Optimization of extended warranty cost for multi-component systems with economic dependence based on group maintenance

During extended warranty(EW)period,maintenance events play a key role in controlling the product systems within normal operations.However,the modelling of failure process and maintenance optimization is complicated owing to the complex features of the product system,namely,components of the multi-component system are interdependent with each other in some form.For the purpose of optimizing the EW pricing decision of the multi-component system scientifically and rationally,taking the series multi-component system with economic dependence sold with EW policy as a research object,this paper optimizes the imperfect preventive maintenance(PM)strategy from the standpoint of EW cost.Taking into consideration adjusting the PM moments of the components in the system,a group maintenance model is developed,in which the system is repaired preventively in accordance with a specified PM base interval.In order to compare with the system EW cost before group maintenance,the system EW cost model before group maintenance is developed.Numerical example demonstrates that offering group maintenance programs can reduce EW cost of the system to a great extent,thereby reducing the EW price,which proves to be a win-win strategy to manufacturers and users.

BCCLR:A Skeleton-Based Action Recognition with Graph Convolutional Network Combining Behavior Dependence and Context Clues

In recent years,skeleton-based action recognition has made great achievements in Computer Vision.A graph convolutional network(GCN)is effective for action recognition,modelling the human skeleton as a spatio-temporal graph.Most GCNs define the graph topology by physical relations of the human joints.However,this predefined graph ignores the spatial relationship between non-adjacent joint pairs in special actions and the behavior dependence between joint pairs,resulting in a low recognition rate for specific actions with implicit correlation between joint pairs.In addition,existing methods ignore the trend correlation between adjacent frames within an action and context clues,leading to erroneous action recognition with similar poses.Therefore,this study proposes a learnable GCN based on behavior dependence,which considers implicit joint correlation by constructing a dynamic learnable graph with extraction of specific behavior dependence of joint pairs.By using the weight relationship between the joint pairs,an adaptive model is constructed.It also designs a self-attention module to obtain their inter-frame topological relationship for exploring the context of actions.Combining the shared topology and the multi-head self-attention map,the module obtains the context-based clue topology to update the dynamic graph convolution,achieving accurate recognition of different actions with similar poses.Detailed experiments on public datasets demonstrate that the proposed method achieves better results and realizes higher quality representation of actions under various evaluation protocols compared to state-of-the-art methods.

Nonuniform Dependence on the Initial Data for Solutions of Conservation Laws

In this paper,we study systems of conservation laws in one space dimension.We prove that for classical solutions in Sobolev spaces H^(s),with s>3/2,the data-to-solution map is not uniformly continuous.Our results apply to all nonlinear scalar conservation laws and to nonlinear hyperbolic systems of two equations.

Time-variant fragility analysis of the bridge system considering time-varying dependence among typical component seismic demands

This paper presents a copula technique to develop time-variant seismic fragility curves for corroded bridges at the system level and considers the realistic time-varying dependence among component seismic demands. Based on material deterioration mechanisms and incremental dynamic analysis, the time-evolving seismic demands of components were obtained in the form of marginal probability distributions. The time-varying dependences among bridge components were then captured with the best fitting copula function, which was selected from the commonly used copula classes by the empirical distribution based analysis method. The system time-variant fragility curves at different damage states were developed and the effects of time-varying dependences among components on the bridge system fragility were investigated. The results indicate the time-varying dependence among components significantly affects the time-variant fragility of the bridge system. The copula technique captures the nonlinear dependence among component seismic demands accurately and easily by separating the marginal distributions and the dependence among them.

Opportunistic maintenance for multi-component systems considering structural dependence and economic dependence

Although opportunistic maintenance strategies are widely used for multi-component systems, all opportunistic mainte- nance strategies only consider economic dependence and do not take structural dependence into account. An opportunistic main- tenance strategy is presented for a multi-component system that considers both structural dependence and economic dependence. The cost relation and time relation among components based on structural dependence are developed. The maintenance strategy for each component of a multi-component system involves one of five maintenance actions, namely, no-maintenance, a minimal maintenance action, an imperfect maintenance action, a perfect maintenance action, and a replacement action. The maintenance action is determined by the virtual age of the component, the life expectancy of the component, and the age threshold values. Monte Carlo simulation is designed to obtain the optimal oppor- tunistic maintenance strategy of the system over its lifetime. The simulation result reveals that the minimum maintenance cost with a strategy that considers structural dependence is less than that with a strategy that does not consider structural dependence. The availability with a strategy that considers structural dependence is greater than that with a strategy that does not consider structural dependence under the same conditions.

Selective maintenance problem for series–parallel system under economic dependence

In view of the high complexity of the objective world, an economic dependence between subsystems(paired and unpaired) is proposed, and then the maintenance cost and time under different economic dependences are formulated in a simple and consistent manner. Selective maintenance problem under economic dependence(EDSMP) is presented based on a series–parallel system in this paper. A case study shows that the system reliability is promoted to a certain extent, which can validate the validity of the EDSMP model. The influence of the ratio of set-up cost on system performance is mainly discussed under different economic dependences. Several existing improvements of classical exhaust algorithm are further modified to solve a large sized EDSMP rapidly. Experimental results illustrate that these improvements can reduce CPU time significantly.Furthermore the contribution of each improvement is defined here, and then their contributions are compared thoroughly.

Reliability and Availability Models of Belt Drive Systems Considering Failure Dependence

Conventional reliability models of belt drive systems in the failure mode of fatigue are mainly based on the static stress strength interference model and its extended models, which cannot consider dynamic factors in the operational duration and be used for further availability analysis. In this paper, time-dependent reliability models, failure rate models and availability models of belt drive systems are developed based on the system dynamic equations with the dynamic stress and the material property degradation taken into account. In the proposed models, dynamic failure dependence and imperfect maintenance are taken into consideration. Furthermore, the issue of time scale inconsistency between system failure rate and system availability is proposed and addressed in the proposed system availability models. Besides, Monte Carlo simulations are carried out to validate the established models. The results from the proposed models and those from the Monte Carlo simulations show a consistency. Furthermore, the case studies show that the failure dependence, imperfect maintenance and the time scale inconsistency have significant influences on system availability. The independence assumption about the belt drive systems results in underestimations of both reliability and availability. Moreover, the neglect of the time scale inconsistency causes the underestimate of the system availability. Meanwhile, these influences show obvious time-dependent characteristics.

Hiding dependence-discovery of F-hiding laws and system laws

Function one direction S-rough sets have dynamic characteristics and law characteristics. By using the function one direction S-rough sets, this article presents the concepts of the f-hiding law, F-hiding law, f-hiding law dependence and F-hiding law dependence. Based on the concepts above, this article proposes the hidingdependence theorem of f-hiding laws, the hiding-dependence theorem of F-hiding laws, the hiding-dependence separation theorem, the hiding dependence-discovery principle of unknown laws. Finally, the application of the hiding dependence of hiding laws in the discovery of system laws is given.

Understand anisotropy dependence of damage evolution and material removal during nanoscratch of MgF_(2) single crystals

To understand the anisotropy dependence of the damage evolution and material removal during the machining process of MgF_(2) single crystals,nanoscratch tests of MgF_(2) single crystals with different crystal planes and directions were systematically performed,and surface morphologies of the scratched grooves under different conditions were analyzed.The experimental results indicated that anisotropy considerably affected the damage evolution in the machining process of MgF_(2) single crystals.A stress field model induced by the scratch was developed by considering the anisotropy,which indicated that during the loading process,median cracks induced by the tensile stress initiated and propagated at the front of the indenter.Lateral cracks induced by tensile stress initiated and propagated on the subsurface during the unloading process.In addition,surface radial cracks induced by the tensile stress were easily generated during the unloading process.The stress change led to the deflection of the propagation direction of lateral cracks.Therefore,the lateral cracks propagated to the workpiece surface,resulting in brittle removal in the form of chunk chips.The plastic deformation parameter indicated that the more the slip systems were activated,the more easily the plastic deformation occurred.The cleavage fracture parameter indicated that the cracks propagated along the activated cleavage planes,and the brittle chunk removal was owing to the subsurface cleavage cracks propagating to the crystal surface.Under the same processing parameters,the scratch of the(001)crystal plane along the[100]crystal-orientation was found to be the most conducive to achieving plastic machining of MgF_(2) single crystals.The theoretical results agreed well with the experimental results,which will not only enhance the understanding of the anisotropy dependence of the damage evolution and removal process during the machining of MgF_(2) crystals,but also provide a theoretical foundation for achieving the high-efficiency and low-damage processing of anisotropic single crystals.

Solvent and electric field dependence of the photocurrent generation in donor：acceptor blend system

This paper reports that the blend films of poly （2-methoxy-5-（2＇-ethyl-hexyloxy）-p-phenylene vinylene） （MEH-PPV） and N,N＇-bis（1-ethylpropyl）-3,4： 9,10-perylene bis （tetracarboxyl diimide） （EP-PDI） with the weight ratio of 1：2.5 have been prepared by spin-coating from chloroform （CF） and chlorobenzene （CB） solutions respectively. The absorption spectra and the morphology of the blend films show that large crystal-like EP-PDI aggregates are formed in film prepared from CB solution, which corresponds to a new absorption shoulder near 590 nm, while there is no shoulder around 590 nm in the UV-Vis absorption spectra of the blend film from CF solution. The electric-field dependence spectra of the photocurrent generation quantum yield of the film from CB solution shows that at weak electric field the EP-PDI aggregates act as more efficient sensitizers, but at strong electric field the quantum yields become almost invariable over the entire spectral range no matter what the state of EP-PDI, monomer or aggregate. At strong electric field, the photocurrent generation yields of both films from CF and CB solution saturate and their yield spectra become spectrally similar, mentioning that at strong electric field the photoexcitons dissociate efficiently and the free charges are collected by the electrodes almost completely.

MISCIBILITY BEHAVIOR AND PRESSURE DEPENDENCE OF THE SYSTEMS POLYSTYRENE/CYCLOPENTANE AND POLYSTYRENE/1-PHENYLDECANE ACCORDING TO A MODIFIED HOLE THEORY

The influence of pressure on miscibility behavior of the systems polystyrene/cyclopentan(?)and polystyrene/1-phenyldecane is studied with a modified hole theory.It is found that the pres-sure dependence of excess volume is responsible for the different kinds of behavior of these two sys-tems.Furthermore,the excess volume is decomposed into two separatc parts,one from the contri-bution of cell volume expansion and the other from the change of hole fraction,and their relation-ship with pressure dependence of the miscibility behavior is analysed.

Influence of a phonon bath in a quantum dot cavity QED system:Dependence of the shape

We present a systematic analysis on the role of the quantum dot （QD） shape in the influence of the phonon bath on the dynamics of a QD cavity QED system. The spectral functions of the phonon bath in three representative QD shapes： spherical, ellipsoidal, and disk, are calculated from the carrier wave functions subjected to the confinement potential provided by the corresponding shape. The obtained spectral functions are used to calculate three main effects brought by the phonon bath, i.e., the coupling renormalization, the off-resonance assisted feeding rate and the pure dephasing rate. It is found that the spectral function of a disk QD has the widest distribution, hence the phonon bath in a disk QD can lead to the smallest renormalization factor, the largest dephasing rate in the short time domains（≤2 ps）, and the oft-resonance assisted feeding can support the widest detuning. Except for the pure dephasing rate in the long time domains, all the influences brought by the phonon bath show serious shape dependence.

System Dependence Graph Construction for Aspect Oriented C++

This paper proposes an extended system dependence graph called AspectSDG to represent control and data dependences for AspeetC＋＋ programs, and presents an approach for the construction of AspectSDG. This approach decomposes aspect-oriented programs into three parts： component codes, aspect codes, and weaving codes. It constructs program dependence graphs （PDGs） for each part, and then connects the PDGs at call sites to form the complete AspectSDG. The AspectSDG can deal with advice precedence correctly, and represent the additional dependences caused by aspect codes. Based on this model, we introduce how to compute a static slice of an AspectC＋ ＋ program.

The Impact of Long Range Dependence of VBR Video Traffic in Queueing System

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Persistence in Non-Autonomous Lotka-Volterra System with Predator-Prey Ratio-Dependence and Density Dependence

The main purpose of this article is considering the persistence non-autonomous Lotka-Volterra system with predator-prey ratio-dependence and density dependence. We get the sufficient conditions of persistence of system, further have the necessary conditions, also the uniform persistence condition, which can be easily checked for the model is obtained.

Exact Failure Probability Formula for Strict Consecutive-k-out-of-n : F Systems with Homogeneous Markov Dependence

An exact formula is presented for calculating the failure probability of strict consecutive k out of n:F systems with homogeneous Markov dependence

Trees species’ dispersal mode and habitat heterogeneity shape negative density dependence in a temperate forest

Conspecific negative density dependence(CNDD)is a potentially important mechanism in maintaining species diversity.While previous evidence showed habitat heterogeneity and species’dispersal modes affect the strength of CNDD at early life stages of trees(e.g.,seedlings),it remains unclear how they affect the strength of CNDD at later life stages.We examined the degree of spatial aggregation between saplings and trees for species dispersed by wind and gravity in four topographic habitats within a 25-ha temperate forest dynamic plot in the Qinling Mountains of central China.We used the replicated spatial point pattern(RSPP)analysis and bivariate paircorrelation function(PCF)to detect the spatial distribution of saplings around trees at two scales,15 and 50 m,respectively.Although the signal was not apparent across the whole study region(or 25-ha),it is distinct on isolated areas with specific characteristics,suggesting that these characteristics could be important factors in CNDD.Further,we found that the gravity-dispersed tree species experienced CNDD across habitats,while for wind-dispersed species CNDD was found in gully,terrace and low-ridge habitats.Our study suggests that neglecting the habitat heterogeneity and dispersal mode can distort the signal of CNDD and community assembly in temperate forests.

Adaptive Graph Convolutional Recurrent Neural Networks for System-Level Mobile Traffic Forecasting

Accurate traffic pattern prediction in largescale networks is of great importance for intelligent system management and automatic resource allocation.System-level mobile traffic forecasting has significant challenges due to the tremendous temporal and spatial dynamics introduced by diverse Internet user behaviors and frequent traffic migration.Spatialtemporal graph modeling is an efficient approach for analyzing the spatial relations and temporal trends of mobile traffic in a large system.Previous research may not reflect the optimal dependency by ignoring inter-base station dependency or pre-determining the explicit geological distance as the interrelationship of base stations.To overcome the limitations of graph structure,this study proposes an adaptive graph convolutional network(AGCN)that captures the latent spatial dependency by developing self-adaptive dependency matrices and acquires temporal dependency using recurrent neural networks.Evaluated on two mobile network datasets,the experimental results demonstrate that this method outperforms other baselines and reduces the mean absolute error by 3.7%and 5.6%compared to time-series based approaches.

Spatio Temporal Tourism Tracking System Based on Adaptive Convolutional Neural Network

Technological developments create a lot of impacts in the tourism industry.Emerging big data technologies and programs generate opportunities to enhance the strategy and results for transport security.However,there is a difference between technological advances and their integration into the methods of tourism study.The rising popularity of Freycinet National Park led to a master plan that would not address cultural and environmental issues.This study addresses the gap by using a synthesized application(app)for demographic surveys and Global Navigation Satellite System(GNSS)technology to implement research processes.This article focuses on managing visitors within the famous Freycinet National Park.Extremely comprehensive structured data were analyzed in three phases,(1)identifying groups of visitors who are more likely to use the walking trails,(2)those who are more and less likely to visit during/peak crowding times,and(3)finally creating an integrated Spatio-temporal dependency model via a machine-based learning system for real-time activity.This research examines innovative techniques that can offer energy resources to managers and tourism agencies,especially in detecting,measuring,and potentially relieving crowding and over-tourism.

A Hybrid Spatial Dependence Model Based on Radial Basis Function Neural Networks (RBFNN) and Random Forest (RF)

The majority of spatial data reveal some degree of spatial dependence. The term “spatial dependence” refers to the tendency for phenomena to be more similar when they occur close together than when they occur far apart in space. This property is ignored in machine learning (ML) for spatial domains of application. Most classical machine learning algorithms are generally inappropriate unless modified in some way to account for it. In this study, we proposed an approach that aimed to improve a ML model to detect the dependence without incorporating any spatial features in the learning process. To detect this dependence while also improving performance, a hybrid model was used based on two representative algorithms. In addition, cross-validation method was used to make the model stable. Furthermore, global moran’s I and local moran were used to capture the spatial dependence in the residuals. The results show that the HM has significant with a R2 of 99.91% performance compared to RBFNN and RF that have 74.22% and 82.26% as R2 respectively. With lower errors, the HM was able to achieve an average test error of 0.033% and a positive global moran’s of 0.12. We concluded that as the R2 value increases, the models become weaker in terms of capturing the dependence.