The influence of the brainstorming process on the creativity of vocational industrial education students in Taiwan the ROC

The purpose of this study was to determine the effectiveness of brainstorming to enhance creativity of vocational industrial education students, and to determine the enhancement effects of creative thinking abilities on different subjects in different technical areas;To accomplish the purpose, 173 students in two classes of the electronic and the machinery departments in Ta-an Senior Vocational Industrial School were involved in this study as subjects. The quasi-experimental design included non-equivalent control groups. Teaching time was five hours in each of the four classes. Experimental groups were taught employing the brainstorming process, while the control group was provided traditional instruction in the introduction of American culture, customs, and national parks. The verbal and figural forms of Torrance Test of Creative Thinking were used to pre-test and post-test all students;After the collection of data, a two-way analysis of covariance (ANCOVA) was used. Before analysis of covariance, a test for homogeneity of regression was conducted to check if it violated the assumption of homogeneity of regression in ANCOVA. If the assumption was violated, the data were analyzed with the analysis of variance for difference between t scores of the pre-test and the post-test;Some conclusions for this research are presented. After participating in the brainstorming session: (1) The experimental group students made significantly greater gains in fluency, flexibility, originality, and total of verbal creative thinking abilities than the control group students. (2) The experimental group students made significantly greater gains in fluency, originality, and total score of figural creative thinking abilities than the control group students, but not in flexibility. The enhancement of figural-elaboration scores for the experimental group occurred for the machinery department students, but not for the electronic department students. (3) There were no significant differences in the enhancement of the scores of fluency, flexibility, originality, and total score in verbal creative thinking abilities between the machinery and the electronic students. (4) There were no significant differences on the enhancement of the scores of fluency, flexibility, originality and total in figural creative thinking abilities between the machinery and the electronic students, while the effects of experimental teaching on the machinery department was better than that of the electronic department on the figural-elaboration scores

3D single breath-hold MR methodology for measuring cardiac parametric mapping at 3T

Mención Internacional en el título de doctorOne of the foremost and challenging subfields of MRI is cardiac magnetic resonance imaging (CMR). CMR is becoming an indispensable tool in cardiovascular medicine by acquiring data about anatomy and function simultaneously. For instance, it allows the non-invasive characterization of myocardial tissues via parametric mapping techniques. These mapping techniques provide a spatial visualization of quantitative changes in the myocardial parameters. Inspired by the need to develop novel high-quality parametric sequences for 3T, this thesis's primary goal is to introduce an accurate and efficient 3D single breath-hold MR methodology for measuring cardiac parametric mapping at 3T. This thesis is divided into two main parts: i) research and development of a new 3D T1 saturation recovery mapping technique (3D SACORA), together with a feasibility study regarding the possibility of adding a T2 mapping feature to 3D SACORA concepts, and ii) research and implementation of a deep learning-based post-processing method to improve the T1 maps obtained with 3D SACORA. In the first part of the thesis, 3D SACORA was developed as a new 3D T1 mapping sequence to speed up T1 mapping acquisition of the whole heart. The proposed sequence was validated in phantoms against the gold standard technique IR-SE and in-vivo against the reference sequence 3D SASHA. The 3D SACORA pulse sequence design was focused on acquiring the entire left ventricle in a single breath-hold while achieving good quality T1 mapping and stability over a wide range of heart rates (HRs). The precision and accuracy of 3D SACORA were assessed in phantom experiments. Reference T1 values were obtained using IR-SE. In order to further validate 3D SACORA T1 estimation accuracy and precision, T1 values were also estimated using an in-house version of 3D SASHA. For in-vivo validation, seven large healthy pigs were scanned with 3D SACORA and 3D SASHA. In all pigs, images were acquired before and after administration of MR contrast agent. The phantom results showed good agreement and no significant bias between methods. In the in-vivo experiments, all T1-weighted images showed good contrast and quality, and the T1 maps correctly represented the information contained in the T1-weighted images. Septal T1s and coefficients of variation did not considerably differ between the two sequences, confirming good accuracy and precision. 3D SACORA images showed good contrast, homogeneity and were comparable to corresponding 3D SASHA images, despite the shorter acquisition time (15s vs. 188s, for a heart rate of 60 bpm). In conclusion, the proposed 3D SACORA successfully acquired a whole-heart 3D T1 map in a single breath-hold at 3T, estimating T1 values in agreement with those obtained with the IR-SE and 3D SASHA sequences. Following the successful validation of 3D SACORA, a feasibility study was performed to assess the potential of modifying the acquisition scheme of 3D SACORA in order to obtain T1 and T2 maps simultaneously in a single breath-hold. This 3D T1/T2 sequence was named 3D dual saturation-recovery compressed SENSE rapid acquisition (3D dual-SACORA). A phantom of eight tubes was built to validate the proposed sequence. The phantom was scanned with 3D dual-SACORA with a simulated heart rate of 60 bpm. Reference T1 and T2 values were estimated using IR-SE and GraSE sequences, respectively. An in-vivo study was performed with a healthy volunteer to evaluate the parametric maps' image quality obtained with the 3D dual-SACORA sequence. T1 and T2 maps of the phantom were successfully obtained with the 3D dual-SACORA sequence. The results show that the proposed sequence achieved good precision and accuracy for most values. A volunteer was successfully scanned with the proposed sequence (acquisition duration of approximately 20s) in a single breath-hold. The saturation time images and the parametric maps obtained with the 3D dual-SACORA sequence showed good contrast and homogeneity. The septal T1 and T2 values are in good agreement with reference sequences and published work. In conclusion, this feasibility study's findings open the door to the possibility of using 3D SACORA concepts to develop a successful 3D T1/T2 sequence. In the second part of the thesis, a deep learning-based super-resolution model was implemented to improve the image quality of the T1 maps of 3D SACORA, and a comprehensive study of the performance of the model in different MR image datasets and sequences was performed. After careful consideration, the selected convolutional neural network to improve the image quality of the T1 maps was the Residual Dense Network (RDN). This network has shown outstanding performance against state-of-the-art methods on benchmark datasets; however, it has not been validated on MR datasets. In this way, the RDN model was initially validated on cardiac and brain benchmark datasets. After this validation, the model was validated on a self-acquired cardiac dataset and on improving T1 maps. The RDN model improved the images successfully for the two benchmark datasets, achieving better performance with the brain dataset than with the cardiac dataset. This result was expected as the brain images have more well-defined edges than the cardiac images, making the resolution enhancement more evident. On the self-acquired cardiac dataset, the model also obtained an enhanced performance on image quality assessment metrics and improved visual assessment, particularly on well-defined edges. Regarding the T1 mapping sequences, the model improved the image quality of the saturation time images and the T1 maps. The model was able to enhance the T1 maps analytically and visually. Analytically, the model did not considerably modify the T1 values while improving the standard deviation in both myocardium and blood. Visually, the model improved the T1 maps by removing noise and motion artifacts without losing resolution on the edges. In conclusion, the RDN model was validated on three different MR datasets and used to improve the image quality of the T1 maps obtained with 3D SACORA and 3D SASHA. In summary, a 3D single breath-hold MR methodology was introduced, including a ready to-go 3D single breath-hold T1 mapping sequence for 3T (3D SACORA), together with the ideas for a new 3D T1/T2 mapping sequence (3D dual-SACORA); and a deep learning-based post-processing implementation capable of improving the image quality of 3D SACORA T1 maps.This thesis has received funding from the European Union Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement N722427.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Carlos Alberola López.- Secretario: María Jesús Ledesma Carbayo.- Vocal: Nathan Mewto

Detection of Hemorrhages and Microaneurysms for Color Fundus Images

Here we address the detection of Hemorrhages and microaneurysms in color fundus images. In pre-Processing we separate red, green, blue color channel from the retinal images. The green channel will pass to the further process. The green color plane was used in the analysis since it shows the best contrast between the vessels and the background retina. Then we extract the GLCM(Gray Level Co-Occurance Matrix) feature. In the GLCMs, several statistics information are derived using the different formulas. These statistics provide information about the texture of an image. Such as Energy, Entropy, Dissimilarity, Contrast, Inverse difference , correlation Homogeneity, Auto correlation, Cluster Shade Cluster Prominence, Maximum probability, Sum of Squares will be calculated for texture image. After feature Extraction, we provide this feature to classifier. Finally it will predict about the retinal whether it is hemorrhages or microaneurysms . After predicting the about the retinal image we will localize the affected place. For segmenting the localized place we will use adaptive thresholding segmentation

6 Seconds of Sound and Vision: Creativity in Micro-Videos

The notion of creativity, as opposed to related concepts such as beauty or interestingness, has not been studied from the perspective of automatic analysis of multimedia content. Meanwhile, short online videos shared on social media platforms, or micro-videos, have arisen as a new medium for creative expression. In this paper we study creative micro-videos in an effort to understand the features that make a video creative, and to address the problem of automatic detection of creative content. Defining creative videos as those that are novel and have aesthetic value, we conduct a crowdsourcing experiment to create a dataset of over 3,800 micro-videos labelled as creative and non-creative. We propose a set of computational features that we map to the components of our definition of creativity, and conduct an analysis to determine which of these features correlate most with creative video. Finally, we evaluate a supervised approach to automatically detect creative video, with promising results, showing that it is necessary to model both aesthetic value and novelty to achieve optimal classification accuracy.Comment: 8 pages, 1 figures, conference IEEE CVPR 201

A Novel Euler's Elastica based Segmentation Approach for Noisy Images via using the Progressive Hedging Algorithm

Euler's Elastica based unsupervised segmentation models have strong capability of completing the missing boundaries for existing objects in a clean image, but they are not working well for noisy images. This paper aims to establish a Euler's Elastica based approach that properly deals with random noises to improve the segmentation performance for noisy images. We solve the corresponding optimization problem via using the progressive hedging algorithm (PHA) with a step length suggested by the alternating direction method of multipliers (ADMM). Technically, all the simplified convex versions of the subproblems derived from the major framework of PHA can be obtained by using the curvature weighted approach and the convex relaxation method. Then an alternating optimization strategy is applied with the merits of using some powerful accelerating techniques including the fast Fourier transform (FFT) and generalized soft threshold formulas. Extensive experiments have been conducted on both synthetic and real images, which validated some significant gains of the proposed segmentation models and demonstrated the advantages of the developed algorithm

An Unusual Transmission Spectrum for the Sub-Saturn KELT-11b Suggestive of a Sub-Solar Water Abundance

We present an optical-to-infrared transmission spectrum of the inflated sub-Saturn KELT-11b measured with the Transiting Exoplanet Survey Satellite (TESS), the Hubble Space Telescope (HST) Wide Field Camera 3 G141 spectroscopic grism, and the Spitzer Space Telescope (Spitzer) at 3.6 $\mu$m, in addition to a Spitzer 4.5 $\mu$m secondary eclipse. The precise HST transmission spectrum notably reveals a low-amplitude water feature with an unusual shape. Based on free retrieval analyses with varying molecular abundances, we find strong evidence for water absorption. Depending on model assumptions, we also find tentative evidence for other absorbers (HCN, TiO, and AlO). The retrieved water abundance is generally $\lesssim 0.1\times$ solar (0.001--0.7$\times$ solar over a range of model assumptions), several orders of magnitude lower than expected from planet formation models based on the solar system metallicity trend. We also consider chemical equilibrium and self-consistent 1D radiative-convective equilibrium model fits and find they too prefer low metallicities ($[M/H] \lesssim -2$, consistent with the free retrieval results). However, all the retrievals should be interpreted with some caution since they either require additional absorbers that are far out of chemical equilibrium to explain the shape of the spectrum or are simply poor fits to the data. Finally, we find the Spitzer secondary eclipse is indicative of full heat redistribution from KELT-11b's dayside to nightside, assuming a clear dayside. These potentially unusual results for KELT-11b's composition are suggestive of new challenges on the horizon for atmosphere and formation models in the face of increasingly precise measurements of exoplanet spectra.Comment: Accepted to The Astronomical Journal. 31 pages, 20 figures, 7 table

Monitoring of a carbon anode paste manufacturing process using machine vision and latent variable methods

Le procédé de réduction électrolytique Hall-Héroult est utilisé pour la fabrication industrielle d’aluminium primaire. Ce procédé nécessite l’utilisation d'anodes de carbone. L’uniformité de la qualité de celles-ci est un paramètre très important pour assurer la stabilité et des performances optimales des cuves d’électrolyse. Malheureusement, les fabricants d'anodes sont actuellement confrontés à une augmentation de la variabilité des matières premières. Cette situation est due à une diminution de la disponibilité de matières premières de bonne qualité à faibles coûts. Pour compenser, les fabricants d'anodes doivent diversifier leur choix de fournisseurs, ce qui augmente la variabilité. Cependant, les usines ne sont pas préparées pour réagir à cette situation tout en maintenant une qualité d'anode stable. Cette situation est due, entre autres, à un manque de mesures quantitatives en temps réel de la qualité des anodes. Plusieurs exemples d’applications industrielles de vision numérique ont été présentés dans la littérature. Par conséquent, il existe une opportunité de développer un tel système pour obtenir une mesure non destructive et en temps réel de la qualité de la pâte d'anode. Le développement du capteur a été fait avec de la pâte et des anodes pressées à l'échelle laboratoire. Un ensemble de caractéristiques de texture d'images calculées à partir de la transformée en ondelettes discrète (DWT) et de matrices de cooccurrence de niveaux de gris (GLCM) ont été sélectionnées. Ces caractéristiques étaient sensibles aux variations dans la formulation et de la quantité de brai dans la pâte. Le capteur est aussi capable de détecter la quantité optimale de brai (OPD) pour différents cokes. Ensuite, la sensibilité et la robustesse du capteur ont été testées avec de la pâte industrielle. Finalement, les usines collectent déjà beaucoup de mesures de procédé en temps réel. Ces données peuvent être utilisées dans une stratégie de monitorage statistique pour détecter et investiguer des déviations de qualité. Une nouvelle méthode statistique multivariée par variables latentes PLS multi-blocs séquentiels (SMB-PLS) a été développée pour améliorer l'interprétation des données industrielles par rapport aux méthodes usuelles de PLS multi-blocs. Cette méthode a également été utilisée pour discuter de la pertinence d’utiliser les caractéristique d'image de la pâte à un modèle statistique pour la surveillance de la variabilité du procédé.The Hall-Héroult electrolysis reduction process used for the industrial aluminium smelting relies on the consumption of carbon anodes. The quality and consistency of these anodes are very important for the stability and performance of the reduction cells. Unfortunately, the anode manufacturers currently face an increase in the raw material variability. This is due to the declining availability of high quality, low cost and consistent materials on the market forcing the anode manufacturers to diversify their suppliers. However, the anode plants are not prepared to compensate for this increase in variability and still maintain consistent anode quality. There is a lack of real-time quality monitoring and control of the baked anodes properties and the most important raw material and process parameters. Machine vision applications have been successful in many industrial applications. Therefore there is an opportunity to develop such a system to obtain a non destructive and online measurement of the anode paste quality. This sensor could then be used in a feedback/feedforward control strategy for attenuating the unmeasured raw material and process variations. The sensor development was performed using laboratory scale paste and pressed anodes. A set of image texture features computed from discrete wavelet transform (DWT) and gray level co-occurrence matrix (GLCM) methods were selected. These features could capture variations in formulation, pitch ratio in the paste and in pitch demand. The sensor was also found to be sensitive to the optimum pitch demand (OPD) of two different cokes. Then, the sensitivity and robustness of the sensor was tested using industrial paste. Finally, the anode plants already collect some real-time process measurement and off-line raw material and baked anode properties that can be used to monitor and troubleshoot process and quality deviations. A new sequential multi-block PLS (SMB-PLS) method was developed to improve the interpretation of complex industrial dataset compared to already available multi-block PLS methods. This method was also used to discuss the relevance of adding real-time paste image feature to a statistical model for monitoring of the process variability