Fuzzy Entropy Theory Research Articles

Towards online monitoring of concrete dam displacement subject to time-varying environments: An improved sequential learning approach

Data-based models are widely applied in concrete dam health monitoring. However, most existing models are restricted to offline modeling, which cannot continuously track the displacement behavior with dynamic evolution patterns, especially in time-varying environments. In this paper, sequential learning is introduced to establish an online monitoring model for dam displacement behavior. This approach starts by considering the timeliness difference between old and new data using the forgetting mechanism, and a novel adaptive forgetting extreme learning machine (AF-ELM) is presented. A primary predictor based on AF-ELM is then formulated, which aims to sequentially learn the complex nonlinear relationship between dam displacement and main environmental factors. Considering the chaotic characteristics contained in the residual sequence of the primary predictor, a multi-scale residual-error correction (REC) strategy is devised based on divide-and-conquer scheme. Specifically, time-varying filter-based empirical mode decomposition is adopted to decompose the raw chaotic residual-error series into a set of subseries with more stationarity, which are further aggregated and reconstructed by fuzzy entropy theory and suitable approximation criterion. Finally, the corrected residual sequence is superimposed with the preliminary predictions from AF-ELM to generate the required modeling results. The effectiveness of the proposed model is verified and assessed by taking a real concrete dam as an example and comparing prediction performance with state-of-the-art models. The results show that AF-ELM performs better in displacement prediction compared with benchmark models, and the multi-scale REC can effectively identify the valuable information within the residual sequence. The proposed online monitoring model can more closely track the dynamic variations of displacement data, which provides a fire-new solution for dam behavior prediction and analysis.

Application of Extended Set Pair Analysis on Wear Risk Evaluation of Backfill Pipeline

Filling slurry can inevitably cause irreversible wear to the pipeline, which represents great costs to mines. This study aims to propose an extended set pair analysis (SPA) for the wear risk evaluation of backfill pipeline. First, to fully describe the wear risk of backfill pipeline, an evaluation index system was established from the aspects of slurry characteristics, pipeline properties, and slurry flow state. Then, the experts grading method was modified with probabilistic linguistic term sets (PLTSs) to obtain subjective weights. Meanwhile, the criteria importance through intercriteria correlation (CRITIC) approach was used to calculate objective weights. By introducing a preference coefficient, they were integrated to determine the comprehensive weights. After that, the classical SPA was extended with membership functions and fuzzy entropy theory, so that the wear risk of backfill pipeline can be evaluated from the perspectives of both the risk level and complexity. Finally, the proposed methodology was applied to assess the wear risk in the Jinchuan nickel mine, Dahongshan copper mine, Hedong gold mine, and Xincheng gold mine. The reliability of evaluation results was further verified through sensitivity and comparative analyses. Results indicate that the proposed methodology is feasible for the wear risk evaluation of backfill pipeline, and can provide guidance on the wear risk management.

Marine economic industrial structure based on fuzzy entropy and least square regression model

China’s economy is in the critical period of transformation from a traditional land economy to a marine economy. China is rich in marine resources, facing important development opportunities and challenges. The adjustment of marine economic industrial structure is of great significance to promote the development of China’s marine economy. To intuitively analyze and clearly understand the effect of industrial structure adjustment of marine economy in recent years in China, using the data of China’s marine industry from 2008 to 2018, fuzzy entropy theory, and least square regression model, the influence of the scientificity and advancement of marine economic industrial structure adjustment on marine economic was analyzed. Results show that the fitting degree of the model is good and significant, with every 1% increase in the scientificity and height of marine economic industrial structure adjustment, the marine economy increases by 1.88% and 2.80%, respectively. The development of marine science and technology can promote the rise of the marine economy.

Fuzzy Covering-Based Three-Way Clustering

This paper investigates the three-way clustering involving fuzzy covering, thresholds acquisition, and boundary region processing. First of all, a valid fuzzy covering of the universe is constructed on the basis of an appropriate fuzzy similarity relation, which helps capture the structural information and the internal connections of the dataset from the global perspective. Due to the advantages of valid fuzzy covering, we explore the valid fuzzy covering instead of the raw dataset for RFCM algorithm-based three-way clustering. Subsequently, from the perspective of semantic interpretation of balancing the uncertainty changes in fuzzy sets, a method of partition thresholds acquisition combining linear and nonlinear fuzzy entropy theory is proposed. Furthermore, boundary regions in three-way clustering correspond to the abstaining decisions and generate uncertain rules. In order to improve the classification accuracy, the k-nearest neighbor (kNN) algorithm is utilized to reduce the objects in the boundary regions. The experimental results show that the performance of the proposed three-way clustering based on fuzzy covering and kNN-FRFCM algorithm is better than the compared algorithms in most cases.

Using Improved Prospect Theory to Develop a Partner Selection Method for Virtual Enterprises with Unknown Weight

A “virtual enterprise” is an effective organization formed by enterprises and partners under market opportunity which can flexibly adapt to the dynamic market demand and improve the competitiveness of enterprises. To select virtual enterprise partners objectively and scientifically, this study proposes the evaluation model of the innovation resource capability of the alternative enterprises under the unknown weight. In the multigranularity hesitation fuzzy language environment, the unknown weight is solved by using fuzzy entropy theory. The risk attitude of decision-making enterprises is introduced by using the improved prospect theory and the selection of partners is comprehensively considered. Finally, a case study is presented to demonstrate the effectiveness of the proposed approach. The research intends to enable the virtual enterprise to choose the partners swiftly such that they can compensate for the shortcomings and optimize the allocation of innovation resources.

Two-dimensional grey cloud clustering-fuzzy entropy comprehensive assessment model for river health evaluation

The river health evaluation is typically complex non-linear system with characteristics of fuzziness and randomness. However, conventional gray clustering method has difficult to effectively describe fuzzy and random information simultaneously. For this purpose, the cloud model and fuzzy entropy theory are introduced to establish 2D gray cloud clustering-fuzzy entropy comprehensive evaluation model. Different with health level models, it reflects river health situation from aspects of health level and corresponding water body complexity simultaneously. The health level is obtained by gray cloud whitened weight function (first sub-system) and fuzzy entropy represents complexity and fuzziness of river health situation (second sub-system). Moreover, multi-level river health evaluation indicator system is constructed with dividing indicators into common and distinct sections according to differences on river characteristics. Meanwhile, indicator weights are determined by renewed combined weighting method based on minimum deviation principle. Finally, we conduct health evaluation work for rivers in the Taihu basin. The evaluation health levels and fuzzy entropy for river A–G are H3 (0.4888, relatively significant); H2 (0.5476, relatively fuzzy); H2 (0.7526, fuzzy); H2 (0.4731, relatively significant); H2 (05138, relatively fuzzy); H3 (0.5822, relatively fuzzy), and H2 (0.4064, relatively significant), respectively. Results are consistent with current river health situation and more intuitive than compared models. Furthermore, evaluation results with four different weighting methods are compared to further demonstrate rationality of the weighting method and evaluation model. Hence, the model proposed is demonstrated to provide new insight for solving river health assessment problem effectively.

Research on Coupling Degree Model of Safety Risk System for Tunnel Construction in Subway Shield Zone

Due to the complexity of construction technology and the seriousness of the safety situation in subway tunnels, it is urgent to find a risk‐control method to monitor and evaluate the construction safety risk system of the subway shield tunnel and to avoid the occurrence of safety risk accidents. Based on the fuzzy entropy theory, this paper establishes a comprehensive index system for the construction safety risk system of shield tunnels. Also, in order to quantitatively analyze the coupling degree among various factors in the safety risk system, the calculation model of coupling degree is established based on the theory of coupling degree in physics. Taking Tianjin subway project as an example, the coupling degree of its safety risk system is calculated. The result shows that the coupling degree of the safety risk system is 0.6997, which indicates that the whole safety risk system is in high‐risk coupling level. Due to the system in an unbalanced development stage, the coupling movement between various factors has a great destructive effect on the construction safety risk system of the subway shield tunnel. This model can not only identify the main risk factors affecting the safety risk system but also monitor the coupling status of the safety risk system in real time and master its development stage. By controlling the main risk factors affecting the safety risk system, the coupling degree of the safety risk system and the probability of the safety risk accident occurrence can be reduced.

An entropy-based approach for assessing the operation of production logistics

Production logistics is increasingly receiving attention from supply chain research. This paper focuses on the lack of quantitative approaches to assess the different operating efficiency of production logistics caused by different production modes or structures. Production logistics is first defined as a whole circulation process. Evaluation indexes are established from a perspective of major factors which make up production-logistics systems. Their computational methods are given as well. Based on the fuzzy entropy theory, an operation entropy model of production-logistics systems is developed to measure the operational efficiency of production logistics. In the case study, two operation order parameters under different conditions are calculated using the evaluation indexes and the operation entropy model developed to accurately compare the level of operational efficiency of the workshop before and after implementing cellular manufacturing. Finally, the result demonstrates the effectiveness of the proposed methodology.

Research on Foundation Pit Construction Risk of Metro Station Based on Fuzzy Entropy Theory

There are many risks in the construction of Foundation Pit of Subway Station because of the complicated conditions and numerous factors influencing foundation pit construction. In order to reduce the construction risk, this paper firstly established the risk evaluation index system of subway station foundation pit construction, which includes seepage breaking, support instability, pit soil landslide, skirting damage, sudden surge damage and mechanical wounding (six first - level indicators, seventeen secondary indicators);Then, the fuzzy entropy theory is applied to calculate the weight of each index, and the construction risk evaluation model of subway station foundation pit is established. Finally, taking LingBin road subway station foundation pit construction in Tianjin as an example, the construction risk of the pit foundation project was evaluated, the evaluation results provide a good basis for the construction risk prevention of the foundation pit.

Mixed Pixel Wise Characterization Based on HDP-HMM and Hyperspectral Image Shape Detection using Hybrid Canny Edge Detection and WPDF

Hyper Spectral Imaging (HSI) gathers and processes information from across the electromagnetic spectrum. The information enclosed in hyperspectral data permits the characterization, recognition and classification of the land-covers with enhanced accuracy and robustness. On the other hand, quite a lot of vital complications must be considered during the classification process of hyperspectral data, among which the maximum quantity of spectral channels, the spatial unevenness of the spectral signature, shape discovery of the images and the value of data. Above all, the maximum quantity of spectral channels and low number of labeled training samples pose the setback of the curse of dimensionality and, accordingly, result in the possibility of overfitting the training data. With the aim of solving all these complications, in this study presented the framework of Support Vector Machine (SVM) together with Fuzzy Sigmoid Kernel Function (SVM-FSK) in the circumstance of HSI classification and analyzing their features in the hyperspectral domain. A Kernel Fisher Discriminant Analysis (KFDA) model is employed for the purpose of dimensionality reduction of HSI. The KFDA dimensionality reduction scheme depends on the selection of the kernel in a higher-dimensional HSI feature space. In order to enhance the gradient level of spatial information, employed Improved Empirical Mode Decomposition (IEMD) with Gaussian Firefly Algorithm (GFA) (IEMD-GFA) to boost the mixed pixel wise SVM-FSK classification accuracy. During the process of IEMD scheme, the identifiable of Intrinsic Mode Functions (IMFs) of spectral band, weight values of IMFs are computed with the help of GFA. In order to identify the shape of HSI, novel hybrid scheme depending on the canny operator and fuzzy entropy theory is formulated. This scheme computes the fuzzy entropy of gradients from an image to make a decision on the threshold for the canny operator. For the purpose of detecting the edges and to discover the shape of the object Weibull Probability Density Function (WPDF) scheme is used. The obtained both spectral and spatial pixels are classified using SVM-FSK and estimated by using Hierarchical Dirichlet Process (HDP)-Hidden Markov Model (HMM). The proposed SVM-FSK is assessed with hyperspectral AVIRIS Indian Pine dataset. It shows that the proposed dimensionality reduction with SVM-FSK classification shows improved classification accuracy in terms of parameters like overall accuracy, standard deviation and mean.

ファジィエントロピー理論に基づくファジィ推論方式

ファジィエントロピー理論に基づくファジィ推論方式