Literature Review on the Statistical Quality Control in Journal of the KSQM for 50 Years

Kwon, Hyuck M.; Hong, Sung Hoon; Lee, Min Koo; Lim, Sung Uk

doi:10.7469/jksqm.2016.44.1.001

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…From the main problems of traditional construction project quality control, we can see that it is necessary to effectively organize all parties involved in the construction by means of information transmission, mutual cooperation of managers, and building a shared platform and establish a more complete quality control of construction projects. e system can solve the shortcomings of traditional construction project quality control [24].…”

Section: Quality Control Experiments Of Construction Project Based On Convolutional Neural Networkmentioning

confidence: 99%

Construction Technology and Quality Control of Power and Electrical Engineering Based on Convolutional Neural Network

Xiao¹

2021

Security and Communication Networks

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In the context of the Internet era, more and more parties have begun to store, process, and analyze data, but the accompanying question is whether people are reasonable about the data under the impact of massive data, effective and efficient analysis, especially the problems faced in this project. This article aims to study the quality control problems faced by electric power and electrical engineering in the construction process through the use of convolutional neural networks. Under this idea, this article proposes a multilayer convolution method. The experimental results show that the use of the improved multilayer convolution method for the convolution method of the convolutional neural network can effectively improve the multiple analysis problems of small datasets in the construction of electric power and electrical engineering; in this way, the relevant data are analyzed; by controlling the quality of construction, the quality problem has been greatly improved. After comparison, it is concluded that the overall construction quality has increased by 35%.

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Section: Quality Control Experiments Of Construction Project Based On Convolutional Neural Networkmentioning

confidence: 99%

Construction Technology and Quality Control of Power and Electrical Engineering Based on Convolutional Neural Network

Xiao¹

2021

Security and Communication Networks

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The Analysis of Knowledge Structure using Co-word Method in Quality Management Field

Park¹

2016

Journal of the Korean society for quality management

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Purpose: This study was designed to analyze the behavioral change of knowledge structures and the trends of research topics in the quality management field. Methods: The network structure and knowledge structure of the words were visualized in map form using co-word analysis, cluster analysis and strategic diagram. Results: Summarizing the research results obtained in this study are as follows. First, the word network derived from co-occurrence matrix had 106 nodes and 5,314 links and its density was analyzed to 0.95. Average betweenness centrality of word network was 2.37. In addition, average closeness centrality and average eigenvector centrality of word network were 0.01. Second, by applying optimal criteria of cluster decision and K-means algorithm to word co-occurrence matrix, 106 words were grouped into seven clusters such as standard & efficiency, product design, reliability, control chart, quality model, 6 sigma, and service quality. Conclusion: According to the results of strategic diagram analysis over time, the traditional research topics of quality management field related to reliability, 6 sigma, control chart topics in the third quadrant were revealed to be declined for their study importance. Research topics related to product design and customer satisfaction were found to be an important research topic over analysis periods. Research topic related to management innovation was emerging state and the scope of research topics related to process model was extended to research topics with system performance. Research topic related to service quality located in the first quadrant was analyzed as the key research topic.

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A Note on the Performance of Pre-Control

Sun-Keun¹

2016

Journal of the Korean society for quality management

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Purpose: This paper evaluates the performance of the pre-control(PC), an alternative to statistical process control techniques and compares with a control chart considering the tolerance of process. Methods: The previous studies for PC have drawbacks that PC with two linked stages, qualification and running, are discussed separately and independently. Hence this paper analyzes the performance of PC by integrating two stages. Results: Average outgoing quality limits to grasp the outcome of PC are provided by computational results for two process capability indexes,   and   and the usefulness of PC from comparative experiments with modified control charts is commented. Conclusion: Helpful guidelines for quality managers to apply PC in practice and areas of process for PC to be more benefit are presented.

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Literature Review on the Statistical Quality Control in Journal of the KSQM for 50 Years

Cited by 3 publications

References 6 publications

Construction Technology and Quality Control of Power and Electrical Engineering Based on Convolutional Neural Network

Construction Technology and Quality Control of Power and Electrical Engineering Based on Convolutional Neural Network

The Analysis of Knowledge Structure using Co-word Method in Quality Management Field

A Note on the Performance of Pre-Control

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