ALBERT

Description: Symmetry, Vol. 9, Pages 183: Community Detection Based on Differential Evolution Using Social Spider Optimization Symmetry doi: 10.3390/sym9090183 Authors: You-Hong Li Jian-Qiang Wang Xue-Jun Wang Yue-Long Zhao Xing-Hua Lu Da-Long Liu Community detection (CD) has become an important research direction for data mining in complex networks. Evolutionary algorithm-based (EA-based) approaches, among many other existing community detection methods, are widely used. However, EA-based approaches are prone to population degradation and local convergence. Developing more efficient evolutionary algorithms thus becomes necessary. In 2013, Cuevas et al. proposed a new differential evolution (DE) hybrid meta-heuristic algorithm based on the simulated cooperative behavior of spiders, known as social spider optimization (SSO). On the basis of improving the SSO algorithm, this paper proposes a community detection algorithm based on differential evolution using social spider optimization (DESSO/CD). In this algorithm, the CD detection process is done by simulating the spider cooperative operators, marriage, and operator selection. The similarity of nodes is defined as local fitness function; the community quality increment is used as a screening criterion for evolutionary operators. Populations are sorted according to their contribution and diversity, making evolution even more different. In the entire process, a random cloud crossover model strategy is used to maintain population diversity. Each generation of the mating radius of the SSO algorithm will be adjusted appropriately according to the iterative times and fitness values. This strategy not only ensures the search space of operators, but also reduces the blindness of exploration. On the other hand, the multi-level, multi-granularity strategy of DESSO/CD can be used to further compensate for resolution limitations and extreme degradation defects based on modular optimization methods. The experimental results demonstrate that the DESSO/CD method could detect the community structure with higher partition accuracy and lower computational cost when compared with existing methods. Since the application of the SSO algorithm in CD research is just beginning, the study is competitive and promising.

Description: Long-span prestressed double-layer composite torsional reticulated shells have beautiful shapes, which make them a solution widely applied in public buildings requiring long-span structures, such as airports and stadiums. However, once the progressive collapse occurs, this will cause serious issues. There are few studies on the damage assessment of long-span spatial structures under accidental loads. This paper proposes a new dynamic damage evaluation index that takes into account the displacement and the cumulative plastic energy dissipation to evaluate the damage of the long-span prestressed double-layer composite torsional reticulated shell. Sleeve structures were applied to the long-span space structures to study their control effect on structural damage. The equivalent load transient unloading method was used to analyze the dynamic time history of the structure. Results show that the structure suffered severe damage and progressive collapse failure after removing four nodes at different positions near the supports. In the position where the plastic hinges first appear and the position with the maximum displacement, the sleeve structures have a poor damage control effect on the structure. Arranging the two sleeve structures in the position of maximum stress, the damage of the structure can be controlled, thus reducing the severe damage and progressive collapse failure to basic intact damage.

Description: This paper investigates a single-machine two-agent scheduling problem to minimize the maximum costs with position-dependent jobs. There are two agents, each with a set of independent jobs, competing to perform their jobs on a common machine. In our scheduling setting, the actual position-dependent processing time of one job is characterized by variable function dependent on the position of the job in the sequence. Each agent wants to fulfil the objective of minimizing the maximum cost of its own jobs. We develop a feasible method to achieve all the Pareto optimal points in polynomial time.

Description: Rolling bearings are the most important parts in rotating machinery, and one of the most vulnerable parts to failure. The rolling bearing is a cyclic symmetrical structure that is stable under normal operating conditions. However, when the rolling bearing fails, its symmetry is destroyed, resulting in unstable performance and causing major accidents. If the performance of rolling bearings can be monitored and evaluated in real time, maintenance strategies can be implemented promptly. In this paper, by using wavelet packet energy entropy (WPEE), the early fault-free features of bearing and the failure samples of similar bearings are decomposed firstly, and the energy value is extracted as the original feature, simultaneously. Secondly, a radial basis function (RBF) neural network model is established by using early fault-free features and similar bearing failure characteristics. The bearing full-life data characteristics of the extracted features are added into the RBF model in an iterative manner to obtain performance degradation Indicator. Boxplot was introduced as an adaptive threshold method to determine the failure threshold. Finally, the results are verified by empirical mode decomposition and Hilbert envelope demodulation. A bearing accelerated life experiment is performed to validate the feasibility and validity of the proposed method. The experimental results show that the method can diagnose early fault points in time and evaluate the degree of bearing degradation, which is of great significance for industrial practical applications.

Description: The maglev train is a whole new method of transportation without wheels, consisting of 20 groups of symmetry suspension units. The magnetic levitation system plays a major role in suspending the maglev train stably and following the track quickly with the desired gap. However, vertical track irregularity in the maglev train line has a dreadful effect on the tracking performance of the magnetic levitation system. The investigations carried out by our team have revealed that the fluctuation of the suspension gap becomes more and more serious with increases in running speed. In this paper, a mathematical model with consideration of vertical track irregularity is established. In order to overcome and suppress the fluctuation of the suspension gap, we propose a new strategy which includes installing an accelerometer on the electromagnet to address this problem. This strategy has already been successfully implemented and applied to the suspension controller for a magnetic levitation system in the Changsha maglev express. Real operation data indicates the tracking performance of the magnetic levitation system was obviously improved.

Description: Graph-based embedding methods receive much attention due to the use of graph and manifold information. However, conventional graph-based embedding methods may not always be effective if the data have high dimensions and have complex distributions. First, the similarity matrix only considers local distance measurement in the original space, which cannot reflect a wide variety of data structures. Second, separation of graph construction and dimensionality reduction leads to the similarity matrix not being fully relied on because the original data usually contain lots of noise samples and features. In this paper, we address these problems by constructing two adjacency graphs to stand for the original structure featuring similarity and diversity of the data, and then impose a rank constraint on the corresponding Laplacian matrix to build a novel adaptive graph learning method, namely locality sensitive discriminative unsupervised dimensionality reduction (LSDUDR). As a result, the learned graph shows a clear block diagonal structure so that the clustering structure of data can be preserved. Experimental results on synthetic datasets and real-world benchmark data sets demonstrate the effectiveness of our approach.

Description: We consider the online scheduling problem on a single machine with the assumption that all jobs have their processing times in , where and . All jobs arrive over time, and each job and its processing time become known at its arrival time. The jobs should be first processed on a single machine and then delivered by a vehicle to some customer. When the capacity of the vehicle is infinite, we provide an online algorithm with the best competitive ratio of . When the capacity of the vehicle is finite, that is, the vehicle can deliver at most c jobs at a time, we provide another best possible online algorithm with the competitive ratio of .

Description: We propose a novel SIR epidemic dynamical control model with media impact, where the state dependent pulse vaccination and medication treatment control strategies are being introduced to prevent the spread of disease at different control threshold values. By using the geometry theory of differential equation and method of successor function, the existence of positive order-1 periodic solution is studied. Further, some sufficient conditions of the orbitally asymptotical stability for positive order-1 periodic solution are given by the analog Poincaré criterion. Furthermore, numerical simulations are carried to illustrate the feasibility of our main results presented here.

Description: This paper is concerned with the optimal Kalman filtering problem for a class of discrete stochastic systems with multiplicative noises and random two-step sensor delays. Three Bernoulli distributed random variables with known conditional probabilities are introduced to characterize the phenomena of the random two-step sensor delays which may happen during the data transmission. By using the state augmentation approach and innovation analysis technique, an optimal Kalman filter is constructed for the augmented system in the sense of the minimum mean square error (MMSE). Subsequently, the optimal Kalman filtering is derived for corresponding augmented system in initial instants. Finally, a simulation example is provided to demonstrate the feasibility and effectiveness of the proposed filtering method.