顫振會導致機械加工不穩定，從而對工件表面、尺寸精度和刀具壽命產生負面影響。因此預測並檢測機械顫振，對於提高產品的生產率和質量有著重要的作用。本文旨在建立一種有效的分析預測模型和銑削穩定性檢測方法。透過建立銑削的動態切削力模型，以了解顫振的潛在機制，並藉由考慮製程阻尼的影響，開發了一種新的顫振模型。在該模型中，利用flank-wavy contact成功地確定了製程阻尼的係數，並可用於繪製基於材料特性、顫振頻率和幅度的穩定性波瓣圖。
然而，由於基於切削動力學模型的離線穩定性估計無法反映顫振的非線性特徵，從而導致穩定性邊界的計算錯誤。因此採用希爾伯特-黃轉換的時頻分析方法來研究銑削的穩定性，此種基於信號的顫振檢測方法能夠即時識別顫振。此外，也提出了一種基於模型和信號的混合顫振檢測方法，並通過分析殘餘切削力來凸顯非線性的顫動行為，因此使用簡單的時域指標即可有效地檢測顫振。
除了基於信號和模型的振動檢測之外，還研究了另一種新方法，即使用深度卷積神經網絡（CNN）進行早期振動檢測。此方法利用連續小波變換（CWT）將切削力轉換成的時頻圖應用於CNN模型，再與傳統的機器學習方法進行比較，以證明該方法的合理性。最後，所提出的方法具有應用於製造環境的潛力。

Chatter vibration causes machining instability which results in negative effects on surface finish, dimensional accuracy, and tool life. Therefore, the prediction and detection of machining chatter vibration play an important role in improving the productivity and quality of the products. This dissertation aims to develop an effective analytical prediction model and a detection method of milling stability. A dynamic cutting force model of the milling process was established to understand the underlying mechanism of chatter. A new model of chatter vibration was then developed by considering the effect of process damping. In this model, the damping process coefficients were successfully defined based on the flank-wavy contact. The successful model can be used to plot the stability lobe diagram based on the material properties and the chatter frequency and amplitude.
However, the off-line stability estimation based on the cutting dynamic model is unable to reflect the nonlinear characteristic of chatter, and therefore causing the miscalculation of the stability boundary. The proposed signal-based chatter detection methodology, on the other hand, was able to identify chatter instantaneously. The time-frequency analysis approach based on Hilbert-Huang transform was used to investigate the milling stability, and a hybrid model- and signal-based chatter detection method was proposed, in which the nonlinear chatter behavior was promoted by analyzing the residual cutting force. The simple time-domain indexes were qualified to detect chatter effectively.
In addition to signal- and model-based chatter detection, another novel approach using deep convolutional neural network (CNN) for in-process early chatter detection was also investigated. This is indeed necessary to take any further action to prevent the development of chatter. The time-frequency image converted from the cutting force using continuous wavelet transform (CWT) was applied to the CNN model. Several comparisons with conventional machine-learning methods were performed to justify the proposed approach. Finally, the proposed approaches have the potentials to be applied to the current manufacturing environment.

[1] D. A. Stephenson and J. S. Agapiou, Metal cutting theory and practice. New York: Marcel Dekker, 1996.
[2] Y. Altintas, Manufacturing Automation: Metal Cutting Mechanics, Machine Tool Vibrations, and CNC Design, 2 ed. Cambridge: Cambridge University Press, 2012.
[3] T. Schmitz and K. S. Smith, Machining Dynamics: Frequency Response for Improved Productivity. 2009, pp. 1-303.
[4] H. E. Merritt, "Theory of Self-Excited Machine-Tool Chatter: Contribution to Machine-Tool Chatter Research - 1," Journal of Manufacturing Science and Engineering, vol. 87, no. 4, pp. 447-454, 1965.
[5] G. Quintana, F. Campa, J. Ciurana, and L. Lacalle, "Productivity improvement through chatter-free milling in workshops," Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, vol. 225, pp. 1163-1174, 2011.
[6] K. Cheng, Machining Dynamics: Fundamentals, Applications and Practices. 2009.
[7] J. Tlusty and M. Polacek, "The stability of the machine tool against self - excited vibration in machining," International research in production engineering, ASME, 1963.
[8] W. A. Kline, R. E. DeVor, and J. R. Lindberg, "The prediction of cutting forces in end milling with application to cornering cuts," International Journal of Machine Tool Design and Research, vol. 22, no. 1, pp. 7-22, 1982.
[9] C.-L. Tssi, "Analysis and prediction of cutting forces in end milling by means of a geometrical model," International Journal of Advanced Manufacturing Technology, vol. 31, pp. 888-896, 2007.
[10] Y. Altintas and S. Engin, "Generalized Modeling of Mechanics and Dynamics of Milling Cutters," CIRP Annals, vol. 50, no. 1, pp. 25-30, 2001.
[11] E. Diez Cifuentes, H. Pérez García, M. Guzmán Villaseñor, and A. Vizán Idoipe, "Dynamic analysis of runout correction in milling," International Journal of Machine Tools and Manufacture, vol. 50, no. 8, pp. 709-717, 2010.
[12] W. A. Kline and R. E. DeVor, "The effect of runout on cutting geometry and forces in end milling," International Journal of Machine Tool Design and Research, vol. 23, no. 2, pp. 123-140, 1983.
[13] Y. Altintas and E. Budak, "Analytical Prediction of Stability Lobes in Milling," CIRP Annals - Manufacturing Technology, vol. 44, pp. 357-362, 1995.
[14] R. P. H. Faassen, N. van de Wouw, J. A. J. Oosterling, and H. Nijmeijer, "Prediction of regenerative chatter by modelling and analysis of high-speed milling," International Journal of Machine Tools and Manufacture, vol. 43, no. 14, pp. 1437-1446, 2003.
[15] P. Palpandian, V. Prabhu Raja, and S. Satish Babu, "Stability Lobe Diagram for High Speed Machining Processes:Comparison of Experimental and Analytical Methods - A Review," International Journal of Innovative Research in Science, Engineering and Technology, vol. 2, no. 3, 2013.
[16] T. Insperger and G. Stépán, "Stability of the milling process," Periodica Polytechnica, Mechanical Engineering, vol. 44, 2000.
[17] J. Yue, "Creating a Stability Lobe Diagram," pp. 301-50, 2006.
[18] E. Abele and U. Fiedler, "Creating Stability Lobe Diagrams during Milling," CIRP Annals - Manufacturing Technology, vol. 53, pp. 309-312, 2004.
[19] N. S. Belwalkar, S. S. Mohite, and G. V. Kashyapi, "Identification of Stability Lobe Diagram for Milling using Vibration Analysis," International Journal on Design & Manufacturing Technologies, vol. 9, no. 2, 2015.
[20] E. Solis, C. R. Peres, J. E. Jiménez, J. R. Alique, and J. C. Monje, "A new analytical–experimental method for the identification of stability lobes in high-speed milling," International Journal of Machine Tools and Manufacture, vol. 44, no. 15, pp. 1591-1597, 2004.
[21] H. B. Lacerda and V. T. Lima, "Evaluation of cutting forces and prediction of chatter vibrations in milling," Journal of the Brazilian Society of Mechanical Sciences and Engineering, vol. 26, pp. 74-81, 2004.
[22] O. Tuysuz and Y. Altintas, "Analytical Modeling of Process Damping in Machining," Journal of Manufacturing Science and Engineering, vol. 141, no. 6, 2019.
[23] T. R. Sisson and R. L. Kegg, "An Explanation of Low-Speed Chatter Effects," Journal of Manufacturing Science and Engineering, vol. 91, no. 4, pp. 951-958, 1969.
[24] C. T. Tyler and T. L. Schmitz, "Analytical process damping stability prediction," Journal of Manufacturing Processes, vol. 15, no. 1, pp. 69-76, 2013.
[25] E. Budak and L. T. Tunc, "Identification and modeling of process damping in turning and milling using a new approach," CIRP Annals, vol. 59, no. 1, pp. 403-408, 2010.
[26] C. T. Tyler, J. Troutman, and T. L. Schmitz, "Radial depth of cut stability lobe diagrams with process damping effects," Precision Engineering, vol. 40, pp. 318-324, 2015.
[27] J. Tlusty and F. Ismail, "Special Aspects of Chatter in Milling," Journal of Vibration and Acoustics, vol. 105, no. 1, pp. 24-32, 1983.
[28] X. Xu, W. Tang, and S. Sun, "Research of Gyroscopic Effects on the Stability of High Speed Milling," Key Engineering Materials, vol. 431-432, pp. 369-372, 2010.
[29] O. Özşahin, E. Budak, and H. N. Özgüven, "Investigating Dynamics of Machine Tool Spindles under Operational Conditions," Advanced Materials Research, vol. 223, pp. 610-621, 2011.
[30] J. Feng, Z. Sun, Z. Jiang, and L. Yang, "Identification of chatter in milling of Ti-6Al-4V titanium alloy thin-walled workpieces based on cutting force signals and surface topography," The International Journal of Advanced Manufacturing Technology, vol. 82, no. 9, pp. 1909-1920, 2016.
[31] C.-C. Wei, M.-K. Liu, and G.-H. Huang, "Chatter Identification of Face Milling Operation via Time-Frequency and Fourier Analysis," International Journal of Automation and Smart Technology, chatter, milling, Hilbert-Huang transform, Fourier transform, stability lobe diagram vol. 6, no. 1, pp. 25-36, 2016.
[32] T. L. Schmitz, K. Medicus, and B. Dutterer, "Exploring once-per-revolution audio signal variance as a chatter indicator," Machining Science and Technology, vol. 6, no. 2, pp. 215-233, 2002.
[33] A. V. Filippov, V. E. Rubtsov, S. Y. Tarasov, O. A. Podgornykh, and N. N. Shamarin, "Detecting transition to chatter mode in peakless tool turning by monitoring vibration and acoustic emission signals," The International Journal of Advanced Manufacturing Technology, vol. 95, no. 1, pp. 157-169, 2018.
[34] J. Gao, Q. Song, and Z. Liu, "Chatter detection and stability region acquisition in thin-walled workpiece milling based on CMWT," The International Journal of Advanced Manufacturing Technology, vol. 98, no. 1-4, pp. 699-713, 2018.
[35] S. Wan, X. Li, W. Chen, and J. Hong, "Investigation on milling chatter identification at early stage with variance ratio and Hilbert–Huang transform," The International Journal of Advanced Manufacturing Technology, vol. 95, no. 9, pp. 3563-3573, 2018.
[36] Y. Mei, R. Mo, H. Sun, and K. Bu, "Chatter detection in milling based on singular spectrum analysis," The International Journal of Advanced Manufacturing Technology, vol. 95, no. 9, pp. 3475-3486, 2018.
[37] R. Du, M. A. Elbestawi, and B. C. Ullagaddi, "Chatter detection in milling based on the probability distribution of cutting force signal," Mechanical Systems and Signal Processing, vol. 6, no. 4, pp. 345-362, 1992.
[38] H. Li, X. Jing, and J. Wang, "Detection and analysis of chatter occurrence in micro-milling process," Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture, vol. 228, pp. 1359-1371, 2014.
[39] S. Tangjitsitcharoen and A. Pongsathornwiwat, "Development of chatter detection in milling processes," The International Journal of Advanced Manufacturing Technology, vol. 65, 2012.
[40] D. Pérez-Canales, L. Vela-Martínez, J. Carlos Jáuregui-Correa, and J. Alvarez-Ramirez, "Analysis of the entropy randomness index for machining chatter detection," International Journal of Machine Tools and Manufacture, vol. 62, pp. 39-45, 2012.
[41] H. Cao, Y. Yue, X. Chen, and X. Zhang, "Chatter detection based on synchrosqueezing transform and statistical indicators in milling process," The International Journal of Advanced Manufacturing Technology, vol. 95, no. 1, pp. 961-972, 2018.
[42] E. Kuljanic, M. Sortino, and G. Totis, "Multisensor approaches for chatter detection in milling," Journal of Sound and Vibration, vol. 312, no. 4, pp. 672-693, 2008.
[43] E. Riviere, V. Stalon, O. Abeele, and E. Filippi, "Chatter detection techniques using microphone," 2006.
[44] L. Sallese, N. Grossi, A. Scippa, and G. Campatelli, "Investigation and Correction of Actual Microphone Response for Chatter Detection in Milling Operations," Measurement and Control, vol. 50, no. 2, pp. 45-52, 2017.
[45] T. Delio, J. Tlusty, and S. Smith, "Use of Audio Signals for Chatter Detection and Control," Journal of Manufacturing Science and Engineering, vol. 114, no. 2, pp. 146-157, 1992.
[46] N. Shamarin, A. Filippov, S. Tarasov, O. Podgornykh, E. Filippova, and A. Vorontsov, Acoustic emission as method of chatter detection in cutting. 2018, p. 020276.
[47] R. Chiou and S. Liang, "Analysis of acoustic emission in chatter vibration with tool wear effect in turning," International Journal of Machine Tools and Manufacture, vol. 40, pp. 927-941, 2000.
[48] R. Tuğci, V. B. Çelen, and A. M. Özbayoğlu, "Comparison of classifiers for chatter detection," in 2013 21st Signal Processing and Communications Applications Conference (SIU), 2013, pp. 1-3.
[49] E. Soliman, "Chatter detection by monitoring spindle drive current," The International Journal of Advanced Manufacturing Technology, vol. 13, pp. 27-34, 1997.
[50] H. Liu, Q. Chen, B. Li, X. Mao, K. Mao, and F. Peng, "On-line chatter detection using servo motor current signal in turning," Science China Technological Sciences, vol. 54, no. 12, pp. 3119-3129, 2011.
[51] C. Yue, H. Gao, X. Liu, S. Y. Liang, and L. Wang, "A review of chatter vibration research in milling," Chinese Journal of Aeronautics, vol. 32, no. 2, pp. 215-242, 2019.
[52] J. Tlusty and G. C. Andrews, "A Critical Review of Sensors for Unmanned Machining," CIRP Annals, vol. 32, no. 2, pp. 563-572, 1983.
[53] M. Lamraoui, M. Barakat, M. Thomas, and M. E. Badaoui, "Chatter detection in milling machines by neural network classification and feature selection," Journal of Vibration and Control, vol. 21, no. 7, pp. 1251-1266, 2013.
[54] W. Rawat and Z. Wang, "Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review," Neural Computation, vol. 29, pp. 1-98, 2017.
[55] R. Yamashita, M. Nishio, R. K. G. Do, and K. Togashi, "Convolutional neural networks: an overview and application in radiology," Insights into Imaging, vol. 9, no. 4, pp. 611-629, 2018.
[56] M. E. Martellotti, " An Analysis of the Milling Process," Transactions of the ASME, vol. 63, no. 8, 1941.
[57] S. Tobias and W. Fishwick, "Theory of regenerative machine tool chatter," The engineer, vol. 205, no. 7, pp. 199-203, 1958.
[58] Y. Altintas, M. Eynian, and H. Onozuka, "Identification of dynamic cutting force coefficients and chatter stability with process damping," CIRP Annals, vol. 57, no. 1, pp. 371-374, 2008.
[59] M. K. Das and S. A. Tobias, "The relation between the static and the dynamic cutting of metals," International Journal of Machine Tool Design and Research, vol. 7, no. 2, pp. 63-89, 1967.
[60] C. Y. Huang and J. J. J. Wang, "Mechanistic Modeling of Process Damping in Peripheral Milling," Journal of Manufacturing Science and Engineering, vol. 129, no. 1, pp. 12-20, 2006.
[61] E. Budak and L. T. Tunc, "A New Method for Identification and Modeling of Process Damping in Machining," Journal of Manufacturing Science and Engineering, vol. 131, no. 5, 2009.
[62] B. Denkena, W. Bickel, and R. Grabowski, "Modeling and simulation of milling processes including process damping effects," Production Engineering, vol. 8, no. 4, pp. 453-459, 2014.
[63] M. A. Elbestawi, F. Ismail, R. Du, and B. C. Ullagaddi, "Modelling Machining Dynamics Including Damping in the Tool-Workpiece Interface," Journal of Manufacturing Science and Engineering, vol. 116, no. 4, pp. 435-439, 1994.
[64] O. Gurdal, E. Ozturk, and N. D. Sims, "Analysis of Process Damping in Milling," Procedia CIRP, vol. 55, pp. 152-157, 2016.
[65] X. Li, W. Zhao, L. Li, N. He, and S. Chi, "Modeling and Application of Process Damping in Milling of Thin-Walled Workpiece Made of Titanium Alloy," Shock and Vibration, vol. 2015, pp. 1-12, 2015.
[66] B. Y. Lee, Y. S. Tarng, and S. C. Ma, "Modeling of the process damping force in chatter vibration," International Journal of Machine Tools and Manufacture, vol. 35, no. 7, pp. 951-962, 1995.
[67] E. Turkes, S. Orak, S. Neseli, and S. Yaldiz, "A new process damping model for chatter vibration," Measurement, vol. 44, no. 8, pp. 1342-1348, 2011.
[68] K. Ahmadi and Y. Altintas, "Identification of Machining Process Damping Using Output-Only Modal Analysis," Journal of Manufacturing Science and Engineering, vol. 136, no. 5, 2014.
[69] A. V. Oppenheim, A. S. Willsky, and S. H. Nawab, Signals and systems, 2nd ed. Upper Saddle River, N.J.: Prentice-Hall International, 1997.
[70] P. Huang, J. Li, J. Sun, and J. Zhou, "Vibration analysis in milling titanium alloy based on signal processing of cutting force," The International Journal of Advanced Manufacturing Technology, vol. 64, no. 5, pp. 613-621, 2013.
[71] D.-H. Kim, J.-Y. Song, S.-K. Cha, and H. Son, "The development of embedded device to detect chatter vibration in machine tools and CNC-based autonomous compensation," Journal of Mechanical Science and Technology, vol. 25, no. 10, p. 2623, 2011.
[72] M. K. Khraisheh, C. Pezeshki, and A. E. Bayoumi, "Time series based analysis for primary chatter in metal cutting," Journal of Sound and Vibration, vol. 180, no. 1, pp. 67-87, 1995.
[73] C. Peng, L. Wang, and T. W. Liao, "A new method for the prediction of chatter stability lobes based on dynamic cutting force simulation model and support vector machine," Journal of Sound and Vibration, vol. 354, pp. 118-131, 2015.
[74] M. C. Yoon and D. H. Chin, "Cutting force monitoring in the end milling operation for chatter detection," Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, vol. 219, no. 6, pp. 455-465, 2005.
[75] N. Huang et al., "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis," Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, vol. 454, pp. 903-995, 1998.
[76] R. Yan and R. X. Gao, "Hilbert–Huang Transform-Based Vibration Signal Analysis for Machine Health Monitoring," IEEE Transactions on Instrumentation and Measurement, vol. 55, no. 6, pp. 2320-2329, 2006.
[77] N. E. Huang and S. S. Shen, "Hilbert-Huang transform and its applications," (in English), 2014.
[78] W. Peng, Z. Hu, L. Yuan, and P. Zhu, "Chatter Identification Using HHT for Boring Process," Proceedings of SPIE - The International Society for Optical Engineering, vol. 9043, 2013.
[79] H. Cao, Y. Lei, and Z. He, "Chatter identification in end milling process using wavelet packets and Hilbert–Huang transform," International Journal of Machine Tools and Manufacture, vol. 69, pp. 11-19, 2013.
[80] H. Cao, K. Zhou, and X. Chen, "Chatter identification in end milling process based on EEMD and nonlinear dimensionless indicators," International Journal of Machine Tools and Manufacture, vol. 92, pp. 52-59, 2015.
[81] Y. Chen, H. Li, L. Hou, J. Wang, and X. Bu, "An intelligent chatter detection method based on EEMD and feature selection with multi-channel vibration signals," Measurement, vol. 127, pp. 356-365, 2018.
[82] G. S. Chen and Q. Z. Zheng, "Online chatter detection of the end milling based on wavelet packet transform and support vector machine recursive feature elimination," The International Journal of Advanced Manufacturing Technology, vol. 95, no. 1, pp. 775-784, 2018.
[83] Y. Chen, H. Li, X. Jing, L. Hou, and X. Bu, "Intelligent chatter detection using image features and support vector machine," The International Journal of Advanced Manufacturing Technology, vol. 102, no. 5, pp. 1433-1442, 2019.
[84] S. Qian, Y. Sun, and Z. Xiong, "Intelligent chatter detection based on wavelet packet node energy and LSSVM-RFE," in 2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), 2015, pp. 1514-1519.
[85] Y. Chen, H. Li, L. Hou, and X. Bu, "Feature extraction using dominant frequency bands and time-frequency image analysis for chatter detection in milling," Precision Engineering, vol. 56, pp. 235-245, 2019.
[86] O. Janssens et al., "Convolutional Neural Network Based Fault Detection for Rotating Machinery," Journal of Sound and Vibration, vol. 377, pp. 331-345, 2016.
[87] Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, 1998.
[88] G. Xu, M. Liu, Z. Jiang, D. Söffker, and W. Shen, "Bearing Fault Diagnosis Method Based on Deep Convolutional Neural Network and Random Forest Ensemble Learning," Sensors (Basel, Switzerland), vol. 19, no. 5, p. 1088, 2019.
[89] W.-Y. Chang, C.-C. Chen, and S.-J. Wu, "Chatter Analysis and Stability Prediction of Milling Tool Based on Zero-Order and Envelope Methods for Real-Time Monitoring and Compensation," International Journal of Precision Engineering and Manufacturing, vol. 20, no. 5, pp. 693-700, 2019.
[90] D. W. Wu, "Application of a comprehensive dynamic cutting force model to orthogonal wave-generating processes," International Journal of Mechanical Sciences, vol. 30, no. 8, pp. 581-600, 1988.
[91] L. T. Tunc and E. Budak, "Identification and Modeling of Process Damping in Milling," Journal of Manufacturing Science and Engineering, vol. 135, no. 2, 2013.
[92] Y.-W. Qui, "On the Study of Milling Chatter," Master, National Taiwan University of Science and Technology, 2016.
[93] N. K. Muhamad Khairussaleh, A. K. Aqella, and I. S. S. Sharifah, "Optimization of Milling Carbon Fibre Reinforced Plastic Using RSM," Procedia Engineering, vol. 184, pp. 518-528, 2017.
[94] M.-K. Liu, M.-Q. Tran, Y.-W. Qui, and C.-H. Chung, "Chatter Detection in Milling Process Based on Time-Frequency Analysis," in the ASME 2017 12th International Manufacturing Science and Engineering Conference, Los Angeles, U.S.A, 2017.
[95] J. A. Bailey, "Friction in metal machining—Mechanical aspects," Wear, vol. 31, no. 2, pp. 243-275, 1975.
[96] B. S. Berger, I. Minis, J. Harley, M. Rokni, and M. Papadopoulos, "Wavelet Based Cutting State Identification," Journal of Sound and Vibration, vol. 213, no. 5, pp. 813-827, 1998.
[97] A.-H. Najmi, J. Sadowsky, O. Morlet, and W. Transform, "The Continuous Wavelet Transform and Variable Resolution Time-Frequency Analysis," vol. 18, 1997.
[98] M. Q. Tran and M. K. Liu, "Chatter Identification in End Milling Process Based on Cutting Force Signal Processing," IOP Conference Series: Materials Science and Engineering, vol. 654, pp. 1-7 , 2019.
[99] M. Misiti, Y. Misiti, G. Oppenheim, and J.-M. Poggi, Wavelet Toolbox. 2002
[100] C. Szegedy et al., "Going deeper with convolutions," in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 1-9.
[101] W. Zhao, S. Li, A. Li, B. Zhang, and Y. Li, "Hyperspectral images classification with convolutional neural network and textural feature using limited training samples," Remote Sensing Letters, vol. 10, no. 5, pp. 449-458, 2019.
[102] H. Shyh-Jier and H. Cheng-Tao, "High-impedance fault detection utilizing a Morlet wavelet transform approach," IEEE Transactions on Power Delivery, vol. 14, no. 4, pp. 1401-1410, 1999.
[103] Y. Jang, S. Kim, K. Kim, and D. Lee, "Deep learning-based classification with improved time resolution for physical activities of children," PeerJ, vol. 6, p. e5764, 2018.