Derivation of analytical seismic fragility functions for common masonry bridge types: methodology and application to real cases

Bridges are, in most cases, the most vulnerable elements in the transportation network during an earthquake; therefore, their seismic vulnerability assessment is necessary for a proper planning of the emergency phase and to define a priority for retrofit interventions. A new simplified approach for the fast evaluation of seismic fragility curves of numerous masonry arch bridge clusters is proposed. The aim of this paper is to propose a quickly procedure to estimate the seismic vulnerability of extended roadway and railway bridge networks in emergency conditions and to optimize the retrofit interventions.This methodology can be applied at regional level for the vulnerability assessment of transportation networks with regard to the specific scenario earthquakes formulated.It would allow us to better manage the phase after the main shock so that it should be possible to rationalise resources for the assessment of bridges, to temporarily close the most vulnerable segments of railway or road network and to maintain the use of the safest ones.     

The Application of BP Neural Networks to Analysis the National Vulnerability

Climate change is the main factor affecting the country’s vulnerability, meanwhile, it is also a complicated and nonlinear dynamic system. In order to solve this complex problem, this paper first uses the analytic hierarchy process (AHP) and natural breakpoint method (NBM) to implement an AHP-NBM comprehensive evaluation model to assess the national vulnerability. By using ArcGIS, national vulnerability scores are classified and the country’s vulnerability is divided into three levels: fragile, vulnerable, and stable. Then, a BP neural network prediction model which is based on multivariate linear regression is used to predict the critical point of vulnerability. The function of the critical point of vulnerability and time is established through multiple linear regression analysis to obtain the regression equation. And the proportion of each factor in the equation is established by using the partial least-squares regression to select the main factors affecting the country’s vulnerability, and using the neural network algorithm to perform the fitting. Lastly, the BP neural network prediction model is optimized by genetic algorithm to get the chaotic time series BP neural network prediction model. In order to verify the practicability of the model, Cambodia is selected to be an example to analyze the critical point of the national vulnerability index.     

A new approach to dynamic fuzzy modeling of genetic regulatory networks

In this paper, the dynamic fuzzy modeling approach is applied for modeling genetic regulatory networks from gene expression data. The parameters of the dynamic fuzzy model and the optimal number of fuzzy rules for the fuzzy gene network can be obtained via the proposed modeling approach from the measured gene expression data. One of the main features of the proposed approach is that the prior qualitative knowledge on the network structure can be easily incorporated in the proposed identification algorithm, so that the faster learning convergence of the algorithm can be achieved. Two sets of data, one the synthetic data, and the other the experimental SOS DNA repair network data with structural knowledge, have been used to validate the proposed modeling approach. It is shown that the proposed approach is effective in modeling genetic regulatory networks.     

ICVSS: A New Method for Vulnerability Quantitative Grading

Vulnerability technology is the basic of network security technology, vulnerability quantitative grading methods, such as CVSS, WIVSS, ICVSS, provide a reference to vulnerability management, but the problems of ignoring the risk elevation caused by a group of vulnerabilities and low accuracy of exploitable level evaluation exist in current vulnerability quantitative grading methods. To solve problems above in current network security quantitative evaluation methods, this paper verified the high relevance degree between type and exploitable score of vulnerability, proposed a new vulnerability quantitative grading method ICVSS, ICVSS can explore attack path using continuity level defined by privilege, add vulnerability type to measure indexes of exploitable metrics and use Analytic Hierarchy Process (AHP) to quantify the influence of vulnerability type on exploitable level. Compared with CVSS and WIVSS, ICVSS is proved that it can discover attack path consist of a sequence of vulnerabilities for network security situation evaluation, and has more accuracy and stability.     

Attacker-defender models and road network vulnerability

The reliability of road networks depends directly on their vulnerability to disruptive incidents, ranging in severity from minor disruptions to terrorist attacks. This paper presents a game theoretic approach to the analysis of road network vulnerability. The approach posits predefined disruption, attack or failure scenarios and then considers how to use the road network so as to minimize the maximum expected loss in the event of one of these scenarios coming to fruition. A mixed route strategy is adopted, meaning that the use of the road network is determined by the worst scenario probabilities. This is equivalent to risk-averse route choice. A solution algorithm suitable for use with standard traffic assignment software is presented, thereby enabling the use of electronic road navigation networks. A variant of this algorithm suitable for risk-averse assignment is developed. A numerical example relating to the central London road network is presented. The results highlight points of vulnerability in the road network. Applications of this form of network vulnerability analysis together with improved solution methods are discussed.     

模糊神经网络在高层建筑横风向振动控制中的应用研究

提出了模糊神经网络方法控制高层建筑横风向风振反应。通过观测部分楼层加速度和控制力输出，建立了模糊神经网络控制器，解决了传统控制中有限的传感器数目对系统振动状态估计的困难．利用模糊神经网络控制器预测结构的控制行为，消除了闭环控制系统中存在的时滞。利用模糊神经网络控制器的自学习能力来确定模糊规则和语言变量隶属函数，解决了土木工程复杂结构模糊控制中，难于依据专家的主观经验来确定模糊控制规则和语言变量隶属函数等困难。模糊神经网络方法的优势在于算法自身的鲁棒性，处理结构非线性、参数不确定性及时变等问题的能力。通过对基准建筑的刚度不确定性分析，讨论了模糊神经网络控制器的鲁棒性。仿真分析表明，模糊神经网络控制策略能有效地抑制高层建筑的横风向风振反应，控制效果略优于LQG控制，而拥有LQG控制不具备的诸多优点。     

A priori least expected time paths in fuzzy,time-variant transportation networks

Dynamics and fuzziness are two significant characteristics of real-world transportation networks. To capture these two features theoretically, this article proposes the concept of a fuzzy, time-variant network characterized by a series of time-dependent fuzzy link travel times. To find an effective route guidance for travelers, the expected travel time is specifically adopted as an evaluation criterion to assess the route generation process. Then the shortest path problem is formulated as a multi-objective 0–1 optimization model for finding the least expected time path over the considered time horizon. Different from the shortest path problem in dynamic and random networks, an efficient method is proposed in this article to calculate the fuzzy expected travel time for each given path. A tabu search algorithm is designed for the problem to generate the best solution under the framework of linear weighted methods. Finally, two numerical experiments are performed to verify the effectiveness and efficiency of the model and algorithm.     

A New Fuzzy Controlled Antenna Network Proposal for Small Satellite Applications

This research contributes to small satellite system development based on electromagnetic modeling and an integrated meta-materials antenna networks design for multimedia transmission contents. It includes an adaptive nonsingular mode tracking control design for small satellites systems using fuzzy waveless antenna networks. By analyzing and modeling based on electromagnetic methods, propagation properties of guided waves from metallic structures with simple or complex forms charge partially or entirely by anisotropic materials such as metamaterials. We propose a system control rule to omit uncertainties, including the inevitable approximation errors resulting from the finite number of fuzzy signal power value basis functions in antenna networks. Moreover, both the stability and the tracking performance of the closed-loop robotic system are experimentally validated. The research lies within the scope of the improvement of speed, effectiveness, and precision of numerical methods applied to electro-magnetic modeling with complex structures, essentially rectangular metallic waveguides filled with isotropic or anisotropic metamaterials. Three axes of our research are presented: waveguides, filters, and antennas. The proposed controller does not require prior knowledge about the dynamics of the fuzzy system controller for antenna networks or the offline learning phase. In addition, this work contributes to solving the problem of non-visibility stations to ensure data transmission in wireless networks. The proposed solution maximizes inter-connection by using a fuzzy controlled antenna network, and the novelty guarantees non-limited interconnection in wireless networks compared to traditional methods.     

神经网络在电路诊断中的应用

目的：阐述了目前电路基于数据库技术和人工智能专家系统及神经网络原理的故障诊断系统。方法：将所记录的模糊症状输入到系统中，通过模糊运算后，运用神经网络学习算法来寻找故障类型。结果：介绍了人工神经网络技术在电路诊断中的应用，并给出系统故障诊断软件的设计，结论：所用专家系统和神经网络相结合的方法改进电子电路故障诊断是可行的。     

Modelling of cluster supply network with cascading failure spread and its vulnerability analysis

In this paper, a new entropy approach is developed to study the vulnerability of cluster supply chain network during the cascading failure spread from a holistic point of view. We use the tools of complex network theory and social network analysis to obtain the network representation of cluster supply chain system and explain its cascading phenomenon. Then we build a new cascading model for cluster supply chain network, and further introduce the concept of network load entropy, to analyse and predict the dynamic behaviours of the vulnerability during the process of failure spreading. Using a cluster supply chain network in an industrial district as a case study, we employ the modelling approach to explore its vulnerability. The simulation results demonstrate that the vulnerability of cluster supply chain network with cascading failures can be identified and predicted efficiently by using the modelling approach. In addition, this work may have practical implications for reducing the cluster supply chain network vulnerability in the cascade control and defence, then obtaining its healthy evolution.     

Diagnosis of Various Skin Cancer Lesions Based on Fine-Tuned ResNet50 Deep Network

With the massive success of deep networks, there have been significant efforts to analyze cancer diseases, especially skin cancer. For this purpose, this work investigates the capability of deep networks in diagnosing a variety of dermoscopic lesion images. This paper aims to develop and fine-tune a deep learning architecture to diagnose different skin cancer grades based on dermatoscopic images. Fine-tuning is a powerful method to obtain enhanced classification results by the customized pre-trained network. Regularization, batch normalization, and hyperparameter optimization are performed for fine-tuning the proposed deep network. The proposed fine-tuned ResNet50 model successfully classified 7-respective classes of dermoscopic lesions using the publicly available HAM10000 dataset. The developed deep model was compared against two powerful models, i.e., InceptionV3 and VGG16, using the Dice similarity coefficient (DSC) and the area under the curve (AUC). The evaluation results show that the proposed model achieved higher results than some recent and robust models.     

Comprehensive Information Security Evaluation Model Based on Multi-Level Decomposition Feedback for IoT

The development of the Internet of Things (IoT) calls for a comprehensive information security evaluation framework to quantitatively measure the safety score and risk (S&R) value of the network urgently. In this paper, we summarize the architecture and vulnerability in IoT and propose a comprehensive information security evaluation model based on multi-level decomposition feedback. The evaluation model provides an idea for information security evaluation of IoT and guides the security decision maker for dynamic protection. Firstly, we establish an overall evaluation indicator system that includes four primary indicators of threat information, asset, vulnerability, and management, respectively. It also includes eleven secondary indicators of system protection rate, attack detection rate, confidentiality, availability, controllability, identifiability, number of vulnerabilities, vulnerability hazard level, staff organization, enterprise grading and service continuity, respectively. Then, we build the core algorithm to enable the evaluation model, wherein a novel weighting technique is developed and a quantitative method is proposed to measure the S&R value. Moreover, in order to better supervise the performance of the proposed evaluation model, we present four novel indicators includes residual risk, continuous conformity of residual risk, head-to-tail consistency and decrease ratio, respectively. Simulation results show the advantages of the proposed model in the evaluation of information security for IoT.     

基于模糊神经网络的数据融合结构损伤识别方法

为了有效利用结构健康监测系统中的多源传感器数据信息,提高损伤检测与评估的识别正确率,该文通过构造模糊神经网络分类器,提出了一种基于模糊神经网络的数据融合损伤识别方法并将之应用于结构健康诊断中。它先通过数据预处理,提取原始响应信号中的特征参数,接着将之作为模糊神经网络的输入,构造模糊神经网络模型进行识别决策,最后运用数据融合算法,计算出数据融合后的决策结果。为了验证所提方法的有效性,通过一个7自由度的建筑模型,分别用单一模糊神经网络决策器和数据融合损伤识别方法进行了损伤识别和比较。研究结果表明:该文所提方法比单一决策结果更准确、可靠。     

A study on group decision-making based fault multi-symptom-domain consensus diagnosis

In the field of fault diagnosis for rotating machines, the conventional methods or the neural network based methods are mainly single symptom domain based methods, and the diagnosis accuracy of which is not always satisfactory. In this paper, in order to utilize multiple symptom domains to improve the diagnosis accuracy, an idea of fault multi-symptom-domain consensus diagnosis is developed. From the point of view of the group decision-making, two particular multi-symptom-domain diagnosis strategies are proposed. The proposed strategies use BP (Back-Propagation) neural networks as diagnosis models in various symptom domains, and then combine the outputs of these networks by two combination schemes, which are based on Dempster–Shafer evidence theory and fuzzy integral theory, respectively. Finally, a case study pertaining to the fault diagnosis for rotor-bearing systems is given in detail, and the results show that the proposed diagnosis strategies are feasible and more efficient than conventional stacked-vector methods.     

LOA-RPL: Novel Energy-Efficient Routing Protocol for the Internet of Things Using Lion Optimization Algorithm to Maximize Network Lifetime

Energy conservation is a significant task in the Internet of Things (IoT) because IoT involves highly resource-constrained devices. Clustering is an effective technique for saving energy by reducing duplicate data. In a clustering protocol, the selection of a cluster head (CH) plays a key role in prolonging the lifetime of a network. However, most cluster-based protocols, including routing protocols for low-power and lossy networks (RPLs), have used fuzzy logic and probabilistic approaches to select the CH node. Consequently, early battery depletion is produced near the sink. To overcome this issue, a lion optimization algorithm (LOA) for selecting CH in RPL is proposed in this study. LOA-RPL comprises three processes: cluster formation, CH selection, and route establishment. A cluster is formed using the Euclidean distance. CH selection is performed using LOA. Route establishment is implemented using residual energy information. An extensive simulation is conducted in the network simulator ns-3 on various parameters, such as network lifetime, power consumption, packet delivery ratio (PDR), and throughput. The performance of LOA-RPL is also compared with those of RPL, fuzzy rule-based energy-efficient clustering and immune-inspired routing (FEEC-IIR), and the routing scheme for IoT that uses shuffled frog-leaping optimization algorithm (RISA-RPL). The performance evaluation metrics used in this study are network lifetime, power consumption, PDR, and throughput. The proposed LOA-RPL increases network lifetime by 20% and PDR by 5%–10% compared with RPL, FEEC-IIR, and RISA-RPL. LOA-RPL is also highly energy-efficient compared with other similar routing protocols.     

一类非线性动态系统的自适应模糊小波神经网络控制

对未知非线性动态系统研究基于模糊小波神经网络的自适应跟踪问题,首先构建一个模糊小波神经网络用于逼近未知的非线性函数的模型,然后根据李亚普诺夫稳定性理论建立自适应率,在线调整的模型参数包括小波网络的权重、小波的伸缩量、偏移量和模糊集合隶属函数的相关参数。提出了一种自适应模糊小波神经网络的滑模控制策略,保证系统的跟踪误差和对外界干扰的抑制被衰减到期望的程度。证明了闭环系统的半全局收敛性和鲁棒性,对倒立摆系统的仿真试验证明了所提控制方法的有效性。     

An efficient gait recognition based on a selective neural network ensemble

The neural network ensemble is a learning paradigm where a collection of neural networks is trained for the same task. Generally, the ensemble shows better generalization performance than a single neural network. In this article, a selective neural network ensemble is applied to gait recognition. The proposed method selects some neural network based on the minimization of generalization error. Since the selection rule is directly incorporated into the cost function, we can obtain adequate component networks to constitute an ensemble. Experiments are performed with the NLPR database to show the performance of the proposed algorithm. © 2008 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 18, 237–241, 2008; Published online in Wiley InterScience (www.interscience.wiley.com).     

Performance of Fuzzy ART neural network and hierarchical clustering for part-machine grouping based on operation sequences

The problem context for this study is one of identifying families of parts having a similar sequence of operations. This is a prerequisite for the implementation of cellular manufacturing, group technology, just-in-time manufacturing systems and for streamlining material flows in general. Given this problem context, this study develops an experimental procedure to compare the performance of a fuzzy ART neural network, a relatively recent neural network method, with the performance of traditional hierarchical clustering methods. For large, industry-type data sets, the fuzzy ART network, with the modifications proposed here, is capable of performance levels equal or superior to those of the widely used hierarchical clustering methods. However, like other ART networks, Fuzzy ART also results in category proliferation problems, an aspect that continues to require attention for ART networks. However, low execution times and superior solution quality make fuzzy ART a useful addition to the set of tools and techniques now available for group technology and design of cellular manufacturing systems.