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.

基于Control Net网通讯的传输方式研究及应用

在散货码头控制系统中,受场区控制设备安装范围影响,结合整体网络设备的统一性,控制网设备选型往往受到一定的影响,控制网的多模光纤传输距离与控制网覆盖范围成为网络传输过程中的一个问题。本文通过控制网络在距离长、限制多的情况下的优化搭接,解决多模光纤控制网远距离传输方式。

Control Net网络在电厂水处理系统中的应用

AB公司的PLC产品的网络功能强大,在实际应用中,可以根据不同情况灵活的运用,Control Net网络在华能太仓电厂水处理系统中得到成功应用,本文介绍在一期工程,二期扩建工程以及一期和二期之间的互连过程中,Control Net网络的应用。

基于Control Net现场总线的过程控制系统

介绍了燃气锅炉基于Control Net现场总线的过程控制系统的硬件构成及系统设计。

Best bounds on the distance between 3-direction quartic box spline surface and its control net

We present the best bounds on the distance between 3-direction quartic box spline surface patch and its control net by means of analysis and computing for the basis functions of 3-direction quartic box spline surface.Both the local bounds and the global bounds are given by the maximum norm of the first differences or second differences or mixed differences of the control points of the surface patch.

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.

INFORMATION DESIGN OF UNIVERSAL ADJUSTMENT PROGRAM OF PLANE CONTROL NETWORKS

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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.

Motor Control Network Effective Connectivity in Regulating Muscle Force Output

Objective of the study: This study aimed at characterizing output features of the higher-order motor control centers (hoMCCs), including secondary (premotor cortex [Pre] and supplementary motor area [SMA]) and association (prefrontal cortex [PFC]) motor regions to the primary motor cortex (M1) during graded force tasks. It is well known that one of the major roles of the primary motor cortex (M1) is controlling motor output such as muscle force. However, it is unclear how the hoMCCs interact with M1 in regulating voluntary muscle contractions. Methods: fMRI data was acquired during graded force tasks and fMRI-based effective connectivity (EC) and muscle force analyses were performed to study the relationship between hoMCCs-M1 effective connectivity and voluntarily exerted handgrip force. Results: The results show that there is a consistent information flow from the hoMCCs to M1 under all force conditions, suggesting a hierarchical control mechanism in the brain in regulating voluntary muscle force. Only the premotor cortex exhibited a significant role in mediating the level of force production through its EC with M1 but that role diminished when the exerted force was high, suggesting perhaps a ceiling and/or fatigue effect on the EC. A flip in the direction of EC from the primary sensory cortex (S1) to the hoMCCs (PFC, SMA, and Pre) at lower force levels while at higher forces EC was observed from the hoMCCs to S1. Conclusion: The hoMCCs regulate M1 output to produce desired voluntary muscle force. Only the Pre-to-M1 connectivity strength directly correlates with the force level especially from low to moderate levels. The hoMCCs are involved in modulating higher force production likely by strengthening M1 output and downgrading inhibition from S1 to M1.

5-29 Establishment of HIRFL Control Network Monitoring Platform

There are a lot of equipments in HIRFL such as faraday cups, view screens power supplies, RF (radio frequency)equipment, vacuum equipment, and so on. A growing number of equipment support network interface in theseyears. Thus the control network system of HIRFL has become more and more big and complicated. So it is veryimportant to ensure that each device is online. We built a network monitoring platform. There are three functions.The first is monitoring the online or offline state of each switch. Second, the devices in the control network belongto different subsystem and are managed by subsystem managers. We cannot guarantee that each device is notdropped, but we can have a warning system which can warn device manager once the device dropped. Third, ifsome ports are abnormal, the information can be immediately reported to the administrator. Therefore, the controlnetwork monitoring platform has three subsystems, switch monitoring system, equipment offline warning systemand switch log analysis system, as shown in Fig. 1.

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.

LonWorks控制网浅析LonWorks Control Net Analysis

LonWorks控制网络是由原苹果公司创始人麦克马库拉(A．C．Markknla)创立于1988年的Echelon公司发明。LonWorks控制网络中的设备可以处理信息和做决策。取消了中央处理机可以减少LonWorks控制网络的成本。同时提高了网络的可靠性。一个节点出故障不影响网络的其他设备的运行。LonWorks已经通过了ANSI美国国家标准协会、ASHRAE美国暖通空调工程师协会、IEEE电子电气工程师协会、AAR美国铁路协会等行业标准。Echelon公司与Oisco公司Microsoft公司Sun公司合作开发了i．LON 1000 internet服务器，使得在internet上访问Lonworks设备。

Finite-time Prescribed Performance Time-Varying Formation Control for Second-Order Multi-Agent Systems With Non-Strict Feedback Based on a Neural Network Observer

This paper studies the problem of time-varying formation control with finite-time prescribed performance for nonstrict feedback second-order multi-agent systems with unmeasured states and unknown nonlinearities.To eliminate nonlinearities,neural networks are applied to approximate the inherent dynamics of the system.In addition,due to the limitations of the actual working conditions,each follower agent can only obtain the locally measurable partial state information of the leader agent.To address this problem,a neural network state observer based on the leader state information is designed.Then,a finite-time prescribed performance adaptive output feedback control strategy is proposed by restricting the sliding mode surface to a prescribed region,which ensures that the closed-loop system has practical finite-time stability and that formation errors of the multi-agent systems converge to the prescribed performance bound in finite time.Finally,a numerical simulation is provided to demonstrate the practicality and effectiveness of the developed algorithm.

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.