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In many game theoretic environments, the precise crisp values of payoffs are not always easy to gather as real-life decision-making problems are not inevitably precise and symmetrically distributed in nature. Hence, there is a need to explore matrix games having unbalanced probabilistic linguistic information. To fill this research gap, this study proposes a concept of probabilistic multiplicative unbalanced linguistic (PM-UL) game in which the players express their responses in terms of PM-UL term. This type of PM-UL game is a useful technique for multiple criteria analysis. Further, to thoroughly capture the uncertainty involved in payoffs of such games, they are converted to probabilistic unbalanced linguistic clouds (PULCs) that describe the uncertainties of qualitative payoffs by implementing the uncertain transformation between PM-UL payoffs and quantitative values. The PULCs are represented by three numerical characteristics, i.e. expectation, entropy and hyperentropy. Hence, this model overcomes the inherent limitation of existing linguistic games where only the fuzziness of concepts is evaluated, by providing a PULC strategy and PULC value of the game. This procedure overcomes the loss of information occurring in the process of transformation of payoff values into quantitative ones. The defined methodology is implemented on a real-life working problem to demonstrate the effect and applicability in decision-making circumstances. [ABSTRACT FROM AUTHOR]

In recent years, urban floods have occurred frequently in China. Therefore, there is an urgent need to strengthen urban flood resilience. This paper proposed a hybrid multi-criteria group decision-making method to assess urban flood resilience based on heterogeneous data, group decision-making methodologies, the pressure-state–response model, and social–economic–natural complex ecosystem theory (PSR-SENCE model). A qualitative and quantitative indicator system is formulated using the PSR-SENCE model. Additionally, a new weighting method for indicators, called the synthesis weighting-group analytic hierarchy process (SW-GAHP), is proposed by considering both intrapersonal consistency and interpersonal consistency of decision-makers. Furthermore, an extensional group decision-making technology (EGDMT) based on heterogeneous data is proposed to evaluate qualitative indicators. The flexible parameterized mapping function (FPMF) is introduced for the evaluation of quantitative indicators. The normal cloud model is employed to handle various uncertainties associated with heterogeneous data. The evaluations for Beijing from 2017 to 2021 reveal a consistent annual improvement in urban flood resilience, with a 14.1% increase. Subsequently, optimization recommendations are presented not only for favorable indicators such as regional economic status, drainability, and public transportation service capacity but also for unfavorable indicators like flood risk and population density. This provides a theoretical foundation and a guide for making decisions about the improvement of urban flood resilience. Finally, our proposed method shows superiority and robustness through comparative and sensitivity analyses. [ABSTRACT FROM AUTHOR]

In China, a substantial portion of highway asphalt pavements are no longer capable of accommodating increasing traffic volumes and necessitate renovation and expansion. Prior to commencing such activities, it is crucial to evaluate the performance of the existing asphalt pavements. This study developed a novel normal cloud framework integrating a comprehensive weighted indicator system for existing asphalt pavement. Five key performance indicators including riding quality index (RQI), rutting area ratio (Rr), cracking area ratio (Cr), patching area ratio (Pr), and pavement structural strength index (PSSI) were selected to holistically represent the pavement condition in highway renovation and expansion projects. Subsequently, a method was proposed to determine the weights of these indicators by integrating the analytic hierarchy process (AHP) and entropy. A normal cloud model was constructed to address data characteristics and representation of indicator fuzziness/randomness through digital cloud modeling. The model was applied to 12 sections of the Jingjintang Expressway (Tianjin section). The results revealed only one section where the normal cloud model differed from the pavement maintenance quality assessment (PQI) model. The 3D ground-penetrating radar detection results of this different section indicated that the normal cloud model more closely aligned with the road structure condition. Compared to absolute pass/fail criteria of the traditional PQI model, the cloud model offered enhanced sensitivity to define graded condition assessments essential for reconstruction planning and decision analysis. Therefore, the normal cloud model is more suitable for assessing the performance of existing asphalt pavements in highway reconstruction and extension projects compared to the PQI model. [ABSTRACT FROM AUTHOR]

The rock mass of coal seam floor plays a crucial role in blocking the uplift and runoff of the underlying confined water in the course of mining under water pressure. Accurately assessing its water resistance performance, which is influenced by numerous factors, is important for preventing accidents caused by inrushing water. This study focuses on a coal mine in the Ningwu coalfield in North China. We establish an evaluation system for the water resistance performance of coal seam floor rock mass, using indices including rock mass thickness, lithological combination characteristics, rock mass quality, structure complexity and bedding plane count. A variable weight–normal cloud (VW–NC) model is formulated to quantitatively evaluate the water resistance performance of the coal seam floor rock mass. Comparative analysis with actual mining conditions and other models reveals that the VW-NC model provides the most precise and accurate results. The study finds that the primary influencing factors on the water resistance performance are weak structural planes, including faults, joints, and bedding planes. The proportions of strong, relatively strong, relatively weak, and weak zones in research area are 20.41%, 59.88%, 12.38%, and 7.33%, respectively.

Abstract Architectural heritage health assessment is the basis of scientific repair and maintenance. However, existing methods do not adequately take into account the fuzziness, randomness and uncertainties unique to architectural heritage assessment. In this paper, a new evaluation model of VM-NCM is constructed by combining variable weight theory and normal cloud model theory. The model enables the combination of qualitative ratings and quantitative calculation, deals with the fuzziness in the assessment process, and resolves the randomness and reflects the uncertainty to a certain extent. Based on constructing the index system combining qualitative and quantitative indexes, the structural index values are acquired by the synergistic coupling of the fine laser point cloud model and finite element structural analysis model. The acquisition of surface index values is completed by the hyperspectral intelligent detection technology of surface materials and diseases. These reduce the generation of ambiguous information in the index detection process. An evaluation study is conducted using the Yingxian wooden pagoda in China as an example. The results show that this method takes into account the fuzziness and randomness in the evaluation process, and obtains more scientific and reliable evaluation results, which provides a research paradigm for assessing the architectural heritage health status.

Architectural heritage health assessment is the basis of scientific repair and maintenance. However, existing methods do not adequately take into account the fuzziness, randomness and uncertainties unique to architectural heritage assessment. In this paper, a new evaluation model of VM-NCM is constructed by combining variable weight theory and normal cloud model theory. The model enables the combination of qualitative ratings and quantitative calculation, deals with the fuzziness in the assessment process, and resolves the randomness and reflects the uncertainty to a certain extent. Based on constructing the index system combining qualitative and quantitative indexes, the structural index values are acquired by the synergistic coupling of the fine laser point cloud model and finite element structural analysis model. The acquisition of surface index values is completed by the hyperspectral intelligent detection technology of surface materials and diseases. These reduce the generation of ambiguous information in the index detection process. An evaluation study is conducted using the Yingxian wooden pagoda in China as an example. The results show that this method takes into account the fuzziness and randomness in the evaluation process, and obtains more scientific and reliable evaluation results, which provides a research paradigm for assessing the architectural heritage health status. [ABSTRACT FROM AUTHOR]

The status of surrounding rocks dramatically influences the safety of construction workers, so the quality assessment of surrounding rocks has great significance. The uniaxial saturated compressive strength of rock (X1), the quality index of surrounding rock (X2), the frictional coefficient of the structural surface (X3), the joint spacing (X4), the state of groundwater(X5), and the integrity coefficient (X6) are selected as the initial evaluation index. Then, the game theory combination weighting-normal cloud model is introduced. Second, the certainty degree matrix of each index is established, and the weight coefficients of assessment indexes are determined based on the game theory combination weighting method. Finally, the quality level of surrounding rocks is judged. Compared with the traditional methods, the proposed model solves the fuzziness and randomness of different indexes, improves the reliability of the assessment process, and enhances the predictive accuracy of assessment results. In addition, it can provide a solution scheme for the evaluation indicators, which are difficult to quantify, and reduce the influence of human factors. The results obtained from the suggested model are consistent with the current specification. Its accuracy approaches 100%, and the method is feasible for the quality level assessment of surrounding rocks, providing a new technique and approach to assessing the risk level of surrounding rocks. [ABSTRACT FROM AUTHOR]

DNA computing is one of the more popular computational methods currently studied, but the requirements for nucleic acid molecules in DNA sequences are high, and it is an important challenge to design reasonable and high-quality DNA sequences while satisfying various constraints. Evolutionary algorithms have good applications in DNA sequence optimization problems, but they still have some limitations. To this end, this paper proposes a JAYA algorithm based on normal clouds, referred to as IJAYA, which uses a combinatorial learning approach to update the optimal and worst positions, which is used to manipulate the subsequent merit search means, and then enhances the local search ability of individuals through the normal cloud model, and finally rejects the worst solutions through a harmony search algorithm to find more reasonable and high-quality solutions. The validity of IJAYA is verified in six benchmark functions, in comparison with multiple variants of JAYA and two statistical tests. In the DNA sequence design optimization problem, the average DNA metrics optimized by IJAYA are: 0 (Continuity), 0 (Hairpin), 59.43 (H-measure), 46.57 (Similarity) and 63.79 (Similarity). The feasibility and practicality of IJAYA was verified by comparing it with the solution algorithms proposed in recent years and ablation experiments. [ABSTRACT FROM AUTHOR]

Hazard evaluation of concrete dam fractures is vital for safe operations. The width (S1), length (S2), and depth (S3) of fractures are adopted as the assessment index, and the game theory combination weighting-normal cloud model is introduced. The normal cloud model of certain dam fractures is subsequently established. The weight coefficients of each index are calculated using game theory combination weighting, and the certainty degree of each index is determined using the cloud model. Finally, the hazard levels of the concrete dam fractures are judged. The proposed model solves the fuzziness and randomness of different indexes; the conclusions demonstrate that the model is feasible for the hazard assessment of concrete dam fractures, and its accuracy is very high; therefore, a new approach can be provided for future hazard-level assessments of concrete dam fractures. [ABSTRACT FROM AUTHOR]

The status of surrounding rocks dramatically influences the safety of construction workers, so the quality assessment of surrounding rocks has great significance. The uniaxial saturated compressive strength of rock (X1), the quality index of surrounding rock (X2), the frictional coefficient of the structural surface (X3), the joint spacing (X4), the state of groundwater(X5), and the integrity coefficient (X6) are selected as the initial evaluation index. Then, the game theory combination weighting-normal cloud model is introduced. Second, the certainty degree matrix of each index is established, and the weight coefficients of assessment indexes are determined based on the game theory combination weighting method. Finally, the quality level of surrounding rocks is judged. Compared with the traditional methods, the proposed model solves the fuzziness and randomness of different indexes, improves the reliability of the assessment process, and enhances the predictive accuracy of assessment results. In addition, it can provide a solution scheme for the evaluation indicators, which are difficult to quantify, and reduce the influence of human factors. The results obtained from the suggested model are consistent with the current specification. Its accuracy approaches 100%, and the method is feasible for the quality level assessment of surrounding rocks, providing a new technique and approach to assessing the risk level of surrounding rocks.

In recent years, urban floods have occurred frequently in China. Therefore, there is an urgent need to strengthen urban flood resilience. This paper proposed a hybrid multi-criteria group decision-making method to assess urban flood resilience based on heterogeneous data, group decision-making methodologies, the pressure-state–response model, and social–economic–natural complex ecosystem theory (PSR-SENCE model). A qualitative and quantitative indicator system is formulated using the PSR-SENCE model. Additionally, a new weighting method for indicators, called the synthesis weighting-group analytic hierarchy process (SW-GAHP), is proposed by considering both intrapersonal consistency and interpersonal consistency of decision-makers. Furthermore, an extensional group decision-making technology (EGDMT) based on heterogeneous data is proposed to evaluate qualitative indicators. The flexible parameterized mapping function (FPMF) is introduced for the evaluation of quantitative indicators. The normal cloud model is employed to handle various uncertainties associated with heterogeneous data. The evaluations for Beijing from 2017 to 2021 reveal a consistent annual improvement in urban flood resilience, with a 14.1% increase. Subsequently, optimization recommendations are presented not only for favorable indicators such as regional economic status, drainability, and public transportation service capacity but also for unfavorable indicators like flood risk and population density. This provides a theoretical foundation and a guide for making decisions about the improvement of urban flood resilience. Finally, our proposed method shows superiority and robustness through comparative and sensitivity analyses.

In China, a substantial portion of highway asphalt pavements are no longer capable of accommodating increasing traffic volumes and necessitate renovation and expansion. Prior to commencing such activities, it is crucial to evaluate the performance of the existing asphalt pavements. This study developed a novel normal cloud framework integrating a comprehensive weighted indicator system for existing asphalt pavement. Five key performance indicators including riding quality index (RQI), rutting area ratio (Rr), cracking area ratio (Cr), patching area ratio (Pr), and pavement structural strength index (PSSI) were selected to holistically represent the pavement condition in highway renovation and expansion projects. Subsequently, a method was proposed to determine the weights of these indicators by integrating the analytic hierarchy process (AHP) and entropy. A normal cloud model was constructed to address data characteristics and representation of indicator fuzziness/randomness through digital cloud modeling. The model was applied to 12 sections of the Jingjintang Expressway (Tianjin section). The results revealed only one section where the normal cloud model differed from the pavement maintenance quality assessment (PQI) model. The 3D ground-penetrating radar detection results of this different section indicated that the normal cloud model more closely aligned with the road structure condition. Compared to absolute pass/fail criteria of the traditional PQI model, the cloud model offered enhanced sensitivity to define graded condition assessments essential for reconstruction planning and decision analysis. Therefore, the normal cloud model is more suitable for assessing the performance of existing asphalt pavements in highway reconstruction and extension projects compared to the PQI model.

In the consensus reaching process (CRP), experts are not infinitely compromising when adjusting opinions, and the "compromise limit" is used to reflect the limited compromise behaviors in the process of expert opinion adjustment. In complex and multiple social relations, experts tend to exhibit different behaviors according to their compromise limits. This paper aims to develop a novel CRP framework to promote a consensus that categorizes and manages experts based on their compromise limits. Firstly, the trust-interest network is defined to represent the multiple relations among experts, and the expert weights are calculated by considering the impact of interest manipulation on trust relations. Secondly, a novel cloud model-based minimum cost consensus model is established, which considers the mutual acceptance between the experts and the group, as well as the changes in the ranges of experts' hesitation and the collective acceptance. Thirdly, three behavior classifications are defined based on individual compromise limits and group acceptance ranges: cooperative behavior, hesitating non-cooperative behavior, and strong non-cooperative behavior, and a CRP optimization model is constructed to manage the three behaviors. Finally, a numerical example is given to illustrate the validity and superiority of the proposed model. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Ground collapse is a common geological disaster in karst area, which would bring great harm to people's life and property. The zoning evaluation on karst collapse susceptibility is beneficial to the classification and treatment of the disaster in order to ensure safety and economy. Karst ground collapse is characterized by suddenness, concealment, multi-factor, randomness and fuzziness; therefore, it is difficult to be fully quantified. The normal cloud model could effectively reflect the fuzziness and randomness of objective things, and integrate them to form the mapping between qualitative and quantitative analyses. In this paper, based on the normal cloud model, the study on susceptibility of karst collapse in Yonghe town, Liuyang City has been conducted in order to provide a basis for the classification and treatment of ground collapse in this area, and also provide a reference for the susceptibility evaluation of karst collapse in other areas. The study area is located to the northeast of Liuyang City, 24 km away from it, and its administrative division is located in Yonghe town, Liuyang City, Hunan Province. Ground collapses in this area are all developed in the distribution area of soluble rock layer. The most developed layer is the second member of the Lower Permian Qixia Formation (P1q2) with thick-layered carbonate rocks. Secondarily, ground collapses are distributed in the middle and upper Carboniferous Hutian Group (C2+3ht). The study area covers an area of 14 km2, and 38 karst collapses have occurred in this area so far. Among these collapses, 22 have been surveyed and filled. Sixteen collapses, four of which have been filled, have been investigated in detail. The collapse sites are mainly distributed in the region of soluble rock where faults are developed and surface water and groundwater are closely connected. Geographically, the collapse sites are mainly distributed in Yueshan Formation-Oujia Formation-Dahe Formation, Yongfu village, Yonghe town, Juxiang community-the old street of Yonghe-Huayuan village, Yanxi town, Nanshan Formation-Lizhen Primary School (old)-Xinwan Formation-Xinping Formation. Six evaluation indexes of karst collapse susceptibility were selected in this study, including karst development degree (dissolution rate of borehole), distance from the fault in the area, thickness of overlying soil layer, characteristics of karst water, distance from the pumping funnel center and current situation of ground collapse (ground collapse density). Firstly, according to the detailed exploration results of existing subsidence pits, the weights of six evaluation indicators of 16 existing subsidence pits were calculated and assigned based on entropy weight method. The calculation results indicate that the weight of development density of existing ground collapses is the largest, and that of the fluctuation amplitude of groundwater is the smallest. Secondly, the scoring standard for the risk level of normal cloud model was determined, and the susceptibility of karst collapse in each unit area in the study area was evaluated combined with the weight of each evaluation index. The evaluation results show that all the karst collapses that occurred are distributed in the area highly subject to karst collapse, indicating that the zoning in the evaluation of this study is reasonable. When the normal cloud model is used to evaluate the susceptibility of karst collapse, the size of grid cells could be controlled by the cloud similarity of comprehensive risk evaluation of karst collapse. For areas with high similarity and small cloud droplet dispersion, the unit area of each evaluation could be appropriately increased in order to reduce the evaluation workload. In addition, the high risk area calculated by analytic hierarchy process is 10.8% larger than that by normal cloud method, but three existing collapse pits distributed in the area at medium-level risk of karst collapse, indicating that normal cloud model takes more advantages than analytic hierarchy process in dealing with fuzzy and random problems such as the risk evaluation of karst collapse. [ABSTRACT FROM AUTHOR]

Abstract 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 (σ c), stress coefficient (σ θ /σ c), elastic deformation energy index (Wet), and brittleness index of rock (σ c/σ t) 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.

This paper studies the risk assessment problem of the direct delivery business from local oil refineries in Sinopec Group. A total of 23 risk factors associated with four segments of the direct delivery business are first identified. Through explaining the respective characteristics, the connotation of each risk factor is analysed in depth. Next, on the basis of the severity and possibility of each risk factor, a multistage risk assessment method for normal cloud model rooted in the extended TOPSIS approach is developed, and then applied to a real-world case. From the investigation, the weaknesses of the present risk assessment process are addressed from various aspects, including the risk factors, segments, and alternatives. Moreover, considering the possible correlation among risk factors, the proposed method is further extended by using the approach of Choquet integral. Additional discussions and recommendations are provided for improving the risk management process of the direct delivery business from local oil refineries. [ABSTRACT FROM AUTHOR]

Aiming at the uncertainty linguistic transformation problem in multi-attribute decision making, a decision-making method based on normal cloud similarity was proposed. Firstly, starting from the normal cloud characteristic curves, a normal cloud similarity measurement method based on Wasserstein distance is proposed by combing with the normal cloud entropy-containing expectation curve, which is using the Wasserstein distance to characterize the similarity characteristics of probability distribution. The properties of the proposed similarity measure are discussed in the paper. Secondly, the performance of the proposed method is compared and analyzed with the existed methods by numerical simulation experiment and time series data classification experiment. The experimental results show that the proposed method has good similarity discrimination ability, high classification accuracy and low CPU time cost. Finally, the method was successfully applied into linguistic multi-attribute decision making, and TOPSIS thought is used to compare and rank the schemes, so as to realize the final decision.

The cloud decision support model is an effective technique for describing an ambiguous mapping between qualitative notions and their quantitative manifestations. However, the existing research on cloud models is based on balanced linguistic term set and cannot address information in an unbalanced linguistic form. Henceforth, to thoroughly capture the intrinsic fuzziness and vagueness of decision information an accurate decision model is required to acquire good solution. In this direction, the proposed work aims to introduce a cloud model based on multiplicative unbalanced linguistic term sets. To match our research requirements, we initially propose a multiplicative unbalanced point-based cloud model. The innovation of the model overcomes the inherent limitation of the existing cloud and is competent of producing new solution according to the cloud model theory. Furthermore, a generalized multiplicative unbalanced linguistic scale function is introduced. The proposed generalized function creates a map between the linguistic data and numerical interval values. Based on this rational conversion method, interval cloud variables are transformed, which overcomes the loss of information existing in the point-based cloud models where linguistic variables are transformed into a specific numerical value. The process is then used to demonstrate the accuracy and efficacy of the suggested model using a numerical example, which is then followed by a comparison study. The overall results typically show that the cloud model has excellent performance and provides better solutions. [ABSTRACT FROM AUTHOR]

Evaluating the state of China's industrial transformation and its ecological protection is essential to addressing industrial ecological security. In this study, nine cities in the Wanjiang Demonstration Zone were selected for analysis using the normal cloud model and the spatial autocorrelation model. The study results showed that (1) the industrial ecological security of the Wanjiang Demonstration Zone showed an upward trend from 2012 to 2019, and the development among cities was unbalanced; (2) the industrial ecological security level in the northeast of the Wanjiang demonstration zone was higher than that in other cities; and (3) the spatial correlation within the demonstration area is positive, and the degree of agglomeration is strong initially but is subsequently attenuated. This indicates that enhancing the ecological security of the city will have a backflow effect that will attenuate connection with surrounding cities. This study elucidates ecological security in the industrial transformation demonstration area, and the following recommendations are made based on the research results: improving the strategic planning of regional industrial ecosystems, creating a new governance pattern featuring coordinated development among regions, and innovating new business forms and models for the development of inter-city linkage.

• A weight method with the criteria variability to choose subgroup representatives. • Two consensus measurements for individual-guided and moderator-guided consensus. • Cloud model-based consensus optimisation with preference consistency. • A consensus hybrid strategy framework considering compromise behaviors. 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. [ABSTRACT FROM AUTHOR]

Copyright of Carsologica Sinica is the property of Institute of Karst Geology, CAGS and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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

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. [ABSTRACT FROM AUTHOR]

In image segmentation, one important problem is the indeterminacy of pixel value. Based on fuzzy and probability theory, Cloud Model can better solve this same concept problem in segmentation. Methods based on Normal Cloud Model have been proposed here for improving the segmentation performance. Single-Kernel Extraction (SKE) and Multi-Kernel Extraction (MKE) methods have been introduced which are used for determining expected value of the Cloud Model during Cloud Transformation. In addition, a Maximum-Similarity Concept Promotion (MSCP) strategy based on Minimum-Distance Concept Promotion (MDCP) has been proposed and its theoretical validity has also been introduced. Image segmentation experiment results show that our algorithms get a strong adaptability and better image segmentation effect evaluation coefficient. [ABSTRACT FROM AUTHOR]

With the large-scale application of 5G technology in smart distribution networks, the operation effects of distribution networks are not clear. Herein, we propose a comprehensive evaluation model of a 5G+ smart distribution network based on the combination weighting and cloud model of the improved Fuzzy Analytic Hierarchy-Entropy Weight Method (FAHPEWM). First, we establish comprehensive evaluation indexes of a 5G+ smart distribution network from five dimensions: reliable operation, economic operation, efficient interaction, technological intelligence, and green emission reduction. Second, by introducing the principle of variance minimization, we propose a combined weighting method based on the improved FAHP-EWM to calculate the comprehensive weight, so as to reduce the defects of subjective arbitrariness and promote objectivity. Finally, a comprehensive evaluation model of 5G+ smart distribution network based on cloud model is proposed by considering the uncertainty of distribution network node information and equipment status information. The example analysis indicates that the overall operation of the 5G+ smart distribution network project is decent, and the weight value calculated by the combined weighting method is more reasonable and accurate than that calculated by the single weighting method, which verifies the effectiveness and rationality of the proposed evaluation method. Moreover, the proposed evaluation method has a certain guiding role for the large-scale application of 5G communication technology in smart distribution networks. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Copyright of Electric Machines & Control / Dianji Yu Kongzhi Xuebao is the property of Electric Machines & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As far as airborne sensors are concerned, the measurement accuracy is an important indicator that cannot be ignored and may directly affect final measurement results. In order to improve the measurement accuracy of a flush air data sensing (FADS), which is an advanced sensor, this paper proposed a new method based on the normal cloud model and the multi-objective programming (MOP). First, the high-precision FADS model is established by using the database obtained with the CFD software and aerodynamics knowledge. Meanwhile, the uncertainty and randomness of signals caused by measurement noise are quantitatively analyzed by using the normal cloud model. Then, in the process of data fusion, a new method for calculating the weights is proposed based on the slack variable method and the Lagrange multiplier method. The simulation results show that the proposed method can improve the measurement accuracy by 3.2% and reduce the dispersion of measurement data by 68.88%.

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. [ABSTRACT FROM AUTHOR]

• It's the first time to apply normal cloud model into wooden pagoda safety assessment. • Two-way mapping of the "quantitative description-qualitative concept". • A four-level safety assessment index system. Currently the safety assessment of wood structure ancient buildings is mainly qualitative description, ignoring accurate quantitative calculation, and there are uncertain factors such as fuzziness and randomness that affect the results in the assessment process. Therefore, based on the cloud model theory of transformation between qualitative and quantitative uncertainties, this paper proposes a normal cloud model-based safety assessment approach for the protection research of wooden pagoda. A multi-level assessment system combining qualitative and quantitative indexes is constructed, and the comprehensive weight of the evaluation indexes is determined by combination weighting method. And combining with the scoring of evaluation indexes through quantitative analysis of point cloud, qualitative evaluation of experts, and published high-level papers, the comprehensive evaluation cloud model is constructed. According to the standard grade division, the criteria cloud model is constructed. And safety performance grade is determined by comparing the cloud similarity between the two clouds. Then this paper sets Yingxian wooden pagoda in China as an example, safety performance is quantified under this approach to determine its safety performance grade. The results show that the pagoda is the closest to third grade, in a certain dangerous state and consistent with the actual situation. The method takes into account the randomness and fuzziness factors, which makes assessment process more comprehensive and objective, provides a research paradigm for the safety assessment of ancient wooden buildings. [ABSTRACT FROM AUTHOR]

The performance of train communication network (TCN) is one of the most important factors to ensure the safety of train operation, and there is a lack of research for the TCN performance currently. To master the state of TCN, a combined method to evaluate and predict the TCN performance is proposed in the paper. First, we select appropriate evaluation indexes and determine their comprehensive weights. Then, the evaluation model of TCN performance is established based on a multidimensional normal cloud model combined with weights. According to the evaluation results, the artificial neural network (ANN) is used to predict the TCN performance to realize the real-time evaluation. Finally, the effectiveness of the evaluation and prediction model of TCN performance is verified by the simulation result, which is obtained from the simulation platform of TCN. The results show that service quality for TCN has been effectively improved, which provides a theoretical reference for evaluation and prediction of TCN performance. [ABSTRACT FROM AUTHOR]

Catenary plays an important role in electrified railway. It is of great significance to sense the status of catenary. However, the current status sensing methods of catenary select weighting coefficients artificially calculate similarity from only one aspect and only consider the fuzziness of indicators without the randomness. Thus, a status sensing system of catenary with multiple indicators is constructed. The main process of this system is as follows: Firstly, fuzzy analytic hierarchy process method and improved criteria importance though inter-criteria correlation are used to get the subjective and objective weights, and least square method is used to obtain the combined weights, which reduces the influence of artificial experience. Secondly, Grey-Technique for Order Preference by Similarity to an Ideal Solution is used to calculate weighted similarity, so as to obtain the health score of each sensing indicator. Thirdly, the normal cloud model is used to handle the health score to obtain the membership degree of sensing objects, which consider both the fuzziness and randomness. Finally, the principle of maximum membership degree is used to determine health status of each sensing object, and the weighted average principle is used to verify the sensing results. Selecting a Chinese railway catenary as an example for verification, the results show that the system can sense the status of catenary accurately. [ABSTRACT FROM AUTHOR]

Risk assessment is an effective method of accident prevention and is vital to actual production. To reduce the risk of mining accidents and realize green and sustainable coal mining, a coal and gas outburst risk assessment method based on the improved comprehensive weight and cloud theory is proposed. The proposed method can effectively solve problems of fuzziness and randomness, index weight deviation, and correlation between indexes in risk assessment, as well as improve the accuracy and rationality of assessment. Nine influencing factors that correspond to coal seam occurrence and geological characteristics, coal seam physical characteristics, and gas occurrence characteristics are selected to establish the risk assessment index system of coal and gas outburst. Using the improved group G1 method and improved CRITIC method to obtain the subjective and objective weights, the ideal point method is used to obtain the comprehensive weight. Using the normal cloud model of cloud theory and the comprehensive weight to assess engineering examples 1–2, the No. 3 coal seam of a mine in Shanxi, and the 21 coal seam of a mine in Henan, the risk grade of coal and gas outburst is determined and then compared with the assessment results obtained from the engineering examples and the actual situations of the above mentioned coal seams. The results show that the coal and gas outburst risks of engineering examples 1–2, No. 3 coal seam, and 21 coal seam are of grades IV, IV, II, and IV, respectively. The No. 3 coal seam and 21 coal seam belong to lower and higher risk categories, respectively. The assessment results are consistent with the actual situation of the coal seams, thereby confirming the rationality and accuracy of the proposed method. This study expands the methods of coal and gas outburst risk assessment and facilitates the formulation of effective preventive measures. [ABSTRACT FROM AUTHOR]

An uncertain multi-attribute decision-making (MADM) problem is studied based on cloud models. Cloud models, referring to fuzziness and randomness, are utilized to depict evaluation and pre-evaluation information which can reflect the future development performance of alternatives. Because of bounded rationality, decision maker's (DM) risk attitudes should be considered when facing uncertainty. Thus, a behavioral MADM (BMADM) method is proposed by considering DM's risk attitudes and pre-evaluation. First, a distance measure for normal cloud models is developed with consideration of both DM's risk preferences and random distribution, aiming at making full use of information. Second, as a basis of applying prospect theory, positive ideal reference point is set by considering both evaluation and pre-evaluation information from three aspects: risk-averse, risk-neutral, and risk-seeking preference coefficients, in which the sign of distance is not necessary to determine. The third element is the establishment of an optimization model for handling incomplete attribute weights, following which is to obtain the ranking of alternatives. The final phase is the application of the proposed method to one case, along with sensitivity and comparison analyses, as a means of illustrating the applicability and feasibility of the new method.

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.

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.

In recent years, some complex linguistic expression techniques with probabilistic information, such as proportional linguistic terms, probabilistic linguistic term sets, have been proposed to describe the uncertainty of opinions and preferences. However, the probability information is expressed as the crisp numbers, which is usually difficult to be estimated and information losses may exist. To capture the inherent fuzziness and vagueness of decision information more comprehensively and accurately, in this article, we introduce a novel concept named dual linguistic term set (DLTS), which consists of two linguistic variables. The DLTS can allow the decision makers to express the probabilistic information by the means of linguistic variables. To describe the hesitant information of the decision makers, a more general concept named hesitant fuzzy DLTS is developed. Afterwards, a multicriteria decision making method with dual linguistic information is proposed based on the normal cloud model. To do so, the DLTS is first transformed into an equivalent two-tuple consisting of two independent normal clouds. Then, new multiplication operation and power operation of normal clouds are defined to obtain the expectation for each attribute value. After aggregating the evaluation information, we further develop a new score function to rank alternatives reasonably. Finally, an illustrative example is given to verify the effectiveness and feasibility of the proposed method, also some comparisons and analyses are provided to show the advantages of the proposed method. [ABSTRACT FROM AUTHOR]

In the field of aviation industry, flush air data sensing (FADS) is an advanced sensor for hypersonic aircraft. However, since the sensor has not been used in engineering practice for a long time, there is still much room for improvement. The main purpose of this paper is to improve the measurement accuracy of FADS from the perspective of fuzzy theory. First, the aerodynamic model of FADS is established based on the knowledge of aerodynamics under supersonic and subsonic conditions. Further, to describe the uncertainty and randomness of redundant signals, normal cloud model, a significant concept in fuzzy theory, is employed. Meanwhile, to reduce the influence of abnormal data on the final measurement accuracy, $3{En}$ principle is adopted to preprocess the data. In the process of data fusion using the aggregation operate, a new method of calculating the objective weight vector is derived based on Lagrange multiplier method and simplex evolutionary method of multi-objective programming (MOP). To illustrate the feasibility and validity of the proposed method, two FADS systems with six measurement taps and thirteen taps are adopted, respectively. The experimental results show that for the FADS with six measurement taps, the proposed method can improve the measurement accuracy by 4.55% and reduce the dispersion of data by 61.38%. For the latter, the proposed method can improve the measurement accuracy by 3.43% and reduce the dispersion of data by 50.14%. To further demonstrate the superiority of the proposed method, a detailed comparison with other six widely used methods is carried out. [ABSTRACT FROM AUTHOR]

With the wide deployment of cloud computing, many security challenges have arisen, such as data and storage integrity and virtualization security. The crisis of trust caused by these security issues has become one of the important factors restricting the wide applications of cloud service. Especially for security-sensitive users, it is challenging to quickly select a cloud service which has the high level of trust and can meet both the user preferences and specific functional demands. This paper explores the multi-granularity selection standard of trust level, the users' preference calculation model, and the cloud service selection algorithm. First, the trust evaluation mechanisms among different entities in the human society are fitted, and the multi-granularity selection standard of trust levels based on Gaussian cloud transformation is constructed. Then, the calculation model of user preferences based on the cloud analytic hierarchy process is developed. Finally, the trusted cloud service selection algorithm based on two-step fuzzy comprehensive evaluation is proposed and experimentally validated.

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Destruction of pipelines because of coal mine gob disasters may result in enormous financial loss and significantly affect public safety. Hence, risk assessment of gob pipelines is of immense significance for field personnel. Given the lack of statistical data and the limitations of expert experience, a prior single risk value is often insufficient to reflect the actual situation and cannot meet the needs of the site. The backward cloud transformation (BCT) algorithm is a method that can fully mine the local information contained in expert experience to restore the overall information. However, the existing BCT algorithm has no solution under certain conditions, which considerably limits its application. This study proposes a comprehensive risk assessment method by combining the structural entropy method with a multi - step backward cloud transformation algorithm based on sampling with replacement (MBCT-SR). First, a simplified model for rapid identification is used to determine whether it is worth calculating risk values. Second, a fault tree that fits the actual situation is established, and the weights of the indexes are determined by the structural entropy weight method. Third, the interval scores of the indexes are transformed into numerical features of the cloud model, which are then logically operated using virtual cloud algorithms. Finally, the risk values of the pipeline can be obtained, the cloud droplet diagram of the risk values is clearly shown by the forward cloud transformation (FCT) algorithm, and the risk level can be obtained by the probability that the cloud droplet falls into each risk level interval. To validate the utility of the proposed method, a case study of a coal mine gob around a long-distance gas pipeline was investigated. • The multi-step backward cloud transform algorithm is firstly used to evaluate the gob pipeline. • The uncertainty problem is solved by combining the interval scoring and normal cloud model. • A new gas pipeline risk assessment system for the gob was established. • Detailed on-site investigation was conducted in the study. • The combination of structure entropy weight method and MBCT-SR effectively improves the accuracy of risk assessment. [ABSTRACT FROM AUTHOR]

Water inrush in karst tunnels is a dynamic process in which internal and external factors are involved. The evaluation of this process is fuzzy, complex, and uncertain. In the current research, few articles give full consideration to the fuzziness and randomness of the water inrush evaluation with useful dynamic feedback. A new assessment method has been proposed for the water inrush evaluation based on a combination of the weighting method and normal cloud model. Specifically, an evaluation index system is forged and each index is quantitatively classified into four grades. A synthetic weighted algorithm combining the analytic hierarchy process, entropy method, and statistical methods is proposed to assign the index weight rationally. Based on the cloud generator algorithm, three numerical characteristics are calculated and a sufficient number of cloud droplets are generated. The membership degree of each index belonging to each grade is constructed and the integrated certain grades are determined. In this paper, the multi-factor normal cloud assessment method is applied to the risk assessment of the Qiyueshan tunnel. The assessment result of the risk grade is accurate, that is, the water inrush risk of different samples at the same risk grade can be reflected in figures. The results not only show high consistency with other assessment methods but are also in good agreement with the excavation results. The proposed cloud model method demonstrates good practical reference for risk assessment of tunnel construction in karst areas and can be applied to tunneling, mining, and other engineering practices in the future. [ABSTRACT FROM AUTHOR]