Abstract: The quality of press-fit assembly is closely related to
reliability and safety of product. The paper proposed a keypoint
detection method based on convolutional neural network to improve
the accuracy of keypoint detection in press-fit curve. It would
provide an auxiliary basis for judging quality of press-fit assembly.
The press-fit curve is a curve of press-fit force and displacement.
Both force data and distance data are time-series data. Therefore,
one-dimensional convolutional neural network is used to process
the press-fit curve. After the obtained press-fit data is filtered, the
multi-layer one-dimensional convolutional neural network is used to
perform the automatic learning of press-fit curve features, and then
sent to the multi-layer perceptron to finally output keypoint of the
curve. We used the data of press-fit assembly equipment in the actual
production process to train CNN model, and we used different data
from the same equipment to evaluate the performance of detection.
Compared with the existing research result, the performance of
detection was significantly improved. This method can provide a
reliable basis for the judgment of press-fit quality.
Abstract: In this paper, ways of modeling dynamic measurement
systems are discussed. Specially, for linear system with single-input
single-output, it could be modeled with shallow neural network.
Then, gradient based optimization algorithms are used for searching
the proper coefficients. Besides, method with normal equation and
second order gradient descent are proposed to accelerate the modeling
process, and ways of better gradient estimation are discussed. It
shows that the mathematical essence of the learning objective is
maximum likelihood with noises under Gaussian distribution. For
conventional gradient descent, the mini-batch learning and gradient
with momentum contribute to faster convergence and enhance model
ability. Lastly, experimental results proved the effectiveness of second
order gradient descent algorithm, and indicated that optimization with
normal equation was the most suitable for linear dynamic models.
Abstract: Electronic apex locators (EAL) has been widely used
clinically for measuring root canal working length with high accuracy,
which is crucial for successful endodontic treatment. In order to
maintain high accuracy in different measurement environments,
this study presented a system for root canal length measurement
based on multifrequency impedance method. This measuring system
can generate a sweep current with frequencies from 100 Hz to
1 MHz through a direct digital synthesizer. Multiple impedance
ratios with different combinations of frequencies were obtained
and transmitted by an analog-to-digital converter and several of
them with representatives will be selected after data process. The
system analyzed the functional relationship between these impedance
ratios and the distance between the file and the apex with statistics
by measuring plenty of teeth. The position of the apical foramen
can be determined by the statistical model using these impedance
ratios. The experimental results revealed that the accuracy of
the system based on multifrequency impedance ratios method to
determine the position of the apical foramen was higher than the
dual-frequency impedance ratio method. Besides that, for more
complex measurement environments, the performance of the system
was more stable.