Abstract: As we all know, bacteria is different from virus which with certain types can be killed by the immune cells in the body. The brucellosis, a bacterial disease, can invade the body by indirect transmission from environment, which has not been researched by combining with immune cells. Considering the effects of immune cells, we put a minimum infection dose of brucellosis invading into the dairy cows as an immunological threshold and get a switch model. In this paper, we accomplish a thorough dynamics analysis of a switch model. On the one hand, we can get a disease-free and bacteria-free steady state and up to three endemic steady states which may be thoroughly analyzed in different cases of a minimum infection dose in a switch model. On the other hand, we calculate the basic reproduction number and know that the disease-free and bacteria-free steady state is a global stability when , and the one of the endemic steady state is a conditionally global stability when . We find that different amounts of may lead to different steady states of brucellosis, and considering the effects of immunology is more serious in mathematics and biology. PubDate: Thu, 23 May 2019 08:05:17 +000

Abstract: Gene expression governs important biological processes such as the cell’s growth cycle and its response to environmental signals. Alterations of this complex network of transcriptional interactions often lead to unstable expression states and disease. Estrogen is a sex hormone known for its roles in cell proliferation. Its expression has been involved in several physiological functions such as regulating the menstrual and reproduction cycles in women. Altered expression states where estrogen levels are atypically high have been associated with an increased incidence of breast, ovarian, and cervix cancer. To better understand the implications of deregulation of the estrogen and estrogen receptor regulatory networks, in this work we generated a dynamical model of gene regulation of the estrogen receptor transcription network based on known regulatory interactions. By using an adaptation to classical Boolean Networks dynamics we identified proliferative and antiproliferative gene expression states of the network and also to identify key players that promote these altered states when perturbed. We also modeled how pairwise gene alterations may contribute to shifts between these two proliferative states and found that the coordinated subexpression of E2F1 and SMAD4 is the most important combination in terms of promoting proliferative states in the network. PubDate: Wed, 22 May 2019 12:05:10 +000

Abstract: This paper investigates the control and synchronization of a class of 3-D uncertain fractional-order chaotic systems with external disturbances. The adding one power integrator control scheme, which is the generalization of the traditional backstepping method, is used to investigate the global stability of the control and synchronization manifold. As a result, several criteria for chaos control and synchronization are obtained. Compared with the previous results, the presented strategies can not only be applied to a class of strict-feedback systems but also be applied to more general class of fractional-order chaotic systems. In addition, the proposed controllers are robust against uncertain parameters and external disturbances. To validate the effectiveness of the proposed criteria, two illustrative examples are given. PubDate: Wed, 22 May 2019 12:05:07 +000

Abstract: ECAP (Equal Channel Angular Pressing) is a well-known technique by which a specimen is pressed into an ECAP die to improve the mechanical properties by the nearly pure shear during the deformation process. In the ECAP processing of can, the specimen is canned with a protection material layer to avoid the cracking during deformation. At present, most simulation studies of ECAP are conducted based on the finite element method, in which large deformation can cause serious mesh distortion, resulting in a decrease of the simulation accuracy. In this study, based on SPH (Smooth Particle Hydrodynamics), we utilize the invalid particles and crack treatment techniques, building an ECAP mathematical model incorporating damage prediction, in order to simulate crack initiation and dynamic extension in the ECAP process. In simulation of pure magnesium during ECAP at room temperature using industrial pure iron as the canned material, the simulation results based on SPH method show that the plastic deformation of the pure magnesium specimen is homogeneous in both the vertical direction and the extrusion direction. The average equivalent strain value of the specimen in the major deformation area is 1.31, which is similar to the finite element simulation result in which the average equivalent strain value of the major deformation area is 1.24. From the damage perspective, the maximum damage values of the inside specimen obtained by the SPH method and the finite element method are both less than 0.16, with both values being far lower than the critical fracture accumulated damage value. The test results well match the simulation results. PubDate: Wed, 22 May 2019 08:05:09 +000

Abstract: In this paper, a fractional-order chaotic circuit based on a novel fractional-order generalized memristor is proposed. It is proved that the circuit based on the diode bridge cascaded with fractional-order inductor has volt-ampere characteristics of pinched hysteresis loop. Then the mathematical model of the fractional-order memristor chaotic circuit is obtained. The impact of the order and system parameters on the dynamic behaviors of the chaotic circuit is studied by phase trajectory, Poincaré Section, and bifurcation diagram method. The order, as an important parameter, can increase the degree of freedom of the system. With the change of the order and parameters, the circuit will exhibit abundant dynamic behaviors such as coexisting upper and lower limit cycle, single scroll chaotic attractors, and double scroll chaotic attractors under different initial conditions. And the system exhibits antimonotonic behavior of antiperiodic bifurcation with the change of system parameters. The equivalent circuit simulations are designed to verify the results of the theoretical analysis and numerical simulation. PubDate: Tue, 21 May 2019 10:05:05 +000

Abstract: In this paper, we propose a novel prediction algorithm based on an improved Elman neural network (NN) ensemble for quality prediction, thus achieving the quality control of designed products at the product design stage. First, the Elman NN parameters are optimized using the grasshopper optimization (GRO) method, and then the weighted average method is improved to combine the outputs of the individual NNs, where the weights are determined by the training errors. Simulations were conducted to compare the proposed method with other NN methods and evaluate its performance. The results demonstrated that the proposed algorithm for quality prediction obtained better accuracy than other NN methods. In this paper, we propose a novel Elman NN ensemble model for quality prediction during product design. Elman NN is combined with GRO to yield an optimized Elman network ensemble model with high generalization ability and prediction accuracy. PubDate: Tue, 21 May 2019 07:05:11 +000

Abstract: This paper addresses variable-time impulsive control for coordinated tracking problem in nonlinear multiagent systems. To make followers coordinately track the leader, a variable-time impulsive controller is designed. Under some well-selected conditions, the comparison system of variable-time impulsive tracking control system is constructed by employing B-equivalence method. And we theoretically demonstrate that the two systems have the same stability property. Coordinated tracking criteria of multiagent systems are obtained by considering the comparison system. Numerical simulation is also provided to illustrate the correctness of theoretical results and the efficiency of the variable-time impulsive controller. PubDate: Mon, 20 May 2019 12:05:11 +000

Abstract: Let be a graph and the adjacency matrix of . The permanent of matrix is called the permanental polynomial of . The permanental sum of is the sum of the absolute values of the coefficients of permanental polynomial of . Computing the permanental sum is #p-complete. In this note, we prove the maximum value and the minimum value of permanental sum of quasi-tree graphs. And the corresponding extremal graphs are also determined. Furthermore,we also determine the graphs with the minimum permanental sum among quasi-tree graphs of order and size , where . PubDate: Mon, 20 May 2019 11:05:10 +000

Abstract: The semidirect drive cutting transmission system of coal cutters is prone to unstable torsional vibration when the resistance values of its driving permanent magnet synchronous motor (PMSM) are affected by changes in temperatures and tough conditions. Besides, the system has the properties of complex electromechanically coupling such as the coupling between electrical parameters and mechanical parameters. Therefore, in this study, the nonlinear torsional vibration equation was established on the basis of the Lagrange-Maxwell theory. Moreover, in light of the nonlinear dynamic bifurcation theory, the system stability was analyzed by taking the resistance value of power motor as the bifurcation parameter. In addition, the influence of subcritical bifurcation on the torsional vibration was studied by investigating the necessary and sufficient conditions for dynamic Hopf bifurcation and classifying the bifurcation types. At last, in order to suppress destabilizing oscillation induced by Hopf bifurcation, the nonlinear feedback controller was constructed, with the introduction of feedback from the motor velocity as well as the selection of voltage value on the shaft as the controlled variable. Meanwhile, the three-order normal form and controlling parameters of the system were obtained with the aid of the multiple scales method and the harmonic balance method. In this way, the Hopf bifurcation point was transferred to control the stability of Hopf bifurcation and the amplitude of limit cycle, thus guaranteeing reliable and safe operation of the system. The numerical simulation results indicate that the designed controller boosts an ideal controlling effect. PubDate: Mon, 20 May 2019 11:05:07 +000

Abstract: The production and storage of major hazard installations (MHIs) bring potential risks to chemical industrial park (CIP). In the production system of MHIs, its dangerous degree is mainly determined by key parameters, and abnormal key parameters often lead to accidents. To predict the real-time risk values of MHIs and improve accident prevention ability of CIP, we need a method that can combine dynamic prediction and assessment. Quantitative risk assessment (QRA) is not capable of modelling risk variations during the operation of a process. Therefore, this paper adopts the data-driven approach. Inspired by visual qualitative analysis and quantitative analysis, a dynamic early warning method is proposed for MHIs. We can get the future trend of these key parameters by using strongly correlation variables to predict key parameters. Fuzzy evaluation analysis is performed on the risk levels of key parameters, and the dynamic evaluation index of these MHIs is obtained. This method can be applied to the dynamic evaluation of MHIs system in CIP. It can contribute to the safety of CIP in some aspects. PubDate: Mon, 20 May 2019 09:05:12 +000

Abstract: Individual socioeconomic status inference from online traces is a remarkably difficult task. While current methods commonly train predictive models on incomplete data by appending socioeconomic information of residential areas or professional occupation profiles, little attention has been paid to how well this information serves as a proxy for the individual demographic trait of interest when fed to a learning model. Here we address this question by proposing three different data collection and combination methods to first estimate and, in turn, infer the socioeconomic status of French Twitter users from their online semantics. We assess the validity of each proxy measure by analyzing the performance of our prediction pipeline when trained on these datasets. Despite having to rely on different user sets, we find that training our model on professional occupation provides better predictive performance than open census data or remote sensed expert annotation of habitual environments. Furthermore, we release the tools we developed in the hope it will provide a generalizable framework to estimate socioeconomic status of large numbers of Twitter users as well as contribute to the scientific discussion on social stratification and inequalities. PubDate: Sun, 19 May 2019 11:05:10 +000

Abstract: This paper is based on the Takagi-Sugeno (T-S) fuzzy models to construct a coronary artery system (CAS) T-S fuzzy controller and considers the uncertainties of system state parameters in CAS. We propose the fuzzy model of CAS with uncertainties. By using T-S fuzzy model of CAS and the use of parallel distributed compensation (PDC) concept, the same fuzzy set is assigned to T-S fuzzy controller. Based on this, a PDC controller whose fuzzy rules correspond to the fuzzy model is designed. By constructing a suitable Lyapunov-Krasovskii function (LKF), the stability conditions of the linear matrix inequality (LMI) are exported. Simulation results show that the method proposed in this paper is correct and effective and has certain practical significance. PubDate: Thu, 16 May 2019 09:05:10 +000

Abstract: With the development of complex renewable energy systems, the frequency control and regulation of the power grid powered by such renewable energies (e.g., wind turbine) are more critical, since the adopted different power generators can lead to frequency variations. To address the frequency regulation of such power grids, we will present a variable coefficient coordinated primary frequency regulation scheme for synchronous generator (SG) and doubly fed induction generator (DFIG). The variable adjustment coefficient of DFIG is defined according to the current reserve capacity, which can be applied to adjust different operation conditions to regulate the frequency variation within a predefined allowable range. Since the DFIG can make full use of the reserve wind power in the system frequency regulation, the proposed method can address both the frequency regulation response and the economic performance. Simulation results indicate that the proposed coordinated control scheme can achieve satisfactory frequency regulation response and lead to reduced demand for frequency regulation of SG. PubDate: Thu, 16 May 2019 09:05:08 +000

Abstract: In recent years, research on location-based services has received a lot of interest, in both industry and academic aspects, due to a wide range of potential applications. Among them, one of the active topic areas is the route planning on a point-of-interest (POI) network. We study the top-k optimal routes querying on large, general graphs where the edge weights may not satisfy the triangle inequality. The query strives to find the top-k optimal routes from a given source, which must visit a number of vertices with all the services that the user needs. Existing POI query methods mainly focus on the textual similarities and ignore the semantic understanding of keywords in spatial objects and queries. To address this problem, this paper studies the semantic similarity of POI keyword searching in the route. Another problem is that most of the previous studies consider that a POI belongs to a category, and they do not consider that a POI may provide various kinds of services even in the same category. So, we propose a novel top-k optimal route planning algorithm based on semantic perception (KOR-SP). In KOR-SP, we define a dominance relationship between two partially explored routes which leads to a smaller searching space and consider the semantic similarity of keywords and the number of single POI’s services. We use an efficient label indexing technique for the shortest path queries to further improve efficiency. Finally, we perform an extensive experimental evaluation on multiple real-world graphs to demonstrate that the proposed methods deliver excellent performance. PubDate: Wed, 15 May 2019 11:05:06 +000

Abstract: Many real-world infrastructure networks, such as power grids and communication networks, always depend on each other by their functional components that share geographic proximity. A lot of works were devoted to revealing the vulnerability of interdependent spatially embedded networks (ISENs) when facing node failures and showed that the ISENs are susceptible to geographically localized attacks caused by natural disasters or terrorist attacks. How to take emergency methods to prevent large scale of cascading failures on interdependent infrastructures is a longstanding problem. Here, we propose an effective strategy for the healing of local structures using the connection profile of a failed node, called the healing strategy by prioritizing minimum degrees (HPMD), in which a new link between two active low-degree neighbors of a failed node is established during the cascading process. Afterwards, comparisons are made between HPMD and three healing strategies based on three metrics: random choice, degree centrality, and local centrality, respectively. Simulations are performed on the ISENs composed of two diluted square lattices with the same size under localized attacks. Results show that HPMD can significantly improve the robustness of the system by enhancing the connectivity of low-degree nodes, which prevent the diffusion of failures from low-degree nodes to moderate-degree nodes. In particular, HPMD can outperform other three strategies in the size of the giant component of networks, critical attack radius, and the number of iterative cascade steps for a given quota of newly added links, which means HPMD is more effective, more timely, and less costly. The high performance of HPMD indicates low-degree nodes should be placed on the top priority for effective healing to resist the cascading of failures in the ISENs, which is totally different from the traditional methods that usually take high-degree nodes as critical nodes in a single network. Furthermore, HPMD considers the distance between a pair of nodes to control the variation in the network structures, which is more applicable to spatial networks than previous methods. PubDate: Wed, 15 May 2019 09:05:07 +000

Abstract: In this paper, the consensus tracking control problem of leader-following nonlinear multiagent systems with iterative learning control is investigated. The model of each following agent consists of second-order unknown nonlinear dynamics and the external disturbance. Moreover, the input of each following agent is subject to saturation constraint. It is assumed that the information of leader is not available to any following agents, and the radial basis function neural network is introduced to approximate the nonlinear dynamics. Then, a distributed adaptive neural network iterative learning control protocol and the adaptive updating laws for the time-varying parameters are proposed, respectively. A new Lyapunov function is constructed to analyze the validity of the presented control protocol. Finally, a numerical example is provided to verify the effectiveness of theoretical results. PubDate: Mon, 13 May 2019 11:05:08 +000

Abstract: By reducing innovation costs, innovation subsidies can help private enterprises convert their production modes to green production. Based on method of computational experiment in social science, we construct a dynamic model for environmental innovation behaviors of private enterprises to simulate their evolution process in different market mechanisms, product competitions, and innovation subsidies and explore the impact of different subsidy modes on environmental technological innovation behaviors. The experimental results show that, under actions of multiagents, the combination of market subsidy and technology transformation subsidy can achieve the highest utilization efficiency of subsidy funds. However, when level of innovation technology is low, the innovation process should be subsidized at the same time to improve the competitiveness of innovative products. Besides, according to the level of innovation technology, flexible innovation subsidy combinations can be adopted to optimize subsidy in the different stages. The experimental results are of great significance for increasing efficiency of innovation subsidy funds and promoting green sustainable development of private enterprises. PubDate: Mon, 13 May 2019 10:05:08 +000

Abstract: This paper proposes a new scheme for solving finite time neural networks adaptive tracking control issue for the nonaffine pure-feedback nonlinear system. The procedure, based on homeomorphism mapping and backstepping, effectively deals with constraint control and design difficulty induced by pure-feedback structure. The most outstanding novelty is that finite time adaptive law is proposed for training weights of neural networks. Furthermore, by combining finite time adaptive law and Lyapunov-based arguments, a valid finite time adaptive neural networks controller design algorithm is presented to ensure that system is practical finite stable (PFS) rather than uniformly ultimately bounded (UUB). Because of using the finite time adaptive law to training weights of neural networks, the closed-loop error system signals are in assurance of bounded in finite time. Benchmark simulations have well demonstrated effectiveness and efficiency of the proposed approach. PubDate: Mon, 13 May 2019 09:05:07 +000

Abstract: This paper treats the exponential stabilization of a class of n-D chaotic systems. A new control approach which is called the exact solution method is presented. The most important feature of this method is that the solution of the system under consideration can be carefully designed to converge exponentially to the origin. Based on this method, the exponential stabilization of a class of n-D chaotic systems and its application in controlling chaotic system with unknown parameter are presented. The Genesio-Tesi system is taken to give the numerical simulation which is completely consistent with the theoretical analysis presented in this paper. PubDate: Sun, 12 May 2019 08:05:11 +000

Abstract: In this paper, we are concerned with Clifford-valued cellular neural networks (CNNs) with discrete delays. Since Clifford algebra is a unital associative algebra and its multiplication is noncommutative, to overcome the difficulty of the noncommutativity of the multiplication of Clifford numbers, we first decompose the considered Clifford-valued neural network into real-valued systems. Second, based on the Banach fixed point theorem, we establish the existence and uniqueness of almost periodic solutions of the considered neural networks. Then, by designing a novel state-feedback controller and constructing a proper Lyapunov function, we study the global asymptotic synchronization of the considered neural networks. Finally, a numerical example is presented to show the effectiveness and feasibility of our results. PubDate: Sun, 12 May 2019 07:05:16 +000

Abstract: Educational Data Mining (EDM) is a research field that focuses on the application of data mining, machine learning, and statistical methods to detect patterns in large collections of educational data. Different machine learning techniques have been applied in this field over the years, but it has been recently that Deep Learning has gained increasing attention in the educational domain. Deep Learning is a machine learning method based on neural network architectures with multiple layers of processing units, which has been successfully applied to a broad set of problems in the areas of image recognition and natural language processing. This paper surveys the research carried out in Deep Learning techniques applied to EDM, from its origins to the present day. The main goals of this study are to identify the EDM tasks that have benefited from Deep Learning and those that are pending to be explored, to describe the main datasets used, to provide an overview of the key concepts, main architectures, and configurations of Deep Learning and its applications to EDM, and to discuss current state-of-the-art and future directions on this area of research. PubDate: Sun, 12 May 2019 00:00:00 +000

Abstract: This paper presents a general steady-state analysis and proposes a minimal compensating voltage (MCV) control scheme for the second generation of electric springs (ES-2) in the power system with substantial penetration of intermittent renewable energy sources. For the steady-state analysis, the relationship among the fluctuating part of the supply voltage, the voltage at the point of common-coupling (PCC), and the compensating voltage provided by ES-2 is derived, which implies that the phase angle related to the PCC voltage can be used as a degree of freedom for the control design to obtain a minimal compensating voltage in a given system. Such a fact is utilized in the control design to obtain the reference of PCC voltage by tuning the above-mentioned phase angle. Once the phase angle of the PCC voltage is chosen, the maximal compensating voltage can be estimated based on the fluctuating part of the supply voltage which can be estimated a priori. Such a fact can be used to design suitable electric springs with appropriate compensating capacity to avoid overcapacity. Numerical simulations are conducted to verify the effectiveness of the steady-state analysis and the proposed control scheme for ES-2. PubDate: Thu, 09 May 2019 12:05:09 +000

Abstract: This paper proposes a constrained solution update strategy for multiobjective evolutionary algorithm based on decomposition, in which each agent aims to optimize one decomposed subproblem. Different from the existing approaches that assign one solution to each agent, our approach allocates the closest solutions to each agent and thus the number of solutions in an agent may be zero and no less than one. Regarding the agent with no solution, it will be assigned one solution in priority, once offspring are generated closest to its subproblem. To keep the same population size, the agent with the largest number of solutions will remove one solution showing the worst convergence. This improves diversity for one agent, while the convergence of other agents is not lowered. On the agent with no less than one solution, offspring assigned to this agent are only allowed to update its original solutions. Thus, the convergence of this agent is enhanced, while the diversity of other agents will not be affected. After a period of evolution, our approach may gradually reach a stable status for solution assignment; i.e., each agent is only assigned with one solution. When compared to six competitive multiobjective evolutionary algorithms with different population selection or update strategies, the experiments validated the advantages of our approach on tackling two sets of test problems. PubDate: Wed, 08 May 2019 11:05:05 +000

Abstract: The article focuses on one of the current problems of manufacturing systems which consist of individual machines equipped with dedicated tools that are replaced when they are worn out. It is assumed that the machines are located within the reach of the robotic arm which carries out transport operations of semifinished products to designated production machines and storage containers in accordance with the production time period. The aim is to find such an arrangement of production activities, respectively, production paths for a given set of orders that will be effective from the time and cost point of view. Moreover, the whole issue is solved with regard to possible failures of individual stands, overfilling of some tanks, etc. The theory and practice of creating and using simulators as tools for the definition and verification of production plans are used to solve this issue. The starting point is the creation of a mathematical simulation model with the necessary but acceptable degree of simplification. The mathematical simulation model is tested on sample data in a feasibility study to perform a detailed usability analysis of the model. The output of the article is a simulation model for which, based on the analysis of simulation results, patterns of possible use in specific types of enterprises are given. PubDate: Tue, 07 May 2019 08:05:06 +000

Abstract: The topic of utilizing coupled map lattice to investigate complex spatiotemporal dynamics has attracted a lot of interest. For exploring the spatiotemporal complexity of a predator-prey system with migration and diffusion, a new three-chain coupled map lattice model is developed in this research. Based on Turing instability analysis, pattern formation conditions for the predator-prey system are derived. Via numerical simulation, rich Turing patterns are found with subtle self-organized structures under diffusion-driven and migration-driven mechanisms. With the variation of migration rates, the predator-prey system exhibits a gradual dynamical transition from diffusion-driven patterns to migration-driven patterns. Moreover, new results, the self-organization of non-Turing patterns, are also revealed. We find that even in the cases where the nonspatial predator-prey system reaches collapse, the migration can still drive pattern self-organization. These non-Turing patterns suggest many new possible ways for the coexistence of predator and prey in space, under the effects of migration and diffusion. PubDate: Sun, 05 May 2019 13:05:15 +000

Abstract: Personalized movie summarization is demand of the current era due to an exponential growth in movies production. The employed methods for movies summarization fail to satisfy the user’s requirements due to the subjective nature of movies data. Therefore, in this paper, we present a user-preference based movie summarization scheme. First, we segmented movie into shots using a novel entropy-based shots segmentation mechanism. Next, temporal saliency of shots is computed, resulting in highly salient shots in which character faces are detected. The resultant shots are then forward propagated to our trained deep CNN model for facial expression recognition (FER) to analyze the emotional state of the characters. The final summary is generated based on user-preferred emotional moments from the seven emotions, i.e., afraid, angry, disgust, happy, neutral, sad, and surprise. The subjective evaluation over five Hollywood movies proves the effectiveness of our proposed scheme in terms of user satisfaction. Furthermore, the objective evaluation verifies the superiority of the proposed scheme over state-of-the-art movie summarization methods. PubDate: Sun, 05 May 2019 09:05:07 +000

Abstract: A defining characteristic of Alzheimer’s disease is difficulty in retrieving semantic memories, or memories encoding facts and knowledge. While it has been suggested that this impairment is caused by a degradation of the semantic store, the precise ways in which the semantic store is degraded are not well understood. Using a longitudinal corpus of semantic fluency data (listing of items in a category), we derive semantic network representations of patients with Alzheimer’s disease and of healthy controls. We contrast our network-based approach with analyzing fluency data with the standard method of counting the total number of items and perseverations in fluency data. We find that the networks of Alzheimer’s patients are more connected and that those connections are more randomly distributed than the connections in networks of healthy individuals. These results suggest that the semantic memory impairment of Alzheimer’s patients can be modeled through the inclusion of spurious associations between unrelated concepts in the semantic store. We also find that information from our network analysis of fluency data improves prediction of patient diagnosis compared to traditional measures of the semantic fluency task. PubDate: Thu, 02 May 2019 12:05:07 +000

Abstract: Because ports are considered to be the heart of the maritime transportation system, thereby assessing port performance is necessary for a nation’s development and economic success. This study proposes a novel metric, namely, “port performance index (PPI)”, to determine the overall performance and utilization of inland waterway ports based on six criteria, port facility, port availability, port economics, port service, port connectivity, and port environment. Unlike existing literature, which mainly ranks ports based on quantitative factors, this study utilizes a Bayesian Network (BN) model that focuses on both quantitative and qualitative factors to rank a port. The assessment of inland waterway port performance is further analyzed based on different advanced techniques such as sensitivity analysis and belief propagation. Insights drawn from the study show that all the six criteria are necessary to predict PPI. The study also showed that port service has the highest impact while port economics has the lowest impact among the six criteria on PPI for inland waterway ports. PubDate: Thu, 02 May 2019 00:00:00 +000

Abstract: This paper concentrates on the component importance measure of a network whose arc failure rates are not deterministic and imprecise ones. Conventionally, a computing method of component importance and a measure method of reliability stability are proposed. Three metrics are analyzed first: Birnbaum measurement, component importance, and component risk growth factor. Based on them, the latter can measure the impact of the component importance on the reliability stability of a system. Examples in some typical structures illustrate how to calculate component importance and reliability stability, including uncertain random series, parallel, parallel-series, series-parallel, and bridge systems. The comprehensive numerical experiments demonstrate that both of these methods can efficiently and accurately evaluate the impact of an arc failure on the reliability of a network system. PubDate: Thu, 02 May 2019 00:00:00 +000