The Cloud Model for Climate Change

In 1995, the Intergovernmental Panel on Climate Change (IPCC) released a thermodynamic model based on the Greenhouse Effect, aiming to forecast global temperatures. This study delves into the intricacies of that model. Some interesting observations are revealed. The IPCC model equated average temperatures with average energy fluxes, which can cause significant errors. The model assumed that all energy fluxes remained constant, and the Earth emitted infrared radiation as if it were a blackbody. Neither of those conditions exists. The IPCC’s definition of Climate Change only includes events caused by human actions, excluding most causes. Satellite data aimed at the tops of clouds may have inferred a high Greenhouse Gas absorption flux. The model showed more energy coming from the atmosphere than absorbed from the sun, which may have caused a violation of the First and Second Laws of Thermodynamics. There were unexpectedly large gaps in the satellite data that aligned with various absorption bands of Greenhouse Gases, possibly caused by photon scattering associated with re-emissions. Based on science, we developed a cloud-based climate model that complied with the Radiation Laws and the First and Second Laws of Thermodynamics. The Cloud Model showed that 81.3% of the outgoing reflected and infrared radiation was applicable to the clouds and water vapor. In comparison, the involvement of CO2 was only 0.04%, making it too minuscule to measure reliably.

Evaluation of toppling rock slopes using a composite cloud model with DEMATEL–CRITIC method

Safety evaluation of toppling rock slopes developing in reservoir areas is crucial. To reduce the uncertainty of safety evaluation, this study developed a composite cloud model, which improved the combination weights of the decision-making trial and evaluation laboratory (DEMATEL) and criteria importance through intercriteria correlation (CRITIC) methods. A safety evaluation system was developed according to in situ monitoring data. The backward cloud generator was used to calculate the numerical characteristics of a cloud model of quantitative indices, and different virtual clouds were used to synthesize some clouds into a generalized one. The synthesized numerical characteristics were calculated to comprehensively evaluate the safety of toppling rock slopes. A case study of a toppling rock slope near the Huangdeng Hydropower Station in China was conducted using monitoring data collected since operation of the hydropower project began. The results indicated that the toppling rock slope was moderately safe with a low safety margin. The composite cloud model considers the fuzziness and randomness of safety evaluation and enables interchange between qualitative and quantitative knowledge. This study provides a new theoretical method for evaluating the safety of toppling rock slopes. It can aid in the predication, control, and even prevention of disasters.

Vulnerability assessment of coastal wetlands in Minjiang River Estuary based on cloud model under sea level rise

The change of coastal wetland vulnerability affects the ecological environment and the economic development of the estuary area.In the past,most of the assessment studies on the vulnerability of coastal ecosystems stayed in static qualitative research,lacking predictability,and the qualitative and quantitative relationship was not objective enough.In this study,the“Source-Pathway-Receptor-Consequence”model and the Intergovernmental Panel on Climate Change vulnerability definition were used to analyze the main impact of sea level rise caused by climate change on coastal wetland ecosystem in Minjiang River Estuary.The results show that:(1)With the increase of time and carbon emission,the area of high vulnerability and the higher vulnerability increased continuously,and the area of low vulnerability and the lower vulnerability decreased.(2)The eastern and northeastern part of the Culu Island in the Minjiang River Estuary of Fujian Province and the eastern coastal wetland of Meihua Town in Changle District are areas with high vulnerability risk.The area of high vulnerability area of coastal wetland under high emission scenario is wider than that under low emission scenario.(3)Under different sea level rise scenarios,elevation has the greatest impact on the vulnerability of coastal wetlands,and slope has less impact.The impact of sea level rise caused by climate change on the coastal wetland ecosystem in the Minjiang River Estuary is mainly manifested in the sea level rise,which changes the habitat elevation and daily flooding time of coastal wetlands,and then affects the survival and distribution of coastal wetland ecosystems.

Temporal and spatial distribution characteristics of water resources in Guangdong Province based on a cloud model

With a focus on the difficulty of quantitatively describing the degree of nonuniformity of temporal and spatial distributions of water resources, quantitative research was carried out on the temporal and spatial distribution characteristics of water resources in Guangdong Province from 1956 to 2000 based on a cloud model. The spatial variation of the temporal distribution characteristics and the temporal variation of the spatial distribution characteristics were both analyzed. In addition, the relationships between the numerical characteristics of the cloud model of temporal and spatial distributions of water resources and precipitation were also studied. The results show that, using a cloud model, it is possible to intuitively describe the temporal and spatial distribution characteristics of water resources in cloud images. Water resources in Guangdong Province and their temporal and spatial distribution characteristics are differentiated by their geographic locations. Downstream and coastal areas have a larger amount of water resources with greater uniformity and stronger stability in terms of temporal distribution. Regions with more precipitation possess larger amounts of water resources, and years with more precipitation show greater nonuniformity in the spatial distribution of water resources. The correlation between the nonuniformity of the temporal distribution and local precipitation is small, and no correlation is found between the stability of the nonuniformity of the temporal and spatial distributions of water resources and precipitation. The amount of water resources in Guangdong Province shows an increasing trend from 1956 to 2000, the nonuniformity of the spatial distribution of water resources declines, and the stability of the nonuniformity of the spatial distribution of water resources is enhanced.

Risk management for mine closure: A cloud model and hybrid semi-quantitative decision method

Mine closure is associated with many negative impacts on society and the environment.If these effects are not rationally addressed,they would pose risks of mine closure.Thus,a risk management method is needed to mitigate these adverse impacts and address mine-closure issues.An integral framework for mine-closure risk management that includes risk assessment and risk treatment was proposed.Given the fuzziness and randomness of the transformation between qualitative and quantitative knowledge in the risk assessment process,a novel risk assessment method based on the cloud model was presented,which fully considers the uncertainty in risks themselves and in the reasoning process.Closed mine reutilization is an effective risk treatment option in response to the identified high risks,but it requires selecting optimal reutilization strategies for the successful implementation of the reuse plan.To this end,a hybrid semi-quantitative decision method is proposed to optimize decision-making.The results of a case study showed that this risk management methodology can help budget planning for risk treatment and provide an instructional framework to effectively reduce the negative effects of closed mines.

Synchro-control of Twin-rudder with Cloud Model

In ships having two rudders, an angle error exists if there is a difference in structural and electrical parameters in two steering gear systems. Such an error also results in reduced efficiency of ship maneuverability during navigation. For the sake of reducing the angle error, a synchro-ballistic control approach based on cloud model is proposed in this paper. First, the mechanism model of steering gear system is introduced. Second, the structure of synchro-control system of twin-rudder is proposed based on the master-slave control strategy. Third, synchro-ballistic controller based on cloud model is designed to solve the nonlinearity and uncertainty of system. Finally, the designed controller is tested via simulation under two different situations. The simulated results demonstrate that this method is simple and has stronger robustness against the variation of states and parameters of plants. Hence, the validity and reliability of the method is proved for synchro-control of two rudders, which is a significant engineering application.

Comprehensive evaluation of 5G+smart distribution network based on combined weighting method-cloud model

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(FAHP-EWM).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.

User’s Relevance of PIR System Based on Cloud Models

A new method to evaluate fuzzily user＇s relevance on the basis of cloud models has been proposed. All factors of personalized information retrieval system are taken into account in this method. So using this method for personalized information retrieval （PIR） system can efficiently judge multi-value relevance, such as quite relevant, comparatively relevant, commonly relevant, basically relevant and completely non-relevant, and realize a kind of transform of qualitative concepts and quantity and improve accuracy of relevance judgements in PIR system. Experimental data showed that the method is practical and valid. Evaluation results are more accurate and approach to the fact better.

Cumulus cloud modeling from images based on VAE-GAN

Background Cumulus clouds are important elements in creating virtual outdoor scenes.Modeling cumulus clouds that have a specific shape is difficult owing to the fluid nature of the cloud.Image-based modeling is an efficient method to solve this problem.Because of the complexity of cloud shapes,the task of modeling the cloud from a single image remains in the development phase.Methods In this study,a deep learning-based method was developed to address the problem of modeling 3D cumulus clouds from a single image.The method employs a three-dimensional autoencoder network that combines the variational autoencoder and the generative adversarial network.First,a 3D cloud shape is mapped into a unique hidden space using the proposed autoencoder.Then,the parameters of the decoder are fixed.A shape reconstruction network is proposed for use instead of the encoder part,and it is trained with rendered images.To train the presented models,we constructed a 3D cumulus dataset that included 2003D cumulus models.These cumulus clouds were rendered under different lighting parameters.Results The qualitative experiments showed that the proposed autoencoder method can learn more structural details of 3D cumulus shapes than existing approaches.Furthermore,some modeling experiments on rendering images demonstrated the effectiveness of the reconstruction model.Conclusion The proposed autoencoder network learns the latent space of 3D cumulus cloud shapes.The presented reconstruction architecture models a cloud from a single image.Experiments demonstrated the effectiveness of the two models.

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

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

Some Physical Aspects of Summer Monsoon Clouds-Comparison of Cloud Model Results with Observations

The physical characteristics of the summer monsoon clouds were investigated. The results of a simple cloud model were compared with the aircraft cloud physical observations collected during the summer monsoon seasons of 1973, 1974, 1976 and 1981 in the Deccan Plateau region.The model predicted profiles of cloud liquid water content (LWC) are in agreement with the observed profiles. There is reasonable agreement between the model predicted cloud vertical thickness and observed rainfall.The observed cloud-drop spectra were found to be narrow and the concentration of drops with diameter > 20um is either low or absent on many occasions. In such clouds the rain-formation cannot take place under natural atmospheric conditions due to the absence of collision-coalescence process. A comparison of the model predicted and observed rainfall suggested that the precipitation efficiency in cumulus clouds of small vertical thickness could be as low as 20 per cent.The clouds forming in the Deccan Plateau region during the summer monsoon are, by and large, cumulus and strato-cumulus type. The vertical thickness of the cumulus clouds is in the range of 1.0-2.0 km. The LWC is found to be more in the region between 1.6-1.9 km A. S. L. , which corresponds to the level at almost 3 / 4 th of the total vertical thickness of the cloud and thereafter the LWC sharply decreased. Nearly 98 per cent of the tops of the low clouds in the region are below freezing level and the most frequent range of occurrence of these cloud-tops is in the range of 2.0-3.0 km A. S. L. The dominant physical mechanism of rain-formation in these summer monsoon clouds is the collision-coalescence process.

基于Cloud Model的大学生毕业质量评分模型的研究

毕业生质量的评价模型应该是动态的、分层次的、多属性的,这样才能给高校教学工作者和管理层提供更加全面和及时的反馈信息,从而更好的推动高等学校人才的培养。本文在已有的评测模型的基础上,引入相对全面的评价指标体系,对测试过程进行系统开发,最后以某届毕业生的评测过程为例,以验证评价模型和系统的正确性、稳定性。

Ensemble Cloud Model Application in Simulating the Catastrophic Heavy Rainfall Event

An attempt has been made in the present research to simulate a deadly flash-flood event over the City of Skopje,Macedonia on 6 August 2016.A cloud model ensemble forecast method is developed to simulate a super-cell storm’s initiation and evolutionary features.Sounding data are generated using an ensemble approach,that utilizes a triple-nested WRF model.A three-dimensional(3-D)convective cloud model(CCM)with a very fine horizontal grid resolution of 250-m is initialized,using the initial representative sounding data,derived from the WRF 1-km forecast outputs.CCM is configured and run with an open lateral boundary conditions LBC,allowing explicit simulation of convective scale processes.This preliminary study showed that the ensemble approach has some advantages in the generation of the initial data and the model initialization.The applied method minimizes the uncertainties and provides a more qualitative-quantitative assessment of super-cell storm initiation,cell structure,evolutionary properties,and intensity.A high-resolution 3-D run is capable to resolve detailed aspects of convection,including high-intensity convective precipitation.The results are significant not only from the aspect of the cloud model’s ability to provide a qualitative-quantitative assessment of intense precipitation but also for a deeper understanding of the essence of storm development,its vortex dynamics,and the meaning of micro-physical processes for the production and release of large amounts of precipitation that were the cause of the catastrophic flood in an urban area.After a series of experiments and verification,such a system could be a reliable tool in weather services for very short-range forecasting(now-casting)and early warning of weather disasters.

Mining Weights of Land Evaluation Factors Based on Cloud Model and Correlation Analysis

The veracity of land evaluation is tightly related to the reasonable weights of land evaluation fac- tors. By mapping qualitative linguistic words into a fine-changeable cloud drops and translating the uncertain factor conditions into quantitative values with the uncertain illation based on cloud model, and then, inte- grating correlation analysis, a new way of figuring out the weight of land evaluation factors is proposed. It may solve the limitations of the conventional ways.

Application of uncertainty reasoning based on cloud model in time series prediction

Time series prediction has been successfully used in several application areas, such as meteoro-logical forecasting, market prediction, network traffic forecasting, etc. , and a number of techniques have been developed for modeling and predicting time series. In the traditional exponential smoothing method, a fixed weight is assigned to data history, and the trend changes of time series are ignored. In this paper, an uncertainty reasoning method, based on cloud model, is employed in time series prediction, which uses cloud logic controller to adjust the smoothing coefficient of the simple exponential smoothing method dynamically to fit the current trend of the time series. The validity of this solution was proved by experiments on various data sets.

Mining multilevel spatial association rules with cloud models

The traditional generalization-based knowledge discovery method is introduced. A new kind of multilevel spatial association of the rules mining method based on the cloud model is presented. The cloud model integrates the vague and random use of linguistic terms in a unified way. With these models, spatial and nonspatial attribute values are well generalized at multiple levels, allowing discovery of strong spatial association rules. Combining the cloud model based method with Apriori algorithms for mining association rules from a spatial database shows benefits in being effective and flexible.

An Evaluation Approach of Subjective Trust Based on Cloud Model

As online trade and interactions on the internet are on the rise, a key issue is how to use simple and effective evaluation methods to accomplish trust decision-making for customers. It is well known that subjective trust holds uncertainty like randomness and fuzziness. However, existing approaches which are commonly based on probability or fuzzy set theory can not attach enough importance to uncertainty. To remedy this problem, a new quantifiable subjective trust evaluation approach is proposed based on the cloud model. Subjective trust is modeled with cloud model in the evaluation approach, and expected value and hyper-entropy of the subjective cloud is used to evaluate the reputation of trust objects. Our experimental data shows that the method can effectively support subjective trust decisions and provide a helpful exploitation for subjective trust evaluation.

Study on the Universality of the Normal Cloud Model

The distribution function is an important tool in the study of the stochastic variances. The normal distribution is very popular in the nature and our society. The idea of membership functions is the foundation of the fuzzy sets theory. While the fuzzy theory is widely used, the completely certain membership function that has no any fuzziness at all has been the bottleneck of the applications of this theory. Cloud models are the effective tools in transforming between qualitative concepts and their quantitative expressions. It can represent the fuzziness and randomness and their relations of uncertain concepts. Also cloud models can show the concept granularity in multi-scale spaces by the digital characteristic Entropy (En). The normal cloud model not only broadens the form conditions of the normal distribution but also makes the normal membership function be the expectation of the random membership degree. In this paper, the universality of the normal cloud model is proved, which is more superior and easier, and can fit the fuzziness and gentleness of human cognitive processing. It would be more applicable and universal in the representation of uncertain notions.

Risk transmission evaluation for parallel construction of warships based on IFCM and the cloud model

To cope with multi-directional transmission coupling,spreading, amplification, and chain reaction of risks during multiproject parallel construction of warships, a risk transmission evaluation method is proposed, which integrates an intuitionistic cloud model with a fuzzy cognitive map. By virtue of expectancy Ex, entropy En, and hyper entropy He, the risk fuzziness and randomness of the knowledge of experts are organically combined to develop a method for converting bi-linguistic variable decision-making information into the quantitative information of the intuitionistic normal cloud(INC) model. Subsequently, the threshold function and weighted summation operation in the traditional fuzzy cognitive map is converted into the INC ordered weighted averaging operator to create the risk transmission model based on the intuitionistic fuzzy cognitive map(IFCM) and the algorithm for solving it. Subsequently, the risk influence sequencing method based on INC and the risk rating method based on nearness are proposed on the basis of Monte Carlo simulation in order to realize the mutual conversion of the qualitative and quantitative information in the risk evaluation results.Example analysis is presented to verify the effectiveness and practicality of the methods.