• Journal of Sensor Science and Technology
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• v.9 no.2
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• pp.127-133
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• 2000

We propose a novel monitoring system for a recycling piggery slurry treatment system through neural networks. Here we tried to model treatment process for each tank(influent, fermentation, aeration, first sedimentation and fourth sedimentation tanks) in the system based on population densities of heterotrophic and lactic acid bacteria. Principle component analysis(PCA) was first applied to identify a relation between input(microbial densities and parameters for the treatment) and output, and then multilayer neural networks were employed to model the treatment process for each tank. PCA filtration of input data as microbial densities was found to facilitate the modeling procedure for the system monitoring even with a relatively lower number of input. Neural networks independently trained for each treatment tank and their subsequent combinatorial data analysis allowed a successful prediction of the treatment system for at least two days.

• Biotechnology and Bioprocess Engineering:BBE
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• v.5 no.5
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• pp.355-361
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• 2000

A recycling reactor system operated under sequential anoxic and oxic conditions for the treatment of swine wastewater has been developed, in which piggery slurry is fermentatively and aerobically treated and then part of the effluent is recycled to the pigsty. This system significantly removes offensive smells (at both the pigsty and the treatment plant), BOD and others, and may be cost effective for small-scale farms. The most dominant heterotrophic were, in order, Alcaligenes faecalis, Brevundimonas diminuta and Streptococcus sp., while lactic acid bacteria were dominantly observed in the anoxic tank. We propose a novel monitoring system for a recycling piggery slurry treatment system through the use of neural networks. In this study, we tried to model the treatment process for each tank in the system (influent, fermentation, aeration, first sedimentation and fourth sedimentation tanks) based upon the population densities of the heterotrophic and lactic acid bacteria. Principal component analysis(PCA) was first applied to identify a relationship between input and output. The input would be microbial densities and the treatment parameters, such as population densities of heterotrophic and lactic acid bacteria, suspended solids(SS), COD, NH$_4$(sup)+-N, ortho-phosphorus (o-P), and total-phosphorus (T-P). then multi-layer neural networks were employed to model the treatment process for each tank. PCA filtration of the input data as microbial densities was found to facilitate the modeling procedure for the system monitoring even with a relatively lower number of imput. Neural network independently trained for each treatment tank and their subsequent combined data analysis allowed a successful prediction of the treatment system for at least two days.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.641-649
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• 2021

Fuel oil consumption (FOC) must be minimized to determine the economic route of a ship; hence, the ship power must be predicted prior to route planning. For this purpose, a numerical method using test results of a model has been widely used. However, predicting ship power using this method is challenging owing to the uncertainty of the model test. An onboard test should be conducted to solve this problem; however, it requires considerable resources and time. Therefore, in this study, a deep feed-forward neural network (DFN) is used to predict ship power using deep learning methods that involve data pattern recognition. To use data in the DFN, the input data and a label (output of prediction) should be configured. In this study, the input data are configured using ocean environmental data (wave height, wave period, wave direction, wind speed, wind direction, and sea surface temperature) and the ship's operational data (draft, speed, and heading). The ship power is selected as the label. In addition, various treatments have been used to improve the prediction accuracy. First, ocean environmental data related to wind and waves are preprocessed using values relative to the ship's velocity. Second, the structure of the DFN is changed based on the characteristics of the input data. Third, the prediction accuracy is analyzed using a combination comprising five hyperparameters (number of hidden layers, number of hidden nodes, learning rate, dropout, and gradient optimizer). Finally, k-means clustering is performed to analyze the effect of the sea state and ship operational status by categorizing it into several models. The performances of various prediction models are compared and analyzed using the DFN in this study.

• Journal of the Korean Regional Science Association
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• v.15 no.1
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• pp.63-73
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• 1999

It is generally believed that there is a trade-off between economic growth and environmental quality since pollutants are generated in the process of production and consumption of commodities. Several researchers have shown this prevailing belief using the short-term input-output models. The literature, however, shows that there have been few attempts to investigate the relationship using long-term forecasting models. This motivates the current paper. This paper attempts to build a reginal growth model in a partial equilibrium framework taking into consideration the requirements of capital invested for pollutant abatement. Model is largely neoclassical. Labor is assumed to move a region with high utility specified in regional per capita average was income and pollution level while capital is partially mobile to a region with high returns. The regional growth is explored in a phase diagram. The paper shows that there are two stable growth equilibria which a region can converge over time and that the equilibria are distinguished by the initial threshold capital stock that a region holds. If the initial capital stock of a region is over(under) than the threshold size, the region converges to the higher (lower) growth equilibrium over time. Moreover, based on this result an environmental quality enhancing policy is analyzed in the phase diagram. It has revealed that the policy calls for the relocation of growth equilibrium points, specifically speaking, it stimulates an increase in labor stock and a decrease in capital stock. Hence the paper has suggested that the prevailing belief which the environmental policy negatively impacts on a regional economic growth is not always true.

• Environmental Engineering Research
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• v.20 no.2
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• pp.155-161
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• 2015

Experimental research shows that the nitric oxides ($NO_X$) concentration track at the outlet of selective catalytic reduction (SCR) catalyst with a transient variation of Adblue dosage has a time delay and it features a characteristic of resistance-capacitance (RC). The phenomenon brings obstacles to get the simultaneously $NO_X$ expected to be reduced and equi-molar ammonia available to SCR reaction, which finally inhibits $NO_X$ conversion efficiency. Generally, engine loads change frequently, which triggers a rapid changing of Adblue dosage, and it aggravates the air quality that are caused by $NO_X$ emission and ammonia slip. In order to increase the conversion efficiency of $NO_X$ and avoid secondary pollution, the paper gives a comprehensive analysis of the SCR system and tells readers the key factors that affect time delay and RC characteristics. Accordingly, a map of time delay is established and a solution method for time constant and proportional constant is carried out. Finally, the paper accurately describes the input-output state relation of SCR system by using "variable RC model with time delay". The model can be used for a real-time correction of Adblue dosage, which can increase the conversion efficiency of $NO_X$ in SCR system and avoid secondary pollution forming. Obviously, the results of the work discover an avenue for the SCR control strategy.

• Atmosphere
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• v.24 no.1
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• pp.123-130
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• 2014

The automated prediction system for seasonal tropical cyclone (TC) activity is established at the National Typhoon Center of the Korea Meteorological Administration (KMA) to provide effective operation and control of the system for user who lacks knowledge of the system. For automation of the system, two procedures which include subjective decisions by user are performed in advance, and their output data are provided as input data. To provide the capability to understand the operational processes for operational user, the input and output data are summarized with each process, and the directory structure is reconstructed following KMA's standard. We introduce a user interface using namelist input parameters to effectively control operational conditions which is fixed or should be manually set in the previous version of the prediction system. To operationally use early prediction which become available through the automation, its performances are evaluated according to initial condition dates. As a result, high correlations between the observed and predicted TC counts are kept for all track clusters even though advancing the initial condition date from May to January.

• Journal of Distribution Science
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• v.15 no.11
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• pp.49-57
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• 2017

Purpose - In Korea, general super markets have a great impact on the market performance of traditional markets. We propose a modified two stage DEA model for evaluating the performance of traditional markets in Incheon, Korea by identifying the influence of external environmental factors including the presence of general super markets as non-discretionary variables in DEA. Research design, data, and methodology - After obtaining bias-corrected estimates of original DEA efficiency scores using the input and output data of 49 traditional markets, we regress them on several external environmental factors by bootstrap-truncated regression. Results - We obtain bias-corrected efficiency scores from the original DEA efficiency scores by bootstrap and among the five environmental factors, the residential population and the presence of general super markets or SSMs can be considered as the driving forces influencing bias-corrected efficiency scores, positively and negatively, respectively. Conclusions - When DEA efficiency scores tend to be overestimated, we need to use a biased-corrected efficiency score by bootstrap. It is important to note that the efficiency of traditional markets can be largely influenced by external environmental factors such as the presence of general super markets or SSMs that traditional markets can not control. Therefore, it is desirable to consider such environmental factors appropriately for a reasonable performance evaluation.

• Environmental and Resource Economics Review
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• v.15 no.4
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• pp.643-672
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• 2006

This study has assessed the industrial human toxicity index by means of toxic substances emissions in South Korean industry. The data used in analysis are the 146 kinds of the toxic chemicals emissions and final demands, total outputs in the input-output table. As a results, human carcinogenic index was $11.86198{\times}10^3$ for overall industries, and $0.26360{\times}10^3$ for average. The industries of higher human toxicity index can be ranked as follows: Mother vehicles and parts (7.85033) > Pig iron and crude steel(4.57409) > Primary iron and steel products(4.36668) > Other transportation equipments and parts(3.43293) > Inorganic basic chemical products(2.64379), etc. Such result can be considered as the priority order of regulation based on industrial characteristics, when the demand and industrial policies should be carried out for the deduction fof toxic substances.

• Journal of Environmental Policy
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• v.9 no.2
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• pp.21-39
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• 2010

In this paper, water consumption annually for industries in Korea was estimated for the first time and based on this, an input-output model was prepared for water consumption analysis. Also making use of this, the direct and indirect water consumption effect according to industrial activities was analyzed and the total effect based on volume was broken down into each factor. The amount of water consumed for industries in Korea (excluding agriculture, forestry and fishery) was estimated about 7 billion and 692 million ton in 2003(excluding sea water). Classifying by industry, the one for electric power & water service accounted for almost half, 49.5%, metalworking industry for 24.3% and chemical industry for 5.0%. As the result of estimation for the direct and indirect water consumption inducement coefficients, the amount of water consumed per the production of one million won ranked the highest for electric power & water service as 113.8 ton and the next highest ones ranked as 49.6 ton for the first metalworking, 16.8 ton for textile and leather goods, and 11.9 ton for general machinery respectively. In the meantime, as the result of breaking down into each factor of total amount of water consumed by industry, it appeared that the ripple effect having on other industries was more than the direct effect.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.14-14
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• 2011

Groundwater in the Waikatoregion is a valuable resource for agriculture, water supply, forestry and industries. The 434,000 ha study area comprises the upper Waikato River catchment from the outflow of Lake Taupo (New Zealand's largest lake) through to Lake Karapiro (a man-made hydro lake with high recreational value) (Figure 1). Water quality in the area is naturally high. However, there are indications that this quality is deteriorating as a result of land use intensification and deforestation. Compounding this concern for decision makers is the lag time between land use changes and the realisation of effects on groundwater and surface water quality. It is expected that the effects of land use changes have not yet fully manifested, and additional intensification may take decadesto fully develop, further compounding the deterioration. Consequently, Environment Waikato (EW) have proposed a programme of work to develop a groundwater model to assist managing water quality and appropriate policy development within the catchment. One of the most important and critical decisions of any modelling exercise is the choice of the modelling platform to be used. It must not inhibit future decision making and scenario exploration and needs to allow as accurate representation of reality as feasible. With this in mind, EW requested that two modelling platforms, MODFLOW/MT3DMS and FEFLOW, be assessed for their ability to deliver the long-term modelling objectives for this project. The two platforms were compared alongside various selection criteria including complexity of model set-up and development, computational burden, ease and accuracy of representing surface water-groundwater interactions, precision in predictive scenarios and ease with which the model input and output files could be interrogated. This latter criteria is essential for the thorough assessment of predictive uncertainty with third-party software, such as PEST. This paper will focus on the attributes of each modelling platform and the comparison of the two approaches against the key criteria in the selection process. Primarily due to the ease of handling and developing input files and interrogating output files, MODFLOW/MT3DMS was selected as the preferred platform. Other advantages and disadvantages of the two modelling platforms were somewhat balanced. A preliminary regional groundwater numerical model of the study area was subsequently constructed. The model simulates steady state groundwater and surface water flows using MODFLOW and transient contaminant transport with MT3DMS, focussing on nitrate nitrogen (as a conservative solute). Geological information for this project was provided by GNS Science. Professional peer review was completed by Dr. Vince Bidwell (of Lincoln Environmental).