Artificial Neural Network for Vibration Frequency Measurement Using Kinect V2

Optical measurement can substantially reduce the required amount of labor and simplify the measurement process. Furthermore, the optical measurement method can provide full-field measurement results of the target object without affecting the physical properties of the measurement target, such as stiffness, mass, or damping. The advent of consumer grade depth cameras, such as the Microsoft Kinect, Intel RealSence, and ASUS Xtion, has attracted significant research attention owing to their availability and robustness in sampling depth information. This paper presents an effective method employing the Kinect sensor V2 and an artificial neural network for vibration frequency measurement. Experiments were conducted to verify the performance of the proposed method. The proposed method can provide good frequency prediction within acceptable accuracy compared to an industrial vibrometer, with the advantages of contactless process and easy pipeline implementation.

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Automatic recognition of facial expressions using microsoft kinect with artificial neural network

2016 International Conference on Engineering & MIS (ICEMIS) ◽

10.1109/icemis.2016.7745376 ◽

2016 ◽

Author(s):

Safae Elhoufi ◽

Maha Jazouli ◽

Aicha Majda ◽

Arsalane Zarghili ◽

Rachid Aalouane

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Facial Expressions ◽

Automatic Recognition ◽

Microsoft Kinect ◽

Recognition Of Facial Expressions ◽

Artificial Neural

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Automatic Estimation of Volumetric Breast Density Using Artificial Neural Network-Based Calibration of Full-Field Digital Mammography: Feasibility on Japanese Women With and Without Breast Cancer

Journal of Digital Imaging ◽

10.1007/s10278-016-9922-9 ◽

2016 ◽

Vol 30 (2) ◽

pp. 215-227 ◽

Cited By ~ 10

Author(s):

Jeff Wang ◽

Fumi Kato ◽

Hiroko Yamashita ◽

Motoi Baba ◽

Yi Cui ◽

...

Keyword(s):

Breast Cancer ◽

Neural Network ◽

Artificial Neural Network ◽

Breast Density ◽

Digital Mammography ◽

Japanese Women ◽

Full Field ◽

Full Field Digital Mammography ◽

Automatic Estimation ◽

Artificial Neural

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A REVIEW PAPER ON STRUCTURAL DAMAGE IDENTIFICATION OF CANTILEVER BEAM BY USING VIBRATION TECHNIQUE, FINITE ELEMENT ANALYSIS & ARTIFICIAL NEURAL NETWORK

International Journal of Research -GRANTHAALAYAH ◽

10.29121/granthaalayah.v3.i12.2015.2896 ◽

2015 ◽

Vol 3 (12) ◽

pp. 125-128

Author(s):

Aakanksha MohanraoGarud ◽

V. G. Bhamre

Keyword(s):

Neural Network ◽

Finite Element Analysis ◽

Artificial Neural Network ◽

Structural Damage ◽

Review Paper ◽

Damage Identification ◽

Frequency Measurement ◽

Element Analysis ◽

Structural Damage Identification ◽

Artificial Neural

In this review paper structural damage identification work in cantilever beam is done by using the Artificial Neural Network as diagnostic parameter. The study is based on the concept that natural frequency is inversely proportional to the mass of the structure. Thus to regulate the proper condition of structure, periodical frequency measurement is necessary. But in dynamic conditions and in complicated structures frequency measurement is difficult, for the same we reviewed various papers to identify the structural damage using various methods. The factors which affects on the damage of structural parts like crack depth, crack location etc. is also discussed in this work. Natural frequency is measured with the help of fast fourier transform by various authors and artificial neural network is also used for identification of the damage in many papers. So in this review work we studied methods of structural damage identification such as vibrations, finite element analysis and artificial neural network.

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Combining the Taguchi Method with an Artificial Neural Network to Construct a Prediction Model for Near-Field Photolithography Experiments

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1243/09544062jmes2055 ◽

2010 ◽

Vol 224 (10) ◽

pp. 2223-2233 ◽

Cited By ~ 5

Author(s):

Z-C Lin ◽

C-B Yang

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Taguchi Method ◽

Prediction Model ◽

Network Model ◽

Neural Network Model ◽

Near Field ◽

Full Field ◽

Artificial Neural ◽

Training Examples

For analysis using the Taguchi method, the L18 or L27 orthogonal array is usually adopted. However, this requires many experiments (18 or 27 runs, respectively), which consumes time and increases costs. In addition, while traditional analysis with the Taguchi model provides a better group of processing parameters, it cannot predict the unexperimented results. This article proposes a progressive Taguchi neural network model that combines the Taguchi method with an artificial neural network and constructs a prediction model for near-field photolithography experiments. This approach establishes a Taguchi neural network that requires fewer experimental runs, while achieving a high predictive precision. The analytical results of the progressive Taguchi neural network model show that, because there are few training examples in the stage 1 preliminary network, there is a significant fluctuation in the network prediction values. In the stage 2 refining network, the prediction effect in the region around the Taguchi factor level points is not bad, but the prediction in the region more remote from the learning and training examples has greater error. The stage 3 precise network can provide optimal prediction results for the full field.

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Computer-aided detection of microcalcification clusters on full-field digital mammograms: multiscale pyramid enhancement and false positive reduction using an artificial neural network

10.1117/12.595753 ◽

2005 ◽

Author(s):

Jun Ge ◽

Jun Wei ◽

Lubomir M. Hadjiiski ◽

Berkman Sahiner ◽

Heang-Ping Chan ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

False Positive ◽

Computer Aided Detection ◽

Full Field ◽

False Positive Reduction ◽

Computer Aided ◽

Artificial Neural ◽

Microcalcification Clusters

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Underwater Inherent Optical Properties Estimation Using a Depth Aided Deep Neural Network

Computational Intelligence and Neuroscience ◽

10.1155/2017/8351232 ◽

2017 ◽

Vol 2017 ◽

pp. 1-8 ◽

Cited By ~ 2

Author(s):

Zhibin Yu ◽

Yubo Wang ◽

Bing Zheng ◽

Haiyong Zheng ◽

Nan Wang ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Optical Properties ◽

Optical Sensors ◽

Deep Neural Network ◽

Marine Biology ◽

Depth Information ◽

Learning Technology ◽

Inherent Optical Properties ◽

Artificial Neural

Underwater inherent optical properties (IOPs) are the fundamental clues to many research fields such as marine optics, marine biology, and underwater vision. Currently, beam transmissometers and optical sensors are considered as the ideal IOPs measuring methods. But these methods are inflexible and expensive to be deployed. To overcome this problem, we aim to develop a novel measuring method using only a single underwater image with the help of deep artificial neural network. The power of artificial neural network has been proved in image processing and computer vision fields with deep learning technology. However, image-based IOPs estimation is a quite different and challenging task. Unlike the traditional applications such as image classification or localization, IOP estimation looks at the transparency of the water between the camera and the target objects to estimate multiple optical properties simultaneously. In this paper, we propose a novel Depth Aided (DA) deep neural network structure for IOPs estimation based on a single RGB image that is even noisy. The imaging depth information is considered as an aided input to help our model make better decision.

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