• Journal of Soil and Groundwater Environment
• /
• v.26 no.6
• /
• pp.144-156
• /
• 2021

This study examined hydrogeological characteristics of groundwater and surface water interaction in the fresh-saline water mixed zone of East Coast lagoon area, Korea, using several technical approaches including hydrological, lithological, and isotopic methods. In addition, the fresh-saline water interface was evaluated using vertical electrical conductivity (EC) data. For this purpose, three monitoring wells (SJ-P1, SJ-P2, and SJ-P3) were installed across the Songji lagoon at depths of 7.4 to 9.0 m, and water level, EC, and temperature at the wells and in the lagoon (SJ-L1) were monitored using automatic transducers from August 1 to October 21, 2021. Isotopic composition of the groundwater, lagoon water, and sea water were also monitored in the mid-September, 2013. The mixing ratios calculated from oxygen and hydrogen isotopic composition decreased with increasing depth in the monitoring wells, indicating saline water intrusion. In the study area, the interaction of groundwater-surface water-sea water was evident, and residual salinity in the sedimentary layers created in the past marine environment showed disorderly characteristics. Moreover, the horizontal flow at the lagoon's edge was more dominant than the vertical flow.

• Journal of the Microelectronics and Packaging Society
• /
• v.10 no.3
• /
• pp.19-27
• /
• 2003

Surface finishes of PCB laminates are important in the solder joint reliability of flip chip package because the types and thicknesses of intermetallic compound(IMC), and compositions and hardness of solders are affected by them. In this study, effects of surface finishes of PCB on the low cycle fatigue resistance of Sn-based lead-free solders; Sn-3.5Ag, Sn-3.5Ag-XCu(X=0.75, 1.5), Sn-3.5Ag-XBi(X=2.5, 7.5) and Sn-0.7Cu were investigated for the Cu and Au/Ni surface finish treatments. Displacement controlled room temperature lap shear fatigue tests showed that fatigue resistance of Sn-3.5Ag-XCu(X=0.75, 1.5), Sn-3.5Ag and Sn-0.7Cu alloys were more or less the same each other but much better than that of Bi containing alloys regardless of the surface finish layer used. In general, solder joints on the Au/Ni finish showed better fatigue resistance than those on the Cu finish. Cross-sectional fractography revealed microcracks nucleation inside of the interfacial IMC near the solder mask edge, more frequently on the Cu than the Au/Ni surface finish. Macro cracks followed the solder/IMC interface in the Bi containing alloys, while they propagated in the solder matrix in other alloys. It was ascribed to the Bi segregation at the solder/IMC interface and the solid solution hardening effect of Bi in the $\beta-Sn$ matrix.

• Journal of Intelligence and Information Systems
• /
• v.23 no.2
• /
• pp.1-17
• /
• 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 Vacuum Society
• /
• v.14 no.2
• /
• pp.78-83
• /
• 2005

Nanometric crystalline silicon (no-Si) embedded in dielectric medium has been paid attention as an efficient light emitting center for more than a decade. In nc-Si, excitonic electron-hole pairs are considered to attribute to radiative recombination. However the surface defects surrounding no-Si is one of non-radiative decay paths competing with the radiative band edge transition, ultimately which makes the emission efficiency of no-Si very poor. In order to passivate those defects - dangling bonds in the $Si:SiO_2$ interface, hydrogen is usually utilized. The luminescence yield from no-Si is dramatically enhanced by defect termination. However due to relatively high mobility of hydrogen in a matrix, hydrogen-terminated no-Si may no longer sustain the enhancement effect on subsequent thermal processes. Therefore instead of easily reversible hydrogen, phosphorus was introduced by ion implantation, expecting to have the same enhancement effect and to be more resistive against succeeding thermal treatments. Samples were Prepared by 400 keV Si implantation with doses of $1\times10^{17}\;Si/cm^2$ and by multi-energy Phosphorus implantation to make relatively uniform phosphorus concentration in the region where implanted Si ions are distributed. Crystalline silicon was precipitated by annealing at $1,100^{\circ}C$ for 2 hours in Ar environment and subsequent annealing were performed for an hour in Ar at a few temperature stages up to $1,000^{\circ}C$ to show improved thermal resistance. Experimental data such as enhancement effect of PL yield, decay time, peak shift for the phosphorus implanted nc-Si are shown, and the possible mechanisms are discussed as well.

• Restorative Dentistry and Endodontics
• /
• v.27 no.2
• /
• pp.150-157
• /
• 2002

One of the latest concepts in bonding are "total etch", in which both enamel and dentin are etched with an acid to remove the smear layers, and "wet dentin" in which the dentin is not dry but left moist before application of the bonding primer Ideally the application of a bonding agent to tooth structure should be insensitive to minor contamination from oral fluids. Clinically, contaminations such as saliva, gingival fluid, blood and handpiece lubricant are often encountered by dentists during cavity preparation. The aim of this study was to evaluate the effect of contamination by hemostatic agents on shear bond strength of compomer restorations. One hundred and ten extracted human maxillary and mandibular molar teeth were collected. The teeth were removed soft tissue remnant and debris and stored in physiologic solution until they were used. Small flat area on dentin of the buccal surface were wet ground serially with 400, 800 and 1200 abrasive papers on automatic polishing machine. The teeth were randomly divided into 11 groups. Each group was conditioned as follows : Group 1: Dentin surface was not etched and not contaminated by hemostatic agents. Group 2: Dentin surface was not etched but was contaminated by Astringedent$^{\circledR}$(Ultradent product Inc., Utah, U.S.A.) Group 3: Dentin surface was not etched but was contaminated by Bosmin$^{\circledR}$(Jeil Pharm, Korea.). Group 4: Dentin surface was not etched but was contaminated by Epri-dent$^{\circledR}$(Epr Industries, NJ, U.S.A.). Group 5: Dentin surface was etched and not contaminated by hemostatic agents. Group 6: Dentin sorface was etched and contaminated by Astringedent$^{\circledR}$. Group 7 : Dentin surface was etched and contaminated by Bosmin$^{\circledR}$. Group 8: Dentin surface was etched and contaminated by Epri-dent$^{\circledR}$. Group 9: Dentin surface was contaminated by Astringedent$^{\circledR}$. The contaminated surface was rinsed by water and dried by compressed air. Group 10: Dentin surface was contaminated by Bosmin$^{\circledR}$. The contaminated surface was rinsed by water and dried by compressed air. Group 11 : Dentin surface was contaminated by Epri-dent$^{\circledR}$. The contaminated surface was rinsed by water and dried by compressed air. After surface conditioning, F2000$^{\circledR}$ was applicated on the conditoned dentin surface The teeth were thermocycled in distilled water at 5$^{\circ}C$ and 55$^{\circ}C$ for 1,000 cycles. The samples were placed on the binder with the bonded compomer-dentin interface parallel to the knife-edge shearing rod of the Universal Testing Machine(Zwick Z020, Zwick Co., Germany) running at a cross head speed or 1.0 mm/min. Group 2 showed significant decrease in shear bond strength compared with group 1 and group 6 showed significant decrease in shear bond strength compared with group 5. There were no significant differences in shear bond strength between group 5 and group 9, 10 and 11.