• Korean Journal of Weed Science
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• v.18 no.2
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• pp.85-94
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• 1998

Herbicides play a very important role in modern agriculture. However, the herbicide applied to the agricultural field may accumulate in the field, converting the advantages to environment pollution. Many small animals in the ecosystem such as alderfly, earthworm, butterfly, loach, frog, firefly, some birds and aquatic organisms have been known to disappear gradually. In addition, several behavior of herbicides including adsorption by soil, movement by water, photodecomposition, volatilization to air, absorption by plant, metabolism by soil microorganisms and so on, are proceeded while the herbicide remained in the environment. In this review, fate and behavior of herbicides in the environment and their effect on ecosystem after their application are focused on four aspects : the first is the absorption and metabolism of herbicides by plant; the second is the residues of herbicides in soil and water environments: the third is the accumulation and release of herbicides in aquatic organisms and the fourth is the translocation of herbicides in model agricultural ecosystem. Many factors may affect the behavior and fate of herbicides after their application, climatic conditions and soil properties seem to be the most important. Therefore, the fate and behavior of herbicide in Taiwan, located on subtropical region, may differ from those in Korea.

• Journal of Environmental Impact Assessment
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• v.23 no.5
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• pp.337-352
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• 2014

The main purposes of this study are to find the causality among residents' loyalty to an environmental festival and its influential factors and, based upon the findings, to explore environmental festivals' developmental vision with special reference to Hampyung Butterfly Festival. Under these aims, this study applies structural equation modeling(SEM). The structural model for SEM analysis is composed of four independent variables which consist of residents' attachment(RA) to their region where its own environmental festival is provided, residents' participational intention(RP) to the festival, the economic effects(EE) of the festival, and the communication(CM) between the residents and the festival providers, one intermediate variable, residents' satisfaction(RS) from the festival, and one final dependent variable, residents' loyalty(RL) to the festival. The causality among these variables is hypothesized as follows; Among the independent variables, RP, EE, and CM have effects only on RS and RA has an effect on both RS and RL. And RS has an effect on RL. The facts found from the SEM are summed up as follows; First, (1) RP and CM turn out to have statistically significant effects on RS, (2) RA is confirmed to have a statistically very significant effect on both RS and RL, and (3) RS is also proved to show a statistically very significant effect on RL. Second, the total effects on RL of independent variables are stronger in the order of RA, CM, and RP. Third, EE seems to have no effect on RS, consequently no effect on RL, either. The reason why EE has no effect looks like to be due to environmental festivals' peculiar features. These findings offer the following suggestions for the future of environmental festivals in the part of festival providers. Firstly, to be successful in the festival, they have to provoke RL above all. Second, to do so, they need to encourage RA, CM, and RP in the mentioned order in the long run. Third, but for a short period, they had better concentrate upon promoting RS.

• Proceedings of The KACE
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• 2018.08a
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• pp.59-62
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• 2018

모순이라는 관점에서 문제를 창의적으로 해결하고자 한 나비 모형은 모순의 유형을 나누어 정의하고 유형별 문제 해결 목표와 추상적 해결 전략을 정의하여 논리적 접근을 가능하게 하였다. 본 연구에서는 모순 문제와 문제에 대한 시간 및 구성요소들의 특성을 이용하여 모순 유형을 결정하고 주어진 문제의 문제 해결 목표와 추상적 해결 전략, 나비 다이어그램을 제시하는 프로그램을 개발한다. 또한 모순 유형 중에서 추상적 해결 전략으로 두 가지 매개 모순을 모두 만족시켜야 하는 문제의 구체적 해결 전략을 개발하기 위하여 시간과 구성요소의 분할과 결합 전략에 대한 알고리즘을 설계한다. 본 연구는 나비 모형을 기반으로 모순 문제의 구체적인 해결 전략을 자동적으로 찾을 수 있도록 돕는다. 궁극적으로는 나비 모형을 기반으로 컴퓨터가 스스로 모순 문제를 해결할 수 있는 알고리즘을 개발한다.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.7
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• pp.191-198
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• 2021

Since the size of training dataset become large and the model is getting deeper to achieve high accuracy in deep learning, the deep neural network training requires a lot of computation and it takes too much time with a single node. Therefore, distributed deep learning is proposed to reduce the training time by distributing computation across multiple nodes. In this study, we propose hybrid allreduce strategy that considers the characteristics of each layer and communication and computational overlapping technique for synchronization of distributed deep learning. Since the convolution layer has fewer parameters than the fully-connected layer as well as it is located at the upper, only short overlapping time is allowed. Thus, butterfly allreduce is used to synchronize the convolution layer. On the other hand, fully-connecter layer is synchronized using ring all-reduce. The empirical experiment results on PyTorch with our proposed scheme shows that the proposed method reduced the training time by up to 33% compared to the baseline PyTorch.

• Computers and Concrete
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• v.32 no.6
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• pp.577-594
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• 2023

Engineered cementitious composites with calcined clay limestone cement (LC³-ECC) as a kind of green, low-carbon and high toughness concrete, has recently received significant investigation. However, the complicated relationship between potential influential factors and LC³-ECC compressive strength makes the prediction of LC³-ECC compressive strength difficult. Regarding this, the machine learning-based prediction models for the compressive strength of LC³-ECC concrete is firstly proposed and developed. Models combine three novel meta-heuristic algorithms (golden jackal optimization algorithm, butterfly optimization algorithm and whale optimization algorithm) with support vector regression (SVR) to improve the accuracy of prediction. A new dataset about LC³-ECC compressive strength was integrated based on 156 data from previous studies and used to develop the SVR-based models. Thirteen potential factors affecting the compressive strength of LC³-ECC were comprehensively considered in the model. The results show all hybrid SVR prediction models can reach the Coefficient of determination (R²) above 0.95 for the testing set and 0.97 for the training set. Radar and Taylor plots also show better overall prediction performance of the hybrid SVR models than several traditional machine learning techniques, which confirms the superiority of the three proposed methods. The successful development of this predictive model can provide scientific guidance for LC³-ECC materials and further apply to such low-carbon, sustainable cement-based materials.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.1
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• pp.184-192
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• 2022

Valve internal leakage is caused by damage to the internal parts of the valve, resulting in accidents and shutdowns of the piping system. This study investigated the possibility of a real-time leak detection method using the acoustic emission (AE) signal generated from the piping system during the internal leakage of a butterfly valve. Datasets of raw time-domain AE signals were collected and postprocessed for each operation mode of the valve in a systematic manner to develop a data-driven model for the detection and classification of internal leakage, by applying machine learning algorithms. The aim of this study was to determine whether it is possible to treat leak detection as a classification problem by applying two classification algorithms: support vector machine (SVM) and convolutional neural network (CNN). The results showed different performances for the algorithms and datasets used. The SVM-based binary classification models, based on feature extraction of data, achieved an overall accuracy of 83% to 90%, while in the case of a multiple classification model, the accuracy was reduced to 66%. By contrast, the CNN-based classification model achieved an accuracy of 99.85%, which is superior to those of any other models based on the SVM algorithm. The results revealed that the SVM classification model requires effective feature extraction of the AE signals to improve the accuracy of multi-class classification. Moreover, the CNN-based classification can be a promising approach to detect both leakage and valve opening as long as the performance of the processor does not degrade.

• Journal of Korean Society of Transportation
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• v.17 no.2
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• pp.115-126
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• 1999

Past nears many Studies have been described for present state and forecasted for the future phenomena in various areas. Many theories and methodologies in transportation have been developed and applied by researchers and planners. On the other hand, many theories and methodologies had disappeared caused by their critical limitations. One of this cause that was discovered of the Chaos in traffic flows. The occurrence of Chaos in traffic flows has affected to the traffic volume and decreased significancy of a simulation result of a specific traffic flow. According to this fact, long-term forecast of traffic flow is difficult, moreover a butterfly effect impedes development and establishment of transportation model. A methodology to solve Chaos character in traffic flow can be able to provide more effective transport planning. This study tackles to enhance and revise the existing theories for the traffic flow applying Chaos theory to estimating travel time.

• Journal of the Korean Institute of Landscape Architecture
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• v.39 no.4
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• pp.98-106
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• 2011

Establishing conservation programs to protect and maintain populations of endangered species are not only a global trend, but also a pursuit endorsed by the Korean Environmental Conservation Act. This study evaluates the feasibility of alternative habitat patches for the endangered butterfly Parnassius bremeri. A portion of habitat of P. bremeri is expected to be fragmented and damaged due to the scheduled construction of the Sangju-Yongduk Highway. A trans fer of the habitat patches of P. bremeri is also scheduled. In order to select an alternative habitat patch, the Mark-Release-Recapture (MRR) method was used to simulate a patch transfer model. The connectedness between habitat are as and the survival of local populations were evaluated for each candidate habitat. It was found that metapopulations with patch distances of <250m showed a 50% connectedness and survival rate in local populations. P. bremeri were expected to migrate at an average distance of 300m. In addition, P. bremeri formed a metapopulation that exhibited intimate patch dynamics that promoted persistence among these patches. Possible candidate habitats including those recommended by local governing bodies were evaluated along with habitats that may counter problems arising from the damage done to the original habitat and habitats that may have a compensatory value equal to that of the original habitat. Based on these criteria, Ansa-myeon township office was selected due to its high scores. This scoring was based on a consideration of a wide range of variables that mark a successful transfer of habitat. These include the amount of funding available, the governing bodies of the possible alternative habitat, and the Expected collaborative effort of local citizens. This decision was collaborated on by incorporating the expertise of various fields of study including biology, ecology, biogeography, ecological engineering, landscape architecture, and social sciences. Therefore, it is suggested that in order to evaluate an alternative habitat for organisms, many social issues as well as ecological issues must be considered.

• The Korean Journal of Malacology
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• v.32 no.3
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• pp.185-188
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• 2016

PANM-DB (version 1.0) was constructed as a web-based interface for the analysis and annotation of Next-Generation Sequencing (NGS) data of Mollusca, Arthropoda, and Nematoda. The database collected the sequences of Protostomes (Mollusca, Arthropoda, and Nematoda) from the NCBI Taxonomy Browser, and the same were compiled in a multi-FASTA format and stored using the formatdb program. This improved the processing of the RNA-seq sequences in terms of speed and hit percentage. PANM-DB has been successfully used for the transcriptome annotation of butterfly, land snail, and other commercial mollusca. We have improved the database by updating the same with new sequences and version 2.0 contains a total of 7,571,246 protein sequences (two times more as compared to version 1.0). Furthermore, the updated version contains the Cephalopoda database. The constructed web interface is available that independently analyses following these updates that is an improvement of the mollusks BLAST server. The updated version of PANM-DB will be helpful for the analysis of the NGS based sequencing data of non-model species, especially Mollusca, Arthropoda, Nematoda.