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Institution

Jiangxi University of Science and Technology

EducationGanzhou, China
About: Jiangxi University of Science and Technology is a education organization based out in Ganzhou, China. It is known for research contribution in the topics: Microstructure & Alloy. The organization has 6958 authors who have published 5576 publications receiving 50650 citations.


Papers
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Journal ArticleDOI
TL;DR: A hierarchical architecture of the smart factory was proposed first, and then the key technologies were analyzed from the aspects of the physical resource layer, the network layer, and the data application layer, which showed that the overall equipment effectiveness of the equipment is significantly improved.
Abstract: Due to the current structure of digital factory, it is necessary to build the smart factory to upgrade the manufacturing industry. Smart factory adopts the combination of physical technology and cyber technology and deeply integrates previously independent discrete systems making the involved technologies more complex and precise than they are now. In this paper, a hierarchical architecture of the smart factory was proposed first, and then the key technologies were analyzed from the aspects of the physical resource layer, the network layer, and the data application layer. In addition, we discussed the major issues and potential solutions to key emerging technologies, such as Internet of Things (IoT), big data, and cloud computing, which are embedded in the manufacturing process. Finally, a candy packing line was used to verify the key technologies of smart factory, which showed that the overall equipment effectiveness of the equipment is significantly improved.

736 citations

Proceedings ArticleDOI
01 Aug 2015
TL;DR: Comparing relevant aspects of Industry 4.0 in relation to strategic planning, key technologies, opportunities, and challenges and the enlightenment for China's manufacturing industries is introduced.
Abstract: Industry 4.0 (the fourth industrial revolution) encapsulates future industry development trends to achieve more intelligent manufacturing processes, including reliance on Cyber-Physical Systems (CPS), construction of Cyber-Physical Production Systems (CPPS), and implementation and operation of smart factories. This paper introduces relevant aspects of Industry 4.0 in relation to strategic planning, key technologies, opportunities, and challenges. Strategic planning includes construction of a CPS network, discussion of two major themes which are based on the smart factory and intelligent production, achieving three integrations (horizontal integration, vertical integration and end-to-end integration) and achieving eight plans which consist of the formulation of system standardization, efficient management etc. Finally, it referred to the enlightenment for China's manufacturing industries, to build China's Industry 4.0.

690 citations

Journal ArticleDOI
TL;DR: A comprehensive review of the state of the art of bone biomaterials and their interactions with stem cells is presented and the promising seed stem cells for bone repair are summarized, and their interaction mechanisms are discussed in detail.
Abstract: Bone biomaterials play a vital role in bone repair by providing the necessary substrate for cell adhesion, proliferation, and differentiation and by modulating cell activity and function. In past decades, extensive efforts have been devoted to developing bone biomaterials with a focus on the following issues: (1) developing ideal biomaterials with a combination of suitable biological and mechanical properties; (2) constructing a cell microenvironment with pores ranging in size from nanoscale to submicro- and microscale; and (3) inducing the oriented differentiation of stem cells for artificial-to-biological transformation. Here we present a comprehensive review of the state of the art of bone biomaterials and their interactions with stem cells. Typical bone biomaterials that have been developed, including bioactive ceramics, biodegradable polymers, and biodegradable metals, are reviewed, with an emphasis on their characteristics and applications. The necessary porous structure of bone biomaterials for the cell microenvironment is discussed, along with the corresponding fabrication methods. Additionally, the promising seed stem cells for bone repair are summarized, and their interaction mechanisms with bone biomaterials are discussed in detail. Special attention has been paid to the signaling pathways involved in the focal adhesion and osteogenic differentiation of stem cells on bone biomaterials. Finally, achievements regarding bone biomaterials are summarized, and future research directions are proposed.

464 citations

Journal ArticleDOI
TL;DR: The unique bandgap structure of the Ag2 O/Ag2 CO3 exhibits high separation efficiency of photogenerated electrons and holes, which effectively protects the Ag 2 CO3 semiconductor to avoid its photoreduction and gives rise to high activity and stability in degradation of the typical water pollutants.
Abstract: Coreshell-like Ag2 O/Ag2 CO3 nanoheterostructures with tailored interface are fabricated by a facile, low-cost and one-step phase transformation method. The unique bandgap structure of the Ag2 O/Ag2 CO3 exhibits high separation efficiency of photogenerated electrons and holes, which effectively protects the Ag2 CO3 semiconductor to avoid its photoreduction and gives rise to high activity and stability in degradation of the typical water pollutants.

436 citations

Journal ArticleDOI
10 Jul 2018-Sensors
TL;DR: A deep neural network model that integrates the CNN and LSTM architectures is developed, and through historical data such as cumulated hours of rain, cumulated wind speed and PM2.5 concentration, the forecasting accuracy of the proposed CNN-LSTM model (APNet) is verified to be the highest in this paper.
Abstract: In modern society, air pollution is an important topic as this pollution exerts a critically bad influence on human health and the environment. Among air pollutants, Particulate Matter (PM2.5) consists of suspended particles with a diameter equal to or less than 2.5 μm. Sources of PM2.5 can be coal-fired power generation, smoke, or dusts. These suspended particles in the air can damage the respiratory and cardiovascular systems of the human body, which may further lead to other diseases such as asthma, lung cancer, or cardiovascular diseases. To monitor and estimate the PM2.5 concentration, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) are combined and applied to the PM2.5 forecasting system. To compare the overall performance of each algorithm, four measurement indexes, Mean Absolute Error (MAE), Root Mean Square Error (RMSE) Pearson correlation coefficient and Index of Agreement (IA) are applied to the experiments in this paper. Compared with other machine learning methods, the experimental results showed that the forecasting accuracy of the proposed CNN-LSTM model (APNet) is verified to be the highest in this paper. For the CNN-LSTM model, its feasibility and practicability to forecast the PM2.5 concentration are also verified in this paper. The main contribution of this paper is to develop a deep neural network model that integrates the CNN and LSTM architectures, and through historical data such as cumulated hours of rain, cumulated wind speed and PM2.5 concentration. In the future, this study can also be applied to the prevention and control of PM2.5.

426 citations


Authors

Showing all 7009 results

NameH-indexPapersCitations
Hua Zhang1631503116769
Wei Li1581855124748
Mingwei Chen10853651351
Hongjie Zhang9276033301
Aibing Yu8693034127
Shiyong Liu7926619061
Chun-Hua Yan7333619972
Xiaobo Ji7336017916
Yang Hou6423514113
Hao Su5730255902
Jian Tian5617513090
Lei Wang54107615189
Jiafu Wan5416712244
Peng Cheng523629193
Heng-Yun Ye472049435
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202336
2022129
2021993
2020912
2019618
2018404