[논문]인공지능 기반의 스마트 센서 기술 개발 동향

신현식; 김종웅

doi:10.6117/kmeps.2022.29.3.01

[국내논문] 인공지능 기반의 스마트 센서 기술 개발 동향
Recent Progress of Smart Sensor Technology Relying on Artificial Intelligence 원문보기

마이크로전자 및 패키징 학회지 = Journal of the Microelectronics and Packaging Society, v.29 no.3, 2022년, pp.1 - 12

신현식 (전북대학교 신소재공학부) , 김종웅 (전북대학교 신소재공학부)

초록
AI-Helper

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.

Abstract ▼ AI-Helper

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.

주제어

표/그림 (13)

그림 Fig. 1. Basic schematic diagrams of (a) Artificial Intelligence category and (b) Machine learning types.^11-15)
그림 Fig. 2. A workflow of machine learning.¹⁶⁾
그림 Fig. 3. Schematic diagrams of (a) Support vector machine, (b) Decision tree and (c) Artificial neural network.¹⁶⁾
그림 Fig. 4. A basic structure of DNN model.³⁸⁾
그림 Fig. 5. A basic structure of CNN model.²⁸⁾
그림 Fig. 6. A basic structure of RNN model.³⁰⁾
그림 Fig. 7. Mechanisms, models and evaluation metrics for classifying Parkinson's disease. (a) Deep neural network architecture of 18 parallel connections. (b) 1D-Convnet structure with 4 convolutional layers, 2 max-pooling layers, 1 fully connected layer. (c) Learning curve showing accuracy of the training set and validation sets. (d) Cross-validation results for Parkinson severity prediction.³³⁾
그림 Fig. 8. AggMap workflow and AggMapNet pipeline based on CNN. (a) A schematic diagram of expressing fluorescence spectral data as Feature maps (Fmaps) with AggMap. (b) A schematic diagram of the process of diagnosing breast cancer using AggMapNet based on CNN.³⁵⁾
그림 Fig. 9. A schematic diagram of signal classification based on deep learning. (a) Deep learning aided data process flow, including a training and an inference process. (b) Schematic diagram of a circular neural network model based on prototype learning. (c) Structure diagram of the feature extractor in the model. (d) The basic unit of the dilated recurrent neural network.³⁶⁾
그림 Fig. 10. Results of applying data on various joint angles to deep learning. (a) Schematic diagram of LSTM algorithm based on RNN. (b-d) Classification confusion matrices using only resistance data, capacitance data, and both resistance and capacitance data. (e) Histogram of classification accuracy of all joint postures using different data.³⁷⁾
그림 Fig. 11. Evaluation indicators based on model performance after applying multiple posture data to deep learning. (a) Datasets in different postures. (b) DNN network training learning curve. (c) Test confusion matrix.³⁸⁾
그림 Fig. 12. A schematic diagram of machine learning using bag-of-words (BoW) algorithm to determine intermixed stimuli.⁴⁴⁾
그림 Fig. 13. A classification mechanism of gas sensors using CNN. (a) Schematic illustration of preprocessing of the sensing data from the micro heater platform-based SMO gas sensor array (i) raw sensing data from the gas sensors. (ii) normalized data using log and base resistance. (iii) the method to collect the data with a 10 s time window. (b) Structure of the convolutional neural network for classifying six gas species (i.e., Air, CO, NH₃, NO₂, CH₄, and C₃H₆O).⁴⁵⁾

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동의어: Packet Collision Rate

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