Efficient Data Processing Techniques in Industrial Sites to Improve Productivity Based on Behavioral Internet

doi:10.22716/sckt.2023.11.1.004

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2023 Vol.11, Issue 1 Preview Page Next Page

Research Article

Efficient Data Processing Techniques in Industrial Sites to Improve Productivity Based on Behavioral Internet 행동 인터넷 기반 생산성 향상을 위한 산업 현장의 효율적인 데이터 처리 모델: Yoon-Su Jeong¹
정윤수¹; ¹Professor, Dept. of Game Software Engineering, Mokwon University

¹목원대학교 게임소프트웨어공학과 교수

31 March 2023. pp. 39-47

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Abstract

전 세계적으로 COVID-19 유행으로 인하여 최근 몇 년 동안 중소기업을 중심으로 산업현장에서는 생산 비용 절감 및 생산성 향상을 위한 다양한 노력을 시도하고 있다. 그러나, 산업현장에서 사용하고 있던 기존 장비와 새로운 IT 기술들을 융합하는 새로운 연구들은 다양하게 진행되고 있지만, 기업 여건(환경, 자금 확보, 유통 등) 문제로 인하여 생산 효율을 단기간에 향상하지 못하고 있으므로 산업 현장의 생산 비용과 시간이 비효율적이다. 본 논문에서는 기업 내 생산시설의 비용 절감 및 시간 단축을 위해서 행동 인터넷 기반의 생산 데이터를 생산 현장 내 구성 요소별로 소규모 단위로 블록 체인화하여 가상 머신에 적용하는 모델을 제안한다. 이때, 제안 모델은 소규모 단위의 데이터가 균형을 맞출 수 있도록 수집된 데이터 간 연계를 균등하게 유지할 수 있도록 K-평균 클러스터링 알고리즘을 사용하여 데이터를 연계한다. 또한, 제안 모델은 생산 현장에서 불규칙적으로 생산되는 데이터를 빠르게 검증하도록 소규모 단위의 데이터들을 블록체인으로 묶어 검증함으로써 데이터의 검증 속도를 빠르게 처리할 뿐만 아니라 데이터 처리를 탄력적으로 운용할 수 있다.

Due to the COVID-19 pandemic worldwide, various efforts have been made to reduce production costs and improve productivity in industrial sites, mainly small and medium-sized enterprises, in recent years. However, although new research is being conducted to fuse existing equipment and new IT technologies used in industrial sites, production costs and time at industrial sites are inefficient because production efficiency is not improved in a short period of time due to corporate conditions (environment, funding, distribution, etc.). In this paper, we propose a model that blocks behavioral Internet-based production data in small units for each component in the production site and applies it to virtual machines to reduce costs and time of production facilities in the enterprise. At this time, the proposed model links the data using a K-means clustering algorithm to ensure that the links between the collected data are evenly maintained so that the data in small units can be balanced. In addition, the proposed model not only processes data verification speed but also operates data processing flexibly by grouping small-scale data into a blockchain to quickly verify data produced irregularly at the production site.

Keywords

Internet of behavior

Blockchain

Clustering algorithm

Data processing

Low cost

References

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Information

Publisher :The Society of Convergence Knowledge
Publisher(Ko) :융복합지식학회
Journal Title :The Society of Convergence Knowledge Transactions
Journal Title(Ko) :융복합지식학회논문지
Volume : 11
No :1
Pages :39-47
DOI :https://doi.org/10.22716/sckt.2023.11.1.004

[1] G. S. Nyman, "Internet of behaviours (iob)", Gote Nyman's (gotepoem) Blog, 2012.

[2] T. DeMarco and T. Lister, Peopleware: productive projects and teams. Addison-Wesley, 2013.

[3] C. Wohlin, D. Smite, and N. B. Moe, "A general theory of software en-gineering: Balancing human, social and organizational capitals", Journal of Systems and Software, Vol. 109, pp. 229-242, 2015. 10.1016/j.jss.2015.08.009

[4] P. Lenberg, R. Feldt, and L. G. Wallgren, "Behavioral software engineering: A definition and systematic literature review", Journal of Systems and software, Vol. 107, pp. 15-37, 2015. 10.1016/j.jss.2015.04.084

[5] C. Garrido-Hidalgo, D. Hortelano, L. Roda-Sanchez, T. Olivares, M. C. Ruiz, and V. Lopez, "Iot heterogeneous mesh network deployment for human-in-the-loop challenges towards a social and sustainable industry 4.0", IEEE Access, Vol. 6, pp. 28417-28 437, 2018. 10.1109/ACCESS.2018.2836677

[6] J. Dugdale, M. T. Moghaddam, and H. Muccini, "Iot4emergency: Internet of things for emergency management", ACM SIGSOFT Software Engineering Notes, Vol. 46, No. 1, pp. 33-36, 2021. 10.1145/3437479.3437489

[7] M. T. Moghaddam, E. Rutten, P. Lalanda, and G. Giraud, "Ias: an iot architectural self-adaptation framework", in European Conference on Software Architecture, Springer, pp. 333-351, 2020. 10.1007/978-3-030-58923-3_22

[8] E. Colangelo, S. Hartleif, S. Hefner, and A. Sauer, "Energy flexibility in production planning", Procedia CIRP, Vol. 104, pp. 1095-1100, 2021. 10.1016/j.procir.2021.11.184

[9] J. Popper, W. Motsch, A. David, T. Petzsche, and M. Ruskowski, "Utilizing multi-agent deep reinforcement learning for flexible job shop scheduling under sustainable viewpoints", in 2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), IEEE, pp. 1-6, 2021. 10.1109/ICECCME52200.2021.9590925 33550886

[10] D. Eryilmaz, J. Apland, and T. M. Smith, "Dynamic electricity pricing-modeling manufacturer response and an application to cement processing", Energy and Environment Research, Vol. 9, No. 2, 2019. 10.5539/eer.v9n2p1

[11] E. Leo, G. Dalle Ave, I. Harjunkoski, and S. Engell, "Stochastic shortterm integrated electricity procurement and production scheduling for a large consumer", Computers & Chemical Engineering, Vol. 145, p.107191, 2021. 10.1016/j.compchemeng.2020.107191

[12] P. M. Castro, G. Dalle Ave, S. Engell, I. E. Grossmann, and I. Harjunkoski, "Industrial demand side management of a steel plant considering alternative power modes and electrode replacement", Industrial & Engineering Chemistry Research, Vol. 59, No. 30, pp. 13642-13656, 2020. 10.1021/acs.iecr.0c01714

[13] I.-Y. Joo and D.-H. Choi, "Distributed optimization framework for energy management of multiple smart homes with distributed energy resources", IEEE Access, Vol. 5, pp. 15551-15560, 2017. 10.1109/ACCESS.2017.2734911

[14] M. Razzanelli, E. Crisostomi, L. Pallottino, and G. Pannocchia, "Distributed model predictive control for energy management in a network of microgrids using the dual decomposition method", Optimal Control Applications and Methods, Vol. 41, No. 1, pp. 25-41, 2020. 10.1002/oca.2504

[15] Machine Learning Repository, https://archive.ics.uci.edu/ml/datasets.php?format=&task=&att=&area=&numAtt=greater100&numIns=&type=seq&sort=nameDown&view=list, 2021.

[16] Y. Meidan, M. Bohadana, A. Shabtai, M. Ochoa, N. O. Tippenhauer, J. D. Guarnizo, and Y. Elovici, "Detection of unauthorized iot devices using machine learning techniques", preprint arXiv:1709.04647, 2017.

[17] libaom software, https://aomedia.googlesource.com/aom/.

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