Normal Cloud Model Research Articles

Rockburst prediction based on optimization of unascertained measure theory with normal cloud

Rockburst is one of the common geological disasters in deep underground areas with high stress. Rockburst prediction is an important measure to know in advance the risk of rockburst hazards to take a scientific approach to the response. In view of the fuzziness and uncertainty between quantitative indexes and qualitative grade assessments in prediction, this study proposes the use of a normal cloud model to optimize the theory of unascertained measures (NC-UM). The uniaxial compressive strength (σc), stress coefficient (σθ/σc), elastic deformation energy index (Wet), and brittleness index of rock (σc/σt) are selected as the index of prediction. After data screening, 249 groups of rockburst case data are selected as the original data set. To reduce the influence of subjective and objective factors of index weight on the prediction results, the game theory is used to synthesize the three weighting methods of Criteria Importance Through Intercriteria Correlation (CRITIC), Entropy Weight (EW), and Analytic Hierarchy Process (AHP) to obtain the comprehensive weight of the index. After validating the model with example data, the results showed that the model was 93.3% accurate with no more than one level of prediction deviation. Compared with the traditional unascertained measure (UM) rockburst prediction model, the accuracy is 15–20% higher than that of the traditional model. It shows that the model is valid and applicable in predicting the rockburst propensity level.

A reliability-based consensus model and regret theory-based selection process for linguistic hesitant-Z multi-attribute group decision making

In real-life decision making problems, reliability of information is one of the most concerning factors. Accordingly, to avoid the involvement of inconsistent information in a multi-attribute group decision making problem, this study aims to develop a consensus model by considering reliability of experts’ opinions in Z-number framework. To do so, first we reflect human uncertainty in the information system by linguistic hesitant-Z-number (LHZN) that has sufficient description power to express hesitancy, vagueness and reliability of evaluation information in a single form. Then, a novel normal cloud model is developed to handle LHZN information rationally without distorting its originality. Furthermore, we develop the Maclaurin symmetric mean operator based on normal cloud to take into account interrelationships among attributes. Thereafter, the weights of the experts are determined based on dynamic importance score of experts which consist of preference similarity score and reliability score. Afterwards, a group consensus degree is measured based on preference values and their reliability to identify the consensus level among experts. Then, to ameliorate the consensus efficiency, experts’ unreliable behaviours are identified and modified with reference to preference value and reliability. Lastly, a regret theory-based selection process is presented to detect the optimal alternative with respect to multiple attributes. The validity, superiority and advantages of the proposed model are exemplified by two case studies of sustainable zero-carbon measures prioritization for Indian transport sector, and failure mode and effect analysis, respectively.

Detecting interdisciplinary semantic drift for knowledge organization based on normal cloud model

To reduce the conceptual ambiguity in interdisciplinary knowledge organization systems (KOSs) and enhance interdisciplinary KOS management, this paper proposes a framework for interdisciplinary semantic drift (ISD) detection based on the normal cloud model (NCM). In this framework, we first analyze the features of interdisciplinary concepts and propose a novel interdisciplinary concept extraction method based on cross-discipline statistical information. Secondly, the high-performance knowledge representation model NCM is adopted to represent each interdisciplinary concept with uncertainty, and then a new ISD degree calculation method is proposed based on the similarity cloud algorithm. Thirdly, to identify the direction of ISD after the degree calculation, we propose an ISD direction identification method according to the theory of knowledge potential energy (KPE). Fourthly, based on the above procedure, we propose an ISD detection algorithm to identify and visualize the ISD process. Finally, we evaluate the proposed framework on the concept of “information entropy” and compare the performance with three baselines. Experimental results demonstrate that our framework outperforms[ all the baselines, and the result is comparable to experts’ judgments (0.808 on Spearman correlation, p<0.001). The research indicates the meaning of an interdisciplinary concept will drift from the high KPE discipline to the low KPE discipline as long as interdisciplinary knowledge potential differences (KPD) exist between these two related disciplines. We further identify three key factors that affect the degree of ISD: the length of the discipline chain of an interdisciplinary concept transfer, the number of source disciplines that an interdisciplinary concept comes from, and the knowledge distance between the source discipline and the target discipline.

Multi-Dimensional Evaluation of Land Comprehensive Carrying Capacity Based on a Normal Cloud Model and Its Interactions: A Case Study of Liaoning Province

Studying land comprehensive carrying capacity (LCCC) is the foundational and key requirement for determining land development planning and urban spatial development patterns of a region. However, the traditional evaluation method discounts the fuzziness and randomness of the evaluation index and its results. The cloud model combines randomness and fuzziness to reveal the correlation between randomness and fuzziness using numerical feature entropy and is used to represent the granularity of a qualitative concept. This study used the Liaoning Province as the study area, and developed a multi-dimensional evaluation index system for LCCC using a normal cloud model. Based on this, the relationship between the different elements of geological condition, resources and environment, economic scale and urban construction were studied using the coupling coordination degree model that reflected not only the system interactions but also the strengths of its degree of coordination. Our results were as follows: (1) numerical feature entropy were evaluated to determine the carrying capacity level of the land, and comprehensive land carrying capacity evaluations were conducted in terms of both quantitative results and the reliability of the results, promoting the scientific application of uncertainty theory in the field of comprehensive land evaluation as well as carrying capacity. (2) Liaoning Province’s prefecture-level cities had distinctly different LCCC, demonstrating “low in the west and high in the east” spatial distribution characteristics. Cities with established economies and relatively strong infrastructures had larger comprehensive land carrying capacities. Overall, there was considerable consistency across the region, though the “low in the west and high in the east” spatial distribution characteristics affected the degree of coordination.

Large-scale group consensus hybrid strategies with three-dimensional clustering optimisation based on normal cloud models

Large-scale group decision-making (LSGDM) is characterised by a large number of experts and a complex consensus reaching process. Clustering is used to divide the large group into a number of manageable subgroups; however, the simultaneous presence of all subgroup members at the negotiation process is rare. Thus, the selection of subgroup representatives for a smooth negotiation is necessary. Few LSGDM consensus recommendation optimisation models truly consider the problems of subgroup representative selection in their strategy to reach a consensus. This article proposes LSGDM consensus hybrid strategy framework with three-dimension clustering optimisation based on normal cloud models (NCMs) whose aims are threefold: (1) the use of NCMs to represent the imprecision of linguistic preferences provided in real complex decision scenarios with a large number of experts; (2) to establish a clustering optimisation method to choose subgroup representatives using three sensible criteria: preference similarity level within the subgroup, preference precision level, and preference consistency level; and (3) to establish two consensus recommendation optimisation strategies for individual negotiation-guided and moderator-guided consensus reaching, respectively. The feasibility and applicability of the proposed method are illustrated via a power curtailment policy assessment example, then some sensitive and comparative analyses are conducted to explicit the effectiveness and advantages of the proposed consensus hybrid strategies.

A teaching evaluation method of English online course based on conscious normal cloud model

Because the traditional English online course teaching evaluation method does not consider the characteristics of cloud numbers, the evaluation weight is low, the result accuracy is not high, and the evaluation time is too long. Therefore, the paper proposes an English online course teaching evaluation method based on the self-conscious normal cloud model. First, analyse the cloud digital features of the conscious normal cloud, and obtain the cloud digital feature distribution of the course evaluation on the basis of determining the weight of the course teaching evaluation. Then, determine the specific input value x and the expected value Ex of the English online course. Finally, calculate the degree of certainty of course teaching evaluation to construct a conscious normal comprehensive evaluation cloud model to realise the comprehensive evaluation of English online course teaching. The experimental results show that the proposed method can effectively improve the evaluation effect of English online course teaching. The accuracy of teaching evaluation can reach 98%, and the evaluation time of each index does not exceed 1 s. It can be seen that the proposed method has certain value and significance.

Vulnerability Evaluation of Port Logistics System Under Public Health Emergencies

It is the goal that evaluating scientifically port logistics system vulnerability level under public health emergencies, exploring the influencing factors of brittleness and improving the production operation and management efficiency of port logistics system. According to the production practice of port logistics system and vulnerability theory, a vulnerability evaluation index system of port logistics system under public health emergencies is established in five aspects, including external risk disturbance, human resource management level, infrastructure level, operation and management level, and intelligence level. The combinatorial weights are obtained by combinatorial empowerment method of game theory, and it is used to process data that evidence theory improved by conflict correction process and mass function with normal cloud model. The vulnerability of Dalian port logistics system is analyzed empirically, and the result is better effectiveness.

A teaching evaluation method of English online course based on conscious normal cloud model

A teaching evaluation method of English online course based on conscious normal cloud model

Cloud model driven assessment of interregional water ecological carrying capacity and analysis of its spatial-temporal collaborative relation

Water ecological carrying capacity (WECC) plays a significant role in the regional sustainable development. This study integrates techniques of normal cloud model, variable-weight combination prediction model, binary and multivariate spatial-temporal matching models, and the improved data envelopment analysis model for analyzing historical and future evolution, spatial-temporal collaborative relation, and input-output efficiency of WECC in the Beijing-Tianjin-Hebei (BTH) and Yangtze River Economic Zone (YREZ). Results reveal a generally fluctuating upward tendency of WECC level in the BTH and YREZ, in which water resources subsystem and socio-economic subsystem should be preferentially monitored in the eastern and midwestern YREZ, respectively. Combination prediction model has lower error than individual ones in terms of WECC forecasts. The binary spatial-temporal matching degrees between two WECC subsystems are equal to or above the medium state in the BTH and YREZ. The multivariate matching degrees decline and extremely fluctuate compared with the binary ones. WECC level and per capita GDP is presented as inverted N-shaped in the western region, while the rest regions are shown as U-shaped or N-shaped. WECC efficiency rises in fluctuation, especially in the central YREZ, while that in the BTH and western YREZ increases slowly with growth rates of 0.28% and 0.26%, respectively. These findings can facilitate improving regional WECC and making rational planning of water resources.

Stability classification probability model of loess deposits based on MCS-Cloud.

The stability classification of loess deposits around tunnels is a vital prerequisite for safe construction in underground environment. Due to the fuzziness and randomness of loess physical and mechanical parameters, the stability prediction of loess deposits shows uncertainty. Existing loess deposit stability classification models rarely consider the uncertainty of influencing factors. A novel classification probability model of loess deposits is proposed for the above problems based on Monte Carlo simulation and multi-dimensional normal cloud (MCS-Cloud). Specifically, five loess parameters, including water content, cohesion, internal friction angle, elastic modulus, and Poisson ratio, were selected as predictors for the stability level of loess deposits. The weights of the predictors were obtained through 50 test samples. After acquiring the numerical characteristics of the normal cloud, the stability level can be comprehensively evaluated with the weighted multi-dimensional normal cloud model. The classification model was applied to the loess tunnel in Yan'an, China. The prediction results are in good agreement with practical engineering, denoting the rationality of the weighted multi-dimensional normal cloud. Finally, the stability classification of loess deposits was discussed from the perspective of uncertainty analysis with the application of MCS. Results proved that the MCS-Cloud model is feasible for classifying the stability of loess deposits surrounding tunnels. The obtained classification probability can be used for quantitative risk assessment of loess tunnels.

Analysis of LNG Storage Tank Safety: A Comprehensive Model Approach with ANP and Normal Cloud

Global energy shortages continue in the post-epidemic era. As clean primary energy, LNG plays an important role in the energy structure adjustment. However, with the widespread use of LNG, storage tank leaks occur frequently. In order to highlight the risk status of critical risk areas of LNG tank leakage, the “Five-high” risk system for the LNG industry, with high-risk locations, high-risk equipment, high-risk processes, high-risk personnel, and high-risk substances as first-level elements, was built. Combined with the Analytic Network Process (ANP) and normal cloud model for quantitative analysis, a safety risk analysis and assessment model for LNG storage tanks based on the ANP-normal cloud model was developed. Safety analysis was conducted with the example of an LNG storage tank area of a factory. The results show that the risk level for LNG tank leakage is level IV, with high-risk equipment being the first-level element with the highest weight and risk. The reliability of the comprehensive model was verified by AHP and fuzzy integrated analysis, and the results were basically consistent with the factory safety evaluation report. The study realizes the quantitative and visual analysis considering the correlation of risk elements and provides a new quantitative method for the safety analysis of LNG energy.

A fuzzy intelligent group recommender method in sparse-data environments based on multi-agent negotiation

Recommender systems have attracted numerous scholars’ attention. Group recommender is a fashionable though challenging topic in the field of recommender systems. Most methods using aggregation technique performs poorly when conflict exists in members’ preferences. The group recommender method based on multi-agent negotiation covers this limitation. However, existing works ignore the imperfect rationality of members during negotiation. To cover up this deficiency, we propose a fuzzy intelligent group recommender method based on multi-agent negotiation. A new concession protocol makes some compensations to the transigent member considering his imperfect rationality. In this work, the dependency structure analysis is introduced to classify opinion terms into aspects, and the fuzzy numbers are introduced to characterize a maze of contradictory opinion terms in textual reviews. Moreover, a new user similarity method is established using normal cloud models. It tackles the problem of sparse data. In addition, a case study on data in TripAdvisor.com is conducted. Its results indicate the good performance of the proposed group recommender method.

Research on Resilience Evaluation and Enhancement of Deep Foundation Pit Construction Safety System

Deep foundation pit (DFP) projects have been a high incidence area of safety accidents because of their own high danger and complexity. Therefore, it is necessary to study the resilience of their construction safety system. This paper systematically identifies the key factors affecting the resilience of deep foundation pit construction based on the analysis of the composition of the deep foundation pit construction safety system (DFPCTSS), the synergistic relationship of its subsystems in the face of the interference and impact of internal and external disaster-causing factors, and the causal mechanism of typical accidents in DFP accidents and the emergent process of system resilience. A resilience evaluation indicator system based on four capacity dimensions of prevention absorption, resistance, recovery, and learning adaptation was constructed by using the fuzzy Delphi method, which is characterized by the resilience emergence process. Then the correlation and weight of evaluation indexes were analyzed based on the DEMATEL–ANP method, the boundary cloud parameters of the resilience evaluation grade were set according to the normal extension cloud model, and the membership degree of the resilience evaluation level was calculated to complete the evaluation of the resilience level. Finally, taking a DFP project of a metro station as an example, the above model was used to evaluate the resilience level of its construction safety system, and suggestions for resilience enhancement were put forward. The results show that the evaluation results are consistent with the actual situation of the project, and the evaluation model is conducive to providing a systematic analysis method and improvement countermeasures for deep foundation pit construction safety management from the perspective of resilience.

An improved deep extreme learning machine to predict the remaining useful life of lithium-ion battery

To aim at the problem of inaccurate prediction of the remaining useful life of the lithium-ion battery, an improved grey wolf optimizer optimizes the deep extreme learning machine (CGWO-DELM) data-driven forecasting method is proposed. This method uses the grey wolf optimization algorithm based on an adaptive normal cloud model to optimize the bias of the deep extreme learning machine, the weight of the input layer, the selection of activation function, and the number of hidden layer nodes. In this article, indirect health factors that can characterize the degradation of battery performance are extracted from the discharge process, and the correlation between them and capacity is analyzed using the Pearson coefficient and Kendel coefficient. Then, the CGWO-DELM prediction model is constructed to predict the capacitance of the lithium-ion battery. The remaining useful life of lithium-ion batteries is indirectly predicted with a 1.44 A·h failure threshold. The prediction results are compared with deep extreme learning machines, long-term memory, other prediction methods, and the current public prediction methods. The results show that the CGWO-DELM prediction method can more accurately predict the remaining useful life of lithium-ion batteries.

A multistage decision-making method with quantum-guided expert state transition based on normal cloud models

Multistage group decision problems are characterized by a changeable decision-making environment and a complex group consensus process, which make it difficult to obtain decision-making results. However, few methods have considered the phenomenon of expert state transition between manageable subgroups in multistage problems. In this paper, we propose a multistage decision-making framework considering quantum-guided expert transition based on normal cloud models. First, a minimum cost consensus model considering individual compromise intervals and the cost function is established based on the normal cloud models to obtain consensual subgroup opinions. Second, an attribute weight optimization model is established based on adjustment satisfaction and fairness satisfaction to aggregate the subgroups. Third, we define the satisfaction determination method of experts in the transition process and then apply the quantum framework to update their state in the next stage. Finally, the feasibility and applicability of the proposed method are illustrated via a power grid emergency evaluation example by conducting analyses and comparisons of the decision results.

Condition assessment of distribution automation remote terminal units based on double-layer improved cloud model

Distribution automation remote terminal units (DRTUs) are the most important devices to sample voltage and current signals, identify the fault position, isolate the fault, and restore the power supply along distribution lines. We cannot adopt time-based maintenance because of the contradiction between the limited manpower and the plenty of DRTUs distributed in broad regions. In this paper, a double-layer improved cloud model (ICM) is proposed for the first time to realize the condition assessment of DRTUs for condition-based maintenance. The first-layer ICM is applied in the main station of whole distribution automation system (DAS) to evaluate the condition of all DRTUs based on the upload limited condition parameters by DRTUs in real time. And the second-layer ICM is applied in field to evaluate one DRTU which belongs to the abnormal DRTUs obtained after the first-layer evaluation. The DRTU test device are used to obtain detailed condition parameters, and these parameters are used in second-layer ICM to obtain accurate condition assessment score. The results in second-layer ICM would be used to optimize the evaluation algorithm in the first-layer evaluation. In the meantime, our proposed ICM combines the normal cloud model and the trapezoidal cloud model to deal with the optimal, negative, and intermediate indicators more effectively. The comparison with traditional evaluation methods shows that our proposed method is more suitable to realize the condition assessment of DRTUs in the distribution power networks.

Large-scale group Delphi method with heterogeneous decision information and dynamic weights

Managing a large number of participants in the Delphi survey encounters many challenges, such as heterogeneous data, high cost, dynamic quality, and various uncertainties, etc. This paper proposes a novel large-scale group Delphi method with heterogeneous decision information and dynamic weights to address these challenges. Heterogeneous data with various uncertainties, including exact numbers, interval numbers, normal cloud models, linguistic terms, and linguistic expressions, are converted into normal cloud models. Thus, both magnitudes and uncertainties of data can be preserved and propagated throughout the whole calculation process. Furthermore, this study also presents a data preprocessing technique consisting of four steps to discard invalid data and low-quality inputs. Moreover, a novel dynamic weights assignment algorithm is proposed to determine the weights for participants. Afterwards, detailed procedures and formal algorithm are developed. A practical application is presented to demonstrate the effectiveness, applicability, and advantages of the proposed methodology with examples and detailed comparative analysis. The application shows that the decision result becomes more stable and consentaneous. The comparative analysis indicates that the proposed methodology overcomes several challenges. Therefore, the proposed method can provide novel ideas for large-scale group decision making with heterogeneous data and various uncertainties in a dynamically changing decision environment. Eventually, this study contributes to several fields, such as the Delphi technique, group decision making, modeling of uncertainty, and heterogeneous data representation and processing, etc.

Mathematical Modeling Method of the Improved Genetic Algorithm for Random Power Fluctuation

In order to solve the problem that the traditional genetic algorithm has a slow search speed and is easy to fall into the local optimal solution, a mathematical modeling method of an improved genetic algorithm in random power fluctuation is proposed. Drawing on the idea of a genetic algorithm (GA) and using the randomness and stability trend of cloud droplets of the normal cloud model, the author proposes a new genetic algorithm, cloud genetic algorithm (CGA). CGA is implemented by the Y-condition cloud generator of the normal cloud model to realize the cross operation, and the basic cloud generator realizes the mutation operation. Finally, the power function optimization experiment and the IIR digital filter optimization design are carried out, and the standard GA, NQGA, CAGA, and LARES algorithms are carried out compared. The experimental results show that the IIR digital filter designed by CGA has the smallest maximum ripple (Ap) in the passband, which is 0.342, and the largest minimum attenuation (As) in the stopband, which is 34.27. It can be observed that the overall performance is better. The validity of the algorithm is proved, and it has a certain reference and application value.

Bacterial foraging optimization algorithm based on normal cloud model for forward kinematics of a 4-DOF parallel manipulator

For the 4-SPU parallel mechanism, the complex multivariate nonlinear equation of the forward kinematics solution is transformed into a minimization problem. This article proposes a new bacterial foraging optimization algorithm based on normal cloud model to obtain the solution of the forward kinematics. Adaptive dynamic adjustment of walking step size of normal forward cloud generator is adopted to improve the fixed step size. Y conditional cloud generator is used to dynamically adjust the migration probability. The simulation results show that new algorithm is an effective method to solve the forward solution optimization with high precision and high speed.

Image segmentation application based on the normal cloud model

Image segmentation application based on the normal cloud model