservable subspaces of LTI control systems,the forms of quotient systems based on observability decomposition in both types of systems.However,there are essential differences between the two types of systems,e.g.,"all plausible definitions of observability in LTI control systems turn out to be equivalent"(by Walter M.Wonham 1985),but there exist nonequivalent definitions of observability in BCNs;the quotient system based on observability decomposition always exists in an LTI control system,while a quotient system based on multiple-experiment observability decomposition does not always exist in a BCN.