Search results

As productions show characteristics of multi-varieties and small batch in a recent new product system, it is more difficult to acquire its failure rate data. With the help of expert experience information, the authors can get the interval estimation of failure rate data under different methods, so how to make the interval convergence with the new information is an important problem to be solved. The paper aims to discuss this issue.

In this paper, the concept of generalized standard grey number is used to characterize the multi-source heterogeneous uncertainty failure rate data into a unified framework. Then, the engineering construction method is used to calculate the average failure rate and build the grey exponential distribution reliability function, whose image is presented as the possible region of the two-curve envelope.

Further, according to the normal distribution assumption of the regional convergence based on the information supplement, the convergence problem of the reliability function is transformed into the convergence of the area of the curve envelope region, and construct the multi-objective programming model with the minimum envelope area and the lowest total cost of information acquisition, acquire the conclusion that the failure rate is equal to the nuclear of the average failure rate when the envelope region converges.

Through the case analysis of the equipment ejection system of the Harbinger system, five groups of results are obtained by Matlab simulation, which verify the rationality and feasibility of the model described in this paper.

Hazard assessment on drought disaster is of great significance for improving drought risk management. Due to the complexity and uncertainty of the drought disaster, the index values have some grey multi-source heterogeneous characteristics. The purpose of this paper is to construct a grey projection incidence model (GPIM) to evaluate the hazard of the drought disaster characterised by the grey heterogeneity information.

First, the index system of the drought hazard risk is established based on the formation mechanism of the drought disaster. Then, the GPIM for the heterogeneous panel data is constructed to assess drought hazard of five cities in Henan Province. Subsequently, based on the assessment results, the grey clustering model is employed for the regional division.

The findings demonstrate that five cities in central Henan Province are divided into three categories, which correspond to three different risk grades, respectively. With respect to different drought risk areas, corresponding countermeasures and suggestions are proposed.

This paper provides a practical and effective new method for the hazard assessment on drought disaster. Meanwhile, these countermeasures and suggestions can help policy makers to improve the efficiency of drought resistance work and reduce the losses caused by drought disasters in Henan Province.

This paper proposes a new GPIM which resolves the assessment problems of the uncertain systems with grey heterogeneous information, such as real numbers, interval grey numbers and three-parameter interval grey numbers. It not only expands the application scope of the grey incidence model, but also enriches the research of panel data.

This research aims to propose a model for the complex decision-making involved in the ecological restoration of mega-infrastructure (e.g. railway engineering). This model is based on multi-source heterogeneous data and will enable stakeholders to solve practical problems in decision-making processes and prevent delayed responses to the demand for ecological restoration.

Based on the principle of complexity degradation, this research collects and brings together multi-source heterogeneous data, including meteorological station data, remote sensing image data, railway engineering ecological risk text data and ecological restoration text data. Further, this research establishes an ecological restoration plan library to form input feature vectors. Random forest is used for classification decisions. The ecological restoration technologies and restoration plant species suitable for different regions are generated.

This research can effectively assist managers of mega-infrastructure projects in making ecological restoration decisions. The accuracy of the model reaches 0.83. Based on the natural environment and construction disturbances in different regions, this model can determine suitable types of trees, shrubs and herbs for planting, as well as the corresponding ecological restoration technologies needed.

Managers should pay attention to the multiple types of data generated in different stages of megaproject and identify the internal relationships between these multi-source heterogeneous data, which provides a decision-making basis for complex management decisions. The coupling between ecological restoration technologies and restoration plant species is also an important factor in improving the efficiency of ecological compensation.

Unlike previous studies, which have selected a typical section of a railway for specialized analysis, the complex decision-making model for ecological restoration proposed in this research has wider geographical applicability and can better meet the diverse ecological restoration needs of railway projects that span large regions.

The purpose of this paper is to improve the existing paradigm of edge computing to maintain a balanced energy usage.

The new greedy algorithm is proposed to balance the energy consumption in edge computing.

The new greedy algorithm can balance energy more efficiently than the random approach by an average of 66.59 percent.

The results are shown in this paper which are better as compared to existing algorithms.

The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system.

Using modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system.

Based on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized.

The innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.

This paper aims to present a multi-source information fusion algorithm based on factor graph for autonomous underwater vehicles (AUVs) navigation and positioning to address the asynchronous and heterogeneous problem of multiple sensors.

The factor graph is formulated by joint probability distribution function (pdf) random variables. All available measurements are processed into an optimal navigation solution by the message passing algorithm in the factor graph model. To further aid high-rate navigation solutions, the equivalent inertial measurement unit (IMU) factor is introduced to replace several consecutive IMU measurements in the factor graph model.

The proposed factor graph was demonstrated both in a simulated and vehicle environment using IMU, Doppler Velocity Log, terrain-aided navigation, magnetic compass pilot and depth meter sensors. Simulation results showed that the proposed factor graph processes all available measurements into the considerably improved navigation performance, computational efficiency and complexity compared with the un-simplified factor graph and the federal Kalman filtering methods. Semi-physical experiment results also verified the robustness and effectiveness.

The proposed factor graph scheme supported a plug and play capability to easily fuse asynchronous heterogeneous measurements information in AUV navigation systems.

The purpose of this study is to address a highly heterogeneous rift margin environment and exhibit considerable spatiotemporal hydro-climatic variations. In spite of limited, random and inaccurate data retrieved from rainfall gauging stations, the recent advancement of satellite rainfall estimate (SRE) has provided promising alternatives over such remote areas. The aim of this research is to take advantage of the technologies through performance evaluation of the SREs against ground-based-gauge rainfall data sets by incorporating its applicability in calibrating hydrological models.

Selected multi satellite-based rainfall estimates were primarily compared statistically with rain gauge observations using a point-to-pixel approach at different time scales (daily and seasonal). The continuous and categorical indices are used to evaluate the performance of SRE. The simple scaling time-variant bias correction method was further applied to remove the systematic error in satellite rainfall estimates before being used as input for a semi-distributed hydrologic engineering center's hydraulic modeling system (HEC-HMS). Runoff calibration and validation were conducted for consecutive periods ranging from 1999–2010 to 2011–2015, respectively.

The spatial patterns retrieved from climate hazards group infrared precipitation with stations (CHIRPS), multi-source weighted-ensemble precipitation (MSWEP) and tropical rainfall measuring mission (TRMM) rainfall estimates are more or less comparably underestimate the ground-based gauge observation at daily and seasonal scales. In comparison to the others, MSWEP has the best probability of detection followed by TRMM at all observation stations whereas CHIRPS performs the least in the study area. Accordingly, the relative calibration performance of the hydrological model (HEC-HMS) using ground-based gauge observation (Nash and Sutcliffe efficiency criteria [NSE] = 0.71; R² = 0.72) is better as compared to MSWEP (NSE = 0.69; R² = 0.7), TRMM (NSE = 0.67, R² = 0.68) and CHIRPS (NSE = 0.58 and R² = 0.62).

Calibration of hydrological model using the satellite rainfall estimate products have promising results. The results also suggest that products can be a potential alternative source of data sparse complex rift margin having heterogeneous characteristics for various water resource related applications in the study area.

This research is an original work that focuses on all three satellite rainfall estimates forced simulations displaying substantially improved performance after bias correction and recalibration.

This paper aims to overcome the defect of single-source temperature measurement method and improve the measurement accuracy of FTR. The friction temperature rise (FTR) of brake affects braking performance seriously. However, it was mainly detected by single-source indirect thermometry, which has obvious deviations.

A three-point temperature measurement system was built based on three kinds of single-resource thermometry. Temperature characteristics of these thermometry were analyzed to achieve a standard FTR curve. Two fusion-monitoring models for FTR based on multi-source information were established by artificial neural network (ANN) and support vector machine (SVM).

Finally, the two models were verified based on the experimental results. The results showed that the fusion-monitoring model of SVM was more accurate than that of ANN in monitoring of FTR.

Then the temperature characteristics of the three single-source thermometry were analyzed, and the fusion-monitoring models based on multi-source information were established by ANN and SVM. Finally, the accuracy of the two models was compared by the experimental results. The more suitable fusion-monitoring model for FTR monitoring was determined which would be of theoretical and practical significance for remedying the monitoring defect of FTR.

Although leadership and organizational scholars have suggested that the virtue of wisdom may promote outstanding leadership behavior, this proposition has rarely been empirically tested. The purpose of this paper is to investigate the relationships between transformational leadership, narcissism, and five dimensions of wisdom as conceptualized by the well-established Berlin wisdom paradigm. General mental ability and emotional intelligence were considered relevant control variables.

Interview, test, and questionnaire data were obtained from 77 employees of a high school and from two or three colleagues of each employee. Data were analyzed using hierarchical regression analyses.

After controlling for general mental ability and emotional intelligence, narcissism and the wisdom dimension relativism of values and life priorities were negatively related to transformational leadership, and the wisdom dimension recognition and management of uncertainty was positively related to transformational leadership. The other three wisdom dimensions, rich factual knowledge about life, rich procedural knowledge about life, and lifespan contextualism, were not significantly related to transformational leadership.

Limitations to be addressed in future studies include the cross-sectional design and the relatively small and specialized sample.

Tentative implications for leadership training and development are outlined.

This multi-method and multi-source study represents the first empirical investigation that examines links between well-established wisdom and leadership constructs in the work context.

To address the gap of divergent conclusions on the impact of knowledge search (KS) on performance, this paper aims to discuss the nonlinear relationships between KS and reverse internationalization enterprise (RIE) performance, and the co-moderation of causation and effectuation (C&E) on KS–performance.

The proposed theoretical model is developed by integrating the theory of knowledge-based view and decision rationality theory. The empirical study is based on survey data collected from 245 RIEs of the Yangtze River Delta and Pearl River Delta regions in China. Hierarchical multiple regression and the appropriate U-test method are used to test the hypotheses.

Empirical results suggest that both focused and multi-focus searches have inverted U-shaped effects on RIE performance. Furthermore, causation weakens the curvilinear effect between multi-focus search and RIE performance, whereas effectuation strengthens the curvilinear effect but weakens the inverted U-shaped relationship between focused search and RIE performance. Results also indicate that the integration of C&E positively moderates the relationship between focused or multi-focus searches and RIE performance.

Findings reveal the nonlinear effects of focused and multi-focus searches on RIE performance and clarify the dispute over the mechanism of KS on performance by proposing the different moderating role of C&E. Moreover, this research provides deeper insight into contingency mechanisms between KS and performance by integrating the co-moderating role of C&E in RIEs.