• Applied Chemistry for Engineering
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• v.23 no.2
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• pp.131-137
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• 2012

Polyaniline (PANI) and polypyrrole (Ppy) hollow sphere structures with controlled shell thicknesses can be easily synthesized than those of using a layer-by-layer method for cathode active material of lithium-ion batteries. Polystyrene (PS) core was synthesized by emulsion polymerization using an anion surfactant. The shell thicknesses of PANI and Ppy were controlled by amounts of aniline and pyrrole monomers. PS was removed by an organic solution. This structure increased in contact with an electrolyte and a specific capacity in lithium-ion batteries. But polymers have disadvantages such as the difficult control of molecular weights and low densities. These disadvantages were completed by controlled shell thicknesses. The amount of aniline monomer increased from 1.2, 2.4, 3.6, 4.8 to 6.0 mL, and the shell thicknesses were 30.2, 38.0, 42.2, 48.2, and 52.4 nm, respectively. And the amount of pyrrole monomer was 0.6, 1.2, 2.4 and 3.6 mL, the shell thicknesses were 16.0, 22.0, 27.0 and 34.0 nm, respectively. In the cathode materials with controlled shell thicknesses, shell thicknesses of the PANI hollow spheres were 30.2, 42.2, and 52.4 nm, and discharge specific capacities of after 10 cycle were ~18, ~29, and ~62 mAh/g, respectively. The shell thicknesses of the Ppy hollow spheres were 16.0, 22.0, 27.0 and 34.0 nm, and discharge specific capacities of after 15 cycle were ~15, ~36, ~56, and ~77 mAh/g, respectively. Thus, shell thicknesses of PANI and Ppy increased, the specific capacities increased.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.43-62
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• 2019

At one time, the anomaly detection sector dominated the method of determining whether there was an abnormality based on the statistics derived from specific data. This methodology was possible because the dimension of the data was simple in the past, so the classical statistical method could work effectively. However, as the characteristics of data have changed complexly in the era of big data, it has become more difficult to accurately analyze and predict the data that occurs throughout the industry in the conventional way. Therefore, SVM and Decision Tree based supervised learning algorithms were used. However, there is peculiarity that supervised learning based model can only accurately predict the test data, when the number of classes is equal to the number of normal classes and most of the data generated in the industry has unbalanced data class. Therefore, the predicted results are not always valid when supervised learning model is applied. In order to overcome these drawbacks, many studies now use the unsupervised learning-based model that is not influenced by class distribution, such as autoencoder or generative adversarial networks. In this paper, we propose a method to detect anomalies using generative adversarial networks. AnoGAN, introduced in the study of Thomas et al (2017), is a classification model that performs abnormal detection of medical images. It was composed of a Convolution Neural Net and was used in the field of detection. On the other hand, sequencing data abnormality detection using generative adversarial network is a lack of research papers compared to image data. Of course, in Li et al (2018), a study by Li et al (LSTM), a type of recurrent neural network, has proposed a model to classify the abnormities of numerical sequence data, but it has not been used for categorical sequence data, as well as feature matching method applied by salans et al.(2016). So it suggests that there are a number of studies to be tried on in the ideal classification of sequence data through a generative adversarial Network. In order to learn the sequence data, the structure of the generative adversarial networks is composed of LSTM, and the 2 stacked-LSTM of the generator is composed of 32-dim hidden unit layers and 64-dim hidden unit layers. The LSTM of the discriminator consists of 64-dim hidden unit layer were used. In the process of deriving abnormal scores from existing paper of Anomaly Detection for Sequence data, entropy values of probability of actual data are used in the process of deriving abnormal scores. but in this paper, as mentioned earlier, abnormal scores have been derived by using feature matching techniques. In addition, the process of optimizing latent variables was designed with LSTM to improve model performance. The modified form of generative adversarial model was more accurate in all experiments than the autoencoder in terms of precision and was approximately 7% higher in accuracy. In terms of Robustness, Generative adversarial networks also performed better than autoencoder. Because generative adversarial networks can learn data distribution from real categorical sequence data, Unaffected by a single normal data. But autoencoder is not. Result of Robustness test showed that he accuracy of the autocoder was 92%, the accuracy of the hostile neural network was 96%, and in terms of sensitivity, the autocoder was 40% and the hostile neural network was 51%. In this paper, experiments have also been conducted to show how much performance changes due to differences in the optimization structure of potential variables. As a result, the level of 1% was improved in terms of sensitivity. These results suggest that it presented a new perspective on optimizing latent variable that were relatively insignificant.

• Membrane Journal
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• v.28 no.6
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• pp.432-443
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• 2018

In this study, propylene/propane separation behavior of Na-type faujasite zeolite membranes is predicted by observing gravimetric adsorptions of propylene and propane on zeolite 13X. The gravimetric adsorptions were measured by using a magnetic suspension balance (MSB) at temperatures of 323, 343, 363 K and a pressure range of 0.02-1 bar. The pressure was increased at 0.1 bar intervals. As adsorption temperature increased, adsorptions of propylene and propane decreased and propylene/propane adsorption selectivity increased. Also, the diffusion coefficients of propylene and propane were increased as the adsorption temperature increased, following the Arrhenius equation. The maximum propylene/propane diffusion selectivity was 0.9753 at 323 K. The perm-selectivity was calculated from the adsorption data of zeolite 13X and compared with the perm-selectivity measured in the single gas permeation experiment for the Na-type faujasite zeolite membrane. The maximum values for the calculated and measured perm-selectivities were observed at a temperature of 323 K. It could be concluded that the prediction of propylene/propane separation of surface diffusion-based membrane by using gravimetric adsorption data is reasonable. Therefore, it is expected that this prediction method can be applied to the screening of adsorption-based microporous membrane for propylene/propane separation.

• Journal of Wetlands Research
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• v.24 no.4
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• pp.331-344
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• 2022

This study analyzed the characteristics of the soil and hydrological environment of abandoned paddy wetlands examined the changes in land cover type in the ecological affect area, analyzed the environmental factors of abandoned paddy wetlands, and examined the changes in land cover type in the ecological impact area. The ecological environment characteristics of the reference abandoned paddy wetlands were investigated through literature research, environmental spatial information service, and preliminary exploration of the abandoned paddy wetlands, and the basic data for the restoration of abandoned paddy wetlands ware provided by examining the changes in land cover type in the ecological impact area for 40 years. Through this study, it will be possible to manage the rapidly increasing number of abandoned farmland to be converted into wetlands so that it can perform functions equivalent to or greater than that of natural wetlands. In particular, as we checked the clues that abandoned paddy wetlands could spread to surrounding ecological influences through land cover changes, the study sites are highly likely to be reference wetlands, and if the topography, soil, water circulation system, and carbon reduction performance are analyzed carefully, it will be possible to standardize the development process. In addition, through the change in land cover, clues were confirmed that the abandoned paddy wetlands could spread to the surrounding ecological affect areas. The land cover type in the ecological impact area, forests was mainly distributed, but generally decreased rapidly in the last 10-20 years, and forests were changing from coniferous forests to broad-leaved forests, mixed forests, or grassland. It has not yet been fully called to the wetland, and it is found that it has maintained the form of barren or grassland, and as can be seen in the case of natural wetlands after more than 30 years after abandoned, it is expected that the transition will gradually proceed to wetlands that are structurally and functionally similar to natural wetlands.

• Journal of the Microelectronics and Packaging Society
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• v.27 no.4
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• pp.83-89
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• 2020

For the past few decades, as part of efforts to protect the environment where fossil fuels, which have been a key energy resource for mankind, are becoming increasingly depleted and pollution due to industrial development, ecofriendly secondary batteries, hydrogen generating energy devices, energy storage systems, and many other new energy technologies are being developed. Among them, the lithium-ion battery (LIB) is considered to be a next-generation energy device suitable for application as a large-capacity battery and capable of industrial application due to its high energy density and long lifespan. However, considering the growing battery market such as eco-friendly electric vehicles and drones, it is expected that a large amount of battery waste will spill out from some point due to the end of life. In order to prepare for this situation, development of a process for recovering lithium and various valuable metals from waste batteries is required, and at the same time, a plan to recycle them is socially required. In this study, we introduce a nanoscale pattern transfer printing (NTP) process of Li2CO3, a representative anode material for lithium ion batteries, one of the strategic materials for recycling waste batteries. First, Li2CO3 powder was formed by pressing in a vacuum, and a 3-inch sputter target for very pure Li2CO3 thin film deposition was successfully produced through high-temperature sintering. The target was mounted on a sputtering device, and a well-ordered Li2CO3 line pattern with a width of 250 nm was successfully obtained on the Si substrate using the NTP process. In addition, based on the nTP method, the periodic Li2CO3 line patterns were formed on the surfaces of metal, glass, flexible polymer substrates, and even curved goggles. These results are expected to be applied to the thin films of various functional materials used in battery devices in the future, and is also expected to be particularly helpful in improving the performance of lithium-ion battery devices on various substrates.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1723-1735
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• 2022

Landslides are one of the most prevalent natural disasters, threating both humans and property. Also landslides can cause damage at the national level, so effective prediction and prevention are essential. Research to produce a landslide susceptibility map with high accuracy is steadily being conducted, and various models have been applied to landslide susceptibility analysis. Pixel-based machine learning models such as frequency ratio models, logistic regression models, ensembles models, and Artificial Neural Networks have been mainly applied. Recent studies have shown that the kernel-based convolutional neural network (CNN) technique is effective and that the spatial characteristics of input data have a significant effect on the accuracy of landslide susceptibility mapping. For this reason, the purpose of this study is to analyze landslide vulnerability using a pixel-based deep neural network model and a patch-based convolutional neural network model. The research area was set up in Gangwon-do, including Inje, Gangneung, and Pyeongchang, where landslides occurred frequently and damaged. Landslide-related factors include slope, curvature, stream power index (SPI), topographic wetness index (TWI), topographic position index (TPI), timber diameter, timber age, lithology, land use, soil depth, soil parent material, lineament density, fault density, normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used. Landslide-related factors were built into a spatial database through data preprocessing, and landslide susceptibility map was predicted using deep neural network (DNN) and CNN models. The model and landslide susceptibility map were verified through average precision (AP) and root mean square errors (RMSE), and as a result of the verification, the patch-based CNN model showed 3.4% improved performance compared to the pixel-based DNN model. The results of this study can be used to predict landslides and are expected to serve as a scientific basis for establishing land use policies and landslide management policies.

• Economic and Environmental Geology
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• v.55 no.1
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• pp.97-109
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• 2022

This study focused on the physicochemical effects of bottom ash dissolved precipitation on the soil and groundwater environment. The iced column and percolation experiments showed that most of the bottom ash particles were drained as the ash-dissolved solution, while the charcoal powder was filtered through the soil. Ion species of Al, As, Cu, Cd, Cr, Pb, Fe, Mn, Ca, K, Si, F, NO₃, SO₄ were analyzed from the eluates collected during the 24 h column test. In the charcoal powder eluates, a high concentration of K was detected at the beginning of the reaction, but it decreased with time. The concentrations of Al and Ca were observed to increase with time, although they existed in trace amount. In the bottom ash eluates, the concentrations of Ca and SO₄ decreased by 30 mg·L^-1 and 67 mg·L^-1, respectively, over 24 h. It is regarded that the infiltration patterns of the bottom ash and biochar in the unsaturated zone were different owing to their particle sizes and solvent properties. It is expected that a significant amount of the bottom ash will mix with the precipitation and percolate below the water table, especially in the case of thin and highly permeable unsaturated zone. The biochar was filtered through the unsaturated zone. The biochar did not dissolve in the groundwater, although it reached the saturation zone. For these reasons, it is considered that the direct contamination by the bottom ash and biochar are unlikely to occur.

• Ecology and Resilient Infrastructure
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• v.9 no.3
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• pp.163-173
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• 2022

Climate change is a key factor that greatly influences changes in the biological seasons and geographical distribution of species. In the ecological field, the BioClimatic predictor (BioClim), which is most related to the physiological characteristics of organisms, is used for vulnerability assessment. However, BioClim values are not provided other than the future period climate average values for each GCM for the Shared Socio-economic Pathways (SSPs) scenario. In this study, BioClim data suitable for domestic conditions was produced using 1 km resolution SSPs scenario detailed data produced by Rural Development Administration, and based on the data, a species distribution model was applied to mainly grow in southern, Gyeongsangbuk-do, Gangwon-do and humid regions. Appropriate habitat distributions were predicted every 30 years for the base years (1981 - 2010) and future years (2011 - 2100) of the Acer pictum subsp. mono. Acer pictum subsp. mono appearance data were collected from a total of 819 points through the national natural environment survey data. In order to improve the performance of the MaxEnt model, the parameters of the model (LQH-1.5) were optimized, and 7 detailed biolicm indices and 5 topographical indices were applied to the MaxEnt model. Drainage, Annual Precipitation (Bio12), and Slope significantly contributed to the distribution of Acer pictum subsp. mono in Korea. As a result of reflecting the growth characteristics that favor moist and fertile soil, the influence of climatic factors was not significant. Accordingly, in the base year, the suitable habitat for a high level of Acer pictum subsp. mono is 3.41% of the area of Korea, and in the near future (2011 - 2040) and far future (2071 - 2100), SSP1-2.6 accounts for 0.01% and 0.02%, gradually decreasing. However, in SSP5-8.5, it was 0.01% and 0.72%, respectively, showing a tendency to decrease in the near future compared to the base year, but to gradually increase toward the far future. This study confirms the future distribution of vegetation that is more easily adapted to climate change, and has significance as a basic study that can be used for future forest restoration of climate change-adapted species.

• Journal of the Korean Society of Radiology
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• v.17 no.4
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• pp.489-495
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• 2023

Radiation shielding facilities are constructed in locations where diagnostic radiation generators are installed, with the aim of preventing exposure for patients and radiation workers. The purpose of this study is seek to compare and validate the trend of attenuation thickness of lead, the primary material in these radiation shielding facilities, at different maximum tube voltages by Monte Carlo simulations and measurement. We employed the Monte Carlo N-Particle 6 simulation code. Within this simulation, we set a lead shielding arrangement, where the distance between the source and the lead sheet was set at 100 cm and the field of view was set at 10 × 10 cm². Additionally, we varied the tube voltages to encompass 80, 100, 120, and 140 kVp. We calculated energy spectra for each respective tube voltage and applied them in the simulations. Lead thicknesses corresponding to attenuation rates of 50, 70, 90, and 95% were determined for tube voltages of 80, 100, 120, and 140 kVp. For 80 kVp, the calculated thicknesses for these attenuation rates were 0.03, 0.08, 0.21, and 0.33 mm, respectively. For 100 kVp, the values were 0.05, 0.12, 0.30, and 0.50 mm. Similarly, for 120 kVp, they were 0.06, 0.14, 0.38, and 0.56 mm. Lastly, at 140 kVp, the corresponding thicknesses were 0.08, 0.16, 0.42, and 0.61 mm. Measurements were conducted to validate the calculated lead thicknesses. The radiation generator employed was the GE Healthcare Discovery XR 656, and the dosimeter used was the IBA MagicMax. The experimental results showed that at 80 kVp, the attenuation rates for different thicknesses were 43.56, 70.33, 89.85, and 93.05%, respectively. Similarly, at 100 kVp, the rates were 52.49, 72.26, 86.31, and 92.17%. For 120 kVp, the attenuation rates were 48.26, 71.18, 87.30, and 91.56%. Lastly, at 140 kVp, they were measured 50.45, 68.75, 89.95, and 91.65%. Upon comparing the simulation and experimental results, it was confirmed that the differences between the two values were within an average of approximately 3%. These research findings serve to validate the reliability of Monte Carlo simulations and could be employed as fundamental data for future radiation shielding facility construction.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.2
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• pp.103-119
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• 2023

Since the Korean government has decided to apply the policy of BIM (Building Information Modeling) to the entire construction industry, it has experienced a positive trend in adoption and utilization. BIM can reduce workloads by building model objects into libraries that conform to standards and enable consistent quality, data integrity, and compatibility. In the domestic architecture, civil engineering, and the overseas landscape architecture sectors, many BIM library standardization studies have been conducted, and guidelines have been established based on them. Currently, basic research and attempts to introduce BIM are being made in Korean landscape architecture field, but the diffusion has been delayed due to difficulties in application. This can be addressed by enhancing the efficiency of BIM work using standardized libraries. Therefore, this study aims to provide a starting point for discussions and present a classification system for objects and attribute information that can be referred to when creating landscape libraries in practice. The standardization of landscape BIM library was explored from two directions: object classification and attribute information items. First, the Korean construction information classification system, product inventory classification system, landscape design and construction standards, and BIM object classification of the NLA (Norwegian Association of Landscape Architects) were referred to classify landscape objects. As a result, the objects were divided into 12 subcategories, including 'trees', 'shrubs', 'ground cover and others', 'outdoor installation', 'outdoor lighting facility', 'stairs and ramp', 'outdoor wall', 'outdoor structure', 'pavement', 'curb', 'irrigation', and 'drainage' under five major categories: 'landscape plant', 'landscape facility', 'landscape structure', 'landscape pavement', and 'irrigation and drainage'. Next, the attribute information for the objects was extracted and structured. To do this, the common attribute information items of the KBIMS (Korean BIM Standard) were included, and the object attribute information items that vary according to the type of objects were included by referring to the PDT (Product Data Template) of the LI (UK Landscape Institute). As a result, the common attributes included information on 'identification', 'distribution', 'classification', and 'manufacture and supply' information, while the object attributes included information on 'naming', 'specifications', 'installation or construction', 'performance', 'sustainability', and 'operations and maintenance'. The significance of this study lies in establishing the foundation for the introduction of landscape BIM through the standardization of library objects, which will enhance the efficiency of modeling tasks and improve the data consistency of BIM models across various disciplines in the construction industry.