• Proceedings of the Materials Research Society of Korea Conference
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• 2010.05a
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• pp.37.2-37.2
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• 2010

Recently $Li_{1.2}Ni_{0.2}Mn_{0.6}O_2$ has been consistently examined and investigated by scientists because of its high lithium storage capacity, which exceeds beyond the conventional theoretical capacity based on conventional chemical concepts. Consequently, $Li_{1.2}Ni_{0.2}Mn_{0.6}O_2$ is considered as one of the most promising cathode candidates for next generation in Li rechargeable batteries. Yet the mechanism and the origin of the overcapacity have not been clarified. Previously, many authors have demonstrated simultaneous oxygen evolution during the first delithiation. However, it may only explain the high capacity of the first charge process, and not of the subsequent cycles. In this work, we report a clarified interpretation of the structural evolution of $Li_{1.2}Ni_{0.2}Mn_{0.6}O_2$, which is the key element in understanding its anomalously high capacity. We identify how the structural evolution of $Li_{1.2}Ni_{0.2}Mn_{0.6}O_2$ occurs upon the electrochemical cycling through careful study of electrochemical profiles, ex-situ X-ray diffraction (XRD), HR-TEM, Raman spectroscopy, and first principles calculation. Moreover, we successfully separated the structural change at subsequent cycles (mainly cation rearrangement) from the first charge process (mainly oxygen evolution with Li extraction) by intentionally synthesizing sample with large particle size. Consequently, the intermediate states of structural evolution could be well resolved. All observations made through various tools lead to the result that spinel-like cation arrangement and lithium environment are created and embedded in layered framework during repeated electrochemical cycling.

• Journal of Powder Materials
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• v.21 no.4
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• pp.286-293
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• 2014

The electrochemical properties of cells assembled with the $LiNiO_2$ (LNO) recycled from cathode materials of waste lithium secondary batteries ($Li[Ni,Co,Mn]O_2$), were evaluated in this study. The leaching, neutralization and solvent extraction process were applied to produce high-purity $NiSO_4$ solution from waste lithium secondary batteries. High-purity NiO powder was then fabricated by the heat-treatment and mixing of the $NiSO_4$ solution and $H_2C_2O_4$. Finally, $LiNiO_2$ as a cathode material for lithium ion secondary batteries was synthesized by heat treatment and mixing of the NiO and $Li_2CO_3$ powders. We assembled the cells using the $LiNiO_2$ powders and evaluated the electrochemical properties. Subsequently, we evaluated the recycling possibility of the cathode materials for waste lithium secondary battery using the processes applied in this work.

• Journal of Korean Society for Geospatial Information Science
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• v.14 no.4 s.38
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• pp.19-26
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• 2006

This study assessed the accuracies of the ground information extracting methods from the LiDAR data. Especially, it compared two kinds of method, one of them is using directly the raw LiDAR data which is point type vector data and the other is using changed data to DSM type as the normal grid type. The methods using Local Maxima and Entropy methods are applied as a former case, and for the other case, this study applies the method using edge detection with filtering and the generated reference surface by the mean filtering. Then, the accuracy assessment are performed with these results, DEM constructed manually and the error permitted limit in scale of digital map. As a results, each DEM mean errors of methods using edge detection with filtering, reference surface, Local Maxima and Entropy are 0.27m, 2.43m, 0.13m and 0.10m respectively. Hence, the method using entropy presented the highest accuracy. And an accuracy from a method directly using the raw LiDAR data has higher accuracy than the method using changed data to DSM type relatively.

• Journal of Korean Society for Geospatial Information Science
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• v.13 no.1 s.31
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• pp.19-25
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• 2005

Nowadays, it is possible to construct the DEMs of urban area effectively and economically by LiDAR system. But the data from LiDAR system has form of DSM which is included various objects as trees and buildings. So the preprocess is necessary to extract the DEMs from LiDAR DSMs for particular purpose as effects analysis of man-made objects for flood prediction. As this study is for extracting DEM from LiDAR DSM of urban area, we detected the edges of various objects using edge detecting algorithm of image process. And, we tried mean value filtering, median value filtering and minimum value filtering or detected edges instead of interpolation method which is used in the previous study and could be modified the source data. it could minimize the modification of source data, and the extracting process of DEMs from DSMs could be simplified and automated.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2005.11a
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• pp.81-104
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• 2005

Accurate 3D models in urban areas are essential for a variety of applications, such as virtual visualization, CIS, and mobile communications. LiDAR(Light Detection and Ranging) is a relatively new technology for directly obtaining 3D points. Because Manual 3D data reconstruction from LiDAR data is very costly and time consuming, many researchs is focused on the automatic extraction of the useful data. In this paper, we classified ground and non-ground points data from LiDAR data by using filtering, and we reconstructed the DTM(Digital Terrain Model) using ground points data, buildings using nonground points data. After the reconstruction, we assessed the accuracy of the DTM and buildings. As a result of, DTM from LiDAR data were 0.16m and 0.59m in high raised apartments areas and low house areas respectively, and buildings were matched with the accuracy of a l/5,000 digital map.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.771-791
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• 2022

Protein-protein interaction (PPI) extraction from original text is important for revealing the molecular mechanism of biological processes. With the rapid growth of biomedical literature, manually extracting PPI has become more time-consuming and laborious. Therefore, the automatic PPI extraction from the raw literature through natural language processing technology has attracted the attention of the majority of researchers. We propose a PPI extraction model based on the large pre-trained language model and adversarial training. It enhances the learning of semantic and syntactic features using BioBERT pre-trained weights, which are built on large-scale domain corpora, and adversarial perturbations are applied to the embedding layer to improve the robustness of the model. Experimental results showed that the proposed model achieved the highest F1 scores (83.93% and 90.31%) on two corpora with large sample sizes, namely, AIMed and BioInfer, respectively, compared with the previous method. It also achieved comparable performance on three corpora with small sample sizes, namely, HPRD50, IEPA, and LLL.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.11
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• pp.4573-4584
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• 2015

The traditional feature extraction algorithms for recognition of communication signals can hardly realize the balance between computational complexity and signals' interclass gathered degrees. They can hardly achieve high recognition rate at low SNR conditions. To solve this problem, a novel feature extraction algorithm based on Holder coefficient was proposed, which has the advantages of low computational complexity and good interclass gathered degree even at low SNR conditions. In this research, the selection methods of parameters and distribution properties of the extracted features regarding Holder coefficient theory were firstly explored, and then interval gray relation algorithm with improved adaptive weight was adopted to verify the effectiveness of the extracted features. Compared with traditional algorithms, the proposed algorithm can more accurately recognize signals at low SNR conditions. Simulation results show that Holder coefficient based features are stable and have good interclass gathered degree, and interval gray relation classifier with adaptive weight can achieve the recognition rate up to 87% even at the SNR of -5dB.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.525-527
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• 2003

Recently, the spatial resolution of earth observation satellites is significantly increased to a few meters. Such high spatial resolution images definitely will provide lots of information for detail-thirsty remote sensing users. However, it is more difficult to develop automated image algorithms for automated image feature extraction and pattern recognition. In this study, we propose a two-stage procedure to extract road information from high resolution satellite images. At first stage, a watershed segmentation technique is developed to classify the image into various regions. Then, a knowledge is built for road and used to extract the road regions. In this study, we use panchromatic and multi-spectral images of the IKONOS satellite as test dataset. The experiment result shows that the proposed technique can generate suitable and meaningful road objects from high spatial resolution satellite images. Apparently, misclassified regions such as parking lots are recognized as road needed further refinement in future research.

• Proceedings of the Korean Institute of Resources Recycling Conference
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• 1993.03a
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• pp.19.1-19
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• 1993

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2017.10a
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• pp.339-340
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• 2017