Resource scheduling of workflow multi-instance migration based on the shuffled leapfrog algorithm

Purpose: When the workflow changed, resource scheduling optimization in the process of the current running instance migration has become a hot issue in current workflow flexible research; purpose of the article is to investigate the resource scheduling problem of workflow multi-instance migration. Design/methodology/approach: The time and cost relationships between activities and resources in workflow instance migration process are analyzed and a resource scheduling optimization model in the process of workflow instance migration is set up; Research is performed on resource scheduling optimization in workflow multi-instance migration, leapfrog algorithm is adopted to obtain the optimal resource scheduling scheme. An example is given to verify the validity of the model and the algorithm. Findings: Under the constraints of resource cost and quantity, an optimal resource scheduling scheme for workflow migration is found, ensuring a minimal running time and optimal cost. Originality/value: A mathematical model for resource scheduling of workflow multi-instance migration is built and the shuffled leapfrog algorithm is designed to solve the model.Peer Reviewe

On-Line Monitoring and Fault Diagnosis of Box Transformer Substation Based on VPRS-RBFNN

Box transformer substation (BTS) is an important power distribution environment. To ensure the safe and stable operation of the power distribution system, it is critical to monitor the BTS operation and diagnose its faults in a reliable manner. In the Internet of Things (IoT) environment, this paper aims to develop a real-time and accurate online strategy for BTS monitoring and fault diagnosis. The framework of our strategy was constructed based on the IoT technique, including a sensing layer, a network layer and an application layer. On this basis, a BTS fault diagnosis method was established with variable precision rough set (VPRS) as the pre-network and the radial basis function neural network (RBFNN) as the back-fed network. The VPRS and the RBFNN were selected, because the BTS faults have many characteristic parameters, with complex nonlinear relationship with fault modes. Finally, a prototype of our strategy was developed and applied to the fault diagnosis of an actual BTS. The results fully demonstrate the effectiveness and feasibility of our strategy

Acquisition Method of User Requirements for Complex Products Based on Data Mining

The vigorous development of big data technology has changed the traditional user requirement acquisition mode of the manufacturing industry. Based on data mining, manufacturing enterprises have the innovation ability to respond quickly to market changes and user requirements. However, in the stage of complex product innovation design, a large amount of design data has not been effectively used, and there are some problems of low efficiency and lack of objectivity of user survey. Therefore, this paper proposes an acquisition method of user requirements based on patent data mining. By constructing a patent data knowledge base, this method combines the Latent Dirichlet Allocation topic model and a K-means algorithm to cluster patent text data to realize the mining of key functional requirements of products. Then, the importance of demand is determined by rough set theory, and the rationality of demand is verified by user importance performance analysis. In this paper, the proposed method is explained and verified by mining the machine tool patent data in CNKI. The results show that this method can effectively improve the efficiency and accuracy of user requirements acquisition, expand the innovative design approach of existing machine tool products, and be applied to other complex product fields with strong versatility

Module division method of complex products for responding to user’s requirements

With the gradual diversification of personalized usage scenarios, user requirements play a direct role in product design decisions. Due to the problem of fuzzy demand caused by user cognitive bias, traditional design methods usually focus on realizing product functions and cannot effectively match user requirements. Therefore, this paper proposes a complex product module division method for user requirements. The method constitutes of three tasks, requirement analysis of module division, design mapping of module division and scheme implementation of module division. Firstly, based on the progressive architecture from initial requirements to precise requirements, the effective user requirements are obtained through similarity recommendation. Secondly, according to the four types of knowledge of function, geometry, physics and design, the design structure matrix is constructed to complete the Requirement-Function-Structure mapping. The improved Fuzzy C-means Algorithm is used to solve the mapping model, and finally a module division scheme that meets the user requirements is obtained. Taking the chip removal machine as an example, the rationality and effectiveness of the method are verified. The results show that the product modules divided by this method can effectively meet the multiple user requirements

A Decision-Making Method for Design Schemes Based on Intuitionistic Fuzzy Sets and Prospect Theory

Conceptual design is a key link in the process of complex product design, and it is very important to select the appropriate design scheme; however, there are many types and inaccuracies of the evaluation data, and there is a problem of mutual influence between the evaluation criteria, which leads to unreliable decision making of the optimal solution. In order to solve this problem, a decision-making method based on intuitionistic fuzzy sets (IFS) and prospect theory is proposed. This method can be used for symmetric and asymmetric evaluation data. The evaluation data are classified according to different expression types and unified using intuitionistic fuzzy numbers. The intuitionistic fuzzy prospect value of decision information is calculated using prospect theory, and the prospect transformation of decision information is completed. At the same time, the Gray Relational Analysis (GRA) method and the Criteria Importance Though Intercriteria Correlation (CRITIC) method are used to calculate the subjective and objective weights of the technical and economic evaluation indexes of the product, and the combination weights are given; then, based on the evidence theory, the basic probability distribution of the evidence chain of all conceptual design schemes is synthesized, and the comprehensive prospect evaluation results of the schemes are obtained to complete the optimization of the conceptual design schemes. Finally, the effectiveness of the proposed method is verified by the conceptual design of the chip removal system of the deep hole machining machine tool. This work provides a promising method for decision makers to optimize the design scheme and provides insights into multi-objective decision-making problems

Nuclear localization of Newcastle disease virus matrix protein promotes virus replication by affecting viral RNA synthesis and transcription and inhibiting host cell transcription

Abstract Nuclear localization of paramyxovirus proteins is crucial for virus life cycle, including the regulation of viral replication and the evasion of host immunity. We previously showed that a recombinant Newcastle disease virus (NDV) with nuclear localization signal mutation in the matrix (M) protein results in a pathotype change and attenuates viral pathogenicity in chickens. However, little is known about the nuclear localization functions of NDV M protein. In this study, the potential functions of the M protein in the nucleus were investigated. We first demonstrate that nuclear localization of the M protein could not only promote the cytopathogenicity of NDV but also increase viral RNA synthesis and transcription efficiency in DF-1 cells. Using microarray analysis, we found that nuclear localization of the M protein might inhibit host cell transcription, represented by numerous up-regulating genes associated with transcriptional repressor activity and down-regulating genes associated with transcriptional activator activity. The role of representative up-regulated gene prospero homeobox 1 (PROX1) and down-regulated gene aryl hydrocarbon receptor (AHR) in the replication of NDV was then evaluated. The results show that siRNA-mediated knockdown of PROX1 or AHR significantly reduced or increased the viral RNA synthesis and viral replication, respectively, demonstrating the important roles of the expression changes of these genes in NDV replication. Together, our findings demonstrate for the first time that nuclear localization of NDV M protein promotes virus replication by affecting viral RNA synthesis and transcription and inhibiting host cell transcription, improving our understanding of the molecular mechanism of NDV replication and pathogenesis

Resource scheduling of workflow multi-instance migration based on the shuffled leapfrog algorithm

Purpose: When the workflow changed, resource scheduling optimization in the process of the current running instance migration has become a hot issue in current workflow flexible research; purpose of the article is to investigate the resource scheduling problem of workflow multi-instance migration. Design/methodology/approach: The time and cost relationships between activities and resources in workflow instance migration process are analyzed and a resource scheduling optimization model in the process of workflow instance migration is set up; Research is performed on resource scheduling optimization in workflow multi-instance migration, leapfrog algorithm is adopted to obtain the optimal resource scheduling scheme. An example is given to verify the validity of the model and the algorithm. Findings: Under the constraints of resource cost and quantity, an optimal resource scheduling scheme for workflow migration is found, ensuring a minimal running time and optimal cost. Originality/value: A mathematical model for resource scheduling of workflow multi-instance migration is built and the shuffled leapfrog algorithm is designed to solve the model

Reliability Evaluation for Aviation Electric Power System in Consideration of Uncertainty

With the rapid development of more electric aircraft (MEA) in recent years, the aviation electric power system (AEPS) has played an increasingly important role in safe flight. However, as a highly reliable system, because of its complicated flight conditions and architecture, it often proves significant uncertainty in its failure occurrence and consequence. Thus, more and more stakeholders, e.g., passengers, aviation administration departments, are dissatisfied with the traditional system reliability analysis, in which failure uncertainty is not considered and system reliability probability is a constant value at a given time. To overcome this disadvantage, we propose a new methodology in the AEPS reliability evaluation. First, we perform a random sampling from the probability distributions of components’ failure rates and compute the system reliability at each sample point; after that, we use variance, confidence interval, and probability density function to quantify the uncertainty of system reliability. Finally, we perform the new method on a series–parallel system and an AEPS. The results show that the power supply reliability of AEPS is uncertain and the uncertainty varies with system time even though the uncertainty of each component’s failure is quite small; therefore it is necessary to quantify system uncertainty for safer flight, and our proposed method could be an effective way to accomplish this quantization task

Reliability Uncertainty Analysis Method for Aircraft Electrical Power System Design Based on Variance Decomposition

As a safety critical system, affected by cognitive uncertainty and flight environment variability, aircraft electrical power system proves highly uncertain in its failure occurrence and consequences. However, there are few studies on how to reduce the uncertainty in the system design stage, which is of great significance for shortening the development cycle and ensuring flight safety during the operation phrase. For this reason, based on the variance decomposition theory, this paper proposes an importance measure index of the influence of component failure rate uncertainty on the uncertainty of power supply reliability (system reliability). Furthermore, an algorithm to calculate the measure index is proposed by combining with the minimum path set and Monte Carlo simulation method. Finally, the proposed method is applied to a typical series-parallel system and an aircraft electrical power system, and a criteria named as “quantity and degree optimization criteria” is drawn from the case study. Results demonstrate that the proposed method indeed realizes the measurement of the contribution degree of component failure rate uncertainty to system reliability uncertainty, and combined with the criteria, proper solutions can be quickly determined to reduce system reliability uncertainty, which can be a theoretical guidance for aircraft electrical power system reliability design