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Background and aims: The quantitative retrieval of soil organic carbon (SOC) storage, particularly for soils with a large potential for carbon sequestration, is of global interest due to its link with the carbon cycle and the mitigation of climate change. However, complex ecosystems with good soil qualities for SOC storage are poorly studied. Methods: The interrelation between SOC and various vegetation remote sensing drivers is understood to demonstrate the link between the carbon stored in the vegetation layer and SOC of the top soil layers. Based on the mapping of SOC in two horizons (0–30 cm and 30–60 cm) we predict SOC with high accuracy in the complex and mountainous heterogeneous páramo system in Ecuador. A large SOC database (in weight % and in Mg/ha) of 493 and 494 SOC sampling data points from 0–30 cm and 30–60 cm soil profiles, respectively, were used to calibrate GPR models using Sentinel-2 and GIS predictors (i.e., Temperature, Elevation, Soil Taxonomy, Geological Unit, Slope Length and Steepness (LS Factor), Orientation and Precipitation). Results: In the 0–30 cm soil profile, the models achieved a R2 of 0.85 (SOC%) and a R2 of 0.79 (SOC Mg/ha). In the 30–60 cm soil profile, models achieved a R2 of 0.86 (SOC%), and a R2 of 0.79 (SOC Mg/ha). Conclusions: The used Sentinel-2 variables (FVC, CWC, LCC/Cab, band 5 (705 nm) and SeLI index) were able to improve the estimation accuracy between 3–21% compared to previous results of the same study area. CWC emerged as the most relevant biophysical variable for SOC prediction. [ABSTRACT FROM AUTHOR]

In this study, a robotic hand control system was designed based on data gloves, aiming to provide more intuitive control and improved operational performance for a remote robotic hand. Compensation measures were proposed for the time lag effect on the remote-control system to address the input and feedback time delays of the remote robot system. A Smith predictor structure was modified by replacing the linear estimator with a recurrent neural network. A convolutional neural network was applied to the long short-term memory (LSTM) model, as it had a better convergence time and learning performance than the multi-layer perceptron model during training. The experimental results demonstrate that the control effect of this scheme is approximately 0.5 s faster than the normal Smith predictive control, proving its effectiveness. [ABSTRACT FROM AUTHOR]

Although Gaussian process regression (GPR) is a powerful Bayesian nonparametric regression model for engineering problems, its predictive performance is highly dependent on a kernel for covariance function of GPR. However, choosing a proper kernel is still challenging even for experts. To choose proper kernel automatically, this study proposes a compositional kernel (CPK) learning using tree-based genetic programming (GEP). The optimal structure of the kernel is defined as a compositional representation based on sums and products of eight base-kernels. The CPK can be encoded as a tree-structure, so that tree-based GEP is employed to discover an optimal tree-structure of the CPK. To avoid overly complex solution in GEP, the proposed method introduced a dynamic maximum tree-depth technique. The novelty of the proposed method is to utilize more flexible and efficient learning capability to learn the relationship between input and output than existing methods. To evaluate the learning capability of the proposed method, seven test functions were firstly investigated with various noise levels, and its predictive accuracy was compared with existing methods. Reliability problems in both parallel and series systems were introduced to evaluate the performance of the proposed method for efficient reliability assessment. The results show that the proposed method generally outperforms or performs similarly to the best one among existing methods. In addition, it is also shown that proper kernel function can significantly improve the performance of GPR as the training data increases. Stated differently, the proposed method can learn the function of being fitted efficiently with less training samples than existing methods. In this context, the proposed method can make powerful and automatic predictive modeling based on GPR in engineering problems. [ABSTRACT FROM AUTHOR]