Data analysis using box and whisker plot for stationary shop analysis

Vignesh, V.; Pavithra, Dalavai; Dinakaran, K.; Thirumalai, Chandrasegar

doi:10.1109/icoei.2017.8300874

Cited by 6 publications

(2 citation statements)

References 21 publications

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“…Thus, when drawing a box-and-whisker plot, it is necessary to first determine the fence; the fence is the first quartile −1.5 × IQR and the third quartile +1.5 × IQR. Importantly, this kind of box plot can be learned and interpreted easily and clearly from the outcome of statistical charts; with the helpful benefit that box-and-whisker plots have a wide application field, such as the analysis of lung data in medicine [19] and the data analysis in stores [20].…”

Section: Box-and-whisker Analysismentioning

confidence: 99%

Identification of the Yield Rate by a Hybrid Fuzzy Control PID-Based Four-Stage Model: A Case Study of Optical Filter Industry

Chen,

Hung,

Lee

et al. 2024

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With the vigorous development of emerging technology and the advent of the Internet generation, high-speed Internet and fast transmission 5G wireless networks contribute to interpersonal communication. Now, the Internet has become popular and widely available, and human life is inseparable from various experiences on the Internet. Many base stations and data centers have been established to convert and switch from electrical transmission to optical transmission; thus, it is entering the new era of optical fiber networks and optical communication technologies. For optical communication, the manufacturing of components for the purpose of high-speed networks is a key process, and the requirement for the stability of its production conditions is very strict. In particular, product yields are always low due to the restriction of high-precision specifications associated with the limitations of too many factors. Given these reasons, this study proposes a hybrid fuzzy control-based model for industry data applications to organize advanced techniques of box-and-whisker plot method, association rule, and decision trees to find out the determinants that affect the yield rate of products and then use the fuzzy control Proportional-Integral-Derivative (PID) method to manage the determinants. Since it is unrealistic to test the real machine online operation at the manufacturing stage, the simulation software supersedes this for improved results, and a mathematical neural network is used to verify the given data to confirm whether its result is similar to that of the simulation. The study suggests that excessive temperature differentials between substrate and cavity can lead to low yields. It suggests using fuzzy control technology for temperature management, which could increase yield, reduce labor costs, and accelerate the transition to high-speed networks by mass-producing high-precision optical filters.

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Section: Box-and-whisker Analysismentioning

confidence: 99%

Identification of the Yield Rate by a Hybrid Fuzzy Control PID-Based Four-Stage Model: A Case Study of Optical Filter Industry

Chen,

Hung,

Lee

et al. 2024

Axioms

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“…Hence, researcher should be detected before analyzing data whether their existence of any outlier. The most common way is box plot also called box and whisker plot method [16] [17]. Outliers can also be detected by fuzzy clustering [18], fuzzy discriminant analysis [19].…”

Section: Introductionmentioning

confidence: 99%

Describing Fuzzy Membership Function and Detecting the Outlier by Using Five Number Summary of Data

Hasan¹,

Sobhan²

2020

AJCM

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One of the most important activities in data science is defining a membership function in fuzzy system. Although there are few ways to describe membership function like artificial neural networks, genetic algorithms etc.; they are very complex and time consuming. On the other hand, the presence of outlier in a data set produces deceptive results in the modeling. So it is important to detect and eliminate them to prevent their negative effect on the modeling. This paper describes a new and simple way of constructing fuzzy membership function by using five-number summary of a data set. Five states membership function can be created in this new method. At the same time, if there is any outlier in the data set, it can be detected with the help of this method. Usually box plot is used to identify the outliers of a data set. So along with the new approach, the box plot has also been drawn so that it is understood that the results obtained in the new method are accurate. Several real life examples and their analysis have been discussed with graph to demonstrate the potential of the proposed method. The results obtained show that the proposed method has given good results. In the case of outlier, the proposed method and the box plot method have shown similar results. Primary advantage of this new procedure is that it is not as expensive as neural networks, and genetic algorithms.

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Diagnosis of coronary artery occlusion by fitting polynomial curve with the ECG signal based on optimization techniques

Jothiramalingam

Anitha

Hemanth

2022

Netw Model Anal Health Inform Bioinforma

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Data analysis using box and whisker plot for stationary shop analysis

Cited by 6 publications

References 21 publications

Identification of the Yield Rate by a Hybrid Fuzzy Control PID-Based Four-Stage Model: A Case Study of Optical Filter Industry

Identification of the Yield Rate by a Hybrid Fuzzy Control PID-Based Four-Stage Model: A Case Study of Optical Filter Industry

Describing Fuzzy Membership Function and Detecting the Outlier by Using Five Number Summary of Data

Diagnosis of coronary artery occlusion by fitting polynomial curve with the ECG signal based on optimization techniques

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