Limits to growth of forest biomass carbon sink under climate change.

Widely recognized as a significant carbon sink, North American forests have experienced a history of recovery and are facing an uncertain future. This growing carbon sink is dictated by recovery from land-use change, with growth trajectory modified by environmental change. To address both processes, we compiled a forest inventory dataset from North America to quantify aboveground biomass growth with stand age across forest types and climate gradients. Here we show, the biomass grows from 90 Mg ha-1 (2000-2016) to 105 Mg ha-1 (2020 s), 128 Mg ha-1 (2050 s), and 146 Mg ha-1 (2080 s) under climate change scenarios with no further disturbances. Climate change modifies the forest recovery trajectory to some extent, but the overall growth is limited, showing signs of biomass saturation. The future (2080s) biomass will only sequester at most 22% more carbon than the current level. Given such a strong sink has limited growth potential, our ground-based analysis suggests policy changes to sustain the carbon sink

Augmented semi-supervised learning for salient object detection with edge computing

[EN] Salient object detection (SOD) from raw sensor images in the edge networks can effectively speed up the decision-making process in the complex environments, because it simulates the mechanism of human attention to identify salient objects from images. The success of supervised deep learning approaches have been widely proved SOD field. However, the imbalanced and limited training data at each edge device pose a huge challenge for us to deploy deep learning methods in the edge computing environments. In this article, we propose a cloud-edge distributed augmented semi-supervised learning architecture for SOD over the edge networks. The framework consists of two components: the base classification networks are employed in different edge nodes, and the reverse augmented network is employed in cloud. First, the base classification networks are trained with data from edge nodes while the reverse augmented network is trained with the whole data. Then, we concatenate each base classification network with reverse augmented network, thus the latter network can help the training of former network. Finally, we integrate the outputs of all base classification network to generate the pseudo-labels, which are used for semi-supervised learning of the augment network. We demonstrated a convincing performance of our semi-supervised learning framework on four bench-marked data-sets. These results show that our augmented semi-supervised learning framework can outperform other optimization strategies on deep learning for the edge computing.Yu, C.; Zhang, Y.; Mukherjee, M.; Lloret, J. (2022). Augmented semi-supervised learning for salient object detection with edge computing. IEEE Wireless Communications. 29(3):109-114. https://doi.org/10.1109/MWC.2020.200035110911429

Prediction of aptamer-protein interacting pairs using an ensemble classifier in combination with various protein sequence attributes

The ranked feature list given by the Relief algorithm. Within the list, a feature with a smaller index indicates that it is more important for aptamer-protein interacting pair prediction. Such a list of ranked features are used to establish the optimal feature set in the IFS procedure. (XLS 56.5 kb

Iterative Learning Control of Hysteresis in Piezoelectric Actuators

We develop convergence criteria of an iterative learning control on the whole desired trajectory to obtain the hysteresis-compensating feedforward input in hysteretic systems. In the analysis, the Prandtl-Ishlinskii model is utilized to capture the nonlinear behavior in piezoelectric actuators. Finally, we apply the control algorithm to an experimental piezoelectric actuator and conclude that the tracking error is reduced to 0.15% of the total displacement, which is approximately the noise level of the sensor measurement

Method of Quantitative Analysis for Multirobot Cooperative Hunting Behaviors

The kinematic behavior of mobile robots can be represented as functions of time. During the operation of a multirobot system, the orbit of a special robot is recorded. The embedding dimension and the delay time are chosen based on the correlation integral method. A chaotic attractor equivalent to the original system is reconstructed in phase space. The multirobot system can be adequately described based on the phase space information, and the dynamic system states can be forecast based on this information. The eigenvalues of the attractor are calculated including the maximum Lyapunov exponent and correlation dimension. The robot collective behavior is described and analyzed quantitatively based on the eigenvalues. The critical factor that affects the interaction of robots is investigated based on quantified parameters. Our analysis results can be used to improve the understanding of robot interaction mechanisms

Multiobjective Optimization of PID Controller of PMSM

PID controller is used in most of the current-speed closed-loop control of permanent magnet synchronous motors (PMSM) servo system. However, Kp, Ki, and Kd of PID are difficult to tune due to the multiple objectives. In order to obtain the optimal PID parameters, we adopt a NSGA-II to optimize the PID parameters in this paper. According to the practical requirement, several objective functions are defined. NSGA-II can search the optimal parameters according to the objective functions with better robustness. This approach provides a more theoretical basis for the optimization of PID parameters than the aggregation function method. The simulation results indicate that the system is valid, and the NSGA-II can obtain the Pareto front of PID parameters