Zhao Fengyu, Jichao Xu and Bo Bergman
In this paper, a two‐section method for measuring is introduced and the variation soruces of measurement process are analysed. Measuring is a special process. Various variation…
Abstract
In this paper, a two‐section method for measuring is introduced and the variation soruces of measurement process are analysed. Measuring is a special process. Various variation source must be firstly decomposed so that the statistical distribution law of measuring process can be established, and then implement monitoring control of the measuring process. A special method to obtain the m easuring variation is discussed, and a monitoring control technique for measuring process is studied based statistical distribution. Towards the end, we briefly introduce software design for the analysis and control of a measurement process.
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Ma Zizong and Zhao Fengyu Xu Jichao
The economic design of specification limits must be determined on an economic basis where we minimize total loss to society, which consists of both the producer and the consumer…
Abstract
The economic design of specification limits must be determined on an economic basis where we minimize total loss to society, which consists of both the producer and the consumer. Economic specification limits have been developed based on the assumption that the quality characteristic is normally distributed. Unfortunately, the assumption is not to meet some practical cases. In this paper, some non‐normal distributions are considered for quality characteristic with geometrical features. An economic model for selecting the optimum specification limits on the basis of minimizing total cost is introduced. A case study is presented to illustrate the application in practice.
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The process of producing software differs in many aspects from that of traditional manufacturing. Software is not manufactured in the classical sense. Development of software more…
Abstract
The process of producing software differs in many aspects from that of traditional manufacturing. Software is not manufactured in the classical sense. Development of software more closely resembles the development effort that goes effort that goes into design new product. In this article, we first describe the foundations of process improvement, which all processes can share. The process improvement differences between software and manufacturing process are then discussed, and a defect driven process inspection and improvement is introduced. Based on the discussion, two experiments were designed and the results of the results were collected. Through the comparison, we found that some efficient quality improvement approaches can be easily adapted in the software improvement and that the inspection efficiency is also significant.
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Mu Shengdong, Wang Fengyu, Xiong Zhengxian, Zhuang Xiao and Zhang Lunfeng
With the advent of the web computing era, the transmission mode of the Internet of Everything has caused an explosion in data volume, which has brought severe challenges to…
Abstract
Purpose
With the advent of the web computing era, the transmission mode of the Internet of Everything has caused an explosion in data volume, which has brought severe challenges to traditional routing protocols. The limitations of the existing routing protocols under the condition of rapid data growth are elaborated, and the routing problem is remodeled as a Markov decision process. this paper aims to solve the problem of high blocking probability due to the increase in data volume by combining deep reinforcement learning. Finally, the correctness of the proposed algorithm in this paper is verified by simulation.
Design/methodology/approach
The limitations of the existing routing protocols under the condition of rapid data growth are elaborated and the routing problem is remodeled as a Markov decision process. Based on this, a deep reinforcement learning method is used to select the next-hop router for each data transmission task, thereby minimizing the length of the data transmission path while avoiding data congestion.
Findings
Simulation results show that the proposed method can significantly reduce the probability of data congestion and increase network throughput.
Originality/value
This paper proposes an intelligent routing algorithm for the network congestion caused by the explosive growth of data volume in the future of the big data era. With the help of deep reinforcement learning, it is possible to dynamically select the transmission jump router according to the current network state, thereby reducing the probability of congestion and improving network throughput.