• Environmental and Resource Economics Review
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• v.19 no.4
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• pp.771-804
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• 2010

This paper estimates optimal production of fish stock using discrete time bio-economic model to make zero profits or to maximize economic profits with maintaining sustainable resource levels under an open access and a sole owner. Particularly, this study generates optimal yields and efforts of large purse seine fisheries which catch mackerel and jack mackerel by using the logistic growth function, Cobb-Douglas production function, fisheries cost and profit functions. As a result, optimal yields of mackerel and jack mackerel under ecological equilibrium of a sole owner were approximately 172,512 tons and 16,937 tons respectively. Also, optimal fishing efforts of mackerel and jack mackerel under the same situation were about 8,508 hauls and 4,915 hauls respectively. In conclusion, the paper suggests that the large purse seine should reduce fishing efforts and increase fish stock to generate higher net present value in optimally managed fishery than that of the present large purse seine.

• Ocean and Polar Research
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• v.37 no.2
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• pp.161-177
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• 2015

This study analyzes the stock assessment of yellow croaker caught mainly by the Korean stow net and gill net fisheries focusing on single species and multiple fisheries. This study standardizes fishing efforts for the two fisheries using the general linear model and uses a surplus production model based on the exponential growth model. The Clarke Yoshimoto Pooley model estimates a maximum sustainable yield(MSY), an allowable biological catch(ABC), fishing efforts for MSY($E_{MSY}$) and for ABC($E_{ABC}$). The bio-economic model is used to estimate the maximum economic yield(MEY) and fishing efforts for MEY($E_{MSY}$). Also, the study employs an economic analysis to estimate the economic interaction between stow net and gill net fisheries. The economic analysis shows the profit accruing to the two fisheries from estimated ABC. Finally, the study compares TACs based on single species and single fishery to TAC based on single species and multiple fisheries. The study proposes that the TAC assessment is necessary for single species and multiple fisheries in order to preserve resources.

• Journal of applied mathematics & informatics
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• v.27 no.5_6
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• pp.1411-1427
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• 2009

We propose a model based on Lotka-Volterra dynamics with two competing spices which are affected not only by harvesting but also by the presence of a predator, the third species. Hyperbolic and linear response functions are considered. We derive the conditions for global stability of the system using Lyapunov function. The optimal harvest policy is studied and the solution is derived in the interior equilibrium case using Pontryagin's maximal principle. Finally, some numerical examples are discussed. The nature of variations in the two prey species and one predator species is studied extensively through graphical illustrations.

• Journal of Energy Engineering
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• v.22 no.2
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• pp.184-196
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• 2013

We need to develop new renewable energy that could fundamentally replace fossil fuel, since the volume of economy and industry of our time becomes uncontrollably enormous. One of the alternative is to develop energy based on marine biomass, which would meet environment and energy needs at the same time. The marine bio-energy productions is supposed to occupy 50% to 500 million TOE in bio-energy production that is based on the Korean 3rd new renewable energy technology development, utilization, supply plan until 2030. This study attempts to apply input-output analysis to investigating the economic impacts of marine bio-energy development project in the Korean national economy. More specifically, this study shows what national economy effect of production-inducing effect, value-added inducing effect, employment-inducing effect, and R&D-inducing effect are explored with demand-driven model. Furthermore, this study attempts to define and classify the marine bio-energy development project sector from I-O table. Also, this study pays particular attention to marine bio-energy development project by taking the industry as exogenous specification and then investigating its economic impacts. The Marine bio-energy development project case 223 billion won, production-inducing effect, value-added inducing effect, and employment-inducing effect are 312 billion won, 87 billion won, 1,151 persons, and 5 billion won respectively. These quantitative information can be usefully utilized in the policy-making for the industrialization of marine bio-energy development project.

• Korean Journal of Environmental Biology
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• v.40 no.2
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• pp.214-223
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• 2022

Among migratory insect pests, Mythimna seperata and Cnaphalocrocis medinalis are invasive pests introduced into South Korea through westerlies from southern China. M. seperata and C. medinalis are insect pests that use rice as a host. They injure rice leaves and inhibit rice growth. To understand the distribution of M. seperata and C. medinalis, it is important to understand environmental factors such as temperature and humidity of their habitat. This study predicted current and future habitat suitability models for understanding the distribution of M. seperata and C. medinalis. Occurrence data, SSPs (Shared Socio-economic Pathways) scenario, and RCP (Representative Concentration Pathway) were applied to MaxEnt (Maximum Entropy), a machine learning model among SDM (Species Distribution Model). As a result, M. seperata and C. medinalis are aggregated on the west and south coasts where they have a host after migration from China. As a result of MaxEnt analysis, the contribution was high in the order of Land-cover data and DEM (Digital Elevation Model). In bioclimatic variables, BIO_4 (Temperature seasonality) was high in M. seperata and BIO_2 (Mean Diurnal Range) was found in C. medinalis. The habitat suitability model predicted that M. seperata and C. medinalis could inhabit most rice paddies.

• Journal of the Korea Society of Computer and Information
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• v.22 no.11
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• pp.65-71
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• 2017

An integrated water environment management system is necessary in improving water quality, properly allocating water resources, and supporting socio-economic development. Specifically, water quality management system using web map can be an efficient approach to accomplish this system. This paper aims to construct a dynamic water quality management system to reflect a water environment management system which includes three sub-models with consideration of their interrelationships (a socio-economic model based on dynamic Input-Output model, a water resources cycle model, and a water pollutants flow model). Based on simulation, the model can precisely estimate trends of water utilization, water quality, and economic development under certain management targets, and propose an optimal plan. This study utilized the model to analyze the potential of using reclaimed water to accomplish local water environment management and sustainable development plan while exploring the applicable approaches. This study indicates that the constructed water environment management system can be effective and easily adopted to assess water resources and environment while improving the trade-off between economic and environment development, as well as formulate regional development plan.

• Journal of Energy Engineering
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• v.23 no.2
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• pp.74-82
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• 2014

The government is planning to expand the bio-gas supply as a method for mitigating greenhouse gas emissions to deal with climate change. By means of a policy instrument, the government is considering an introduction of the Renewable Fuel Standard (RFS) whose targets include bio-gas. This paper attempts to look into the economic effects of expanding the bio-gas supply by applying an input-output (I-O) analysis using a 2011 I-O table. The bio-gas supply sector consists of liquefied petroleum gas supply sector and city gas supply sector, based on the tenets of introducing the RFS. The production-inducing effect, value-added creation effect, and employment-inducing effect of the bio-gas sector are analyzed. The supply shortage effect and the price pervasive effect are also investigated. The results show that the production or investment of 1.0 won in the bio-gas supply sector induces the production of 1.0539 won and the value-added of 0.1998 won in the national economy. Moreover, the production or investment of 1.0 billion won, supply shortage of 1.0 won, and a price increase of 10.0% in the bio-gas supply sector touch off the employment of 0.5279 person, 1.6229 won, and an increase in overall price level by 0.0183%, respectively.

• Biomedical Science Letters
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• v.24 no.4
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• pp.418-425
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• 2018

Amyloid brain positron emission tomography (PET) images are visually and subjectively analyzed by the physician with a lot of time and effort to determine the ${\beta}$-Amyloid ($A{\beta}$) deposition. We designed a convolutional neural network (CNN) model that predicts the $A{\beta}$-positive and $A{\beta}$-negative status. We performed 18F-florbetaben (FBB) brain PET on controls and patients (n=176) with mild cognitive impairment and Alzheimer's Disease (AD). We classified brain PET images visually as per the on the brain amyloid plaque load score. We designed the visual geometry group (VGG16) model for the visual assessment of slice-based samples. To evaluate only the gray matter and not the white matter, gray matter masking (GMM) was applied to the slice-based standard samples. All the performance metrics were higher with GMM than without GMM (accuracy 92.39 vs. 89.60, sensitivity 87.93 vs. 85.76, and specificity 98.94 vs. 95.32). For the patient-based standard, all the performance metrics were almost the same (accuracy 89.78 vs. 89.21), lower (sensitivity 93.97 vs. 99.14), and higher (specificity 81.67 vs. 70.00). The area under curve with the VGG16 model that observed the gray matter region only was slightly higher than the model that observed the whole brain for both slice-based and patient-based decision processes. Amyloid brain PET images can be appropriately analyzed using the CNN model for predicting the $A{\beta}$-positive and $A{\beta}$-negative status.

• Ocean and Polar Research
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• v.28 no.1
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• pp.25-35
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• 2006

Fisheries buyback programs have been implemented from 1994 in Korea, and its scale is estimated to have a value of 930 billion won, which is compounded for eight years since 1994. The paper evaluates the programs' economic and financial viability, and predicts efficient ways about how much and how long to reduce fisheries vessels so as to pursue a target biomass at MSY, For the specific purpose of the paper, aggregate fisheries stock dynamics and catch functions are specified and estimated by yearly catch and fishing effort data from 1970 to 2001, using ASPIC model and Schaefer's logistic production model. Results show that the fisheries stock in Korea has steadily declined since 1970, and that Korean fisheries overexploitation has steadily increased. Using cost-benefit analysis method, the buyback program holds the economic and financial feasibility even if the scale of buyback programs is not sufficient to avoid the downward trend in fisheries stock and harvest. The potential investment scale is predicted in several alternative scenarios using the sensitivity analysis method. The results recommend the annual reduction of 46%, 12% or 20% for the next one year, five years or three years, respectively so that the target biomass at MSY may be reached in 25 years.

• Korean Journal of Remote Sensing
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• v.34 no.4
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• pp.611-624
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• 2018

The percentage of moisture content in rice before harvest is crucial to reduce the economic loss in terms of yield, quality and drying cost. This paper discusses the application of artificial neural network (ANN) in developing a reliable prediction model using the low altitude fixed-wing unmanned air vehicle (UAV) based reflectance value of green, red, and NIR and statistical moisture content data. A comparison between the actual statistical data and the predicted data was performed to evaluate the performance of the model. The correlation coefficient (R) is 0.862 and the mean absolute percentage error (MAPE) is 0.914% indicate a very good accuracy of the model to predict the moisture content in rice before harvest. The model predicted values are matched well with the measured values($R^2=0.743$, and Nash-Sutcliffe Efficiency = 0.730). The model results are very promising and show the reliable potential to predict moisture content with the error of prediction less than 7%. This model might be potentially helpful for the rice production system in the field of precision agriculture (PA).