Study on the Detection Method for Daylily Based on YOLOv5 under Complex Field Environments

Intelligent detection is vital for achieving the intelligent picking operation of daylily, but complex field environments pose challenges due to branch occlusion, overlapping plants, and uneven lighting. To address these challenges, this study selected an intelligent detection model based on YOLOv5s for daylily, the depth and width parameters of the YOLOv5s network were optimized, with Ghost, Transformer, and MobileNetv3 lightweight networks used to optimize the CSPDarknet backbone network of YOLOv5s, continuously improving the model’s performance. The experimental results show that the original YOLOv5s model increased mean average precision (mAP) by 49%, 44%, and 24.9% compared to YOLOv4, SSD, and Faster R-CNN models, optimizing the depth and width parameters of the network increased the mAP of the original YOLOv5s model by 7.7%, and the YOLOv5s model with Transformer as the backbone network increased the mAP by 0.2% and the inference speed by 69% compared to the model after network parameter optimization. The optimized YOLOv5s model provided precision, recall rate, mAP, and inference speed of 81.4%, 74.4%, 78.1%, and 93 frames per second (FPS), which can achieve accurate and fast detection of daylily in complex field environments. The research results can provide data and experimental references for developing intelligent picking equipment for daylily

Study on the Influence of PCA Pre-Treatment on Pig Face Identification with Random Forest

To explore the application of a traditional machine learning model in the intelligent management of pigs, in this paper, the influence of PCA pre-treatment on pig face identification with RF is studied. By this testing method, the parameters of two testing schemes, one adopting RF alone and the other adopting RF + PCA, were determined to be 65 and 70, respectively. With individual identification tests carried out on 10 pigs, accuracy, recall, and f1-score were increased by 2.66, 2.76, and 2.81 percentage points, respectively. Except for the slight increase in training time, the test time was reduced to 75% of the old scheme, and the efficiency of the optimized scheme was greatly improved. It indicates that PCA pre-treatment positively improved the efficiency of individual pig identification with RF. Furthermore, it provides experimental support for the mobile terminals and the embedded application of RF classifiers

Balanced Standing on One Foot of Biped Robot Based on Three-Particle Model Predictive Control

Balancing is a fundamental task in the motion control of bipedal robots. Compared to two-foot balancing, one-foot balancing introduces new challenges, such as a smaller supporting polygon and control difficulty coming from the kinematic coupling between the center of mass (CoM) and the swinging leg. Although nonlinear model predictive control (NMPC) may solve this problem, it is not feasible to implement it on the actual robot because of its large amount of calculation. This paper proposes the three-particle model predictive control (TP-MPC) approach. It combines with the hierarchical whole-body control (WBC) to solve the one-leg balancing problem in real time. The bipedal robot’s torso and two legs are modeled as three separate particles without inertia. The TP-MPC generates feasible swing leg trajectories, followed by the WBC to adjust the robot’s center of mass. Since the three-particle model is linear, the TP-MPC requires less computational cost, which implies real-time execution on an actual robot. The proposed method is verified in simulation. Simulation results show that our method can resist much larger external disturbance than the WBC-only control scheme

Fracture Evolution of Overburden Strata and Determination of Gas Drainage Area Induced by Mining Disturbance

Overburden strata fracture evolution is critical to dynamic disaster prevention and gas-relief drainage, so it is important to accurately determine the evolution relationships with mining disturbance. In this paper, experiments and numerical simulation were adopted jointly to characterize the time-varying fracture area of overlying strata. The experimental results showed that the roof strata gradually broke and collapsed with coal mining, which indicated the fractures of overburden strata developed in an upward direction. The fracture development causes were explained by numerical simulation, which showed that stress increase exceeded the strength of coal and rock strata, and fractures were formed and expanded. Both experiments and numerical simulation results showed the two sides and the top of fracture areas provided channels and spaces for gas migration and reservoir, respectively. In addition, the breaking angle of overburden strata and the height of fracture areas were analyzed quantitatively. Through microseismic monitoring at the mining site, the fracture scales and ranges of overburden strata were verified by the energy and frequency of microseismic events, which were consistent with the support of maximum resistance. The position of drainage boreholes was considered based on the results of overburden strata fracture evolution. Our study is aimed at promoting coal mining in safety and improving gas drainage with a sustainable approach

A chemically mediated artificial neuron

Brain–machine interfaces typically rely on electrophysiological signals to interpret and transmit neurological information. In biological systems, however, neurotransmitters are chemical-based interneuron messengers. This mismatch can potentially lead to incorrect interpretation of the transmitted neuron information. Here we report a chemically mediated artificial neuron that can receive and release the neurotransmitter dopamine. The artificial neuron detects dopamine using a carbon-based electrochemical sensor and then processes the sensory signals using a memristor with synaptic plasticity, before stimulating dopamine release through a heat-responsive hydrogel. The system responds to dopamine exocytosis from rat pheochromocytoma cells and also releases dopamine to activate pheochromocytoma cells, forming a chemical communication loop similar to interneurons. To illustrate the potential of this approach, we show that the artificial neuron can trigger the controllable movement of a mouse leg and robotic hand.Agency for Science, Technology and Research (A*STAR)Ministry of Education (MOE)National Research Foundation (NRF)Submitted/Accepted versionWe acknowledge financial support from the National Key Research and Development Program of China (2017YFA0205302, L.W.); Natural Science Foundation of Jiangsu Province—Major Project (BK20212012, L.W.); National Key R&D Program of China (2021YFB3601200, M.W.); National Natural Science Foundation of China (81971701, B.H.); the Natural Science Foundation for Young Scholars of Jiangsu Province (BK20210596, T.W.); the Natural Science Foundation of Jiangsu Province (BK20201352, B.H.); the Program of Jiangsu Specially-Appointed Professor (B.H. and T.W.); Science Foundation of Nanjing University of Post and Telecommunications (NUPTSF, NY221004, T.W.); the Agency for Science, Technology and Research (A*STAR) under its AME Programmatic Funding Scheme (Project #A18A1b0045, X.C.); the National Research Foundation (NRF), Prime Minister’s Office, Singapore, under its NRF Investigatorship (NRF-NRFI2017-07, X.C.); and Singapore Ministry of Education (MOE2017-T2-2-107, X.C.)