231 research outputs found
Application and Research of Industrial Robot Technology in Intelligent Manufacturing Field
At present, The use of robotics and automation is growing at a breathtaking speed, injecting strong momentum into economic and social development. With the acceleration of the new round of technological revolution and industrial transformation, the robot industry is ushered in a window period of upgrading and changing development. The world's major industrial developed countries have taken robots as the forefront and focus of competition in the technology industry. This article mainly introduces the basic structure of industrial robots and their applications in the field of automotive assembly and pump manufacturing. It also analyzes the shortcomings of domestic robots and the current solutions
Polycentric development in China’s mega-city regions, 2001-08: A comparison of the Yangtze and Pearl River Deltas
Large-scale urban regions are increasingly functioning as the territorial backbone of the global economy. Many of these mega-city regions are polycentric in that they consist of a range of densely interwoven cities and towns. The purpose of this chapter is to analyse the geographies of these polycentric networks in what are arguably China’s two most important mega-city regions: the Yangtze River Delta (YRD) and the Pearl River Delta (PRD). To this end, we deployed a methodology that allowed the analysis of the shifting spatial organization of mega-city regions through the lens of the headquarters–branches linkages of corporations; that is, we explored the mega-city regions’ constituent urban networks by looking at the ownership linkages running from a corporation’s headquarters to the corporation’s branches. In the process, this research extended and refined the statistical tools that are often deployed to measure polycentricity. Our results suggest that in both the YRD and the PRD there are more and more linkages interconnecting the mega-city region. The two regions share the following features: the general level of polycentricity is increasing, even though the concentration of headquarters is also increasing; and the growth of the general level of polycentricity mainly originates from higher levels of network density. There are, however, also fundamental differences between the YRD and the PRD: firms in the PRD are more likely to set up branches beyond the prefectures’ boundaries, which results in higher levels of network density than in the YRD; there is a relatively 'flatter' intercity network in the YRD compared to the PRD, in which there are more firms’ links interconnecting the four major cities (Guangzhou, Shenzhen, Dongguan and Foshan), rather than other small and medium-size cities
Extension of the general unit hydrograph theory for the spread of salinity in estuaries
From both practical and theoretical perspectives, it is essential to be able to express observed salinity distributions in terms of simplified theoretical models, which enable qualitative assessments to be made in many problems concerning water resource utilization (such as intake of fresh water) in estuaries. In this study, we propose a general and analytical salt intrusion model inspired by Guo's general unit hydrograph theory for flood hydrograph prediction in a watershed. To derive a simple, general and analytical model of salinity distribution, we first make four hypotheses on the longitudinal salinity gradient based on empirical observations; we then derive a general unit hydrograph for the salinity distribution along a partially mixed or well-mixed estuary. The newly developed model can be well calibrated using a minimum of three salinity measurements along the estuary axis and does converge towards zero when the along-estuary distance approaches infinity asymptotically. The theory has been successfully applied to reproduce the salt intrusion in 21 estuaries worldwide, which suggests that the proposed method can be a useful tool for quickly assessing the spread of salinity under a wide range of riverine and tidal conditions and for quantifying the potential impacts of human-induced and natural changes.51979296; 52279080; 2019ZT08G090; 440001-2023-10716; LA/P/0069/2020info:eu-repo/semantics/publishedVersio
Multimodal Short Video Rumor Detection System Based on Contrastive Learning
With short video platforms becoming one of the important channels for news
sharing, major short video platforms in China have gradually become new
breeding grounds for fake news. However, it is not easy to distinguish short
video rumors due to the great amount of information and features contained in
short videos, as well as the serious homogenization and similarity of features
among videos. In order to mitigate the spread of short video rumors, our group
decides to detect short video rumors by constructing multimodal feature fusion
and introducing external knowledge after considering the advantages and
disadvantages of each algorithm. The ideas of detection are as follows: (1)
dataset creation: to build a short video dataset with multiple features; (2)
multimodal rumor detection model: firstly, we use TSN (Temporal Segment
Networks) video coding model to extract video features; then, we use OCR
(Optical Character Recognition) and ASR (Automatic Character Recognition) to
extract video features. Recognition) and ASR (Automatic Speech Recognition)
fusion to extract text, and then use the BERT model to fuse text features with
video features (3) Finally, use contrast learning to achieve distinction: first
crawl external knowledge, then use the vector database to achieve the
introduction of external knowledge and the final structure of the
classification output. Our research process is always oriented to practical
needs, and the related knowledge results will play an important role in many
practical scenarios such as short video rumor identification and social opinion
control
Now and Future of Artificial Intelligence-based Signet Ring Cell Diagnosis: A Survey
Since signet ring cells (SRCs) are associated with high peripheral metastasis
rate and dismal survival, they play an important role in determining surgical
approaches and prognosis, while they are easily missed by even experienced
pathologists. Although automatic diagnosis SRCs based on deep learning has
received increasing attention to assist pathologists in improving the
diagnostic efficiency and accuracy, the existing works have not been
systematically overviewed, which hindered the evaluation of the gap between
algorithms and clinical applications. In this paper, we provide a survey on SRC
analysis driven by deep learning from 2008 to August 2023. Specifically, the
biological characteristics of SRCs and the challenges of automatic
identification are systemically summarized. Then, the representative algorithms
are analyzed and compared via dividing them into classification, detection, and
segmentation. Finally, for comprehensive consideration to the performance of
existing methods and the requirements for clinical assistance, we discuss the
open issues and future trends of SRC analysis. The retrospect research will
help researchers in the related fields, particularly for who without medical
science background not only to clearly find the outline of SRC analysis, but
also gain the prospect of intelligent diagnosis, resulting in accelerating the
practice and application of intelligent algorithms
CupCleaner: A Data Cleaning Approach for Comment Updating
Recently, deep learning-based techniques have shown promising performance on
various tasks related to software engineering. For these learning-based
approaches to perform well, obtaining high-quality data is one fundamental and
crucial issue. The comment updating task is an emerging software engineering
task aiming at automatically updating the corresponding comments based on
changes in source code. However, datasets for the comment updating tasks are
usually crawled from committed versions in open source software repositories
such as GitHub, where there is lack of quality control of comments. In this
paper, we focus on cleaning existing comment updating datasets with considering
some properties of the comment updating process in software development. We
propose a semantic and overlapping-aware approach named CupCleaner (Comment
UPdating's CLEANER) to achieve this purpose. Specifically, we calculate a score
based on semantics and overlapping information of the code and comments. Based
on the distribution of the scores, we filter out the data with low scores in
the tail of the distribution to get rid of possible unclean data. We first
conducted a human evaluation on the noise data and high-quality data identified
by CupCleaner. The results show that the human ratings of the noise data
identified by CupCleaner are significantly lower. Then, we applied our data
cleaning approach to the training and validation sets of three existing comment
updating datasets while keeping the test set unchanged. Our experimental
results show that even after filtering out over 30\% of the data using
CupCleaner, there is still an improvement in all performance metrics. The
experimental results on the cleaned test set also suggest that CupCleaner may
provide help for constructing datasets for updating-related tasks
Learn to Grasp via Intention Discovery and its Application to Challenging Clutter
Humans excel in grasping objects through diverse and robust policies, many of
which are so probabilistically rare that exploration-based learning methods
hardly observe and learn. Inspired by the human learning process, we propose a
method to extract and exploit latent intents from demonstrations, and then
learn diverse and robust grasping policies through self-exploration. The
resulting policy can grasp challenging objects in various environments with an
off-the-shelf parallel gripper. The key component is a learned intention
estimator, which maps gripper pose and visual sensory to a set of sub-intents
covering important phases of the grasping movement. Sub-intents can be used to
build an intrinsic reward to guide policy learning. The learned policy
demonstrates remarkable zero-shot generalization from simulation to the real
world while retaining its robustness against states that have never been
encountered during training, novel objects such as protractors and user
manuals, and environments such as the cluttered conveyor.Comment: Accepted to IEEE Robotics and Automation Letters (RA-L
Flipbot: Learning Continuous Paper Flipping via Coarse-to-Fine Exteroceptive-Proprioceptive Exploration
This paper tackles the task of singulating and grasping paper-like deformable
objects. We refer to such tasks as paper-flipping. In contrast to manipulating
deformable objects that lack compression strength (such as shirts and ropes),
minor variations in the physical properties of the paper-like deformable
objects significantly impact the results, making manipulation highly
challenging. Here, we present Flipbot, a novel solution for flipping paper-like
deformable objects. Flipbot allows the robot to capture object physical
properties by integrating exteroceptive and proprioceptive perceptions that are
indispensable for manipulating deformable objects. Furthermore, by
incorporating a proposed coarse-to-fine exploration process, the system is
capable of learning the optimal control parameters for effective paper-flipping
through proprioceptive and exteroceptive inputs. We deploy our method on a
real-world robot with a soft gripper and learn in a self-supervised manner. The
resulting policy demonstrates the effectiveness of Flipbot on paper-flipping
tasks with various settings beyond the reach of prior studies, including but
not limited to flipping pages throughout a book and emptying paper sheets in a
box.Comment: Accepted to International Conference on Robotics and Automation
(ICRA) 202
Multi-view 3D Face Reconstruction Based on Flame
At present, face 3D reconstruction has broad application prospects in various
fields, but the research on it is still in the development stage. In this
paper, we hope to achieve better face 3D reconstruction quality by combining
multi-view training framework with face parametric model Flame, propose a
multi-view training and testing model MFNet (Multi-view Flame Network). We
build a self-supervised training framework and implement constraints such as
multi-view optical flow loss function and face landmark loss, and finally
obtain a complete MFNet. We propose innovative implementations of multi-view
optical flow loss and the covisible mask. We test our model on AFLW and
facescape datasets and also take pictures of our faces to reconstruct 3D faces
while simulating actual scenarios as much as possible, which achieves good
results. Our work mainly addresses the problem of combining parametric models
of faces with multi-view face 3D reconstruction and explores the implementation
of a Flame based multi-view training and testing framework for contributing to
the field of face 3D reconstruction
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