1,721,003 research outputs found
Sviluppo di un modello per la classificazione automatica di componenti meccanici prodotti in lotti mediante un’applicazione della geometria di Minkowsky all’analisi delle immagini
No full textOptimized Digital Twin Networks
No full textIn this paper we define an optimized digital twin knowledge network based both on the carrying capacity and preferential attachment. It is a logistic growth scale-free network which solves a min-max problem. The main advantage of this model is to build an integrated system (humans-digital twins) aiming to the max efficiency with limited costs of management
K-means Clustering and Hadamard Metric for Graphs Modelling
No full textModelling a geometric surface or contour surface is a modern field involving a wide range of topics such as mathematics, computer science, engineering design. The main task in modelling a geometric surface is to approximation the shape of the contour surface of a 3D solid object the most efficient way. In this paper we will give a method to optimize the geometric model, by defining a local sampling of the mesh, based on k-means method and minimization of the Hausdorff measure with an homologous model
On the Optimal Design of a Scale-Free Supply Network
No full textIn this paper we define a scale-free network based both on the preferential attachement parameter, of the Barabasi-Albert model, and on the new parameter of carrying capacity under a logistic growth. The main advantage is that by using this new parameter the network will grow as a set of communites each one with a limited number of nodes, each community with only one hub and a very little number of connections between communities, thus minimizing the number of links. With this model, which fulfills the 80–20 Pareto rule, we will also get an optimal designed network characterized by the limited cost of management
Generalized cauchy process: Difference iterative forecasting model
No full textThe contribution of this article is mainly to develop a new stochastic sequence forecasting model, which is also called the difference iterative forecasting model based on the Generalized Cauchy (GC) process. The GC process is a Long-Range Dependent (LRD) process described by two independent parameters: Hurst parameter H and fractal dimension D. Compared with the fractional Brownian motion (fBm) with a linear relationship between H and D, the GC process can more flexibly describe various LRD processes. Before building the forecasting model, this article demonstrates the GC process using H and D to describe the LRD and fractal properties of stochastic sequences, respectively. The GC process is taken as the diffusion term to establish a differential iterative forecasting model, where the incremental distribution of the GC process is obtained by statistics. The parameters of the forecasting model are estimated by the box dimension, the rescaled range, and the maximum likelihood methods. Finally, a real wind speed data set is used to verify the performance of the GC difference iterative forecasting model
Brain Tumor Segmentation Based on Bendlet Transform and Improved Chan-Vese Model
No full textAutomated segmentation of brain tumors is a difficult procedure due to the variability and blurred boundary of the lesions. In this study, we propose an automated model based on Bendlet transform and improved Chan-Vese (CV) model for brain tumor segmentation. Since the Bendlet system is based on the principle of sparse approximation, Bendlet transform is applied to describe the images and map images to the feature space and, thereby, first obtain the feature set. This can help in effectively exploring the mapping relationship between brain lesions and normal tissues, and achieving multi-scale and multi-directional registration. Secondly, the SSIM region detection method is proposed to preliminarily locate the tumor region from three aspects of brightness, structure, and contrast. Finally, the CV model is solved by the Hermite-Shannon-Cosine wavelet homotopy method, and the boundary of the tumor region is more accurately delineated by the wavelet transform coefficient. We randomly selected some cross-sectional images to verify the effectiveness of the proposed algorithm and compared with CV, Ostu, K-FCM, and region growing segmentation methods. The experimental results showed that the proposed algorithm had higher segmentation accuracy and better stability
Shearlet Transform and the Application in Image Processing
No full textShearlet is a multi-dimensional function used for sparse representation, which has many excellent characteristics such as multi-resolution and multi-direction. It can detect the position of singular points and the direction of singular curves, and is more sensitive to the geometric structure of the image. Therefore, this paper introduces the shearlet transform and its application in image processing, and introduces the bendlet transform proposed on this basis
Stability Analysis of Self-propelled Hydrodynamic Irrigation Machines Used for Food Industry Crops
No full textSome critical limit conditions for the stability of the self-propelled hydrodynamic irrigation machine used for food industry crops, have been studied, and experimental and numerical tests have been carried out for their determination. The strength forces necessary for the machine overturn have been calculated by a computer code realized in Matlab R2019a, and the corresponding values are listed as function of the soil slope angle ψ of the weight W and the pipeline strength force., With this aim, different operative conditions for the considered machine have been examined so that the pipeline strength force, under the following conditions: water filled pipeline of and empty pipeline;dry and wet soil. By analyzing the data measured in the open field, on a considered machine with a coil diameter of 3 m, the different contributes to the total rewinding strength have been examined during the considered tests. Further, it has been possible to deduce that by changing; only the value of the water pressure, the total value of the rewinding strength force increased by 100 daN, which is clearly due; to the changing pressure which increases the stiffness of the polyethylene pipeline. Moreover, other very dangerous limit conditions were determined during the rewinding phase of the pipeline on overflooded soil (also due to a rain storm), with a pipeline completely unwound on the soil and sunk into it. In these critical conditions, it has been noted that, to perform the operating phase, it is possible to reach a very high T value, which can cause the machine overturning even for ψ = 0 (horizontal case)
Methodologies for assessing the quality of 3D models obtained using close-range photogrammetry
Full text linkAlthough reality-based models are widely used to describe the geometric surfaces of an entity in a digital space, a systematic and universally recognised treatment of issues such as accuracy is lacking. The topic is certainly complex as this analysis should involve not only shape approximation but also other attributes (e.g., colour). Wanting to limit ourselves to geometry alone, this work proposes solutions for assessing the quality of photogrammetric models, differentiating them according to possible scenarios: sometimes, homologous models obtained using different techniques and technologies are available. In these cases, a comparison between digital reconstructions can serve to effectively quantify accuracy; more often, no terms of comparison are available, and one is forced to derive indicators from the same photogrammetric process to describe quality. We propose for this scenario a statistical analysis on the covariance matrix of the estimated coordinates for the tie points. The main goal is to provide a range of possible approaches to the conscious management of survey data
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