Normal Cloud Research Articles

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.

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

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.

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 deep learning framework for time series classification using normal cloud representation and convolutional neural network optimization

AbstractTime Series Classification (TSC) started getting a lot of attention recently, mostly due to the important real‐world applications of time series such as in the financial industry. In this context, various approaches have been proposed to treat this important and challenging problem in data mining. These approaches include deep learning models that are outperformed the traditional classification techniques. However, the choice of an optimal and efficient deep neural network for TSC is still a major problem. Motivated by this challenge, we propose in this paper, an optimized deep learning framework for TSC tasks, using normal cloud representation and convolutional neural networks optimization (NCR‐CNNO). Our approach consists of two phases: In the first one, we convert the raw time series to a matrix of characteristics using two‐dimensional normal cloud representation. In the second phase, we suggest a CNN to handle the matrix generated in the first phase, as well as, an optimization model is proposed to optimize the unnecessary and redundant parameters. Experiments conducted on extensive time series datasets demonstrate that NCR‐CNNO yields significant improvement in the performance of time series classification compared to the state of the art.

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.

CUNA: A privacy preserving medical records storage in cloud environment using deep encryption

Securing the medical images in cloud environment becomes mandatory because of massive image accumulation and information storage in cloud premises. Large accommodation of information in the cloud brings another risk of data security. Privacy preserving data mining techniques investigate privacy of data to be maintained before or either after the data mining technique. Lightweight technique to store the patient data securely in the cloud is focused. The proposed study is focused on designing a robust model in which medical records privacy is preserved using Customizable Unique node access (CUNA) architecture. The system design undergoes data preparation, encryption, cloud transfer, decryption and analysis modules. The input to the proposed system is patient health care data such as CT images, Medical records, Clinical treatment procedures etc. The CT images are encrypted with respect to their medical records, further transferred into the cloud by hiding the text based confidential data into the images. Inspite of normal cloud storage process, the proposed approach utilized deep cryptography technique to make authenticated storage mechanism adaptive in the cloud. CUNA is the customized commonly allocated key assigned at every node. The unique key word is encrypted, and utilized to transfer the privacy data of the patients. The data can be retrieved from the cloud, after the decryption of CUNA code. The correlation of the key is systematically achieved using Gaussian regression process algorithm (GRA). The proposed approach is compared with various state-of-art approaches and obtained the accuracy of 92% with image MSE of 0.0012%.

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.

Multicriteria Group Decision Making Based on Intuitionistic Normal Cloud and Cloud Distance Entropy

The uncertainty of information is an important issue that must be faced when dealing with decision-making problems. Randomness and fuzziness are the two most common types of uncertainty. In this paper, we propose a multicriteria group decision-making method based on intuitionistic normal cloud and cloud distance entropy. First, the backward cloud generation algorithm for intuitionistic normal clouds is designed to transform the intuitionistic fuzzy decision information given by all experts into an intuitionistic normal cloud matrix to avoid the loss and distortion of information. Second, the distance measurement of the cloud model is introduced into the information entropy theory, and the concept of cloud distance entropy is proposed. Then, the distance measurement for intuitionistic normal clouds based on numerical features is defined and its properties are discussed, based on which the criterion weight determination method under intuitionistic normal cloud information is proposed. In addition, the VIKOR method, which integrates group utility and individual regret, is extended to the intuitionistic normal cloud environment, and thus the ranking results of the alternatives are obtained. Finally, the effectiveness and practicality of the proposed method are demonstrated by two numerical examples.

Investigation on the influence of the Bohai Sea Geological Environment on the submarine stratum stability based on data mining: an intelligent prediction model.

The Bohai Sea has unique characteristics of the marine geological environment, with strong water closure and weak self-purification ability. Assessment of the submarine stratum stability plays an essential part in ocean engineering construction, preventing marine geological disasters and controlling environmental pollution for the Bohai strait. Nevertheless, limited studies have investigated quantitative classification of the submarine stratum stability or the influence of Bohai Sea's engineering geological environment on the submarine stratum stability using laboratory experiments and the data mining method. Thus, this study carried out physical and mechanical experiments based on the submarine borehole soil samples from the Bohai Sea. And a database containing 60 groups of submarine soil samples' mechanical parameters was established. The physical and mechanical parameters of the Bohai sea's submarine soil, i.e., depth, water content, void ratio, density, internal friction angle, and cohesion, are the prediction indicators. And the submarine soil samples' unconfined compressive strength and elastic modulus are used for quantifying the stability grade of Bohai Sea's submarine stratum. Furthermore, the submarine stratum stability prediction model was established based on the multiple linear regression analysis method and the nonlinear regression analysis method (i.e., BPNN, normal cloud, and TOPSIS method). The testing results indicate that the submarine stratum stability prediction model obtained by the linear regression analysis does not have obvious linear characteristics and gives a poor prediction performance (MAE=0.9333, MAPE=0.3, RMSE=1.2111). In contrast, the nonlinear analysis methods presented a higher fitting version. Compared to the cloud model and the TOPSIS method, the BPNN model with the lowest testing error (MAE=0.2, MAPE=0.1, RMSE=0.4472) presented the best prediction accuracy. The proposed submarine stratum prediction model has been validated as an effective submarine stratum stability prediction method and has good prospects for further ocean engineering applications.

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.