Analisis Komputasi Paralel pada Image Encoding Framework untuk Konversi Citra Data Deret Waktu Sistem Kontrol Industri
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https://doi.org/10.24843/MITE.2023.v22i02.P06
Abstrak
Sensor pada sistem kontrol industri mengirimkan serangkaian data tiap waktu dikenal dengan data deret waktu ke kontroler. Data memiliki informasi penting bagi kontroler untuk menentukan sinyal kontrol bagi aktuator. Munculnya anomali pada data deret waktu dapat dideteksi dengan metode Convolutional Neural Network (CNN) memanfaatkan teknik image encoding seperti Gramian Angular Field (GAF) dan Markov Transition Field (MTF). Teknik ini mengubah data deret waktu menjadi citra melalui serangkaian tahap mulai persiapan data, encoding data, dan konversi citra. Pembagian data berukuran besar menjadi sejumlah segmen yang lebih kecil membutuhkan proses encoding dan konversi yang berulang. Proses berulang yang dikerjakan secara serial membutuhkan waktu yang lama sehingga memperlambat deteksi anomali dan tanggapan yang harus dilakukan. Penelitian ini menerapkan komputasi paralel dengan Joblib dan Mpire pada image encoding GAF dan MTF yang disediakan oleh pustaka pyts berbasis Python. Konfigurasi n_jobs menentukan jumlah inti logika CPU yang digunakan untuk mengeksekusi program. Penerapan nilai n_jobs = 8 yang disesuaikan dengan jumlah inti logika CPU komputer penelitian menghasilkan penghematan waktu proses rata-rata sebesar 63% (Joblib) dan 49% (Mpire) yang secara teoritis akan mampu mendeteksi anomali yang muncul minimal tiap 62.73 ms (Joblib) dan 86.20 ms (Mpire) dibandingkan dengan komputasi serial yakni setiap 167.51 ms.
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Referensi
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[2] D. M. Hawkins, Identification of Outliers. Dordrecht: Springer Netherlands, 1980. doi: 10.1007/978-94-015-3994-4.
[3] A. Blázquez-García, A. Conde, U. Mori, and J. A. Lozano, “A Review on Outlier/Anomaly Detection in Time Series Data,” ACM Comput. Surv., vol. 54, no. 3, pp. 1–33, Apr. 2022, doi: 10.1145/3444690.
[4] C. C. Aggarwal, Outlier Analysis. Cham: Springer International Publishing, 2017. doi: 10.1007/978-3-319-47578-3.
[5] K. Shaukat et al., “A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives,” in Advances in Information and Communication, K. Arai, Ed., in Advances in Intelligent Systems and Computing. Cham: Springer International Publishing, 2021, pp. 865–877. doi: 10.1007/978-3-030-73100-7_60.
[6] G. Pang, C. Shen, L. Cao, and A. V. D. Hengel, “Deep Learning for Anomaly Detection: A Review,” ACM Comput. Surv., vol. 54, no. 2, pp. 1–38, Apr. 2021, doi: 10.1145/3439950.
[7] H. Rahadian, S. Bandong, A. Widyotriatmo, and E. Joelianto, “Open Source OPC UA Data Traffic Characteristic and Anomaly Detection using Image-Encoding based Convolutional Neural Network,” in 2022 7th International Conference on Electric Vehicular Technology (ICEVT), Bali, Indonesia: IEEE, Sep. 2022, pp. 52–59. doi: 10.1109/ICEVT55516.2022.9925002.
[8] S. Oh, S. Oh, T.-W. Um, J. Kim, and Y.-A. Jung, “Methods of Pre-Clustering and Generating Time Series Images for Detecting Anomalies in Electric Power Usage Data,” Electronics, vol. 11, no. 20, p. 3315, Oct. 2022, doi: 10.3390/electronics11203315.
[9] C.-C. Wang and C.-H. Kuo, “Detecting dyeing machine entanglement anomalies by using time series image analysis and deep learning techniques for dyeing-finishing process,” Adv. Eng. Inform., vol. 55, p. 101852, Jan. 2023, doi: 10.1016/j.aei.2022.101852.
[10] G. Liu, Y. Niu, W. Zhao, Y. Duan, and J. Shu, “Data anomaly detection for structural health monitoring using a combination network of GANomaly and CNN,” Smart Struct. Syst., vol. 29, no. 1, pp. 53–62, Jan. 2022, doi: 10.12989/SSS.2022.29.1.053.
[11] H. Y. Yatbaz, E. Ever, and A. Yazici, “Activity Recognition and Anomaly Detection in E-Health Applications Using Color-Coded Representation and Lightweight CNN Architectures,” IEEE Sens. J., vol. 21, no. 13, pp. 14191–14202, Jul. 2021, doi: 10.1109/JSEN.2021.3061458.
[12] J. Faouzi and H. Janati, “pyts: A Python Package for Time Series Classification,” J. Mach. Learn. Res., vol. 21, no. 46, pp. 1–6, 2020.
[13] T. Fu, “A review on time series data mining,” Eng. Appl. Artif. Intell., vol. 24, no. 1, pp. 164–181, Feb. 2011, doi: 10.1016/j.engappai.2010.09.007.
[14] W. Jiang, “Time series classification: nearest neighbor versus deep learning models,” SN Appl. Sci., vol. 2, no. 4, p. 721, Apr. 2020, doi: 10.1007/s42452-020-2506-9.
[15] B. Lindemann, B. Maschler, N. Sahlab, and M. Weyrich, “A survey on anomaly detection for technical systems using LSTM networks,” Comput. Ind., vol. 131, p. 103498, Oct. 2021, doi: 10.1016/j.compind.2021.103498.
[16] I. González, A. J. Calderón, J. Figueiredo, and J. M. C. Sousa, “A Literature Survey on Open Platform Communications (OPC) Applied to Advanced Industrial Environments,” Electronics, vol. 8, no. 5, p. 510, May 2019, doi: 10.3390/electronics8050510.
[17] A. Cartwright, “OPC-UA: the Flow of Data,” Ai Build TechBlog, May 27, 2021. https://medium.com/ai-build-techblog/opc-ua-the-flow-of-data-7c3e5c870a4c (accessed May 21, 2023).
[18] N. Muhlbauer, E. Kirdan, M.-O. Pahl, and G. Carle, “Open-Source OPC UA Security and Scalability,” in 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Vienna, Austria: IEEE, Sep. 2020, pp. 262–269. doi: 10.1109/ETFA46521.2020.9212091.
[19] Z. Wang and T. Oates, “Imaging time-series to improve classification and imputation,” in Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015.
[20] M. Wang, W. Wang, X. Zhang, and H. H.-C. Iu, “A New Fault Diagnosis of Rolling Bearing Based on Markov Transition Field and CNN,” Entropy, vol. 24, no. 6, p. 751, May 2022, doi: 10.3390/e24060751.
[21] I. A. S. Dewi Paramitha, G. M. A. Sasmita, and I. M. S. Raharja, “Analisis Data Log IDS Snort dengan Algoritma Clustering Fuzzy C-Means,” Maj. Ilm. Teknol. Elektro, vol. 19, no. 1, p. 95, Oct. 2020, doi: 10.24843/MITE.2020.v19i01.P14.
[22] A. Jayadi, T. Susanto, and F. D. Adhinata, “Sistem Kendali Proporsional pada Robot Penghindar Halangan (Avoider) Pioneer P3-DX,” Maj. Ilm. Teknol. Elektro, vol. 20, no. 1, p. 47, Mar. 2021, doi: 10.24843/MITE.2021.v20i01.P05.
[23] Q. Kong, T. Siauw, and A. M. Bayen, Python programming and numerical methods: a guide for engineers and scientists. London: Elsevier, Academic Press, 2021.
[24] “Python Multiprocessing: The Complete Guide,” Super Fast Python, Jun. 26, 2022. https://superfastpython.com/multiprocessing-in-python/ (accessed Feb. 26, 2023).
[25] T. Kim, Y. Cha, B. Shin, and B. Cha, “Survey and Performance Test of Python-based Libraries for Parallel Processing,” in The 9th International Conference on Smart Media and Applications, Jeju Republic of Korea: ACM, Sep. 2020, pp. 154–157. doi: 10.1145/3426020.3426057.
[26] S. Jansen, “MPIRE for Python: MultiProcessing Is Really Easy!,” Medium, Oct. 27, 2021. https://towardsdatascience.com/mpire-for-python-multiprocessing-is-really-easy-d2ae7999a3e9 (accessed Feb. 22, 2023).
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Diterbitkan
2023-12-19
##submission.howToCite##
RAHADIAN, Helmy; WAHID, Muhammad Rizalul; ARIFIN, Zaenal.
Analisis Komputasi Paralel pada Image Encoding Framework untuk Konversi Citra Data Deret Waktu Sistem Kontrol Industri.
Jurnal Teknologi Elektro, [S.l.], v. 22, n. 2, p. 193-202, dec. 2023.
ISSN 2503-2372.
Tersedia pada: <https://ojs.unud.ac.id./index.php/mite/article/view/98662>. Tanggal Akses: 21 apr. 2025
doi: https://doi.org/10.24843/MITE.2023.v22i02.P06.
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