Nitrogen loss assessment and environmental consequences in the loess soil of China

Attention is focused on fertilizer nitrogen loss and the environmental consequences in Shaanxi Province in loess region of China, including N losses to the atmosphere via ammonia volatilization, nitrification and denitrification, N losses to groundwater by leaching, and crop uptake by roots. Three soils were selected, Entisol, Anthrosol and Luvisol from north, central and south Shaanxi, respectively. Nitrification and NH4+ fixation were measured using a closed chamber method in the laboratory. Denitrification was tested in the laboratory with intact soil cores, C2H2 inhibition techniques. N2O emission was assessed via in situ measurement of N2O in the soil profile and at the soil surface in field experiments. Fertilizer use and crop yields obtained by the farmers were investigated on a large scale in Shaanxi Province. Transformation of fertilizer NH4+ to NO3- was within nine days in the Entisol and Anthrosols, but it took 40 days in Luvisol due to NH4+ fixation by clay minerals. In the pot experiment open to the wind and sunshine with different water content, applied N fertilizer recovery was 74.2% for the Luvisol and 61.3% for the Entisol. The results for the Luvisol showed lower nitrogen recovery as initial soil water content increased. When the fertilizer was incorporated, the recovery was 91.6% at 8% and 68.9% at 28% water content. Recovery increased with increasing soil clay content. Large amount of nitrate was accumulated at 200-400 cm depth in the soil profile and accounted for 362-543, 144-677 and 165-569 kg N ha-1 in terrace and bottom land in north Shaanxi, terrace land in Guanzhong and south Shaanxi, respectively. N2O measurements also showed that N2O spatial variation in the profile could be ranked as, 10 cm < 30 cm < 150 cm < 90 cm < 60 cm. Temporal variation was correlated with rainfall or irrigation. Closed chamber measurements or calculations from profile concentrations resulted in N2O emission of less than 1 kg N2O ha-1 y-1. An investigation showed that soil fertility in the Guanzhong area is high, but yield has not increased with increasing N fertilizer application during the last five years. Over-application of N fertilizer was very common in the Guanzhong area and ranged from 100 to 382 kg N ha-1 for wheat and from 106 to 530 kg N ha-1 for maize. The results of the experiments indicate that the N fertilizer recovery efficiency is about 30% and the consequences of N losses are seriously threatening the environment by leaching to the groundwater and by denitrification to the atmosphere

Reference adaptation for robots in physical interactions with unknown environments

In this paper, we propose a method of reference adaptation for robots in physical interactions with unknown environments. A cost function is constructed to describe the interaction performance, which combines trajectory tracking error and interaction force between the robot and the environment. It is minimized by the proposed reference adaptation based on trajectory parametrization and iterative learning. An adaptive impedance control is developed to make the robot be governed by the target impedance model. Simulation and experiment studies are conducted to verify the effectiveness of the proposed method

Adaptive control for robot navigation in human environments based on social force model

In this paper, we introduce a novel control scheme based on the social force model for robots navigating in human environments. Social proxemics potential field is constructed based on the theory of proxemics and used to generate social interaction force for design of robot motion control. A combined kinematic/dynamic control is proposed to make the robot follow the target social force model, in the presence of kinematic velocity constraints. Under the proposed framework, given a specific social convention, robot is able to generate and modify its path smoothly without violating the proxemics constraints. The validity of the proposed method is verified through experimental studies using the V-rep platform

Optimal critic learning for robot control in time-varying environments

In this paper, optimal critic learning is developed for robot control in a time-varying environment. The unknown environment is described as a linear system with time-varying parameters, and impedance control is employed for the interaction control. Desired impedance parameters are obtained in the sense of an optimal realization of the composite of trajectory tracking and force regulation. Q-function based critic learning is developed to determine the optimal impedance parameters without the knowledge of the system dynamics. Simulation results are presented and compared with existing methods, and the efficacy of the proposed method is verified

Adaptive control of robotic manipulators with unified motion constraints

In this paper, we present an adaptive control of robotic manipulators with parametric uncertainties and motion constraints. Position and velocity constraints are considered and they are unified and converted into the constraint of the nominal input. An adaptive neural network control is developed to achieve trajectory tracking, while the problems of motion constraints are addressed by considering the saturation effect of the nominal input. The uniform boundedness of all closed-loop signals is verified through Lyapunov analysis. Simulation and experiment results on a 2 DOF robotic manipulator demonstrate the effectiveness of the proposed method

LLCaps: Learning to Illuminate Low-Light Capsule Endoscopy with Curved Wavelet Attention and Reverse Diffusion

Wireless capsule endoscopy (WCE) is a painless and non-invasive diagnostic tool for gastrointestinal (GI) diseases. However, due to GI anatomical constraints and hardware manufacturing limitations, WCE vision signals may suffer from insufficient illumination, leading to a complicated screening and examination procedure. Deep learning-based low-light image enhancement (LLIE) in the medical field gradually attracts researchers. Given the exuberant development of the denoising diffusion probabilistic model (DDPM) in computer vision, we introduce a WCE LLIE framework based on the multi-scale convolutional neural network (CNN) and reverse diffusion process. The multi-scale design allows models to preserve high-resolution representation and context information from low-resolution, while the curved wavelet attention (CWA) block is proposed for high-frequency and local feature learning. Furthermore, we combine the reverse diffusion procedure to further optimize the shallow output and generate the most realistic image. The proposed method is compared with ten state-of-the-art (SOTA) LLIE methods and significantly outperforms quantitatively and qualitatively. The superior performance on GI disease segmentation further demonstrates the clinical potential of our proposed model. Our code is publicly accessible.Comment: To appear in MICCAI 2023. Code availability: https://github.com/longbai1006/LLCap

Continuous critic learning for robot control in physical human-robot interaction

In this paper, optimal impedance adaptation is investigated for interaction control in constrained motion. The external environment is modeled as a linear system with parameter matrices completely unknown and continuous critic learning is adopted for interaction control. The desired impedance is obtained which leads to an optimal realization of the trajectory tracking and force regulation. As no particular system information is required in the whole process, the proposed interaction control provides a feasible solution to a large number of applications. The validity of the proposed method is verified through simulation studies

Adaptive optimal control for linear discrete time-varying systems

In this paper, adaptive optimal control is proposed for time-varying discrete linear system subject to unknown system dynamics. The idea of the method is a direct application of the Q-learning adaptive dynamic programming for time-varying system. In order to derive the optimal control policy, a actor-critic structure is constructed and time-varying least square method is adopted for parameter adaptation. It has shown that the derived control policy can robustly stabilize the time varying system and guarantee an optimal control performance at the same time. As no particular system information is required throughout the process, the proposed techniques provide a potential feasible solution to a large variety of control application. The validity of the proposed method is verified through simulation studies