• 한국환경복원기술학회지
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• 제27권3호
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• pp.29-43
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• 2024

Legally protected species are one of the crucial considerations in the field of natural ecology when conducting environmental impact assessments (EIAs). The occurrence of legally protected species, especially 'Endangered Wildlife' designated by Ministry of Environment, significantly influences the progression of projects subject to EIA, necessitating clear investigations and presentations of their habitats. In perspective of statistics, a minimum of 30 occurrence coordinates is required for population prediction, but most of endangered wildlife has insufficient coordinates and it posing challenges for distribution prediction through modeling. Consequently, this study aims to propose modeling methodologies applicable when coordinate data are limited, focusing on Rodgersia podophylla, representing characteristics of endangered wildlife and northern plant species. For this methodology, 30 random sampling coordinates were used as input data, assuming little survey data, and modeling was performed using individual models included in BIOMOD2. After that, the modeling results were evaluated by using discrimination capacity and the reality reflection ability. An optimal modeling technique was proposed by ensemble the remaining models except for the MaxEnt model, which was found to be less reliable in the modeling results. Alongside discussions on discrimination capacity metrics(e.g. TSS and AUC) presented in modeling results, this study provides insights and suggestions for improvement, but it has limitations that it is difficult to use universally because it is not a study conducted on various species. By supporting survey site selection in EIA processes, this research is anticipated to contribute to minimizing situations where protected species are overlooked in survey results.

• 대한기계학회논문집A
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• 제25권5호
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• pp.788-796
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• 2001

VR(Virtual reality) technologies have given engineers the ability to design, test, and evaluate engineering systems in a virtual environment. The virtual plant is the highlight of the application of the VR technology to plant engineering. Plant design, maintenance, control, management, operation are integrated in the virtual plant. The VR monitoring system including the concept of the virtual plant is developed to replace a current control room that has number of gages and warning lamps in two-dimensional panels which shows the operating status of a plant. The operating status of the plant is displayed in the VR monitoring system through the realistic computer graphics. Sophisticated, realistic and prompt control becomes possible. The VR monitoring system consists of advanced visualization, walk-through simulation and navigation. In the virtual environment, a user can navigate and interact with each component of a plant. In addition, the user can access the information by just clicking interesting component. The VR monitoring system is operated with various modules, such as (1) virtual plant constructed with Graphic Management System (GMS), (2) Touch & Tell System, and (3) Equipment DB System of Part. In order to confirm the usefulness of the VR monitoring system, a pilot gas plant which is currently being used for plant operator training is taken as application. The end of the paper gives an outlook on the future work and a brief conclusion.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.1782-1787
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• 1997

A combined algorithm called APEXS(Alarm Processing and Diagnosis Expert System) for power plants has been developed on the time-based alarm processing with a proper alarm prioritization and a diagnosis with a qualitative model(QM), qualitative interpreter(QI), and a state-transition trees(STT).

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 2004년도 추계학술발표회 발표논문집
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• pp.501-502
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• 2004

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.1017-1021
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• 1991

This paper is to explore the potential use of the modeling capacity of neural networks for control applications. The tasks are carried out by two neural networks which act as a plant identifier and a system controller, respectively. Using information stored in the identification network control action has been developed. Without supervising control signals are generated by a gradient type iterative algorithm.

• 한국조경학회:학술대회논문집
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• 한국조경학회 1999년도 정기대회 및 학술논문발표회
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• pp.18-19
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• 1999

• 한국안전학회지
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• 제35권1호
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• pp.25-33
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• 2020

In most plant layout optimization researches, MILP(Mixed Integer Linear Programming) problems, in which the objective function includes the costs of pipelines connecting process equipment and cost associated with safety issues, have been employed. Based on these MILP problems, various optimization solvers have been applied to investigate the optimal solutions. To consider safety issues on the objective function of MILP problems together, the accurate information about the impact and the frequency of potential accidents in a plant should be required to evaluate the safety issues. However, it is really impossible to obtain accurate information about potential accidents and this limitation may reduce the reliability of a plant layout problem. Moreover, in real industries such as plant engineering companies, the plant layout is previously fixed and the considerations of various safety instruments and systems have been performed to guarantee the plant safety. To reflect these situations, the two step optimization problems have been designed in this study. The first MILP model aims to minimize the costs of pipelines and the land size as complying sufficient spaces for the maintenance and safety. After the plant layout is determined by the first MILP model, the optimal locations of blast walls have been investigated to maximize the mitigation impacts of blast walls. The particle swarm optimization technique, which is one of the representative sampling approaches, is employed throughout the consideration of the characteristics of MILP models in this study. The ethylene oxide plant is tested to verify the efficacy of the proposed model.

• Journal of Biosystems Engineering
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• 제35권2호
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• pp.91-99
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• 2010

A Model of small scale total mixed rations(TMR) plant which can be utilized round bales was developed, tested and analyzed in this study. This study consist of two parts. One is development of a small scale TMR plant model which was already reported at the previous paper. This is the second part of the study. For the study, a series of tests of the model plant were performed and its costs was analyzed. Also, the break-even point of the model plant by comparing with market price of commercial TMR feed was determined. Results of the research are summarized as follows ; As the results of mixing test, the average coefficient of variation(CV) value for mixing of the feed was 13.0 % at the gate of the mixer. The production cost was estimated as 8,298 won/head for dairy cattle farm and 2,495 won/head for beef cattle farm, when producing 8 batch a day. Also, it is recommended to utilize the model plant when farm size is over 79 heads for dairy cattle farm and 113 heads for beef cattle farm. As an overall conclusion, the model plant designed for farm size TMR feed mill will be very useful model for both beef cattle and dairy farms in Korea. Also it is expected that the capital investment for the model plant can be recovered with 8 months compare with purchasing commercial TMR feed if the model plant feeds 1,000 beef cattle approximately.

• 콘크리트학회논문집
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• 제21권2호
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• pp.153-158
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• 2009

콘크리트 표면에 4개의 전극배열을 이용한 비파괴 표면측정법을 통하여 철근의 피복을 벗겨내지 않고도 콘크리트 내 철근의 부식상태 및 철근의 배치상태를 추정할 수 있다. 표면측정법 모델링은 부식된 철근과 주위 콘크리트사이의 계면 임피던스가 콘크리트 표면에서 측정된 임피던스와 어떤 관련이 있는지를 보여준다. 본 연구에서는 콘크리트에 매립된 철근 크기와 콘크리트 피복두께 등의 탐지를 위하여 철근부식 표면측정 모델링을 이용하였다. 모델링 결과 철근 크기가 D10에서 D35로 커짐에 따라 표면임피던스 변화가 나타났으며, 또한 콘크리트 피복두께가 0.02 m에서 0.1 m로 커짐에 따라 그 변화에 대한 영향이 표면측정 임피던스를 통해 나타났다. 그러므로 표면측정법 모델링을 통하여 콘크리트 내 철근부식반응을 시뮬레이션 할 수 있으며, 동시에 철근직경과 매입 깊이 등에 대한 정보도 얻을 수 있음 알 수 있다.

• 한국농공학회논문집
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• 제49권4호
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• pp.13-22
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• 2007

The uncertainty in water quality model predictions is inevitably high due to natural stochasticity, model uncertainty, and parameter uncertainty. An integrated modeling system under uncertainty was described and demonstrated for use in watershed management and receiving-water quality prediction. A watershed model (HSPF), a receiving water quality model (WASP), and a wetland model (NPS-WET) were incorporated into an integrated modeling system (modified-BASINS) and applied to the Hwaseong Reservoir watershed. Reservoir water quality was predicted using the calibrated integrated modeling system, and the deterministic integrated modeling output was useful for estimating mean water quality given future watershed conditions and assessing the spatial distribution of pollutant loads. A Monte Carlo simulation was used to investigate the effect of various uncertainties on output prediction. Without pollution control measures in the watershed, the concentrations of total nitrogen (T-N) and total phosphorous (T-P) in the Hwaseong Reservoir, considering uncertainty, would be less than about 4.8 and 0.26 mg 4.8 and 0.26 mg $L^{-1}$, respectively, with 95% confidence. The effects of two watershed management practices, a wastewater treatment plant (WWTP) and a constructed wetland (WETLAND), were evaluated. The combined scenario (WWTP + WETLAND) was the most effective at improving reservoir water quality, bringing concentrations of T-N and T-P in the Hwaseong Reservoir to less than 3.54 and 0.15 mg ${L^{-1}$, 26.7 and 42.9% improvements, respectively, with 95% confidence. Overall, the Monte Carlo simulation in the integrated modeling system was practical for estimating uncertainty and reliable in water quality prediction. The approach described here may allow decisions to be made based on probability and level of risk, and its application is recommended.