Attractor-Based Simultaneous Design of the Minimum Set of Control Nodes and Controllers in Boolean Networks

Design of control strategies for gene regulatory networks is a challenging and important topic in systems biology. In this paper, the problem of finding both a minimum set of control nodes (control inputs) and a controller is studied. A control node corresponds to a gene that expression can be controlled. Here, a Boolean network is used as a model of gene regulatory networks, and control specifications on attractors, which represent cell types or states of cells, are imposed. It is important to design a gene regulatory network that has desired attractors and has no undesired attractors. Using a matrix-based representation of BNs, this problem can be rewritten as an integer linear programming problem. Finally, the proposed method is demonstrated by a numerical example on a WNT5A network, which is related to melanoma.

Verification of Real-Time Pricing Systems Based on Probabilistic Boolean Networks

In this paper, verification of real-time pricing systems of electricity is considered using a probabilistic Boolean network (PBN). In real-time pricing systems, electricity conservation is achieved by manipulating the electricity price at each time. A PBN is widely used as a model of complex systems, and is appropriate as a model of real-time pricing systems. Using the PBN-based model, real-time pricing systems can be quantitatively analyzed. In this paper, we propose a verification method of real-time pricing systems using the PBN-based model and the probabilistic model checker PRISM. First, the PBN-based model is derived. Next, the reachability problem, which is one of the typical verification problems, is formulated, and a solution method is derived. Finally, the effectiveness of the proposed method is presented by a numerical example.

Modeling effects of abiotic factors on the abundances of eight woody species in the Harana forest using artificial networks,random forest,and generalized linear models

Background Abiotic factors exert different impacts on the abundance of individual tree species in the forest but little has been known about the impact of abiotic factors on the individual plant,particularly,in a tropical forest.This study identified the impact of abiotic factors on the abundances of Podocarpus falcatus,Croton macrostachyus,Celtis africana,Syzygium guineense,Olea capensis,Diospyros abyssinica,Feliucium decipenses,and Coffea arabica.A systematic sample design was used in the Harana forest,where 1122 plots were established to collect the abundance of species.Random forest(RF),artificial neural network(ANN),and generalized linear model(GLM)models were used to examine the impacts of topographic,climatic,and edaphic factors on the log abundances of woody species.The RF model was used to predict the spatial distribution maps of the log abundances of each species.Results The RF model achieved a better prediction accuracy with R^(2)=71%and a mean squared error(MSE)of 0.28 for Feliucium decipenses.The RF model differentiated elevation,temperature,precipitation,clay,and potassium were the top variables that influenced the abundance of species.The ANN model showed that elevation induced a nega-tive impact on the log abundances of all woody species.The GLM model reaffirmed the negative impact of elevation on all woody species except the log abundances of Syzygium guineense and Olea capensis.The ANN model indicated that soil organic matter(SOM)could positively affect the log abundances of all woody species.The GLM showed a similar positive impact of SOM,except for a negative impact on the log abundance of Celtis africana at p<0.05.The spatial distributions of the log abundances of Coffee arabica,Filicium decipenses,and Celtis africana were confined to the eastern parts,while the log abundance of Olea capensis was limited to the western parts.Conclusions The impacts of abiotic factors on the abundance of woody species may vary with species.This ecological understanding could guide the restoration activity of individual species.The prediction maps in this study provide spatially explicit information which can enhance the successful implementation of species conservation.

Integrating Multiple Linear Regression and Infectious Disease Models for Predicting Information Dissemination in Social Networks

Social network is the mainstream medium of current information dissemination,and it is particularly important to accurately predict its propagation law.In this paper,we introduce a social network propagation model integrating multiple linear regression and infectious disease model.Firstly,we proposed the features that affect social network communication from three dimensions.Then,we predicted the node influence via multiple linear regression.Lastly,we used the node influence as the state transition of the infectious disease model to predict the trend of information dissemination in social networks.The experimental results on a real social network dataset showed that the prediction results of the model are consistent with the actual information dissemination trends.

CRB:A new rumor blocking algorithm in online social networks based on competitive spreading model and influence maximization

The virtuality and openness of online social platforms make networks a hotbed for the rapid propagation of various rumors.In order to block the outbreak of rumor,one of the most effective containment measures is spreading positive information to counterbalance the diffusion of rumor.The spreading mechanism of rumors and effective suppression strategies are significant and challenging research issues.Firstly,in order to simulate the dissemination of multiple types of information,we propose a competitive linear threshold model with state transition(CLTST)to describe the spreading process of rumor and anti-rumor in the same network.Subsequently,we put forward a community-based rumor blocking(CRB)algorithm based on influence maximization theory in social networks.Its crucial step is to identify a set of influential seeds that propagate anti-rumor information to other nodes,which includes community detection,selection of candidate anti-rumor seeds and generation of anti-rumor seed set.Under the CLTST model,the CRB algorithm has been compared with six state-of-the-art algorithms on nine online social networks to verify the performance.Experimental results show that the proposed model can better reflect the process of rumor propagation,and review the propagation mechanism of rumor and anti-rumor in online social networks.Moreover,the proposed CRB algorithm has better performance in weakening the rumor dissemination ability,which can select anti-rumor seeds in networks more accurately and achieve better performance in influence spread,sensitivity analysis,seeds distribution and running time.

Stability of piecewise-linear models of genetic regulatory networks

This paper investigates the stability of the equilibria of the piecewise-linear models of genetic regulatory networks on the intersection of the thresholds of all variables. It first studies circling trajectories and derives some stability conditions by quantitative analysis in the state transition graph. Then it proposes a common Lyapunov function for convergence analysis of the piecewise-linear models and gives a simple sign condition. All the obtained conditions are only related to the constant terms on the right-hand side of the differential equation after bringing the equilibrium to zero.

Stability analysis of discrete-time BAM neural networks based on standard neural network models

To facilitate stability analysis of discrete-time bidirectional associative memory (BAM) neural networks, they were converted into novel neural network models, termed standard neural network models (SNNMs), which interconnect linear dynamic systems and bounded static nonlinear operators. By combining a number of different Lyapunov functionals with S-procedure, some useful criteria of global asymptotic stability and global exponential stability of the equilibrium points of SNNMs were derived. These stability conditions were formulated as linear matrix inequalities (LMIs). So global stability of the discrete-time BAM neural networks could be analyzed by using the stability results of the SNNMs. Compared to the existing stability analysis methods, the proposed approach is easy to implement, less conservative, and is applicable to other recurrent neural networks.

Interval standard neural network models for nonlinear systems

A neural-network-based robust control design is suggested for control of a class of nonlinear systems. The design ap- proach employs a neural network, whose activation functions satisfy the sector conditions, to approximate the nonlinear system. To improve the approximation performance and to account for the parameter perturbations during operation, a novel neural network model termed standard neural network model (SNNM) is proposed. If the uncertainty is bounded, the SNNM is called an interval SNNM (ISNNM). A state-feedback control law is designed for the nonlinear system modelled by an ISNNM such that the closed-loop system is globally, robustly, and asymptotically stable. The control design equations are shown to be a set of linear matrix inequalities (LMIs) that can be easily solved by available convex optimization algorithms. An example is given to illustrate the control design procedure, and the performance of the proposed approach is compared with that of a related method reported in literature.

Domain Decomposition of an Optimal Control Problem for Semi-Linear Elliptic Equations on Metric Graphs with Application to Gas Networks

We consider optimal control problems for the flow of gas in a pipe network. The equations of motions are taken to be represented by a semi-linear model derived from the fully nonlinear isothermal Euler gas equations. We formulate an optimal control problem on a given network and introduce a time discretization thereof. We then study the well-posedness of the corresponding time-discrete optimal control problem. In order to further reduce the complexity, we consider an instantaneous control strategy. The main part of the paper is concerned with a non-overlapping domain decomposition of the semi-linear elliptic optimal control problem on the graph into local problems on a small part of the network, ultimately on a single edge.

无模型自适应滑模控制的微波加热过程温度控制

微波加热模型具有无限维、非线性和时变等特点,导致控制器难于设计和实现。针对此问题,提出了一种适用于微波加热过程的无模型自适应滑模控制方法。首先,对微波加热过程传热数学模型进行分析,建立了微波加热过程输入功率与温度之间的全格式动态线性化数据模型。然后,根据该数据模型设计了无模型自适应滑模控制器,并给出了数据模型中相关未知时变参数和未知干扰的估计算法。最后,利用COMSOL和MATLAB进行仿真,仿真结果验证了所提控制方法的有效性。

基于一元线性回归模型的供水网络中水表读数虚高问题研究

为了定量研究供水网络中总表漏水程度和分表读数虚高程度,根据水量平衡分析法的原理,结合大数据分析技术,建立了一元线性回归方程,回归常数代表总表漏水量程度,回归系数代表分表读数虚高程度。通过针对2021年某高校供水管网的实证研究表明,总表日均漏水量为15.5958吨,分表读数虚高率为1.07%。该方法对供水管网漏损率的精准评估等问题的解决提供了新的思路和方法。

基于复杂网络的复杂产品系统关键节点辨识

复杂产品系统具有规模大、结构复杂等特点,从系统结构的视角辨识复杂产品系统关键节点,对于“事前”定位潜在风险源具有重要意义。针对目前“事后”追溯风险源难以保证复杂产品系统可靠运行及风险管理前瞻性等问题,考虑复杂产品系统的层级结构和子系统间风险的相互影响,提出了一种可用于“事前”风险管理的关键节点辨识方法。首先依据系统结构建立复杂网络;然后,构造零模型并结合网络统计特征初步划分节点集合,在此基础上引入线性阈值模型分析节点风险传播影响力,并基于风险的动态传播特征选取关键节点;最后,采用新舟700飞机的系统结构进行实证分析。结果表明,提出的方法有效地辨识了复杂产品系统的关键节点。

多种残差补偿的贝叶斯网络下的短期交通预测

为了解决道路车流量的数据生成条件时变场景下的交通预测问题,本文建立道路交通控制与交通流预测数据之间的联系,提出一种基于多种残差补偿的贝叶斯网络的短期交通预测方法。提取城市中大规模多路口主干道车道及车辆信息构造多个平行的贝叶斯网络,使用贝叶斯关系及期望最大化算法进行短期交通预测。再通过数据自相关残差补偿、车辆换道和多路口连通性的线性残差补偿提高了预测的精度,解决了传统研究对相邻路口和换道导致的误差等因素处理能力不足的问题。仿真结果表明:使用贝叶斯网络预测交通流,并基于车辆行为的残差进行精度补偿,可以更准确地预测复杂的交通演化场景的短期交通流。

基于自适应小波回声神经网络的光纤陀螺测角仪温度误差补偿技术

基于光纤陀螺的测角仪可以实现对各项角运动参数的一体化动态精密测量,但在实际应用中,光纤陀螺测角仪受到温度变化的影响,导致测量精度下降。针对这一问题,本文提出了一种基于自适应小波回声神经网络的光纤陀螺测角仪温度误差补偿技术。为了提高温度误差建模的进度,提高传统神经网络的逼近能力,通过自适应前向线性预测滤波器对建模用测角仪温度漂移数据进行预处理,并采用自适应小波回声神经网络建立温度漂移模型,能够避免传统神经网络结构设计的盲目性和局部最优等问题,增强了网络学习能力和泛化能力,并利用自适应律代替神经网络梯度进行网络训练,提升神经网络的逼近精度和收敛速度。实验结果表明,该模型可以提高光纤陀螺测角仪的测量精度和环境适应性,为光纤陀螺测角仪的性能优化和实际应用提供了可靠的技术支撑。

复杂工况下航空电子通信网络数据传输延时补偿研究

为了解决航空电子通信网络数据在复杂工况下受到环境噪声干扰的影响大以及数据传输延时过大的问题,以复杂工况为背景,研究航空电子通信网络数据传输延时补偿。采用非线性变换算法分析通信网络数据状态,得到影响延时因素的离散性值及线性变化。建立数据传输延时状态模型,分析每个节点实时状态,计算数据传输状态和延时参数,并求解二者间的关系。将数据传输延时问题抽象为先到先服务的排队服务,并计算数据发送、到达速率与延时时间的关系。在此基础上,求解最大时间节点的实时延迟数值,按照延迟优先级顺序实行差值填补,完成数据传输延时补偿。结果表明,所提方法有良好的补偿精度和补偿效果,可降低航空电子通信的延迟时间,实现实时命令接收和消息传递功能,说明所提方法具有一定的实用价值。

人工智能在视网膜液监测中的应用指南(2024)

老年性黄斑变性(SMD)是一种复杂的、高度遗传的、多因素作用的疾病,患者黄斑区结构会发生衰老性改变,表现为视网膜进行性变性和视力逐渐丧失。全世界约有2亿人受到SMD的影响,并且随着人口老龄化的加剧,发病率不断上升。近年来人工智能(AI)技术发展迅猛,AI技术在医学领域的应用为医疗行业的发展带来新的可能。利用AI对视网膜液进行定性定量评估,不仅可以在新生血管性SMD的诊断过程中发挥重要作用,还可以在治疗过程中根据治疗效果及时调整治疗方案,为患者提供更加个性化的治疗。本指南总结了AI在SMD治疗中的应用,包括AI在视网膜液监测技术中的应用进展、临床应用及未来发展,为眼科医生评估患者病情、设计治疗方案及判断预后提供足够的帮助。

基于误差分治的神经网络验证

随着神经网络技术的快速发展,其在自动驾驶、智能制造、医疗诊断等安全攸关领域得到了广泛应用,神经网络的可信保障变得至关重要.然而,由于神经网络具有脆弱性,轻微的扰动经常会导致错误的结果,因此采用形式化验证的手段来保障神经网络安全可信是非常重要的.目前神经网络的验证方法主要关注分析的精度,而易忽略运行效率.在验证一些复杂网络的安全性质时,较大规模的状态空间可能会导致验证方法不可行或者无法求解等问题.为了减少神经网络的状态空间,提高验证效率,提出一种基于过近似误差分治的神经网络形式化验证方法.该方法利用可达性分析技术计算非线性节点的上下界,并采用一种改进的符号线性松弛方法减少了非线性节点边界计算过程中的过近似误差.通过计算节点过近似误差的直接和间接影响,将节点的约束进行细化,从而将原始验证问题划分为一组子问题,其混合整数规划(MILP)公式具有较少的约束数量.所提方法已实现为工具NNVerifier,并通过实验在经典的3个数据集上训练的4个基于ReLU的全连接基准网络进行性质验证和评估.实验结果表明,NNVerifier的验证效率比现有的完备验证技术提高了37.18%.

基于数据驱动知识显式嵌入的配电网最优需求响应策略

充分挖掘多元需求侧资源的灵活性,对于提升分布式新能源广泛接入背景下的新型配电网运行可靠性和经济性具有重要意义。然而,目前关于需求响应策略的解析化方法大多基于较为理想的用户行为观测和参数假设,纯数据驱动方法难以兼顾电网侧运行的复杂约束,策略的可用性存疑。为此,文章提出基于数据驱动知识显式嵌入的需求响应策略,首先,考虑到需求侧资源灵活性的强时段耦合特性,提出需求侧资源动态模型,定量分析需求侧灵活性资源的响应特性;其次,提出数据驱动知识的显式解析方法,将需求侧灵活性描述为混合整数线性模型并嵌入至配电网优化运行模型中,实现灵活实用的新型配电网供需交互与协调运行。最后,通过仿真算例验证所提方法兼具解析模型和数据驱动方法的优势,为不完全信息观测条件下源网荷协调运行提供较为实用化的解决方案。

小样本纱线质量预测的机器学习算法适用性分析

为了解决当前基于神经网络的纱线质量预测模型针对小样本预测精度偏低和预测精度不稳定的问题,建立了随机森林(RF)算法预测模型、多层感知机神经网络(MLP)算法预测模型和线性回归(LR)算法预测模型,就各算法模型在小样本情况下对不同数据特点的数据集的敏感性、不同数据维度的敏感性和不同训练样本数的敏感性进行了预测性能对比试验。用决定系数和均方根误差进行模型预测性能评估。试验结果表明:在小样本情况下,相比于MLP算法和LR算法,大多数情况下RF算法预测准确性更高、预测精度稳定性更好、对小训练样本量的适应性更好,具有较高的综合预测性能。

多元线性回归模型与多层感知器神经网络在铀矿测井泥质含量预测中的应用

在铀矿资源勘探工作中,泥质含量的测定对于确定地下岩层的性质和砂岩型铀矿床的分布具有重要意义。文章旨在避免常规测井解释计算方法受到希尔奇系数选取准确性的限制,提出了利用多元线性回归模型和多层感知器(MLP,Multilayer Perceptron)神经网络对测井数据进行分析与预测的方法。通过选取某地区的测井数据,采用多元线性回归模型和MLP神经网络进行了泥质含量关系模型的构建和验证。结果显示,多元线性回归模型在泥质含量低层位出现过拟合现象,而MLP神经网络则表现出更高的预测准确性,MLP神经网络在泥质含量预测中优于传统多元线性回归模型,为铀矿勘探中泥质含量的准确预测提供了有效工具,并有望改进现有的泥质含量评价方法。这些研究成果可显著提升测井解释的效率和准确性,对后续铀矿勘探开发工作的开展具有积极影响。