• Imaging Science in Dentistry
• /
• v.54 no.1
• /
• pp.49-56
• /
• 2024

Purpose: This study evaluated the impact of artifacts generated by metal crowns on the detection of proximal caries lesions in teeth at various distances using cone-beam computed tomography (CBCT). Additionally, the diagnostic impacts of tube current and metal artifact reduction (MAR) were investigated. Materials and Methods: Thirty teeth were arranged within 10 phantoms, each containing 1 first premolar, 1 second premolar, and 1 second molar. A sound first molar (for the control group) or a tooth with a metal crown was placed. Of the 60 proximal surfaces evaluated, 15 were sound and 45 exhibited enamel caries. CBCT scans were acquired using an OP300 Maxio unit (Instrumentarium, Tuusula, Finland), while varying the tube current (4, 8, or 12.5 mA) and enabling or disabling MAR. Five observers assessed mesial and distal surfaces using a 5-point scale. Multi-way analysis of variance was employed for data comparison, with P<0.05 indicating statistical significance. Results: The area under the curve (AUC) varied from 0.40 to 0.60 (sensitivity: 0.28-0.45, specificity: 0.44-0.80). The diagnostic accuracy was not significantly affected by the presence of a metal crown, milliamperage, or MAR(P>0.05). However, the overall AUC and specificity were significantly lower for surfaces near a crown (P<0.05). Conclusion: CBCT-based caries detection was not influenced by the presence of a metal crown, variations in milliamperage, or MAR activation. However, the diagnostic accuracy was low and was further diminished for surfaces near a crown. Consequently, CBCT is not recommended for the detection of incipient caries lesions.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2010.05a
• /
• pp.636-637
• /
• 2010

• Proceedings of the KOR-KST Conference
• /
• 2007.05a
• /
• pp.375-382
• /
• 2007

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2013.04a
• /
• pp.134-134
• /
• 2013

• Journal of the Institute of Convergence Signal Processing
• /
• v.5 no.4
• /
• pp.281-286
• /
• 2004

The wear process of end mill is a so complicated process that a more reliable technique is required for the monitoring and controling the tool life and its performance. This research presents a new tool wear monitoring method based on the sound signal generated on the machining. The experiment carried out continuous-side-milling for using the high-speed steel end mill under wet condition. The sound pressure was measured at 0.5m from the cutting zone by a dynamic microphone, and was analyzed at frequency domain. The tooth passing frequency appears as a harmonics form, and end mill wear is related with the first harmonic. It can be concluded from the result that the tool wear is correlate with the intensity of the measured sound at tooth passing frequency estimation of end mill wear using sound is possible through frequency analysis at tooth passing frequency under the given circumstances.

• 대한공업교육학회지
• /
• v.31 no.1
• /
• pp.185-199
• /
• 2006

In this paper, to detect lateral direction sound pressure fiber optic sensor using Fabry-Perot interferometeric sensor array was fabricated and experimented. This parallel sensor array composed of one light source and the light split into each sensor using directional coupler and to see the output signal the array system do not need any digital signal processor. As a lateral direction sound source arbitrary sound frequency of 100Hz, 200Hz, and 655Hz using by nondirectional speaker were applied to the array sensor which installed on $60cm{\times}60cm{\times}60cm$ latticed structure. The detected signals from the two sensors were analyzed in the time and frequency domains. It was confirmed that the suggested sensor array detected applied sound source well but there were a little amplitude differences in between the sensors. Because the sensor supported simply at both ends theoretical analysis was performed and its solution was suggested. To compare the theoretical and experimental results arbitrary sound frequency of 2kHz was applied to the sensor array. It shows that experimental results was good agreement with theoretical results.

• Journal of Biomedical Engineering Research
• /
• v.44 no.2
• /
• pp.147-156
• /
• 2023

Efforts to employ smart home sensors to monitor the indoor activities of elderly single residents have been made to assess the feasibility of a safe and healthy lifestyle. However, the bathroom remains an area of blind spot. In this study, we have developed and evaluated a new edge computer device that can automatically detect water usage activities in the bathroom and record the activity log on a cloud server. Three kinds of sound as flushing, showering, and washing using wash basin generated during water usage were recorded and cut into 1-second scenes. These sound clips were then converted into a 2-dimensional image using MEL-spectrogram. Sound data augmentation techniques were adopted to obtain better learning effect from smaller number of data sets. These techniques, some of which are applied in time domain and others in frequency domain, increased the number of training data set by 30 times. A deep learning model, called CRNN, combining Convolutional Neural Network and Recurrent Neural Network was employed. The edge device was implemented using Raspberry Pi 4 and was equipped with a condenser microphone and amplifier to run the pre-trained model in real-time. The detected activities were recorded as text-based activity logs on a Firebase server. Performance was evaluated in two bathrooms for the three water usage activities, resulting in an accuracy of 96.1% and 88.2%, and F1 Score of 96.1% and 87.8%, respectively. Most of the classification errors were observed in the water sound from washing. In conclusion, this system demonstrates the potential for use in recording the activities as a lifelog of elderly single residents to a cloud server over the long-term.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.11 no.2
• /
• pp.203-208
• /
• 2016

This paper presents a fault detection and data processing algorithm for acoustic sensor systems using the multiple sensor algorithm that has originally developed for the wireless sensor nodes. The multiple sensor algorithm can increase the reliability of the sensor systems by utilizing and comparing the measurements of the multiple sensors. In the acoustic sensor system, the equivalent sound level($L_{eq}$) is used to detect the faulty sensor. The experiment was conducted to demonstrate the feasibility of the multiple acoustic sensor algorithm, and the results show that the algorithm can detect the faulty sensor and validate the data.

• The Journal of the Acoustical Society of Korea
• /
• v.16 no.6
• /
• pp.100-105
• /
• 1997

To analyze the multipath propagation effects on the source bearing detection of HLA(Horizontal Line Array), the conversion mechanism of the multipath into the bearing is described, and the bearing is estimated from the multipath modeled with typical sound velocity structures of the East and the South Sea of Korea. The erroneous bearing is observed from the beamforming outputs simulated with the modeled multipath, and the erroneous phenomena are analyzed. In case of the East Sea, since the multipath propagation with a high receiving angle occurs due to strong inverse slope of the sound velocity structure, it is possible that the estimated source bearing is different from the real source bearing, and that the number of the source is misrecognized.

• Journal of Ocean Engineering and Technology
• /
• v.34 no.4
• /
• pp.277-284
• /
• 2020

Underwater acoustics, which is the study of phenomena related to sound waves in water, has been applied mainly in research on the use of sound navigation and range (SONAR) systems for communication, target detection, investigation of marine resources and environments, and noise measurement and analysis. The main objective of underwater acoustic remote sensing is to obtain information on a target object indirectly by using acoustic data. Presently, various types of machine learning techniques are being widely used to extract information from acoustic data. The machine learning techniques typically used in underwater acoustics and their applications in passive SONAR systems were reviewed in the first two parts of this work (Yang et al., 2020a; Yang et al., 2020b). As a follow-up, this paper reviews machine learning applications in SONAR signal processing with a focus on active target detection and classification.