A study on the failure prediction of composite laminates in bending

Failure prediction for composite materials under given loading conditions is important for efficient design in structural applications. Over the past several decades, there are numerous failure criteria proposed to more accurately predict the failure composite laminates. A lot of research was conducted to evaluate and validate the failure prediction capability for failure criteria. The most failure criteria are studied for in-plane loading conditions. Mechanical behavior of composite laminates varies depending on the loading conditions. Even if failure criterion is accurate under the in-plane loads, it cannot be accurate for out-of-plane loads such as bending. In many industrial structures, composite laminates is under out-of-plane load as well as in-plane loads. For the structural stability of the composite structures, it is important to accurately predict failure of composite laminates under bending. In this study, the failure prediction of composite laminates under bending is investigated. The non-linear finite element analysis using Arc-length method is performed. 2D strain-based interactive failure theory [1] that is more accurately final failure of composite laminate under multi-axial loading is applied to predict the final failure of composite laminates under bending. In order to compare the accuracy of the failure predictions, a 3-point bending test are performed for un-symmetric cross-ply [0/90]8 and quasi-isotropic [0/±45/90]2s composite laminates. Also, it is compared with the other failure criteria such as maximum strain, maximum stress and Tsai-Wu theories. Finally, the predicted results using 2D strain-based interactive failure theory more agree well with the experiment than other failure theories. Acknowledgements This work was supported under the framework of Aerospace Technology Development Program (No. 10074270, Development of Manufacturing Core Technology for 3-Dimnesional Woven Integrated Composite Wing Structure of 5,000 Pound VLJ Aircraft) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea) This work was supported by the New & Renewable Energy Core Technology Program of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea. (No. 20143030021130) References [1] S. Y. Lee and J. H. Roh, “Two-dimensional strain-based interactive failure theory for multidirectional composite laminates,” Composite Part B: Engineering, vol. 69, pp.69-75, 2015

Ball Trajectory Inference from Multi-Agent Sports Contexts Using Set Transformer and Hierarchical Bi-LSTM

As artificial intelligence spreads out to numerous fields, the application of AI to sports analytics is also in the spotlight. However, one of the major challenges is the difficulty of automated acquisition of continuous movement data during sports matches. In particular, it is a conundrum to reliably track a tiny ball on a wide soccer pitch with obstacles such as occlusion and imitations. Tackling the problem, this paper proposes an inference framework of ball trajectory from player trajectories as a cost-efficient alternative to ball tracking. We combine Set Transformers to get permutation-invariant and equivariant representations of the multi-agent contexts with a hierarchical architecture that intermediately predicts the player ball possession to support the final trajectory inference. Also, we introduce the reality loss term and postprocessing to secure the estimated trajectories to be physically realistic. The experimental results show that our model provides natural and accurate trajectories as well as admissible player ball possession at the same time. Lastly, we suggest several practical applications of our framework including missing trajectory imputation, semi-automated pass annotation, automated zoom-in for match broadcasting, and calculating possession-wise running performance metrics

6MapNet: Representing soccer players from tracking data by a triplet network

Although the values of individual soccer players have become astronomical, subjective judgments still play a big part in the player analysis. Recently, there have been new attempts to quantitatively grasp players' styles using video-based event stream data. However, they have some limitations in scalability due to high annotation costs and sparsity of event stream data. In this paper, we build a triplet network named 6MapNet that can effectively capture the movement styles of players using in-game GPS data. Without any annotation of soccer-specific actions, we use players' locations and velocities to generate two types of heatmaps. Our subnetworks then map these heatmap pairs into feature vectors whose similarity corresponds to the actual similarity of playing styles. The experimental results show that players can be accurately identified with only a small number of matches by our method.Comment: 12 pages, 4 figures, In 8th Workshop on Machine Learning and Data Mining for Sports Analytics (MLSA21

The Effects of Cognitive Appraisal and Emotion on Consumer Behavior: The Critical Role of Recollection in the Luxury Cruise Setting

Abstract de la ponencia[EN] The purposes of this study were: (1) to integrate the cognitive appraisal theory and script theory; (2) to examine the bonding character of recollection; and (3) to assess the relationships between consumers ‘appraisals, positive/negative emotions, recollection, storytelling and repurchase intention. A review of previous studies revealed 14 theoretical hypotheses. The proposed hypotheses were tested utilizing data collected from 300 luxury cruise passengers. Confirmatory factor analysis and structural equation modeling were utilized to test the proposed theoretical relationships. According to the results, this work was the first to integrate the cognitive appraisal approach and script theory and also depicted a new angle from which marketers can better understand cruise travelers’ behaviorJoo, E.; Shin, H.; Kim, I.; Choi, J.; Jang, J.; Hyun, S. (2016). The Effects of Cognitive Appraisal and Emotion on Consumer Behavior: The Critical Role of Recollection in the Luxury Cruise Setting. En CARMA 2016: 1st International Conference on Advanced Research Methods in Analytics. Editorial Universitat Politècnica de València. 167-167. https://doi.org/10.4995/CARMA2016.2015.3135OCS16716

Long-term Time Series Forecasting based on Decomposition and Neural Ordinary Differential Equations

Long-term time series forecasting (LTSF) is a challenging task that has been investigated in various domains such as finance investment, health care, traffic, and weather forecasting. In recent years, Linear-based LTSF models showed better performance, pointing out the problem of Transformer-based approaches causing temporal information loss. However, Linear-based approach has also limitations that the model is too simple to comprehensively exploit the characteristics of the dataset. To solve these limitations, we propose LTSF-DNODE, which applies a model based on linear ordinary differential equations (ODEs) and a time series decomposition method according to data statistical characteristics. We show that LTSF-DNODE outperforms the baselines on various real-world datasets. In addition, for each dataset, we explore the impacts of regularization in the neural ordinary differential equation (NODE) framework.Comment: Accepted at IEEE BigData 202