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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2020.1
- 수록면
- 167 - 175 (9page)
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The detection and recognition of abnormal driving becomes crucial for achieving safety in Intelligent Transportation Systems (ITS). This paper presents a feature extraction method based on spectral data to train a neural network model for driving behavior recognition. The proposed method uses a two stage signal processing approach to derive time-saving and efficient feature vectors. For the first stage, the feature vector set is obtained by calculating variances from each frequency bin containing the power spectrum data. The feature set is further reduced in the second stage where an intersection method is used to select more significant features that are finally applied for training a neural network model. A stream of live signals are fed to the trained model which recognizes the abnormal driving behaviors. The driving behaviors considered in this study are weaving, sudden braking and normal driving. The effectiveness of the proposed method is demonstrated by comparing with existing methods, which are Particle Swarm Optimization (PSO) and Convolution Neural Network (CNN). The experiments show that the proposed approach achieves satisfactory results with less computational complexity.
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참고문헌 (15)
참고문헌 신청
J. D. Newman / 2005 / Home Blood Glucose Biosensors : A Commercial Perspective / Biosensors and bioelectronics 20:2435~2453
S. Park / 2015 / Self-Monitoring Blood Meter: Is Your Glucose Meter Accurate? / The journal of Korean Diabetes 16(1)
S. Kim / 2013 / Glucose Motoring System, Self-testing / Medical Device Market Research Report 8
A. Pfützner / 2012 / Clinical Assessment of the Accuracy of Blood Glucose Measurement Devices / Current medical research and opinion 28:525~531
L. Heinemann / 2010 / Quality of Glucose Measurement with Blood Glucose Meters at the Point-of-care : Relevance of Interfering Factors / Diabetes technology & therapeutics 12:847~857
S. Park / 2015 / Self-Monitoring Blood Meter: Is Your Glucose Meter Accurate? / The journal of Korean Diabetes 16(1)
S. Kim / 2013 / Glucose Motoring System, Self-testing / Medical Device Market Research Report 8
A. Pfützner / 2012 / Clinical Assessment of the Accuracy of Blood Glucose Measurement Devices / Current medical research and opinion 28:525~531
L. Heinemann / 2010 / Quality of Glucose Measurement with Blood Glucose Meters at the Point-of-care : Relevance of Interfering Factors / Diabetes technology & therapeutics 12:847~857
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