• Korean Journal of Heritage: History & Science
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• v.56 no.4
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• pp.40-52
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• 2023

The basic purpose of building material called tiles is waterproofing and damp proofing, and they were restricted to use on important buildings to symbolize authority. This is especially true during the Three Kingdoms period, although unearthed examples are rare. Most of these tiles are found in ruins in the Silla and Baekje regions. Tiles were excavated from the Buwon-dong ruins that show the oldest manufacturing technique in the Gaya region to date, and tiles from the early Three Kingdoms period were recently excavated from the Gimhae Bonghwang-dong ruins, which is presumed to be the site of the royal palace of Geumgwan Gaya. These are important materials that show the appearance of tiles from the early days of Gimhae, the ancient capital of Geumgwan Gaya. The tiles excavated from the Bonghwang-dong ruins are reddish-yellow because a small amount of sand was mixed in the tile material and baked at a low temperature. The tiles are thin, no traces of fabric were identified, but traces of clay bands were identified. Tapping tool marks and traces of an anvil used in pottery production are clearly observed on the inside and outside, indicating that the tiles were made in the same way as earthenware manufacturing methods. If this is connected to the genealogy of the potters who made Gaya earthenware, it is estimated that tiles and earthenware were produced together as in the Songrim-ri ruins in Bulo-dong, Incheon, Songgok-dong ruins in Gyeongju, and Mulcheon-ri ruins. To date, tiles excavated from the Gimhae area have been identified only in places believed to be the Geumgwan Gaya City Wall (Royal Palace) in the Gimhae Basin. Considering what has been recorded so far and the geographical scenery, the Bonghwang-dong remains are the only city wall candidate site, and this is clearly revealed through the existence of the excavated tiles, which proves this. Considering that a small number of tiles were excavated during this time, it is estimated that the role of tiles as a luxury product with a symbolic meaning was greater than that of roofing materials, and there were strict restrictions and controls on its use.

• The Journal of Korean Academy of Prosthodontics
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• v.40 no.1
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• pp.1-17
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• 2002

The purpose of this study is to examine the possibility of thermal injury to bone tissues during an implant site preparation under the same condition as a typical clinical practice of $Br{\aa}nemark$ implant system. All the burs for $Br{\aa}nemark$ implant system were studied except the round bur The experiments involved 880 drilling cases : 50 cases for each of the 5 steps of NP, 5 steps of RP, and 7 steps of WP, all including srew tap, and 30 cases of 2mm twist drill. For precision drilling, a precision handpiece restraining system was developed (Eungyong Machinery Co., Korea). The system kept the drill parallel to the drilling path and allowed horizontal adjustment of the drill with as little as $1{\mu}m$ increment. The thermocouple insertion hole. that is 0.9mm in diameter and 8mm in depth, was prepared 0.2mm away from the tapping bur the last drilling step. The temperatures due to countersink, pilot drill, and other drills were measured at the surface of the bone, at the depths of 4mm and 8mm respectively. Countersink drilling temperature was measured by attaching the tip of a thermocouple at the rim of the countersink. To assure temperature measurement at the desired depths, 'bent-thermocouples' with their tips of 4 and 8mm bent at $120^{\circ}$ were used. The profiles of temperature variation were recorded continuously at one second interval using a thermometer with memory function (Fluke Co. U.S.A.) and 0.7mm thermocouples (Omega Co., U.S.A.). To simulate typical clinical conditions, 35mm square samples of bovine scapular bone were utilized. The samples were approximately 20mm thick with the cortical thickness on the drilling side ranging from 1 to 2mm. A sample was placed in a container of saline solution so that its lower half is submerged into the solution and the upper half exposed to the room air, which averaged $24.9^{\circ}C$. The temperature of the saline solution was maintained at $36.5^{\circ}C$ using an electric heater (J. O Tech Co., Korea). This experimental condition was similar to that of a patient s opened mouth. The study revealed that a 2mm twist drill required greatest attention. As a guide drill, a twist drill is required to bore through a 'virgin bone,' rather than merely enlarging an already drilled hole as is the case with other drills. This typically generates greater amount of heat. Furthermore, one tends to apply a greater pressure to overcome drilling difficulty, thus producing even greater amount heat. 150 experiments were conducted for 2mm twist drill. For 140 cases, drill pressure of 750g was sufficient, and 10 cases required additional 500 or 100g of drilling pressure. In case of the former. 3 of the 140 cases produced the temperature greater than $47^{\circ}C$, the threshold temperature of degeneration of bone tissue (1983. Eriksson et al.) which is also the reference temperature in this study. In each of the 10 cases requiring extra pressure, the temperature exceeded the reference temperature. More significantly, a surge of heat was observed in each of these cases This observations led to addtional 20 drilling experiments on dense bones. For 10 of these cases, the pressure of 1,250g was applied. For the other 10, 1.750g were applied. In each of these cases, it was also observed that the temperature rose abruptly far above the thresh old temperature of $47^{\circ}C$, sometimes even to 70 or $80^{\circ}C$. It was also observed that the increased drilling pressure influenced the shortening of drilling time more than the rise of drilling temperature. This suggests the desirability of clinically reconsidering application of extra pressures to prevent possible injury to bone tissues. An analysis of these two extra pressure groups of 1,250g and 1,750g revealed that the t-statistics for reduced amount of drilling time due to extra pressure and increased peak temperature due to the same were 10.80 and 2.08 respectively suggesting that drilling time was more influenced than temperature. All the subsequent drillings after the drilling with a 2mm twist drill did not produce excessive heat, i.e. the heat generation is at the same or below the body temperature level. Some of screw tap, pilot, and countersink showed negative correlation coefficients between the generated heat and the drilling time. indicating the more the drilling time, the lower the temperature. The study also revealed that the drilling time was increased as a function of frequency of the use of the drill. Under the drilling pressure of 750g, it was revealed that the drilling time for an old twist drill that has already drilled 40 times was 4.5 times longer than a new drill The measurement was taken for the first 10 drillings of a new drill and 10 drillings of an old drill that has already been used for 40 drillings. 'Test Statistics' of small samples t-test was 3.49, confirming that the used twist drills require longer drilling time than new ones. On the other hand, it was revealed that there was no significant difference in drilling temperature between the new drill and the old twist drill. Finally, the following conclusions were reached from this study : 1 Used drilling bur causes almost no change in drilling temperature but increase in drilling time through 50 drillings under the manufacturer-recommended cooling conditions and the drilling pressure of 750g. 2. The heat that is generated through drilling mattered only in the case of 2mm twist drills, the first drill to be used in bone drilling process for all the other drills there is no significant problem. 3. If the drilling pressure is increased when a 2mm twist drill reaches a dense bone, the temperature rises abruptly even under the manufacturer-recommended cooling conditions. 4. Drilling heat was the highest at the final moment of the drilling process.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.139-156
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• 2021

The biggest reason for using a deep learning model in image classification is that it is possible to consider the relationship between each region by extracting each region's features from the overall information of the image. However, the CNN model may not be suitable for emotional image data without the image's regional features. To solve the difficulty of classifying emotion images, many researchers each year propose a CNN-based architecture suitable for emotion images. Studies on the relationship between color and human emotion were also conducted, and results were derived that different emotions are induced according to color. In studies using deep learning, there have been studies that apply color information to image subtraction classification. The case where the image's color information is additionally used than the case where the classification model is trained with only the image improves the accuracy of classifying image emotions. This study proposes two ways to increase the accuracy by incorporating the result value after the model classifies an image's emotion. Both methods improve accuracy by modifying the result value based on statistics using the color of the picture. When performing the test by finding the two-color combinations most distributed for all training data, the two-color combinations most distributed for each test data image were found. The result values were corrected according to the color combination distribution. This method weights the result value obtained after the model classifies an image's emotion by creating an expression based on the log function and the exponential function. Emotion6, classified into six emotions, and Artphoto classified into eight categories were used for the image data. Densenet169, Mnasnet, Resnet101, Resnet152, and Vgg19 architectures were used for the CNN model, and the performance evaluation was compared before and after applying the two-stage learning to the CNN model. Inspired by color psychology, which deals with the relationship between colors and emotions, when creating a model that classifies an image's sentiment, we studied how to improve accuracy by modifying the result values based on color. Sixteen colors were used: red, orange, yellow, green, blue, indigo, purple, turquoise, pink, magenta, brown, gray, silver, gold, white, and black. It has meaning. Using Scikit-learn's Clustering, the seven colors that are primarily distributed in the image are checked. Then, the RGB coordinate values of the colors from the image are compared with the RGB coordinate values of the 16 colors presented in the above data. That is, it was converted to the closest color. Suppose three or more color combinations are selected. In that case, too many color combinations occur, resulting in a problem in which the distribution is scattered, so a situation fewer influences the result value. Therefore, to solve this problem, two-color combinations were found and weighted to the model. Before training, the most distributed color combinations were found for all training data images. The distribution of color combinations for each class was stored in a Python dictionary format to be used during testing. During the test, the two-color combinations that are most distributed for each test data image are found. After that, we checked how the color combinations were distributed in the training data and corrected the result. We devised several equations to weight the result value from the model based on the extracted color as described above. The data set was randomly divided by 80:20, and the model was verified using 20% of the data as a test set. After splitting the remaining 80% of the data into five divisions to perform 5-fold cross-validation, the model was trained five times using different verification datasets. Finally, the performance was checked using the test dataset that was previously separated. Adam was used as the activation function, and the learning rate was set to 0.01. The training was performed as much as 20 epochs, and if the validation loss value did not decrease during five epochs of learning, the experiment was stopped. Early tapping was set to load the model with the best validation loss value. The classification accuracy was better when the extracted information using color properties was used together than the case using only the CNN architecture.