Short-term Inbetweening of 3D Human Motions

Creating computer generated human animations without the use of motion capture technology is a tedious and time consuming activity. Although there are several publications regarding animation synthesis using data driven methods, not many are dedicated towards the task of inbetweening, which consists of generating transition movements between frames. A modified version of LSTM, called Recurrent Transition Network (RTN), solves the inbetweening task for walking motion based on ten initial frames and two final frames. In this work, we are interested on the short-term inbetweening task, where we need to use the least amount of frames to generate the missing frames for short-term transitions. We are also interested on different kinds of movements, such as martial arts and Indian dance. Thus, we adapt the Recurrent Transition Network (RTN) to require only the two firts frames and the last one, called ARTN, and propose a simple post processing method combining ARTN with linear interpolation, called ARTN+. The results show that the average error of ARTN+ is less than the average error of each method (RTN and interpolation) separately in the martial arts and Indian dance dataset.

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Impact of Network Structure on Short-Term Voltage Stability Using Data-Driven Method

2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia) ◽

10.1109/isgt-asia.2019.8881311 ◽

2019 ◽

Author(s):

Wanjun Huang ◽

Xinran Zhang ◽

David J. Hill

Keyword(s):

Network Structure ◽

Voltage Stability ◽

Data Driven ◽

Short Term ◽

Using Data

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Short-term electricity consumption prediction for buildings using data-driven swarm intelligence based ensemble model

Energy and Buildings ◽

10.1016/j.enbuild.2020.110558 ◽

2021 ◽

Vol 231 ◽

pp. 110558

Author(s):

Kangji Li ◽

Jing Tian ◽

Wenping Xue ◽

Gang Tan

Keyword(s):

Swarm Intelligence ◽

Electricity Consumption ◽

Data Driven ◽

Ensemble Model ◽

Short Term ◽

Using Data ◽

Consumption Prediction

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A Novel Stacked Long Short-Term Memory Approach of Deep Learning for Streamflow Simulation

Sustainability ◽

10.3390/su132313384 ◽

2021 ◽

Vol 13 (23) ◽

pp. 13384

Author(s):

Majid Mirzaei ◽

Haoxuan Yu ◽

Adnan Dehghani ◽

Hadi Galavi ◽

Vahid Shokri ◽

...

Keyword(s):

Spatial Distribution ◽

Short Term Memory ◽

Data Driven ◽

Rainfall Runoff ◽

Short Term ◽

Runoff Simulation ◽

Physically Based ◽

Long Short Term Memory ◽

Using Data ◽

Modelling Approach

Rainfall-Runoff simulation is the backbone of all hydrological and climate change studies. This study proposes a novel stochastic model for daily rainfall-runoff simulation called Stacked Long Short-Term Memory (SLSTM) relying on machine learning technology. The SLSTM model utilizes only the rainfall-runoff data in its modelling approach and the hydrology system is deemed a blackbox. Conversely, the distributed and physically-based hydrological models, e.g., SWAT (Soil and Water Assessment Tool) preserve the physical aspect of hydrological variables and their inter-relations while taking a wide range of data. The two model types provide specific applications that interest modelers, who can apply them according to their project specification and objectives. However, sparse distribution of point-data may hinder physical models’ performance, which may not be the case in data-driven models. This study proposes a specific SLSTM model and investigates the SLSTM and SWAT models’ data dependency in terms of their spatial distribution. The study was conducted in the two distinct river basins of Samarahan and Trusan, Malaysia, with over 20 years of hydro-climate data. The Trusan basin’s rain gauges are scattered downstream of the basin outlet and Samarahan’s are located around the basin, with one station within each basin’s limits. The SWAT was developed and calibrated following its general modelling approach, however, the SLSTM performance was also tested using data preprocessing with principal component analysis (PCA). Results showed that the SWAT performance for daily streamflow simulation at Samarahan has been superior to that of Trusan. Both the SLSTM and PCA-SLSTM models, however, showed better performance at Trusan with PCA-SLSTM outperforming the SLSTM. This demonstrates that the SWAT model is greatly affected by the spatial distribution of its input data, while data-driven models, irrespective of the spatial distribution of their entry data, can perform well if the data adequacy condition is met. However, considering the structural difference between the two models, each has its specific application in a water resources context. The study of catchments’ response to changes in the hydrology cycle requires a physically-based model like SWAT with proper spatial and temporal distribution of its entry data. However, the study of a specific phenomenon without considering the underlying processes can be done using data-driven models like SLSTM, where improper spatial distribution of data cannot be a restricting factor.

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Utility of Big Data in Predicting Short-Term Blood Glucose Levels in Type 1 Diabetes Mellitus Through Machine Learning Techniques

Sensors ◽

10.3390/s19204482 ◽

2019 ◽

Vol 19 (20) ◽

pp. 4482 ◽

Cited By ~ 6

Author(s):

Ignacio Rodríguez-Rodríguez ◽

Ioannis Chatzigiannakis ◽

José-Víctor Rodríguez ◽

Marianna Maranghi ◽

Michele Gentili ◽

...

Keyword(s):

Machine Learning ◽

Prediction Models ◽

Data Driven ◽

Machine Learning Techniques ◽

Average Error ◽

Short Term ◽

Glucose Levels ◽

Learning Techniques ◽

Short Term Prediction

Machine learning techniques combined with wearable electronics can deliver accurate short-term blood glucose level prediction models. These models can learn personalized glucose–insulin dynamics based on the sensor data collected by monitoring several aspects of the physiological condition and daily activity of an individual. Until now, the prevalent approach for developing data-driven prediction models was to collect as much data as possible to help physicians and patients optimally adjust therapy. The objective of this work was to investigate the minimum data variety, volume, and velocity required to create accurate person-centric short-term prediction models. We developed a series of these models using different machine learning time series forecasting techniques suitable for execution within a wearable processor. We conducted an extensive passive patient monitoring study in real-world conditions to build an appropriate data set. The study involved a subset of type 1 diabetic subjects wearing a flash glucose monitoring system. We comparatively and quantitatively evaluated the performance of the developed data-driven prediction models and the corresponding machine learning techniques. Our results indicate that very accurate short-term prediction can be achieved by only monitoring interstitial glucose data over a very short time period and using a low sampling frequency. The models developed can predict glucose levels within a 15-min horizon with an average error as low as 15.43 mg/dL using only 24 historic values collected within a period of sex hours, and by increasing the sampling frequency to include 72 values, the average error is reduced to 10.15 mg/dL. Our prediction models are suitable for execution within a wearable device, requiring the minimum hardware requirements while at simultaneously achieving very high prediction accuracy.

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