Clustering phase of a general constraint satisfaction problem model <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e2047" altimg="si7.svg"><mml:mi>d</mml:mi></mml:math>-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e2052" altimg="si91.svg"><mml:mi>k</mml:mi></mml:math>-CSP

Wei, Xu; Gong, Fuzhou; Zhou, Guangyan

doi:10.1016/j.physa.2019.122708

Cited by 2 publications

(2 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Panel data is the direct embodiment of the dynamic clustering of dam deformation, and its Spatio-temporal characteristic attributes are mainly described from three aspects [35]: (a) absolute index, which is the original value of panel data, (b) growing index, which is the embodiment of the growth rate of sample objects, and (c) fluctuation index, which is the embodiment of the growth rate and change rate of the sample.…”

Section: Matrix Expression Of Spatiotemporal Characteristic Level Of ...mentioning

confidence: 99%

“…Because panel data clustering is a concentrated hierarchical clustering algorithm, the CSP index is particularly suitable for single link concentrated hierarchical clustering algorithms [33,34]. The advantage of the CSP index is to solve and optimize the multi-weight factor problem [35,36], especially the clustering problem. The zoning rules of the ultra-high arch dam are established through the idea of the CSP index, which can simulate the water level in different stages of the reservoir in the whole process and realize the dynamic cluster analysis of dam deformation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deformation Analysis of an Ultra-High Arch Dam under Different Water Level Conditions Based on Optimized Dynamic Panel Clustering

et al. 2022

View full text Add to dashboard Cite

During the operation period, the deformation of an ultra-high arch dam is affected by the large fluctuation of the reservoir water level. Under the dual coupling of the ultra-high dam and the complex water level conditions, the traditional variational analysis method cannot be sufficiently applied to its deformation analysis. The deformation analysis of the ultra-high arch dam, however, is very important in order to judge the dam safety state. To analyze the deformation law of different parts of an ultra-high arch dam, the panel data clustering theory is used to construct a Spatio-temporal characteristic model of dam deformation. In order to solve the difficult problem of the fluctuating displacement of dam deformation with water level effect, three displacement component indexes (absolute quantity, growing, and fluctuation) are proposed to characterize dam deformation. To further optimize the panel clustering deformation model, the objective weight coefficient of clustering comprehensive distance is calculated based on the CRITIC (CRiteria Importance Through Inter-criteria Correlation) method. The zoning rules of the ultra-high arch dam are established by using the idea of the CSP (Constraint Satisfaction Problem) index, and the complex water level of the reservoir is simulated in the whole process. Finally, the dynamic cluster analysis of dam deformation is realized. Through a case study, three typical working conditions including the rapid rise and fall of water level and the normal operation are calculated, and the deformation laws of different deformation zones are analyzed. The results show that the model can reasonably describe the deformation law of an ultra-high arch dam under different water levels, conveniently and intuitively select representative measuring points and key monitoring parts, effectively reducing the analysis workload of lots of measuring points, and improve the reliability of arch dam deformation analysis.

show abstract

Section: Matrix Expression Of Spatiotemporal Characteristic Level Of ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deformation Analysis of an Ultra-High Arch Dam under Different Water Level Conditions Based on Optimized Dynamic Panel Clustering

et al. 2022

View full text Add to dashboard Cite

show abstract

The solution space structure of planted constraint satisfaction problems with growing domains

Wei¹,

Zhang²

2022

J. Stat. Mech.

Self Cite

View full text Add to dashboard Cite

Planting a solution into the random revised B (RB) model, which is a prototype of the random constraint satisfaction problem (CSP) with growing domains, can generate very hard satisfiable CSP benchmarks. We study the solution space structure of the planted RB model. In the thermodynamic limit, by the first moment method we find that this model goes through four phase transitions as the constraint density increases. In the replica symmetric phase, what we call the independent phase transition occurs, after which the planted cluster (cluster containing the planted solution) is separated from the giant cluster. From then on, the solution space except the planted cluster goes through the same clustering phase transition and the same satisfiability phase transition as on the random RB model. The planted cluster goes through the isolated phase transition, after which the planted cluster contains only one solution. When the instances have about 102 variables, experiments show that the last three phase transitions have already appeared as in the thermodynamic limit. This phase diagram provides strong evidence that this model can generate very hard satisfiable CSP benchmarks. Finally, we find when the constraint density goes to infinity, there is a single smooth valley in the energy landscape.

show abstract

Clustering phase of a general constraint satisfaction problem model d-k-CSP

Cited by 2 publications

References 33 publications

Deformation Analysis of an Ultra-High Arch Dam under Different Water Level Conditions Based on Optimized Dynamic Panel Clustering

Deformation Analysis of an Ultra-High Arch Dam under Different Water Level Conditions Based on Optimized Dynamic Panel Clustering

The solution space structure of planted constraint satisfaction problems with growing domains

Contact Info

Product

Resources

About