• Journal of the Korea institute for structural maintenance and inspection
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• v.18 no.4
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• pp.50-58
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• 2014

Due to the cost burden of new construction, the necessity of repair and retrofitting of aged structures is sharply increasing as the domain of repair and retrofitting construction is expanding. Because of the necessity, new technologies for repair and retrofitting are continuously studied in Korea and foreign countries. Steel adhesive method, fiber reinforced plastic (FRP) surface adhesive method, and external prestressing method are used to perform the repair and retrofitting works in Korea. In order to consider a repair method using steel mesh reinforced cement mortar (SMCM), 3-point flexural member test was conducted considering repair area and layer number of SMCM. Five types of specimens including ordinary reinforced concrete (RC) specimen with dimensions of $1400{\times}500{\times}200$ (mm) were cast for testing the deflection measurement, a LVDT was installed at the top center of the specimens. Also, a steel strain gauge and a concrete strain gauge were placed at the center of the specimens. A steel strain gauge was also installed on the shear reinforcement. The 3 point flexural member test results showed that the maximum load of SMCM reinforced specimen was higher than that of basic RC specimen in all of the load-displacement curves. Also, the results showed that, when the whole lower part of the basic RC specimen was reinforced, the maximum load and strain were 1.18 and 1.37 times higher than that of the basic RC specimen, respectively. Each specimen showed a slightly different failure behavior where the difference of the results was caused by the difference in the adhesive level between SMCM and RC. Particularly, in SM-B1 specimen, SMCM spalled off during the experiment. This failure behavior showed that the adhesive performance for RC must be improved in order to utilize SMCM as repair and retrofitting material.

• Journal of the korean academy of Pediatric Dentistry
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• v.27 no.3
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• pp.419-430
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• 2000

The purpose of this study was to compare the microleakage pattern of preventive resin restoration using conventional composite resin and flowable composite resin that recently developed. 60 sound premolar teeth were allocated to three groups. Flowable composite resin was used for the experimental groups(Group I and II) and conventional resin for the control group(Group III). After composite filling and sealant application, all teeth were thermocycled and evaluated for microleakage under light microscope. Additionally, a variety of voids formed inside restorations were also evaluated. Data were analyzed statistically using Kruskal-Wallis test and/or Mann-Whitney U-test. The results of the present study were as follows. 1. Microleakage found in all samples was only limited to the interface of restoration margin and enamel. 2. The flowable composite resin groups (Group I, II) generally showed less microleakage than control groups (conventional preventive resin restoration) (p<0.05) 3. Various types of voids were observed in most specimens. Especially, there was a tendency for more and larger voids to be found in group I, II than group III (p<0.05).

• Progress in Medical Physics
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• v.23 no.4
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• pp.269-278
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• 2012

Aquaplast Thermoplastic (AT) is a tissue-equivalent oral compensator that has been developed to improve dose uniformity at the common boundary and around the treated area during radiotherapy in patients with head and neck cancer. In order to assess the usefulness of AT, the degree of improvement in dose distribution and physical properties were compared to those of oral compensators made using paraffin, alginate, and putty, which are materials conventionally used in dental imprinting. To assess the physical properties, strength evaluations (compression and drop evaluations) and natural deformation evaluations (volume change over time) were performed; a Gafchromic EBT2 film and a glass dosimeter inserted into a developed phantom for dose verification were used to measure the common boundary dose and the beam profile to assess the dose delivery. When the natural deformation of the oral compensators was assessed over a two-month period, alginate exhibited a maximum of 80% change in volume from moisture evaporation, while the remaining tissue-equivalent properties, including those of AT, showed a change in volume that was less than 3%. In a free-fall test at a height of 1.5 m (repeated 5 times as a strength evaluation), paraffin was easily damaged by the impact, but AT exhibited no damage from the fall. In compressive strength testing, AT was not destroyed even at 8 times the force needed for paraffin. In dose verification using a glass dosimeter, the results showed that in a single test, the tissue-equivalent (about 80 Hounsfield Units [HU]) AT delivered about 4.9% lower surface dose in terms of delivery of an output coefficient (monitor unit), which was 4% lower than putty and exhibited a value of about 1,000 HU or higher during a dose delivery of the same formulation. In addition, when the incident direction of the beam was used as a reference, the uniformity of the dose, as assessed from the beam profile at the boundary after passing through the oral compensators, was 11.41, 3.98, and 4.30 for air, AT, and putty, respectively. The AT oral compensator had a higher strength and lower probability of material transformation than the oral compensators conventionally used as a tissue-equivalent material, and a uniform dose distribution was successfully formed at the boundary and surrounding area including the mouth. It was also possible to deliver a uniformly formulated dose and reduce the skin dose delivery.

• Composites Research
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• v.35 no.4
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• pp.248-254
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• 2022

In this study, TiB₂-steel composite with high-fractional TiB₂ reinforcement was fabricated by gas pressure infiltration process and the microstructure analysis and compressive strength and hardness were evaluated. To elucidate the correlation between microstructure and mechanical properties for fabricated composite, after the compression test of TiB₂-steel composite, the fracture surface was analyzed and the fracture behavior on compression test was predicted. As a result of the compression fracture surface analysis, interfacial failure trace between the steel matrix and the reinforcement was observed, and the interface between the steel matrix and the reinforcement was analyzed using TEM. From the result of microstructure analysis on the fabricated composite, it was confirmed that, in addition to TiB₂ reinforcement and steel matrix, TiC phase and coarse (Fe,M)₂B (M=Cr,Mn) phase were formed. Throughout the thermodynamic calculation, it was confirmed that TiC and (Fe,M)₂B can be formed as a stable phase under the process condition. The fabricated TiB₂-steel composite had a significantly increased hardness, and the compressive strength and Young's modulus were improved by 3.07 times and 1.95 times, respectively, compared to steel matrix. It seems that the coarse (Fe,M)₂B (M=Cr,Mn) phase formed throughout the composite causes the deterioration of mechanical properties, and by controlling the formation of the (Fe,M)₂B (M=Cr,Mn) phase, it is judged that the mechanical properties of the TiB₂-steel composite can be further improved.

• The Journal of Korean Academy of Prosthodontics
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• v.61 no.3
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• pp.189-197
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• 2023

Purpose. This study aimed to compare and evaluate the shear bond strength between various temporary prostheses resin blocks fabricated by subtractive and additive manufacturing methods bonded to self-curing reline resin. Materials and methods. The experimental groups were divided into 4 groups according to the manufacturing methods of the resin block specimens and each specimen was fabricated by subtractive manufacturing (SM), additive manufacturing stereolithography apparatus manufacturing (AMS), additive manufacturing digital light processing manufacturing (AMD) and conventional self-curing (CON). To bond the resin block specimens and self-curing resin, the reline resin was injected and polymerized into the same location of each resin block using a silicone mold. The shear bond strength was measured using a universal testing machine, and the surface of the adhesive interface was examined by scanning electron microscopy. To compare between groups, one-way ANOVA was done followed by Tukey post hoc test (α = 0.05). Results. The shear bond strength showed higher values in the order of CON, SM, AMS, and AMD group. There were significant differences between CON and AMS groups, as well as between CON and AMD groups. but there were no significant differences between CON and SM groups (P > .05). There were significant differences between SM and AMD groups, but there were no significant differences between SM and AMS groups. The AMS group was significantly different from the AMD group (P < .001). The most frequent failure mode was mixed failures in CON and AMS groups, and adhesive failures in SM and AMD groups. Conclusion. The shear bond strength of SM group showed lower but not significant bond strength compared to the CON group. The additive manufacturing method groups (AMS and AMD) showed significantly lower bond strength than the CON group, with the AMD group the lowest. There was also a significant difference between the AMD and SM group.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.73-85
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• 2013

Nowadays, in today's information society, the importance of the knowledge service using the information to creative value is getting higher day by day. In addition, depending on the development of IT technology, it is ease to collect and use information. Also, many companies actively use customer information to marketing in a variety of industries. Into the 21st century, companies have been actively using the culture arts to manage corporate image and marketing closely linked to their commercial interests. But, it is difficult that companies attract or maintain consumer's interest through their technology. For that reason, it is trend to perform cultural activities for tool of differentiation over many firms. Many firms used the customer's experience to new marketing strategy in order to effectively respond to competitive market. Accordingly, it is emerging rapidly that the necessity of personalized service to provide a new experience for people based on the personal profile information that contains the characteristics of the individual. Like this, personalized service using customer's individual profile information such as language, symbols, behavior, and emotions is very important today. Through this, we will be able to judge interaction between people and content and to maximize customer's experience and satisfaction. There are various relative works provide customer-centered service. Specially, emotion recognition research is emerging recently. Existing researches experienced emotion recognition using mostly bio-signal. Most of researches are voice and face studies that have great emotional changes. However, there are several difficulties to predict people's emotion caused by limitation of equipment and service environments. So, in this paper, we develop emotion prediction model based on vision-based interface to overcome existing limitations. Emotion recognition research based on people's gesture and posture has been processed by several researchers. This paper developed a model that recognizes people's emotional states through body gesture and posture using difference image method. And we found optimization validation model for four kinds of emotions' prediction. A proposed model purposed to automatically determine and predict 4 human emotions (Sadness, Surprise, Joy, and Disgust). To build up the model, event booth was installed in the KOCCA's lobby and we provided some proper stimulative movie to collect their body gesture and posture as the change of emotions. And then, we extracted body movements using difference image method. And we revised people data to build proposed model through neural network. The proposed model for emotion prediction used 3 type time-frame sets (20 frames, 30 frames, and 40 frames). And then, we adopted the model which has best performance compared with other models.' Before build three kinds of models, the entire 97 data set were divided into three data sets of learning, test, and validation set. The proposed model for emotion prediction was constructed using artificial neural network. In this paper, we used the back-propagation algorithm as a learning method, and set learning rate to 10%, momentum rate to 10%. The sigmoid function was used as the transform function. And we designed a three-layer perceptron neural network with one hidden layer and four output nodes. Based on the test data set, the learning for this research model was stopped when it reaches 50000 after reaching the minimum error in order to explore the point of learning. We finally processed each model's accuracy and found best model to predict each emotions. The result showed prediction accuracy 100% from sadness, and 96% from joy prediction in 20 frames set model. And 88% from surprise, and 98% from disgust in 30 frames set model. The findings of our research are expected to be useful to provide effective algorithm for personalized service in various industries such as advertisement, exhibition, performance, etc.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.1-17
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• 2017

The deep learning framework is software designed to help develop deep learning models. Some of its important functions include "automatic differentiation" and "utilization of GPU". The list of popular deep learning framework includes Caffe (BVLC) and Theano (University of Montreal). And recently, Microsoft's deep learning framework, Microsoft Cognitive Toolkit, was released as open-source license, following Google's Tensorflow a year earlier. The early deep learning frameworks have been developed mainly for research at universities. Beginning with the inception of Tensorflow, however, it seems that companies such as Microsoft and Facebook have started to join the competition of framework development. Given the trend, Google and other companies are expected to continue investing in the deep learning framework to bring forward the initiative in the artificial intelligence business. From this point of view, we think it is a good time to compare some of deep learning frameworks. So we compare three deep learning frameworks which can be used as a Python library. Those are Google's Tensorflow, Microsoft's CNTK, and Theano which is sort of a predecessor of the preceding two. The most common and important function of deep learning frameworks is the ability to perform automatic differentiation. Basically all the mathematical expressions of deep learning models can be represented as computational graphs, which consist of nodes and edges. Partial derivatives on each edge of a computational graph can then be obtained. With the partial derivatives, we can let software compute differentiation of any node with respect to any variable by utilizing chain rule of Calculus. First of all, the convenience of coding is in the order of CNTK, Tensorflow, and Theano. The criterion is simply based on the lengths of the codes and the learning curve and the ease of coding are not the main concern. According to the criteria, Theano was the most difficult to implement with, and CNTK and Tensorflow were somewhat easier. With Tensorflow, we need to define weight variables and biases explicitly. The reason that CNTK and Tensorflow are easier to implement with is that those frameworks provide us with more abstraction than Theano. We, however, need to mention that low-level coding is not always bad. It gives us flexibility of coding. With the low-level coding such as in Theano, we can implement and test any new deep learning models or any new search methods that we can think of. The assessment of the execution speed of each framework is that there is not meaningful difference. According to the experiment, execution speeds of Theano and Tensorflow are very similar, although the experiment was limited to a CNN model. In the case of CNTK, the experimental environment was not maintained as the same. The code written in CNTK has to be run in PC environment without GPU where codes execute as much as 50 times slower than with GPU. But we concluded that the difference of execution speed was within the range of variation caused by the different hardware setup. In this study, we compared three types of deep learning framework: Theano, Tensorflow, and CNTK. According to Wikipedia, there are 12 available deep learning frameworks. And 15 different attributes differentiate each framework. Some of the important attributes would include interface language (Python, C ++, Java, etc.) and the availability of libraries on various deep learning models such as CNN, RNN, DBN, and etc. And if a user implements a large scale deep learning model, it will also be important to support multiple GPU or multiple servers. Also, if you are learning the deep learning model, it would also be important if there are enough examples and references.

• Journal of the korean academy of Pediatric Dentistry
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• v.34 no.3
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• pp.420-429
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• 2007

The purpose of this study was to compare the micro ensile bond strength to bovine dentin of several adhesives (SM, Scotch $Bond^{TM}$ Multipurpose; SB, $Adper^{TM}$ Single Bond 2; SE, $Clearfil^{(R)}$ SE Bond; AQ, AQ $Bond^{TM}$; TS, $Clearfil^{(R)}$ tri-S Bond). Except SM and SB, they have a simplified one- or two-step application protocols in compare with the dentin adhesives conventional three-step protocols. For the microtensile bond strength test, the labial surfaces of bovine incisors were used. Following exposure of dentin layer, according to their manufacturer's directions, each dentin adhesives were applied and composite resin blocks were constructed. The teeth were sectioned for specimen and tested microtensile bond strength. Also observed the fracture mode of interface. The obtained results were as follows : 1. The microtensile bond strength values ranged from 51.34 to 24.04 MPa on dentin(in decreasing order, SE, SM, SB, AQ and TS). 2. The highest microtensile bond strength was by SE and SM on bovine dentin, and the lowest by AQ and TS. 3. SM, SB and SE showed cohesive failures and adhesive failure but AQ, TS presented almost adhesive failures. In summary, microtensile bond strengths of single-step adhesives (AQ and TS) on bovine dentin were significantly lower than those of multi-step adhesives (SM, SB and SE) (p<0.05).

• Economic and Environmental Geology
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• v.40 no.4
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• pp.361-374
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• 2007

Rare earth elements(REEs) have been used as an useful tool in understanding the various geological processes such as evolution and differentiation in the crust. The REEs also have been used as an analog of actinides for radioactive wastes at the water-rock interactions. Using physicochemical properties of the REEs and actinides, we have shown that Eu is an optimum analogue for understanding the behavior of Am in subsurface environments. Factors affecting sorption behavior of radioactive nuclides in groundwater were investigated by batch experiments. Four nuclides such as $^{241}Am,\;^{152}Eu,\;^{160}Tb\;and\;^{60}Co$ were selected to test our hypothesis, and $^{160}Tb$ and $^{60}Co$ were specifically used to compare to the sorption behavior between $^{241}Am-^{152}Eu$ and other radioactive nuclides. Four different rock samples and one groundwater were used in the batch experiments where solution pH for all experiments was fixed at 5.5. Our results demonstrate that $^{241}Am,\;^{152}Eu,\;and\;^{160}Tb$ show similar sorption behavior whereas $^{60}Co$ is different in sorption behavior at the mineral-water interface, suggesting that the sorption behavior of $^{60}Co$ is affected by different rock types. Our results also show that 1) Eu in REEs is optimum analogue of fate and transport of Am in subsurface environments, and 2) mineral compositions such as $SiO_2,\;TiO_2,\;P_2O_5$ and distribution of REEs such as Eu anomaly play key roles in affecting sorption behavior of radioactive nuclides even though physicochemical properties of geological materials such as specific surface area and cation exchange capacity can not be ruled out.

• Journal of Bio-Environment Control
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• v.20 no.2
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• pp.93-100
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• 2011

Maintenance of adequate soil water potential during the period of crop growth is necessary to support optimum plant growth and yields. A better understanding of soil water movement within and below the rooting zone can facilitate optimal irrigation scheduling aimed at minimizing the adverse effects of water stress on crop growth and development and the leaching of water below the root zone which can have adverse environmental effects. The objective of this study was to evaluate the feasibility of using a portable irrigation controller with an Watermark sensor for the cultivation of drip-irrigated vegetable crops in a greenhouse. The control capability of the irrigation controller for a soil water potential of -20 kPa was evaluated under summer conditions by cultivating 45-day-old tomato plants grown in three differently textured soils (sandy loam, loam, and loamy sands). Water contents through each soil profile were continuously monitored using three Sentek probes, each consisting of three capacitance sensors at 10, 20, and 30 cm depths. Even though a repeatable cycling of soil water potential occurred for the potential treatment, the lower limit of the Watermark (about 0 kPa) obtained in this study presented a limitation of using the Watermark sensor for optimal irrigation of tomato plants where -20 kPa was used as a point for triggering irrigations. This problem might be related to the slow response time and inadequate soil-sensor interface of the Watermark sensor as compared to a porous and ceramic cup-based tensiometer with a sensitive pressure transducer. In addition, the irrigation time of 50 to 60 min at each of the irrigation operation gave a rapid drop of the potential to zero, resulting in over irrigation of tomatoes. There were differences in water content among the three different soil types under the variable rate irrigation, showing a range of water contents of 16 to 24%, 17 to 28%, and 24 to 32% for loamy sand, sandy loam, and loam soils, respectively. The greatest rate increase in water content was observed in the top of 10 cm depth of sandy loam soil within almost 60 min from the start of irrigation.