• Resources Recycling
• /
• v.24 no.3
• /
• pp.44-50
• /
• 2015

Recycling technologies would be required in consideration of increasing demand in lithium ion batteries (LIBs). In this study, the leaching behavior of Ni, Co and Mn is investigated with ammoniacal medium for spent cathode active materials, which are separated from a commercial LIB pack in hybrid electric vehicles. The leaching behavior of each metal is analyzed in the presence of reducing agent and pH buffering agent. The existence of reducing agent is necessary to increase the leaching efficiency of Ni and Co. The leaching of Mn is insignificant even with the existence of reducing agent in contrast to Ni and Co. The most conspicuous difference between acid and ammoniacal leaching would be the selective leaching behavior between Ni/Co and Mn. The ammoniacal leaching can reduce the cost of basic reagent that makes the pH of leachate higher for the precipitation of leached metals in the acid leaching.

• Journal of the Korean Geographical Society
• /
• v.46 no.1
• /
• pp.67-80
• /
• 2011

Choropleth mapping of population distribution is based on the assumption that people are uniformly distributed throughout each enumeration unit. Dasymetric mapping technique improves choropleth mapping by refining spatially aggregated data with residential information. Further, pycnophylactic interpolation can upgrade dasymetric mapping by considering population distribution of neighboring areas, while preserving the volumes of original units. This study proposed a combined solution of dasymetric mapping and pycnophylactic interpolation to improve the accuracy of population density distribution. Specifically, the dasymetric method accounts for the spatial distribution of population within each census unit, while pycnophylactic interpolation considers population distribution of neighboring area. This technique is demonstrated with 1990 census data of the Athens, GA. with land use land cover information derived from remotely-sensed imagery for the areal extent of populated areas. The results are evaluated by comparison between original population counts of smaller census units (census block groups) and population counts of the grid map built from larger units (census tracts) aggregated to the same areal units. The estimated populations indicate a satisfactory level of accuracy. Population distribution acquired by the suggested method can be re-aggregated to any type of geographic boundaries such as electoral boundaries, school districts, and even watershed for a variety of applications.

• Proceedings of the Korean Society of Crop Science Conference
• /
• 2017.06a
• /
• pp.172-172
• /
• 2017

Pollen germination and its' viability in bellflower hybrid system are of great importance. The present study was conducted to investigate the several factors underlying the pollen germination of Platycodon grandiflorum and obtain the basic data for effective artificial pollination for the production of sound specifies. The pollens of Platycodon grandiflorum started germination from one hour after planting, and the germination was actively progressed as time dependent manner. For lighting conditions, the germination of pollens under the light was faster by more than twice than that without the light. Furthermore, the germination was better in the high temperature rather than in the low temperature. The germination rate was higher in the $30^{\circ}C$. For the carbon source, the germination rate was better at the concentration of 15% regardless of the kinds. In particular, the highest value was observed with glucose. The germination rate was decreased substantially as the increasing with the higher pH. The dynamic germination of pollens was observed at the pH 5. With respect to the growth regulator, the higher concentration of NAA induced the higher the germination rate. $GA_3$ showed a good germination rate in $0.05mg{\cdot}L^{-1}$. Meanwhile, for kinetin, lower concentration increased the germination rate, unlike NAA. The higher concentrations of boric acid degraded the germination rate, and the addition of boric acid of $10mg{\cdot}L^{-1}$ demonstrated higher germination rate than the addition of other growth regulators. Notably, the addition of asparaginic acid exhibited the similar results in all test sectors regardless of concentration, whereas a little higher result was observed in the high concentration sector. Taken together, the results concluded that the boric acid was considered as one of the essential minerals that played an important role on the germination of pollens.

• Polymer(Korea)
• /
• v.35 no.4
• /
• pp.289-295
• /
• 2011

Biomaterials for retinal tissue engineering must demonstrate several critical features for potential utility, including mechanical integrity, biocompatibility, and slow biodegradation. Silk film biomaterials were designed and characterized to meet these functional requirements. We prepared natural/synthetic hybrid silk/PLGA films using 0, 10, 20, 40, and 80 wt% of silk by a solvent evaporation method. MIT assay was used to confirm the number of cells attached on film at 1, 2, and 3 days, respectively. The morphology of cellular adhesion on films was also confirmed by scanning electron microscope (SEM). RT-PCR was conducted to confrrm mRNA expression of retinal pigment epithelitun (RPE) using RPE65 as a RPEs marker and the expression of cytokeratin were determined by immunofluorescence staining. We confirmed that the silk/PLGA film of 20~40 wt% silk was superior for the adhesion and proliferation of RPEs.

• Journal of Intelligence and Information Systems
• /
• v.16 no.4
• /
• pp.99-112
• /
• 2010

Ensemble learning is a method for improving the performance of classification and prediction algorithms. It is a method for finding a highly accurateclassifier on the training set by constructing and combining an ensemble of weak classifiers, each of which needs only to be moderately accurate on the training set. Ensemble learning has received considerable attention from machine learning and artificial intelligence fields because of its remarkable performance improvement and flexible integration with the traditional learning algorithms such as decision tree (DT), neural networks (NN), and SVM, etc. In those researches, all of DT ensemble studies have demonstrated impressive improvements in the generalization behavior of DT, while NN and SVM ensemble studies have not shown remarkable performance as shown in DT ensembles. Recently, several works have reported that the performance of ensemble can be degraded where multiple classifiers of an ensemble are highly correlated with, and thereby result in multicollinearity problem, which leads to performance degradation of the ensemble. They have also proposed the differentiated learning strategies to cope with performance degradation problem. Hansen and Salamon (1990) insisted that it is necessary and sufficient for the performance enhancement of an ensemble that the ensemble should contain diverse classifiers. Breiman (1996) explored that ensemble learning can increase the performance of unstable learning algorithms, but does not show remarkable performance improvement on stable learning algorithms. Unstable learning algorithms such as decision tree learners are sensitive to the change of the training data, and thus small changes in the training data can yield large changes in the generated classifiers. Therefore, ensemble with unstable learning algorithms can guarantee some diversity among the classifiers. To the contrary, stable learning algorithms such as NN and SVM generate similar classifiers in spite of small changes of the training data, and thus the correlation among the resulting classifiers is very high. This high correlation results in multicollinearity problem, which leads to performance degradation of the ensemble. Kim,s work (2009) showedthe performance comparison in bankruptcy prediction on Korea firms using tradition prediction algorithms such as NN, DT, and SVM. It reports that stable learning algorithms such as NN and SVM have higher predictability than the unstable DT. Meanwhile, with respect to their ensemble learning, DT ensemble shows the more improved performance than NN and SVM ensemble. Further analysis with variance inflation factor (VIF) analysis empirically proves that performance degradation of ensemble is due to multicollinearity problem. It also proposes that optimization of ensemble is needed to cope with such a problem. This paper proposes a hybrid system for coverage optimization of NN ensemble (CO-NN) in order to improve the performance of NN ensemble. Coverage optimization is a technique of choosing a sub-ensemble from an original ensemble to guarantee the diversity of classifiers in coverage optimization process. CO-NN uses GA which has been widely used for various optimization problems to deal with the coverage optimization problem. The GA chromosomes for the coverage optimization are encoded into binary strings, each bit of which indicates individual classifier. The fitness function is defined as maximization of error reduction and a constraint of variance inflation factor (VIF), which is one of the generally used methods to measure multicollinearity, is added to insure the diversity of classifiers by removing high correlation among the classifiers. We use Microsoft Excel and the GAs software package called Evolver. Experiments on company failure prediction have shown that CO-NN is effectively applied in the stable performance enhancement of NNensembles through the choice of classifiers by considering the correlations of the ensemble. The classifiers which have the potential multicollinearity problem are removed by the coverage optimization process of CO-NN and thereby CO-NN has shown higher performance than a single NN classifier and NN ensemble at 1% significance level, and DT ensemble at 5% significance level. However, there remain further research issues. First, decision optimization process to find optimal combination function should be considered in further research. Secondly, various learning strategies to deal with data noise should be introduced in more advanced further researches in the future.

• Journal of Korean Society of Transportation
• /
• v.22 no.3 s.74
• /
• pp.145-157
• /
• 2004

The cycle length design model of the Korean traffic responsive signal control systems is devised to vary a cycle length as a response to changes in traffic demand in real time by utilizing parameters specified by a system operator and such field information as degrees of saturation of through phases. Since no explicit guideline is provided to a system operator, the system tends to include ambiguity in terms of the system optimization. In addition, the cycle lengths produced by the existing model have yet been verified if they are comparable to the ones minimizing delay. This paper presents the studies conducted (1) to find shortcomings embedded in the existing model by comparing the cycle lengths produced by the model against the ones minimizing delay and (2) to propose a new direction to design a cycle length minimizing delay and excluding such operator oriented parameters. It was found from the study that the cycle lengths from the existing model fail to minimize delay and promote intersection operational conditions to be unsatisfied when traffic volume is low, due to the feature of the changed target operational volume-to-capacity ratio embedded in the model. The 64 different neural network based cycle length design models were developed based on simulation data surrogating field data. The CORSIM optimal cycle lengths minimizing delay were found through the COST software developed for the study. COST searches for the CORSIM optimal cycle length minimizing delay with a heuristic searching method, a hybrid genetic algorithm. Among 64 models, the best one producing cycle lengths close enough to the optimal was selected through statistical tests. It was found from the verification test that the best model designs a cycle length as similar pattern to the ones minimizing delay. The cycle lengths from the proposed model are comparable to the ones from TRANSYT-7F.