• Journal of Environmental Science International
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• v.22 no.11
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• pp.1509-1517
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• 2013

So far, most studies associated with soil carbon sequestration have been focused on long term aspect. However, information regarding soil carbon sequestration in short term aspect is limited. This study was conducted to determine changes of soil organic carbon content and stability of carbon in response to compost application rate and tillage management during rice growing season(150 days) in short term aspect. Under pot experiment condition, compost was mixed with an arable soil at rates corresponding to 0, 6, 12, and 24 Mg/ha. To determine effect of tillage on soil carbon sequestration, till and no-till treatments were set up in soils amended with application rate of 12 Mg/ha. Compost application and tillage management did not significantly affect soil organic carbon(SOC) content in soil at harvest time. Bulk density of soil was not changed significantly with compost application and tillage management. These might result from short duration of experiment. While hot water extractable organic carbon(HWEOC) content decreased with compost application, humic substances(HS) increased. Below ground biomass of rice increased with application of compost and till operation. From the above results, continuos application of compost and reduce tillage might improve increase in soil organic carbon content and stability of carbon in long term aspect.

• Journal of the Korea Safety Management & Science
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• v.10 no.2
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• pp.195-203
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• 2008

The purpose of this study is to develop a guideline of process capability evaluation and to apply this guideline improving the quality of products, especially in the small and medium enterprises. In this study we deal in the concept of process capability evaluation, the calculation of process capability index, and the application of a case study. Man must compare the state of process with the standards in evaluating of the process capability. Control chart can be used as a yardstick for judgement for the long term period and the distribution shape of histogram for the short term period. Man should regard to the significant figure by the calculation of process capability index.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1480-1491
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• 2015

In new deregulated electricity market, short-term price forecasting is key information for all market players. A better forecast of market-clearing price (MCP) helps market participants to strategically set up their bidding strategies for energy markets in the short-term. This paper presents a new prediction strategy to improve the need for more accurate short-term price forecasting tool at spot market using an artificial neural networks (ANNs). To build the forecasting ANN model, a three-layered feedforward neural network trained by the improved Levenberg-marquardt (LM) algorithm is used to forecast the locational marginal prices (LMPs). To accurately predict LMPs, actual power generation and load are considered as the input sets, and then the difference is used to predict price differences in the spot market. The proposed ANN model generalizes the relationship between the LMP in each area and the unconstrained MCP during the same period of time. The LMP calculation is iterated so that the capacity between the areas is maximized and the mechanism itself helps to relieve grid congestion. The addition of flow between the areas gives the LMPs a new equilibrium point, which is balanced when taking the transfer capacity into account, LMP forecasting is then possible. The proposed forecasting strategy is tested on the spot market of the Nord Pool. The validity, the efficiency, and effectiveness of the proposed approach are shown by comparing with time-series models

• Journal of The Korean Society of Agricultural Engineers
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• v.46 no.5
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• pp.41-52
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• 2004

In this study, parameters of long and short term runoff model were optimized using genetic algorithm as a basic research for integrated water management in a watershed. In case of Korea where drought and flood occurr frequently, the integrated water management is necessary to minimize possible damage of drought and flood. Modified TANK model was optimized as a long term runoff model and storage-function model was optimized as a short term runoff model. Besides distinguished parameters were applied to modified TANK model for supplementing defect that the model estimates less runoff in the storm period. As a result of application, simulated long and short term runoff results showed 7% and 5% improvement compared with before optimized on the average. In case of modified TANK model using distinguished parameters, the simulated runoff after optimized showed more interrelationship than before optimized. Therefore, modified TANK model can be applied for the long term water balance as an integrated water management in a watershed. In case of storage-function model, simulated runoff in the storm period showed high interrelationship with observed one. These optimized models can be applied for the runoff analysis of watershed.

• Journal of Korean Society on Water Environment
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• v.29 no.1
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• pp.19-28
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• 2013

Artificial neural networks were used for time series modelling of algal dynamics of whole year and by season at the Paldang dam station (confluence area). The modelling was based on comprehensive weekly water quality data from 1997 to 2004 at the Paldang dam station. The results of validation of seasonal models showed that the timing and magnitude of the observed chlorophyll a concentration was predicted better, compared with the ANN model for whole year. Internal weightings of the inputs in trained neural networks were obtained by sensitivity analysis for identification of the primary driving mechanisms in the system dynamics. pH, COD, TP determined most the dynamics of chlorophyll a, although these inputs were not the real driving variable for algal growth. Short-term prediction models that perform one or two weeks ahead predictions of chlorophyll a concentration were designed for the application of Harmful Algal Alert System in Lake Paldang. Short-term-ahead ANN models showed the possibilities of application of Harmful Algal Alert System after increasing ANN model's performance.

• International journal of advanced smart convergence
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• v.7 no.2
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• pp.73-85
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• 2018

The purpose of this study is to develop a short-term ICT training course that helps teachers from non-computing disciplines in developing countries acquire flipped-learning content creation skills. A field application is performed by applying the developed ICT training course to secondary school teachers of non-ICT subject specialisms in Laos. In the field study, participating teachers' teaching efficacy on ICT and satisfaction toward the training course are measured. The result of t-test on ICT teaching efficacy showed statistically significant increases in teachers' self-efficacy related to ICT use, both personal efficacy and outcome expectancy. The satisfaction survey performed after training showed that trainees were highly satisfied with the training course. The results of this field study could be used to propose a short-term teacher education model that could be applicable to teachers in other developing countries.

• Industrial Engineering and Management Systems
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• v.14 no.3
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• pp.318-324
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• 2015

This paper proposes a dynamic short-term load forecasting method that utilizes a new sequential learning algorithm based on Relevance Vector Machine (RVM). The method performs general optimization of weights and hyperparameters using the current relevance vectors and newly arriving data. By doing so, the proposed algorithm is trained with the most recent data. Consequently, it extends the RVM algorithm to real-time and nonstationary learning processes. The results of application of the proposed algorithm to prediction of electrical loads indicate that its accuracy is comparable to that of existing nonparametric learning algorithms. Further, the proposed model reduces computational complexity.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.409-410
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• 2022

This paper proposes a fusion model based on Long-Short Term Memory networks (LSTM) and CNN deep learning methods, and applied to multi-category news datasets, and achieved good results. Experiments show that the fusion model based on deep learning has greatly improved the precision and accuracy of text sentiment classification. This method will become an important way to optimize the model and improve the performance of the model.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.76-83
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• 2020

Postal logistics organizations are characterized as having high labor intensity and short response times. These characteristics, along with rapid change in mail volume, make load scheduling a fundamental concern. Load analysis of major postal infrastructures such as post offices, sorting centers, exchange centers, and delivery stations is required for optimal postal logistics operation. In particular, the performance of mail traffic forecasting is essential for optimizing the resource operation by accurate load analysis. This paper addresses a traffic forecast problem of postal parcel that arises at delivery stations of Korea Post. The main purpose of this paper is to describe a method for predicting short-term traffic of postal parcel based on self-similarity analysis and to introduce an application of the traffic prediction model to postal logistics system. The proposed scheme develops multiple regression models by the clusters resulted from feature engineering and individual models for delivery stations to reinforce prediction accuracy. The experiment with data supplied by main postal delivery stations shows the advantage in terms of prediction performance. Comparing with other technique, experimental results show that the proposed method improves the accuracy up to 45.8%.

• Journal of Information Processing Systems
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• v.18 no.6
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• pp.719-728
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• 2022

The prediction of short-term traffic flow is the theoretical basis of intelligent transportation as well as the key technology in traffic flow induction systems. The research on short-term traffic flow prediction has showed the considerable social value. At present, the support vector regression (SVR) intelligent prediction model that is suitable for small samples has been applied in this domain. Aiming at parameter selection difficulty and prediction accuracy improvement, the artificial bee colony (ABC) is adopted in optimizing SVR parameters, which is referred to as the ABC-SVR algorithm in the paper. The simulation experiments are carried out by comparing the ABC-SVR algorithm with SVR algorithm, and the feasibility of the proposed ABC-SVR algorithm is verified by result analysis. Continuously, the simulation experiments are carried out by comparing the ABC-SVR algorithm with particle swarm optimization SVR (PSO-SVR) algorithm and genetic optimization SVR (GA-SVR) algorithm, and a better optimization effect has been attained by simulation experiments and verified by statistical test. Simultaneously, the simulation experiments are carried out by comparing the ABC-SVR algorithm and wavelet neural network time series (WNN-TS) algorithm, and the prediction accuracy of the proposed ABC-SVR algorithm is improved and satisfactory prediction effects have been obtained.