Agile Scheduling for MES in a Complex Information Environment

Since Manufacturing Execution System (MES) is a bridge which links the upper planning system of the enterprise and the control system of the shop floor, various kinds of the information with different characteristics flow through the system. The information environment of MES and its effect on MES scheduling are analyzed. A methodological proposal is given to address the problem of agile scheduling in a complex information environment, based on which a microeconomic market and game theoretic model-based scheduling approach is presented. The future development of this method is also discussed.

Security-Critical Components Recognition Algorithm for Complex Heterogeneous Information Systems

With the skyrocketing development of technologies,there are many issues in information security quantitative evaluation(ISQE)of complex heterogeneous information systems(CHISs).The development of CHIS calls for an ISQE model based on security-critical components to improve the efficiency of system security evaluation urgently.In this paper,we summarize the implication of critical components in different filed and propose a recognition algorithm of security-critical components based on threat attack tree to support the ISQE process.The evaluation model establishes a framework for ISQE of CHISs that are updated iteratively.Firstly,with the support of asset identification and topology data,we sort the security importance of each asset based on the threat attack tree and obtain the security-critical components(set)of the CHIS.Then,we build the evaluation indicator tree of the evaluation target and propose an ISQE algorithm based on the coefficient of variation to calculate the security quality value of the CHIS.Moreover,we present a novel indicator measurement uncertainty aiming to better supervise the performance of the proposed model.Simulation results show the advantages of the proposed algorithm in the evaluation of CHISs.

Aggregation operators and CRITIC-VIKOR method for confidence complex q-rung orthopair normal fuzzy information and their applications

Supply chain management is an essential part of an organisation's sustainable programme.Understanding the concentration of natural environment,public,and economic influence and feasibility of your suppliers and purchasers is becoming progressively familiar as all industries are moving towards a massive sustainable potential.To handle such sort of developments in supply chain management the involvement of fuzzy settings and their generalisations is playing an important role.Keeping in mind this role,the aim of this study is to analyse the role and involvement of complex q-rung orthopair normal fuzzy(CQRONF)information in supply chain management.The major impact of this theory is to analyse the notion of confidence CQRONF weighted averaging,confidence CQRONF ordered weighted averaging,confidence CQRONF hybrid averaging,confidence CQRONF weighted geometric,confidence CQRONF ordered weighted geometric,confidence CQRONF hybrid geometric operators and try to diagnose various properties and results.Furthermore,with the help of the CRITIC and VIKOR models,we diagnosed the novel theory of the CQRONF-CRITIC-VIKOR model to check the sensitivity analysis of the initiated method.Moreover,in the availability of diagnosed operators,we constructed a multi-attribute decision-making tool for finding a beneficial sustainable supplier to handle complex dilemmas.Finally,the initiated operator's efficiency is proved by comparative analysis.

Research on Extraction Method of Surface Information Based on Multi-Feature Combination Such as Fractal Texture

Because of the developed economy and lush vegetation in southern China, the following obstacles or difficulties exist in remote sensing land surface classification: 1) Diverse surface composition types;2) Undulating terrains;3) Small fragmented land;4) Indistinguishable shadows of surface objects. It is our top priority to clarify how to use the concept of big data (Data mining technology) and various new technologies and methods to make complex surface remote sensing information extraction technology develop in the direction of automation, refinement and intelligence. In order to achieve the above research objectives, the paper takes the Gaofen-2 satellite data produced in China as the data source, and takes the complex surface remote sensing information extraction technology as the research object, and intelligently analyzes the remote sensing information of complex surface on the basis of completing the data collection and preprocessing. The specific extraction methods are as follows: 1) extraction research on fractal texture features of Brownian motion;2) extraction research on color features;3) extraction research on vegetation index;4) research on vectors and corresponding classification. In this paper, fractal texture features, color features, vegetation features and spectral features of remote sensing images are combined to form a combination feature vector, which improves the dimension of features, and the feature vector improves the difference of remote sensing features, and it is more conducive to the classification of remote sensing features, and thus it improves the classification accuracy of remote sensing images. It is suitable for remote sensing information extraction of complex surface in southern China. This method can be extended to complex surface area in the future.

Using object-based analysis to derive surface complexity information for improved filtering of airborne laser scanning data

Airborne laser scanning （ALS） is a technique used to obtain Digital Surface Models （DSM） and Digital Terrain Models （DTM） efficiently, and filtering is the key procedure used to derive DTM from point clouds. Generating seed points is an initial step for most filtering algorithms, whereas existing algorithms usually define a regular window size to generate seed points. This may lead to an inadequate density of seed points, and further introduce error type I, especially in steep terrain and forested areas. In this study, we propose the use of object- based analysis to derive surface complexity information from ALS datasets, which can then be used to improve seed point generation. We assume that an area is complex if it is composed of many small objects, with no buildings within the area. Using these assumptions, we propose and implement a new segmentation algorithm based on a grid index, which we call the Edge and Slope Restricted Region Growing （ESRGG） algorithm. Surface complexity information is obtained by statistical analysis of the number of objects derived by segmentation in each area. Then, for complex areas, a smaller window size is defined to generate seed points. Experimental results show that the proposed algorithm could greatly improve the filtering results in complex areas, especially in steep terrain and forested areas.

Computational implementation of a GIS developed tool for prediction of dynamic ground movement and deformation due to underground extraction sequence

In the last century, there has been a significant development in the evaluation of methods to predict ground movement due to underground extraction. Some remarkable developments in three-dimensional computational methods have been supported in civil engineering, subsidence engineering and mining engineering practice. However, ground movement problem due to mining extraction sequence is effectively four dimensional （4D）. A rational prediction is getting more and more important for long-term underground mining planning. Hence, computer-based analytical methods that realistically simulate spatially distributed time-dependent ground movement process are needed for the reliable long-term underground mining planning to minimize the surface environmental damages. In this research, a new computational system is developed to simulate four-dimensional （4D） ground movement by combining a stochastic medium theory, Knothe time-delay model and geographic information system （GIS） technology. All the calculations are implemented by a computational program, in which the components of GIS are used to fulfill the spatial-temporal analysis model. In this paper a tight coupling strategy based on component object model of GIS technology is used to overcome the problems of complex three-dimensional extraction model and spatial data integration. Moreover, the implementation of computational of the interfaces of the developed tool is described. The GIS based developed tool is validated by two study cases. The developed computational tool and models are achieved within the GIS system so the effective and efficient calculation methodology can be obtained, so the simulation problems of 4D ground movement due to underground mining extraction sequence can be solved by implementation of the developed tool in GIS.

Using the multi-living agent concept to investigate complex information systems

In this paper, we propose the multi-living agent （MLA） concept based on the of analysis the characteristics of complex information systems, especially those systems that require multi-functional operations under strict restraint strong countermeasures （SRSC） environment. First, we investigate the representation of the livelihood of the system under the SRSC conditions, and obtain the basic dynamical presentation of the MLA from the profile of the system＇s function. Next, we propose the concept of the living self-organization based on the self-organization profile of the system, and derive a Two-Set model of the living self-organization mechanism. Further, based on the above results, we present a basic construction model of MLA-based information systems in the field of information security and countermeasures. A three-level negotiation-coordination mechanism is also derived. Finally, we present two practical examples to show how to use the MLA concept to analyze and study real complex information systems. In our opinion, the proposal of the MLA will bridge the gap between the research of the application and the basic application level of science. It provides the principle research methods and the supporting theories for the construction and analysis of complex information systems in the information security and countermeasures field.

Exploiting global information in complex network repair processes

Robustness of complex networks has been studied for decades,with a particular focus on network attack.Research on network repair,on the other hand,has been conducted only very lately,given the even higher complexity and absence of an effective evaluation metric.A recently proposed network repair strategy is self-healing,which aims to repair networks for larger components at a low cost only with local information.In this paper,we discuss the effectiveness and efficiency of self-healing,which limits network repair to be a multi-objective optimization problem and makes it difficult to measure its optimality.This leads us to a new network repair evaluation metric.Since the time complexity of the computation is very high,we devise a greedy ranking strategy.Evaluations on both real-world and random networks show the effectiveness of our new metric and repair strategy.Our study contributes to optimal network repair algorithms and provides a gold standard for future studies on network repair.

A New Method for Measurement and Reduction of Software Complexity

This paper develops an improved structural software complexity metrics named information flow complexity which is closely related to the reliability of software. Together with the three software complexity metrics, the total software complexity is measured and some rules to reduce the complexity are presented in the paper. To illustrate and explain the process of measurement and reduction of software complexity, several examples and experiments are given. It is proposed that software complexity metrics can be measured earlier in software development and can provide substantial information of software systems whose reliabil- ity can be modeled and used in the determination of initial parameter estimation.

A Comparative Analysis on Weibo and Twitter

Weibo is the Twitter counterpart in China that has attracted hundreds of millions of users. We crawled an almost complete Weibo user network that contains 222 million users and 27 billion links in 2013. This paper analyzes the structural properties of this network, and compares it with a Twitter user network. The topological properties we studied include the degree distributions, connected components, distance distributions, reciprocity,clustering coefficient, Page Rank centrality, and degree assortativity. We find that Weibo users have a higher diversity index, higher Gini index, but a lower reciprocity and clustering coefficient for most of the nodes. A surprising observation is that the reciprocity of Weibo is only about a quarter of the reciprocity of the Twitter user network. We also show that Weibo adoption rate correlates with economic development positively, and Weibo network can be used to quantify the connections between provinces and regions in China. In particular, point-wise mutual information is shown to be accurate in quantifying the strength of connections. We developed an interactive analyzing software framework for this study, and released the data and code online.

The Concept and Operations of Blind Number

：This paper gives the definition and operations of blind number, and discusses its operationproperties. Blind number is a mathematical tool to express and deal with complex information with severalkinds of uncertainty.