Training Shapes Research Articles

Análisis implícito del flujo transitorio de agua con aire disuelto

Se aplica el método de diferencias finitas implícito (MDFI) para resolver el flujo transitorio en un sistema que transporta agua con aire disuelto utilizando una malla espacio-tiempo rectangular fija (o variable) definida por el método del paso de tiempo especificado. El contenido de aire en el fluido modifica tanto la velocidad de la onda como el número de Courant, lo cual hace inconveniente aplicar el Método de las Características (MC) tradicional y otros esquemas explícitos debido a su imposibilidad para simular los cambios en la magnitud, forma y frecuencia del tren de presiones. La conclusión es que el MDFI entrega una solución precisa y estable, con un buen nivel de ajuste respecto de un caso clásico reportado en la literatura, siendo una alternativa válida para la solución del transitorio en sistemas que transportan agua con aire disuelto.

The balancing act: the variation among deaneries across Great Britain regarding provision of focus on diabetes and endocrinology training

Diabetes and endocrinology is a medical specialty, and a five-year dual accreditation training programme in diabetes and endocrinology and general internal medicine is offered, with active participation in medical on-call rota. Some deaneries offer some respite from the ever-increasing general medical workload by offering a few months of training focusing only on specialty work in diabetes and endocrinology. The authors wanted to see if this experience is available uniformly to all the trainees in diabetes and endocrinology/ general internal medicine across Great Britain. To assess this, a survey of specialist training registrars on a dual accreditation programme for diabetes and general internal medicine from all deaneries in England, Scotland and Wales was performed by directly interviewing and asking them about any relaxation in either their on-call or ward commitments to focus on diabetes and endocrine specialty during their five years of training. The results showed that the acute take/general medical commitment-free training periodfocusing only on diabetes and endocrinology ranges from zero in some deaneries to nearly three years in others. This simple survey highlights the extent of variability that exists in dual diabetes/endocrinology and general internal medicine training programmes across deaneries in England, Scotland and Wales, which may increase further once the training programme is reduced to four years after new changes from Shape of Training.

Multi-Scale Shape Boltzmann Machine: A Shape Model Based on Deep Learning Method

Shape modelling is very important in many tasks of computer vision in the internet of things. Shape Boltzmann Machine (SBM) is a strong shape model, having ability to capture the details of object shape by introducing the Local Receptive Fields (LRF) and weight sharing into a deep learning architecture. However, applying LRF only in a single layer restrict its capabilities of learning more de-tails of object shape and representation of local shape parts. In this paper, we propose a new shape model based on Deep Boltzmann Machine (DBM) which we call Multi-Scale Shape Boltzmann Machine (MSSBM). By introducing weight sharing and LRF hierarchically in a deep architecture, MSSBM is capable of learning the true binary distributions of training shapes and generating more realistic shapes than the existing models, such as Deep Belief Network (DBN), DBM, SBM. Such capabilities make MSSBM suitable for many vision tasks, for example, image segmentation, object detection and inpainting, by enforcing shape prior knowledge. We demonstrate the performance of MSSBM through several experiments on three different datasets, in which exploitation of the details of shape structure is important for capturing the statistical variability of the underlying shape distributions. Experimental results show that MSSBM is a strong model for representing binary shapes that contains complex structure features.

Aerodynamic Optimization of the Head Shape of a High-speed Train Based on Adjoint Method

The adjoint optimization method has been well developed and applied in the field of aviation. In this paper, the adjoint method is used to optimize the head shape of a simplified high-speed train, and the feasibility of this method in aerodynamic shape optimization of high speed-trains is discussed. The optimization results show that the aerodynamic drag coefficient of the optimized train is 0.9% lower than that of the original one, while the main feature of the head shape is not changed significantly. The adjoint method has important engineering application value in the aerodynamic optimization of high-speed trains.

Numerical Simulation of High Speed Train’s Head Shape and Resistance Characteristics

To develop Chinese standard EMU with speed of 350 kilometers per hour, the paper introduces a numerical simulation method to evaluate the resistance characteristics of four kinds of trains with different head shapes and gives suggestion on type selection. According to resistance characteristics of three parts of main part including pantograph cavity, windshield and air conditioner fairing, the present research carry out the local optimum design of train and calculate selected types, involving 11 types of train shapes. It is found that the optimal design is capable to effectively reduce operating aerodynamic resistance of the train and improve the vehicle aerodynamic performance.

Study on the Aerodynamic Performance of the High-Speed Train Head with Symmetrical and Asymmetric Nose Shape

This study focused on the use of mesh morphing on optimizing high-speed train shape. Three shape variables were studied in this paper: the angle of cab-window, length of train-nose and width of train-nose. The original high-speed train head was optimized in symmetrical and asymmetric deformation. Total drag coefficient and lift coefficient of head car and tail car were optimized using Kriging model and NSGA- Ⅱ. As a result, three objective aerodynamic performance of the optimal train in symmetrical deformation is slightly improved comparing with the original ones and that in asymmetric deformation is improved significantly. Also, results of the symmetrical and asymmetric optimized high-speed train running in different speed were compared with that of the original high-speed train. Proposed method in this paper has been proved to be an efficient and feasible approach to study and optimize the aerodynamic performance of different high-speed trains as well as other structures.

How do workplaces, working practices and colleagues affect UK doctors’ career decisions? A qualitative study of junior doctors’ career decision making in the UK

ObjectivesThis study draws on an in-depth investigation of factors that influenced the career decisions of junior doctors.SettingJunior doctors in the UK can choose to enter specialty training (ST) programmes within...

Image Segmentation Using Disjunctive Normal Bayesian Shape and Appearance Models.

The use of appearance and shape priors in image segmentation is known to improve accuracy; however, existing techniques have several drawbacks. For instance, most active shape and appearance models require landmark points and assume unimodal shape and appearance distributions, and the level set representation does not support construction of local priors. In this paper, we present novel appearance and shape models for image segmentation based on a differentiable implicit parametric shape representation called a disjunctive normal shape model (DNSM). The DNSM is formed by the disjunction of polytopes, which themselves are formed by the conjunctions of half-spaces. The DNSM's parametric nature allows the use of powerful local prior statistics, and its implicit nature removes the need to use landmarks and easily handles topological changes. In a Bayesian inference framework, we model arbitrary shape and appearance distributions using nonparametric density estimations, at any local scale. The proposed local shape prior results in accurate segmentation even when very few training shapes are available, because the method generates a rich set of shape variations by locally combining training samples. We demonstrate the performance of the framework by applying it to both 2-D and 3-D data sets with emphasis on biomedical image segmentation applications.

How can general paediatric training be optimised in highly specialised tertiary settings? Twelve tips from an interview-based study of trainees

ObjectivesBoth general and subspecialty paediatric trainees undertake attachments in highly specialised tertiary hospitals. Trainee feedback suggests that mismatches in expectations between trainees and supervisors and a perceived lack of educational...

3-D Active Contour Segmentation Based on Sparse Linear Combination of Training Shapes (SCoTS).

SCoTS captures a sparse representation of shapes in an input image through a linear span of previously delineated shapes in a training repository. The model updates shape prior over level set iterations and captures variabilities in shapes by a sparse combination of the training data. The level set evolution is therefore driven by a data term as well as a term capturing valid prior shapes. During evolution, the shape prior influence is adjusted based on shape reconstruction, with the assigned weight determined from the degree of sparsity of the representation. For the problem of lung nodule segmentation in X-ray CT, SCoTS offers a unified framework, capable of segmenting nodules of all types. Experimental validations are demonstrated on 542 3-D lung nodule images from the LIDC-IDRI database. Despite its generality, SCoTS is competitive with domain specific state of the art methods for lung nodule segmentation.

Texture Evolution during Isothermal, Isostrain and Isobaric Loading of Polycrystalline Shape Memory NiTi.

In situ neutron diffraction was used to provide insight into martensite variant microstructures during isothermal, isobaric, and isostrain loading in shape memory NiTi. Results show variant microstructures were equivalent for the corresponding strain and more importantly, the reversibility and equivalency was immediately evident in variant microstructures that were first formed isobarically but then reoriented to a near random self-accommodated microstructure following isothermal deformation. Variant microstructures formed isothermally were not significantly affected by a subsequent thermal cycle under constant strain. In all loading cases considered, the resulting variant microstructure correlated with strain and did not correlate with stress. Based on the ability to select a variant microstructure for a given strain despite thermomechanical loading history, the results demonstrated here can be obtained by following any sequence of thermomechanical loading paths over multiple cycles. Thus for training shape memory alloys (repeating thermomechanical cycling to obtain the desired variant microstructure), optimal paths can be selected so as to minimize the number of training cycles required thereby increasing the overall stability and fatigue life of these alloys in actuator or medical applications.

Automatic construction of statistical shape models using deformable simplex meshes with vector field convolution energy

Background In the active shape model framework, principal component analysis (PCA) based statistical shape models (SSMs) are widely employed to incorporate high-level a priori shape knowledge of the structure to be segmented to achieve robustness. A crucial component of building SSMs is to establish shape correspondence between all training shapes, which is a very challenging task, especially in three dimensions.Methods We propose a novel mesh-to-volume registration based shape correspondence establishment method to improve the accuracy and reduce the computational cost. Specifically, we present a greedy algorithm based deformable simplex mesh that uses vector field convolution as the external energy. Furthermore, we develop an automatic shape initialization method by using a Gaussian mixture model based registration algorithm, to derive an initial shape that has high overlap with the object of interest, such that the deformable models can then evolve more locally. We apply the proposed deformable surface model to the application of femur statistical shape model construction to illustrate its accuracy and efficiency.ResultsExtensive experiments on ten femur CT scans show that the quality of the constructed femur shape models via the proposed method is much better than that of the classical spherical harmonics (SPHARM) method. Moreover, the proposed method achieves much higher computational efficiency than the SPHARM method.ConclusionsThe experimental results suggest that our method can be employed for effective statistical shape model construction.

The role of residual stress states in modeling the cyclic two-way shape memory behavior of high-temperature NiTiPd alloys and actuation components

A multi-mechanism, three-dimensional, material model that captures the cyclic training and subsequent two-way shape memory behaviors of high-temperature ternary NiTiPd material is presented. To this end, available experimental test data on this alloy have been used for characterization of the model parameters and the subsequent validation of its predictions. Numerical simulations involving many thermal cycles and stress levels are used to investigate some of the key factors which influence the magnitude and stability of TWSM actuation strains. In particular, the developed model is used to provide quantitative measurements of the internal stresses induced in the NiTiPd SMA material after cyclic training, and how they predominantly affect the behavior of the material under zero-load TWSME. This is further substantiated by a detailed analysis of the global bending response of a trained beam strip, where emphasis is placed on the interplay between the two major agents responsible for the resulting TWSME actuation rotations: (1) intrinsic internal state variables, reflecting the microstructural changes occurring at the material point during thermal cycling, and (2) the extrinsic global residual state resulting from the inhomogeneous stress fields at different points over the section of the trained beam upon release of the training loads.

Surrogate Based Optimization of Aerodynamic Noise for Streamlined Shape of High Speed Trains

Aerodynamic noise increases with the sixth power of the running speed. As the speed increases, aerodynamic noise becomes predominant and begins to be the main noise source at a certain high speed. As a result, aerodynamic noise has to be focused on when designing new high-speed trains. In order to perform the aerodynamic noise optimization, the equivalent continuous sound pressure level (SPL) has been used in the present paper, which could take all of the far field observation probes into consideration. The Non-Linear Acoustics Solver (NLAS) approach has been utilized for acoustic calculation. With the use of Kriging surrogate model, a multi-objective optimization of the streamlined shape of high-speed trains has been performed, which takes the noise level in the far field and the drag of the whole train as the objectives. To efficiently construct the Kriging model, the cross validation approach has been adopted. Optimization results reveal that both the equivalent continuous sound pressure level and the drag of the whole train are reduced in a certain extent.

Multi-resolution multi-object statistical shape models based on the locality assumption.

Statistical shape models learned from a population of previously observed training shapes are nowadays widely used in medical image analysis to aid segmentation or classification. However, providing an appropriate and representative training population of preferably manual segmentations is typically either very labor-intensive or even impossible. Therefore, statistical shape models in practice frequently suffer from the high-dimension-low-sample-size (HDLSS) problem resulting in models with insufficient expressiveness. In this paper, a novel approach for learning representative multi-resolution multi-object statistical shape models from a small number of training samples that adequately model the variability of each individual object as well as their interrelations is presented. The method is based on the assumption of locality, which means that local shape variations have limited effects in distant areas and, therefore, can be modeled independently. This locality assumption is integrated into the standard statistical shape modeling framework by manipulating the sample covariance matrix (non-zero covariances between distant landmarks are set to zero). To allow for multi-object modeling, a method for computing distances between points located on different object shapes is proposed. Furthermore, different levels of locality are introduced by deriving a multi-resolution scheme, which is equipped with a method to combine variability information modeled at different levels into a single shape model. This combined representation of global and local variability in a single shape model allows the use of the classical active shape model strategy for model-based image segmentation. An extensive evaluation based on a public data base of 247 chest radiographs is performed to show the modeling and segmentation capabilities of the proposed approach in single- and multi-object HDLSS scenarios. The new approach is not only compared to the classical shape modeling method but also to three state-of-the-art shape modeling approaches specifically designed to cope with the HDLSS problem. The results show that the new approach significantly outperforms all other approaches in terms of generalization ability and model-based segmentation accuracy.

Multi-Objective Aerodynamic Optimization of the Streamlined Shape of High-Speed Trains Based on the Kriging Model.

Minimizing the aerodynamic drag and the lift of the train coach remains a key issue for high-speed trains. With the development of computing technology and computational fluid dynamics (CFD) in the engineering field, CFD has been successfully applied to the design process of high-speed trains. However, developing a new streamlined shape for high-speed trains with excellent aerodynamic performance requires huge computational costs. Furthermore, relationships between multiple design variables and the aerodynamic loads are seldom obtained. In the present study, the Kriging surrogate model is used to perform a multi-objective optimization of the streamlined shape of high-speed trains, where the drag and the lift of the train coach are the optimization objectives. To improve the prediction accuracy of the Kriging model, the cross-validation method is used to construct the optimal Kriging model. The optimization results show that the two objectives are efficiently optimized, indicating that the optimization strategy used in the present study can greatly improve the optimization efficiency and meet the engineering requirements.

Multi-objective Optimization Design of the Streamlined Head Shape of Super High-speed Trains

Multi-objective Optimization Design of the Streamlined Head Shape of Super High-speed Trains

Shape Optimization of High-Speed Rail by Biomimetic

The Southern Corridor High-Speed Rail (HSR) has the potential to connect Kuala Lumpur and Singapore with only 90 minutes of travel time. The HSR needs to travel at minimum drag in order to promote ride comfort and environmental sustainability. Eight combinations of train head bio-models are generated from a morphological chart of three key concepts formed by biomimetic method, referring to the peregrine falcon, belted kingfisher, sailfish and dolpine. The three key concepts are the type of train nose, cross-section shape, and train nose shape. The bio-models are drawn by computer-aided drawing software and the air flow around the bio-models are simulated in order to obtain the drag coefficients in counterflow condition. The drag coefficient of each bio-models are compared and validated with published drag coefficient of Harmony CRH380A, which is 0.1971. The bio-models with lowest drag coefficient is the bio-model with sharp-streamlined nose and round-based crosssection, which is 0.1559. The drag coefficient of Harmony CRH380A is reduced by 20.9% if the windscreen is removed and the paraboloid nose is replaced with sharp nose. These bio-models can be a guideline for future studies in developing a more sustainable train shape in the railway industry.

Automatic extraction of generic focal features on 3D shapes via random forest regression analysis of geodesics-in-heat

Flexible definition and automatic extraction of generic features on 3D shapes is important for feature-centric geometric analysis, however, existing techniques fall short in measuring and locating semantic features from users' psychological standpoints. This paper makes an initial attempt to propose a learning based generic modeling approach for user-central definition and automatic extraction of features on arbitrary shapes. Instead of purely resorting to certain local geometric extremes to simply formulate feature metrics, it enables the users to arbitrarily specify application-specific features on training shapes, so that similar features can be automatically extracted from other same-category shapes with isometric or near-isometric deformations. Our key originality is built upon an observation: the geodesic distance from one point to desired feature point on testing shape should be similar to the points on training shapes. To this end, we propose a novel regression model to bridge the massive random-sampled local properties and the desired feature points via incorporating their corresponding geodesic distances into the powerful random forest framework. On that basis, an effective voting strategy is proposed to estimate the locations of the user-specified features on new shapes. Our extensive experiments and comprehensive evaluations have demonstrated many attractive advantages of our method, including being fully-automatic, robust to noise and partial holes, invariant under isometric and near-isometric deformation, and also scale-invariant, which can well facilitate to many downstream geometry-processing applications such as semantic mesh segmentation, mesh skeleton extraction, etc.

Musical training shapes neural responses to melodic and prosodic expectation

Current research on music processing and syntax or semantics in language suggests that music and language share partially overlapping neural resources. Pitch also constitutes a common denominator, forming melody in music and prosody in language. Further, pitch perception is modulated by musical training. The present study investigated how music and language interact on pitch dimension and whether musical training plays a role in this interaction. For this purpose, we used melodies ending on an expected or unexpected note (melodic expectancy being estimated by a computational model) paired with prosodic utterances which were either expected (statements with falling pitch) or relatively unexpected (questions with rising pitch). Participants' (22 musicians, 20 nonmusicians) ERPs and behavioural responses in a statement/question discrimination task were recorded. Participants were faster for simultaneous expectancy violations in the melodic and linguistic stimuli. Further, musicians performed better than nonmusicians, which may be related to their increased pitch tracking ability. At the neural level, prosodic violations elicited a front-central positive ERP around 150ms after the onset of the last word/note, while musicians presented reduced P600 in response to strong incongruities (questions on low-probability notes). Critically, musicians' P800 amplitudes were proportional to their level of musical training, suggesting that expertise might shape the pitch processing of language. The beneficial aspect of expertise could be attributed to its strengthening effect of general executive functions. These findings offer novel contributions to our understanding of shared higher-order mechanisms between music and language processing on pitch dimension, and further demonstrate a potential modulation by musical expertise.