DOI QR코드

DOI QR Code

Recent Progress of Smart Sensor Technology Relying on Artificial Intelligence

인공지능 기반의 스마트 센서 기술 개발 동향

  • Shin, Hyun Sik (School of Advanced Materials Engineering, Jeonbuk National University) ;
  • Kim, Jong-Woong (School of Advanced Materials Engineering, Jeonbuk National University)
  • 신현식 (전북대학교 신소재공학부) ;
  • 김종웅 (전북대학교 신소재공학부)
  • Received : 2022.09.08
  • Accepted : 2022.09.30
  • Published : 2022.09.30

Abstract

With the rapid development of artificial intelligence technology that gives existing sensors functions similar to human intelligence is drawing attention. Previously, researches were mainly focused on an improvement of fundamental performance indicators as sensors. However, recently, attempts to combine artificial intelligence such as classification and prediction with sensors have been explored. Based on this, intelligent sensor research has been actively reported in almost all kinds of sensing fields such as disease detection, motion detection, and gas sensor. In this paper, we introduce the basic concepts, types, and driving mechanisms of artificial intelligence and review some examples of its use.

인공지능 기술의 급속한 발전으로 기존 센서에 인간의 지능과 유사한 기능을 부여하기 위한 연구가 큰 주목을 받고 있다. 기존에는 주로 센서로써의 기초 성능지표, 예를 들어 감도 및 속도 등을 향상시키기 위한 연구가 주로 진행되었지만, 최근에는 분류나 예측 등의 인공지능을 센서에 결합하기 위한 시도가 확대되고 있다. 이를 바탕으로 최근 질병 감지 센서, 모션 감지 센서 및 가스 센서 등 거의 센서 전 분야에서 지능형 센서에 대한 연구 결과가 활발히 보고되고 있다. 본 논문에서는 인공지능의 기본적인 개념, 종류 및 메커니즘과 더불어, 최근 보고된 지능형 센서에의 적용 사례에 대해 알아보고자 한다.

Keywords

Acknowledgement

이 논문은 한국연구재단 지원의 중견연구자 지원사업과제 (2022R1A2C1010353) 및 한국 산업기술통상자원부 지원의 디스플레이 혁신공정 플랫폼구축 기술개발사업(20006467)의 지원으로 작성되었습니다.

References

  1. Z. Zhang, F. Wen, Z. Sun, X. Guo, T. He and C. LEE, "Artificial Intelligence-Enabled Sensing Technologies in the 5G/Internet of Things Era: From Virtual Reality/Augmented Reality to the Digital Twin", Advanced Intelligent Systems, 2100228 (2022).
  2. N. Ha, K. Xu, G. Ren, A. Mitchell and J. Z. Ou, "Machine Learning-Enabled Smart Sensor Systems", Advanced Intelligent Systems, 2(9), 2000063 (2020). https://doi.org/10.1002/aisy.202000063
  3. Y. Djenouri, A. Belhadi, G. Srivastava, E. H. Houssein and J. C. W. Lin, "Sensor data fusion for the industrial artificial intelligence of things", Expert Systems, 39(5), e12875 (2022). https://doi.org/10.1111/exsy.12875
  4. H. Nazemi, A. Joseph, J. Park and A. Emadi, "Advanced micro-and nano-gas sensor technology: A review", Sensors, 19(6), 1285 (2019).
  5. K. Zhou, K. Dai, C. Liu and C. Shen,"Flexible conductive polymer composites for smart wearable strain sensors", SmartMat, 1(1), e1010 (2020). https://doi.org/10.1002/smm2.1010
  6. Y. Yang, Y. Song, X. Bo, J. Min, O. S. Pak, L. Zhu and W. Gao, "A laser-engraved wearable sensor for sensitive detection of uric acid and tyrosine in sweat", Nature Biotechnology, 38(2), 217-224 (2020). https://doi.org/10.1038/s41587-019-0321-x
  7. S.B. Choi, J. S. Meena and J. W. Kim, "Technical Trends of Ti3C2TX MXene-based Flexible Electrodes", J. Microelectron. Packag. Soc., 29(1), 17-33 (2022).
  8. Y. Guo, M. Zhong, Z. Fang, P. Wan and G. Yu, "A wearable transient pressure sensor made with MXene nanosheets for sensitive broad-range human-machine interfacing", Nano Letters, 19(2), 1143-1150 (2019). https://doi.org/10.1021/acs.nanolett.8b04514
  9. A. Massaro, G. Ricci, S. Selicato, S. Raminelli and A. Galiano, "Decisional support system with Artificial Intelligence oriented on health prediction using a wearable device and big data", In 2020 IEEE International Workshop on Metrology for Industry 4.0 & IoT (pp. 718-723). IEEE. (2020).
  10. B. Muthu, C. B. Sivaparthipan, G. Manogaran, R. Sundarasekar, S. Kadry, A. Shanthini and A. Dasel, "A. IOT based wearable sensor for diseases prediction and symptom analysis in healthcare sector", Peer-to-peer Networking and Applications, 13(6), 2123-2134 (2020). https://doi.org/10.1007/s12083-019-00823-2
  11. N. Sharma, R. Sharma and N. Jindal, "Machine learning and deep learning applications-a vision", Global Transitions Proceedings, 2(1), 24-28 (2021). https://doi.org/10.1016/j.gltp.2021.01.004
  12. L. Meng, B. McWilliams, W. Jarosinski, H. Y. Park, Y. G. Jung, Lee, J and J. Zhang, "Machine learning in additive manufacturing: a review", Jom, 72(6), 2363-2377 (2020). https://doi.org/10.1007/s11837-020-04155-y
  13. C. Janiesch, P. Zschech and K. Heinrich, "Machine learning and deep learning", Electronic Markets, 31(3), 685-695 (2021). https://doi.org/10.1007/s12525-021-00475-2
  14. D. Jakhar and I. Kaur, "Artificial intelligence, machine learning and deep learning: definitions and differences", Clinical and Experimental Dermatology, 45(1), 131-132 (2020). https://doi.org/10.1111/ced.14029
  15. I. H. Sarker, "Machine learning: Algorithms", real-world applications and research directions. SN Computer Science, 2(3), 1-21 (2021). https://doi.org/10.1007/s42979-020-00382-x
  16. J. Wei, X. Chu, X. Y. Sun, K. Xu, H. X. Deng, J. Chen and M. Lei, "Machine learning in materials science", InfoMat, 1(3), 338-358 (2019). https://doi.org/10.1002/inf2.12028
  17. J. Cervantes, F. Garcia-Lamont, L. Rodriguez-Mazahua and A. Lopez, "A comprehensive survey on support vector machine classification: Applications, challenges and trends", Neurocomputing, 408, 189-215 (2020). https://doi.org/10.1016/j.neucom.2019.10.118
  18. Z. H. Kok, A. R. M. Shariff, M. S. M. Alfatni and S. KhairunnizaBejo, "Support vector machine in precision agriculture: a review", Computers and Electronics in Agriculture, 191, 106546 (2021). https://doi.org/10.1016/j.compag.2021.106546
  19. P. Dey, S. K. Chaulya and S. Kumar, "Secure decision tree twin support vector machine training and classification process for encrypted IoT data via blockchain platform", Concurrency and Computation: Practice and Experience, 33(16), e6264 (2021). https://doi.org/10.1002/cpe.6264
  20. B. Charbuty and A. Abdulazeez, "Classification based on decision tree algorithm for machine learning", Journal of Applied Science and Technology Trends, 2(01), 20-28 (2021). https://doi.org/10.38094/jastt20165
  21. A. Agrawal and A. Choudhary, "Deep materials informatics: Applications of deep learning in materials science", Mrs Communications, 9(3), 779-792 (2019). https://doi.org/10.1557/mrc.2019.73
  22. D. H. Kim, "Artificial intelligence-based Modeling Mechanisms for Material Analysis and Discovery", Journal of Intelligent Pervasive and Soft Computing, 1(01), 10-15 (2022).
  23. K. S. Garud, S. Jayaraj and M. Y. Lee, "A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models", International Journal of Energy Research, 45(1), 6-35 (2021). https://doi.org/10.1002/er.5608
  24. S. Salman and X. Liu, "Overfitting mechanism and avoidance in deep neural networks", arXiv preprint arXiv:1901.06566 (2019).
  25. A. Labach, H. Salehinejad and S. Valaee, "Survey of dropout methods for deep neural networks", arXiv preprint arXiv: 1904.13310 (2019).
  26. M. Koklu, I. Cinar and Y. S. Taspinar, "Classification of rice varieties with deep learning methods", Computers and Electronics in Agriculture, 187, 106285 (2021). https://doi.org/10.1016/j.compag.2021.106285
  27. Q. Zhang, M. Zhang, T. Chen, Z. Sun, Y. Ma and B. Yu, "Recent advances in convolutional neural network acceleration", Neurocomputing, 323, 37-51 (2019). https://doi.org/10.1016/j.neucom.2018.09.038
  28. L. Alzubaidi, J. Zhang, A. J. Humaidi, A. Al-Dujaili, Y. Duan, O. Al-Shamma and L. Farhan, "Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions", Journal of Big Data, 8(1), 1-74 (2021). https://doi.org/10.1186/s40537-020-00387-6
  29. A. Sherstinsky, "Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network", Physica D: Nonlinear Phenomena, 404, 132306 (2020). https://doi.org/10.1016/j.physd.2019.132306
  30. Y. Luo, Z. Chen and T. Yoshioka, "Dual-path rnn: efficient long sequence modeling for time-domain single-channel speech separation", In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 46-50). IEEE. (2020).
  31. A.H Shahid and M.P Singh, "A deep learning approach for prediction of Parkinson's disease progression", Biomedical Engineering Letters, 10(2), 227-239 (2020). https://doi.org/10.1007/s13534-020-00156-7
  32. R. Atri, K. Urban, B. Marebwa, T. Simuni, C. Tanner, A. Siderowf and L. Lancashire. "Deep Learning for Daily Monitoring of Parkinson's Disease Outside the Clinic Using Wearable Sensors", Sensors, 22(18), 6831 (2022). https://doi.org/10.3390/s22186831
  33. I. El Maachi, G.A Bilodeau and W. Bouachir, "Deep 1D-Convnet for accurate Parkinson disease detection and severity prediction from gait", Expert Systems with Applications, 143, 113075 (2020). https://doi.org/10.1016/j.eswa.2019.113075
  34. D. L. Lovelace, L. R. McDaniel and D. Golden, "Long-term effects of breast cancer surgery, treatment, and survivor care", Journal of Midwifery & Women's Health, 64(6), 713-724 (2019). https://doi.org/10.1111/jmwh.13012
  35. Y. Jin, N. Du, Y. Huang, W. Shen, Y. Tan, Y. Z. Chen and C. Tan, "Fluorescence Analysis of Circulating Exosomes for Breast Cancer Diagnosis Using a Sensor Array and Deep Learning", ACS sensors, 1524-1532 (2022).
  36. Y. Lu, H. Tian, J. Cheng, F. Zhu, B. Liu, S. Wei and Z. L. Wang, "Decoding lip language using triboelectric sensors with deep learning", Nature Communications, 13(1), 1-12 (2022). https://doi.org/10.1038/s41467-021-27699-2
  37. L. Wen, M. Nie, P. Chen, Y. N. Zhao, J. Shen, C. Wang and Sun, L, "Wearable multimode sensor with a seamless integrated structure for recognition of different joint motion states with the assistance of a deep learning algorithm", Microsystems & Nanoengineering, 8(1), 1-14 (2022). https://doi.org/10.1038/s41378-021-00337-z
  38. N. Zhu, G. Zhao, X. Zhang and Z. Jin, "Falling motion detection algorithm based on deep learning", IET Image Processing (2021).
  39. C. Chi, X. Sun, N. Xue, T. Li and C. Liu, "Recent progress in technologies for tactile sensors. Sensors", 18(4), 948 (2018). https://doi.org/10.3390/s18040948
  40. Y. Wan, Y. Wang and C. F. Guo, "Recent progresses on flexible tactile sensors", Materials Today Physics, 1, 61-73 (2017). https://doi.org/10.1016/j.mtphys.2017.06.002
  41. Y. Liu, R. Bao, J. Tao, J. Li, M. Dong and C. Pan, "Recent progress in tactile sensors and their applications in intelligent systems", Science Bulletin, 65(1), 70-88 (2020). https://doi.org/10.1016/j.scib.2019.10.021
  42. Q. Duan, T. Zhang, C. Liu, R. Yuan, G. Li, P. Jun Tiw and R. Huang, "Artificial Multisensory Neurons with Fused Haptic and Temperature Perception for Multimodal In-Sensor Computing", Advanced Intelligent Systems, 2200039 (2022).
  43. J. Y. Park, W. J. Lee, H. J. Nam and S. H. Choa, "Technology of stretchable interconnector and strain sensors for stretchable electronics", J. Microelectron. Packag. Soc, 25(4), 25-34 (2018).
  44. J. H. Lee, J. S. Heo, Y. J. Kim, J. Eom, H. J. Jung, J. W. Kim and S. K. Park, "A Behavior-Learned Cross-Reactive Sensor Matrix for Intelligent Skin Perception", Advanced Materials, 32(22), 2000969 (2020). https://doi.org/10.1002/adma.202000969
  45. M. Kang, I. Cho, J. Park, J. Jeong, K. Lee, B. Lee and I. Park, "High Accuracy Real-Time Multi-Gas Identification by a Batch-Uniform Gas Sensor Array and Deep Learning Algorithm", ACS Sensors, 7(2), 430-440 (2022). https://doi.org/10.1021/acssensors.1c01204
  46. J. Lee, Y. Jung, S. H. Sung, G. Lee, J. Kim, J. Seong and S. Jeon, "High-performance gas sensor array for indoor air quality monitoring: The role of Au nanoparticles on WO 3, SnO2, and NiO-based gas sensors", Journal of Materials Chemistry A, 9(2), 1159-1167 (2021). https://doi.org/10.1039/D0TA08743B