Cloud Model Research Articles

Energy-Saving Evaluation and Comprehensive Benefit Analysis of Power Transmission/Distribution System Based on Cloud Model

Energy-Saving Evaluation and Comprehensive Benefit Analysis of Power Transmission/Distribution System Based on Cloud Model

Text sentiment analysis and classification based on bidirectional Gated Recurrent Units (GRUs) model

This paper explores the importance of text sentiment analysis and classification in the field of natural language processing, and proposes a new approach to sentiment analysis and classification based on the bidirectional gated recurrent units (GRUs) model. The study firstly analyses the word cloud model of the text with six sentiment labels, and then carries out data preprocessing, including the steps of removing special symbols, punctuation marks, numbers, stop words and non-alphabetic parts. Subsequently, the data set is divided into training set and test set, and through model training and testing, it is found that the accuracy of the validation set is increased from 85% to 93% with training, which is an increase of 8%; at the same time, the loss value of the validation set decreases from 0.7 to 0.1 and tends to be stable, and the model is gradually close to the actual value, which can effectively classify the text emotions. The confusion matrix shows that the accuracy of the model on the test set reaches 94.8%, the precision is 95.9%, the recall is 99.1%, and the F1 score is 97.4%, which proves that the model has good generalisation ability and classification effect. Overall, the study demonstrated an effective method for text sentiment analysis and classification with satisfactory results.

Dynamic risk early warning system for tunnel construction based on two-dimensional cloud model

Due to the uncertainty of geological conditions and construction status during tunnel construction, the risk of tunnel construction will vary with updates to environmental information. The current risk management practice for tunnel construction projects in China is static and relies on the subjective judgment of experts and practitioners, so it is necessary to propose a risk assessment methodology that takes into account dynamically updated risk information and uncertainty. This paper proposes a dynamic risk warning system for tunnel construction that integrates knowledge-driven consequence datasets and data-driven likelihood datasets of risk events, using a probability algorithm based on a two-dimensional cloud model for risk decision-making. The algorithm quantifies the risk probabilities by randomly simulating the risk discrimination results under the influence of uncertainty multiple times and statistically analyzing the distribution pattern of a large number of simulation results within the constructed quantitative risk matrix. Decisions on risk level and warning state are made based on risk probabilities. Moreover, this study conducts extensive simulation tests to determine the optimal number of simulated cloud droplets and develops a software toolbox to facilitate rapid feedback on tunnel construction risk. The proposed algorithm is applied to the construction of Guiyang Metro Line 3. The results show that the standard deviation of the simulation results is less than 0.025 and the algorithm has good stability. The two-dimensional cloud model offers a superior reflection of risk uncertainty compared with its one-dimensional counterpart, making it more appropriate for assessing the risks associated with tunnel construction—a complex scenario characterized by intricate index relationships and significant uncertainty.

The Evolution of Meteorological Satellite Cloud-Detection Methodologies for Atmospheric Parameter Retrievals

The accurate detection of clouds is an important first step in the processing of remotely sensed satellite data analyses and subsequent cloud model predictions. While initial cloud retrieval technology began with the exploitation of one or two bands of satellite imagery, it has accelerated rapidly in recent years as sensor and retrieval technology, creating a new era in space observation exploration. Additionally, the initial emphasis in satellite retrieval technology focused on cloud detection for cloud forecast models, but more recently, cloud screening in satellite-acquired data is playing an increasingly critical role in the investigation of cloud-free data for the retrieval of soil moisture, vegetation cover, ocean color concentration and sea surface temperatures, as well as the environmental monitoring of a host of products, e.g., atmospheric aerosol data, to study the Earth’s atmospheric and climatic systems. With about 60% of the Earth covered by clouds, on average, it is necessary to accurately detect clouds in remote sensing data to screen cloud contaminate data in remote sensing analyses. In this review, the evolution of cloud-detection methodologies is highlighted with advancement in sensor hardware technology and machine learning algorithmic advances. The review takes into consideration the meteorological sensors usually used for atmospheric parameters estimation (thermodynamic profiles, aerosols, cloud microphysical parameters). Moreover, a discussion is presented on methods for obtaining the cloud-truth data needed to determine the accuracy of these cloud-detection approaches.

Group decision making method for third-party logistics management: An interval rough cloud optimization model

Group decision making in third-party logistics service selection plays an essential role for improving service quality, increasing efficiency and reducing the net cost. Fuzzy and uncertain linguistic variables are commonly used to represent experts‘rankings in optimization problems. To recognize the limits of human cognition and subjectivity of human evaluations, several optimization approaches have been studied to select remanufacturing alternatives in decision making processes, however these methods have certain deficiencies such as lacking manipulation tools of diverse information, randomness, use of predefined parameters increasing uncertainty, interpersonal relations among evaluation criteria. The integration of interval numbers, rough approximations, and cloud model theory plays a significant role to model incomplete and inadequate information occurring in decision making problems. This research paper focuses on the integration of dual interval rough integrated cloud model with best-worst optimization technique, Multi-Attributive Border Approximation area Comparison (MABAC) and Weighted Aggregated Sum Product Assessment (WASPAS) approaches. A novel min–max optimization model based dual interval rough integrated cloud values is designed to compute the weight coefficients and consistency ratio for each criteria. The consistency of proposed optimization model is checked using a consistency ratio test. Secondly, the alternatives are ranked using the proposed DIRI cloud based MABAC and WASPAS approaches using interval clouds based weighted sum and weighted product coefficients, approximation area values and distance formulae. The significance of the proposed model is highlighted with a case study of third-party logistics service management of an electronic firm. The rationality and out-performance of the proposed methodology is studied by a comparative analysis with existing approaches and detailed sensitivity analysis on different variations of criteria weights and parameter.

Research on the hybrid chaos-coud salp swarm algorithm

Salp swarm algorithm (SSA) is a new swarm intelligence optimization algorithm, which has the advantages of simple structure, almost no parameter setting. However, SSA also has the shortcomings of slow convergence speed in the early stage and low optimization accuracy in the later stage when searching for the optimal solution. To address the problems, this study proposes a hybrid chaos-cloud salp swarm algorithm (CC-SSA). First, to accelerate the convergence speed, a positive normal cloud generator is used to perform local search on the superior salp individuals. Second, to enhance the diversity of CC-SSA and avoid it from falling into local optimum, the chaotic map is used to perform global search on the inferior salp individuals by adding global perturbation. Third, to control the execution ratio of global and local search, the mixed control parameter (MLMS) and population allocation coefficient (r) are introduced to organically combine three algorithms, SSA, chaotic map, and cloud model. Finally, to evaluate the performance of the proposed algorithm, it is compared with other 9 conventional algorithms on 12 classic functions. The experimental results show that CC-SSA has an average accuracy rate of 92.92 %, which ranks first. In addition, the average iteration of CC-SSA scores the third. Therefore, compared to conventional optimization algorithms, CC-SSA has better performance in terms of execution time and optimize accuracy.

Optimal power flow method with consideration of uncertainty sources of renewable energy and demand response

Optimal power flow (OPF) calculation methods are important for the power system operation and mainly focus on the deterministic power flow calculation, neglecting the impact of demand response on online security calculation of power systems with renewable energy sources. Therefore, this paper proposes an OPF calculation method that considers the uncertainties of wind power, photovoltaic (PV) power generation and demand-side response. Firstly, the research focuses on the renewable energy grid, considering the uncertainties of wind power and PV power generation, and establishes uncertainty models for wind power and PV output. Secondly, based on cloud model theory, an uncertainty model for demand response is established. According to the established models, an efficient OPF model is constructed with a linearized submodels considering multiple uncertainties. By testing on the IEEE 30-bus system as a typical example, we found the effectiveness and superiority of the proposed OPF calculation method can benefit the power system economic operation and demand side resource utilization.

DOML: A new modeling approach to Infrastructure-as-Code

One of the main DevOps practices is the automation of resource provisioning and deployment of complex software. This automation is enabled by the explicit definition of Infrastructure-as-Code (IaC), i.e., a set of scripts, often written in different modeling languages, which defines the infrastructure to be provisioned and applications to be deployed.We introduce the DevOps Modeling Language (DOML), a new Cloud modeling language for infrastructure deployments. DOML is a modeling approach that can be mapped into multiple IaC languages, addressing infrastructure provisioning, application deployment and configuration.The idea behind DOML is to use a single modeling paradigm which can help to reduce the need of deep technical expertise in using different specialized IaC languages.We present the DOML’s principles and discuss the related work on IaC languages. Furthermore, the advantages of the DOML for the end-user are demonstrated in comparison with some state-of-the-art IaC languages such as Ansible, Terraform, and Cloudify, and an evaluation of its effectiveness through several examples and a case study is provided.

Accurately Computing the Interacted Volume of Molecules over Their 3D Mesh Models.

For quickly predicting the rational arrangement of catalysts and substrates, we previously proposed a method to calculate the interacted volumes of molecules over their 3D point cloud models. However, the nonuniform density in molecular point clouds may lead to incomplete contours in some slices, reducing the accuracy of the previous method. In this paper, we propose a two-step method for more accurately computing molecular interacted volumes. First, by employing a prematched mesh slicing method, we layer the 3D triangular mesh models of the electrostatic potential isosurfaces of two molecules globally, transforming the volume calculation into finding the intersecting areas in each layer. Next, by subdividing polygonal edges, we accurately identify intersecting parts within each layer, ensuring precise calculation of interacted volumes. In addition, we present a concise overview for computing intersecting areas in cases of multiple contour intersections and for improving computational efficiency by incorporating bounding boxes at three stages. Experimental results demonstrate that our method maintains high accuracy in different experimental data sets, with an average relative error of 0.16%. On the same experimental setup, our average relative error is 0.07%, which is lower than the previous algorithm's 1.73%, improving the accuracy and stability in calculating interacted volumes.

Comprehensive evaluation of the response relationship between meteorological drought and hydrological drought in the Yalong River Basin, China

Extreme climate events are becoming increasingly severe owing to global warming. In recent years, the Yalong River has experienced frequent droughts, which has seriously hindered the development of hydropower. Based on standardized precipitation evapotranspiration index (SPEI) and standardized runoff index (SRI) values, this study employed transfer entropy, multivariate Copula functions, and a cloud model to study the response relationship and risk situation between meteorological and hydrological droughts. The results showed that: (1) The smaller the scale of SPEI and SRI, the more sensitive the identification of drought and the faster the detection of drought trend changes. (2) Combined with small-scale drought index analyses, the trend and severity of meteorological and hydrological droughts propagating from the upper and middle reaches to the lower reaches is increasing from year to year. (3) The Gumbel-Hougaard Copula function reveals the relationship between meteorological and hydrological drought, with the longest joint return period of 13–15 months. (4) In cloud model risk evaluation, the degree, uncertainty and stochasticity of drought risk in the basin are increasing, and the risk degree of interannual meteorological and seasonal hydrological droughts in the basin was high. This study provides theoretical support for optimizing water resource allocation and drought risk situation analysis in the basin.

Performance Assessment of Existing Asphalt Pavement in China’s Highway Reconstruction and Expansion Project Based on Coupling Weighting Method and Cloud Model Theory

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

Research on path planning for clear aligner flexible manufacturing

In recent years, clear aligner can enhance individual appearance with dental defects, so it used more and more widely. However, in manufacturing process, there are still some problems, such as low degree of automation and high equipment cost. The problem of coordinate system mismatch between gingival curve point cloud and dental CAD model is faced to. The PCA-ICP registration algorithm is proposed, which includes coarse match algorithm and improve-ICP registration algorithm. The principal component analysis (PCA) based method can roughly find the posture relationship between the two point clouds. Using z-level dynamic hierarchical, the ICP registration can accurately find the posture between these two clouds. The final registration maximum distance error is 0.03 mm, which is smaller than robot machining error. Secondly, the clear aligner machining process is conducted to verify the registration effectiveness. Before machining, the path is generated based on the well registered gingival curve. After full registration, the tool path is calculated by establishing a local coordinate system between the workpiece and the tool to avoid interference. This path is calculated and generated as an executable program for ABB industrial robots. Finally, the robot was used for flexible cutting of clear aligners and was able to extract products, ensuring the effectiveness of the proposed research. This method can effectively solve the limitations of traditional milling path planning under such complex conditions.

Are These Planets or Brown Dwarfs? Broadly Solar Compositions from High-resolution Atmospheric Retrievals of ∼10–30 M Jup Companions

Using Keck Planet Imager and Characterizer high-resolution (R ∼ 35,000) spectroscopy from 2.29 to 2.49 μm, we present uniform atmospheric retrievals for eight young substellar companions with masses of ∼10–30 M Jup, orbital separations spanning ∼50–360 au, and T eff between ∼1500 and 2600 K. We find that all companions have solar C/O ratios and metallicities to within the 1σ–2σ level, with the measurements clustered around solar composition. Stars in the same stellar associations as our systems have near-solar abundances, so these results indicate that this population of companions is consistent with formation via direct gravitational collapse. Alternatively, core accretion outside the CO snowline would be compatible with our measurements, though the high mass ratios of most systems would require rapid core assembly and gas accretion in massive disks. On a population level, our findings can be contrasted with abundance measurements for directly imaged planets with m < 10 M Jup, which show tentative atmospheric metal enrichment compared to their host stars. In addition, the atmospheric compositions of our sample of companions are distinct from those of hot Jupiters, which most likely form via core accretion. For two companions with T eff ∼ 1700–2000 K (κ And b and GSC 6214–210 b), our best-fit models prefer a nongray cloud model with >3σ significance. The cloudy models yield 2σ−3σ lower T eff for these companions, though the C/O and [C/H] still agree between cloudy and clear models at the 1σ level. Finally, we constrain 12CO/13CO for three companions with the highest signal-to-noise ratio data (GQ Lup b, HIP 79098b, and DH Tau b) and report vsini and radial velocities for all companions.

Research on Long-span Bridge Point Cloud Morphology Identification

With the rapid development of 3D laser scanning technology and the trend of accurate and convenient acquisition of 3D point cloud data, the 3D point cloud data that was challenging to acquire in the past has also become one of the main ways to represent the 3D form of bridges. Among several bridge types, suspension bridges have high research value due to their widespread use in long-span bridges. The main cable, as the main load-bearing member of the suspension bridge, exhibits characteristics such as a large span, heavy components, and numerous influencing factors in long-span bridges. Thus, controlling the formation of the main cables is a problem frequently encountered in bridge engineering. Using 3D point cloud scanning data, this paper constructs an overall 3D point cloud model of the bridge and proposes an automatic extraction algorithm for bridge components based on this model. Subsequently, the paper illustrates the integration of the algorithm analysis and control of user interaction platform building, aiming to build up long span bridge digital base, therefore realizing variation recognition and digital tracking of long bridge space form.

Effective Data Sharing in an Edge–Cloud Model: Security Challenges and Solutions

Effective Data Sharing in an Edge–Cloud Model: Security Challenges and Solutions

LOD2-Level+ Low-Rise Building Model Extraction Method for Oblique Photography Data Using U-NET and a Multi-Decision RANSAC Segmentation Algorithm

Oblique photography is a regional digital surface model generation technique that can be widely used for building 3D model construction. However, due to the lack of geometric and semantic information about the building, these models make it difficult to differentiate more detailed components in the building, such as roofs and balconies. This paper proposes a deep learning-based method (U-NET) for constructing 3D models of low-rise buildings that address the issues. The method ensures complete geometric and semantic information and conforms to the LOD2 level. First, digital orthophotos are used to perform building extraction based on U-NET, and then a contour optimization method based on the main direction of the building and the center of gravity of the contour is used to obtain the regular building contour. Second, the pure building point cloud model representing a single building is extracted from the whole point cloud scene based on the acquired building contour. Finally, the multi-decision RANSAC algorithm is used to segment the building detail point cloud and construct a triangular mesh of building components, followed by a triangular mesh fusion and splicing method to achieve monolithic building components. The paper presents experimental evidence that the building contour extraction algorithm can achieve a 90.3% success rate and that the resulting single building 3D model contains LOD2 building components, which contain detailed geometric and semantic information.

Research on EU natural gas security and countermeasures based on two-dimensional cloud model

The disruption of natural gas supply in the European Union (EU) and the ensuing price volatility triggered by the Ukrainian crisis have underscored the critical importance of natural gas security. This study aims to assess the current state of gas security in 27 EU countries that heavily rely on natural gas imports. We propose a comprehensive framework comprising seven dimensions to delineate natural gas security, complemented by a set of 13 evaluation indicators. Employing an enhanced AHP-entropy weight method, TOPSIS method, and two-dimensional cloud model, we rigorously evaluate the natural gas security performance across these 27 EU nations. Through the identification of primary influencing factors and the categorization of natural gas security levels, we establish an overarching ranking of gas security in the EU27. Our findings reveal that the EU's natural gas security risk level is classified as level III, with availability and affordability emerging as key dimensions significantly impacting security performance. This assessment not only offers valuable insights for energy-import-dependent countries and regions but also furnishes assessment tools and indicators crucial for evaluating natural gas security across diverse geopolitical contexts.

Air Pollution Monitoring Model in Real-time with cloud based Channel Ranging and Stego Privacy Preservation

&lt;p&gt;Monitoring air quality in polluted environments is crucial for human health, but traditional methods using meteorological equipment have limitations in complex terrains and are costly. This study proposes a novel approach to address air pollution monitoring in urban areas by visually assessing the haziness of distant photos. We explore the correlation between air quality indexes (e.g., AQI, PM2.5, PM10) and haziness levels in monitoring images. Results indicate that objective indicators accurately reflect air pollution levels, regardless of image size. To implement this observation practically, we introduce a new method called the "Channel ranging based prediction model in cloud with steganography-based privacy preservation framework." This model calculates a ratio between dark and bright channel information in scaled images, serving as a visual index of air pollution. Experimental findings demonstrate the effectiveness of this metric in terms of correlation, computational speed, and classification accuracy compared to existing metrics.&lt;/p&gt;

Cloud model-based multi-stage multi-attribute decision-making method under probabilistic interval-valued hesitant fuzzy environment

Due to the complexity of multi-stage multi-attribute decision-making (MADM) problems, decision-makers (DMs) frequently provide incomplete and hesitant evaluation information. A crucial difficulty in multi-stage MADM problems is how to make a valid decision under uncertain environment. Firstly, we employ probabilistic interval-valued hesitant fuzzy set (PIVHFS) to characterize the hesitancy of DMs, and then build an optimization model to obtain the probability information based on the score function, deviation function of probabilistic interval-valued hesitant fuzzy element (PIVHFE) and information entropy. Secondly, cloud model is introduced to optimize the calculation process and an optimization model is developed to realize the transformation from PIVHFS to cloud model. Thirdly, a method to solve the stage weights is proposed by synthesizing the within-stage and between-stage decision information. Similarly, the attribute weights are obtained by constructing a mathematical model based on the within-attribute and between-attribute decision information. The noted method can utilize decision information to its fullest. Then, the regret theory is applied to a multi-stage MADM problem. Finally, the applicability and effectiveness of the method are illustrated through case analysis and methods comparison.

A Novel High-Fidelity Simulation for Finishing Operations: Hybrid Image Mosaic and Wavelet Decomposition.

In finishing simulations, achieving accurate results can be challenging due to the minimal amount of material removal and the limited measurement range of surface micro-topography instruments. To overcome these limitations, a novel high-fidelity modeling method combining image mosaic and wavelet decomposition technologies is proposed in this paper. We achieve the stitching of narrow field and high pixel micro morphology images through four steps: image feature extraction, overlapped feature matching, feature fusion, and stitching effect evaluation. On this basis, the wavelet decomposition method is employed to separate detection signals based on their respective frequencies, allowing the establishment of a datum plane and a roughness surface. The point cloud model undergoes a transformation into a continuous geometric model via the Poisson reconstruction algorithm. In the case study, four sample images of an aluminum alloy sheet after barrel finishing were collected using the ZeGage Plus optical profiler. Each image has an actual size of 834.37 μm × 834.37 μm. Subsequently, a comparison was carried out between the physical and simulation experiments. The results clearly indicate that the proposed method has the potential to enhance the accuracy of the finishing simulation by over 30%. The error between the resulting model and the actual surface of the part can be controlled within 1 μm.