Research on the Key Technologies of Relation Extraction from Quality Text for Industrial Manufacturing

Author(s):  
Jun Wang ◽  
Mingyan Song ◽  
Qiang Ye ◽  
Tong Zhang
Author(s):  
Ocident Bongomin ◽  
Gilbert Gilibrays Ocen ◽  
Eric Oyondi Nganyi ◽  
Alex Musinguzi ◽  
Timothy Omara

The 21st century has witnessed a number of incredible changes ranging from the way of life and the technologies that emerged. Currently, we have entered a new paradigm shift called industry 4.0 where science fictions have become science facts, and technology fusion is the main driver. Therefore, ensuring that any advancement in technology reach and benefit all is the ideal opportunity for everyone. In this paper, disruptive technologies of industry 4.0 have been explored and quantified in terms of the number of their appearances in literature. This research mainly aimed at identifying industry 4.0 key technologies which have been ill-defined by previous researchers and to enlighten the required skills of industry 4.0. Comprehensive literature survey covering the field of engineering, production, and management from both academia and business was done from publication databases: Google scholar, ScienceDirect, Scopus, Sage, Taylor & Francis and Emerald insight. The results of the study show that 35 disruptive technologies were quantified and 13 key technologies: Internet of things, Big data, 3D printing, Cloud computing, Autonomous robots, Virtual and augmented reality, Cyber physical system, Artificial intelligence, Smart sensors, Simulation, Nanotechnology, Drones and Biotechnology were identified. Moreover, both technical and personal skills to be imparted into the human workforce for industry 4.0 were identified. The study reveals the need to investigate the capabilities and the readiness of some developing countries in adapting industry 4.0 in terms of the changes in the education systems and industrial manufacturing settings. In addition, the study proposes the need to address the ways for integration of industry 4.0 concepts into the current education system.


2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Ocident Bongomin ◽  
Gilbert Gilibrays Ocen ◽  
Eric Oyondi Nganyi ◽  
Alex Musinguzi ◽  
Timothy Omara

The 21st century has witnessed precipitous changes spanning from the way of life to the technologies that emerged. We have entered a nascent paradigm shift (industry 4.0) where science fictions have become science facts, and technology fusion is the main driver. Thus, ensuring that any advancement in technology reach and benefit all is the ideal opportunity for everyone. In this study, disruptive technologies of industry 4.0 were explored and quantified in terms of the number of their appearances in published literature. The study aimed at identifying industry 4.0 key technologies which have been ill-defined by previous researchers and to enumerate the required skills of industry 4.0. Comprehensive literature survey covering the field of engineering, production, and management was done in multidisciplinary databases: Google Scholar, Science Direct, Scopus, Sage, Taylor & Francis, and Emerald Insight. From the electronic survey, 35 disruptive technologies were quantified and 13 key technologies: Internet of Things, Big Data, 3D printing, Cloud computing, Autonomous robots, Virtual and Augmented reality, Cyber-physical system, Artificial intelligence, Smart sensors, Simulation, Nanotechnology, Drones, and Biotechnology were identified. Both technical and personal skills to be imparted into the human workforce for industry 4.0 were reported. The review identified the need to investigate the capability and the readiness of developing countries in adapting industry 4.0 in terms of the changes in the education systems and industrial manufacturing settings. This study proposes the need to address the integration of industry 4.0 concepts into the current education system.


Author(s):  
Ocident Bongomin ◽  
Gilbert Gilibrays Ocen ◽  
Eric Oyondi Nganyi ◽  
Alex Musinguzi ◽  
Timothy Omara

The 21st century has witnessed precipitous changes spanning from the way of life to the technologies that emerged. We have entered a nascent paradigm shift (industry 4.0) where science fictions have become science facts, and technology fusion is the main driver. Thus, ensuring that any advancement in technology reach and benefit all is the ideal opportunity for everyone. In this study, disruptive technologies of industry 4.0 was explored and quantified in terms of the number of their appearances in published literature. The study aimed at identifying industry 4.0 key technologies which have been ill-defined by previous researchers and to enumerate the required skills of industry 4.0. Comprehensive literature survey covering the field of engineering, production, and management was done from multidisciplinary databases: Google scholar, ScienceDirect, Scopus, Sage, Taylor & Francis and Emerald insight. Results of the electronic survey showed that 35 disruptive technologies were quantified and 13 key technologies: Internet of things, Big data, 3D printing, Cloud computing, Autonomous robots, Virtual and Augmented reality, Cyber physical system, Artificial intelligence, Smart sensors, Simulation, Nanotechnology, Drones and Biotechnology were identified. Both technical and personal skills to be imparted into the human workforce for industry 4.0 were reported. The study identified the need to investigate the capability and the readiness of developing countries in adapting industry 4.0 in terms of the changes in the education systems and industrial manufacturing settings. The study proposes the need to address integration of industry 4.0 concepts into the current education system.


Author(s):  
Koichiro Iida ◽  
Hideki Gorohmaru ◽  
Kazuhiro Nagayama ◽  
Koichi Ishibashi ◽  
Yoshiko Shoji ◽  
...  

Author(s):  
Prachi Jain ◽  
Shikhar Murty ◽  
Mausam . ◽  
Soumen Chakrabarti

This paper analyzes the varied performance of Matrix Factorization (MF) on the related tasks of relation extraction and knowledge-base completion, which have been unified recently into a single framework of knowledge-base inference (KBI) [Toutanova et al., 2015]. We first propose a new evaluation protocol that makes comparisons between MF and Tensor Factorization (TF) models fair. We find that this results in a steep drop in MF performance. Our analysis attributes this to the high out-of-vocabulary (OOV) rate of entity pairs in test folds of commonly-used datasets. To alleviate this issue, we propose three extensions to MF. Our best model is a TF-augmented MF model. This hybrid model is robust and obtains strong results across various KBI datasets.


2020 ◽  
Vol 3 (3) ◽  
pp. 122
Author(s):  
Andi Silvan

AbstractThis study takes the topic of predicting corporate bankruptcies. This research dqlam use traditional methods Altman Z-Score and Zmijewski. The purpose of this study was to obtain in-depth information about predicting bankruptcy of companies that are not necessarily directly to bankruptcy, but there is financial distress.Based on the results of research conducted on the four (4) non industrial manufacturing company listed on the Indonesia Stock Exchange (BEI). Obtaining the value z-score represents the average company are in good condition, which means no financial distress. Acquisition value of x-score has a value of less than 0 (zero) which means that the company is in good condition and is predicted not experiencing financial difficulties. This study led to the conclusion that the Altman Z-Score and Zmijewski method can be used to predict corporate bankruptcy. Keywords: Financial Ratios, Bankruptcy, Company.


2014 ◽  
Author(s):  
Miao Fan ◽  
Deli Zhao ◽  
Qiang Zhou ◽  
Zhiyuan Liu ◽  
Thomas Fang Zheng ◽  
...  

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