Search

Abstract It is difficult to make scientific decisions for multistage risk decision-making problems, which are mainly influenced by these challenges, such as uncertainty of information, the passage of time and complexity of the problems The multistage risk decision-making problem is analyzed for normal cloud model considering behavior characteristics in this paper. Firstly, considering the fuzziness and randomness of multi-stage risk decision-making information, the relative entropy and modified relative entropy are defined as the distance measures for the normal cloud model from the perspective of full utilization of information. Secondly, the prospect theory is extended to normal cloud model environment. Considering the behavior characteristics of reference dependence, two reference points are set up considering the short-term development and long-term development. Thirdly, the optimization model is built to obtain the attribute weights and stage weights based on the principle of maximizing the difference between ranking values. The ranking of alternatives is determined by the final ranking values. The case analysis and the method comparison illustrate the feasibility and rationality of the proposed method. Highlights • Relative entropy is defined as a distance measure for the normal cloud model. • Two reference points are set up considering the behavior characteristics. • An optimization model is built to obtain the weights through maximizing the difference among ranking values. [ABSTRACT FROM AUTHOR]

The uniform sampling strategy is widely used in time series segmentation, but unable to handle time warping problem or preserve the latent patterns in time series. To solve these shortcomings, a brand new data-driven segmentation method is proposed, which could segment time series into subsequences with different lengths adaptively. Then a time series classification method under the bag-of-word framework is proposed. Two kinds of mutually complementary features, i.e., interval feature and normal cloud model feature, are extracted from subsequences. And then time series are encoded into Fisher Vectors. Finally, a linear support vector machine is used as the classifier. Experiments on 43 UCR datasets show that the newly proposed method has promising classification accuracies comparing with state of the art baselines. Moreover, due to the data-driven segmentation and timesaving local feature extraction, the method has low time complexity, which is also demonstrated in the experiments. [ABSTRACT FROM AUTHOR]

When data mining research is conducted, it is difficult to obtain precise domain knowledge to set a similarity threshold. Furthermore, noise and missing values are inevitable. Missing values and noise without pre-processing are challenges for many algorithms. A time-series clustering method is proposed based on the normal cloud model and complex networks. Matrix profile similarity measurement, normal cloud model generation and filtering, cloud model expectation curve weighting, degree centrality reweighting, and community discovery in complex networks are the five stages of the proposed clustering algorithm. Local features are considered, and the effects of missing values are reduced when performing similarity measurements. The normal cloud model can be used to set thresholds adaptively. The Louvain algorithm accomplishes the clustering task in complex networks without specifying the clusters. Experiments are conducted on 94 datasets and are compared with 8 clustering methods in the UCR time-series clustering benchmark study. Experimental results indicate that the proposed method can perform well on many datasets. [Display omitted] • It is unnecessary to specify the number of clusters. • Similarity threshold can be set adaptively by using normal cloud model. • Local shape features of time series are considered. • The impact of missing values and noise can be reduced. [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., Competing Interests: The authors declare no competing interests.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

Z-number is the general representation of real-life information with reliability, and it has adequate description power from the point of view of human perception. This study develops an innovative method for addressing multicriteria group decision-making (MCGDM) problems withZ-numbers under the condition that the weight information is completely unknown. ProcessingZ-numbers requires effective support of reliable tools. Then, the normal cloud model can be employed to analyze theZ-number construct. First, the potential information involved inZ-numbers is invoked, and a novel concept of normalZ+-value is proposed with the aid of the normal cloud model. The operations, distance measurement, and power aggregation operators of normalZ+-values are defined. Moreover, an MCGDM method is developed by incorporating the defined distance measurement and power aggregation operators into the MultiObjective Optimization by Ratio Analysis plus the Full Multiplicative Form. Finally, an illustrative example concerning air pollution potential evaluation is provided to demonstrate the proposed method. Its feasibility and validity are further verified by a sensitivity analysis and comparison with other existing methods. [ABSTRACT FROM AUTHOR]

A systematic work has been presented for condition assessment of suspension bridges in this study. Initially, a four-layer index system is built up. Subsequently, 45 experts are invited to determine the index weights by processing the experts’ opinions using the Group Decision-Making (GDM). The assigned weights seem more reasonable (especially weights of the tower and auxiliary facility) when compared with those in the existing China’s code. Next, assessment algorithms, the Local Variable Weight Model (LVWM) and Normal Cloud Model (NCM), are established based on the characteristics of the bridge condition assessment. The LVWM adjusts weight properly to make assessment results in line with the actual situation under extreme cases. The NCM describes not only the fuzziness but also the randomness in the assessment process. Finally, a case study is illustrated to verify the effectiveness of the methodology. For sake of highlighting the advantages of the LVWM, two more models are subjected to the case study, which are the Constant Weight Model (CWM) and Traditional Variable Weight Model (TVWM). Consequently, the assessment result of the LVWM is more in keeping with the actual situation than those of the CWM or TVWM. [ABSTRACT FROM AUTHOR]

This paper deals with the identification of anomalies in wind turbine (WT) gearbox by temperature trend analysis approach. Support vector regression (SVR) is adopted to build two models for forecasting operating temperature of WT gearbox. One model is trained with historical supervisory control and data acquisitions (SCADA) data in the normal state, and the other is trained with abnormal state data. The prediction accuracy of two models is compared, and the sequences of relative error (SRE) index for two models are calculated. Then, two trend cloud model, namely normal cloud, and abnormal cloud, are built based on an improved inverse normal cloud generator, meanwhile the SRE are used as inputs of the generator, and the parameters of different trend cloud models are obtained as outputs. The closeness degree of the current state related to the normal or abnormal cloud can be calculated using the current SCADA data, and the principle of maximum closeness degree is adopted to judge the anomaly. The proposed approach has been used to analyze a real gearbox failure occurred in a 1.5 MW WT. The results obtained confirm the feasibility and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

In mountainous regions, rockfall is a typical geological disaster which might bring immense casualties and economic losses, but also endanger the safety of civil engineering construction. Many tunnels are being built in the southwest of China, thus a comprehensive assessment for rockfall risk is needed. For this purpose, in this paper, based on normal cloud model theory, we created a multi-index evaluation model for the rockfall risk assessment. Then, according to previous research and specific geological conditions, potential tunnel dangers are classified into four ranks, and some geological factors are considered as the principal factors. In order to fully express the opinions of experts, the qualitative indices were quantified by continuous value scale. Moreover, the value of each index is determined by expert scoring. In view of different evaluation units, we used the normalization method to make geological indices dimensionless. And three numerical characteristics (Ex, En, and He) were calculated by the cloud generator algorithm with MATLAB. In this study, we assigned the weight of indices by simple dependent function to avoid the influence of subjective. Finally, by means of a normal cloud generator, we determined the integrated certainty grades. To ensure the accuracy of the normal cloud model method, it was tested in rockfall cases in Jiefangcun tunnel. And the results obtained by the cloud model method are in good agreement with the practical situation. Moreover, the results are better than those of the AHP-FUZZY and artificial neural networks methods after comparison. The cloud model-based method realizes a multi-criteria assessment of the rockfall risk in tunnel portal section and provides a practical guide on safe tunnel construction for similar projects. [ABSTRACT FROM AUTHOR]

Water Resource Sustainable Utilization (WRSU) is becoming increasingly important, given growing water resource shortages and widening gaps between water supply and demand. Most existing studies have focused on WRSU levels without a dedicated strategy-oriented framework. In addition, uncertainties occur in the process of indicator quantification and grading, leading to a lack of accuracy in the assessment results. Therefore, in this study, stemming from water resource, societal, economic, and environmental dimensions, an indicator system with qualitative description was introduced by Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis to enable development and selection of sustainable water use strategies. A normal cloud model that is capable of addressing uncertainties was used to determine WRSU levels. The comprehensive evaluation results can both reflect the WRSU levels and select the most suitable strategy. The model's utility was demonstrated by applying it to the case of Shandong province in China. Based on the results, most areas of Shandong province appear to be facing serious unsustainable issues. Appropriate development strategies based on the WRSU levels were provided for improving sustainable use of water resources. The proposed method offers an efficient means for WRSU assessment and strategy development. Moreover, it has the potential to be applied to other water resource issues. [ABSTRACT FROM AUTHOR]

The Fruit Fly Optimization Algorithm (FOA) is one of the latest swarm intelligence-based methods inspired by the foraging behavior of fruit fly swarm. To improve the global search ability and solution accuracy of the FOA, a bimodal adaptive fruit fly optimization algorithm using normal cloud learning (BCMFOA) is proposed in this paper. Based on the labor allocation characteristics of the swarm foraging behavior, the fruit fly population is divided into two groups in the optimization process according to their duties of searching or capturing. The search group is mainly based on the fruit fly's olfactory sensors to find possible global optima in a large range, while the capture group makes use of their keen visions to exploit neighborhood of the current best food source found by the search group. Moreover, the randomness and fuzziness of the foraging behavior of fruit fly swarm during the olfactory phase are described by a normal cloud model. Using a normal cloud generator and an adaptive parameter updation strategy, the search range of the fruit fly population is adaptively adjusted. Therefore, the ability of FOA to avoid local optima is enhanced greatly. Twenty-three benchmark functions are used to test the performance of the proposed BCMFOA algorithm. Numerical results show that the proposed method can significantly improve the global search ability and solution accuracy of FOA. Compared with existing methods such as PSO, DE, AFAS, the experimental results indicate that BCMFOA has better or comparative convergence performance and accuracy. The application to the multi-parameter estimation of a permanent magnet synchronous motor further confirms its good performance. [ABSTRACT FROM AUTHOR]

• Propose an effective interdisciplinary concepts extraction method. • Employ the normal cloud model (NCM) for interdisciplinary semantic drift identification. • Conduct an empirical study on the interdisciplinary concept "information entropy". • Visualize the semantic drift degree and direction of each interdisciplinary concept. 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. [ABSTRACT FROM AUTHOR]

The Artificial Bee Colony (ABC) algorithm is a bionic intelligent optimization method. The cloud model is a kind of uncertainty conversion model between a qualitative concept T ˜ that is presented by nature language and its quantitative expression, which integrates probability theory and the fuzzy mathematics. A developed ABC algorithm based on cloud model is proposed to enhance accuracy of the basic ABC algorithm and avoid getting trapped into local optima by introducing a new select mechanism, replacing the onlooker bees’ search formula and changing the scout bees’ updating formula. Experiments on CEC15 show that the new algorithm has a faster convergence speed and higher accuracy than the basic ABC and some cloud model based ABC variants.

• A dynamic and adaptive large-scale group Delphi method is proposed. • Heterogeneous data with various uncertainties are modeled by normal cloud models. • A novel dynamic weights assignment algorithm is proposed. • A four-step data preprocessing process is proposed to filter out bad data. • The effectiveness and advantages are demonstrated by applications and comparisons. 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. [ABSTRACT FROM AUTHOR]

To satisfy the requirements of on-line status assessment of wind turbines, a fuzzy comprehensive evaluation method combined with normal cloud model based on SCADA data was proposed. Firstly, a concept was introduced, The Dynamic Inferior, which can take both the variation of assessment indices into the calculation process of inferior degree and the changing of the wind turbine operating condition into consideration. A Markov chain model was adapted to predict the variation of assessment indices. Then, a normal cloud model with the dynamic inferior degree as inputs was adopted to calculate the membership degree of different assessment indices to overcome the subjectivity of normal membership functions. Furthermore, a fuzzy comprehensive assessment method was developed to assess the status of wind turbines online. Finally, the method was tested on the historical SCADA data of wind turbines. The results showed that this method was capable of not only accurately assessing the status of wind turbines online, but could also warn of problems early, which can prevent the occurrence of serious complications. [ABSTRACT FROM AUTHOR]

The analytic hierarchy process (AHP) is the most popular extension to the pairwise comparisonsmethod which is based on the observation that it is much easier to rank several objects when restricted to two objects at one time. As the pairwise comparisons are subjective, the use of linguistic expressions rather than numerical values is straightforward and friendlier due to the uncertainties that are inherent in human judgments. In this paper, to handle the uncertainty and hesitancy in practical decisionmaking situations, we represent pairwise comparisons in AHP using hesitant cloud linguistic term sets (HCLTSs) which are proposed based on hesitant fuzzy linguistic term sets (HFLTSs) and normal cloud models. Then, the synthetic cloud model aggregation algorithm is proposed to transform the HCLTS pairwise comparison matrix into the positive reciprocal synthetic cloud matrix. A prioritizationmethod using the geometricmean technique is adopted, and the rankingmethod based on comparing of the parameters of normal cloud models is proposed. Thus, we extend the traditional AHP method in hesitant and uncertain environment, and we call it HCLTS-AHP method. The comparative linguistic expressions of preferences becomemore flexible and richer and are more similar to human beings' cognitive models. Furthermore, the synthetic cloud model is consistent with objectivity and the calculations are easy to implement. An illustrated example is applied to the ranking of four alternatives to show the usefulness of the proposed HCLTS-AHP method. [ABSTRACT FROM AUTHOR]

Medium-and-long-term load forecasting plays an important role in energy policy implementation and electric department investment decision. Aiming to improve the robustness and accuracy of annual electric load forecasting, a robust weighted combination load forecasting method based on forecast model filtering and adaptive variable weight determination is proposed. Similar years of selection is carried out based on the similarity between the history year and the forecast year. The forecast models are filtered to select the better ones according to their comprehensive validity degrees. To determine the adaptive variable weight of the selected forecast models, the disturbance variable is introduced into Immune Algorithm-Particle Swarm Optimization (IA-PSO) and the adaptive adjustable strategy of particle search speed is established. Based on the forecast model weight determined by improved IA-PSO, the weighted combination forecast of annual electric load is obtained. The given case study illustrates the correctness and feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

Recreational fishing is an important component of the global capture fishery, given its significant role in the economy, social culture, and ecology. Compared to the mature development found in industrialized countries, recreational fishing in developing countries or transitional economy is a new industry with abundant jobs and high revenues. China's recreational fishery industry has shown an upward trend since 2003. This paper built an evaluation framework from both static (present competitive strength) and dynamic (potential competitive strength) perspectives to evaluate recreational fishery competitiveness in 30 Chinese provinces based on normal cloud model. The results showed the following: (1) The evaluation framework can effectively evaluate recreational fishery competitiveness. Meanwhile, there is significant randomness and fuzziness discrepancy between different indicators. 'Industry' indicators had the lowest fuzziness and randomness. (2) The development of recreational fishery competitiveness in China is not strong and uneven, with more than two-thirds of the total provinces with the lowest competitiveness level. Regarding spatial distribution, the eastern coastal provinces had relatively strong competitiveness, whereas the western inland provinces were weak. (3) There was stability divergence of competitiveness results among different grades. In general, the competitiveness of provinces belonging to grades I and V was the most stable, while other provinces are relatively unstable with the characteristics of dynamic changes. (4) The main advantageous factors for recreational fishery competitiveness in most provinces were 'service', whereas the main disadvantageous factor was 'resources'. The abundance of fishery resources might not be a key factor resulting in regional recreational fishery competitiveness discrepancies. This study can provide a reference for decision-makers to formulate an assessment framework for recreational fishery industry and provide ideas for the formulation of recreational fishery planning at the national level. • Defining the concept and mechanism of recreational fishery competitiveness. • Building an evaluation framework from both static and dynamic perspectives to evaluate recreational fishery competitiveness. • Analyzing the competitiveness of the provinces based on normal cloud model in China and discussing the stability divergence of competitiveness results among different grades. • Discussing the implications for understanding the recreational fishery industry connotation decision-making. [ABSTRACT FROM AUTHOR]

This paper chooses 8 cities and counties of Bijie area as the research target. With the research foundation of natural disaster risk theory and drought risk formation principle, we start from dangerousness, exposure , vulnerability and the ability to prevent disaster to filter out 23 indexes range from social status, economy, weather and geology to construct the assessment system of Bijie drought risk. To solve the uncertainty of quantitative description for drought risk assessment index and the judge of assessment result, cloud model and entropy weight method are implemented to decide how much percentage the city or county belong on each index. In the end, we use the weight of 23 indexes to get the drought risk level each city or county belongs to. The result reveal that Weining has the highest risk while Bijie, Qianxi, Zhijin, Hezhang have higher risk of drought occurence and Dafang and Jinsha have normal risk, Nayong has lower risk. This research aims to help governments at all levels to prevent drought and provide decision basis to reduce drought loss.

The representation and processing of uncertainty information is one of the key basic issues of the intelligent information processing in the face of growing vast information, especially in the era of network. There have been many theories, such as probability statistics, evidence theory, fuzzy set, rough set, cloud model, etc., to deal with uncertainty information from different perspectives, and they have been applied into obtaining the rules and knowledge from amount of data, for example, data mining, knowledge discovery, machine learning, expert system, etc. Simply, This is a cognitive transformation process from data to knowledge (FDtoK). However, the cognitive transformation process from knowledge to data (FKtoD) is what often happens in human brain, but it is lack of research. As an effective cognition model, cloud model provides a cognitive transformation way to realize both processes of FDtoK and FKtoD via forward cloud transformation (FCT) and backward cloud transformation (BCT). In this paper, the authors introduce the FCT and BCT firstly, and make a depth analysis for the two existing single-step BCT algorithms. We find that these two BCT algorithms lack stability and sometimes are invalid. For this reason we propose a new multi-step backward cloud transformation algorithm based on sampling with replacement (MBCT-SR) which is more precise than the existing methods. Furthermore, the effectiveness and convergence of new method is analyzed in detail, and how to set the parameters m, r appeared in MBCT-SR is also analyzed. Finally, we have error analysis and comparison to demonstrate the efficiency of the proposed backward cloud transformation algorithm for some simulation experiments. [ABSTRACT FROM AUTHOR]

Medium-and-long-term load forecasting plays an important role in energy policy implementation and electric department investment decision. Aiming to improve the robustness and accuracy of annual electric load forecasting, a robust weighted combination load forecasting method based on forecast model filtering and adaptive variable weight determination is proposed. Similar years of selection is carried out based on the similarity between the history year and the forecast year. The forecast models are filtered to select the better ones according to their comprehensive validity degrees. To determine the adaptive variable weight of the selected forecast models, the disturbance variable is introduced into Immune Algorithm-Particle Swarm Optimization (IA-PSO) and the adaptive adjustable strategy of particle search speed is established. Based on the forecast model weight determined by improved IA-PSO, the weighted combination forecast of annual electric load is obtained. The given case study illustrates the correctness and feasibility of the proposed method.

It is difficult to make scientific decisions for multistage complex decision-making problems, which are heavily affected by multi-level attributes, stage changes, and multi-source heterogeneous information. The normal cloud model is an uncertainty transformation model for constructing mappings between numerical values and their linguistic representations, which can be used to describe multi-source evaluation information for a better reflection of the distribution characteristics and uncertainties. This study aims to develop an effective multi-level multi-attribute decision-making method based on normal cloud models to solve multistage evaluation problems with multi-source heterogeneous information. Firstly, an optimization model is established to capture stage weights and lower-level attribute weights based on cloud distance. Secondly, a bidirectional cloud projection measure is proposed to obtain the measurement values of upper-level attributes based on horizontal and vertical reference points. Thirdly, the comprehensive evaluation values of alternatives are obtained based on Shapley weights of the upper-level attributes. Finally, an illustrative example is presented to clarify the feasibility and superiority of the proposed method. The result indicates that our method is (1) a flexible evaluation framework based on target reference points in fuzzy environments, (2) a powerful measurement tool for aggregating multi-source information, and (3) an objective decision-making method considering the interrelationships between objects. It is of great significance for the enterprises to optimize their operation mechanisms and resource allocation based on these evaluation results. • The normal cloud model is used to deal with multi-source information. • The bidirectional cloud projection measure is proposed to aggregate multi-source information with two reference points. • The Shapley function is used to obtain the optimal fuzzy measure and interaction relationships among attributes. • A multistage decision-making method based on two reference points is established. [ABSTRACT FROM AUTHOR]

• Fourteen factors were collected to establish a deformation risk assessment system. • DEA was used to analyze the efficiency of the factors affecting the deformation. • Water content was the most efficient factor in the deformation after DEA. • The deformation risk assessment model was provided using a normal cloud model. • The proposed model was verified by five typical soft rock tunnels. Deformation of the surrounding rock is an anticipated geological hazard when driving tunnels through soft rock. If deformation is not restricted, it can lead to serious engineering safety hazards. Therefore, studying the factors that affect the deformation of soft rock tunnels is highly significant. This study selected twelve main influencing factors involving subjective and objective factors and four actual indexes to establish a deformation risk assessment system for a soft rock tunnel. Data envelopment analysis (DEA) was innovatively utilized to analyze the efficiency of the twelve factors which affect the deformation of soft rock tunnels. The analytical results showed that the water content of the soft rock was the most efficient factor, followed by the tunnel depth, uniaxial compressive strength, excavation method, strength-stress ratio and groundwater seepage condition. A risk assessment model for the deformation of a soft rock tunnel was established using a normal cloud model and verified by the deformation occurred in typical soft rock tunnel cases. Risk assessment results confirmed that the proposed model was consistent with actual deformation risk degree. This model can guidie significance for the assessment of soft rock tunnel engineering. [ABSTRACT FROM AUTHOR]

For task allocation of mobile crowd sensing, aiming at the problem that the task cannot​ be completed normally due to the change of sensing state and the data quality is reduced because the sensor willingness is not satisfied, a task allocation method with active learning ability based on the normal cloud model is proposed. Firstly, the data quality, sensing environment and network state of sensors are evaluated, and the threshold is set according to the power of sensor and the total amount of data sent, and on the basis, the sensor sensing ability is monitored in real time. Then, the multi-granularity standard distribution cloud and sensor state cloud are established through normal cloud model when the sensing state changes. Furthermore, the willingness score of the sensor to the task is obtained by cosine method. Finally, according to the willingness list, tasks are allocated by the maximum sensor willingness and the minimum number of sensors. Simulation experiment verifies that the task can be allocated when the sensing state is changed, and the quality of sensing data is better. Besides, in the incentive effect, the actual proportion of sensing sensors and the average numbers of tasks completed by sensors are better, and the incentive budget is smaller when the same data quality is obtained. [ABSTRACT FROM AUTHOR]

Springs are a source of drinking water and a famous tourist attraction in Jinan, China. In this paper, a multi-index evaluation method was proposed based on a normal cloud model. This model is new graphic model, which could synthetically picture the randomness and fuzziness of concepts. Ten parameters were selected, and water quality was classified into five levels. Three numerical characteristics were calculated, and the weights were assigned by an integrated weighting algorithm. The uncertainty of each spring was calculated by a cloud generator and the integrated certainty grades of water quality were determined. To ensure the accuracy of the normal cloud model, the proposed method was used to assess the water quality of springs in Jinan, China. The results obtained by the proposed method were compared with that of the other four methods. The results obtained by different methods are highly consistent. The proposed cloud model-based method can reflect the water quality level and provides a practical guide for water quality evaluation, as demonstrated in Jinan springs. [ABSTRACT FROM AUTHOR]

Urban Large-scale Public Spaces (ULPS) are important areas of urban culture and economic development, which are also places of the potential safety hazard. ULPS safety assessment has played a crucial role in the theory and practice of urban sustainable development. The primary objective of this study is to explore the interaction between ULPS safety risk and its influencing factors. In the first stage, an index sensitivity analysis method was applied to calculate and identify the safety risk assessment index system. Next, a Delphi method and information entropy method were also applied to collect and calculate the weight of risk assessment indicators. In the second stage, a Dempster-Shafer Theory (DST) method with evidence fusion technique was utilized to analyze the interaction between the ULPS safety risk level and the multiple-index variables, measured by four observed performance indicators, i.e., environmental factor, human factor, equipment factor, and management factor. Finally, an empirical study of DST approach for ULPS safety performance analysis was presented.

The Artificial Bee Colony (ABC) algorithm is a bionic intelligent optimization method. The cloud model is a kind of uncertainty conversion model between a qualitative concept T ˜ that is presented by nature language and its quantitative expression, which integrates probability theory and the fuzzy mathematics. A developed ABC algorithm based on cloud model is proposed to enhance accuracy of the basic ABC algorithm and avoid getting trapped into local optima by introducing a new select mechanism, replacing the onlooker bees’ search formula and changing the scout bees’ updating formula. Experiments on CEC15 show that the new algorithm has a faster convergence speed and higher accuracy than the basic ABC and some cloud model based ABC variants. [ABSTRACT FROM AUTHOR]