Semantic Segmentation of Fruits on Multi-sensor Fused Data in Natural Orchards

Semantic segmentation is a fundamental task for agricultural robots to understand the surrounding environments in natural orchards. The recent development of the LiDAR techniques enables the robot to acquire accurate range measurements of the view in the unstructured orchards. Compared to RGB images, 3D point clouds have geometrical properties. By combining the LiDAR and camera, rich information on geometries and textures can be obtained. In this work, we propose a deep-learning-based segmentation method to perform accurate semantic segmentation on fused data from a LiDAR-Camera visual sensor. Two critical problems are explored and solved in this work. The first one is how to efficiently fused the texture and geometrical features from multi-sensor data. The second one is how to efficiently train the 3D segmentation network under severely imbalance class conditions. Moreover, an implementation of 3D segmentation in orchards including LiDAR-Camera data fusion, data collection and labelling, network training, and model inference is introduced in detail. In the experiment, we comprehensively analyze the network setup when dealing with highly unstructured and noisy point clouds acquired from an apple orchard. Overall, our proposed method achieves 86.2% mIoU on the segmentation of fruits on the high-resolution point cloud (100k-200k points). The experiment results show that the proposed method can perform accurate segmentation in real orchard environments

Design of Video Teaching System Based on Virtual Reality Technology

With the progress of the times and the development of network technology, great changes have taken place in the mode of education and teaching, and the traditional mode of education and teaching has been unable to meet the current teaching situation and requirements. Under this background, many scholars have developed a variety of teaching models adapted to the background of the times, which has achieved good results. In order to realize the reform and innovation of teaching mode, this paper designs a film and television teaching system based on virtual reality technology. In the era of Internet of Things and big data, Internet of Things technology is used as support to realize the transmission and sharing of teaching information in film and television teaching system. In the research, data mining technology is used to collect relevant data of teaching resources and teaching information, and relevant algorithms in data mining are used to realize data processing. Experiment of film and television teaching system in different stages of education system and the application of film and television teaching theory courses, experiment courses for students and education development index are analyzed. According to the end of the experiment, the influence of results show that the film and television teaching system in the use of elementary school, junior high school, high school and university level are 25.32%, 31.44%, 18.46% and 47.76% respectively. The film and television teaching system plays a significant role in students’ experimental course scores. The use of the film and television teaching system has raised the development index of education and teaching in each stage of education from 1.12, 1.33, 1.47 and 1.36 to 2.14, 2.21, 2.36 and 2.44 respectively, indicating that the film and television teaching system has a certain promoting effect on the development of education and teaching

A Survey of Document-Level Information Extraction

Document-level information extraction (IE) is a crucial task in natural language processing (NLP). This paper conducts a systematic review of recent document-level IE literature. In addition, we conduct a thorough error analysis with current state-of-the-art algorithms and identify their limitations as well as the remaining challenges for the task of document-level IE. According to our findings, labeling noises, entity coreference resolution, and lack of reasoning, severely affect the performance of document-level IE. The objective of this survey paper is to provide more insights and help NLP researchers to further enhance document-level IE performance

An Expert's Guide to Training Physics-informed Neural Networks

Physics-informed neural networks (PINNs) have been popularized as a deep learning framework that can seamlessly synthesize observational data and partial differential equation (PDE) constraints. Their practical effectiveness however can be hampered by training pathologies, but also oftentimes by poor choices made by users who lack deep learning expertise. In this paper we present a series of best practices that can significantly improve the training efficiency and overall accuracy of PINNs. We also put forth a series of challenging benchmark problems that highlight some of the most prominent difficulties in training PINNs, and present comprehensive and fully reproducible ablation studies that demonstrate how different architecture choices and training strategies affect the test accuracy of the resulting models. We show that the methods and guiding principles put forth in this study lead to state-of-the-art results and provide strong baselines that future studies should use for comparison purposes. To this end, we also release a highly optimized library in JAX that can be used to reproduce all results reported in this paper, enable future research studies, as well as facilitate easy adaptation to new use-case scenarios.Comment: 36 pages, 25 figures, 13 table

COURSE-KEEPING CONTROL FOR DIRECTIONALLY UNSTABLE LARGE TANKERS USING THE MIRROR-MAPPING TECHNIQUE

This study examines the course-keeping control of directionally unstable large oil tankers involving a pole in the right half plane. Treated as an unstable plant in control engineering, tankers are theoretically and experimentally investigated during the controller design process. First, the unstable plant is mirror-mapped to its corresponding stable minimum phase plant using the mirror-mapping technique, which enables an easy controller design. Then, a linear proportional-differential and a first-order filter controller is designed based on the closed-loop gain shaping algorithm, which requires only one controller parameter to be properly selected based on the system’s characteristics. Numerical simulation results confirmed that the designed controller can successfully stabilise an unstable plant subjected to external wind and wave disturbances. The controller designed with the proposed method is suitable for course-keeping control of directionally unstable large tankers. The controller design method is simple with an uncomplicated structure that can easily be implemented in engineering endeavours. Moreover, the rudder motion is small and soft