Normal Cloud Research Articles

Multiobjective Optimization of Cloud Manufacturing Service Composition with Improved Particle Swarm Optimization Algorithm

Aiming at the problems of low search efficiency and inaccurate optimization of existing service composition optimization methods, a new multiobjective optimization model of cloud manufacturing service composition was constructed, which took service matching degree, composition synergy degree, cloud entropy, execution time, and execution cost as optimization objectives, and an improved particle swarm optimization algorithm (IPSOA) was proposed. In the IPSOA, the integer encoding method was used for particle encoding. The inertia coefficient and two acceleration coefficients were improved by introducing the normal cloud model, sine function, and cosine function. The global search ability of IPSOA in the early stage was improved, and its prematurity was restrained to form a more comprehensive solution space. In the later stage, IPSOA focused on the local fine search and improved the optimization precision. Taking automatic guided forklift manufacturing task as an example, the correctness of the proposed multiobjective optimization model of cloud manufacturing service composition and the effectiveness of its solution algorithm were verified. The performance of IPSOA was analyzed and compared with standard genetic algorithm (SGA) and traditional particle swarm optimization (PSO). Under the same conditions, IPSOA had a faster convergence speed than PSO and SGA and had better performance than PSO.

A novel classification approach based on integrated connection cloud model and game theory

The classification of problems under uncertainty environments involves the precise description and weighting of fuzzy and random indicators. Although the one-dimensional normal cloud model can effectively handle the fuzziness and randomness of the indicator in an infinite interval, it may ignore the changing tendency of the rating boundary, and become complex with the increment of indicator and sample numbers. To overcome above shortcomings, a novel classification based on the integrated connection cloud model and game theory was proposed here to depict fuzzy indicators randomly distributing in finite intervals and simultaneously embody the importance and inherent information of indicators in harmony. Namely, the connection numbers theory and the game theory based combination weighting method were first utilized to aggregate the connection cloud model of the individual indicator. Next, integrated connection clouds were simulated to express the interval-valued classification standards, and the connection degrees were calculated to identify the rank from the identity, discrepancy, and contrary relationships. Finally, the validity and feasibility of the approach proposed here were further verified by example application to the classification of surrounding rock stability and comparisons with other methods. Results indicate that the integrated cloud model based classification approach presents a precise description of fuzzy indicators distributing randomly in the asymmetrical intervals and better computational efficiency relative to the classification method using the one-dimensional cloud model. It also minimizes errors caused by the multiplication singularity point, human subjectivity and neglect of the intrinsic information of indicators.

Application of Cloud Model in Qualitative Forecasting for Stock Market Trends.

Forecasting stock prices plays an important role in setting a trading strategy or determining the appropriate timing for buying or selling a stock. The use of technical analysis for financial forecasting has been successfully employed by many researchers. The existing qualitative based methods developed based on fuzzy reasoning techniques cannot describe the data comprehensively, which has greatly limited the objectivity of fuzzy time series in uncertain data forecasting. Extended fuzzy sets (e.g., fuzzy probabilistic set) study the fuzziness of the membership grade to a concept. The cloud model, based on probability measure space, automatically produces random membership grades of a concept through a cloud generator. In this paper, a cloud model-based approach was proposed to confirm accurate stock based on Japanese candlestick. By incorporating probability statistics and fuzzy set theories, the cloud model can aid the required transformation between the qualitative concepts and quantitative data. The degree of certainty associated with candlestick patterns can be calculated through repeated assessments by employing the normal cloud model. The hybrid weighting method comprising the fuzzy time series, and Heikin–Ashi candlestick was employed for determining the weights of the indicators in the multi-criteria decision-making process. Fuzzy membership functions are constructed by the cloud model to deal effectively with uncertainty and vagueness of the stock historical data with the aim to predict the next open, high, low, and close prices for the stock. The experimental results prove the feasibility and high forecasting accuracy of the proposed model.

A static risk assessment model for underwater shield tunnel construction

The shield method is widely used in underwater tunnel construction. However, the shield construction process faces many uncertain risk factors that increase the level of safety risk during shield tunnel construction. Therefore, risk assessment has become a necessary task in the early stages of tunnel construction. In this study, a new risk assessment model for underwater shield tunnel construction is proposed that combines a normal cloud model with an entropy weight method. The model contains 20 assessment indexes and gives the range of each index corresponding to different risk levels, which comprehensively reflects the influencing factors of risk. By using a normal cloud model, the fuzziness and randomness of risk assessment data are taken into account effectively, and the reliability of assessment results is increased. Based on an entropy weight method, the rules and characteristics of the evaluation data in the model are considered, and the weight coefficient of the evaluation index is determined to avoid the subjectivity of the expert weight method. The risk assessment model is applied using the Xiangjiang shield tunnel as an example. The results show that the overall construction risk level of the Xiangjiang tunnel is level II, which is consistent with the site risk situation and shows that the model can objectively and accurately evaluate the construction risk level of an underwater shield tunnel.

The active learning multi-task allocation method in mobile crowd sensing based on normal cloud model

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.

Urban rail transit operation safety evaluation based on an improved CRITIC method and cloud model

Urban rail transit operation safety based on a cloud model and improved Criteria Importance Through Intercriteria Correlation (CRITIC) method was evaluated, considering both existing expertise and uncertainties such as fuzziness and randomness of such evaluation. A two-level index system was constructed to evaluate urban rail transit operation safety. The evaluation scale was then chosen. The cloud model index system to obtain the evaluation set and cloud was determined. An improved CRITIC method was adopted to specify the weights of all indices. The normal cloud model for the comprehensive index cloud and evaluation set was measured next. The rationale and validity of the model were verified, and the results noted. The index system could indeed be scientifically, systematically and objectively evaluated based on a cloud model and improved CRITIC method. The evaluation results agreed well with practical results. This study provides a reference and theoretical basis for the operational safety planning and management for urban rail transit.

Dual Linguistic Term Set and Its Application Based on the Normal Cloud Model

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.

Deep feature-preserving normal estimation for point cloud filtering

Point cloud filtering, the main bottleneck of which is removing noise (outliers) while preserving geometric features, is a fundamental problem in 3D field. The two-step schemes involving normal estimation and position update have been shown to produce promising results. Nevertheless, the current normal estimation methods including optimization ones and deep learning ones, often either have limited automation or cannot preserve sharp features. In this paper, we propose a novel feature-preserving normal estimation method for point cloud filtering with preserving geometric features. It is a learning method and thus achieves automatic prediction for normals. For training phase, we first generate patch based samples which are then fed to a classification network to classify feature and non-feature points. We finally train the samples of feature and non-feature points separately, to achieve decent results. Regarding testing, given a noisy point cloud, its normals can be automatically estimated. For further point cloud filtering, we iterate the above normal estimation and a current position update algorithm for a few times. Various experiments demonstrate that our method outperforms state-of-the-art normal estimation methods and point cloud filtering techniques, in terms of both quality and quantity.

Groundwater assessment based on analytic hierarchy process and the normal cloud model in Weinan, China

Groundwater quality is facing a grim challenge due to the development of Weinan city. A multi-index comprehensive evaluation method—analytic hierarchy process and normal cloud model are proposed to evaluate the groundwater quality of Weinan city. Seven parameters including TH (total hardness), TDS (total dissolved solids), SO4 2-, Cl−, COD, NO2 −, Mn were selected to characterize the groundwater quality. The results show that the groundwater quality level of W6 and B557 are excellent water quality (I), H32, W15, W23, and W14 are good water quality (II), only B24 is medium or average water quality (III). Compared with other methods, the results obtained by the proposed method are in good agreement with the NI method but far from SFI method. This study confirms the suitability of the Analytic hierarchy process and Normal cloud model method in groundwater quality evaluation in Weinan city, and provides a reasonable basis for decision-makers of groundwater protection and utilization in Weinan city.

A Normal Cloud Model-Based Method for Risk Assessment of Water Inrush and Its Application in a Super-Long Tunnel Constructed by a Tunnel Boring Machine in the Arid Area of Northwest China

Tunnel water inrush is complex, fuzzy, and random, and it is affected by many factors, such as hydrology, geology, and construction. However, few papers have considered the impact of dynamic monitoring on water inrush in previous research. In this study, considering geological, hydrological, and construction factors, as well as dynamic monitoring, a new multi-index evaluation method is proposed to analyze the risk of tunnel water inrush based on the normal cloud model. A new weight algorithm combining analytic hierarchy process and entropy method is used to calculate the index weight. The certainty degree of each evaluation index belonging to the corresponding cloud can be obtained by the cloud model theory. The final level of tunnel water inrush is determined via the synthetic certainty degree. The proposed method is applied to analyze the risk of water inrush in the SS (Shuang-san) tunnel constructed by a tunnel boring machine in the arid area of Northwest China. The evaluation results are not only basically identical to the results calculated by the ideal point and gray relation projection methods, but also agree well with the actual excavation results. This demonstrates that this new risk assessment method of water inrush has high accuracy and feasibility. Simultaneously, it also provides a new research idea to analyze the probability of tunnel water inrush and can provide a reference for related projects.

The Short-Term Economical Operation Problem for Hydropower Station Using Chaotic Normal Cloud Model Based Discrete Shuffled Frog Leaping Algorithm

The short-term economical operation (STEO) in hydropower station is nonlinear mixed integer problem, satisfying complex hydrologic constraints simultaneously. In this paper, we decomposed STEO into unit commitment (UC) and economical load dispatch (ELD) sub-problems. In terms of premature convergence and inefficient search performance of conventional shuffled frog leaping algorithm (SFLA), we proposed chaotic normal cloud model based discrete SFLA (CNCM-DSFLA). The CNCM-DSFLA incorporates novel population initialization based on renewed chaotic logistic mapping, three frog sub-populations including leader frog, follower frog and mutation frog, heuristic frog activation mechanism and frog mutation based on normal cloud model. The CNCM-DSFLA is applied to solve UC sub-problem and ELD sub-problem is handled based on optimal economic load distribution table. Moreover, reserve capacity supplement and repair, and minimum startup and shutdown repair strategies are introduced to deal with multiple hydrologic and electrical constraints. Finally, CNCM-DSFLA is verified by a case in Three Gorges hydropower station. Simulation results demonstrate that CNCM-DSFLA gets higher-quality solutions with less total water consumption and shorter computation time in comparison with other methods. Thus, validity and superiority of CNCM-DSFLA are verified and it can provide novel effective way for solving STEO problem in complex hydropower station system.

Research and Development of Cloud Manufacturing Service Composition System Based on Intelligent Optimal Algorithm

For solving the problems of manufacturing resource allocation and industrial structure, an Intelligent Cloud Manufacturing Service Composition (ICMSC) method is proposed with cloud-entropy-genetic-algorithm. The development platform hierarchical construction and the development tools application are studied, and a CMSC system is developed. The traditional genetic operators are improved by using the normal cloud model. The improved cloud-entropy-genetic-algorithm combined with business intelligence is applied to the service composition and optimization module of CMSC system. Taking the CMSC of medical oxygen inhaler as an example, service selection, composition and optimization are carried out in CMSC system. The results show that it is feasible to apply the ICMSC method based on cloud-entropy-genetic-algorithm to CMSC system.

Multiobjective Long-Term Generation Scheduling of Cascade Hydroelectricity System Using a Quantum-Behaved Particle Swarm Optimization Based on Decomposition

Multiobjective long-term generation scheduling (MOLTGS) plays a vital role in coordinating the contradiction between the generation and reliability of cascade hydroelectricity system (CHS). In this paper, a multiobjective quantum-behaved particle swarm optimization based on decomposition (MOQPSO/D) is presented to solve the MOLTGS problem of maximizing total hydroelectricity generation and firm hydroelectricity output. In MOQPSO/D, the improved logistic map is adopted to initialize the population in the feasible space, where various equality and inequality constraints are handled by a constraint handling method based on conversion and repair strategies. An improved Tchebycheff decomposition with a modified generator of direction vectors, a modified QPSO operator and normal cloud mutation are the key components of MOQPSO/D in addressing the nonlinearity and complexity of MOLTGS. The feasibility and effectiveness of MOQPSO/D are verified first on numerical experiments of benchmark instances and then on real engineering examples of the Three Gorges (TG) and Gezhouba (GZB) plants within three representative years. The results show that MOQPSO/D can not only perform better robustness, convergence and diversity performance than other seven competitors but also output a group of Pareto optimal schemes within a reasonable amount of time. So, this paper provides a new effective alternative to solve the MOLTGS problem of CHS when considering both generation and reliability objectives.

Risk-Based Multi-Attribute Decision-Making for Normal Cloud Model Considering Pre-Evaluation Information

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.

Load response potential evaluation for distribution networks: A hybrid decision-making model with intuitionistic normal cloud and unknown weight information

Load response potential evaluation of distribution networks is crucial in accelerating cooperative optimization of transmission and distribution networks, but the related research is comparatively rare. The purpose of this study is to establish a hybrid decision-making model considering unknown weight information and decision-makers’ psychological behavior to solve load response potential evaluation problems under uncertain environment. Firstly, a comprehensive evaluation attribute system with qualitative and quantitative nature is established. Secondly, intuitionistic normal clouds are utilized to depict linguistic evaluation, which avoids information distortion. Thirdly, a distance measure for intuitionistic normal clouds considering cloud droplets’ distribution characteristics is defined to compute grey relational degree of decision-making information with ideal information, thereby determining decision-makers’ dynamic weights. Furthermore, indifference threshold-based attribute ratio analysis is introduced to derive attribute weights. TODIM (an acronym in Portuguese for interactive and multicriteria decision-making) is extended to intuitionistic normal cloud environment to rank-order alternatives, which makes the framework more applicable for the actual evaluation. The case study shows that the proposed model provides more reasonable results, and the optimal and suboptimal alternatives are sensitive to the attenuation parameter, suggesting that loss avoidance behavior should deserve attention. This research has practical applied value for dispatchers in power grid dispatching management.

Mobile Cloud Data Privacy using Lightweight Data Sharing System

The data security problem in mobile cloud becomes more and more severe and it prevents further development of mobile cloud. There are substantial studies that have been conducted to improve the cloud security. However the most of them are not applicable for mobile cloud since mobile devices only have limited computing resources and power. So we propose a light weight data sharing scheme (LDSS) for mobile computing it adopts CP-ABE (Cipher text policy attribute based encryption) an access control technology using normal cloud environment but changes a structure of access control tree to make suitable for mobile cloud environment. It is important to use the resources provided by Cloud Service Provide to store and share data. Thus LDSS can effectively reduce the over head on the mobile device side when users are sharing a data in mobile cloud environment..

Comprehensive risk assessment and engineering application of mine water inrush based on normal cloud model and local variable weight

ABSTRACT Mine water inrush disaster occurs after the critical risk degree is reached under the non-linear dynamic coupling action of hydrogeological conditions, water hazard sources and excavation disturbance of artificial machinery. In this paper, the risk assessment unit is divided from the time and space dimensions, and the risk assessment evaluation index set is determined based on the “4M1E” management system, taking into account the fuzziness, dynamics, and balance of the evaluation indicators. This method changes the defect of the index weight in the previous comprehensive evaluation method as the constant weight. The game theory set model is selected to construct the initial weight vector of the evaluation index, and the final weight vector is determined by the local variable weight equalization function combined with the indicator state value. The normal cloud model is used to process the evaluation indicator status values, and the comprehensive evaluation matrix is determined to solve the ambiguity and randomness of the evaluation indicators. Finally, a comprehensive assessment model for mine water inrush disasters is formed, and a dynamic assessment framework is used to conduct dynamic risk assessment of mine water inrush. The research results show that the dynamic comprehensive evaluation model provides a theoretical basis for the early warning and prevention of mine water inrush disasters.

A novel learning cloud Bayesian network for risk measurement

Bayesian network (BN) is a popularly used approach for risk analysis. Because it is a graphic model being able to deal with randomness yet unable to model ambiguity, the fuzzy set theory is often combined with it to create a so-called fuzzy BN. Instead of using the classical fuzzy set theory, this paper intends to combine a normal Cloud model with the BN. In the normal Cloud model, an element belonging to a certain qualitative concept is not certain and precise as well. The Cloud BN is a generalization of the fuzzy BN. It is more adaptive for the uncertainty description of linguistic concepts, for example, the risks. Using the normal Cloud model, the following numerical characteristics of the variables can be estimated: the expectation, the dispersion degree compared with the expectation, and the dispersion degree of entropy. Consequently, the risk assessment contains a richer set of analytical information. Cloud BNs attract growing research interests. Compared to its precedents, the Cloud BN in this paper has a learning capability. Since the risk factors may have a combined effect, the causal relationships among the variables can be very complex, and hidden variables may exist. The learning mechanism allows for automatic structure discovery from data, giving rise to a dynamically evolving network. The proposed learning Cloud BN is able to represent the real risk situation better than its precedents. Its effectiveness and applicability are demonstrated by an illustrative case for risk prediction of the face instability in an underground tunnel construction project.

Safety evaluation and risk reasoning of metro station based on IFPN

In order to realize the accurate evaluation of the safety status of metro station and the high-efficiency identification of the risk cause, an improved fuzzy petri net is introduced to describe the safety risk propagation process of the metro station. The normal gray cloud whiten weight model combined with whitening weight function in gray clustering system and cloud theory is introduced, and the uncertainty of the initial token of place is comprehensively described. The Apriori algorithm is used to extract the transition confidence and weight according to the strong association rules between places, and an IFPN analysis model to support the evolution of the safety state of metro station is established. Through the forward reasoning, the interpretation of the risk propagation path is realized, and the safety status of the metro station is solved. Through the reverse reasoning, the risk is identified and the risk place that has the greatest impact on the safety status of metro station is investigated. Finally, the model is reasoned with an example to verify the effectiveness of the evaluation method.

A new quantitative method for risk assessment of water inrush in karst tunnels based on variable weight function and improved cloud model

Water inrush disaster seriously affects the safety of karst tunnel construction. It’s essential to assess the risk level of water inrush in karst tunnels accurately, and take some effective countermeasures to reduce the damage to the project. We integrates the variable weight theory and cloud model theory to construct the VW&ICM calculation model to evaluate the risk of construction of karst tunnels. First, we select the index factors that affect the risk of water inrush from karst tunnels. Secondly, according to the theory of variable weight, we construct the zoning variable weight model, and the normalization criterion of the data from index factors is established. Thirdly, according to the attribute mathematic theory, we select the numerical characteristics of the improved cloud model, and the normal cloud generator is used to establish the point membership function. Finally, the risk index data is brought into the variable weight vector function and the point membership function to obtain the variable weight vector W and the membership degree matrix R. The risk overall evaluation vector B is further calculated, and the membership degree fluctuation is comprehensively analyzed according to the principle of maximum membership degree. The scope determines the risk of water inrush from karst tunnels. Finally, calculate the variable weight vector W and the membership matrix R, and the overall risk evaluation vector B is further integrated by W and R. According to the max-subjection principle and the fluctuation rule of membership, we get the risk level of water inrush in karst tunnel. The VM&ICM not only can quantitatively evaluate risk by considering the uncertainty of risk assessment and the influence of indicator size on weight, but also can make an analysis of the reliability to make the assessment result more convincing. The model makes improvement in weakening the influence of subjective factors on assessment results and in allocating the weight of indicators. A simple and practical software package is developed, which greatly improves the computational efficiency of VW&ICM method. The VW&ICM calculation model is applied to the risk assessment of water inrush for karst tunnels, and the results are basically consistent with the on-site construction situation.