Multi-Target Recognition of Bananas and Automatic Positioning for the Inflorescence Axis Cutting Point

Multi-target recognition and positioning using robots in orchards is a challenging task in modern precision agriculture owing to the presence of complex noise disturbance, including wind disturbance, changing illumination, and branch and leaf shading. To obtain the target information for a bud-cutting robotic operation, we employed a modified deep learning algorithm for the fast and precise recognition of banana fruits, inflorescence axes, and flower buds. Thus, the cutting point on the inflorescence axis was identified using an edge detection algorithm and geometric calculation. We proposed a modified YOLOv3 model based on clustering optimization and clarified the influence of front-lighting and backlighting on the model. Image segmentation and denoising were performed to obtain the edge images of the flower buds and inflorescence axes. The spatial geometry model was constructed on this basis. The center of symmetry and centroid were calculated for the edges of the flower buds. The equation for the position of the inflorescence axis was established, and the cutting point was determined. Experimental results showed that the modified YOLOv3 model based on clustering optimization showed excellent performance with good balance between speed and precision both under front-lighting and backlighting conditions. The total pixel positioning error between the calculated and manually determined optimal cutting point in the flower bud was 4 and 5 pixels under the front-lighting and backlighting conditions, respectively. The percentage of images that met the positioning requirements was 93 and 90%, respectively. The results indicate that the new method can satisfy the real-time operating requirements for the banana bud-cutting robot.

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Binary Classification Model Based on Machine Learning Algorithm for the Short-Circuit Detection in Power System

Proceedings of the 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence ◽

10.1145/3377713.3377753 ◽

2019 ◽

Author(s):

Qiwei Lu ◽

Jinpei Cheng ◽

Dianlin Guo ◽

Mengmeng Su ◽

Xuewei Wu ◽

...

Keyword(s):

Machine Learning ◽

Power System ◽

Learning Algorithm ◽

Binary Classification ◽

Short Circuit ◽

Classification Model ◽

Machine Learning Algorithm ◽

Model Based

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NLM-HS: Navigation Learning Model Based on a Hippocampal–Striatal Circuit for Explaining Navigation Mechanisms in Animal Brains

Brain Sciences ◽

10.3390/brainsci11060803 ◽

2021 ◽

Vol 11 (6) ◽

pp. 803

Author(s):

Jie Chai ◽

Xiaogang Ruan ◽

Jing Huang

Keyword(s):

Prefrontal Cortex ◽

Learning Algorithm ◽

Learning Model ◽

Cognitive Map ◽

Model Based ◽

Animal Navigation ◽

Environmental Cognition ◽

Neurophysiological Studies ◽

Planning Algorithm ◽

Two Stages

Neurophysiological studies have shown that the hippocampus, striatum, and prefrontal cortex play different roles in animal navigation, but it is still less clear how these structures work together. In this paper, we establish a navigation learning model based on the hippocampal–striatal circuit (NLM-HS), which provides a possible explanation for the navigation mechanism in the animal brain. The hippocampal model generates a cognitive map of the environment and performs goal-directed navigation by using a place cell sequence planning algorithm. The striatal model performs reward-related habitual navigation by using the classic temporal difference learning algorithm. Since the two models may produce inconsistent behavioral decisions, the prefrontal cortex model chooses the most appropriate strategies by using a strategy arbitration mechanism. The cognitive and learning mechanism of the NLM-HS works in two stages of exploration and navigation. First, the agent uses a hippocampal model to construct the cognitive map of the unknown environment. Then, the agent uses the strategy arbitration mechanism in the prefrontal cortex model to directly decide which strategy to choose. To test the validity of the NLM-HS, the classical Tolman detour experiment was reproduced. The results show that the NLM-HS not only makes agents show environmental cognition and navigation behavior similar to animals, but also makes behavioral decisions faster and achieves better adaptivity than hippocampal or striatal models alone.

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Roboterbasierte spanende Bearbeitung*/Robot-Based Milling Operation. Machine Learning Algorithm for a model-based feed-forward torque control

wt Werkstattstechnik online ◽

10.37544/1436-4980-2019-05-54 ◽

2019 ◽

Vol 109 (05) ◽

pp. 352-357

Author(s):

C. Brecher ◽

L. Gründel ◽

L. Lienenlüke ◽

S. Storms

Keyword(s):

Machine Learning ◽

Position Control ◽

Learning Algorithm ◽

Milling Process ◽

Industrial Robots ◽

Torque Control ◽

Feed Forward ◽

Control Loops ◽

Model Based ◽

Machine Learning Approach

Die Lageregelung von konventionellen Industrierobotern ist nicht auf den dynamischen Fräsprozess ausgelegt. Eine Möglichkeit, das Verhalten der Regelkreise zu optimieren, ist eine modellbasierte Momentenvorsteuerung, welche in dieser Arbeit aufgrund vieler Vorteile durch einen Machine-Learning-Ansatz erweitert wird. Hierzu wird die Umsetzung in Matlab und die simulative Evaluation erläutert, die im Anschluss das Potenzial dieses Konzeptes bestätigt.   The position control of conventional industrial robots is not designed for the dynamic milling process. One possibility to optimize the behavior of the control loops is a model-based feed-forward torque control which is supported by a machine learning approach due to many advantages. The implementation in Matlab and the simulative evaluation are explained, which subsequently confirms the potential of this concept.

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