• Journal of Sensor Science and Technology
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• v.33 no.1
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• pp.34-39
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• 2024

Energy harvesting technology that converts the wasted energy resources into electrical energy is emerging as a semipermanent power source for self-powered electronics and wireless low-power sensor systems. Among the various energy conversion techniques, flexible piezoelectric energy harvesters (f-PEHs), using materials with piezoelectric effects, have attracted significant interest because they can harvest a small mechanical energy into electrical signals without constraints of time and space in various environments. In this study, we used a flexible piezoelectric composite film fabricated by dispersing BaHf_xTi_(1-x)O₃ (x = 0, 0.01, 0.05, 0.1) piezoelectric powders inside a polymeric matrix to facilitate f-PEHs. The fabricated f-PEH with optimal Hf contents (x = 0.05) generated a maximum output voltage of 0.95 V and current signal of 130 nA with stable electrical/mechanical disabilities under periodically bending deformations. In addition, we demonstrated a cantilever-type f-PEH and investigated its potential as a sensor by characterizing the output performance under mechanical vibrations at various frequencies. This study provides the breakthrough for realizing self-powered energy harvesting and sensing systems by adopting the lead-free piezoelectric composites under vibrational environments.

• Journal of IKEEE
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• v.27 no.1
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• pp.116-121
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• 2023

In this paper, we develop a deep learning structure for a complex microbial incubator that applies deep learning prediction result information. The proposed complex microbial incubator consists of pre-processing of complex microbial data, conversion of complex microbial data structure, design of deep learning network, learning of the designed deep learning network, and GUI development applied to the prototype. In the complex microbial data preprocessing, one-hot encoding is performed on the amount of molasses, nutrients, plant extract, salt, etc. required for microbial culture, and the maximum-minimum normalization method for the pH concentration measured as a result of the culture and the number of microbial cells to preprocess the data. In the complex microbial data structure conversion, the preprocessed data is converted into a graph structure by connecting the water temperature and the number of microbial cells, and then expressed as an adjacency matrix and attribute information to be used as input data for a deep learning network. In deep learning network design, complex microbial data is learned by designing a graph convolutional network specialized for graph structures. The designed deep learning network uses a cosine loss function to proceed with learning in the direction of minimizing the error that occurs during learning. GUI development applied to the prototype shows the target pH concentration (3.8 or less) and the number of cells (10⁸ or more) of complex microorganisms in an order suitable for culturing according to the water temperature selected by the user. In order to evaluate the performance of the proposed microbial incubator, the results of experiments conducted by authorized testing institutes showed that the average pH was 3.7 and the number of cells of complex microorganisms was 1.7 × 10⁸. Therefore, the effectiveness of the deep learning structure for the complex microbial incubator applying the deep learning prediction result information proposed in this paper was proven.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.5 no.1
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• pp.39-52
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• 2007

Quantitative analysis on release behavior of the $^{85}Kr\;and\;^{14}C$ fission gases from the spent fuel material during the voloxidation and OREOX process has been performed. This thermal treatment step in a remote fabrication process to fabricate the dry-processed fuel from spent fuel has been used to obtain a fine powder The fractional release percent of fission gases from spent fuel materials with burn-up ranges from 27,000 MWd/tU to 65,000 MWd/tU have been evaluated by comparing the measured data with these initial inventories calculated by ORIGEN code. The release characteristics of $^{85}Kr\;and\;^{14}C$ fission gases during the voloxidation process at $500^{\circ}C$ seem to be closely linked to the degree of conversion efficiency of $UO_2\;to\;U_3O_8$ powder, and it is thus interpreted that the release from grain-boundary would be dominated during this step. The high release fraction of the fission gas from an oxidized powder during the OREOX process would be due to increase both in the gas diffusion at a temperature of $500^{\circ}C$ in a reduction step and in U atom mobility by the reduction. Therefore, it is believed that the fission gases release inventories in the OREOX step come from the inter-grain and inter-grain on $UO_2$ matrix. It is shown that the release fraction of $^{85}Kr\;and\;^{14}C$ fission gases during the voloxidation step would be increased as fuel burn-up increases, ranging from 6 to 12%, and a residual fission gas would completely be removed during the OREOX step. It seems that more effective treatment conditions for a removal of volatile fission gas are of powder formation by the oxidation in advance than the reduction of spent fuel at the higher temperature.

• Korean Journal of Ecology and Environment
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• v.47 no.spc
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• pp.48-56
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• 2014

Weekly changes of water environments and phytoplankton community with the salinity gradients were investigated at Gyoungpo Lake from April to November in 1998 and 2012. Underwater crossam in Gyoungpo Lake was removed in 2004. Thereafter, average salinity of Gyoungpo lake increased from 7.5 ppt in 1998 to 20 ppt in 2012. A total of 99 and 80 species of phytoplankton was observed from the sampled in 1998 and 2012, respectively. The number of common species during the 2 separate years was 40. Transparency, SS, $NO_3-N$ concentration and N/P ratio in 2012 were lower than those in 1998. During the period of water shortage (April, May) of 2012 transparency decreased due to decreased salinity and increased SS and Chl. a. Correlation coefficients between species and community scores of DCA ordination based on data matrix of the phytoplankton revealed larger variation among sampling seasons in 1998 than in 2012. The increase of seawater influx and conversion rates following the removal of the underwater crossbeam might explain such a differential variation. Gymnodium sp., Peridinium sp., Prorocentrum sp., Nitzschia longissima, Schroederia setigera, Lyngbya sp., Asterococcus limneticus, Asterococcus superbus and Cyclotella meneghiniana were found to well adapt at the high salinities in 2012. Comparatively, Asterrionella formosa, Nitzschia frustulum, Chlorella ellipsoidea, Scenedesmus bijuga and Scenedesmus ellipsoideus were observed at lower salinities in 1998. Two quite contrasting phytoplankton communities were found in the two seasons of a year, spring with limited precipitation and summer, the flood season.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.258-266
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• 2023

This study analyzed methodologies for estimating carbon stocks of perennial woody crops and the research cases in overseas countries. As a result, we found that Australia, Bulgaria, Canada, and Japan are using the stock-difference method, while Austria, Denmark, and Germany are estimating the change in the carbon stock based on the gain-loss method. In some overseas countries, the researches were conducted on estimating the carbon stock change using image data as tier 3 phase beyond the research developing country-specific factors as tier 2 phase. In South Korea, convergence studies as the third stage were conducted in forestry field, but advanced research in the agricultural field is at the beginning stage. Based on these results, we suggest directions for the following four future researches: 1) securing national-specific factors related to emissions and removals in the agricultural field through the development of allometric equation and carbon conversion factors for perennial woody crops to improve the completeness of emission and removals statistics, 2) implementing policy studies on the cultivation area calculation refinement with fruit tree-biomass-based maturity, 3) developing a more advanced estimation technique for perennial woody crops in the agricultural sector using allometric equation and remote sensing techniques based on the agricultural and forestry satellite scheduled to be launched in 2025, and to establish a matrix and monitoring system for perennial woody crop cultivation areas in the agricultural sector, Lastly, 4) estimating soil carbon stocks change, which is currently estimated by treating all agricultural areas as one, by sub-land classification to implement a dynamic carbon cycle model. This study suggests a detailed guideline and advanced methods of carbon stock change calculation for perennial woody crops, which supports 2050 Carbon Neutral Strategy of Ministry of Agriculture, Food, and Rural Affairs and activate related research in agricultural sector.