Cloud Model Research Articles

Design concept evaluation based on cloud rough model and modified AHP-VIKOR: An application to lithography tool manufacturing process

Design concept evaluation is a wide domain of research as the production process of new product development is mainly based on the evaluations of experts. As different types of uncertainties like vagueness, randomness, and diversity essentially exist in the evaluation information, the manipulation models that deal with just one type of uncertainty are not appropriate for ranking the design alternatives. The integration of multiple approaches into a single mathematical model to create an effective framework is more useful for uncertain information manipulation. This paper presents an innovative group decision-making approach to evaluate design concepts by integrating cloud rough numbers, analytic hierarchy process (AHP) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method to introduce a novel cloud rough AHP-VIKOR model. The concept of cloud rough numbers is utilized by fusing the merits of the cloud model theory and rough approximations in addressing randomness in experts’ judgments and intrapersonal uncertainty for handling consensus inconsistency. Some new modified algebraic operations are introduced to remove the errors in already existing operations of cloud rough numbers. The cloud rough AHP technique is introduced to compute weights of design criteria by computing the cloud rough numbers of linguistic values and their reciprocals. Based on cloud rough values of positive and negative ideals, the individual regret, group utility values and evaluation indices are determined. Using the distance of evaluation indices from minimized cloud rough value, the design alternatives are ranked using different formulae. The significance of the proposed approach is illustrated with a real-world application in lithography tool manufacturing process. The out-performance and reliability of the proposed model is studied by a sensitivity analysis of different parameters in the method, and a comparison analysis with existing approaches.

3D modeling and volume measurement of bulk grains stored in large warehouses using bi-temporal multi-site terrestrial laser scanning data

Terrestrial laser scanning (TLS) is a promising technology for quantity checking huge grain stocks with low cost, light workload and high efficiency. Existing applications of TLS in bulk grain measurement and quantification lack the ability to capture complete structural information of grain bulks and thus will result in large errors. In this paper, we propose a bi-temporal TLS scheme for fast 3D modeling and accurate volume measurement of bulk grains stored in large warehouses. The scheme uses bi-temporal multi-site TLS datasets acquired under both empty and full or high loading conditions to obtain complete surface information about grain bulk’s structure. In order for a grain bulk’s all external surfaces and the 3D volumetric model reconstructed therefrom to be automatically derived from the bi-temporal TLS dataset, several dedicated methods are developed for the scheme. A fully automated marker-free strategy exploring structurally semantic information inherent in the large grain storehouses is adopted to register multi-scan TLS point cloud data captured in large-scale granary scenes. Also, a local minima-based region growing technique is devised to extract underlying surfaces from a granary scene point cloud model. Experiments show that the proposed 3D modeling and volume measurement scheme can work effectively and quickly in TLS-based granary field applications and repeated test data demonstrate its correctness, repeatability and accuracy. Compared with the conventional manual measurement approach, the bi-temporal TLS scheme can not only achieve much higher measurement precision, but also greatly improve efficiency by significantly reducing cost, workload, and manpower. It has good potential for use in the area of nation-wide grain inventory inspection in China.

Product design evaluation based on improved CRITIC and Comprehensive Cloud-TOPSIS – Applied to automotive styling design evaluation

To tackle the challenge of achieving more reasonably calculation of evaluation indicator weights and a more scientifically rigorous scoring system for ranking in product design evaluation, a novel evaluation model that leverages improved CRITIC (Criteria Improved Criteria Importance) and CC-TOPSIS (Comprehensive Cloud-Technique for Order Preference by Similarity to Ideal Solution) is proposed. Firstly, the process commences with the collection of qualitative product evaluations from respondents through questionnaires, presented in the form of fuzzy confidence intervals. Subsequently, these evaluations are transformed into generalized trapezoidal fuzzy numbers, and their center of gravity values are calculated. Secondly, after the weight for each evaluation indicator is calculated employing an enhanced CRITIC approach, a weight cloud model utilizing the backward cloud methodology is constructed. Subsequently, the scoring cloud model is generated as a backward cloud, followed by the application of an enhanced synthesis operator to amalgamate the weight cloud model with the scoring cloud model, culminating in the formation of a comprehensive cloud model. Finally, the product design schemes are ranked using the Cloud-TOPSIS method, and a cloud chart analysis provides insights into optimization directions. The utilization of this methodology in assessing automotive styling design serves as a compelling showcase of its practicality and efficiency, in addition to, the method's resilience is further bolstered through a sensitivity analysis of the evaluation indicators. This method adeptly computes the weights of evaluation indicators, while also addressing the inherent variability in weight values and incorporates the benefits of Cloud Model and TOPSIS in the evaluation of product design alternatives, ultimately elevating the rationality and the scientific underpinnings of the product design evaluation process.

Research on assessment of air traffic control operation quality based on track data

AbstractEvaluating the quality of air traffic control operations is crucial for enhancing airspace management. Thus, this paper proposes a data mining approach for conducting a comprehensive assessment of control operation quality (COQ) in increasingly complex operation environments. First, the authors establish a COQ evaluation index system that combines both subjective and objective measures. Key index parameters are determined using wavelet filtering and interval estimation techniques on the basis of data mining results. Second, the authors apply an entropy‐weighted cloud model to label data samples and classify COQ into ‘excellent’, ‘good’, and ‘fair’ levels. Finally, the authors establish an support vector machine‐based COQ assessment model using XGBoost feature combinations to verify the practical feasibility and scientific validity of their approach.

Risk assessment of shield tunneling crossing building based on variable weight theory and cloud model

Shield tunneling crossing buildings is a complex process involving multiple influencing factors. The conventional risk assessments for shield tunneling crossing buildings ignore the dynamic changes in index weights and fail to promptly reflect the impact of changes in index status on evaluation results. To solve this problem, a risk assessment model for shield tunneling crossing buildings is proposed based on the variable weight theory and cloud model. Firstly, a risk assessment indicator system for shield tunneling crossing buildings is constructed. The variable weight theory is used to calculate the weights of the evaluation indicators reasonably, taking into account the dynamic changes of the evaluation indicators and their impacts on the evaluation results. The cloud model theory is introduced to solve the randomness and fuzziness in the risk assessment of shield tunneling crossing buildings. The new model is applied to a case study of the risk assessment of shield tunneling crossing buildings during the construction of the Changsha Metro Line 4. Compared with the traditional weight model, the variable weight theory model calculates the weights of the indicators more reasonably. At the same time, it is verified that the model has good applicability and accuracy in the risk assessment of shield tunneling crossing buildings. This model provides a new approach to guide the safe operation management and disposal decision-making of shield tunneling crossing buildings.

Analysis of Spatiotemporal Variation Characteristics and Driving Factors of Drought in Yinshanbeilu Inner Mongolia Based on a Cloud Model

Drought refers to a meteorological disaster that results in insufficient soil moisture due to a long-term lack of rainfall and disrupts the moisture balance of crops. Yinshanbeilu in Inner Mongolia is an arid and semi-arid region, and the onset of drought and its subsequent transmission is a key challenge in water resource management. This study takes Yinshanbeilu as the study area, analyses the changing characteristics of multi-timescale drought from 1971 to 2020 based on the Standardised Precipitation Index (SPI), and analyses the stochasticity and stability of the drought by using a cloud model. Finally, the cross-wavelet transform method and Pearson’s test are used to explore the correlation between atmospheric circulation factors, PRE and PET, and drought. The results indicate that (1) on the annual scale, the frequency of drought in Yinshanbeilu mainly ranges from 22% to 28%, with a high frequency of light droughts, a low frequency of severe droughts, a high frequency of droughts in the east and west, and a low frequency of droughts in the north and south; on the seasonal scale, the frequency of droughts in winter is the highest, with a rate of 34.6%, and the lowest frequency of droughts is in autumn, with a rate of 28.3%. (2) There is a decreasing trend in Entropy (En) and Hyper-Entropy (He), and an increasing trend in Expectation (Ex) for the inter-annual SPI-12 cloud model. Spatially, Ex and He are negatively correlated, while En and He are positively correlated. The inter-annual variation in cloud eigenvalues is greater than the inter-site variation, so the cloud model better reflects the spatial stochasticity and stability of regional inter-annual SPI. For the seasonal-scale SPI-3 cloud model, Ex is smaller in all seasons, En is also smaller, and He is larger. (3) Sunspot, PRE (precipitation), and PET (Potential Evapotranspiration) are all positively correlated with SPI and have the highest correlation. This study reveals the characteristics and causes of variations of drought in Yinshanbeilu, which can be applied to future research areas related to regional drought risk management.

Retracted: IOT and Blockchain-Based Cloud Model for Secure Data Transmission for Smart City

Retracted: IOT and Blockchain-Based Cloud Model for Secure Data Transmission for Smart City

Point Cloud Registration for Measuring Shape Dependence of Soft Tissue Deformation by Digital Twins in Head and Neck Surgery

Introduction: A 2½ D point cloud registration method was developed to generate digital twins of different tissue shapes and resection cavities by applying a machine learning (ML) approach. This demonstrates the feasibility of quantifying soft tissue shifts. Methods: An ML model was trained using simulated surface scan data obtained from tumor resections in a pig head cadaver model. It hereby uses 438 2½ D scans of the tissue surface. Tissue shift was induced by a temperature change from 7.91 ± 4.1°C to 36.37 ± 1.28°C. Results: Digital twins were generated from various branched and compact resection cavities (RCs) and cut tissues (CT). A temperature increase induced a tissue shift with a significant volume increase of 6 mL and 2 mL in branched and compact RCs, respectively (p = 0.0443; 0.0157). The volumes of branched and compact CT were decreased by 3 and 4 mL (p < 0.001). In the warm state, RC and CT no longer fit together because of the significant tissue deformation. Although not significant, the compact RC showed a greater tissue deformation of 1 μL than the branched RC with 0.5 μL induced by the temperature change (p = 0.7874). The branched and compact CT forms responded almost equally to changes in temperature (p = 0.1461). Conclusions: The simulation experiment of induced soft tissue deformation using digital twins based on 2½ D point cloud models proved that our method helps to quantify shape-dependent tissue shifts.

A Study on Refraction Error Compensation Method for Underwater Spinning Laser Scanning Three-Dimensional Imaging.

Laser scanning 3D imaging technology, because it can obtain accurate three-dimensional surface data, has been widely used in the search for wrecks and rescue operations, underwater resource development, and other fields. At present, the conventional underwater spinning laser scanning imaging system maintains a relatively fixed light window. However, in low-light situations underwater, the rotation of the scanning device causes some degree of water fluctuation, which warps the light strip data that the system sensor receives about the object's surface. To solve this problem, this research studies an underwater 3D scanning and imaging system that makes use of a fixed light window and a spinning laser (FWLS). A refraction error compensation algorithm is investigated that is based on the fundamentals of linear laser scanning imaging, and a dynamic refraction mathematical model is established based on the motion of the imaging device. The results of the experiment on error analysis in an optimal underwater environment indicate that the error in reconstructing the radius is decreased by 60% (from 2.5 mm to around 1 mm) when compensating for the measurement data of a standard sphere with a radius of 20 mm. Moreover, the compensated point cloud data exhibit a higher degree of correspondence with the model of the standard spherical point cloud. Furthermore, we examine the impact of physical noise, measurement distance, and partial occlusion of the object on the imaging system inside an authentic underwater setting. This study is a good starting point for looking at the refractive error of an underwater laser scanning imaging system. It also provides to us some ideas for future research on the refractive error of other scanning imaging methods.

A Dynamic Workflow Scheduling Method based on MCDM Optimization that Manages Priority Tasks for Fault Tolerance

Because it offers efficient on-demand service delivery over the internet, cloud computing has become more and more popular. An architectural model for cloud computing energy management is provided by the suggested Ant Lion algorithm. Virtual Machines (VMs) in cloud systems are assigned to hosts based not so much on their overall and long-term use, but rather on their immediate resource consumption, including RAM availability. The placement and scheduling processes are frequently computationally demanding and have the potential to affect the performance of deployed virtual machines. In this research work, we offer a strategy that considers the historical resource use of virtual machines (VMs) over time while scheduling them in the cloud. Our goal is to use the Ant lion approach to schedule virtual machines (VMs) in a way that maximizes performance by evaluating the utilization levels of prior VMs. The goal is to reduce the degradation of performance brought about by Cloud administration tasks like as virtual machine deployment, which might impact systems that have already been installed. Furthermore, congested virtual machines (VMs) sometimes take up resources from nearby VMs, increasing the VMs' actual CPU use. Our results show that by learning and adjusting to system behavior over time, our strategy outperforms conventional instant-based physical machine selection. We offer the idea of scheduling virtual machines (VMs) using resource monitoring data from past VM resource use. By using the Ant lion classifier, four fewer physical machines are needed.

Deep learning with blockchain based cyber security threat intelligence and situational awareness system for intrusion alert prediction

Network security situation assessment (NSSA) is imperative and active defense technology in the network security situation. By examining NSSA data, one can examine the threat of network security and examine the network attack phase and hence fully grasp the complete network security situation. With the quick design of 5 G, the cloud model and Internet of things (IoT), the network platform is increasingly complicated and resulting in diversity of network threats which discover the accuracy. Thus, a new blockchain based cyber-security threat intelligence (CTI) and situational awareness system is devised for intrusion alert prediction. A blockchain-based CTI model is considered where data acquired are allowed to linear normalization. Here, the cyber situational awareness engine is used for alert segregation, which is implemented with entropy weighting power k means algorithm wherein weights generated during alert segregation are updated using Adaptive Transit Search (ATS). Then, the feature selection is implemented using hybrid Soergel and Lorentzian. The selected features are fed to Deep Maxout Network (DMN) for performing intrusion alert prediction. Finally, the cyber attack mitigation is carried out by blacklisting based on predicted result. The modified DMN outperformed with highest F-measure of 95.2%, precision of 96.9% and recall of 94.7%.

Improving the approximation quality of tensor product B-spline surfaces by local parameterization

Abstract Freeform surfaces like tensor product B-spline surfaces have been proven to be a suitable tool to model laser scanner point clouds, especially those representing artificial objects. However, when it comes to the modelling of point clouds representing natural surfaces with a lot of local structures, tensor product B-spline surfaces reach their limits. Refinement strategies are usually used as an alternative, but their functional description is no longer nearly as compact as that of classical tensor product B-spline surfaces, making subsequent analysis steps considerably more cumbersome. In this publication, the approximation quality of classical tensor product B-spline surfaces is improved by means of local parameterization. By using base surfaces with a local character, relevant information about local structures of the surface to be estimated are stored in the surface parameters during the parameterization step. As a consequence, the resulting tensor product B-spline surface is able to represent these structures even with only a small number of control points. The developed locally parameterized B-spline surfaces are used to model four data sets with different characteristics. The results reveal a clear improvement compared to the classical tensor product B-spline surfaces in terms of correctness, goodness-of-fit and stability.

Tconns: a novel time-varying context-aware offloading strategy for mobile edge computing

Mobility is a fundamental feature of mobile edge computing. Due to the mobility of users, the contextual attributes of cloudlets such as server resources and network state will dynamically change with time during offloading, showing time-varying and fuzzy characteristics. To this end, how to make efficient offloading decision to provide low-latency, low-power and highly reliable services in MEC has become a critical issue. In this paper, we propose a time-varying context-aware cloudlet decision algorithm based on neutrosophic set, TConNS {text {(The Code of TConNS is available at https://github.com/zhengLabs/NSO)}}. Firstly, we establish a representation model of the multi-dimensional time-varying context of candidate cloudlets, including the mobile residence time. Secondly, we adopt the backward generator of cloud model theory to transform the contextual raw data into a single-valued neutrosophic set with the expression ability for fuzzy information. Finally, we use a series of appropriate operations under the own unique computing system of neutrosophic set to obtain the best cloudlet. Extensive experiments show that TConNS reduces the average response time by about 49% and the average energy consumption by about 46%, and also reduces the number of task failures.

AHP-based quantitative evaluation of radiation safety in nuclear technology utilization units

In order to protect human health from the hazards of nuclear radiation, it is necessary to standardize the management of irradiation equipment. According to the principle of “man-machine-management-environment” intrinsic safety, analytic hierarchy process (AHP) and cloud model method were adopted. A quantitative safety evaluation index system was established from four aspects: safety management, safety environment, irradiation equipment and experimental team. Based on the actual situation of an experimental institution, the safety grade of this experimental institution was systematically analyzed. The safety grade of this experimental organization was excellent, but it still needs to improve certain aspects. Corresponding management recommendations were put forward for nuclear technology utilization units of different safety levels.

A novel gradient descent optimizer based on fractional order scheduler and its application in deep neural networks

To improve convergence speed of deep neural network trained by gradient descent methods (GDMs), this paper proposed a novel fractional order gradient descent optimizer (FOAdam) based on Caputo operator and Adam algorithm, and analyzed the impact of fractional order on convergence performance using optimization theory and simulation. Furthermore, to address the trade-off between convergence speed and precision under fixed-order conditions, a fractional order scheduler (FOS) was designed based on the Connections Cloud Model (CCM). Numerical experiments conducted on MNIST and CIFAR-10 datasets showed that FOS-FOAdam outperformed other mainstream GDMs in terms of convergence speed and precision. Finally, FOS-FOAdam was applied in recognizing and classifying image datasets of cuttings and cores at the logging site, demonstrating good prospects for practical engineering applications.

A hybrid MCDM approach based on combined weighting method, cloud model and COPRAS for assessing road construction workers' safety climate.

The purpose of this paper is to propose a novel approach to assess the safety climate level of different groups of workers in a construction company and predict safety performance and implement targeted improvement measures. This paper utilizes the BP neural network and random forest algorithm to establish a weight learning mechanism for calculating the weights of safety climate evaluation criteria. The cloud model is employed to construct the decision matrix for different groups under the evaluation criteria. Meanwhile, the paper utilizes the COPRAS method to compare the safety climate of different groups. The findings show the accuracy of the CM-COPRAS model is assessed by comparing it with the other methods. The three models are almost consistent in assessing the safety climate for working age groups, accident experience groups, and work type groups, with slight differences in the evaluation results for the education groups. The consistency of the computational results of the CM-COPRAS model with the results of the existing research, i.e., that the education level is positively proportional to the safety climate supports the reasonableness and validity of the CM-COPRAS model. The paper proposes a hybrid MCDM method that integrates the Combined weighting method, Cloud model, and COPRAS for safety climate level evaluation in different construction worker groups. A case study is presented to demonstrate the applicability of the proposed method and to compare it with other methods to validate the effectiveness of the present method.

Aircraft Skin Damage Detection and Assessment From UAV Images Using GLCM and Cloud Model

Aircraft Skin Damage Detection and Assessment From UAV Images Using GLCM and Cloud Model

Vulnerability Analysis of Bohai Rim Ports in the Context of High-Quality Development

The article scientifically analyzes the connotation of port vulnerability, and based on the principles of exposure-sensitivity-adaptability and the five major theories of high-quality development, constructs a high-quality development perspective port vulnerability evaluation index system. Taking 11 major ports in the Bohai Rim as the research object, firstly, the cloud model was applied to quantify the qualitative indicators, the improved catastrophe series method was used to calculate the vulnerability of each port, and the entropy weight method was introduced to determine the priority of the indicators. Secondly, by comparing and analyzing the results obtained by the entropy weighted fuzzy optimization method, it was found that the evaluation results of the two methods were consistent, thereby confirming the credibility and correctness of this method. The evaluation results show that the overall vulnerability level of major ports in the Bohai Rim region is relatively high, with disaster resistance at a moderate or below level. Huanghua Port has the highest vulnerability, while Qingdao Port has the lowest vulnerability. Finally, the key vulnerability factors of each port are analyzed, and corresponding countermeasures are proposed to provide reference for the risk management of ports.

Link-16 anti-jamming performance evaluation based on grey relational analysis and cloud model

Link-16 anti-jamming performance evaluation based on grey relational analysis and cloud model

Research on Promoting the Micro-Governance of Sports in China’s Urban Communities under the Concept of Common Construction, Common Governance and Sharing

Abstract The study first constructed a system of evaluation indicators, including five secondary and 21 tertiary indicators such as financial resources, site facilities and human resources. The weights were calculated using the combination assignment method, combining subjective weights based on the G1 method and objective weights from the projection seeking model algorithm, emphasizing the importance of human resources. The cloud model was applied to grade governance effectiveness, and most indicators were in the medium and high levels. The complexity and diversity of governance were revealed through quantitative and qualitative comparative Analysis (QCA) of a sample of 10 communities in H city. The results of the study showed that the similarity of community sports facilities as a percentage of expenditures in the 10 community samples in City H reached the high performance level (0.8601), while the similarity of the indicators of sports training and instruction, percentage of human resources in sports, public demand, and management of social organizations was at a lower level. To improve the effectiveness, it is recommended to strengthen the introduction and training of human resources, the construction of hardware facilities and publicity and promotion, and to optimize the organizational security. At the same time, modern technologies such as big data are utilized to establish a more efficient governance system to fully implement the concept.