• Journal of the Korean Society of Environmental Restoration Technology
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• v.25 no.3
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• pp.83-95
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• 2022

This study aims to present primary data for habitat restoration and artificial breeding conditions of L. unmunsana by identifying the habitat conditions and the larvae's food sources. In order to investigate the habitat characteristics of the adult L. unmunsana and land snails, which are the primary food sources for the larvae, field surveys were conducted on a total of 10 habitats in south-central parts of Korea including Sanseongcheon, Jeonju. The results revealed that the L. unmunsana habitat in the Sanseongcheon area had a broadleaf forest with a multi-layered vegetation structure, adjacent water features, and the north/northeast/northwest slopes with little effect of artificial lighting. The adult L. unmunsana in the Sanseongcheon area appeared from the end of May to the end of June, and was especially intensively observed around the middle of June. The most active time was from 23:30 to 00:30 with a temperature range of 19~22℃ and higher than 80% humidity. The peak count of the observed adults L. unmunsana was a total of 774 on June 11, 2021. In the case of land snails, 11 families and 23 species were observed in 10 habitats of L. unmunsana, and Euphaedusa fusaniana was the most extensive and the most observed in the five survey areas. The land snails of L. unmunsana habitats are mostly found under the organic layers of leaves and a fallen tree branch in broadleaf forests, where a thick organic material layer buffers temperature changes and provides high humidity for various snails. These habitat conditions are suitable for the larva of L. unmunsana and land snails to inhabit, feed, hide and hibernate.

• Composites Research
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• v.35 no.3
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• pp.188-195
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• 2022

This paper proposes a multiscale process-structural analysis methodology and applies to a battery housing part made of the short fiber-reinforced and fabric-reinforced composite layers. In particular, uncertainties of the material properties within the microscale representative volume element (RVE) were considered. The random spatial distribution of matrix properties in the microscale RVE was realized by the Karhunen-Loeve Expansion (KLE) method. Then, effective properties of the RVE reflecting on spatially varying matrix properties were obtained by the computational homogenization and mapped to a macroscale FE (finite element) model. Morever, through the hybrid process simulation, a FE (finite element) model mapping residual stress and fiber orientation from compression molding simulation is combined with one mapping fiber orientation from the draping process simulation. The proposed method is expected to rigorously evaluate the design requirements of the battery housing part and composite materials having various material configurations.

• Journal of Adhesion and Interface
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• v.23 no.2
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• pp.17-24
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• 2022

Colloidal quantum dots (QDs) have gained attention for applications in quantum dot light emitting diodes (QLEDs) due to their high photoluminescence quantum yield, narrow emission spectra, and tunable bandgap. Nevertheless, non-radiative recombination induced by electron and hole imbalance deteriorates the device efficiency and stability. To overcome the problem, researchers have been trying to enhance hole transport properties of hole transporting layers (HTL) and/or slow down the electron injection in electron transport layer (ETL). Here, we summarize two approaches: i) development of interfacial materials between QD and ETL (or HTL); ii) engineering of HTL by blending or multi-layer approaches.

• Nuclear Engineering and Technology
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• v.55 no.7
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• pp.2578-2590
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• 2023

In this work, a 24-month two-batch fuel management strategy for the APR1400 using LEU + has been investigated, where enrichments of 5.9 and 5.2 w/o are utilized in lieu of the conventional 4-5 w/o UO₂ fuel. In addition, an Accident Tolerant Fuel (ATF) clad based on the swaging technology is applied to APR1400 fuel assemblies. In this special ATF clad design, both outer and inner SS316 layers protect the conventional zircaloy clad. Erbia (Er₂O₃) is introduced as a burnable absorber with two-fold goals to lower the critical boron concentration in the long-cycle LEU + loaded core as well as to handle the LEU + fuel in the existing front-end fuel facilities without renewing the license. Two types of fuel assemblies with different loading of gadolinia (Gd₂O₃) are considered to control both the reactivity and the core radial power distribution. The erbia burnable absorber is uniformly admixed with UO₂ in all fuel pins except for the gadolinia-bearing ones. In this study, two core designs were devised with different erbia loading, and core performance and safety parameters were evaluated for each case in comparison with a core design without any burnable absorbers. The core analysis was done using the two-step method. First, cross-sections are generated by the SERPENT 2 Monte Carlo code, and the 3-D neutronic analysis is performed with an in-house multi-physics nodal code KANT.

• Computers and Concrete
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• v.32 no.1
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• pp.61-74
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• 2023

This article investigates the wave propagation analysis of the imperfect functionally graded (FG) sandwich plates based on a novel simple four-variable integral quasi-3D higher-order shear deformation theory (HSDT). The thickness stretching effect is considered in the transverse displacement component. The presented formulation ensures a parabolic variation of the transverse shear stresses with zero-stresses at the top and the bottom surfaces without requiring any shear correction factors. The studied sandwich plates can be used in several sectors as areas of aircraft, construction, naval/marine, aerospace and wind energy systems, the sandwich structure is composed from three layers (two FG face sheets and isotropic core). The material properties in the FG faces sheet are computed according to a modified power law function with considering the porosity which may appear during the manufacturing process in the form of micro-voids in the layer body. The Hamilton principle is utilized to determine the four governing differential equations for wave propagation in FG plates which is reduced in terms of computation time and cost compared to the other conventional quasi-3D models. An eigenvalue equation is formulated for the analytical solution using a generalized displacements' solution form for wave propagation. The effects of porosity, temperature, moisture concentration, core thickness, and the material exponent on the plates' dispersion relations are examined by considering the thickness stretching influence.

• Information Systems Review
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• v.23 no.1
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• pp.135-154
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• 2021

In the network economy, the informatization of Small and Medium enterprises(SME) plays an important role in determining productivity while being competitive in the businesses. Informatization of SME has become important along with the recent trend of the fourth industrial revolution. Based on the IT Business Value Model, this study analyzes the key factors of information service of SME with the structure model. In addition, multi-level model was conducted by dividing the layers according to the size of the SME. The analysis confirmed that complementary organizational resources are a key factor in determining the informatization of SME. In addition, the effect of informatization of SME on the scale of SME varies depending on the type of entry into the industrial complex.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.3
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• pp.379-392
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• 2024

With the rapid increase in data, saving storage space and improving the efficiency of data transmission have become critical issues, making the research on the efficiency of data compression technologies increasingly important. Lossless algorithms can precisely restore original data but have limited compression ratios, whereas lossy algorithms provide higher compression rates at the expense of some data loss. There has been active research in data compression using deep learning-based algorithms, especially the autoencoder model. This study proposes a new side-channel analysis data compressor utilizing autoencoders. This compressor achieves higher compression rates than Deflate while maintaining the characteristics of side-channel data. The encoder, using locally connected layers, effectively preserves the temporal characteristics of side-channel data, and the decoder maintains fast decompression times with a multi-layer perceptron. Through correlation power analysis, the proposed compressor has been proven to compress data without losing the characteristics of side-channel data.

• Geomechanics and Engineering
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• v.37 no.4
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• pp.341-358
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• 2024

This paper reports several plane-strain trapdoor tests conducted to investigate the effects of reinforcement on soil arching development under localized surface loading with a loading plate width three times the trapdoor width. An analogical soil composed of aluminum rods with three different diameters was used as the backfill and Kraft paper with two different stiffness values was used as the reinforcement material. Four reinforcement arrangements were investigated: (1) no reinforcement, (2) one low stiffness reinforcement R1, (3) one high stiffness reinforcement R2, and (4) two low stiffness reinforcements R1 with a backfill layer in between. The stiffness of R2 was approximately twice that of R1; therefore, two R1 had approximately the same total stiffness as one R2. Test results indicate that the use of reinforcement minimized soil arching degradation under localized surface loading. Soil arching with reinforcement degraded more at unloading stages as compared to that at loading stages. The use of stiffer reinforcement had the advantages of more effectively minimizing soil arching degradation. As compared to one high stiffness reinforcement layer, two low stiffness reinforcement layers with a backfill layer of certain thickness in between promoted soil arching under localized surface loading. Due to different states of soil arching development with and without reinforcement, an analytical multi-stage soil arching model available in the literature was selected in this study to calculate the average vertical pressures acting on the trapdoor or on the deflected reinforcement section under both the backfill self-weight and localized surface loading.

• Journal of the Korean Magnetics Society
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• v.23 no.4
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• pp.122-125
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• 2013

Perpendicular magnetic anisotropy (PMA) is the phenomenon of magnetic thin film which is preferentially magnetized in a direction perpendicular to the film's plane. Amorphous multilayer with PMA has been studied as the good candidate to realization of high density STT-MRAM (Spin Transfer Torque-Magnetic Random Access Memory). The current issue of high density STT-MRAM is a decrease in the switching current of the device and an application of amorphous materials which are most suitable devices. The amorphous ferromagnetic material has low saturated magnetization, low coercivity and high thermal stability. In this study, we presented amorphous ferromagnetic multilayer that consists of an amorphous alloy CoSiB and a nonmagnetic material Pd. We investigated the change of PMA of the $[CoSiB\;t_{CoSiB}/Pd\;1.3nm]_5$ multilayer ($t_{CoSiB}$ = 0.1, 0.2, 0.3, 0.4, 0.5, 0.6 nm, and $t_{Pd}$ = 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6 nm) and $[CoSiB\;0.3nm/Pd\;1.3nm]_n$ multilayer (n = 3, 5, 7, 9, 11, 13). This multilayer is measured by VSM (Vibrating Sample Magnetometer) and analyzed magnetic properties like a coercivity ($H_c$) and a magnetization ($M_s$). The coercivity in the $[CoSiB\;t_{CoSiB}\;nm/Pd\;1.3nm]_5$ multi-layers increased with increasing $t_{CoSiB}$ to reach a maximum at $t_{CoSiB}$ = 0.3 nm and then decreased for $t_{CoSiB}$ > 0.3 nm. The lowest saturated magnetization of $0.26emu/cm^3$ was obtained in the $[CoSiB\;0.3nm/Pd\;1.3nm]_3$ multilayer whereas the highest coercivity of 0.26 kOe was obtained in the $[CoSiB\;0.3nm/Pd\;1.3nm]_5$ mutilayer. Additional Pd layers did not contribute to the perpendicular magnetic anisotropy. The single domain structure evolved in to a striped multi-domain structure as the bilayer repetition number n was increased above 7 after which (n > 7) the hysteresis loops had a bow-tie shapes.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.95-108
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• 2017

Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.