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http://dx.doi.org/10.6117/kmeps.2022.29.3.01

Recent Progress of Smart Sensor Technology Relying on Artificial Intelligence  

Shin, Hyun Sik (School of Advanced Materials Engineering, Jeonbuk National University)
Kim, Jong-Woong (School of Advanced Materials Engineering, Jeonbuk National University)
Publication Information
Journal of the Microelectronics and Packaging Society / v.29, no.3, 2022 , pp. 1-12 More about this Journal
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.
Keywords
Artificial intelligence; Sensor; Machine learning; Deep learning; Intelligent sensor;
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1 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).
2 Y. Wan, Y. Wang and C. F. Guo, "Recent progresses on flexible tactile sensors", Materials Today Physics, 1, 61-73 (2017).   DOI
3 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).   DOI
4 A. Sherstinsky, "Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network", Physica D: Nonlinear Phenomena, 404, 132306 (2020).   DOI
5 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).   DOI
6 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).   DOI
7 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).   DOI
8 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).
9 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).   DOI
10 N. Zhu, G. Zhao, X. Zhang and Z. Jin, "Falling motion detection algorithm based on deep learning", IET Image Processing (2021).
11 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).   DOI
12 D. Jakhar and I. Kaur, "Artificial intelligence, machine learning and deep learning: definitions and differences", Clinical and Experimental Dermatology, 45(1), 131-132 (2020).   DOI
13 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).
14 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).   DOI
15 C. Janiesch, P. Zschech and K. Heinrich, "Machine learning and deep learning", Electronic Markets, 31(3), 685-695 (2021).   DOI
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).   DOI
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).   DOI
18 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).   DOI
19 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).   DOI
20 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).
21 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).   DOI
22 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).   DOI
23 H. Nazemi, A. Joseph, J. Park and A. Emadi, "Advanced micro-and nano-gas sensor technology: A review", Sensors, 19(6), 1285 (2019).
24 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).   DOI
25 M. Koklu, I. Cinar and Y. S. Taspinar, "Classification of rice varieties with deep learning methods", Computers and Electronics in Agriculture, 187, 106285 (2021).   DOI
26 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).   DOI
27 A. Labach, H. Salehinejad and S. Valaee, "Survey of dropout methods for deep neural networks", arXiv preprint arXiv: 1904.13310 (2019).
28 I. H. Sarker, "Machine learning: Algorithms", real-world applications and research directions. SN Computer Science, 2(3), 1-21 (2021).   DOI
29 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).   DOI
30 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).
31 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).   DOI
32 A. Agrawal and A. Choudhary, "Deep materials informatics: Applications of deep learning in materials science", Mrs Communications, 9(3), 779-792 (2019).   DOI
33 S. Salman and X. Liu, "Overfitting mechanism and avoidance in deep neural networks", arXiv preprint arXiv:1901.06566 (2019).
34 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).   DOI
35 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).
36 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).
37 K. Zhou, K. Dai, C. Liu and C. Shen,"Flexible conductive polymer composites for smart wearable strain sensors", SmartMat, 1(1), e1010 (2020).   DOI
38 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).   DOI
39 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).   DOI
40 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).   DOI
41 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).   DOI
42 N. Sharma, R. Sharma and N. Jindal, "Machine learning and deep learning applications-a vision", Global Transitions Proceedings, 2(1), 24-28 (2021).   DOI
43 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).   DOI
44 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).   DOI
45 C. Chi, X. Sun, N. Xue, T. Li and C. Liu, "Recent progress in technologies for tactile sensors. Sensors", 18(4), 948 (2018).   DOI
46 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).