• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.119-138
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• 2016

Since the Industrial Revolution, which made the mass production and mass distribution of standardized goods possible, machine-made (manufactured) products have accounted for the majority of the market. However, in recent years, the phenomenon of purchasing even more expensive handmade products has become a noticeable trend as consumers have started to acknowledge the value of handmade products, such as the craftsman's commitment, belief in their quality and scarcity, and the sense of self-esteem from having them,. Consumer interest in these handmade products has shown explosive growth and has been coupled with the recent development of three-dimensional (3D) printing technologies. Etsy.com is the world's largest online handmade platform. It is no different from any other online platform; it provides an online market where buyers and sellers virtually meet to share information and transact business. However, Etsy.com is different in that shops within this platform only deal with handmade products in a variety of categories, ranging from jewelry to toys. Since its establishment in 2005, despite being limited to handmade products, Etsy.com has enjoyed rapid growth in membership, transaction volume, and revenue. Most recently in April 2015, it raised funds through an initial public offering (IPO) of more than 1.8 billion USD, which demonstrates the huge potential of online handmade platforms. After the success of Etsy.com, various types of online handmade platforms such as Handmade at Amazon, ArtFire, DaWanda, and Craft is ART have emerged and are now competing with each other, at the same time, which has increased the size of the market. According to Deloitte's 2015 holiday survey on which types of gifts the respondents plan to buy during the holiday season, about 16% of U.S. consumers chose "homemade or craft items (e.g., Etsy purchase)," which was the same rate as those for the computer game and shoes categories. This indicates that consumer interests in online handmade platforms will continue to rise in the future. However, this high interest in the market for handmade products and their platforms has not yet led to academic research. Most extant studies have only focused on machine-made products and intelligent services for them. This indicates a lack of studies on handmade products and their intelligent services on virtual platforms. Therefore, this study used signaling theory and prior research on the effects of sellers' characteristics on their performance (e.g., total sales and price premiums) in the buyer-seller relationship to identify the key influencing e-Image factors (e.g., reputation, size, information sharing, and length of relationship). Then, their impacts on the performance of shops within the online handmade platform were empirically examined; the dataset was collected from Etsy.com through the application of web harvesting technology. The results from the structural equation modeling revealed that the reputation, size, and information sharing have significant effects on the total sales, while the reputation and length of relationship influence price premiums. This study extended the online platform research into online handmade platform research by identifying key influencing e-Image factors on within-platform shop's total sales and price premiums based on signaling theory and then performed a statistical investigation. These findings are expected to be a stepping stone for future studies on intelligent online handmade services as well as handmade products themselves. Furthermore, the findings of the study provide online handmade platform operators with practical guidelines on how to implement intelligent online handmade services. They should also help shop managers build their marketing strategies in a more specific and effective manner by suggesting key influencing e-Image factors. The results of this study should contribute to the vitalization of intelligent online handmade services by providing clues on how to maximize within-platform shops' total sales and price premiums.

• Restorative Dentistry and Endodontics
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• v.25 no.1
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• pp.46-55
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• 2000

During bonding procedure of composite resin, the prepared cavity can be contaminated by saliva. In this study, marginal microleakage and shear bond strength of a composite resin to primed enamel and dentin treated with artificial saliva(Taliva$^{(R)}$) were evaluated. For the marginal microleakage test, Class V cavities were prepared in the buccal surfaces of fifty molars. The samples were randomly assigned into 5 groups with 10 samples in each group. Control group was applied with a bonding system (Scotchbond$^{TM}$ Multi-Purpose plus) according to manufacture's directions without saliva contamination. Experimental groups were divided into 4 groups and contaminated with artificial saliva for 30 seconds after priming: Experimental 1 group ; artificial saliva was dried with compressed air only, Experimental 2 group ; artificial saliva was rinsed and dried. Experimental 3 group ; cavities were etched with 35% phosphoric acid for 15 seconds after rinsing and drying artificial saliva. Experimental 4 group ; cavities were etched with 35% phosphoric acid for 15 seconds and primer was reapplied after rinsing and drying artificial saliva. All the cavities were applied a bonding agent and filled with a composite resin (Z-100$^{TM}$). Specimens were immersed in 0.5% basic fuschin dye for 24 hours and embedded in transparent acrylic resin and sectioned buccolingually with diamond wheel saw. Four sections were obtained from one specimen. Degree of marginal leakage was scored under stereomicroscope and their scores were averaged from four sections. The data were analyzed by Kruscal-Wallis test and Fisher's LSD. For the shear bond strength test, the buccal or occlusal surfaces of one hundred molar teeth were ground to expose enamel(n=50) or dentin(n=50) using diamond wheel saw and its surface was smoothed with Lapping and Polishing Machine(South Bay Technology Co., U.S.A.). Samples were divided into 5 groups. Treatment of saliva-contaminated enamel and dentin surfaces was same as the marginal microleakage test and composite resin was bonded via a gelatin capsule. All specimens were stored in distilled water for 48 hours. The shear bond strengths were measured by universal testing machine (AGS-1000 4D, Shimaduzu Co., Japan) with a crosshead speed of 5 mm/minute. Failure mode of fracture sites was examined under stereomicroscope. The data were analyzed by ANOVA and Tukey's studentized range test. The results of this study were as follows : 1. Enamel marginal microleakage showed no significant difference among groups. 2. Dentinal marginal microleakages of control, experimental 2 and 4 groups were lower than those of experimental 1 and 3 groups (p<0.05). 3. The shear bond strength to enamel was the highest value in control group (20.03${\pm}$4.47MPa) and the lowest value in experimental 1 group (13.28${\pm}$6.52MPa). There were significant differences between experimental 1 group and other groups (p<0.05). 4. The shear bond strength to dentin was higher in control group (17.87${\pm}$4.02MPa) and experimental 4 group (16.38${\pm}$3.23MPa) than in other groups, its value was low in experimental 1 group (3.95${\pm}$2.51 MPa) and experimental 2 group (6.72${\pm}$2.26MPa)(p<0.05). 5. Failure mode of fractured site on the enamel showed mostly adhesive failures in experimental 1 and 3 groups. 6. Failure mode of fractured site on the dentin did not show adhesive failures in control group, but showed mostly adhesive failure in experimental groups. As a summary of above results, if the primed tooth surface was contaminated with artificial saliva, primer should be reapplied after re-etching it.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.481-493
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• 2023

In high-density urban areas, the urban heat island effect increases urban temperatures, leading to negative impacts such as worsened air pollution, increased cooling energy consumption, and increased greenhouse gas emissions. In urban environments where it is difficult to secure additional green spaces, rooftop greening is an efficient greenhouse gas reduction strategy. In this study, we not only analyzed the current status of the urban heat island effect but also utilized high-resolution satellite data and spatial information to estimate the available rooftop greening area within the study area. We evaluated the mitigation effect of the urban heat island phenomenon and carbon sequestration capacity through temperature predictions resulting from rooftop greening. To achieve this, we utilized WorldView-2 satellite data to classify land cover in the urban heat island areas of Busan city. We developed a prediction model for temperature changes before and after rooftop greening using machine learning techniques. To assess the degree of urban heat island mitigation due to changes in rooftop greening areas, we constructed a temperature change prediction model with temperature as the dependent variable using the random forest technique. In this process, we built a multiple regression model to derive high-resolution land surface temperatures for training data using Google Earth Engine, combining Landsat-8 and Sentinel-2 satellite data. Additionally, we evaluated carbon sequestration based on rooftop greening areas using a carbon absorption capacity per plant. The results of this study suggest that the developed satellite-based urban heat island assessment and temperature change prediction technology using Random Forest models can be applied to urban heat island-vulnerable areas with potential for expansion.

• The Journal of Korean Academy of Prosthodontics
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• v.53 no.4
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• pp.318-324
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• 2015

Purpose: This study compared shear bond strengths of five self-adhesive cements with phosphate monomer to zirconium oxide ceramic with and without airborn particle abrasion. Materials and methods: One hundred zirconia samples were air-abraded ($50{\mu}mAl_2O_3$). One hundred composite resin cylinders were fabricated. Composite cylinders were bonded to the zirconia samples with either Permacem 2.0 (P), $Clearfil^{TM}$ SA Luting (C), $Multilink^{(R)}$ Speed (M), $RelyX^{TM}$ U200 Automix (R), G-Cem $LinkAce^{TM}$ (G). All bonded specimens were stored in distilled water ($37^{\circ}C$) for 24 h and half of them were additionally aged by thermocycling ($5^{\circ}C$, $55^{\circ}C$, 5,000 times). The bonded specimens were loaded in shear force until fracture (1 mm/min) by using Universal Testing Machine (Model 4201, Instron Co, Canton, MA, USA). The failure sites were inspected under field-emission scanning electron microscopy. The data was analyzed with ANOVA, Tukey HSD post-hoc test and paired samples t-test ($\alpha$=.05). Results: Before and after thermocycling, $Multilink^{(R)}$ Speed (M) revealed higher shear-bond strength than the other cements. G-Cem $LinkAce^{TM}$ (G) showed significantly lower bond strengths after thermocycling than before treatment (P<.05), but the other groups were not significantly different (P>.05). Conclusion: Most self-adhesive cements with phosphate monomer showed high shear bond strength with zirconia ceramic and weren't influenced by thermocycling, so they seem to valuable to zirconia ceramic bonding.

• The Journal of Korean Academy of Prosthodontics
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• v.57 no.2
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• pp.127-133
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• 2019

Purpose: The purpose of present study was to investigate fracture strength and mode of failure of endodontically treated teeth restored with metal cast post-core system, prefabricated fiber post system, and newly introduced polyetherketoneketone (PEKK) post-core system. Materials and methods: A total of 21 mandibular premolar were randomly grouped into 3 groups of 7 each according to the post material. Group A was for metal cast post core; Group B for prefabricated glass fiber post and resin core; and Group C for milled PEKK post cores. All specimens were restored with metal crown. The fracture strength of each specimen was measured by applying a static load of 135-degree to the tooth at 2 mm/min crosshead speed using a universal testing machine. After the fracture strength measurement, the mode of failure was observed. The results were analyzed using Kruscal-Wallis test and post hoc Mann-Whitney U test at confidence interval ${\alpha}=.05$. Results: Fracture resistance of PEKK post core was lower than those of cast metal post and fiber reinforced post with composite resin core. In the aspect of fracture mode most of the root fracture occurred in the metal post core, whereas the post detachment occurred mainly in the fiber reinforced post. In the case of PEKK post core, teeth and post were fractured together. Conclusion: It is necessary to select appropriate materials of post for extensively damaged teeth restoration and clinical application of the PEKK post seems to require more research on improvement of strength.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.979-995
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• 2023

As wildfires are difficult to predict, real-time monitoring is crucial for a timely response. Geostationary satellite images are very useful for active fire detection because they can monitor a vast area with high temporal resolution (e.g., 2 min). Existing satellite-based active fire detection algorithms detect thermal outliers using threshold values based on the statistical analysis of brightness temperature. However, the difficulty in establishing suitable thresholds for such threshold-based methods hinders their ability to detect fires with low intensity and achieve generalized performance. In light of these challenges, machine learning has emerged as a potential-solution. Until now, relatively simple techniques such as random forest, Vanilla convolutional neural network (CNN), and U-net have been applied for active fire detection. Therefore, this study proposed an active fire detection algorithm using state-of-the-art (SOTA) deep learning techniques using data from the Advanced Himawari Imager and evaluated it over East Asia and Australia. The SOTA model was developed by applying EfficientNet and lion optimizer, and the results were compared with the model using the Vanilla CNN structure. EfficientNet outperformed CNN with F1-scores of 0.88 and 0.83 in East Asia and Australia, respectively. The performance was better after using weighted loss, equal sampling, and image augmentation techniques to fix data imbalance issues compared to before the techniques were used, resulting in F1-scores of 0.92 in East Asia and 0.84 in Australia. It is anticipated that timely responses facilitated by the SOTA deep learning-based approach for active fire detection will effectively mitigate the damage caused by wildfires.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.

• Journal of Korean Orthopaedic Sports Medicine
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• v.2 no.1
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• pp.28-36
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• 2003

Purpose: To determine and to compare the effects of cyclic loading on the fixation strength of different femoral fixation methods in ACL reconstruction. Materials and Methods: Biomechanical test using an Instron(R) machine (Model No.5569. Mass, U.S.A) were carried out to compare the pull out strength of six different femoral fixation techniques after a cyclic loading in 72 Yorkshire pig knees. The graft-bone complex was cyclically loaded between 30N and 150N at 50 mm/min rate for 1000 cycles and maximal tensile testing was performed. A preload of 30N was applied to the graft along the axis of the tunnel 15 minutes. ANOVA and the Duncan multiple comparison test was used for the statistical analysis. Results: The mean maximum tensile strength of femoral fixation before and after the cyclic loading test were 1003.4$\pm$145N and 601.1$\pm$154N in hamstring-LA screw(R) group, 595.5$\pm$104N and 360.7$\pm$56N in hamstring-Bioscrew(R) group, 1431.7$\pm$135N and 710.7$\pm$114N in hamstring-Semifix(R) group, 603.6$\pm$54N and 459.1$\pm$46N in hamstring-Endobutton(R) fixation group, 1067.4$\pm$145 and 601.8$\pm$134N in the BPTB-Titanium interference screw group, and 987.1$\pm$168N and 588.7$\pm$124N in the BPTB-Bioscrew(R) group. And these data illustrated that cyclic loading reduces the maximum tensile strength by 40 $\%$, 39 $\%$, 50 $\%$, 24 $\%$, 44 $\%$, 40 $\%$ respectively. Conclusions: With the results of these experiments it should be emphasized that rehabilitation exercises after anterior cruciate ligament reconstruction should be executed with precaution as the repetitive flexion and extension of the knee would compromise the maximum tensile strength of the graft tendon.

• Restorative Dentistry and Endodontics
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• v.26 no.5
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• pp.409-417
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• 2001

The purpose of this study was to investigate the influence of light irradiation over self-priming adhesive on dentin bonding. After acid etching the exposed dentin, a self-priming adhesive (Prime&Bond$^{\circledR}$NT dental adhesive system Dentsply DeTrey, GmbH, Konstanz, Germany) was applied and light irradiation was done for 20 sec with regular intensity (600 mW/$\textrm{cm}^2$) in group I and for 3 sec with ultra-high intensity (1930 mW/$\textrm{cm}^2$) in group III. No light irradiation was done over self-priming adhesive in groups II and IV. Composite resin was added on the self-priming adhesive and irradiated for 40 sec with regular intensity (600 mW/$\textrm{cm}^2$) in groups I and II and for 3 sec with ultra-high intensity (1930 mW/$\textrm{cm}^2$) in groups III and IV. To see the effect of light curing time on dentin bonding, another 3 group specimens were prepared. Without light-irradiation over self-priming adhesive, added composite resin was irradiated for 3, 6, or 12 sec with ultra-high intensity light. After bonded specimens were stored in 37$^{\circ}C$ distilled water for 24 hours, shear bond strength were measured using a universal testing machine (4202, Instron, Instron Co., U.S.A.) and fractured surfaces were examined under a stereomicroscope (SZ-PT Olympus, Japan). Statistical analysis were done with one-way, two-way ANOVA and chi-square test. The results were as follows : 1. The shear bond strengths from the groups irradiated over self-priming adhesive were significantly higher than those from the groups without irradiation (p<0.05). 2. There was no significant shear bond strength difference between regular intensity light irradiation groups and ultra-high intensity ones (p>0.05). 3. There was no significant shear bond strength difference among various irradiation time groups with ultra-high intensity ones (p>0.05). 4. In stereomicroscopic examination of fractured surfaces, adhesive-cohesive mixed failure mode was mostly seen in all groups, and there was no significant difference in failure mode among groups (p>0.05).

• Journal of Dental Rehabilitation and Applied Science
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• v.27 no.2
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• pp.125-140
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• 2011

The remnant of temporary cement on the intaglio surface of cast restoration may have a negative effect on the retentive strength of permanent cement. This study was to evaluate the effect of temporary cement cleaning methods on the retentive strength of cementation type implant prostheses. Prefabricated implant abutments - height 5.5mm, diameter 4.5mm, 6 degree axial wall taper with chamfer margins were used. Forty copings-abutment specimens were divided into four groups(each n=10) according to the cleaning methods for temporary cement(Temp-$Bond^{(R)}$) as follows : no temporary cementation(the control group), orange solvent, ultrasonic cleaning, air borne-particle abrasion. After the application of temporary cement and the separation, the cleaning procedure was performed according to the protocol of each group. The specimens were cemented with $Premier^{(R)}$ Implant $Cement^{TM}$. After the permanent cementation, the specimens were subjected to thermocycling and pulled out from the specimens with a universal testing machine at a cross-head speed of 0.5mm/min. After the retentive strength test, all the specimens were cleaned using ultrasonic cleaning, abraded with air borne-particles, and steam-cleaned. Likewise, the specimens were temporarily cemented(Temp-$Bond^{(R)}$ NE), cleaned according to the protocol of each group, cemented with $Premier^{(R)}$ Implant $Cement^{TM}$ and subjected to thermocycling and measurement of their retentive strength. The mean of group with orange solvent were significantly lower than those of other groups(p<0.05). There was no significance between group with ultrasonic cleaning and group with air borne-particle abrasion. Group with ultrasonic cleaning and group with air-particle abrasion were no significance at control group. There was no significance between group cemented with Temp-$Bond^{(R)}$ and group cemented with Temp-$Bond^{(R)}$ NE. Within the limitation of this study, it can be concluded that the temporary cement cleaning method with only orange solvent may have a negative effect on the retentive strength of permanent cement. Ultrasonic cleaning and air borne-particle abrasion methods are recommended for the temporary cement cleaning method on cementation type implant prostheses.