An Online Tool Temperature Monitoring Method Based on Physics-Guided Infrared Image Features and Artificial Neural Network for Dry Cutting

Artificial neural network models were developed to classify road pavement types into the transverse-tined, the longitudinal-tined, NGCS(Next Generation Concrete Surface), Diamond Grinding, and Stone Mastic Asphalt by utilizing tire-pavement noise and road surface images. Tire-pavement noise data were collected by OBSI(On-Board Sound Intensity) method, and analyzed to obtain sound intensity level, sound pressure level, and sound quality indices. Road surface image data was analyzed through image feature extraction algorithms of Hough transformation and HOG(Histogram of gradient). The important features among the acoustic and image characteristics were selected by a random forest model. The acoustic features selected by the random forest algorithm are the overall sound intensity level of 400~5kHz 1/3-octave bands, the sound intensities (W/m2) of 800~2kHz 1/3-octave bands, loudness, fluctuation strength and tonality. The image features selected are the number of longitudinal lines extracted from Hough transform algorithm and HOG of the central cell. The two groups of the selected features were applied separately or together to an artificial neural network model to find classification performance. The classification accuracy rates of the models using acoustic features only, image features only and both acoustic and image features combined were 90.8%, 88.8%, and 97.3%, respectively.

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Segmentation Algorithm for Apple Recognition using Image Features and Artificial Neural Network

Acta Optica Sinica ◽

10.3788/aos20082811.2104 ◽

2008 ◽

Vol 28 (11) ◽

pp. 2104-2108 ◽

Cited By ~ 7

Author(s):

张亚静 Zhang Yajing ◽

李民赞 Li Minzan ◽

乔军 Qiao Jun ◽

刘刚 Liu Gang

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Image Features ◽

Segmentation Algorithm ◽

Artificial Neural

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Computer-aided diagnosis of mammography using an artificial neural network: predicting the invasiveness of breast cancers from image features

10.1117/12.237977 ◽

1996 ◽

Cited By ~ 2

Author(s):

Joseph Y. Lo ◽

Jeffrey Kim ◽

Jay A. Baker ◽

Carey E. Floyd, Jr.

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Computer Aided Diagnosis ◽

Image Features ◽

Breast Cancers ◽

Computer Aided ◽

Artificial Neural ◽

Aided Diagnosis

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Integrity monitoring method for dry storage casks using artificial neural network

Nuclear Engineering and Design ◽

10.1016/j.nucengdes.2020.110741 ◽

2020 ◽

Vol 366 ◽

pp. 110741

Author(s):

Hyong Chol Kim ◽

Sam Hee Han ◽

Young Jin Lee

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Monitoring Method ◽

Integrity Monitoring ◽

Dry Storage ◽

Artificial Neural

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Analyzing the performance of artificial neural network (ANN)-, fuzzy logic (FL)-, and least square (LS)-based models for online tool condition monitoring

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-016-8548-x ◽

2016 ◽

Vol 87 (1-4) ◽

pp. 1145-1158 ◽

Cited By ~ 14

Author(s):

Aydin Salimiasl ◽

Ahmet Özdemir

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Fuzzy Logic ◽

Condition Monitoring ◽

Tool Condition Monitoring ◽

Least Square ◽

Online Tool ◽

Tool Condition ◽

Artificial Neural ◽

Artificial Neural Network Ann

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Analysing the Quality of Phaseolus Vulgaris Family of Legumes using Artificial Neural Network and “Bag of Features” Techniques

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.i7540.0881019 ◽

2019 ◽

Vol 8 (10) ◽

pp. 1270-1274

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Phaseolus Vulgaris ◽

Development Program ◽

The Philippines ◽

Image Features ◽

Support Vector ◽

Bag Of Features ◽

Artificial Neural

The Philippine Council for Agriculture, Forestry and National Resources Research and Development-Department of Science and Technology (PCAARRD-DOST) have recognized the importance of cultivating legumes as priority crop among others in the vegetable industry under the National Vegetable Research & Development Program. They have further emphasized the need for innovating the methods to improve the processes in terms of producing better quality of products. The study developed a prototype compiled application based on the trained and validated dataset using ANN (Artificial Neural Network) machine. The BoF (Bag of Features) technique was utilized for image features extraction in the SVM (Support Vector Machine) environment for quality classification of Phaseolus Vulgaris family of legumes. These are commonly cultivated in the Philippines. The combined methods yielded an accuracy of 90.2%.

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