Abstract: When dealing with evolving or multidimensional complex systems, network theory provides us with elegant ways of describing their constituting components, through, respectively, time-varying and multilayer complex networks. Nevertheless, the analysis of how these components are related is still an open problem. We here propose a general framework for analysing the evolution of a (complex) system, by describing the structure created by the difference between multiple networks by means of the Information Content metric. Differently from other approaches, which focus on assessing the magnitude of the change, the proposed one allows understanding if the observed changes are due to random noise or to structural (targeted) modifications; in other words, it allows describing the nature of the force driving the changes and discriminating between stochastic fluctuations and intentional modifications. We validate the framework by means of sets of synthetic networks, as well as networks representing real technological, social, and biological evolving systems. We further propose a way of reconstructing network correlograms, which allow converting the system’s evolution to the frequency domain. PubDate: Sun, 16 Dec 2018 08:20:06 +000

Abstract: It is well known that all agents in a multiagent system can asymptotically converge to a common value based on consensus protocols. Besides, the associated convergence rate depends on the magnitude of the smallest nonzero eigenvalue of Laplacian matrix . In this paper, we introduce a superposition system to superpose to the original system and study how to change the convergence rate without destroying the connectivity of undirected communication graphs. And we find the result if the eigenvector of eigenvalue has two identical entries , then the weight and existence of the edge do not affect the magnitude of , which is the argument of this paper. By taking advantage of the inequality of eigenvalues, conditions are derived to achieve the largest convergence rate with the largest delay margin, and, at the same time, the corresponding topology structure is characterized in detail. In addition, a method of constructing invalid algebraic connectivity weights is proposed to keep the convergence rate unchanged. Finally, simulations are given to demonstrate the effectiveness of the results. PubDate: Sun, 16 Dec 2018 08:06:08 +000

Abstract: Inspired by Shalev’s model of loss aversion, we investigate the effect of loss aversion on a bimatrix game where the payoffs in the bimatrix game are characterized by triangular fuzzy variables. First, we define three solution concepts of credibilistic loss aversion Nash equilibria, and their existence theorems are presented. Then, three sufficient and necessary conditions are given to find the credibilistic loss aversion Nash equilibria. Moreover, the relationship among the three credibilistic loss aversion Nash equilibria is discussed in detail. Finally, for bimatix game with triangular fuzzy payoffs, we investigate the effect of loss aversion coefficients and confidence levels on the three credibilistic loss aversion Nash equilibria. It is found that an increase of loss aversion levels of a player leads to a decrease of his/her own payoff. We also find that the equilibrium utilities of players are decreasing (increasing) as their own confidence levels when players employ the optimistic (pessimistic) value criterion. PubDate: Sun, 16 Dec 2018 00:00:00 +000

Abstract: Unmanned ground vehicles (UGVs) are well suited to tasks that are either too dangerous or too monotonous for people. For example, UGVs can traverse arduous terrain in search of disaster victims. However, it is difficult to design these systems so that they perform well in a variety of different environments. In this study, we evolve controllers and physical characteristics of a UGV with transformable wheels to improve its mobility in a simulated environment. The UGV’s mission is to visit a sequence of coordinates while automatically handling obstacles of varying sizes by extending wheel struts radially outward from the center of each wheel. Evolved finite state machines (FSMs) and artificial neural networks (ANNs) are compared, and a set of controller design principles are gathered from analyzing these experiments. Results show similar performance between FSM and ANN controllers but differing strategies. Finally, we show that a UGV’s controller and physical characteristics can be effectively chosen by examining results from evolutionary optimization. PubDate: Sun, 16 Dec 2018 00:00:00 +000

Abstract: This paper is concerned with the target tracking problem of an autonomous surface vehicle in the presence of a maneuvering target. The velocity information of target is totally unknown to the follower vehicle, and only the relative distance and angle between the target and follower are obtained. First, a reduced-order extended state observer is used to estimate the unknown relative dynamics due to the unavailable velocity of the target. Based on the reduced-order extended state observer, an antidisturbance guidance law for target tracking is designed. The input-to-state stability of the closed-loop target tracking guidance system is analyzed via cascade theory. Furthermore, the above result is extended to the case that collisions between the target and leader are avoided during tracking, and a collision-free target tracking guidance law is developed. The main feature of the proposed guidance law is twofold. First, the target tracking can be achieved without using the velocity information of the target. Second, collision avoidance can be achieved during target tracking. Simulation results show the effectiveness of the proposed antidisturbance guidance law for tracking a maneuvering target with the arbitrary bounded velocity. PubDate: Thu, 13 Dec 2018 08:29:34 +000

Abstract: Discovering and modeling community structure exist to be a fundamentally challenging task. In domains such as biology, chemistry, and physics, researchers often rely on community detection algorithms to uncover community structures from complex systems yet no unified definition of community structure exists. Furthermore, existing models tend to be oversimplified leading to a neglect of richer information such as nodal features. Coupled with the surge of user generated information on social networks, a demand for newer techniques beyond traditional approaches is inevitable. Deep learning techniques such as network representation learning have shown tremendous promise. More specifically, supervised and semisupervised learning tasks such as link prediction and node classification have achieved remarkable results. However, unsupervised learning tasks such as community detection remain widely unexplored. In this paper, a novel deep generative model for community detection is proposed. Extensive experiments show that the proposed model, empowered with Bayesian deep learning, can provide insights in terms of uncertainty and exploit nonlinearities which result in better performance in comparison to state-of-the-art community detection methods. Additionally, unlike traditional methods, the proposed model is community structure definition agnostic. Leveraging on low-dimensional embeddings of both network topology and feature similarity, it automatically learns the best model configuration for describing similarities in a community. PubDate: Thu, 13 Dec 2018 08:18:14 +000

Abstract: In this work we explore two potential mechanisms inducing multiple equilibria for weakly reversible networks with mass-action kinetics. The study is performed on a class of polynomial dynamic systems that, under some mild assumptions, are able to accommodate in their state-space form weakly reversible mass-action kinetic networks. The contribution is twofold. We provide an explicit representation of the set of all positive equilibria attained by the system class in terms of a set of (positive parameter dependent) algebraic relations. With this in hand, we prove that deficiency-one networks can only admit multiple equilibria via folding of the equilibrium manifold, whereas a bifurcation leading to multiple branches is only possible in networks with deficiencies larger than one. Interestingly, some kinetic networks within this latter class are capable of sustaining multiple equilibria for any reaction simplex, as we illustrate with one example. PubDate: Wed, 12 Dec 2018 00:00:00 +000

Abstract: Water quality prediction is the basis of water environmental planning, evaluation, and management. In this work, a novel intelligent prediction model based on the fuzzy wavelet neural network (FWNN) including the neural network (NN), the fuzzy logic (FL), the wavelet transform (WT), and the genetic algorithm (GA) was proposed to simulate the nonlinearity of water quality parameters and water quality predictions. A self-adapted fuzzy -means clustering was used to determine the number of fuzzy rules. A hybrid learning algorithm based on a genetic algorithm and gradient descent algorithm was employed to optimize the network parameters. Comparisons were made between the proposed FWNN model and the fuzzy neural network (FNN), the wavelet neural network (WNN), and the neural network (ANN). The results indicate that the FWNN made effective use of the self-adaptability of NN, the uncertainty capacity of FL, and the partial analysis ability of WT, so it could handle the fluctuation and the nonseasonal time series data of water quality, while exhibiting higher estimation accuracy and better robustness and achieving better performances for predicting water quality with high determination coefficients over 0.90. The FWNN is feasible and reliable for simulating and predicting water quality in river. PubDate: Wed, 12 Dec 2018 00:00:00 +000

Abstract: This article presents a review on two methods based on dynamic mode decomposition and its multiple applications, focusing on higher order dynamic mode decomposition (which provides a purely temporal Fourier-like decomposition) and spatiotemporal Koopman decomposition (which gives a spatiotemporal Fourier-like decomposition). These methods are purely data-driven, using either numerical or experimental data, and permit reconstructing the given data and identifying the temporal growth rates and frequencies involved in the dynamics and the spatial growth rates and wavenumbers in the case of the spatiotemporal Koopman decomposition. Thus, they may be used to either identify and extrapolate the dynamics from transient behavior to permanent dynamics or construct efficient, purely data-driven reduced order models. PubDate: Wed, 12 Dec 2018 00:00:00 +000

Abstract: Adaptive Monte Carlo localization (AMCL) algorithm has a limited pose accuracy because of the nonconvexity of the laser sensor model, the complex and unstructured features of the working environment, the randomness of particle sampling, and the final pose selection problem. In this paper, an improved AMCL algorithm is proposed, aiming to build a laser radar-based robot localization system in a complex and unstructured environment, with a LIDAR point cloud scan-matching process after the particle score calculating process. The weighted mean pose of AMCL particle swarm is used as the initial pose of the scan matching process. The LIDAR point cloud is matched with the probability grid map from coarse to fine using the Gaussian-Newton method, which results in more accurate poses. Moreover, the scan-matching pose is added into the particle swarm as a high-weight particle. So the particle swarm after resampling will be more concentrated in the correct position. The particle filter and the scan-matching process form a closed loop, thus enhancing the localization accuracy of mobile robots. The experiment results demonstrate that the proposed improved AMCL algorithm is superior to the traditional AMCL algorithm in the complex and unstructured environment, by exploiting the high-accuracy characteristic of scan matching while inheriting the stability of AMCL. PubDate: Tue, 11 Dec 2018 09:27:09 +000

Abstract: This paper studies distributed optimization having flocking behavior and local constraint set. Multiagent systems with continuous-time and second-order dynamics are studied. Each agent has a local constraint set and a local objective function, which are known to only one agent. The objective is for multiple agents to optimize a sum of the local functions with local interaction and information. First, a bounded potential function to construct the controller is given and a distributed optimization algorithm that makes a group of agents avoid collisions during the evolution is presented. Then, it is proved that all agents track the optimal velocity while avoiding collisions. The proof of the main result is divided into three steps: global set convergence, consensus analysis, and optimal set convergence. Finally, a simulation is included to illustrate the results. PubDate: Tue, 11 Dec 2018 06:33:14 +000

Abstract: An integrated guidance integrated estimation/guidance law is designed for exoatmospheric interceptors equipped with divert thrusters and optical seekers to intercept maneuvering targets. This paper considers an angles-only guidance problem for exoatmospheric maneuvering targets. A bounded differential game-based guidance law is derived against maneuvering targets using zero-effort-miss (ZEM). Estimators based the extended Kalman filter (EKF) and the unscented Kalman filter (UKF) are designed to estimate LOS rates that are contaminated by noise and target maneuver. Furthermore, to improve the observability of the range, an observability enhancement differential game guidance law is derived. The guidance law and the estimator are integrated together in the guidance loop. The proposed integrated estimation/guidance law has been tested in several three-dimensional nonlinear interception scenarios. Numerical simulations on a set of Monte-Carlo simulations prove the validity and superiority of the proposed guidance law in hit-to-kill interception. PubDate: Mon, 10 Dec 2018 08:48:06 +000

Abstract: The aim of this paper is to present a computational model of the CA1 region of the hippocampus, whose properties include (a) attenuation of receptors for external stimuli, (b) delay and decay of postsynaptic potentials, (c) modification of internal weights due to propagation of postsynaptic potentials through the dendrite, and (d) modification of weights for the analog memory of each input due to a pattern of long-term synaptic potentiation (LTP) with regard to its decay. The computer simulations showed that CA1 model performs efficient LTP induction and high rate of sub-millisecond coincidence detection. We also discuss a possibility of hardware implementation of pyramidal cells of CA1 region of the hippocampus. PubDate: Mon, 10 Dec 2018 07:45:39 +000

Abstract: Protein-protein interactions (PPIs), as an important molecular process within cells, are of pivotal importance in the biochemical function of cells. Although high-throughput experimental techniques have matured, enabling researchers to detect large amounts of PPIs, it has unavoidable disadvantages, such as having a high cost and being time consuming. Recent studies have demonstrated that PPIs can be efficiently detected by computational methods. Therefore, in this study, we propose a novel computational method to predict PPIs using only protein sequence information. This method was developed based on a deep learning algorithm-stacked sparse autoencoder (SSAE) combined with a Legendre moment (LM) feature extraction technique. Finally, a probabilistic classification vector machine (PCVM) classifier is used to implement PPI prediction. The proposed method was performed on human, unbalanced-human, H. pylori, and S. cerevisiae datasets with 5-fold cross-validation and yielded very high predictive accuracies of 98.58%, 97.71%, 93.76%, and 96.55%, respectively. To further evaluate the performance of our method, we compare it with the support vector machine- (SVM-) based method. The experimental results indicate that the PCVM-based method is obviously preferable to the SVM-based method. Our results have proven that the proposed method is practical, effective, and robust. PubDate: Mon, 10 Dec 2018 00:00:00 +000

Abstract: Objective. Urumqi is one of the key areas of HIV/AIDS infection in Xinjiang and in China. The AIDS epidemic is spreading from high-risk groups to the general population, and the situation is still very serious. The goal of this study was to use four data mining algorithms to establish the identification model of HIV infection and compare their predictive performance. Method. The data from the sentinel monitoring data of the three groups of high-risk groups (injecting drug users (IDU), men who have sex with men (MSM), and female sex workers (FSW)) in Urumqi from 2009 to 2015 included demographic characteristics, sex behavior, and serological detection results. Then we used age, marital status, education level, and other variables as input variables and whether to infect HIV as output variables to establish four prediction models for the three datasets. We also used confusion matrix, accuracy, sensitivity, specificity, precision, recall, and the area under the receiver operating characteristic (ROC) curve (AUC) to evaluate classification performance and analyzed the importance of predictive variables. Results. The final experimental results show that random forests algorithm obtains the best results, the diagnostic accuracy for random forests on MSM dataset is 94.4821%, 97.5136% on FSW dataset, and 94.6375% on IDU dataset. The k-nearest neighbors algorithm came out second, with 91.5258% diagnostic accuracy on MSM dataset, 96.3083% diagnostic accuracy on FSW dataset, and 90.8287% diagnostic accuracy on IDU dataset, followed by support vector machine (94.0182%, 98.0369%, and 91.3571%). The decision tree algorithm was the poorest among the four algorithms, with 79.1761% diagnostic accuracy on MSM dataset, 87.0283% diagnostic accuracy on FSW dataset, and 74.3879% accuracy on IDU. Conclusions. Data mining technology, as a new method of assisting disease screening and diagnosis, can help medical personnel to screen and diagnose AIDS rapidly from a large number of information. PubDate: Mon, 10 Dec 2018 00:00:00 +000

Abstract: Unpaired image translation is a challenging problem in computer vision, while existing generative adversarial networks (GANs) models mainly use the adversarial loss and other constraints to model. But the degree of constraint imposed on the generator and the discriminator is not enough, which results in bad image quality. In addition, we find that the current GANs-based models have not yet been implemented by adding an auxiliary domain, which is used to constrain the generator. To solve the problem mentioned above, we propose a multiscale and multilevel GANs (MMGANs) model for image translation. In this model, we add an auxiliary domain to constrain generator, which combines this auxiliary domain with the original domains for modelling and helps generator learn the detailed content of the image. Then we use multiscale and multilevel feature matching to constrain the discriminator. The purpose is to make the training process as stable as possible. Finally, we conduct experiments on six image translation tasks. The results verify the validity of the proposed model. PubDate: Sun, 09 Dec 2018 07:33:41 +000

Abstract: Based on the current status of the development of equipment manufacturing enterprises, the main influencing factors and main evaluation indicators of the equipment manufacturing enterprises in the context of dual-channel marketing are analyzed and determined. According to the organizational layout and transaction form of equipment manufacturing enterprises, the problem of equipment manufacturing enterprise location decision-making with online channel and offline channel trading mode is divided into two categories. One is the single-plant location decision problem, and the other is the multiplant location decision problem. In the single-plant location decision problem, we have established the conceptual model and mathematical model. Based on prospect theory, we use multicriteria decision-making method to quantify and standardize the evaluation index and determine the reference value, and the priority of the scheme is determined according to the final comprehensive value. In the multiplant location decision problem, we have established the conceptual model and the logical model and established the mathematical model with the goal of minimizing the cost and time. Finally, an example is given to solve the location problem of single plant and multiple plants. The feasibility of the model is proved by the solution and verification. PubDate: Sun, 09 Dec 2018 07:29:46 +000

Abstract: We aim to study the characteristics and mechanism of the aerodynamic noise sources for a high-speed train in a tunnel at the speeds of 50 m/s, 70 m/s, 83 m/s, and 97 m/s by means of the numerical wind tunnel model and the nonreflective boundary condition. First, the large eddy simulation model was used to simulate the fluctuating flow field around a 1/8 scale model of a high-speed train that consists of three connected vehicles with bogies in the tunnel. Next, the spectral characteristics of the aerodynamic noise source for the high-speed train were obtained by performing a Fourier transform on the fluctuating pressure. Finally, the mechanism of the aerodynamic noise was studied using the sound theory of cavity flow and the flow field structure. The results show that the spectrum pattern of the sound source energy presented broadband and multipeak characteristics for the high-speed train. The dominant distribution frequency range is from 100 Hz to 4 kHz for the high-speed train, accounting for approximately 95.1% of the total sound source energy. The peak frequencies are 400 Hz and 800 Hz. The sound source energy at 400 Hz and 800 Hz is primarily from the bogie cavities. The spectrum pattern of the sound source energy has frequency similarity for the bottom structure of the streamlined part of the head vehicle. The induced mode of the sound source energy is probably the dynamic oscillation mode of the cavity and the resonant oscillation mode of the cavity for the under-car structure at 400 Hz and 800 Hz, respectively. The numerical computation model was checked by the wind tunnel test results. PubDate: Sun, 09 Dec 2018 00:00:00 +000

Abstract: In this study, we examine the impact of the dominant enterprise’s fairness concern on decisions in e-supply chains. Considering that the network platform’s service level obviously influences product sales, an e-supply chain consisting of a single manufacturer and a single network platform is constructed. In this setting, four models of whether fairness concern is considered by the different dominant parties are investigated, and optimal decisions of the four models are compared and analyzed to study the impact of fairness concern. The findings show that when fairness concern is not taken into account, the profits when enterprises are dominating system are higher than when they are not dominating system. When fairness concern is considered, the dominant enterprise’s fairness concern is beneficial to increase the subordinate enterprise’s profit, but it will reduce its profit. And when the network platform dominates system and considers fairness concern, the sales price and the service level are the highest, indicating that consumers can get an enjoyable shopping experience. To sales price, it is negatively correlated with the fairness concern coefficient if manufacturer dominates the system, while it is positively correlated with the fairness concern coefficient if network platform dominates the system. Regardless of who has the fairness concern, fairness concern can improve the service level and increase consumer stickiness. PubDate: Thu, 06 Dec 2018 08:49:28 +000

Abstract: In this study, we develop two Ant Colony Optimization (ACO) models as new metaheuristic models for solving the time-constrained Travelling Salesman Problem (TSP). Here, the time-constrained TSP means a TSP in which several cities have constraints that the agents have to visit within prescribed time limits. In our ACO models, only agents that achieved tour under certain conditions defined in respective ACO models are allowed to modulate pheromone deposition. The agents in one model are allowed to deposit pheromone only if they achieve a tour satisfying strictly the above purpose. The agents in the other model is allowed to deposit pheromone not only if they achieve a tour satisfying strictly the above purpose, but also if they achieve a tour satisfying the above purpose in some degree. We compare performance of two developed ACO models by focusing on pheromone deposition. We confirm that the later model performs well to some TSP benchmark datasets from TSPLIB in comparison to the former and the traditional AS (Ant System) models. Furthermore, the agent exhibits critical properties; i.e., the system exhibits complex behaviors. These results suggest that the agents perform adaptive travels by coordinating some complex pheromone depositions. PubDate: Thu, 06 Dec 2018 08:24:46 +000

Abstract: Multidimensional data that occur in a variety of applications in clinical diagnostics and health care can naturally be represented by multidimensional arrays (i.e., tensors). Tensor decompositions offer valuable and powerful tools for latent concept discovery that can handle effectively missing values and noise. We propose a seamless, application-independent feature extraction and multiple-instance (MI) classification method, which represents the raw multidimensional, possibly incomplete, data by means of learning a high-order dictionary. The effectiveness of the proposed method is demonstrated in two application scenarios: (i) prediction of frailty in older people using multisensor recordings and (ii) breast cancer classification based on histopathology images. The proposed method outperforms or is comparable to the state-of-the-art multiple-instance learning classifiers highlighting its potential for computer-assisted diagnosis and health care support. PubDate: Thu, 06 Dec 2018 00:00:00 +000

Abstract: Decision support systems founded on rule-based knowledge representation should be equipped with rule management mechanisms. Effective exploration of new knowledge in every domain of human life requires new algorithms of knowledge organization and a thorough search of the created data structures. In this work, the author introduces an optimization of both the knowledge base structure and the inference algorithm. Hence, a new, hierarchically organized knowledge base structure is proposed as it draws on the cluster analysis method and a new forward-chaining inference algorithm which searches only the so-called representatives of rule clusters. Making use of the similarity approach, the algorithm tries to discover new facts (new knowledge) from rules and facts already known. The author defines and analyses four various representative generation methods for rule clusters. Experimental results contain the analysis of the impact of the proposed methods on the efficiency of a decision support system with such knowledge representation. In order to do this, four representative generation methods and various types of clustering parameters (similarity measure, clustering methods, etc.) were examined. As can be seen, the proposed modification of both the structure of knowledge base and the inference algorithm has yielded satisfactory results. PubDate: Thu, 06 Dec 2018 00:00:00 +000

Abstract: We have retrieved and analyzed several millions of Twitter messages corresponding to the Spanish general elections held on the 20th of December 2015 and repeated on the 26th of June 2016. The availability of data from two electoral campaigns that are very close in time allows us to compare collective behaviors of two analogous social systems with a similar context. By computing and analyzing the time series of daily activity, we have found a significant linear correlation between both elections. Additionally, we have revealed that the daily number of tweets, retweets, and mentions follow a power law with respect to the number of unique users that take part in the conversation. Furthermore, we have verified that the topologies of the networks of mentions and retweets do not change from one election to the other, indicating that their underlying dynamics are robust in the face of a change in social context. Hence, in the light of our results, there are several recurrent collective behavioral patterns that exhibit similar and consistent properties in different electoral campaigns. PubDate: Wed, 05 Dec 2018 10:11:43 +000

Abstract: We consider a stochastic continuous submodular huge-scale optimization problem, which arises naturally in many applications such as machine learning. Due to high-dimensional data, the computation of the whole gradient vector can become prohibitively expensive. To reduce the complexity and memory requirements, we propose a stochastic block-coordinate gradient projection algorithm for maximizing continuous submodular functions, which chooses a random subset of gradient vector and updates the estimates along the positive gradient direction. We prove that the estimates of all nodes generated by the algorithm converge to some stationary points with probability 1. Moreover, we show that the proposed algorithm achieves the tight approximation guarantee after iterations for DR-submodular functions by choosing appropriate step sizes. Furthermore, we also show that the algorithm achieves the tight approximation guarantee after iterations for weakly DR-submodular functions with parameter by choosing diminishing step sizes. PubDate: Wed, 05 Dec 2018 09:43:11 +000

Abstract: How are ownership relationships distributed in the geographical space' Is physical proximity a significant factor in investment decisions' What is the impact of the capital city' How can the structure of investment patterns characterize the attractiveness and development of economic regions' To explore these issues, we analyze the network of company ownership in Hungary and determine how are connections are distributed in geographical space. Based on the calculation of the internal and external linking probabilities, we propose several measures to evaluate the attractiveness of towns and geographic regions. Community detection based on several null models indicates that modules of the network coincide with administrative regions, in which Budapest is the absolute centre, and where county centres function as hubs. Gravity model-based modularity analysis highlights that, besides the strong attraction of Budapest, geographical distance has a significant influence over the frequency of connections and the target nodes play the most significant role in link formation, which confirms that the analysis of the directed company-ownership network gives a good indication of regional attractiveness. PubDate: Wed, 05 Dec 2018 08:10:06 +000

Abstract: The integration of renewable power supplies into existing electrical grids, or other major technology transitions in electric power, is a complex sociotechnical process. While the technical challenges are well-understood, the process of adapting electricity policy and market rules to these new technologies is understudied. Planning and market rules are a critical determinant of the technical success of renewable energy integration efforts and the financial viability of renewable energy investments. Organizational adaptation can be particularly complex in electric power, where transmission grids cross multiple political boundaries and decisions are made not by central authorities or governments, but in cooperative regional frameworks that must accommodate many divergent interests. We add to a recently emerging literature on the governance of regional organizations that plan and operate electric power grids by developing and illustrating a novel approach to the study of political power in multistakeholder electricity organizations. We use semistructured interviews with participants in a specific regional electric grid authority, the PJM Regional Transmission Operator in the Mid-Atlantic United States, to elicit perceptions of where tensions arise in stakeholder-driven processes for changing PJM’s rules and perceptions of those groups of stakeholders that possess political power. We treat these perceptions as hypotheses that can be evaluated empirically using five years of data from PJM on how stakeholders voted on a wide variety of regional electricity policy issues. Representing voting behavior as a network, we use a community detection method to identify strong coalitions of stakeholders in PJM that provide support for some stakeholder perceptions of political power and refute other perceptions. The degree distribution of the voting network exhibits a fat tail relative to those in other canonical graph models. We show, using relatively simple network metrics including degree, betweenness, and the mixing parameter, that the reason for this fat tail in the degree distribution is the existence of “swing” voters in RTO stakeholder networks. These voters are identifiable in the tail of the degree distribution of the voting network and are influential in pushing highly contentious rule change proposals towards passage or failure. The method we develop is generalizable to other contexts and provides a new framework for the study of regional electricity policy formation. PubDate: Wed, 05 Dec 2018 08:01:43 +000

Abstract: A fractional Kalman filter-based multirate sensor fusion algorithm is presented to fuse the asynchronous measurements of the multirate sensors. Based on the characteristics of multirate and delay measurement, the state is reestimated at the time when the delayed measurement occurs by using weighted fractional Kalman filter, and then the state estimation is updated at the current time when the delayed measurement arrives following the similar pattern of Kalman filter. The simulation examples are given to illustrate the effectiveness of the proposed fusion method. PubDate: Wed, 05 Dec 2018 00:00:00 +000

Abstract: Bankruptcy of listed companies or shareholders delisting usually causes the crisis spreading in stock markets. Based on the systematic analysis of the epidemic diseases and rumors spreading on the complex networks, the SIR model is introduced to research the crisis spreading in shareholding networks of listed companies and their main holders on the basis of the data about ownership structure in Chinese Stock Markets. The characteristics of shareholding networks are studied, and the parameters for the SIR model are obtained by empirical approach. Then, the numerical computation method is successfully used to analyze the crisis spreading in the networks when the networks meet random failures or intentional attacks. We find the networks have good robustness against the random failures. However, the crisis will spread at a high speed and cause catastrophic damage if there are some failures or attacks on hub vertices in the networks. Under this condition, the networks show obvious vulnerability. Last but not least, the controllability of the networks under the condition of intentional attacks and random failures is studied. The results show that if the network is controlled globally, it is more reliable to allow a politically good new or an appropriate exciting economical policy to play the role in orienting markets under the control of public opinions as the crisis occurs. However, under normal circumstances, controlling a small part of driver vertices representing listed companies, applying appropriate control strategies, and using its characteristics of high efficiency of sending information can effectively control the stock market. Our research provides a new reference to further exploration about the transmission mechanism of the crisis based SIR model and further research on the controllability of crisis spreading in financial markets. PubDate: Tue, 04 Dec 2018 07:23:02 +000

Abstract: The complexity and resilience of urban public transit network (PTN) are the interdisciplinary study area between transportation engineering and system science, which is a good demonstration of applying complex network theory to promote the development of engineering science. The deep understanding of this study helps to provide a new perspective for analyzing the reliability of urban PTN. Following study process of the complexity and resilience of complex network, this paper reviews the complexity and resilience of PTN from four topics, i.e., the PTN complexity, the static resilience of PTN, the dynamic resilience (cascading failures based resilience) of single layered PTN, and the dynamic resilience of interdependent PTN. In the literature review, multiple key items are, respectively, extracted for each topic, and the engineering applicability of each topic is also analyzed, which are both for obtaining the key features of this study area. Finally, in order to realize the development trend of cyclic and forward—complex network theory, network resilience theory, transforming into a realistic model and method that is close to actual public transit operation, engineering application and practice, and contributing to complex network theory, the study status is summarized and the future development trend is prospected. PubDate: Tue, 04 Dec 2018 06:19:22 +000