Information Entropy Method Research Articles

Fault diagnosis method for lithium-ion batteries based on relative-range-feature and improved Theil index

ABSTRACT With the widespread adoption of electric vehicles (EVs), battery failures have become a significant concern. To safeguard the lives and property of drivers, accurate and prompt diagnosis of battery failures is crucial. This paper proposes a novel battery fault diagnosis method based on the Relative-Range-Feature (RRF) and an improved Theil index, utilizing actual operating data from EVs. Firstly, the trend component of the voltage is extracted utilizing Singular Spectrum Analysis (SSA) to eliminate noise from the original voltage data. Secondly, to address the challenge of inconspicuous early fault features, a new RRF feature extraction method is introduced. This method significantly amplifies the feature differences between normal and faulty batteries. An improved Theil index is proposed to achieve prompt fault detection with high robustness. Finally, a faulty battery localization algorithm that combines Multidimensional scaling (MDS) and Mahalanobis distance (MD) is proposed. This algorithm excels in distinguishing faulty batteries from normal ones. Validation using actual vehicle data demonstrates that the proposed algorithm offers significant advantages in reliability, effectiveness, and robustness. Compared to the information entropy and correlation coefficient methods, this method demonstrates superior applicability.

A hydraulic motor fault diagnosis method based on weighted multi-channel information fusion

Abstract In response to the limitations of the existing single-sensor hydraulic motor fault diagnosis model, which includes significant fluctuations in fault identification accuracy, low data utilization, poor reliability, and insufficient generalization ability under variable working conditions, a novel hydraulic motor fault diagnosis method based on weighted fusion of multi-channel data and migration learning is proposed. Firstly, in order to fully extract the fault information in the multi-channel data set of the hydraulic motor, a multi-channel fusion method based on information entropy weighting is proposed. The information entropy method is employed to calculate the fusion weight of each channel of data, and the sampled data of each channel is weighted and fused. Subsequently, the fusion data from the source domain is employed to pre-train the deep transfer model, with the model parameters obtained from this pre-training serving as the initialization parameters for the target domain model. Further, the parameters of the target domain model’s feature extractor are fixed, and the parameters of its classifier are fine-tuned using the target domain’s fusion data. The distance between the source and target domains is reduced by incorporating an attention mechanism and constructing a loss function. The migration from the source domain to the target domain is achieved, which enables the classifier to adapt to the novel target sample recognition task. Ultimately, the experimental results of hydraulic motor migration diagnosis under variable operating conditions demonstrate that the proposed method is efficacious for hydraulic motor fault diagnosis. In comparison to conventional models such as CNN, LSTM and ResNet, the proposed method exhibits superior migration diagnosis accuracy and strong generalization and robustness under variable operating conditions.

Customer Requirement Importance Determination Method Based on Uncertain Language Information Entropy

To reflect the uncertainty of the objective environment in an effective way, a customer demand selection method based on information entropy of uncertain language is proposed. First, the customer representative uses uncertain language variables to represent the competitiveness of the company’s products and those of its competitors. Secondly, the method of information entropy was used to determine the competitive priority of each customer demand item. Finally, taking the improvement of the PE crusher as an example, the practicability and reliability of the proposed method are illustrated.

Research on Particle Swarm Image Threshold Segmentation Method Based on Rough Set

Image processing is an important way to obtain information and is widely used in important fields such as military, medical and transportation. Image segmentation plays an important role in image processing. In view of the inherent complexity and correlation of the image itself, how to deal with the uncertainty in the image segmentation process is the main work to obtain a more accurate image segmentation result. We propose an image single-threshold segmentation algorithm using the maximum dependency of variable precision rough set (VPRS). The algorithm uses VPRS to represent the image, and uses the maximum dependency and particle swarm optimization to solve the optimal image segmentation threshold, which effectively handles the uncertainty in image segmentation. The experiments show that the single-threshold segmentation algorithm has certain practicability and flexibility, the segmentation effect is better than the maximum average information entropy method, and the segmentation efficiency is significantly higher than the ordinary iterative method.

The Interlink between Stakeholder Influence and Sustainable Practices: A Case Study of Thai Agriculture Enterprise

Nowadays, agriculture businesses have been significantly impacted by rapid global changes, compelling the agro-industry to adopt sustainable development practices to remain resilient. Moreover, the application of stakeholder theory has become essential in business management to achieve inclusive growth and fulfill sustainable business. Understanding the interlink between stakeholder pressure and the motivation to transform an enterprise’s practices into sustainable development is imperative. Therefore, this study aims to evaluate the direct pressure of stakeholder groups on sustainable practices in agriculture enterprises in Thailand through a questionnaire survey. This paper focused on the influence of primary and secondary stakeholders and evaluated the weighting of sustainability practices. The survey was conducted on employees who work in enterprises that apply Thailand’s Sufficiency Economy Philosophy (SEP). The research adopted the regression and information entropy methods for result analysis. The results showed that employees, shareholders, and competitors are significant stakeholder groups that drive sustainable capital covering economics, nature, society, and human capital. Last, stakeholder management is an outstanding practice in a SEP thinking enterprise. Concurrently, human capital is the highest priority to fulfill this alternative pathway to be successful in enterprise sustainability.

Intelligent fault diagnosis of bearings driven by double-level data fusion based on multichannel sample fusion and feature fusion under time-varying speed conditions

Bearing fault diagnosis holds paramount importance in guaranteeing the smooth operation of mechanical systems. However, single-channel vibration sensor data poses limitations in providing a comprehensive representation of bearing fault characteristics, and the fault diagnosis model extracts fault features with insufficient representativeness, resulting in low reliability and poor generalization capability of the model in the face of variable operating conditions. Therefore, a novel intelligent fault diagnosis model of bearings driven by double-level data fusion (DLDF) is developed in this paper. Initially, the fusion weights of each channel's data are obtained using the information entropy method. Then, a novel multi-modal image fusion strategy is proposed to integrate the time-frequency representations of vibration data from channels X, Y, and Z at a specific measurement point. Finally, the shallow and deep feature components extracted from the CNN model and the feature components obtained through different mapping methods are fused to extract more representative fault features, achieving a reliable diagnosis of bearing faults under time-varying speed conditions. The efficacy and reliability of the proposed approach are demonstrated through the fault diagnosis outcomes of two sets of bearing experimental data under time-varying speed conditions.

CoCoSo framework for multi-attribute decision-making with triangular fuzzy neutrosophic sets: “Innovation and entrepreneurship” evaluation case

The “innovation and Entrepreneurship” education focuses on cultivating students’ “innovation and Entrepreneurship” abilities, reflecting the fundamental requirements of economic progress and social development for the knowledge, ability, and quality structure of talents. Local universities should actively promote the reform of talent cultivation models, establish a scientific “innovation and Entrepreneurship” education target system based on the actual situation of the school, strengthen the construction of “innovation and Entrepreneurship” education courses, build various “innovation and Entrepreneurship” practice platforms, pay attention to the interaction between the first and second classrooms, as well as practical and training platforms, serve the growth and talent development of students, and achieve a comprehensive improvement in the quality of school education, teaching, and talent cultivation. The “innovation and Entrepreneurship” education quality evaluation of local colleges could be considered as multiple attribute decision-making (MADM). Recently, the Combined Compromise Solution (CoCoSo) method and information entropy method was employed to deal with MADM. The triangular fuzzy neutrosophic sets (TFNSs) are employed as a better tool for expressing uncertain information during the “innovation and Entrepreneurship” education quality evaluation of local colleges. In this paper, the triangular fuzzy neutrosophic number CoCoSo (TFNN-CoCoSo) based on the TFNN grey rational coefficients (TFNNGRC) from TFNN positive ideal solution (TFNNPIS) is constructed to cope with the MADM under TFNSs. The information entropy method is employed to compute the weight values based on the TFNNGRC from TFNNPIS under TFNSs. Finally, a numerical example of “innovation and Entrepreneurship” education quality evaluation of local colleges is constructed and some decision comparisons are constructed to verify the TFNN-CoCoSo method.

From field soil sampling to watershed model: Upscaling by integrating information entropy and interpolation method

Soil property data plays a crucial role in watershed hydrology and non-point source (H/NPS) modeling, but how to improve modeling accuracy with affordable soil samplings and the effects of sampling information on H/NPS modeling remains to be further explored. In this study, the number of sampling points and soil properties were optimized by the information entropy and the spatial interpolation method. Then the sampled properties were parameterized and the effects of different parameterization schemes on H/NPS modeling were tested using the Soil and Water Assessment Tool (SWAT). The results indicated that the required sampling points increased successively for soil bulk density (SOL_BD), soil saturated hydraulic conductivity (SOL_K) and soil available water capacity (SOL_AWC). Compared to the traditional database (Harmonized world soil database), the NSE and R2 performance by new scheme increased by 22.8% and 10.5%, respectively. The entropy-based optimization reduced the sampling points by 13.2%, indicating a more cost-effective scheme. Compared to hydrological simulation, sampled properties showed greater effects on NPS modeling, especially for nitrogen. This proposed method/framework can be generalized to other watersheds by upscaling field soil sampling information to the watershed scale, thus improving H/NPS simulation.

Information-entropy enabled identifying topological photonic phase in real space

The topological photonics plays an important role in the fields of fundamental physics and photonic devices. The traditional method of designing topological system is based on the momentum space, which is not a direct and convenient way to grasp the topological properties, especially for the perturbative structures or coupled systems. Here, we propose an interdisciplinary approach to study the topological systems in real space through combining the information entropy and topological photonics. As a proof of concept, the Kagome model has been analyzed with information entropy. We reveal that the bandgap closing does not correspond to the topological edge state disappearing. This method can be used to identify the topological phase conveniently and directly, even the systems with perturbations or couplings. As a promotional validation, Su–Schrieffer–Heeger model and the valley-Hall photonic crystal have also been studied based on the information entropy method. This work provides a method to study topological photonic phase based on information theory, and brings inspiration to analyze the physical properties by taking advantage of interdisciplinarity.Graphical

Condition Information Entropy and Rough Set Method Based on Particle Swarm Optimization Applied in the Natural Quality Evaluation of Cultivated Land

The evaluation of the natural quality of cultivated land is crucial for preserving arable land and achieving a balance between the quantity and quality of arable land. Therefore, a timely assessment of the natural quality of cultivated land is needed to monitor its changes. However, current methods often focus on a single specified crop, neglecting the variations that occur across different specified crops. Since the indicator weight recognition method is only suitable for a single crop, this paper proposes a novel model evaluating the natural quality of cultivated land based on the method of “hidden light–temperature index and yield ratio coefficient”. In addition, the condition information entropy and rough set method based on particle swarm optimization (CIERS-PSO) were proposed to evaluate the natural quality of cultivated land in Enshi. Firstly, condition information entropy and rough set are adopted to determine the importance of the indicator automatically. Then, particle swarm optimization (PSO) is utilized to obtain the optimal weights of the first-level and second-level indicators. Finally, the proposed model and evaluation method were adopted to evaluate the natural qualities of the cultivated land. The experimental results demonstrated that the combination of the “hidden light–temperature index and yield ratio coefficient” model and the CIERS-PSO method can automatically identify the indicator weights for the evaluation of natural quality in multi-crop cultivated land. It could obtain better evaluation accuracy even if the sample size is small.

Fault diagnosis of lithium-ion batteries based on wavelet packet decomposition and Manhattan average distance

ABSTRACT As lithium-ion batteries are widely used in electric vehicles, safety accidents caused by battery failures emerge one after another. Nevertheless, failures caused by changes in the internal structure or characteristics of the battery, such as sudden and progressive failures, are still a serious problem for electric vehicles, challenging existing fault diagnosis methods. This paper first performs wavelet packet decomposition on the battery’s raw voltage signal to obtain high-quality low-frequency and high-frequency characteristic signal components. Then performs singular value decomposition on the characteristic signal components to extract the corresponding singular value characteristic parameters, and introduces the Manhattan average distance algorithm to battery faults. Diagnosing and locating faulty battery units using the Laida criterion (3-σ criterion) outlier detection method. Finally, actual vehicle data were used to verify the reliability, stability, accuracy of the method, and compared with the traditional Manhattan distance, correlation coefficient, information entropy methods. The method in this paper has good fault detection effects on vehicles with sudden and progressive faults vehicles.

A Convolutional Neural Network and Attention-Based Retrieval of Temperature Profile for a Satellite Hyperspectral Microwave Sensor

As numerical weather forecasting advances, there is a growing demand for higher-quality atmospheric data. Hyperspectral instruments can capture more atmospheric information and increase vertical resolution, but there has been limited research into retrieval algorithms for obtaining hyperspectral microwaves in the future. This study proposes an atmospheric temperature profile detection algorithm based on Convolutional Neural Networks (CNN) and Local Attention Mechanisms for local feature extraction, applied to hyperspectral microwave sensors. The study utilizes the method of information entropy to extract more effective channels in the vicinities of 60 GHz, 118 GHz, and 425 GHz. The algorithm uses the brightness temperature as the input of the network. The algorithm addresses common issues encountered in conventional networks, such as overfitting, gradient explosion, and gradient vanishing. Additionally, this method isolates the three oxygen-sensitive frequency bands for modularized local feature extraction training, thereby avoiding abrupt changes in brightness temperature between adjacent frequency bands. More importantly, the algorithm considers the correlation between multiple channels and information redundancy, focusing on variations in local information. This enhances the effectiveness of hyperspectral microwave channel information extraction. We simulated the brightness temperatures of the selected channels through ARTS and divided them into training, validation, and test sets. The retrieval capability of the proposed method is validated on a test dataset, achieving a root mean square error of 1.46 K and a mean absolute error of 1.4 K for temperature profile. Detailed comparisons are also made between this method and other commonly used networks for atmospheric retrieval. The results demonstrate that the proposed method significantly improves the accuracy of temperature profile retrieval, particularly in capturing fine details, and is more adaptable to complex environments. The model also exhibits scalability, extending from one-dimensional (pressure level) to three-dimensional space. The error for each pressure level is controlled within 0.7 K and the average error is within 0.4 K, demonstrating effectiveness across different scales with impressive results. The computational efficiency and accuracy have both been improved when handling a large amount of radiation data.

Elimination of seismic characteristics of solid-filled in ultra-deep fractured-vuggy reservoirs

A large number of high-quality fractured-vuggy reservoirs (FVRs) are buried in ultra-deep (6500–9000 m) carbonate layers in the Tarim Basin, represented by the strong “beaded” reflection (SBR) on seismic profiles. This characteristic corresponds to the vast majority of high yield oil and gas wells, but there have also been a number of instances where the reservoir was filled with solid, leading to exploration failure. To avoid drilling failure, it is crucial to accurately identify fake reservoirs using geophysical methods. This paper proposes a novel method to eliminate the seismic characteristics of solid-filled in FVRs. Based on the waveform component decomposition (WCD) data, combined with drilling and logging data, an adaptive component reconstruction (ACR) can be completed to distinguish whether the reservoir is filled with solid or not, achieving the goal of eliminating the seismic characteristics of filling. Firstly, supervised WCD is performed on the target seismic data. Then, the waveform characteristics, which indicate the difference in the FVRs filled with solids, are combined accordingly with the sensitive attribute characteristics (instantaneous amplitude, heterogeneous anomalies, arc length, etc.) of each component data, and the feature templates of filled and non-filled FVRs are defined. Finally, the information entropy method is applied to optimise the component set that can distinguish the solid filled and non-filled FVRs and complete the component reconstruction. Forward modelling shows that this method can effectively distinguish the characteristics of FVRs that have been filled by solids from those that not. This method has achieved favourable results in the M block in the Tarim Basin. Successfully removed some or all the characteristic information of solid filled reservoirs confirmed by drilling from seismic data. The effectiveness of the method in identifying solid filled reservoirs has been confirmed by drilling, and the prediction rate of FVRs non-filled with solids has increased from 78% to 95%. This method can be used for reference in blocks with similar geological characteristics.

An advanced decision-support system for ranking unbalanced bids

Awarding a contract to a bidder who has unbalanced the bid may cost more money to the owner. An owner who determines that a bid has been unbalanced has the legal right to reject the bid. Although several models have been developed to detect unbalanced bids, some researchers still assert that the number of cases in which owners mistakenly identify balanced bids as unbalanced is too great to ignore. One of the reasons of this failure is the use of an inappropriate benchmark against which the offered unit prices are compared. In this paper it is argued that the use of the geometric mean of the unit prices provided by the bidders as a benchmark helps to obtain robust solutions and an advanced decision-making model is proposed in which the Information Entropy and VIKOR methods are used to detect unbalanced bids. The results obtained from an example project drawn from the literature demonstrate how owners may be misled if they use an inappropriate benchmark in detecting unbalanced bids. This study contributes to the body of knowledge by highlighting the negative impacts of using flawed benchmarks in this process, an area of research that is mostly ignored in the unbalanced bidding literature.

Analysis of the Utility of Chinese Traditional Culture in Contemporary Digital Media Communication Based on a Big Data Platform

Abstract Chinese traditional culture is profound and has important historical and cultural values, and in the digital information era, the dissemination of Chinese traditional culture is facing new challenges and opportunities. This paper begins with the utility mechanism of Chinese traditional culture dissemination and develops a path for Chinese traditional culture dissemination using digital media. On this basis, a digital platform for Chinese traditional culture is established through the modular division of digital platforms and joint visual and interaction design. Then, the hierarchical analysis method (AHP) is combined with (PCA) to complete the weighting. Then, the subjective and objective weights are balanced by the information entropy method (IE). Finally, the comprehensive evaluation model of the utility of digital communication in Chinese traditional culture is constructed by combining with the method of the distance between the superiority and inferiority solutions (TOPSIS), and the evaluation index system of the utility of cultural communication is established. Among them, the benefit value has the highest weight of 0.2007. The other indicators in order of weight are cultural, emotional experience (0.1735), cultural content experience (0.1687), interactive experience (0.1533), social impact (0.1531), and digital function experience (0.1507). Finally, the communication effect of traditional Chinese culture is analyzed using the digital platform of this paper. The average score is 8.75, with excellent communication effects. Based on the utility analysis of digital media communication, this paper provides a digital path for the communication of Chinese traditional culture and establishes a comprehensive evaluation method of communication utility, which provides an effective research method for the utility of digital media communication of traditional culture.

An Extended EDAS Approach Based on Cumulative Prospect Theory for Multiple Attributes Group Decision Making with Interval-Valued Intuitionistic Fuzzy Information

The interval-valued intuitionistic fuzzy sets (IVIFSs), based on the intuitionistic fuzzy sets (IFSs), combine the classical decision method and its research and application is attracting attention. After a comparative analysis, it becomes clear that multiple classical methods with IVIFSs’ information have been applied to many practical issues. In this paper, we extended the classical EDAS method based on the Cumulative Prospect Theory (CPT) considering the decision experts (DEs)’ psychological factors under IVIFSs. Taking the fuzzy and uncertain character of the IVIFSs and the psychological preference into consideration, an original EDAS method, based on the CPT under IVIFSs (IVIF-CPT-EDAS) method, is created for multiple-attribute group decision making (MAGDM) issues. Meanwhile, the information entropy method is used to evaluate the attribute weight. Finally, a numerical example for Green Technology Venture Capital (GTVC) project selection is given, some comparisons are used to illustrate the advantages of the IVIF-CPT-EDAS method and a sensitivity analysis is applied to prove the effectiveness and stability of this new method.

Jadeite Jewelry Design Based on Product User Information Analysis and Entropy Method

Jadeite Jewelry Design Based on Product User Information Analysis and Entropy Method

A Multi-Point Geostatistical Modeling Method Based on 2D Training Image Partition Simulation

In this paper, a multi-point geostatistical (MPS) method based on variational function partition simulation is proposed to solve the key problem of MPS 3D modeling using 2D training images. The new method uses the FILTERSIM algorithm framework, and the variational function is used to construct simulation partitions and training image sequences, and only a small number of training images close to the unknown nodes are used in the partition simulation to participate in the MPS simulation. To enhance the reliability, a new covariance filter is also designed to capture the diverse features of the training patterns and allow the filter to downsize the training patterns from any direction; in addition, an information entropy method is used to reconstruct the whole 3D space by selecting the global optimal solution from several locally similar training patterns. The stability and applicability of the new method in complex geological modeling are demonstrated by analyzing the parameter sensitivity and algorithm performance. A geological model of a uranium deposit is simulated to test the pumping of five reserved drill holes, and the results show that the accuracy of the simulation results of the new method is improved by 11.36% compared with the traditional MPS method.

Investigating the spatio-temporal interactive relationship between land use structure and ecosystem services in urbanizing China

The negative impacts of urbanization-triggered land use changes on ecosystem services have attracted worldwide attention. However, the interactive relationships between land use structure and ecosystem services remain unclear, which is not conducive to sustainable regional ecological management. This study employed the information entropy and ecosystem services value assessment method to measure the land use structure and ecosystem services at the county level in China between 2000 and 2020, respectively. Then the spatial correlation analysis and decoupling model were used to explore the spatial and temporal interactive relationships between them, respectively. The results indicated that the average information entropy of land use structure (IELUS) and total ecosystem services value (ESV) in China both showed a continuous growth trend during the study period. ESV and IELUS were found to have a significant positive spatial correlation and were dominated by low-value agglomeration. The dominant types of the interactive relationship between IELUS and ESV were strong decoupling and expansion negative decoupling, and the number of counties with expansion negative decoupling decreased rapidly during the study period. These results suggest that differentiated land-use structure management policies ought to be adopted to protect regional ecosystem services.

Measuring and analyzing the information entropy value of key Audit matters (KAMs) disclosure at the system and reporting scale

Drawing upon information entropy theory, this study deduces a measurement formula for the information entropy for key audit matters (KAMs) disclosure, considering both system scale and audit report scale. The information entropy of an audit report within a specific system is calculated as the weighted average logarithm value of the continuous product of KAM disclosure frequencies. Subsequently, a systematic calculation of KAM disclosure information entropy is conducted using 14,837 financial statement audit reports from listed companies traded on the Shanghai and Shenzhen Stock Exchanges, covering the FY 2017-20 period. The findings indicate that: (1) The information entropy of KAM disclosure has decreased from 2017 to 2020, while the homogenization degree of KAM disclosure has increased. (2) The information entropy and evenness of KAM disclosure on the main board are significantly higher than those of the ChiNext and the STAR Market. (3) The transportation, storage and postal services, manufacturing, finance, power supply, heating, gas and water supply industries exhibit the highest overall information entropy of KAM disclosure, indicating good quality. (4) The information entropy and evenness of KAM disclosure are higher for the big 4 accounting firms compared to domestic accounting firms and big 8 accounting firms. (5) The KAMs disclosed when the Certified Public Accountant (CPA) provides unqualified opinions, emphasizing that matter paragraphs possess higher information value, and are more disorderly and balanced. The measurement method and related results of information entropy offer reference and comparative value for future research on the quality of KAMs disclosure.