• Analyses & Alternatives
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• v.7 no.2
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• pp.35-77
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• 2023

This study aims to solve the entangled loop between demographic transition (DT) and economic growth by analyzing cross-country data. We undertake a national-level group analysis to verify the compressed transition of demographic variables over time. Assuming that the LA (latecomer advantage) on DT over time exists, we verify that the DT of the latecomer is compressed by providing a formal proof of LA on DT over income. As a DT has the double-kinked functions of income, we check them in multiple aspects: early maturation, leftward threshold, and steeper descent under a contour map and econometric methods. We find that the developing countries (the latecomer) have speedy DT (CDT, compressed DT) as well as speedy income such that DT of the latecomers starts at lower levels of income, lasts for a shorter period, and finishes at the earlier stage of economic development compared to that of developed countries (the early mover). To check the balance of DT, we classify countries into four groups of DT---balanced, slow, unilateral, and rapid transition countries. We identify that the main causes of rapid transition are due to the strong family planning programs of the government. Finally, we check the effect of latecomer's CDT on economic growth inversely: we undertake the simulation of the CDT effect on economic growth and the aging process for the latecomer. A worrying result is that the CDT of the latecomer shows a sharp upturn of the working-age population, followed by a sharp downturn in a short period. Compared to early-mover countries, the latecomer countries cannot buy more time to accommodate the workable population for the period of demographic bonus and prepare their aging societies for demographic onus. Thus, we conclude that CDT is not necessarily advantageous to developing countries. These outcomes of the latecomer's CDT can be re-interpreted as follows. Developing countries need power sources to pump up economic development, such as the following production factors: labor, physical and financial capital, and economic systems. As for labor, the properties of early maturation and leftward thresholds on DTs of the latecomer mean that demographic movement occurs at an unusually early stage of economic development; this is similar to a plane that leaks fuel before or just before take-off, with the result that it no longer flies higher or farther. What is worse, the property of steeper descent represents the falling speed of a plane so that it cannot be sustained at higher levels, and then plummets to all-time lows.

• Korean journal of applied entomology
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• v.51 no.4
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• pp.431-438
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• 2012

The developmental time of the nymphs of Myzus persicae was studied in the laboratory (six constant temperatures from 15 to $30^{\circ}C$ with 50~60% RH, and a photoperiod of 14L:10D) and in a green-pepper plastic house. Mortality of M. persicae in laboratory was high in the first(6.7~13.3%) and second instar nymphs(6.7%) at low temperatures and high in the third (17.8%) and fourth instar nymphs(17.8%) at high temperatures. Mortality was 66.7% at $33^{\circ}C$ in laboratory and $26.7^{\circ}C$ in plastic house. The total developmental time was the longest at $14.6^{\circ}C$ (14.4 days) and shortest at $26.7^{\circ}C$ (6.0 days) in plastic house. The lower threshold temperature of the total nymphal stage was $3.0^{\circ}C$ in laboratory. The thermal constant required for nymphal stage was 111.1DD. The relationship between developmental rate and temperature was fitted nonlinear model by Logan-6 which has the lowest value on Akaike information criterion (AIC) and Bayesian information criterion (BIC). The distribution of completion of each developmental stage was well described by the 3-parameter Weibull function ($r^2=0.95{\sim}0.97$). This model accurately described the predicted and observed occurrences. Thus the model is considered to be good for use in predicting the optimal spray time for Myzus persicae.

• Journal of Korea Water Resources Association
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• v.53 no.2
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• pp.107-119
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• 2020

This study analyzed past drought characteristics based on the observed rainfall data and performed a long-term outlook for future extreme droughts using Representative Concentration Pathways 8.5 (RCP 8.5) climate change scenarios. Standardized Precipitation Index (SPI) used duration of 1, 3, 6, 9 and 12 months, a meteorological drought index, was applied for quantitative drought analysis. A single long-term time series was constructed by combining daily rainfall observation data and RCP scenario. The constructed data was used as SPI input factors for each different duration. For the analysis of meteorological drought observed relatively long-term since 1954 in Korea, 12 rainfall stations were selected and applied 10 general circulation models (GCM) at the same point. In order to analyze drought characteristics according to climate change, trend analysis and clustering were performed. For non-stationary frequency analysis using sampling technique, we adopted the technique DEMC that combines Bayesian-based differential evolution ("DE") and Markov chain Monte Carlo ("MCMC"). A non-stationary drought frequency analysis was used to derive Severity-Duration-Frequency (SDF) curves for the 12 locations. A quantitative outlook for future droughts was carried out by deriving SDF curves with long-term hydrologic data assuming non-stationarity, and by quantitatively identifying potential drought risks. As a result of performing cluster analysis to identify the spatial characteristics, it was analyzed that there is a high risk of drought in the future in Jeonju, Gwangju, Yeosun, Mokpo, and Chupyeongryeong except Jeju corresponding to Zone 1-2, 2, and 3-2. They could be efficiently utilized in future drought management policies.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.175-197
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• 2021

Recently, with the development of deep learning technology, research on unstructured data analysis is being actively conducted, and it is showing remarkable results in various fields such as classification, summary, and generation. Among various text analysis fields, text classification is the most widely used technology in academia and industry. Text classification includes binary class classification with one label among two classes, multi-class classification with one label among several classes, and multi-label classification with multiple labels among several classes. In particular, multi-label classification requires a different training method from binary class classification and multi-class classification because of the characteristic of having multiple labels. In addition, since the number of labels to be predicted increases as the number of labels and classes increases, there is a limitation in that performance improvement is difficult due to an increase in prediction difficulty. To overcome these limitations, (i) compressing the initially given high-dimensional label space into a low-dimensional latent label space, (ii) after performing training to predict the compressed label, (iii) restoring the predicted label to the high-dimensional original label space, research on label embedding is being actively conducted. Typical label embedding techniques include Principal Label Space Transformation (PLST), Multi-Label Classification via Boolean Matrix Decomposition (MLC-BMaD), and Bayesian Multi-Label Compressed Sensing (BML-CS). However, since these techniques consider only the linear relationship between labels or compress the labels by random transformation, it is difficult to understand the non-linear relationship between labels, so there is a limitation in that it is not possible to create a latent label space sufficiently containing the information of the original label. Recently, there have been increasing attempts to improve performance by applying deep learning technology to label embedding. Label embedding using an autoencoder, a deep learning model that is effective for data compression and restoration, is representative. However, the traditional autoencoder-based label embedding has a limitation in that a large amount of information loss occurs when compressing a high-dimensional label space having a myriad of classes into a low-dimensional latent label space. This can be found in the gradient loss problem that occurs in the backpropagation process of learning. To solve this problem, skip connection was devised, and by adding the input of the layer to the output to prevent gradient loss during backpropagation, efficient learning is possible even when the layer is deep. Skip connection is mainly used for image feature extraction in convolutional neural networks, but studies using skip connection in autoencoder or label embedding process are still lacking. Therefore, in this study, we propose an autoencoder-based label embedding methodology in which skip connections are added to each of the encoder and decoder to form a low-dimensional latent label space that reflects the information of the high-dimensional label space well. In addition, the proposed methodology was applied to actual paper keywords to derive the high-dimensional keyword label space and the low-dimensional latent label space. Using this, we conducted an experiment to predict the compressed keyword vector existing in the latent label space from the paper abstract and to evaluate the multi-label classification by restoring the predicted keyword vector back to the original label space. As a result, the accuracy, precision, recall, and F1 score used as performance indicators showed far superior performance in multi-label classification based on the proposed methodology compared to traditional multi-label classification methods. This can be seen that the low-dimensional latent label space derived through the proposed methodology well reflected the information of the high-dimensional label space, which ultimately led to the improvement of the performance of the multi-label classification itself. In addition, the utility of the proposed methodology was identified by comparing the performance of the proposed methodology according to the domain characteristics and the number of dimensions of the latent label space.

• Korean Journal of Ecology and Environment
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• v.55 no.2
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• pp.99-110
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• 2022

We investigated physicochemical properties and isotopic compositions of organic matter (δ¹³C_TOC and δ ¹⁵N_TN) in the old fish farming (OFF) site after the cessation of aquaculture farming. Based on this approach, our objective is to determine the organic matter origin and their relative contributions preserved at sediments of fish farming. Temporal and spatial distribution of particulate and sinking organic matter(OFF sites: 2.0 to 3.3 mg L^-1 for particulate matter concentration, 18.8 to 246.6 g m^-2 day^-1 for sinking organic matter rate, control sites: 2.0 to 3.5 mg L^-1 for particulate matter concentration, 25.5 to 129.4 g m^-2 day^-1 for sinking organic matter rate) between both sites showed significant difference along seasonal precipitations. In contrast to variations of δ¹³C_TOC and δ¹⁵N_TN values at water columns, these isotopic compositions (OFF sites: -21.5‰ to -20.4‰ for δ¹³ C_TOC, 6.0‰ to 7.6‰ for δ¹⁵N_TN, control sites: -21.6‰ to -21.0‰ for δ¹³C_TOC, 6.6‰ to 8.0‰ for δ¹⁵N_TN) investigated at sediments have distinctive isotopic patterns(p<0.05) for seawater-derived nitrogen sources, indicating the increased input of aquaculture-derived sources (e.g., fish fecal). With respect to past fish farming activities, representative sources(e.g., fish fecal and algae) between both sites showed significant difference (p<0.05), confirming predominant contribution (55.9±4.6%) of fish fecal within OFF sites. Thus, our results may determine specific controlling factor for sustainable use of fish farming sites by estimating the discriminative contributions of organic matter between both sites.