• Resources Recycling
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• v.28 no.5
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• pp.60-67
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• 2019

A valuable metal recovery from waste resources such as spent rechargeable secondary batteries is of critical issues because of a sharp increase in the amount of waste resources. In this context, it is necessary to research not only recycling waste lithium-ion batteries (LIBs), but also reusing valuable metals (e.g., Li, Co, Ni, Mn etc.) recovered from waste LIBs. In particular, the lithium hydroxide ($LiOH{\cdot}xH_2O$), which is of precursors that can be prepared by the recovery of Li in waste LIBs, can be reused as a catalyst, a carbon dioxide absorbent, and again as a precursor for cathode materials of LIB. However, most studies of recycling the waste LIBs have been focused on the preparation of lithium carbonate with a recovery of Li. Herein, we show the preparation of high purity lithium hydroxide powder along with the precipitation process, and the systematic study to find an optimum condition is also carried out. The lithium carbonate, which is recovered from waste LIBs, was used as starting materials for synthesis of lithium hydroxide. The optimum precipitation conditions for the preparation of LiOH were found as follows: based on stirring, reaction temperature $90^{\circ}C$, reaction time 3 hr, precursor ratio 1:1. To synthesize uniform and high purity lithium hydroxide, 2-step precipitation process was additionally performed, and consequently, high purity $LiOH{\cdot}xH_2O$ powder was obtained.

• The Korean Journal of Archival Studies
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• no.35
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• pp.161-206
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• 2013

'Maeul' is a concept connoting a community. As a place where ordinary people's lives are planned and realized, Maeul is the foundation of their daily lives as well as a place where they work, rest and enjoy pastime activities. In Korea, however, most Maeul communities are dismantled while going though the modern period representing colonization and developmental dictatorship. Growth-oriented industrialization and urbanization turned into such adverse effects as individualization, a sense of loss and a sense of alienation. Recently, through innovations from below, Maeuls are restored, and through Maeul communities restored this way, every Maeul and many researchers carry out activities to build a healthy civil society. This study was conducted on such a background. For a healthy restoration of Maeul communities and a sustainable operation of those communities, it is necessary to establish archives where record the trace of Maeul members' daily lives and relations between those members. The archive of Maeul communities is a place that contains each Maeul's local characteristics as well as human relations as well. It is because this place can be space where Maeul members can record their history, communicate with each other and make a better future. The archive of Maeul communities can be made into various different models, which can be operated by reflecting the identity of a community such as main agents and characteristics, objectives and orientation of objects recorded. Rather than when Maeul communities exist as individuals, they can display more important functions and better effect when they form a network. Therefore, it is needed to provide various and creative methodologies different from the existing government-led record management. Not only on the form of archives, but also all over their functions, such as collection, arrangement, classification, evaluation, management and utilization, Maeul and Maeul residents' norms, orientation and realistic conditions should be thoroughly reflected. Starting from a chance to look back at individuals' lives, the archive of Maeul communities will be a new chapter to restore and build a healthy community in our society and overcome social contradictions from below. Moreover, the archive of Maeul communities has a great significance that it will broaden its prospect creatively with a new paradigm, not only mechanically turning the existing public sector-centered record management into a non-governmental sector.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.316-328
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• 2021

In this study, the leaf Na content prediction model for spring potato was established using 400-1000 nm hyperspectral sensor to develop the multispectral sensor for the salinity monitoring in reclaimed land. The irrigation conditions were standard, drought, and salinity (2, 4, 8 dS/m), and the irrigation amount was calculated based on the amount of evaporation. The leaves' Na contents were measured 1st and 2nd weeks after starting irrigation in the vegetative, tuber formative, and tuber growing periods, respectively. The reflectance of the leaves was converted from 5 nm to 10 nm, 25 nm, and 50 nm of FWHM (full width at half maximum) based on the 10 nm wavelength intervals. Using the variance importance in projections of partial least square regression(PLSR-VIP), ten band ratios were selected as the variables to predict salinity damage levels with Na content of spring potato leaves. The MLR(Multiple linear regression) models were estimated by removing the band ratios one by one in the order of the lowest weight among the ten band ratios. The performance of models was compared by not only R2, MAPE but also the number of band ratios, optimal FWHM to develop the compact multispectral sensor. It was an advantage to use 25 nm of FWHM to predict the amount of Na in leaves for spring potatoes during the 1st and 2nd weeks vegetative and tuber formative periods and 2 weeks tuber growing periods. The selected bandpass filters were 15 bands and mainly in red and red-edge regions such as 430/440, 490/500, 500/510, 550/560, 570/580, 590/600, 640/650, 650/660, 670/680, 680/690, 690/700, 700/710, 710/720, 720/730, 730/740 nm.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.218-233
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• 2022

The long-term (1986~2020) predictability of the number of days of heat and cold damages for each growth stage of soybean is evaluated using the daily maximum and minimum temperature (Tmax and Tmin) data produced by Pusan National University Coupled General Circulation Model (PNU CGCM)-Weather Research and Forecasting (WRF). The Predictability evaluation methods for the number of days of damages are Normalized Standard Deviations (NSD), Root Mean Square Error (RMSE), Hit Rate (HR), and Heidke Skill Score (HSS). First, we verified the simulation performance of the Tmax and Tmin, which are the variables that define the heat and cold damages of soybean. As a result, although there are some differences depending on the month starting with initial conditions from January (01RUN) to May (05RUN), the result after a systematic bias correction by the Variance Scaling method is similar to the observation compared to the bias-uncorrected one. The simulation performance for correction Tmax and Tmin from March to October is overall high in the results (ENS) averaged by applying the Simple Composite Method (SCM) from 01RUN to 05RUN. In addition, the model well simulates the regional patterns and characteristics of the number of days of heat and cold damages by according to the growth stages of soybean, compared with observations. In ENS, HR and HSS for heat damage (cold damage) of soybean have ranged from 0.45~0.75, 0.02~0.10 (0.49~0.76, -0.04~0.11) during each growth stage. In conclusion, 01RUN~05RUN and ENS of PNU CGCM-WRF Chain have the reasonable performance to predict heat and cold damages for each growth stage of soybean in South Korea.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.73-95
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• 2021

This study uses Node2vec graph embedding method and Light GBM link prediction to explore undeveloped export candidate countries in Korea's food and beverage industry. Node2vec is the method that improves the limit of the structural equivalence representation of the network, which is known to be relatively weak compared to the existing link prediction method based on the number of common neighbors of the network. Therefore, the method is known to show excellent performance in both community detection and structural equivalence of the network. The vector value obtained by embedding the network in this way operates under the condition of a constant length from an arbitrarily designated starting point node. Therefore, it has the advantage that it is easy to apply the sequence of nodes as an input value to the model for downstream tasks such as Logistic Regression, Support Vector Machine, and Random Forest. Based on these features of the Node2vec graph embedding method, this study applied the above method to the international trade information of the Korean food and beverage industry. Through this, we intend to contribute to creating the effect of extensive margin diversification in Korea in the global value chain relationship of the industry. The optimal predictive model derived from the results of this study recorded a precision of 0.95 and a recall of 0.79, and an F1 score of 0.86, showing excellent performance. This performance was shown to be superior to that of the binary classifier based on Logistic Regression set as the baseline model. In the baseline model, a precision of 0.95 and a recall of 0.73 were recorded, and an F1 score of 0.83 was recorded. In addition, the light GBM-based optimal prediction model derived from this study showed superior performance than the link prediction model of previous studies, which is set as a benchmarking model in this study. The predictive model of the previous study recorded only a recall rate of 0.75, but the proposed model of this study showed better performance which recall rate is 0.79. The difference in the performance of the prediction results between benchmarking model and this study model is due to the model learning strategy. In this study, groups were classified by the trade value scale, and prediction models were trained differently for these groups. Specific methods are (1) a method of randomly masking and learning a model for all trades without setting specific conditions for trade value, (2) arbitrarily masking a part of the trades with an average trade value or higher and using the model method, and (3) a method of arbitrarily masking some of the trades with the top 25% or higher trade value and learning the model. As a result of the experiment, it was confirmed that the performance of the model trained by randomly masking some of the trades with the above-average trade value in this method was the best and appeared stably. It was found that most of the results of potential export candidates for Korea derived through the above model appeared appropriate through additional investigation. Combining the above, this study could suggest the practical utility of the link prediction method applying Node2vec and Light GBM. In addition, useful implications could be derived for weight update strategies that can perform better link prediction while training the model. On the other hand, this study also has policy utility because it is applied to trade transactions that have not been performed much in the research related to link prediction based on graph embedding. The results of this study support a rapid response to changes in the global value chain such as the recent US-China trade conflict or Japan's export regulations, and I think that it has sufficient usefulness as a tool for policy decision-making.