Grey Clustering Research Articles

Study and Neural Network Analysis on Durability of Basalt Fibre Concrete

In order to investigate the law of basalt fibre to enhance the durability of concrete, this paper selects basalt fibre length as the main factor, supplemented by novel research methods such as neural networks, to study the rule of concrete resistance to multiple types of salt erosion. Tests have shown that large doses of mineral admixtures and basalt fibres can prolong the time that concrete is eroded by salt solutions; the age of maintenance has a small effect on the mechanical and durability of the concrete; the increase in length of basalt fibres enhances the mechanical properties of the concrete, but weakens the durability. This is exacerbated by the mixing of fibres, but the increase is not significant; the effect of length on concrete resistance to mass loss, corrosion resistance factor of compressive strength, and resistance to chloride ion attack is ranked as follows: 6 mm > 12 mm > 18 mm > 6 mm + 12 mm > 6 mm + 12 mm + 18 mm. The opposite is true for effective porosity; the highest compressive strength corrosion resistance coefficient was found in the length of 6 mm, with an average increase of 6.2% compared to 18 mm, and the mixed group was generally smaller than the single mixed group. The average increase in chloride content was 25.1% for length 18 mm compared to 6 mm; the triple-doped L6-12-18 group was the largest, with an average increase of 33.9% in effective porosity over the minimum 6 mm group. Based on the data from the above indoor trials, artificial neural network models and grey cluster analysis were used to predict and analyse the data, and the prediction and categorisation results were accurate and reliable, providing a reference for subsequent studies.

Measuring the efficiency of public hospitals: A multistage data envelopment analysis in Fujian Province, China.

The present study aimed to evaluate the operational efficiency of public hospitals in Fujian Province and the factors responsible for the inefficiency of these hospitals and provide relevant suggestions for health policymakers in allocating service resources. In the first stage of the research, the variables affecting the efficiency of hospitals were extracted by qualitative and quantitative methods, including literature optimization, gray related analysis and gray clustering evaluation. In the second stage, the data envelopment analysis (DEA) method was used to evaluate the operational efficiency of 49 hospitals of different levels and types selected by sampling in 2020. Finally, a Tobit regression model with introduced institutional factors and background factors was established to study the main influencing factors of hospital inefficiency. In the first stage, 10 input variables and 10 output variables necessary from the mangers' point of view were identified to test efficiency. In the second stage, the average comprehensive TE, PTE, and SE of 49 sample hospitals was 0.802, 0.888, and 0.902, respectively. 22.45% of these hospitals met the effective criteria, i.e., the overall effective rate was 22.45%. The low SE value of the hospital was the main reason hindering the improvement of the comprehensive efficiency value. The overall effective rate of secondary public hospitals (30.77%) was higher than that of tertiary public hospitals (19.44%), and the overall effective rate of public specialized hospitals (30%) was higher than that of general public hospitals (18.92%). Based on the third stage results, the bed occupancy rate (BOR) and the proportion of beds (POB) were major factors affecting the operation efficiency of grade III hospitals (p < 0.01). However, the operating efficiency of grade II hospitals was significantly affected by POB and regional per capita GDP(GDPPC) (p < 0.05). Moreover, the impact of BOR and GDPPC was positive, and POB was negatively correlated with hospital operation efficiency. The study results indicated that the overall operation efficiency of public hospitals in Fujian Province is low. This study revealed that intervention should be strengthened from a policy and management perspective to improve the operation efficiency of public hospitals.

Artificial Intelligence Model Based on Grey Clustering to Access Quality of Industrial Hygiene: A Case Study in Peru

Industrial hygiene is a preventive technique that tries to avoid professional illnesses and damage to health caused by several possible toxic agents. The purpose of this study is to simultaneously analyze different risk factors (body vibration, lighting, heat stress and noise), to obtain an overall risk assessment of these factors and to classify them on a scale of levels of Unacceptable, Not recommended or Acceptable. In this work, an artificial intelligence model based on the grey clustering method was applied to evaluate the quality of industrial hygiene. The grey clustering method was selected, as it enables the integration of objective factors related to hazards present in the workplace with subjective employee evaluations. A case study, in the three warehouses of a beer industry in Peru, was developed. The results obtained showed that the warehouses have an acceptable level of quality. These results could help industries to make decisions about conducting evaluations of the different occupational agents and determine whether the quality of hygiene represents a risk, as well as give certain recommendations with respect to the factors presented.

Abnormal State Detection of OLTC Based on Improved Fuzzy C-means Clustering

An accurate extraction of vibration signal characteristics of an on-load tap changer (OLTC) during contact switching can effectively help detect its abnormal state. Therefore, an improved fuzzy C-means clustering method for abnormal state detection of the OLTC contact is proposed. First, the wavelet packet and singular spectrum analysis are used to denoise the vibration signal generated by the moving and static contacts of the OLTC. Then, the Hilbert-Huang transform that is optimized by the ensemble empirical mode decomposition (EEMD) is used to decompose the vibration signal and extract the boundary spectrum features. Finally, the gray wolf algorithm-based fuzzy C-means clustering is used to denoise the signal and determine the abnormal states of the OLTC contact. An analysis of the experimental data shows that the proposed secondary denoising method has a better denoising effect compared to the single denoising method. The EEMD can improve the modal aliasing effect, and the improved fuzzy C-means clustering can effectively identify the abnormal state of the OLTC contacts. The analysis results of field measured data further verify the effectiveness of the proposed method and provide a reference for the abnormal state detection of the OLTC.

GERT-Q-learning model for intelligent QoS dynamic optimization of Inmarsat STN based on grey clustering of delay and delay variation

PurposeThis paper aims to implement quality of service(QoS) dynamic optimization for the integrated satellite-terrestrial network(STN) of the fifth-generation Inmarsat system(Inmarsat-5).Design/methodology/approachThe structure and operational logic of Inmarsat-5 STN are introduced to build the graphic evaluation and review technique(GERT) model. Thus, the equivalent network QoS metrics can be derived from the analytical algorithm of GERT. The center–point mixed possibility functions of average delay and delay variation are constructed considering users' experiences. Then, the grey clustering evaluation of link QoS is obtained combined with the two-stage decision model to give suitable rewards for the agent of GERT-Q-learning, which realizes the intelligent optimization mechanism under real-time monitoring data.FindingsA case study based on five time periods of monitoring data verifies the adaptability of the proposed method. On the one hand, grey clustering based on possibility function enables a more effective measurement of link QoS from the users' perspective. On the other hand, the method comparison intuitively shows that the proposed method performs better.Originality/valueWith the development trend of integrated communication, STN has become an important research object in satellite communications. This paper establishes a modular and extensible optimization framework whose loose coupling structure and flexibility facilitate management and development. The grey-clustering-based GERT-Q-Learning model has the potential to maximize design and application benefits of STN throughout its life cycle.

Research on service quality evaluation of medical caring and nursing combined institutions for the aged based on interval MTS-grey cloud clustering

The lack of a scientific and complete service quality evaluation system for Medical Caring and Nursing Combined Institutions for the Aged is a critical factor that makes it difficult to improve the quality of their services. Based on the SERVQUAL model, the service quality evaluation index system of Medical Caring and Nursing Combined Institutions for the Aged is constructed from tangibles, security, reliability, responsiveness, and empathy. Considering the ambiguity, randomness, grey characteristics, and the interaction between indicators in the service evaluation process of Medical Caring and Nursing Combined Institutions for the Aged, the interval Mahalanobis-Taguchi system (MTS) is introduced into the grey cloud clustering model, and a service quality evaluation model of the interval MTS— grey cloud clustering is proposed. The Medical Caring and Nursing Combined Institutions for the Aged in four typical cities of Jiangsu Province are taken as examples in this study. Feasibility of the proposed method is verified, and targeted measures are thus proposed to provide stronger support and reference for improving the service quality of these institutions.

Radiomic features derived from pretherapeutic MRI predict chemoradiation response in locally advanced rectal cancer.

The standard treatment for locally advanced rectal cancer (LARC) is neoadjuvant concurrent chemoradiotherapy (CRT) followed by surgical excision. Current evidence suggests a favorable prognosis for those with pathological complete response (pCR), and surgery may be spared for them. We trained and validated regression models for CRT response prediction with selected radiomic features extracted from pretreatment magnetic resonance (MR) images to recruit potential candidates for this watch-and-wait strategy. We retrospectively enrolled patients with LARC who underwent pre-CRT MR imaging between 2010 and 2019. Pathological complete response in surgical specimens after CRT was defined as the ground truth. Quantitative features derived from both unfiltered and filtered images were extracted from manually segmented region of interests on T2-weighted images and selected using variance threshold, univariate statistical tests, and cross-validation least absolute shrinkage and selection operator (Lasso) regression. Finally, a regression model using selected features with high coefficients was optimized and evaluated. Model performance was measured by classification accuracies and area under the receiver operating characteristic (AUROC). We extracted 1223 radiomic features from each MRI study of 133 enrolled patients. After tumor excision, 34 (26 %) of 133 patients had pCR in resected specimens. When 25 image-derived features were selected from univariate analysis, classification AUROC was 0.86 and 0.79 with the addition of six clinical features on the hold-out internal validation dataset. When 11 image-derived features were used, the optimized linear regression model had an AUROC value of 0.79 and 0.65 with the addition of six clinical features on the hold-out dataset. Among the radiomic features, texture features including gray level variance, strength, and cluster prominence had the highest coefficient by Lasso regression. Radiomic features derived from pretreatment MR images demonstrated promising efficacy in predicting pCR after CRT. However, radiomic features combined with clinical features did not result in remarkable improvement in model performance.

Evaluation method for the hyperspectral image camouflage effect based on multifeature description and grayscale clustering

Hyperspectral images have a special attribute with both spectral and spatial information, which is of great significance for the evaluation of the stealth performance of camouflaged targets. Aiming at the problems of a single evaluation index and the low credibility of traditional optical camouflage evaluation methods, this paper proposes a grayscale clustering camouflage effect evaluation method based on multifeature descriptions of hyperspectral images using similarity indicators that reflect different spectral characteristics of the target and background. From the perspective of spectrum and human visual contrast, a comprehensive evaluation index system including spectral distance feature, spectral derivative feature, curve shape feature and spatial texture feature is constructed by combining spatial–spectral multi-feature constraints. At the same time, an improved Delphi method is proposed to simulate the expert decision-making process, and better evaluation weights are obtained by comparison and screening. The comprehensive evaluation of camouflage effect based on whitening function gray clustering is realized. The proposed method can not only give the “excellent” and “bad” of camouflage effect qualitatively, but also calculate the comprehensive score of camouflage effect by model.

An ultrasound-assisted nitrogen protection system for sustainable indigo dyeing of cotton fabrics

The traditional process of denim dyeing is highly environmentally damaging due to high temperature used for reduction and dyeing as well as the heavy use of sodium dithionite. It can be efficient if dyeing can be carried out with an environmental reducing agent under nitrogen protection or if the liquor ratio can be reduced. However, at such a low liquor ratio, dye molecules form agglomerates, which results in inefficient dye-uptake and uneven dyeing. This study investigated the possibility of applying ultrasound in conjunction with nitrogen protection to the dyeing of cotton fabrics with indigo reduced by the reducing agent DK. Comparing the reducing agent DK with the other reducing agents, it was found that reducing agent DK had stronger reducing and better properties for indigo dyed fabrics. Furthermore, the synergistic effect of nitrogen protection and ultrasound prevented the early oxidation and agglomeration of the leuco-indigo. It was evident that there was an increased contact area between the leuco-indigo and the cotton fiber, as well as a decrease in the crystallinity of the cellulose macromolecules. And the increased amorphous region of the cellulose macromolecules facilitated indigo-leuco’s penetration into the fiber, resulting in enhanced dyeing depths and uniformity. In addition, orthogonal experiments and gray clustering analysis were utilized to optimize the dyeing process. As a result of the optimized dyeing process, the K/ S and S values of dyed denim fabric reached 16.70 and 0.12, respectively, which was 42% and 72% higher than the traditional sodium dithionite dyeing process. It was found that the dyeing liquid was more susceptible to biological and chemical degradation. In addition, the total economic costs were reduced by 11.702%. It is conceivable that eco-friendly dyeing strategies presented here have superior dyeing properties which can significantly enhance the sustainability of denim dyeing processes.

A Method of Power Supply Health State Estimation Based on Grey Clustering and Fuzzy Comprehensive Evaluation

Health state estimation can evaluate the current degradation state of equipment, and the evaluation results can provide a basis for formulating equipment maintenance strategies. At present, in the research on health state estimation, only one level of indicators of the equipment is usually considered, meaning incomplete estimation results. In addition, the current evaluation methods rarely consider the impact of data noise on the evaluation results, which can easily lead to abnormal evaluation results. To solve the above two problems, this paper first introduces the state indicators such as ripple voltage and output voltage, as well as the ratio of theoretical use time into the evaluation indicators. Furthermore, a health state estimation method combining grey clustering and fuzzy comprehensive evaluation methods is established. This method can consider the characteristics of multiple data groups at the same time, thus the impact of data noise on the evaluation results can be reduced. During the evaluation, the grey clustering method is used to evaluate the clustering coefficient vector of the power supply under a single group of data. After that, the clustering coefficient vector of multiple groups of data is used as the membership vector of fuzzy comprehensive evaluation, and the fuzzy comprehensive evaluation method is used to evaluate the health state of a power supply under multiple groups of data. An example shows that this method is effective in estimating the health state of a normally degraded power supply.

Service quality measurement of agricultural cold chain logistics supply chain based on grey clustering

Service quality measurement of agricultural cold chain logistics supply chain based on grey clustering

Service quality measurement of agricultural cold chain logistics supply chain based on grey clustering

Service quality measurement of agricultural cold chain logistics supply chain based on grey clustering

An evaluation method of English online learning behaviour based on feature mining

In order to overcome the problems of low precision of feature retrieval and poor clustering effect of traditional online learning behaviour evaluation methods, this paper designed an English online learning behaviour evaluation method based on feature mining. Firstly, the unbiased estimation theory is used to quantitatively sample English online learning behaviour. After data preprocessing, the clustering centre is divided. The data is allocated to different clusters through clustering processing, and the feature mining results are obtained through iteration. Then the evaluation index system is constructed, and the final evaluation results are obtained on the basis of the grey interval clustering treatment of the evaluation index. According to the test results, the retrieval accuracy of behaviour features of method of this paper is closer to one, and its clustering effect on different online learning behaviour features is good, which proves that it has achieved the design expectation.

Research on the health literacy promotion strategies of rural middle-aged and elderly people based on a two-stage grey clustering model

Research on the health literacy promotion strategies of rural middle-aged and elderly people based on a two-stage grey clustering model

A consensus model for linguistic group decision making considering emotion analysis

In a problem of linguistic group decision-making (GDM), various emotional characteristics of decision-makers’ (DMs) behind their preference series, which is defined as emotion factor, can impact consensus reaching process. As words mean different things to different people, this study comprehensively proposes a novel consensus model with emotion factors based on personalized individual semantics. An improved grey clustering method facilitates to obtain emotion factors. Finally, the proposal model can provide more accurate judgments for moderator's management and further promote consensus.

Higher education evaluation system based on grey clustering evaluation model

Higher education evaluation system based on grey clustering evaluation model

GCA-CNN based transformer digital twin model construction and fault diagnosis and condition evaluation analysis

Oil-immersed power transformer is the most important piece of equipment in the transmission system, and the stable operation of this equipment is of great significance to the normal work of the power system. At present, deep learning has been widely used in transformer condition evaluation and fault detection. About the shortage of deep learning algorithm models, this paper proposes a transformer digital twin model construction and fault diagnosis and condition evaluation analysis based on gray clustering algorithm (GCA) and conventional neural network (CNN). The data are first collected and filtered by combining the operation data, condition information amount and faulty features of power transformers, and then the condition features and fault features are combined to evaluate and detect the condition and faults of power transformers using GCA-CNN respectively. The whitening weight function is determined by expert scoring, the state evaluation matrix is established, the evaluation coefficients are obtained to calculate the evaluation weights, and the transformer state is obtained according to the clustering coefficients; 2000 pieces of raw data are input into the model to obtain the output fault types. Finally, the results are derived and compared with the real results. This paper uses real data from a power plant in Yunnan, and according to the results, the model established in this paper has higher accuracy and better evaluation and detection effects.

Research on the Risk Management of Shantytown Renovation Project Based on Grey Clustering Method

In this paper, the risk evaluation model of shantytown transformation is constructed with the second area shantytown transformation project of Q city as a research case. Firstly, the brainstorming method and Delphi method are used to determine the risk evaluation index system of shantytown transformation, secondly, the weights of the evaluation model are determined by a linear combination of the C-OWA operator assignment method and entropy weighting method, which reduces the subjective influence of the assignment, and finally, the grey clustering method is used to assess the risk level of the shantytown transformation, and relevant measures are proposed to reduce the risk of the shantytown transformation according to the results, which reduces to a certain degree the the possibility of risk occurrence in the process of shantytown transformation, providing theoretical guidance for the shantytown transformation project in Q city.

Clustering Analysis of the Evaluation Data on Students’ Learning under a Blended Environment

This paper explores the establishment of the effectiveness evaluation index system for students’ learning under blended learning environment, so as to provide some theoretical reference and guidance for the evaluation of the effect of students’ learning in blended environment. In current studies, there are few quantitative analyses on the application cases or teaching practice, and there is also a lack of evaluation studies on the students as the main body of the learning process. Therefore, this paper carries out a study on the clustering analysis of the evaluation data on students’ learning in blended environment, displaying the learning effect satisfaction model for students under blended learning environment, and providing the idea of constructing a learning effect evaluation model accordingly. This paper characterizes the learning effect of students at each stage in the form of sets and formulates evaluation index system. The analytic hierarchy process-grey clustering evaluation of students’ learning effect in blended environment is carried out. On the basis of analytic hierarchy process, the evaluation index weight is determined and the evaluation grade is fixed based on the grey clustering analysis. The experimental results verify the effectiveness of the evaluation model.

Risk management of retrofit project in old residential areas under green development

The green retrofit of old residential areas is an important approach to promote urban renewal and to realize the sustainable development of existing buildings. However, the various risks in retrofit projects hinder the progress. In this study, a list of risk factors of retrofit projects in Old Residential Areas (ORAs) is built from whole life cycle of a project, the opinions of participants on the possibility of risk factors and the degree of impact on the project were collected through the questionnaire survey, a risk assessment model based on C-OWA and grey cluster analysis is developed to evaluate the risk, and risk management and control are proposed based on different risk levels. A case study is carried out by taking a retrofit project in Chongqing A-zone as an example to assess the comprehensive risk in this project, and corresponding recommendations for risk control of the retrofit project are provided. The research findings provide references for risk management of retrofit project in ORAs.