Research on Modeling and Experiment of Glass Substrate Peeling Based on Adhesion Theory

In this paper, the peeling of glass substrates is modeled, in a setting of automatic polishing and grinding for flat panel display glass substrates. The mechanical model of glass substrates-adhesive pad structure is established. The vacuum adsorbing force between them is regarded as adhesive force. The model is simplified as a distributed spring group which can describe the desorption and shear behavior of the glass substrates-adhesive pad structure. The corresponding analytical solution method is proposed. Finally, experiment is conducted to verify the accuracy and feasibility of the proposed mechanical model

Overview of Tool Wear Monitoring Methods Based on Convolutional Neural Network

Tool wear monitoring is of great significance for the development of manufacturing systems and intelligent manufacturing. Online tool condition monitoring is a crucial technology for cost reduction, quality improvement, and manufacturing intelligence in modern manufacturing. However, it remains a difficult problem to monitor the status of tools online, in real-time and accurately in the industry. In the research status of mainstream technology, the convolution neural network may be a good solution to this problem, based on the appropriate sensor system and correct signal processing methods. Therefore, this paper outlines the state-of-the-art systems encountered in the open access literature, focusing on information collection, feature selection–extraction technologies based on deep convolutional neural networks, and monitoring network architecture and modeling methods. Based on typical cases, this paper focuses on the application of the convolution neural network in tool wear monitoring. From the application results, it is feasible and reliable to apply convolution neural networks in tool wear monitoring. Additionally, it can improve the prediction accuracy, which is of great significance for the future development of technology. This paper can be a guide for the researchers and manufacturers in the area of tool wear monitoring for explaining the latest trends and requirements

Overview of Tool Wear Monitoring Methods Based on Convolutional Neural Network

Tool wear monitoring is of great significance for the development of manufacturing systems and intelligent manufacturing. Online tool condition monitoring is a crucial technology for cost reduction, quality improvement, and manufacturing intelligence in modern manufacturing. However, it remains a difficult problem to monitor the status of tools online, in real-time and accurately in the industry. In the research status of mainstream technology, the convolution neural network may be a good solution to this problem, based on the appropriate sensor system and correct signal processing methods. Therefore, this paper outlines the state-of-the-art systems encountered in the open access literature, focusing on information collection, feature selection–extraction technologies based on deep convolutional neural networks, and monitoring network architecture and modeling methods. Based on typical cases, this paper focuses on the application of the convolution neural network in tool wear monitoring. From the application results, it is feasible and reliable to apply convolution neural networks in tool wear monitoring. Additionally, it can improve the prediction accuracy, which is of great significance for the future development of technology. This paper can be a guide for the researchers and manufacturers in the area of tool wear monitoring for explaining the latest trends and requirements

Efficient Robot Skills Learning with Weighted Near-Optimal Experiences Policy Optimization

Autonomous learning of robotic skills seems to be more natural and more practical than engineered skills, analogous to the learning process of human individuals. Policy gradient methods are a type of reinforcement learning technique which have great potential in solving robot skills learning problems. However, policy gradient methods require too many instances of robot online interaction with the environment in order to learn a good policy, which means lower efficiency of the learning process and a higher likelihood of damage to both the robot and the environment. In this paper, we propose a two-phase (imitation phase and practice phase) framework for efficient learning of robot walking skills, in which we pay more attention to the quality of skill learning and sample efficiency at the same time. The training starts with what we call the first stage or the imitation phase of learning, updating the parameters of the policy network in a supervised learning manner. The training set used in the policy network learning is composed of the experienced trajectories output by the iterative linear Gaussian controller. This paper also refers to these trajectories as near-optimal experiences. In the second stage, or the practice phase, the experiences for policy network learning are collected directly from online interactions, and the policy network parameters are updated with model-free reinforcement learning. The experiences from both stages are stored in the weighted replay buffer, and they are arranged in order according to the experience scoring algorithm proposed in this paper. The proposed framework is tested on a biped robot walking task in a MATLAB simulation environment. The results show that the sample efficiency of the proposed framework is much higher than ordinary policy gradient algorithms. The algorithm proposed in this paper achieved the highest cumulative reward, and the robot learned better walking skills autonomously. In addition, the weighted replay buffer method can be made as a general module for other model-free reinforcement learning algorithms. Our framework provides a new way to combine model-based reinforcement learning with model-free reinforcement learning to efficiently update the policy network parameters in the process of robot skills learning

An analysis on the spatiotemporal behavior of inbound tourists in Jiaodong Peninsula based on Flickr geotagged photos

Exploring the spatiotemporal behavior characteristics of inbound tourists is of great practical significance to the management and planning of attractions. Based on Flickr geotagged photos and metadata, the research analyzes the spatiotemporal behavior characteristics of inbound tourists in Jiaodong Peninsula from the perspective of tourist flow network. We decompose the time series characteristics of tourists with STL decomposition algorithm, and predict the inbound tourism trend through the time series model. We propose the Hot Status Index (HSI) based on network centrality, extract and analyze the distribution pattern of inbound tourism hotspots with the structural hole method. The k-core decomposition algorithm is introduced to analyze the hierarchical characteristics of the tourist flow network. And Fast Unfolding algorithm is adopted to analyze the characteristics of community aggregation under different scales, dividing the scenic area into four communities. The results show that the time series model can accurately estimate the trend of the number of tourists. And there is a “competition” effect among attractions in Jiaodong Peninsula. The attractions show a hierarchy effect, of which the core layer has obvious small-world characteristics, with a clustering coefficient of 0.768. The inter-city tourist flow in Jiaodong Peninsula reveals a closed “multi-triangle” distribution pattern, mainly in the marginal coastal cities. Qingdao old town presents community stability, and other urban communities have a scale effect, mainly comprising two tourism circles

Robotic Manipulation Planning for Automatic Peeling of Glass Substrate Based on Online Learning Model Predictive Path Integral

Autonomous planning robotic contact-rich manipulation has long been a challenging problem. Automatic peeling of glass substrates of LCD flat panel displays is a typical contact-rich manipulation task, which requires extremely high safe handling through the manipulation process. To this end of peeling glass substrates automatically, the system model is established from data and is used for the online planning of the robot motion in this paper. A simulation environment is designed to pretrain the process model with deep learning-based neural network structure to avoid expensive and time-consuming collection of real-time data. Then, an online learning algorithm is introduced to tune the pretrained model according to the real-time data from the peeling process experiments to cover the uncertainties of the real process. Finally, an Online Learning Model Predictive Path Integral (OL-MPPI) algorithm is proposed for the optimal trajectory planning of the robot. The performance of our algorithm was validated through glass substrate peeling tasks of experiments

Analyzing the Soil Microbial Characteristics of <i>Poa alpigena</i> Lindm. on Bird Island in Qinghai Lake Based on Metagenomics Analysis

Poa alpigena Lindm. is a dominant forage grass that is widely distributed on the Qinghai-Tibetan Plateau and is often used in the restoration of degraded grasslands. Soil microorganisms are major players in the cycling of materials in terrestrial ecosystems. In this study, based on high-throughput sequencing, the rhizosphere and non-rhizosphere soils of Poa alpigena L. on Bird Island, Qinghai Lake, were used to investigate the effects of Poa alpigena L. on the composition and structure of soil microbial communities, and to establish associated soil microbial gene pools. Results revealed that microorganisms in the soil of Poa alpigena L. on Bird Island belonged to 62 phyla, 112 classes, 245 orders, 518 families, 1610 genera, and 5704 species. The dominant soil bacteria in rhizosphere and non-rhizosphere soils were Proteobacteria (49.62%, 47.13%) and Actinobacteria (30.31% and 31.67%), whereas the dominant fungi were Ascomycota (3.15% and 3.37%) and Basidiomycota (0.98% and 1.06%). Alpha diversity analysis revealed that the microbial richness and diversity in non-rhizosphere soil were significantly higher than those in rhizosphere soil, mainly influenced by soil water content and total nitrogen content. Furthermore, on the basis of LEfSe analysis, Alphaproteobacteria and Betaproteobacteria were identified as prominent differential taxa for rhizosphere and non-rhizosphere soils, respectively. The key differential metabolic pathways of rhizosphere soil microorganisms were those associated with the ATP-binding cassette (ABC) transporter, basal metabolism, and cytochrome P450 metabolism, whereas those of non-rhizosphere soil microorganisms included the gene expression-related pathways, methane metabolism, and pathway associated with degradation of aromatic compounds. These findings indicated that the rhizosphere soil of Poa alpigena L. is selective for microorganisms that play important roles in the oxidation of methane and regulation of the greenhouse effect on Bird Island, and that the soil environment on this island may be subject to contamination with aromatic compounds

Effects of Warming on Microbial Community Characteristics in the Soil Surface Layer of Niaodao Wetland in the Qinghai Lake Basin

Lakeshore wetlands are important terrestrial ecosystems worldwide, and the lakeshore wetlands of the Tibetan Plateau are sensitive to climate change. Therefore, in the context of global warming, studying the effects of temperature rise on surface soil microbial communities is essential for wetland biodiversity conservation. In this study, we used metagenomic sequencing to examine changes in the structure of surface soil microbial communities and their metabolic pathways in the Niaodao lakeshore wetland (NLW) in Qinghai Lake at 1.2 &deg;C warming. Under natural control and warming conditions, Proteobacteria and Actinobacteria were the most dominant bacterial phyla, and Ascomycota and Basidiomycota were the predominant fungal phyla. Soil pH, electrical conductivity, and temperature affected the relative abundances of the dominant soil microbes. Effect size estimation in a linear discriminant analysis revealed 11 differential pathways between warming and natural conditions. Warming considerably enhanced the peptidoglycan biosynthetic pathways but inhibited the ATP-binding cassette transporter pathway. Warming treatment affected &alpha;-diversity indices, with an increase in the Shannon, Chao1, and richness indices and a decrease in the Simpson index compared with the index changes for the natural control conditions. Analysis of similarities showed significant differences between warming and control samples. Overall, temperature rise altered surface soil microbial community structure and increased surface soil microbial diversity and abundance in NLW

Psychological Disorders of Patients With Allergic Rhinitis in Chengdu, China: Exploratory Research

BackgroundThe number of patients with allergic rhinitis (AR) has exceeded 500 million worldwide due to the unstable curative effect that can easily produce mental and psychological disorders. However, most of the relevant existing literature is one-on-one retrospective analyses or targeted meta-analyses of AR with psychological disorders like irritability, depression, and anxiety, while “multi-hospital + interdisciplinary” multiple regression analyses are scarce. ObjectiveThis study aims to precisely identify the psychological disorders of patients with AR who were diagnosed and treated in the five most renowned hospitals in Chengdu, China over the past 5 years using 10 classification methods so as to attract attention and care from otolaryngologists. MethodsThe Symptom Checklist 90 (SCL-90) was used to group and score the mental state of 827 strictly screened patients with AR according to 9 classification criteria. The scores were then compared within groups. Intergroup comparisons were made between the study group and the Chinese norm, and the positive factors for psychological disorders were extracted. Four symptoms in the study group, that is, nasal itching, sneezing, clear discharge, and nasal congestion, were scored on a visual analog scale. Partial correlation analysis was performed between the extracted positive factors for psychological disorders and the symptom scores by the multiple regression statistical method. ResultsAmong 827 patients, 124 (15%) had no mental health impairments, 176 (21.3%) had mild impairments, 474 (57.3%) had mild to moderate impairments, 41 (5%) had moderate to severe impairments, and 12 (1.4%) had severe impairments. The average score of the SCL-90 for all 827 patients was 2.64 (SD 0.25), which corresponded to mild to moderate mental health impairments. The 827 patients scored significantly higher for the 4 positive factors: depression, anxiety, psychosis, and other (sleep, diet). Depression was positively correlated with sneezing and clear discharge, anxiety was positively correlated with nasal itching and congestion, psychosis was positively correlated with nasal itching and sneezing, and other (sleep, diet) was positively correlated with clear discharge and nasal congestion. ConclusionsPatients with AR have mild to moderate mental health impairments, with women and those with abnormal BMI, aged ≥45 years, with a monthly salary <¥5110 (US $700), with a disease duration <13 years, residing in urban areas, with a high school or above education, or who are indoor laborers being at high risk and requiring more care, follow-up, and comprehensive therapy from otolaryngologists