Robustness of coupled networks with multiple support from functional components at different scales

Robustness is an essential component of modern network science. Here, we investigate the robustness of coupled networks where the functionality of a node depends not only on its connectivity, here measured by the size of its connected component in its own network, but also the support provided by at least M links from another network. We here develop a theoretical framework and investigate analytically and numerically the cascading failure process when the system is under attack, deriving expressions for the proportion of functional nodes in the stable state, and the critical threshold when the system collapses. Significantly, our results show an abrupt phase transition and we derive the minimum inner and inter-connectivity density necessary for the system to remain active. We also observe that the system necessitates an increased density of links inside and across networks to prevent collapse, especially when conditions on the coupling between the networks are more stringent. Finally, we discuss the importance of our results in real-world settings and their potential use to aid decision-makers design more resilient infrastructure systems

Large structural impact localization based on multi-agent system

In practical applications of structural health monitoring, a huge amount of distributed sensors are usually used to monitor structures of large dimensions. In order to obtain fast and accurate evaluation of a structure, a multi-agent system is introduced to manage different sensor sets and to fuse distributed information. In this paper, a multi-agent system based on impact location is presented to deal with the impact load localization problem for large-scale structures. The monitoring system firstly detects whether an impact event happens in the monitored subregion, and focuses on the impact source on the sub-region boundary to obtain the sensor network data with blackboard systems. Then the collaborative evaluation of both the acoustic emission and the inverse analysis localization method is employed to obtain precise and fast localization result. Finally, a reliable assessment for the whole structure is provided by fusing evaluation results from the sub-regions. The performance of the proposed multi-agent system is illustrated by means of experimental on a large aerospace aluminum plate structure. Extensive testing of the proposed system demonstrated its effectiveness for the impact load localization in each sub-region, particularly for impacts lying next to the borders of the sub-regions

Online Multiple Instance Joint Model for Visual Tracking

Although numerous online learning strategies have been proposed to handle the appearance variation in visual tracking, the existing methods just perform well in certain cases since they lack effective appearance learning mech-anism. In this paper, a joint model tracker (JMT) is pre-sented, which consists of a generative model based on Mul-tiple Subspaces and a discriminative model based on im-proved Multiple Instance Boosting (MIBoosting). The gen-erative model utilizes a series of local constructed sub-spaces to update the Multiple Subspaces model and con-siders the energy dissipation of dimension reduction in up-dating step. The discriminative model adopts the Gaussian Mixture Model (GMM) to estimate the posterior probability of the likelihood function. These two parts supervise each other to update in multiple instance way which helps our tracker recover from drift. Extensive experiments on var-ious databases validate the effectiveness of our proposed method over other state-of-the-art trackers. 1

Research on economic policy system evaluation methods

Great progress has been made in construction of economic policy system in recent years. Nevertheless, there are also difficulties in accurate inquiry, efficient use and comparative analysis. In a bid to address critical problems of the existed system, this paper builds an intelligent policy system platform. By adopting the big data and artificial intelligence technologies, a number of functions such as accurate matching, system classification, and the analyses of relevance, life cycle, response level, time-effectiveness, self-consistency, maneuverability and so on are made available, so as to put in place a policy evaluation management system which is systematic, multi-dimensional and intelligent

Accurate Identification of Broken Rock Mass Structure and Its Application in Stability Analysis of Underground Caverns Surrounding Rock

In view of the broken rock mass in the surrounding rock of large underground caverns, the 3D distribution model of the joints is obtained through the on-site investigation of the joints and the digital imaging technology, and the image processing software. Based on the analysis of the mutual cutting degree of the joints and the geometric shape of the block, the concepts and calculation methods of the degree of fragmentation and the degree of bite are proposed, and the degree of rock mass fragmentation is quantitatively described. Furthermore, the multi-factor analysis method is used to establish the quality evaluation method and the standard of broken rock mass based on the degree of rock fragmentation and the degree of bite, and the failure mode and safety criterion of broken rock mass of surrounding rock are proposed. Applying the discrete element numerical analysis method, the mechanical parameters of different broken rock masses are obtained. The reinforcement analysis of the surrounding rock of different broken rock masses shows that the degree of fragmentation, the degree of bite, and the classification of surrounding rock proposed in this paper can implement precise reinforcement measures for the surrounding rock of different broken rock masses, as it provides an important theoretical basis for the surrounding rock safety of large underground caverns in the broken rock mass and has wide applicability

Different Toppling Bank Slope Failures under Hydrodynamic Action during Impoundment of the Miaowei Hydropower Station Reservoir

Toppling is a common deformation and failure phenomenon in the reservoir bank slopes of hydropower projects. This paper studies the genesis and evolution of different toppling bodies during water impoundment at the Miaowei Hydropower Station Reservoir on the Lancang River in southwest China. Toppling properties were determined and second failure characteristics analyzed in different reservoir impoundment stages. Different degrees of toppling deformation were primarily affected by the transverse bending stress, while the regional tectonic stress has been shown to have a significant effect on the transverse bending of the rock layers. Combined with the on-site investigation and monitoring results, the failure mechanisms of the different toppling deformation bodies were analyzed. The second failure of the toppling rock mass caused by the reservoir impoundment process is mainly the hydrodynamic splitting along fractures, wave impaction and softening on the slope foot. The transverse bending effect of gravity is transmitted upward through joint misalignment, rotation and slip, accelerating the speed of secondary toppling failure and forming a compression-shear failure along the toppling tension crack. A model to predict the scope and time of failure in the toppling deformation banks under the action of reservoir hydrodynamics was proposed

Deformation Characteristics and Stability Prediction of Mala Landslide at Miaowei Hydropower Station under Hydrodynamic Action

In recent years, with the completion of the construction of large-scale hydropower projects in China, a series of engineering geological problems that occurred during the operation of the hydropower station have become an important issue affecting the normal operation of hydropower stations. Landslides on reservoir slopes triggered especially by water storage and other factors related to the construction of hydropower stations seriously affect the normal operation of the hydropower station and lead to other geological disasters. Research indicates that many reservoir-area landslides are triggered by hydrodynamic forces resulting from water level fluctuations in hydroelectric power stations. The Mala landslide of Miaowei Hydropower Station in the Lancang River Basin of China is taken as the engineering example to study the influence of hydrodynamic forces on the deformation characteristics and stability trends of the landslide. This paper explores the formation mechanism and influencing factors of the Mala landslide by conducting a field investigation of the Mala landslide and analyzing the monitoring data. Additionally, this paper also discusses the impacts of water storage, rainfall, and engineering construction on landslide induction. It is considered that the evolution of the Mala landslide from the initial stage of water storage to the current state mainly includes four stages: small-scale bank collapse stage, creep deformation stage, accelerated sliding stage, and uniform sliding stage. Moreover, the changes in the trend of landslide stability are analyzed using the two-dimensional finite element method. The research results provide a valuable reference for understanding the formation mechanism and predicting the deformation of reservoir landslides, which has considerable engineering practical significance

Failure Mechanism and Long Short-Term Memory Neural Network Model for Landslide Risk Prediction

Rockslides along a stepped failure surface have characteristics of stepped deformation characteristic and it is difficult to predict the failure time. In this study, the deformation characteristics and disaster prediction model of the Fengning granite rockslide were analyzed based on field surveys and monitoring data. To evaluate the stability, the shear strength parameters of the sliding surface were determined based on the back-propagation neural network and three-dimensional discrete element numerical method. Through the correlation analysis of deformation monitoring results with rainfall and blasting, it is shown that the landslide was triggered by excavation, rainfall, and blasting vibrations. The landslide displacement prediction model was established by using long short-term memory neural network (LSTM) based on the monitoring data, and the prediction results are compared with those using the BP model, SVM model and ARMA model. Results show that the LSTM model has strong advantages and good reliability for the stepped landslide deformation with short-term influence, and the predicted LSTM values were very consistent with the measured values, with a correlation coefficient of 0.977. Combined with the distribution characteristics of joints, the damage influence scope of the landslide was simulated by three-dimensional discrete element, which provides decision-making basis for disaster warning after slope instability. The method proposed in this paper can provide references for early warning and treatment of geological disasters

Failure Mechanism and Long Short-Term Memory Neural Network Model for Landslide Risk Prediction

Rockslides along a stepped failure surface have characteristics of stepped deformation characteristic and it is difficult to predict the failure time. In this study, the deformation characteristics and disaster prediction model of the Fengning granite rockslide were analyzed based on field surveys and monitoring data. To evaluate the stability, the shear strength parameters of the sliding surface were determined based on the back-propagation neural network and three-dimensional discrete element numerical method. Through the correlation analysis of deformation monitoring results with rainfall and blasting, it is shown that the landslide was triggered by excavation, rainfall, and blasting vibrations. The landslide displacement prediction model was established by using long short-term memory neural network (LSTM) based on the monitoring data, and the prediction results are compared with those using the BP model, SVM model and ARMA model. Results show that the LSTM model has strong advantages and good reliability for the stepped landslide deformation with short-term influence, and the predicted LSTM values were very consistent with the measured values, with a correlation coefficient of 0.977. Combined with the distribution characteristics of joints, the damage influence scope of the landslide was simulated by three-dimensional discrete element, which provides decision-making basis for disaster warning after slope instability. The method proposed in this paper can provide references for early warning and treatment of geological disasters

A Split-and-Merge-Based Uterine Fibroid Ultrasound Image Segmentation Method in HIFU Therapy.

High-intensity focused ultrasound (HIFU) therapy has been used to treat uterine fibroids widely and successfully. Uterine fibroid segmentation plays an important role in positioning the target region for HIFU therapy. Presently, it is completed by physicians manually, reducing the efficiency of therapy. Thus, computer-aided segmentation of uterine fibroids benefits the improvement of therapy efficiency. Recently, most computer-aided ultrasound segmentation methods have been based on the framework of contour evolution, such as snakes and level sets. These methods can achieve good performance, although they need an initial contour that influences segmentation results. It is difficult to obtain the initial contour automatically; thus, the initial contour is always obtained manually in many segmentation methods. A split-and-merge-based uterine fibroid segmentation method, which needs no initial contour to ensure less manual intervention, is proposed in this paper. The method first splits the image into many small homogeneous regions called superpixels. A new feature representation method based on texture histogram is employed to characterize each superpixel. Next, the superpixels are merged according to their similarities, which are measured by integrating their Quadratic-Chi texture histogram distances with their space adjacency. Multi-way Ncut is used as the merging criterion, and an adaptive scheme is incorporated to decrease manual intervention further. The method is implemented using Matlab on a personal computer (PC) platform with Intel Pentium Dual-Core CPU E5700. The method is validated on forty-two ultrasound images acquired from HIFU therapy. The average running time is 9.54 s. Statistical results showed that SI reaches a value as high as 87.58%, and normHD is 5.18% on average. It has been demonstrated that the proposed method is appropriate for segmentation of uterine fibroids in HIFU pre-treatment imaging and planning