Brief Paper: Secret Key and Tag Generation for IIoT Systems Based on
                    Edge Computing

Koh, Giheon; Yu, Heungsik; Kim, Sungun

doi:10.33851/jmis.2021.8.1.57

Cited by 3 publications

(1 citation statement)

References 5 publications

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“…Quality control has always been an integral part of manufacturing [2]. The internet of things (IoT) allows for data collection at every point of the manufacturing process, using networked sensors and intelligent devices and putting those technologies to use directly on the manufacturing floor, collecting data to drive artificial intelligence (AI) and predictive analytics [3,4]. A huge increase in data volume has created a new paradigm in product quality management and predictive maintenance [5].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Process Quality Performance Using Statistical Analysis and Long Short-Term Memory

Pheng¹,

Chuluunsaikhan

Ryu

et al. 2022

Applied Sciences

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In the manufacturing industry, the process capability index (Cpk) measures the level and capability required to improve the processes. However, the Cpk is not enough to represent the process capability and performance of the manufacturing processes. In other words, considering that the smart manufacturing environment can accommodate the big data collected from various facilities, we need to understand the state of the process by comprehensively considering diverse factors contained in the manufacturing. In this paper, a two-stage method is proposed to analyze the process quality performance (PQP) and predict future process quality. First, we propose the PQP as a new measure for representing process capability and performance, which is defined by a composite statistical process analysis of such factors as manufacturing cycle time analysis, process trajectory of abnormal detection, statistical process control analysis, and process capability control analysis. Second, PQP analysis results are used to predict and estimate the stability of the production process using a long short-term memory (LSTM) neural network, which is a deep learning algorithm-based method. The present work compares the LSTM prediction model with the random forest, autoregressive integrated moving average, and artificial neural network models to convincingly demonstrate the effectiveness of our proposed approach. Notably, the LSTM model achieved higher accuracy than the other models.

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Section: Introductionmentioning

confidence: 99%

Prediction of Process Quality Performance Using Statistical Analysis and Long Short-Term Memory

Pheng¹,

Chuluunsaikhan

Ryu

et al. 2022

Applied Sciences

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Smart Hardware Development under Industrial IOT (IIOT) 4.0: A Survey Report

Bhatt

Bindal

2021

2021 6th International Conference on Signal Processing, Computing and Control (ISPCC)

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Artificial intelligence and internet of things oriented sustainable precision farming: Towards modern agriculture

Sharma,

Tselykh

et al. 2023

Open Life Sciences

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Agriculture encompasses the study, practice, and discipline of plant cultivation. Agriculture has an extensive history dating back thousands of years. Depending on climate and terrain, it began independently in various locations on the planet. In comparison to what could be sustained by foraging and gathering, agriculture has the potential to significantly increase the human population. Throughout the twenty-first century, precision farming (PF) has increased the agricultural output. precision agriculture (PA) is a technology-enabled method of agriculture that assesses, monitors, and evaluates the needs of specific fields and commodities. The primary objective of this farming method, as opposed to conventional farming, is to increase crop yields and profitability through the precise application of inputs. This work describes in depth the development and function of artificial intelligence (AI) and the internet of things (IoT) in contemporary agriculture. Modern day-to-day applications rely extensively on AI and the IoT. Modern agriculture leverages AI and IoT for technological advancement. This improves the accuracy and profitability of modern agriculture. The use of AI and IoT in modern smart precision agricultural applications is highlighted in this work and the method proposed incorporates specific steps in PF and demonstrates superior performance compared to existing classification methods. It achieves a remarkable accuracy of 98.65%, precision of 98.32%, and recall rate of 97.65% while retaining competitive execution time of 0.23 s, when analysing PF using the FAOSTAT benchmark dataset. Additionally, crucial equipment and methods used in PF are described and the vital advantages and real-time tools utilised in PA are covered in detail.

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Brief Paper: Secret Key and Tag Generation for IIoT Systems Based on Edge Computing

Cited by 3 publications

References 5 publications

Prediction of Process Quality Performance Using Statistical Analysis and Long Short-Term Memory

Prediction of Process Quality Performance Using Statistical Analysis and Long Short-Term Memory

Smart Hardware Development under Industrial IOT (IIOT) 4.0: A Survey Report

Artificial intelligence and internet of things oriented sustainable precision farming: Towards modern agriculture

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