Teaching equipment of newton’s second law Gallego Becerra, Hugo Armando; García, Sebastián Martínez; Henao Melo, Luis Guillermo
IOP conference series. Materials Science and Engineering,
06/2021, Letnik:
1154, Številka:
1
Journal Article
Recenzirano
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Abstract
Mechanic is the branch of physics that is responsible for the study of the movement and balance of bodies in a system, within this can also be considered the bodies at rest. For the first ...case the scientist Isaac Newton considered the study of the movement of bodies, where he defines that any physical system which is subjected to different actions, as long as such actions are not nullified, will result in a change in the state of the body. The force is the magnitude that quantitatively measures the intensity, direction and direction of that interactions, having said that, if the summation of all forces of a system is different from zero, it can be concluded that the system will be in motion and therefore have an associated acceleration. Based on the above definition, was executed by the research group Design and Construction of Prototypes for Demonstration Experiments (DICOPED) in the design and construction of a prototype that allows to verify the Newton’s Second Law, looking to develop an autonomous, robust and low-cost prototype, that allows the use of the technology and tools present in the means, encourage and facilitate the teaching of basic concepts but no less important of physics to students of basic and higher education.
Nelle Scuole di Architettura, come il MIT - Massachusetts Institute of Technology - o l'ETH di Zurigo, la prototipazione fisica rappresenta unesperienza di didattica efficace, il metodo formativo si ...basa sull'"imparare facendo" e i laboratori costituiscono le "officine" in cui realizzare "al vero" l'idea architettonica (Paris, 2017). Nei Design-Build projects, sviluppati in tutto il mondo come strategia di architectural education, gli studenti sviluppano e costruiscono in scala reale vere e proprie strutture edilizie (Folic et al., 2016). In particolare, stimolo per la progettazione tecnologica la tassonomia proposta da Stan Ruecker in relazione alle Digital Humanities (Ruecker, 2015) che individua tre diverse categorie di prototipi a seconda dellobiettivo per cui vengono realizzati: experiment, development and provocation prototypes. Un team di docenti e studenti della Scuola di Architettura del Politecnico di Torino, in collaborazione con l'associazione culturale Atelier Mobile e l'artista Carlos Valverde, ha sviluppato una proposta di architettura effimera in grado di valorizzare uno spazio residuale della cittå di Torino all'interno del cosiddetto Bunker, un ex area industriale dismessa alla fine degli anni '80, situata a Nord della cittå di Torino.
Extreme instance imbalance among categories and combinatorial explosion make the recognition of Human-Object Interaction (HOI) a challenging task. Few studies have addressed both challenges directly. ...Motivated by the success of few-shot learning that learns a robust model from a few instances, we formulate HOI as a few-shot task in a meta-learning framework to alleviate the above challenges. Due to the fact that the intrinsical characteristic of HOI is diverse and interactive, we propose a Semantic-guided Attentive Prototypes Network (SAPNet) framework to learn a semantic-guided metric space where HOI recognition can be performed by computing distances to attentive prototypes of each class. Specifically, the model generates attentive prototypes guided by the category names of actions and objects, which highlight the commonalities of images from the same class in HOI. In addition, we design two alternative prototypes calculation methods, i.e., Prototypes Shift (PS) approach and Hallucinatory Graph Prototypes (HGP) approach, which explore to learn a suitable category prototypes representations in HOI. Finally, in order to realize the task of few-shot HOI, we reorganize 2 HOI benchmark datasets with 2 split strategies, i.e., HICO-NN, TUHOI-NN, HICO-NF, and TUHOI-NF. Extensive experimental results on these datasets have demonstrated the effectiveness of our proposed SAPNet approach.
•A state of the art of all the developed solid-state caloric prototypes was presented.•The devices were classified as magnetocaloric, electrocaloric, elastocaloric or barocaloric.•The main design ...features and the energy performances shown were reported for each device.•A point of view of the future perspectives is proposed in the conclusions.
The solid-state processes for cooling and heat pump technologies are based on the well-known caloric effects (magneto-, electro-, elasto- and baro-caloric). With an interest arisen in 1976, year of the development of the first room-temperature magnetic refrigerator, up to a few years ago, magnetocaloric was among the most investigated caloric cooling techniques and it was considered among the main solid-state alternatives to vapor-compression cooling and heat-pumping. During such period, a remarkable number of prototypes of magnetic refrigerators or heat pumps was built. Recently, the attention toward all the four caloric effects has recently grown; therefore, an increase in projects on solid-state prototype developing (not only on magnetocaloric) was observed. The intention of this paper is to offer a state-of-the-art of all the solid-state prototypes for cooling and heat pumps processes, devoted to room temperature operations, developed before the year 2019.
K-Means algorithm is a popular clustering method. However, it has two limitations: 1) it gets stuck easily in spurious local minima, and 2) the number of clusters <inline-formula><tex-math ...notation="LaTeX">k</tex-math></inline-formula> has to be given a priori. To solve these two issues, a multi-prototypes convex merging based K-Means clustering algorithm (MCKM) is presented. First, based on the structure of the spurious local minima of the K-Means problem, a multi-prototypes sampling (MPS) is designed to select the appropriate number of multi-prototypes for data with arbitrary shapes. Then, a merging technique, called convex merging (CM), merges the multi-prototypes to get a better local minima without <inline-formula><tex-math notation="LaTeX">k</tex-math></inline-formula> being given a priori. Specifically, CM can obtain the optimal merging and estimate the correct <inline-formula><tex-math notation="LaTeX">k</tex-math></inline-formula>. By integrating these two techniques with K-Means algorithm, the proposed MCKM is an efficient and explainable clustering algorithm for escaping the undesirable local minima of K-Means problem without given <inline-formula><tex-math notation="LaTeX">k</tex-math></inline-formula> first. Two theoretical proofs are given to guarantee that the cost of MCKM (MPS+CM) can achieve a constant factor approximation to the optimal cost of the K-Means problem. Experimental results performed on synthetic and real-world data sets have verified the effectiveness of the proposed algorithm.
Learning with limited data is a key challenge for visual recognition. Many few-shot learning methods address this challenge by learning an instance embedding function from seen classes and apply the ...function to instances from unseen classes with limited labels. This style of transfer learning is task-agnostic: the embedding function is not learned optimally discriminative with respect to the unseen classes, where discerning among them leads to the target task. In this paper, we propose a novel approach to adapt the instance embeddings to the target classification task with a set-to-set function, yielding embeddings that are task-specific and are discriminative. We empirically investigated various instantiations of such set-to-set functions and observed the Transformer is most effective --- as it naturally satisfies key properties of our desired model. We denote this model as FEAT (few-shot embedding adaptation w/ Transformer) and validate it on both the standard few-shot classification benchmark and four extended few-shot learning settings with essential use cases, i.e., cross-domain, transductive, generalized few-shot learning, and low-shot learning. It archived consistent improvements over baseline models as well as previous methods, and established the new state-of-the-art results on two benchmarks.
We present a simple, fully-convolutional model for real-time (<inline-formula><tex-math notation="LaTeX">>30</tex-math> ...<mml:math><mml:mrow><mml:mo>></mml:mo><mml:mn>30</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="zhou-ieq1-3014297.gif"/> </inline-formula> fps) instance segmentation that achieves competitive results on MS COCO evaluated on a single Titan Xp, which is significantly faster than any previous state-of-the-art approach. Moreover, we obtain this result after training on only one GPU . We accomplish this by breaking instance segmentation into two parallel subtasks: (1) generating a set of prototype masks and (2) predicting per-instance mask coefficients. Then we produce instance masks by linearly combining the prototypes with the mask coefficients. We find that because this process doesn't depend on repooling, this approach produces very high-quality masks and exhibits temporal stability for free. Furthermore, we analyze the emergent behavior of our prototypes and show they learn to localize instances on their own in a translation variant manner, despite being fully-convolutional. We also propose Fast NMS, a drop-in 12 ms faster replacement for standard NMS that only has a marginal performance penalty. Finally, by incorporating deformable convolutions into the backbone network, optimizing the prediction head with better anchor scales and aspect ratios, and adding a novel fast mask re-scoring branch, our YOLACT++ model can achieve 34.1 mAP on MS COCO at 33.5 fps, which is fairly close to the state-of-the-art approaches while still running at real-time.
Visible-infrared person re-identification (VI-ReID) is an important task in night-time surveillance applications, since visible cameras are difficult to capture valid appearance information under ...poor illumination conditions. Compared to traditional person re-identification that handles only the intra-modality discrepancy, VI-ReID suffers from additional cross-modality discrepancy caused by different types of imaging systems. To reduce both intra- and cross-modality discrepancies, we propose a Hierarchical Cross-Modality Disentanglement (Hi-CMD) method, which automatically disentangles ID-discriminative factors and ID-excluded factors from visible-thermal images. We only use ID-discriminative factors for robust cross-modality matching without ID-excluded factors such as pose or illumination. To implement our approach, we introduce an ID-preserving person image generation network and a hierarchical feature learning module. Our generation network learns the disentangled representation by generating a new cross-modality image with different poses and illuminations while preserving a person's identity. At the same time, the feature learning module enables our model to explicitly extract the common ID-discriminative characteristic between visible-infrared images. Extensive experimental results demonstrate that our method outperforms the state-of-the-art methods on two VI-ReID datasets. The source code is available at: https://github.com/bismex/HiCMD.
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