• The Journal of Korean Academy of Prosthodontics
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• v.49 no.2
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• pp.120-127
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• 2011

Purpose: The purpose of this study was to examine the effects of chromium chloride addition on coloration, mechanical property and microstructure of 3Y-TZP. Materials and methods: Chromium chloride was weighed as 0.06, 0.12, and 0.25 wt% and each measured amount was dissolved in alcohol. $ZrO_2$ powder was mixed with each of the individual slurry to prepare chromium doped zirconia specimen. The color, physical properties and microstructure were observed after the zirconia specimen were sintered at $1450^{\circ}C$. In order to evaluate the color, spectrophotometer was used to analyze the value of $L^*$, $C^*$, $a^*$ and $b^*$, after placing the specimen on a white plate, and measured according to the International Commission on Illumination (CIE) standard, Illuminant D65 and SCE system. The density was measured in the Archimedes method, while microstructures were evaluated by using the scanning electron microscopy (SEM) and XRD. Fracture toughness was calculated Vickers indentation method and indentation size was measured by using the optical microscope. The data were analyzed with 1-way ANOVA test (${\alpha}$ = 0.05). The Tukey multiple comparison test was used for post hocanalysis. Results: 1. Chromium chloride rendered zirconia a brownish color. While chromium chloride content was increased, the color of zirconia was changed from brownish to brownish-red. 2. Chromium chloride content was increased; density of the specimen was decreased. 3. More chromium chloride in the ratio showed increase size of grains. 4. But the addition of chromium chloride did not affect the crystal phase of zirconia, and all specimens showed tetragonal phase. 5. The chromium chloride in zirconia did not showed statistically significant difference in fracture toughness, but addition of 0.25 wt% showed a statistically significant difference (P<.05). Conclusion: Based on the above results, this study suggests that chromium chlorides can make colored zirconia while adding in a liquid form. The new colored zirconia showed a slight difference in color to that of the natural tooth, nevertheless this material can be used as an all ceramic core material.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.2
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• pp.129-152
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• 2024

In the application of deep learning object detection via CCTV in tunnels, a large number of false positive detections occur due to the poor environmental conditions of tunnels, such as low illumination and severe perspective effect. This problem directly impacts the reliability of the tunnel CCTV-based accident detection system reliant on object detection performance. Hence, it is necessary to reduce the number of false positive detections while also enhancing the number of true positive detections. Based on a deep learning object detection model, this paper proposes a false positive data training method that not only reduces false positives but also improves true positive detection performance through retraining of false positive data. This paper's false positive data training method is based on the following steps: initial training of a training dataset - inference of a validation dataset - correction of false positive data and dataset composition - addition to the training dataset and retraining. In this paper, experiments were conducted to verify the performance of this method. First, the optimal hyperparameters of the deep learning object detection model to be applied in this experiment were determined through previous experiments. Then, in this experiment, training image format was determined, and experiments were conducted sequentially to check the long-term performance improvement through retraining of repeated false detection datasets. As a result, in the first experiment, it was found that the inclusion of the background in the inferred image was more advantageous for object detection performance than the removal of the background excluding the object. In the second experiment, it was found that retraining by accumulating false positives from each level of retraining was more advantageous than retraining independently for each level of retraining in terms of continuous improvement of object detection performance. After retraining the false positive data with the method determined in the two experiments, the car object class showed excellent inference performance with an AP value of 0.95 or higher after the first retraining, and by the fifth retraining, the inference performance was improved by about 1.06 times compared to the initial inference. And the person object class continued to improve its inference performance as retraining progressed, and by the 18th retraining, it showed that it could self-improve its inference performance by more than 2.3 times compared to the initial inference.