Academic literature on the topic 'Optimal cluster density'
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Journal articles on the topic "Optimal cluster density"
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Wang, Zheng, Changxin Liu, and Kejing Mao. "Industry cluster: spatial density and optimal scale." Annals of Regional Science 49, no. 3 (June 18, 2011): 719–31. http://dx.doi.org/10.1007/s00168-011-0452-6.
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Zhao, Chao, Junchuang Yang, and Kexin Wen. "An Improved Clustering Algorithm Based on Density Peak and Nearest Neighbors." Mathematical Problems in Engineering 2022 (August 10, 2022): 1–10. http://dx.doi.org/10.1155/2022/5499213.
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Aiming at the problems that the initial cluster centers are randomly selected and the number of clusters is manually determined in traditional clustering algorithm, which results in unstable clustering results, we propose an improved clustering algorithm based on density peak and nearest neighbors. Firstly, an improved density peak clustering method is proposed to optimize the cutoff distance and local density of data points. It avoids that random selection of initial cluster centers is easy to fall into the local optimal solution. Furthermore, a K-value selection method is presented to choose the optimal number of clusters, which is determined by the sum of the squared errors within the clusters. Finally, we employ the idea of the K-nearest neighbors to carry out the assignment for outliers. Experiments on the UCI real data sets indicate that our proposed algorithm can achieve better clustering results compared with several known algorithms.
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Wang, Zhenggang, Xuantong Li, Jin Jin, Zhong Liu, and Wei Liu. "Unsupervised Clustering of Neighborhood Associations and Image Segmentation Applications." Algorithms 13, no. 12 (November 25, 2020): 309. http://dx.doi.org/10.3390/a13120309.
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Irregular shape clustering is always a difficult problem in clustering analysis. In this paper, by analyzing the advantages and disadvantages of existing clustering analysis algorithms, a new neighborhood density correlation clustering (NDCC) algorithm for quickly discovering arbitrary shaped clusters. Because the density of the center region of any cluster sample dataset is greater than that of the edge region, the data points can be divided into core, edge, and noise data points, and then the density correlation of the core data points in their neighborhood can be used to form a cluster. Further more, by constructing an objective function and optimizing the parameters automatically, a locally optimal result that is close to the globally optimal solution can be obtained. This algorithm avoids the clustering errors caused by iso-density points between clusters. We compare this algorithm with other five clustering algorithms and verify it on two common remote sensing image datasets. The results show that it can cluster the same ground objects in remote sensing images into one class and distinguish different ground objects. NDCC has strong robustness to irregular scattering dataset and can solve the clustering problem of remote sensing image.
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Tsegaye, Seneshaw, Thomas M. Missimer, Jong-Yeop Kim, and Jason Hock. "A Clustered, Decentralized Approach to Urban Water Management." Water 12, no. 1 (January 9, 2020): 185. http://dx.doi.org/10.3390/w12010185.
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Current models in design of urban water management systems and their corresponding infrastructure using centralized designs have commonly failed from the perspective of cost effectiveness and inability to adapt to the future changes. These challenges are driving cities towards using decentralized systems. While there is great consensus on the benefits of decentralization; currently no methods exist which guide decision-makers to define the optimal boundaries of decentralized water systems. A new clustering methodology and tool to decentralize water supply systems (WSS) into small and adaptable units is presented. The tool includes two major components: (i) minimization of the distance from source to consumer by assigning demand to the closest water source, and (ii) maximization of the intra-cluster homogeneity by defining the cluster boundaries such that the variation in population density, land use, socio-economic level, and topography within the cluster is minimized. The methodology and tool were applied to Arua Town in Uganda. Four random cluster scenarios and a centralized system were created and compared with the optimal clustered WSS. It was observed that the operational cost of the four cluster scenarios is up to 13.9 % higher than the optimal, and the centralized system is 26.6% higher than the optimal clustered WSS, consequently verifying the efficacy of the proposed method to determine an optimal cluster boundary for WSS. In addition, optimal homogeneous clusters improve efficiency by encouraging reuse of wastewater and stormwater within a cluster and by minimizing leakage through reduced pressure variations.
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Ren, Min, Peiyu Liu, Zhihao Wang, and Jing Yi. "A Self-Adaptive Fuzzyc-Means Algorithm for Determining the Optimal Number of Clusters." Computational Intelligence and Neuroscience 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/2647389.
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For the shortcoming of fuzzyc-means algorithm (FCM) needing to know the number of clusters in advance, this paper proposed a new self-adaptive method to determine the optimal number of clusters. Firstly, a density-based algorithm was put forward. The algorithm, according to the characteristics of the dataset, automatically determined the possible maximum number of clusters instead of using the empirical rulenand obtained the optimal initial cluster centroids, improving the limitation of FCM that randomly selected cluster centroids lead the convergence result to the local minimum. Secondly, this paper, by introducing a penalty function, proposed a new fuzzy clustering validity index based on fuzzy compactness and separation, which ensured that when the number of clusters verged on that of objects in the dataset, the value of clustering validity index did not monotonically decrease and was close to zero, so that the optimal number of clusters lost robustness and decision function. Then, based on these studies, a self-adaptive FCM algorithm was put forward to estimate the optimal number of clusters by the iterative trial-and-error process. At last, experiments were done on the UCI, KDD Cup 1999, and synthetic datasets, which showed that the method not only effectively determined the optimal number of clusters, but also reduced the iteration of FCM with the stable clustering result.
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Srinivasa Raju, K., and D. Nagesh Kumar. "Selection of global climate models for India using cluster analysis." Journal of Water and Climate Change 7, no. 4 (March 28, 2016): 764–74. http://dx.doi.org/10.2166/wcc.2016.112.
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Global climate models (GCMs) are gaining importance due to their capability to ascertain climate variables that will be useful to develop long, medium and short term water resources planning strategies. The applicability of K-Means cluster analysis is explored for grouping 36 GCMs from Coupled Model Intercomparison Project 5 for maximum temperature (MAXT), minimum temperature (MINT) and a combination of maximum and minimum temperature (COMBT) over India. Cluster validation methods, namely the Davies–Bouldin Index (DBI) and F-statistic, are used to obtain an optimal number of clusters of GCMs for India. The indicator chosen for evaluation of GCMs is the probability density function based skill score. It is noticed that the optimal number of clusters for MAXT, MINT and COMBT scenarios are 3, 2 and 2, respectively. Accordingly, suitable ensembles of GCMs are suggested for India for MAXT, MINT and COMBT individually. The suggested methodology can be extended to any number of GCMs and indicators, with minor modifications.
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Zang, Wenke, Liyan Ren, Wenqian Zhang, and Xiyu Liu. "Automatic Density Peaks Clustering Using DNA Genetic Algorithm Optimized Data Field and Gaussian Process." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 08 (May 9, 2017): 1750023. http://dx.doi.org/10.1142/s0218001417500239.
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Clustering by fast search and finding of Density Peaks (called as DPC) introduced by Alex Rodríguez and Alessandro Laio attracted much attention in the field of pattern recognition and artificial intelligence. However, DPC still has a lot of defects that are not resolved. Firstly, the local density [Formula: see text] of point [Formula: see text] is affected by the cutoff distance [Formula: see text], which can influence the clustering result, especially for small real-world cases. Secondly, the number of clusters is still found intuitively by using the decision diagram to select the cluster centers. In order to overcome these defects, this paper proposes an automatic density peaks clustering approach using DNA genetic algorithm optimized data field and Gaussian process (referred to as ADPC-DNAGA). ADPC-DNAGA can extract the optimal value of threshold with the potential entropy of data field and automatically determine the cluster centers by Gaussian method. For any data set to be clustered, the threshold can be calculated from the data set objectively rather than the empirical estimation. The proposed clustering algorithm is benchmarked on publicly available synthetic and real-world datasets which are commonly used for testing the performance of clustering algorithms. The clustering results are compared not only with that of DPC but also with that of several well-known clustering algorithms such as Affinity Propagation, DBSCAN and Spectral Cluster. The experimental results demonstrate that our proposed clustering algorithm can find the optimal cutoff distance [Formula: see text], to automatically identify clusters, regardless of their shape and dimension of the embedded space, and can often outperform the comparisons.
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Zhou, Rong, Yong Zhang, Shengzhong Feng, and Nurbol Luktarhan. "A Novel Hierarchical Clustering Algorithm Based on Density Peaks for Complex Datasets." Complexity 2018 (July 18, 2018): 1–8. http://dx.doi.org/10.1155/2018/2032461.
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Clustering aims to differentiate objects from different groups (clusters) by similarities or distances between pairs of objects. Numerous clustering algorithms have been proposed to investigate what factors constitute a cluster and how to efficiently find them. The clustering by fast search and find of density peak algorithm is proposed to intuitively determine cluster centers and assign points to corresponding partitions for complex datasets. This method incorporates simple structure due to the noniterative logic and less few parameters; however, the guidelines for parameter selection and center determination are not explicit. To tackle these problems, we propose an improved hierarchical clustering method HCDP aiming to represent the complex structure of the dataset. A k-nearest neighbor strategy is integrated to compute the local density of each point, avoiding to select the nonnecessary global parameter dc and enables cluster smoothing and condensing. In addition, a new clustering evaluation approach is also introduced to extract a “flat” and “optimal” partition solution from the structure by adaptively computing the clustering stability. The proposed approach is conducted on some applications with complex datasets, where the results demonstrate that the novel method outperforms its counterparts to a large extent.
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SHIEH, HORNG-LIN, and CHENG-CHIEN KUO. "A NOVEL VALIDITY INDEX FOR THE SUBTRACTIVE CLUSTERING ALGORITHM." International Journal of Pattern Recognition and Artificial Intelligence 25, no. 04 (June 2011): 547–63. http://dx.doi.org/10.1142/s0218001411008798.
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This paper proposes a new validity index for the subtractive clustering (SC) algorithm. The subtractive clustering algorithm proposed by Chiu is an effective and simple method for identifying the cluster centers of sampling data based on the concept of a density function. The SC algorithm continually produces the cluster centers until the final potential compared with the original is less than a predefined threshold. The procedure is terminated when there are only a few data points around the most recent cluster. The choice of the threshold is an important factor affecting the clustering results: if it is too large, then too few data points will be accepted as cluster centers; if it is too small, then too many cluster centers will be generated. In this paper, a modified SC algorithm for data clustering based on a cluster validity index is proposed to obtain the optimal number of clusters. Six examples show that the proposed index achieves better performance results than other cluster validities do.
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Tang, Mingzhu, Caihua Meng, Huawei Wu, Hongqiu Zhu, Jiabiao Yi, Jun Tang, and Yifan Wang. "Fault Detection for Wind Turbine Blade Bolts Based on GSG Combined with CS-LightGBM." Sensors 22, no. 18 (September 7, 2022): 6763. http://dx.doi.org/10.3390/s22186763.
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Aiming at the problem of class imbalance in the wind turbine blade bolts operation-monitoring dataset, a fault detection method for wind turbine blade bolts based on Gaussian Mixture Model–Synthetic Minority Oversampling Technique–Gaussian Mixture Model (GSG) combined with Cost-Sensitive LightGBM (CS-LightGBM) was proposed. Since it is difficult to obtain the fault samples of blade bolts, the GSG oversampling method was constructed to increase the fault samples in the blade bolt dataset. The method obtains the optimal number of clusters through the BIC criterion, and uses the GMM based on the optimal number of clusters to optimally cluster the fault samples in the blade bolt dataset. According to the density distribution of fault samples in inter-clusters, we synthesized new fault samples using SMOTE in an intra-cluster. This retains the distribution characteristics of the original fault class samples. Then, we used the GMM with the same initial cluster center to cluster the fault class samples that were added to new samples, and removed the synthetic fault class samples that were not clustered into the corresponding clusters. Finally, the synthetic data training set was used to train the CS-LightGBM fault detection model. Additionally, the hyperparameters of CS-LightGBM were optimized by the Bayesian optimization algorithm to obtain the optimal CS-LightGBM fault detection model. The experimental results show that compared with six models including SMOTE-LightGBM, CS-LightGBM, K-means-SMOTE-LightGBM, etc., the proposed fault detection model is superior to the other comparison methods in the false alarm rate, missing alarm rate and F1-score index. The method can well realize the fault detection of large wind turbine blade bolts.
Dissertations / Theses on the topic "Optimal cluster density"
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Xu, Sanlin, and SanlinXu@yahoo com. "Mobility Metrics for Routing in MANETs." The Australian National University. Faculty of Engineering and Information Technology, 2007. http://thesis.anu.edu.au./public/adt-ANU20070621.212401.
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A Mobile Ad hoc Network (MANET) is a collection of wireless mobile nodes forming a temporary network without the need for base stations or any other preexisting network infrastructure. In a peer-to-peer fashion, mobile nodes can communicate with each other by using wireless multihop communication. Due to its low cost,
high flexibility, fast network establishment and self-reconfiguration, ad hoc networking has received much interest during the last ten years. However, without a fixed infrastructure, frequent path changes cause significant numbers of
routing packets to discover new paths, leading to increased network congestion and transmission latency over fixed networks. Many on-demand routing protocols have been developed by using various routing mobility metrics to choose the most reliable
routes, while dealing with the primary obstacle caused by node mobility.
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In the first part, we have developed an analysis framework for mobility metrics in random mobility model. Unlike previous research, where the mobility metrics were mostly studied by simulations, we derive the analytical expressions of mobility
metrics, including link persistence, link duration, link availability, link residual time, link change rate and their path equivalents. We also show relationships between the different
metrics, where they exist. Such exact expressions constitute precise mathematical relationships between network connectivity and node mobility.
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We further validate our analysis framework in Random Walk Mobility
model (RWMM). Regarding constant or random variable node velocity, we construct the transition matrix of Markov Chain Model through the analysis of the PDF of node separation after one epoch. In addition, we present intuitive and simple expressions for the link residual time and link duration, for the RWMM, which relate them directly to the ratio between transmission range and node speed. We also illustrate the relationship between link change rate and link duration. Finally, simulation results for all mentioned mobility metrics are reported which match well the proposed analytical framework.
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In the second part, we investigate the mobility metric applications on caching strategies and hierarchy routing algorithm. When on-demand routing employed, stale route cache
information and frequent new-route discovery in processes in MANETs generate considerable routing delay and overhead. This thesis proposes a practical route caching strategy to minimize routing delay and/or overhead by setting route cache timeout to a mobility metric, the expected path residual time. The strategy is independent of network traffic load and adapts to various
non-identical link duration distributions, so it is feasible to implement in a real-time route caching scheme. Calculated results
show that the routing delay achieved by the route caching scheme is only marginally more than the theoretically determined minimum. Simulation in NS-2 demonstrates that the end-to-end delay from DSR routing can be remarkably reduced by our caching scheme. By using overhead analysis model, we demonstrate that the minimum routing overhead can be achieved by increasing timeout to around twice the expected path residual time, without significant increase in routing delay.
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Apart from route cache, this thesis also addresses link cache strategy which has the potential to utilize route information more efficiently than a route cache scheme. Unlike some previous link cache schemes delete links at some fixed time after they enter the cache, we proposes using either the expected path duration or the link residual time as the link cache timeout. Simulation results in NS-2 show that both of the proposed link caching schemes can improve network performance in the DSR by reducing dropped data packets, latency and routing overhead, with the link residual time scheme out-performing the path duration scheme.
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To deal with large-scale MANETs, this thesis presents an adaptive k-hop clustering algorithm (AdpKHop), which selects clusterhead (CH) by our CH selection metrics. The proposed CH selection criteria enable that the chosen CHs are closer to the cluster centroid and more stable than other cluster members with respect to node mobility. By using merging threshold
which is based on the CH selection metric, 1-hop clusters can merge to k-hop clusters, where the size of each k-hop cluster adapts to the node mobility of the chosen CH. Moreover, we propose a routing overhead analysis model for k-hop clustering
algorithm, which is determined by a range of network parameters, such as link change rate (related to node mobility), node degree and cluster density. Through the overhead analysis, we show that an optimal k-hop cluster density does exist, which is
independent of node mobility. Therefore, the corresponding optimal
cluster merging threshold can be employed to efficiently organise k-hop clusters to achieve minimum routing overhead, which is highly desirable in large-scale networks.
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The work presented in this thesis provides a sound basis for future research on mobility analysis for mobile ad hoc networks, in aspects such as mobility metrics, caching strategies and k-hop clustering routing protocols.
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Tam, Mary Christina. "Ab initio Calculations of Optical Rotation." Diss., Virginia Tech, 2006. http://hdl.handle.net/10919/27214.
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Coupled cluster (CC) and density functional theory (DFT) are highly regarded as robust quantum chemical methods for accurately predicting a wide variety of properties, such as molecular structures, thermochemical data, vibrational spectra, etc., but there has been little focus on the theoretical prediction of optical rotation. This property, also referred to as circular birefringence, is inherent to all chiral molecules and occurs because such samples exhibit different refractive indices for left- and right- circularly polarized light. This thesis focuses on the theoretical prediction of this chiroptic property using CC and DFT quantum chemical models. Several
small chiral systems have been studied, including (S)-methyloxirane, (R)-epichlorohydrin, (R)-methylthiirane, and the conformationally flexible molecules, (R)-3-chloro-1-butene and
(R)-2-chlorobutane. All predicted results have been compared to recently published gas-phase cavity ringdown polarimetry data. When applicable, well-converged Gibbs free energy differences among confomers were determined using complete-basis-set extrapolations of CC energies in order to obtain Boltzmann-averaged specific rotations. The overall results indicate that the theoretical rotation is highly dependent on the choice of optimized geometry and basis set (diffuse functions are shown to be extremely important), and that there is a large difference between the CC and DFT predicted values, with DFT usually predicting magnitudes that are larger than those of coupled cluster theory.Ph. D.
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Sinha, Roy Rajarshi. "Ab initio simulation of optical properties of noble-metal clusters." Thesis, Aix-Marseille, 2018. http://www.theses.fr/2018AIXM0017/document.
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L'intérêt de la recherche fondamentale pour les morceaux nanométriques de métaux nobles est principalement dû à la résonance localisée des plasmons de surface (LSPR) dans l'absorption optique. Différents aspects, liés à la compréhension théorique de la LSPR dans le cas de clusters de métaux nobles de taille dite intermédiaire, sont étudiés dans ce manuscrit. Afin d'avoir une vision plus large nous utilisons deux approches : l'approche électromagnétique classique et le formalisme ab initio en temps réel de la théorie de la fonctionnelle de la densité dépendant du temps (RT-TDDFT). Une comparaison systématique et détaillée de ces deux approches souligne et quantifie les limitations de l'approche électromagnétique lorsqu'elle est appliquée à des systèmes de taille quantique. Les différences entre les excitations plasmoniques collectives et celles impliquant les électrons d, ainsi que leurs interactions, sont étudiées grâce au comportement spatial des densités correspondantes. Ces densités sont obtenues en appliquant une transformée de Fourier dans l'espace à la densité obtenue par les simulations DFT utilisant une perturbation delta-kick. Dans ce manuscrit, des clusters de métaux nobles nus et protégés par des ligands sont étudiés. En particulier, motivé par de récents travaux sur les phénomènes d'émergence de plasmon, l'étude par TD-DFT de nano-alliages Au-Cu de taille tout juste inférieure à 2nm à fourni de subtiles connaissances sur les effets d'alliages sur la réponse optique de tels systèmesThe fundamental research interest in nanometric pieces of noble metals is mainly due to the localized surface-plasmon resonance (LSPR) in the optical absorption. Different aspects related to the theoretical understanding of LSPRs in `intermediate-size' noble-metal clusters are studied in this thesis. To gain a broader perspective both the real-time \ai formalism of \td density-functional theory (RT-TDDFT) and the classical electromagnetics approach are employed. A systematic and detailed comparison of these two approaches highlights and quantifies the limitations of the electromagnetics approach when applied to quantum-sized systems. The differences between collective plasmonic excitations and the excitations involving $d$-electrons, as well as the interplay between them are explored in the spatial behaviour of the corresponding induced densities by performing the spatially resolved Fourier transform of the time-dependent induced density obtained from a RT-TDDFT simulation using a $\delta$-kick perturbation. In this thesis, both bare and ligand-protected noble-metal clusters were studied. In particular, motivated by recent experiments on plasmon emergence phenomena, the TDDFT study of Au-Cu nanoalloys in the size range just below 2~nm produced subtle insights into the general effects of alloying on the optical response of these systems
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Xu, Sanlin. "Mobility Metrics for Routing in MANETs." Phd thesis, 2007. http://hdl.handle.net/1885/49272.
Full textAbstract:
A Mobile Ad hoc Network (MANET) is a collection of wireless mobile nodes forming a temporary network without the need for base stations or any other pre–existing network infrastructure. In a peer-to-peer fashion, mobile nodes can communicate with each other by using wireless multihop communication. Due to its low cost, high flexibility, fast network establishment and self-reconfiguration, ad hoc networking has received much interest during the last ten years. However, without a fixed infrastructure, frequent path changes cause significant numbers of routing packets to discover new paths, leading to increased network congestion and transmission latency over fixed networks. Many on-demand routing protocols have been developed by using various routing mobility metrics to choose the most reliable routes, while dealing with the primary obstacle caused by node mobility. ¶ In the first part, we have developed an analysis framework for mobility metrics in random mobility model. ... ¶ In the second part, we investigate the mobility metric applications on caching strategies and hierarchy routing algorithm. ...
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Burgess, Robertson Wesley. "A TDDFT study of the optical absorption spectra of gold and silver clusters." Thesis, 2012. http://hdl.handle.net/1959.13/936786.
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Research Doctorate - Doctor of Philosophy (PhD)The absorption cross-section over the optical range of frequencies of gold and silver clusters of up to 171 atoms was calculated using time-dependent density functional theory. Calculations were performed using the package Octopus and used the explicit time propagation method. The wavefunctions were calculated over a real-space grid and exchange-correlation interactions were including using the local density approximation. Structures were cleaved from a bulk crystal and included high-symmetry structures as well as structures with lower levels of symmetry. The evolution of the absorption spectra over cluster size was investigated and several trends were identified. As cluster size increases the absorption spectra becomes smoother. For gold clusters with more than approximately 70 atoms, the absorption spectra have several common features, including an absorption peak at around 2.5-3.0 eV, commonly attributed to a plasmonic oscillation. Absorption spectra were compared to past calculations and experimental measurements where available. For gold clusters above approximately 150 atoms, the calculated absorption spectra are in reasonable agreement with Mie theory calculations and experimental measurements. The effect of different calculation methods and approximations on the calculated absorption cross-section was also identified. The inclusion of spin-polarisation and the use of an exchange-correlation potential using the generalised gradient approximation had minor impact on the calculated absorption spectra. A new method of analysing the nature of peaks in the absorption spectra was also investigated. This method entailed exciting the system at a single frequency, and analysing the evolution of the electron density over time. This initial investigation indicated a difference in the evolution of the system when it was oscillated at a frequency corresponding to a plasmonic response as compared to a frequency corresponding to an electron hole excitation. This possibly indicates a method for investigating the nature of a plasmonic response in clusters of this size. This thesis demonstrates that with current computing power the optical absorption spectra of metallic clusters can be calculated using time-dependent density functional theory over a continuous range of cluster sizes from several atoms up almost to the point at which classical calculations become accurate. It identifies what calculation parameters are important to the optical absorption spectra for future calculations to agree with classical calculations as more computing power becomes available.
Books on the topic "Optimal cluster density"
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Non-Linear Optical Response in Atoms, Molecules and Clusters: An Explicit Time Dependent Density Functional Approach. Springer, 2014.
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Goncharov, Vladimir. Non-Linear Optical Response in Atoms, Molecules and Clusters: An Explicit Time Dependent Density Functional Approach. Springer, 2014.
Find full textBook chapters on the topic "Optimal cluster density"
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Ching, W. Y. "Local Density Calculation of the Optical Properties of Insulators." In Density Functional Theory of Molecules, Clusters, and Solids, 189–222. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-011-0487-6_8.
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Ranjan, Prabhat, Ajay Kumar, and Tanmoy Chakraborty. "Structural, Electronic, and Optical Properties of AuCu n λ (λ = 0, ± 1; n = 1−8) Nanoalloy Clusters: A Density Functional Theory Study." In Chemical Nanoscience and Nanotechnology, 43–62. Series statement: AAP research notes on nanoscience & nanotechnology: Apple Academic Press, 2019. http://dx.doi.org/10.1201/9780429398254-4.
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Saxena, Priyank, and R. Sukesh Kumar. "Restoration of CT Images Corrupted With Fixed Valued Impulse Noise Using an Optimum Decision-Based Filter." In Advances in Multimedia and Interactive Technologies, 220–39. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5246-8.ch008.
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The main aim of this chapter is to perform the restoration of computed tomography (CT) images acquired at the reduced level of radiation dose. Reduction in radiation dose affects the image quality as it increases noise and decreases low contrast resolution. In this chapter, an optimum decision-based filter (ODBF) is proposed as an image-space denoising technique, to detect and restore the low dose CT (LDCT) images corrupted with fixed valued impulse noise (salt and pepper) of unequal density. The detection stage employs k-means clustering to discriminate the noise-free pixels from the noisy-pixels by splitting the image data into three clusters of different intensities. The restoration stage employs mask else trimmed median (METM) estimation followed by an optional adaptive mask sizing for restoration of noisy pixels. The proposed method demonstrates noticeable improvement over other existing methods in restoration of LDCT images while maintaining the image contrast and edge details.
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Fakeye, Attah, Olusegun Ige, and Olufemi Ogunsanwo. "Multivariate Assessment of California Bearing Ratio with Contrasted Geotechnical Properties of Soils in Ilorin-Lokoja Highway." In Engineering Geology. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.93523.
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California Bearing Ratio (CBR) is an important parameter used in designing pavement layers in road construction but testing this parameter requires time, labor, and huge cost. The study therefore applies multivariate approach to evaluate CBR based on contrasted geotechnical parameters along Ilorin-Lokoja highway. The results obtained showed that the migmatite-gneiss-derived soils are slightly more fines (< 0.075 mm; 7.4–59.6%), more plastic (PI; 1.6–39%), and have low strength (MDD = 1.8 mg/m3; CBR = 29.0%) than the metasediments (11–57.7%, 2.0–30%, 1.6 mg/m3, 23.6%) and older granite soils (8.2–32.7%, 2.6–13.4%, 1.7 mg/m3, 27.8%), respectively. The principal component analysis (PCA) revealed three major components (eigenvalues >1) which accounted for 83.8% of the total variance at the rate of 33.4, 14.7, and 11.4%. Major contributing variables for the components were fines (R = 0.87), plasticity index (R = 0.7), and coarse sand (R = 0.67%). Spatial distribution of these groups established interplay of sediment-gradation and moisture-connection evident in hierarchical cluster analysis that revealed patterns of homogeneity and soil relationships. Regression analysis established five models from predictor variables such as fines, activity, free swell, liquid and plastic limits, weighted plasticity index, optimum moisture content, and maximum dry density with the coefficient of determination (R2 = 0.33) and root mean square error (RMSE) of 7.80.
Conference papers on the topic "Optimal cluster density"
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Ouenes, A., R. Dirksen, M. Paryani, S. Rehman, and M. Bari. "Completion Optimization While Drilling – Geomechanical Steering towards Fracable Rock for Optimal Selection of Stage Spacing and Cluster Density in Unconventional Wells." In SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition. Society of Petroleum Engineers, 2017. http://dx.doi.org/10.2118/188087-ms.
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Suciu, Claudiu Valentin. "Molecular Dynamics Study of Wetting on Brushlike Nanopillar and Wavelike Nanorough Surfaces." In 2007 First International Conference on Integration and Commercialization of Micro and Nanosystems. ASMEDC, 2007. http://dx.doi.org/10.1115/mnc2007-21210.
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A Molecular Dynamics technique is proposed to simulate the motion of water nano-droplets on brushlike nanopillar and wavelike nanorough surfaces. Firstly, a brushlike nanopillar structure is obtained by deposition of a hexagonal packing of alkyl linear chains CnH2n+1 (n = 1–18) on a (0001) type flat surface, consisted of hexagonal packed carbon atoms. Distance between the grafted alkyl chains is selected in the 0.5–1.4 nm range, and the distance between the carbon atoms of the base surface is set to 0.1421nm. Next, the (0001) type flat surface is folded in order to obtain a wavelike nano-roughness. Water cluster is consisted of 729–2197 molecules, and after 25ps it reaches a diameter of 3–5 nm, which corresponds to a liquid phase of 1g/cm3 density, at an equilibrium temperature of 293K. Lennard-Jones potential is used to describe all the interactions into the considered system. By the appropriate input of the Lennard-Jones parameters one controls the hydrophilic level of the base surface. Influences of the intermolecular distance and the length of the grafted alkyls, as well as the influences of the nano-wavelength and the hydrophilic level of the base surface on the contact angle are illustrated. Such results are useful for the appropriate design of ultrahydrophobic nano-surfaces, and for the optimal design of nanoporous materials, able to produce surface dissipation of the mechanical energy.
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Liu, Kai, Andrés Tovar, Emily Nutwell, and Duane Detwiler. "Towards Nonlinear Multimaterial Topology Optimization Using Unsupervised Machine Learning and Metamodel-Based Optimization." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-46534.
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This work introduces a multimaterial density-based topology optimization method suitable for nonlinear structural problems. The proposed method consists of three stages: continuous density distribution, clustering, and metamodel-based optimization. The initial continuous density distribution is generated following a synthesis strategy without penalization, e.g., the hybrid cellular automaton (HCA) method. In the clustering stage, unsupervised machine learning (e.g., K-means clustering) is used to optimally classify the continuous density distribution into a finite number of clusters based on their similarity. Finally, a metamodel (e.g., Kriging interpolation) is generated and iteratively updated following a global optimization algorithm (e.g., genetic algorithms) to ultimately converge to an optimal material distribution. The proposed methodology is demonstrated with the design of multimaterial stiff (minimum compliance) structures, compliant mechanisms, and a thin-walled S-rail structure for crashworthiness.
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Sahoo, Rakesh Kumar, Shakti Shankar Ray, and Sridhar Sahu. "Optical absorption in Al2N2 cluster: A time dependent density functional study." In DAE SOLID STATE PHYSICS SYMPOSIUM 2018. AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5113422.
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Bai, Bing, Hanxiao Wang, Changyi Zhang, Zhenfeng Tong, and Wen Yang. "Effect of Element Segregation on Thermal Aging Behavior of 17-4PH Martensitic Stainless Steel for Nuclear Power Plant." In 2017 25th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/icone25-66977.
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The valve stem used in the main steam system of nuclear power plant is usually 17-4PH martensitic stainless steel. When it served in 300 C° for a long time, the thermal aging embrittlement of valve stem will be significant, with the performance of the ductile brittle transition temperature (DBTT) and the hardness increased, the upper stage energy (USE) decreased. It will seriously affect the safety and economic operation of nuclear power plant (NPP). It is important to study the thermal aging effect of the 17-4PH steel for safe operation of nuclear power plant. In this work, Three-Dimensional Atom Probe (3DAP), Energy Dispersive X-Ray Spectroscopy (EDX), Scanning Electron Microscope (SEM) and Optical Microscope (OM) are used to analyze the element distribution in 17-4PH steel. The results show that lath martensite will grow significantly under high temperature for a long time. More δ-ferrite will be found between lath martensite, and some carbide aggregates at its interface. In addition, the number density of Cu clusters in the17-4PH steel is increased. It is found that Ni and Mn have obvious segregation with the Cu cluster.
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Kan, Wu, and Jon E. Olson. "Numerical Investigation of Complex Hydraulic Fracture Development in Naturally Fractured Reservoirs." In SPE Hydraulic Fracturing Technology Conference. SPE, 2015. http://dx.doi.org/10.2118/spe-173326-ms.
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Abstract Complex fracture networks have become more evident in shale reservoirs due to the interaction between pre-existing natural and hydraulic fractures. Accurate characterization of fracture complexity plays an important role in optimizing fracturing design, especially for shale reservoirs with high-density natural fractures. In this study, we simulated simultaneous multiple fracture propagation within a single fracturing stage using a complex hydraulic fracture development model. The model was developed to simulate complex fracture propagation by coupling rock mechanics and fluid mechanics. A simplified three-dimensional displacement discontinuity method was implemented to more accurately calculate fracture displacements and fracture-induced dynamic stress changes than our previously developed pseudo-3d model. The effects of perforation cluster spacing, differential stress (SHmax - Shmin) and various geometry natural fracture patterns on injection pressure and fracture complexity were investigated. The single stage simulation results shown that (1) higher differential stress suppresses fracture length and increases injection pressure; (2) there is an optimal choice for the number of fractures per stage to maximize effective fracture surface area, beyond which increasing the number of fractures actually decreases effective fracture area; and (3) fracture complexity is a function of natural fracture patterns (various regular pattern geometries were investigated). Natural fractures with small relative angle to hydraulic fractures are more likely to control fracture propagation path. Also, natural fracture patterns with more long fractures tend to increase the likelihood to dominate the preferential fracture trend of fracture trajectory. Our numerical model can provide a physics-based complex fracture network that can be imported into reservoir simulation models for production analysis. The overall sensitivity results presented should serve as guidelines for fracture complexity analysis.
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Ray, Shakti S., and Sridhar Sahu. "Optical absorption in BnMg clusters: A time dependent density functional study." In DAE SOLID STATE PHYSICS SYMPOSIUM 2018. AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5113452.
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Shinde, Ravindra, and Meenakshi Tayade. "Optical absorption in B[sub 13] cluster: A time-dependent density functional approach." In SOLID STATE PHYSICS: PROCEEDINGS OF THE 57TH DAE SOLID STATE PHYSICS SYMPOSIUM 2012. AIP, 2013. http://dx.doi.org/10.1063/1.4791302.
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Liu, Mei-Jun, Meng Zhang, Qiang Zhang, Guan-Jun Yang, Cheng-Xin Li, Chang-Jiu Li, and Qiang Zhang. "Variation of Vaporized Coating Material in Free Plasma Jet of Plasma Spray-Physical Vapor Deposition Process." In ITSC2018, edited by F. Azarmi, K. Balani, H. Li, T. Eden, K. Shinoda, T. Hussain, F. L. Toma, Y. C. Lau, and J. Veilleux. ASM International, 2018. http://dx.doi.org/10.31399/asm.cp.itsc2018p0403.
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Abstract In the Plasma Spray-Physical Vapor Deposition (PS-PVD) process, the vapor atom of feedstock material is one deposition unit of the columnar structure coating. It is reported that the gas phase may be transformed into cluster when the powder feeding rate increases from small to large or the sedimentation distance increases from a certain distance to another distance. In order to understanding the variation of vaporized coating material in free plasma jet, the gaseous material capacity of plasma jet must be fundamentally understood. In this work, the thermal characteristics of plasma were firstly measured by optical emission spectrometry (OES). The results show that the free plasma jet is in the local thermal equilibrium due to a typical electron number density from 2.1×1015 to 3.1×1015 cm-3. In this condition, the temperature of gaseous zirconia can be equal to the plasma temperature. A model was developed to obtain the vapor pressure of gaseous ZrO2 molecules as a two dimensional map of jet axis and radial position corresponding to different average plasma temperatures. The overall gaseous material capacity of free plasma jet was further established. At a position of plasma jet, clusters may form when the gaseous material exceeds local maximum gaseous material capacity.
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Bugrien, J., K. Mwitondi, and F. Shuweihdi. "A kernel density smoothing method for determining an optimal number of clusters in continuous data." In RISK ANALYSIS 2014. Southampton, UK: WIT Press, 2014. http://dx.doi.org/10.2495/risk140151.
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