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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.

The selection of sensitive temperature measurement points is the premise of thermal error modeling and compensation. However, most of the sensitive temperature measurement point selection methods do not consider the influence of the variability of thermal sensitive points on thermal error modeling and compensation. This paper considers the variability of thermal sensitive points, and aims to propose a sensitive temperature measurement point selection method and thermal error modeling method that can reduce the influence of thermal sensitive point variability.

Taking the truss robot as the experimental object, the finite element method is used to construct the simulation model of the truss robot, and the temperature measurement point layout scheme is designed based on the simulation model to collect the temperature and thermal error data. After the clustering of the temperature measurement point data is completed, the improved attention mechanism is used to extract the temperature data of the key time steps of the temperature measurement points in each category for thermal error modeling.

By comparing with the thermal error modeling method of the conventional fixed sensitive temperature measurement points, it is proved that the method proposed in this paper is more flexible in the processing of sensitive temperature measurement points and more stable in prediction accuracy.

The Grey Attention-Long Short Term Memory (GA-LSTM) thermal error prediction model proposed in this paper can reduce the influence of the variability of thermal sensitive points on the accuracy of thermal error modeling in long-term processing, and improve the accuracy of thermal error prediction model, which has certain application value. It has guiding significance for thermal error compensation prediction.

This study aims to apply new modifications by changing the nonlinear logarithmic calculation steps in the method based on the removal effects of criteria (MEREC) method. Geometric and harmonic mean from multiplicative functions is used for the modifications made while extracting the effects of the criteria on the overall performance one by one. Instead of the nonlinear logarithmic measure used in the MEREC method, it is desired to obtain results that are closer to the mean and have a lower standard deviation.

The MEREC method is based on the removal effects of the criteria on the overall performance. The method uses a logarithmic measure with a nonlinear function. MEREC-G using geometric mean and MEREC-H using harmonic mean are introduced in this study. The authors compared the MEREC method, its modifications and some other objective weight determination methods.

MEREC-G and MEREC-H variants, which are modifications of the MEREC method, are shown to be effective in determining the objective weights of the criteria. Findings of the MEREC-G and MEREC-H variants are more convenient, simpler, more reasonable, closer to the mean and have fewer deviations. It was determined that the MEREC-G variant gave more compatible findings with the entropy method.

Decision-making can occur at any time in any area of life. There are various criteria and alternatives for decision-making. In multi-criteria decision-making (MCDM) models, it is a very important distinction to determine the criteria weights for the selection/ranking of the alternatives. The MEREC method can be used to find more reasonable or average results than other weight determination methods such as entropy. It can be expected that the MEREC method will be more used in daily life problems and various areas.

Objective weight determination methods evaluate the weights of the criteria according to the scores of the determined alternatives. In this study, the MEREC method, which is an objective weight determination method, has been expanded. Although a nonlinear measurement model is used in the literature, the contribution was made in this study by using multiplicative functions. As an important originality, the authors demonstrated the effect of removing criteria in the MEREC method in a sensitivity analysis by actually removing the alternatives one by one from the model.

This paper aims to build a scientific evaluation index system for regional low-carbon circular economic development. Taking Sichuan Province as the empirical research object, the paper evaluates its low-carbon circular economy (LCCE) development level and proposes policy recommendations for climate change improvement based on the evaluation results.

This paper, first, built an evaluation index system with 30 indicators within six subsystems, namely, economic development, social progress, energy consumption, low-carbon emissions, carbon sink capacity and environmental carrying capacity. Second, develop an “entropy weight-grey correlation” evaluation method. Finally, from a practical point of view, measure the development level of LCCE in Sichuan Province, China, from 2008 to 2018.

It was found that Sichuan LCCE development had a general downward trend from 2008 to 2012 and a steady upward trend from 2012 to 2018; however, the overall level was low. The main factors affecting the LCCE development are lagging energy consumption and environmental carrying capacity subsystem developments.

This paper puts forward relevant suggestions for improving the development of a low-carbon economy and climate change for the reference of policymakers.

This paper built an evaluation index system with 30 indicators for regional low carbon circular economic development. The evaluation method of “entropy weight-grey correlation” is used to measure the development level of regional LCCE in Sichuan Province, China.

Since 2000, China, along with the USA, UK, France, Japan and many other developed countries have drawn up new blueprints for the development of a marine economy. At present, international marine economics research has entered into a new period of development, and the research methods of ocean econometrics are becoming more complex and mature. The purpose of this paper is to review the progress of international marine econometrics research and gives the development direction of marine econometrics.

The Web of Science core collection database was utilized, harvesting data from 1996 to May 2018, measuring the marine economy research from 1,489 articles as its sample, using CiteSpace visualization analysis tools.

Mapping the knowledge map from annual international marine economic metrology, literature identification, keywords, involving disciplines and related journals, countries (regions) and research and analyzing the research status of reveals the research frontiers of international marine economy measurement (learning) by using CiteSpace.

The conceptions and characteristics of marine econometrics are defined and analyzed, and the theoretical method of marine econometrics is sorted out. Mapping the knowledge diagram of marine econometrics and discussing the research status of international marine economics, and clarifying the existing problems, future opportunities and challenges of international marine econometrics research.

The present paper assesses the decision problem of selecting Unmanned Aerial Vehicle Systems (SARP) by the hybrid MPSI-SPOTIS approach for deployment in border control and transborder illicit combat.

By the hybrid MCDA MPSI-SPOTIS approach, and from the database available in Gettinger (2019), models were filtered by Endurance, Range, Maximum Take-Off Weight (MTOW), and Payload, fitting within the classification of Categories EB 0 and 2. Category EB 1 was not considered in this study due to the limited number of models in the data source.

The use of the Multi-Criteria Decision Analysis (MCDA) tool MPSI-SPOTIS allowed the determination of weights by stochastic criteria, applied in a ranking method resistant to reverse ordering. The application of the method identified the Raybird-3 (Cat EB 0) and Searcher (Mk3) (Cat EB 2) models as the best alternatives. From a proposed clustering, other selection possibilities with close performance in the evaluation were presented. The cost criterion was not taken into consideration due to the absence of information in the data source employed. Future studies are suggested to include criteria related to the life cycle and acquisition cost of the models.

The cost criterion was not taken into consideration due to the absence of information in the data source used. Future studies are suggested to include criteria related to the life cycle and acquisition cost of the models.

This paper aims to propose a technology selection method applied to complex defense acquisitions when multiple factors influence the decision makers and it is hard to obtain a major optimum solution in multitask and multi-mission platform.

Fault diagnosis methods based on blind source separation (BSS) for rolling element bearings are necessary tools to prevent any unexpected accidents. In the field application, the actual signal acquisition is usually hindered by certain restrictions, such as the limited number of signal channels. The purpose of this study is to fulfill the weakness of the existed BSS method.

To deal with this problem, this paper proposes a blind source extraction (BSE) method for bearing fault diagnosis based on empirical mode decomposition (EMD) and temporal correlation. First, a single-channel undetermined BSS problem is transformed into a determined BSS problem using the EMD algorithm. Then, the desired fault signal is extracted from selected intrinsic mode functions with a multi-shift correlation method.

Experimental results prove the extracted fault signal can be easily identified through the envelope spectrum. The application of the proposed method is validated using simulated signals and rolling element bearing signals of the train axle.

This paper proposes an underdetermined BSE method based on the EMD and the temporal correlation method for rolling element bearings. A simulated signal and two bearing fault signal from the train rolling element bearings show that the proposed method can well extract the bearing fault signal. Note that the proposed method can extract the periodic fault signal for bearing fault diagnosis. Thus, it should be helpful in the diagnosis of other rotating machinery, such as gears or blades.

The purpose of the work is to provide a comprehensive review of the digital image correlation (DIC) technique for those who are interested in performing the DIC technique in the area of manufacturing.

No methodology was used because the paper is a review article.

no fundings.

Herein, the historical development, main strengths and measurement setup of DIC are introduced. Subsequently, the basic principles of the DIC technique are outlined in detail. The analysis of measurement accuracy associated with experimental factors and correlation algorithms is discussed and some useful recommendations for reducing measurement errors are also offered. Then, the utilization of DIC in different manufacturing fields (e.g. cutting, welding, forming and additive manufacturing) is summarized. Finally, the current challenges and prospects of DIC in intelligent manufacturing are discussed.

Purpose – The purpose of this research is to investigate the relationship of lead (Pb) and zinc (Zn) contents in sediment of Faunus ater (F. ater) population density and to analyze the relationship between Pb and Zn accumulation in F. ater with F. ater density in Reuleng River, Leupung, Aceh Besar.

Design/Methodology/Approach – Sampling was conducted in November 2016 until January 2017. Density of F. ater was analyzed by density formula while its relationship to Pb and Zn in sediments and F. ater was conducted by correlation analysis method.

Findings – The results showed that correlation between Pb and Zn in sediments and in F. ater varies at each locations on every month of sampling. Pb and Zn content in sediments found a fluctuating relationship in each month of sampling with density of F. ater. Correlation of Pb content in sediments with F. ater density showed a medium correlation in January 2017 with r-value = 0.665. Zn in sediment has a very strong correlation to F. ater density in November 2016 with r-value = 0.891. Pb in F. ater has a medium correlation to F. ater density in January 2017 with r-value = 0.436. Furthermore, accumulation of Zn in F. ater to its density does show some apparent correlation in each month of sampling.

Research Limitation/Implications – This research gives information about the relationship of Pb and Zn contents in sediment to density of F. ater and to analyze correlation of Pb and Zn in F. ater to density of F. ater in Reuleng River, Leupung, Aceh Besar district.

Originality/Value – This is the first time research is conducted about on the correlation between lead and zinc to obtain the density of F. ater.

In the continuous development of high-speed railways, ensuring the safety of the operation control system is crucial. Electromagnetic interference (EMI) faults in signaling equipment may cause transportation interruptions, delays and even threaten the safety of train operations. Exploring the impact of disturbances on signaling equipment and establishing evaluation methods for the correlation between EMI and safety is urgently needed.

This paper elaborates on the necessity and significance of studying the impact of EMI as an unavoidable and widespread risk factor in the external environment of high-speed railway operations and continuous development. The current status of research methods and achievements from the perspectives of standard systems, reliability analysis and safety assessment are examined layer by layer. Additionally, it provides prospects for innovative ideas for exploring the quantitative correlation between EMI and signaling safety.

Despite certain innovative achievements in both domestic and international standard systems and related research for ensuring and evaluating railway signaling safety, there’s a lack of quantitative and strategic research on the degradation of safety performance in signaling equipment due to EMI. A quantitative correlation between EMI and safety has yet to be established. On this basis, this paper proposes considerations for research methods pertaining to the correlation between EMI and safety.

This paper overviews a series of methods and outcomes derived from domestic and international studies regarding railway signaling safety, encompassing standard systems, reliability analysis and safety assessment. Recognizing the necessity for quantitatively describing and predicting the impact of EMI on high-speed railway signaling safety, an innovative approach using risk assessment techniques as a bridge to establish the correlation between EMI and signaling safety is proposed.