Multidimensional Cloud Model Research Articles

Probability prediction method for rockburst intensity based on rough set and multidimensional cloud model uncertainty reasoning

Probability prediction method for rockburst intensity based on rough set and multidimensional cloud model uncertainty reasoning

Multi-Dimensional Cloud Model-Based Assessment and Its Application to the Risk of Supply Chain Financial Companies

The multi-dimensional cloud model is proposed as the expansion of the one-dimensional cloud model. The features of ambiguity and stochasticity in complex information situations are considered; thus, this optimized model can be utilized upon multiple value classifications and ordering via which the objects' attributes of physical and social can be reflected. Therefore, this promoted model is wildly used. This paper provides a knowledge graph by reviewing the theoretical research of the multi-dimensional cloud model and its related bibliographies, and Cite Space is applied here to give a visualization conclusion. In recent years, a multitude of theories and methods have emerged to address the challenges posed by fuzzy and stochastic uncertainty in various domains, such as image segmentation, data mining, prediction techniques, and comprehensive evaluation of multiple metrics and dimensions using uncertain linguistic variables.

Risk evaluation and management mechanism of green electricity market based on multi-dimensional cloud model

In the process of continuous promotion in the green electricity market, various risks have emerged due to the inherent uncertainty and complex trading environment of the market. Evaluating and controlling the risks through a multi-dimensional evaluation is crucial for the smooth and healthy development of the green electricity market. This work proposes a risk assessment method for the green electricity market based on the multi-dimensional cloud model. First, the comprehensive indicator evaluation system is established for both pre-transaction and post-transaction dimensions by analyzing the operating mechanism of the green electricity market. Second, each dimensional cloud is formed by the evaluation clouds of all indicator levels. The comprehensive risk level cloud is generated based on the indicator values and the evaluation clouds of each indicator. Finally, the sources of market risk are identified, and the corresponding risk management methods are proposed on the basis of the risk evaluation level and membership degree of each indicator. The case study results demonstrate that the proposed method can accurately evaluate and reduce the potential risks existing in the green electricity market through the two-stage detection mechanism.

Multispectral versus texture features from ZiYuan-3 for recognizing on deciduous tree species with cloud and SVM models

Tree species recognition accuracy greatly affects forest remote sensing mapping and forestry resource monitoring. The multispectral and texture features of the remote sensing images from the ZiYuan-3 (ZY-3) satellite at two phenological phases of autumn and winter (September 29th and December 7th) were selected for constructing and optimizing sensitive spectral indices and texture indices. Multidimensional cloud model and support vector machine (SVM) model were constructed by the screened spectral and texture indices for remote sensing recognition of Quercus acutissima (Q. acutissima) and Robinia pseudoacacia (R. pseudoacacia) on Mount Tai. The results showed that, the correlation intensities of the constructed spectral indices with tree species were preferable in winter than in autumn. The spectral indices constructed by band 4 showed the superior correlation compared with other bands, both in the autumn and winter time phases. The optimal sensitive texture indices for both phases were mean, homogeneity and contrast for Q. acutissima, and contrast, dissimilarity and second moment for R. pseudoacacia. Spectral features were found to have a higher recognition accuracy than textural features for recognizing on both Q. acutissima and R. pseudoacacia, and winter showing superior recognition accuracy than autumn, especially for Q. acutissima. The recognition accuracy of the multidimensional cloud model (89.98%) does not show a superior advantage over the one-dimensional cloud model (90.57%). The highest recognition accuracy derived from a three-dimensional SVM was 84.86%, which was lower than the cloud model (89.98%) in the same dimension. This study is expected to provide technical support for the precise recognition and forestry management on Mount Tai.

Assessment on water resources carrying capacity in karst areas under a novel multi-dimensional cloud model—a case study

Assessment on water resources carrying capacity in karst areas under a novel multi-dimensional cloud model—a case study

Grid-Related Fine Action Segmentation Based on an STCNN-MCM Joint Algorithm during Smart Grid Training

Smart grid-training systems enable trainers to achieve the high safety standards required for power operation. Effective methods for the rational segmentation of continuous fine actions can improve smart grid-training systems, which is of great significance to sustainable power-grid operation and the personal safety of operators. In this paper, a joint algorithm of a spatio-temporal convolutional neural network and multidimensional cloud model (STCNN-MCM) is proposed to complete the segmentation of fine actions during power operation. Firstly, the spatio-temporal convolutional neural network (STCNN) is used to extract action features from the multi-sensor dataset of hand actions during power operation and to predict the next moment’s action to form a multi-outcome dataset; then, a multidimensional cloud model (MCM) is designed based on the motion features of the real power operation; finally, the corresponding probabilities are obtained from the distribution of the predicted data in the cloud model through the multi-outcome dataset for action-rsegmentation point determination. The results show that STCNN-MCM can choose the segmentation points of fine actions in power operation in a relatively efficient way, improve the accuracy of action division, and can be used to improve smart grid-training systems for the segmentation of continuous fine actions in power operation.

Green Construction Grade Evaluation of Large Channels Based on Uncertain AHP-Multidimensional Cloud Model

(1) Background. With the increasing national investment in infrastructure in recent years, water conservancy construction has been developed rapidly, which has greatly improved people’s living standards, but at the same time, the damage to the environment and the waste of resources caused by construction is becoming more and more obvious. In the prospect of sustainable development, the application of a complete set of green construction evaluation system is imminent. (2) Methods. This paper takes the green construction of large channels as the research object, takes the Green Building Evaluation Standard (GB/T 50378-2019) as the guideline, takes the control items of “safety and durability, health and comfort, resource saving, environmental livability, improvement and innovation” in the Standard as the construction reference system, and combines the construction characteristics of water conservancy projects. It determines the evaluation index and carries out green construction grade evaluation by uncertain AHP (hierarchical analysis)-multidimensional cloud model. (3) Results. The green construction grade evaluation of large channels was carried out with the example, and the final green construction grade evaluation result of “three stars” was obtained. (4) Conclusions. The evaluation results are consistent with the traditional AHP (hierarchical analysis)-cloud model, but the evaluation model proposed in this paper takes into account the fuzziness, randomness and correlation among the evaluation factors, which is more in line with the actual situation. It plays a guiding role for the green construction of large channels.

Randomness-oriented Multi-dimensional Cloud-based belief rule Base approach for complex system modeling

Many complex system modeling problems have to face two major challenges, randomness and interpretability, which are the motives and major contributions of this study. For the randomness challenge, the cloud model is adopted which reveals randomness by assuming the points within an interval following a certain distribution. For the interpretability challenge, the Belief Rule Base (BRB) is adopted which is essentially a white-box with direct access to experts and decision-makers. Therefore, a new approach is proposed to meet the two challenges by combining the cloud model and BRB, namely Cloud-BRB. In the new Cloud-BRB approach, new rule activation and matching degree calculation procedures have been proposed to better accommodate the cloud model into BRB. Moreover, new weight calculation procedures have also been proposed based on the multi-dimensional cloud model which connects the multiple attributes in practical problems and multiple attributes in BRB. A practical case of risk assessment on tunnel-induced damages to pipes is studied. Case study results show the consistency with previous approaches, which validate the efficiency of the proposed Cloud-BRB approach. As the belief distribution and randomness have been included in the Cloud-BRB approach, the results are more informative for decision-makers than previous approaches. Furthermore, Pipe No. 10 has been used to demonstrate the key influential factors identification process by the Cloud-BRB approach.

Comprehensive evaluation on water resources carrying capacity based on improved AGA-AHP method

The combined weight method (CWM) for comprehensive water resources carrying capacity evaluation is proposed in this paper to evaluate the regional water resources carrying capacity. Firstly, the accelerating genetic algorithm based on analytic hierarchy process (AGA-AHP) method is improved to optimize the expert evaluation matrix and determine the subjective weight, in which entropy weight method is used to determine objective weight, and the combined weight formula is put forward to get the combined weight of evaluation index. Secondly, the certainty degree is calculated by using the multi-dimensional normal cloud model. The obstacle factors of evaluation index are diagnosed. Finally, taking Henan Province as an example, the regional water resources carrying capacity is evaluated by using CWM method. The comparison between single cloud model, AHP-TOPSIS and CWM is made. It is proved that CWM method takes the fuzziness and randomness into account in the evaluation process, and the efficient and intuitive results can be obtained through evaluation. The obstacle degree and the time changing pattern of the index can be clearly diagnosed, which can provide a new idea for the evaluation method of water resources carrying capacity.

A Rockburst Proneness Evaluation Method Based on Multidimensional Cloud Model Improved by Control Variable Method and Rockburst Database

Abstract Rockbursts are common geological disasters in underground engineering, and rockburst proneness evaluation is an important research subject. In this study, a multidimensional cloud model was used to evaluate the rockburst proneness level, and the control variable method was used to establish 15 multidimensional cloud (MC) models. The key factors affecting the accuracy of multidimensional cloud model evaluation are numerical characteristics, weight, and normalization methods. The optimal numerical characteristics calculation method of a multidimensional cloud model was determined, and an improved CRITIC (IC) weight method was optimised by introducing the relative standard deviation and an improved quantisation coefficient. Six rockburst indexes were used as input for the multidimensional cloud model, including the elastic deformation energy index Wet, maximum tangential stress σθMPa of a cavern, uniaxial compressive strength σcMPa, uniaxial tensile strength στMPa, strength brittleness coefficient B1=σc/στ, and stress coefficient σθ/σc. The model was used to learn 271 groups of complete rockburst cases, and the MC-IC rockburst proneness evaluation method was established. The performance of the proposed MC-IC rockburst proneness method was verified by an 8-fold cross-validation and confusion matrix (precision, recall, F1). The method was tested to evaluate 20 groups of rockburst cases from the Jiangbian Hydropower Station, and the accuracy reaches 95%. The evaluation results were compared with three empirical criteria: four cloud-based methods, three unsupervised learning methods, and four supervised learning methods; the accuracy of the method established in this paper is 93.33%. The results showed that the MC-IC method had an excellent performance in evaluating rockburst proneness and can provide a practical basis for identifying rockburst hazard areas in deep engineering.

Enhancing dam safety evaluation using dam digital twins

Comprehensive evaluation of dams in a dynamic and proactive way is accepted as an effective strategy to improve dam safety. However, there is a lack of efficient and standardised approaches for managing monitoring-related information that can help to provide dynamic and reliable information for continuous dam evaluation. With the importance of digital twins (DTs) being proven in better data integration and interoperability, a DT-based approach for comprehensive dam evaluation and its data integration method based on extended industry foundation classes (IFC) are provided in this study. This paper presents a new data structure and corresponding data classification strategy, using which information required for continuous evaluation can be effectively organised. Considering that existing evaluation methods cannot fully characterise uncertainties in the evaluation process, a cloud model based comprehensive evaluation framework is proposed. In this approach, a multi-rule cloud reasoning model is utilised to evaluate monitoring points. Also, a multi-dimensional cloud model and an improved Criteria Importance Though Intercriteria Correlation (CRITIC) method are leveraged to assess evaluation indices affected by multiple factors. Finally, the results of a case study verify that the proposed DT-based solution realises a continuous dam safety evaluation, which contributes to automated and efficient dam condition monitoring.

Evaluation and Application of Surrounding Rock Stability Based on the Improved Weighting Multidimensional Cloud Model

Aiming at the uncertainty in the existing tunnel surrounding rock stability evaluation methods and the inability to make scientific explanations in the process of determining the weights of multiple evaluation indicators, this paper combines subjective weighting based on the nonlinear characteristics of the surrounding rock stability evaluation criteria. Based on the advantages of the method AHP and the objective weighting method CRITIC, a comprehensive weighting method with multiple evaluation indicators is proposed, which is integrated with the multidimensional normal cloud model to establish a surrounding rock stability evaluation method for the improved weighted multidimensional cloud model and accurately obtain the safety level of tunnel surrounding rock construction. The multidimensional cloud model based on improved weighting not only solves the two major problems of randomness and ambiguity in surrounding rock stability evaluation but also fully considers the influence of the information contained in each evaluation factor on the results. The surrounding rock stability evaluation method in this paper is applied to the tunnel construction of the Beijing-Shanghai Expressway and Jinan Expressway connecting line construction project, and the evaluation results have a scientific guiding role for construction risk control.

Classification and Visualization of Surrounding Rock Mass Stability Based on Multi-Dimensional Cloud Model

The classification of the stability of surrounding rock is an uncertain system with multiple indices. The Multi-dimensional Cloud Model provides an advanced solution through the use of an improved model of One-dimensional Cloud Model. Setting each index as a one-dimensional attribute, the Multi-dimensional Cloud Model can set the digital characteristics of each index according to the cloud theory. The Multi-dimensional cloud generator can calculate the certainty of each grade, and then determine the stability levels of the surrounding rock according to the principle of maximum certainty. Using this model to 5 coal mine roadway surrounding rock examples and comparing the results with those of One-dimensional and Two-dimensional Cloud Models, we find that the Multi-dimensional Cloud Model can provide a more accurate solution. Since the classification results of the Multi-dimensional Cloud Model are difficult to be presented intuitively and visually, we reduce the Multi-dimensional Cloud Model to One-dimensional and Two-dimensional Cloud Models in order to visualize the results achieved by the Multi-dimensional Cloud Model. This approach provides a more accurate and intuitive method for the classification of the surrounding rock stability, and it can also be applied to other types of classification problems.

Rock burst risk assessment in deep-buried underground caverns: a novel analysis method

A rock burst often occurs during the construction period of deep-buried underground caverns. How to predict and prevent it is an urgent problem in underground engineering, especially in large hydropower stations. Combining grey correlation method, principal component analysis (PCA) and cloud theory, a novel analysis method that is proposed to evaluate rock burst. First, seven indices, namely, Rc, Rc/σ1, Rc/σt, σθ/Rc, Wet, H and KV, are selected. Considering the relationship between these indices, the grey correlation method is used to analyze these indices and reduce them. According to the correlation coefficients, the five indices, namely, Rc/σ1, Rc/σt, σθ/Rc, Wet and KV, consist of the final evaluated system. Second, the weight of each index is calculated using principal component analysis (PCA). Take into consideration of the ambiguity and randomness of rock burst; the multi-dimensional cloud model is used to evaluate the rock burst level. The proposed model is applied to a case study of the Jiangbian hydropower station to certify the feasibility and effectiveness of the novel method. The results are basically consistent with the actual rock burst level. At last, the selection of the evaluation system and the accuracy of the multi-dimensional cloud model are discussed. This novel method provides a new idea for the risk assessment of rock burst in deep-buried underground caverns.

A New Approach to Realizing the Soft-and Operation in Cloud Model-Based Control

Considering fuzziness, randomness, and the association between them, cloud model-based control is a new way to address uncertainty in the inference system. Similar to fuzzy control theory, this method includes an important step of dealing with the logic concept “and”, which is defined as the operation of soft-and between several antecedents and has not been scientifically solved in the current literatures. The traditional method of realizing soft-and is to use multi-dimensional cloud model theory, which lacks a theoretical basis. Based on the fuzzy and random theory, this paper proposes a novel approach using numeric simulation to calculate the soft-and in the cloud control system. In this method, the theory to determine the distribution of the minimum value between two random variables is applied. Compared with the traditional method, the considered approach is more reliable and reasonable, and its result is also in accordance with the standard fuzzy inference system.

A novel multi-dimensional cloud model coupled with connection numbers theory for evaluation of slope stability

Abstract Stability evaluation of a slope involves various fuzzy and correlation indicators randomly distributed in finite intervals. A novel multi-dimensional connection cloud model was presented here to address multiple uncertainties and distribution characteristics of indicators, and to depict the randomness and fuzziness of the measured index value belonging to the classification standard in the slope stability analysis. In the model, when simulating fuzzy and random characteristics of evaluation indicators in finite intervals, the numerical characteristics of connection cloud model were assigned on the basis of the analysis of identical-discrepancy-contrary (IDC) relationships between measured indicators and the classification standard to overcome the subjectivity. Considering the effect of indicator correlation in a unified way, the integrated connection degree of a grade was further specified for the evaluation sample. Moreover, case studies and comparisons of the proposed model with one-dimensional normal cloud model, extension model, and support vector machine (SVM) were performed to confirm the validity and reliability. The results indicate that this model employed to evaluate slope stability can clearly depict the random and fuzzy distribution features of measured data in finite intervals, and its calculation process is quicker and simpler than that of one-dimensional normal cloud model.

Comprehensive prediction method for failure rate of transmission line based on multi‐dimensional cloud model

In allusion to the prediction for the failure rate of transmission lines under multiple external environmental factors, a comprehensive prediction method for the failure rate of transmission lines based on an multi-dimensional cloud model and Cholesky decomposition was proposed in this study. According to the historical data in meteorological early warning system and icing forecasting system, the mutual correlation degrees of these multiple external environmental factors are got by the grey relational algorithm and the comprehensive prediction model for the failure rate of transmission lines was established based on an multi-dimensional cloud model and Cholesky decomposition. In light with the external environmental factors in the next year, the failure rate of transmission lines was predicted by this proposed method. The test results based on actual data in Guizhou Power Grid show that this proposed comprehensive prediction method for the failure rate of transmission lines has high accuracy as the prediction error is about 2.05% and a much low computational burden as it only takes 30.25 s which is highly suitable for practical applications.

Novel Prediction Method for Highway Distresses in Permafrost Regions Based on Qualitative Reasoning of Multidimensional and Multirules Cloud Model

Highway in permafrost regions has numerous diseases during operation, due to instability and degradation of permafrost. To predict distress sections of a newly built highway in permafrost regions, we proposed a new method based on the multidimensional and multirules reasoning cloud model. Herein, the evaluation parameters affecting the highway distresses in permafrost regions, i.e., annual average ground temperature, ice content, and frozen-heave factor, were as the data input, whereas the distress degree was as the data output; all of the aforementioned were described by a cloud model. Based on the analysis of distress large data, inference rules and a cloud reasoning prediction model were established. Subsequently, distress degrees of the 10 equidistance highway sections were predicted on the Qinghai-Tibet highway by using the cloud model, and actual distress degree and predicted distress degree were compared by using the regression analysis algorithm. The results showed that the relevance between the actual distress degree and the predicted distress degree was 0.738. The study provides a feasible and effective method to predict the potential distress sections of the newly built highway and better plan infrastructure project on permafrost regions.

Land Eco-Security Assessment Based on the Multi-Dimensional Connection Cloud Model

The evaluation of land eco-security is challenging because it is involved with various uncertainty factors. Although the normal cloud model provides an idea for dealing with the randomness and fuzziness of indicators for the evaluation of land eco-security, it cannot simulate the distribution state of the evaluation indicators in a finite interval and their calculation process is complicated for multi-factor problems. Herein, a novel multi-dimensional connection cloud model is discussed to remedy these defects. In this model, combined with the range of evaluation factors in each grade, the identity-discrepancy-contrary principle of set pair theory is adopted to determine the digital characteristics of the multi-dimensional cloud model, which can uniformly describe the certainty and uncertainty relationships between the measured indices and the evaluation criteria and also improve the fuzzy-randomness of evaluation indicators closer to the actual distribution characteristics. The case study and the comparison of the proposed model with the normal cloud model and the matter element model were performed to confirm the validity and reliability of the proposed model. Results show that this model can overcome the subjectivity in determining the digital characteristics of the normal cloud model, providing a novel method for the comprehensive evaluation of land eco-security.

Water eutrophication assessment based on rough set and multidimensional cloud model

This investigation developed a hybrid rough set (RST) and multidimensional cloud model (RSMCM) to leverage the unique strengths of RST and cloud modeling to evaluate the trophic level. In the proposed hybrid model, RST is used to decrease the data scale and extract the qualitative rules, and the multidimensional cloud model is employed to quantitatively analyze the average values, uniformity and stability of water eutrophication. The experimental results reveal that the hybrid model achieves more accurate assessment results than other mainstream models. Therefore, the hybrid model is a promising alternative for a water eutrophication information system and offers a quantitative measure for evaluating the uniformity and stability of eutrophication in utilities management and for operations staff.