• Title/Summary/Keyword: Model Optimization

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A Modeling Optimization for Numerical Analysis of GPR in Multi-Grounding Systems (다중 접지계 GPR 수치 해석을 위한 최적 모델링 기법)

  • Lee, Jae-Bok;Chang, Sug-Hun;Myung, Sung-Ho;Cho, Yeon-Gyu
    • The Journal of Korean Institute of Electromagnetic Engineering and Science
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    • v.17 no.11 s.114
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    • pp.1120-1131
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    • 2006
  • This paper describes the numerical analysis techniques using the Combined Integration/Matrix Method to calculate ground potential rise which can be occurred in the various grounding systems. Combined Integration/Matrix Method is used to reduce the error and computation time with the analytical integration equation and the proper segmentaion of earth embedded conductor. To do it, optimal segmentaion method for the buried conductors is presented through error analysis which is capable of applying the practical scaled various grounding systems. The optimum length of segmented element is much co-related with the buried depth of grounding electrode and the maximum length of buried electrode. As a result, less 3 precent errors was obtained by proposed model. The proposed model is applied to verify an effect of multi-grounding problems which was aroused much controversy with separated or common grounding between the high power grounding system and low power grounding system such as signal and telecommunication grounding.

Development of Low-fat Meat Processing Technology Using Interactions between Meat Proteins and Hydrocolloids- I Optimization of Interactions between Meat Proteins and Hydrocolloids by Model Study (식육단백질과 친수성 콜로이드의 상호결합 특성을 이용한 저지방 육제품 제조기술 개발 - I 모델연구를 이용한 상호반응의 최적화)

  • Chin, Koo-Bok;Chung, Bo-Kyung
    • Journal of the Korean Society of Food Science and Nutrition
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    • v.31 no.3
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    • pp.438-444
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    • 2002
  • Interactions between meat proteins and hydrocolloids in a model system may play an important role for the improvement of textural properties in low-fat sausage mixtures. The objective of this study was to determine gel properties as affected by the type and level of hydrocolloid, various pH values of meat protein-hydrocolloid mixture before cooking, and internal cooking temperatures. The desirable heat-induced gels (HIGs) were formed at least pH values above 6.0. The addition of konjac flour (KF), kappa-carrageenan (CN) and locust bean gum (LBG) to extracted salt soluble proteins (2%) improved the gel strength with increased levels (0.5∼1.5%) and HIGs containing CN had the highest (p<0.05) gel strength. The increase of cooking temperature increased gel strength, depending on pH and type of hydrocolloid. However, the minimun internal cooking temperature to make viscoelastic HIGs was 70$^{\circ}C$. These results indicated that desirable HIGs were manufactured with each hydrocolloid concentration of 1% and minimum cooking temperature of 70$^{\circ}C$ with pH values higher than 6.0.

Optimal Forest Management for Improving Economic and Public Functions in Mt.Gari Leading Forest Management Zone (가리산 선도산림경영단지의 경제적·공익적 기능 증진을 위한 산림관리 최적화 방안)

  • Kim, Dayoung;Han, Hee;Chung, Joosang
    • Journal of Korean Society of Forest Science
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    • v.110 no.4
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    • pp.665-677
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    • 2021
  • This study analyzed the optimization method of forest management to enhance economic and public functions, as well as the interrelationship among timber production, carbon storage, and water conservation functions in Mt.Gari leading forest management zone. For these purposes, a forest management planning model was developed using Multi-Objective Linear Programming. The model had an objective function to maximize the total NPV (Net Present Value) of weighted timber production, carbon storage, water conservation, and constraints to limit the rate of change in timber production, percentage of each age-class and tree species area, percentage of conifers and broad-leaved trees area in each management zone, minimum timber production and timber sales amount. Based on the description of forest inventory and the comprehensive plan of Mt.Gari, we analyzed stand information and management constraints of the study area. We compared management alternatives using different weights in the objective function. Therefore, the total NPV was maximized in the alternative considering the three functions in equal proportion, rather than the alternatives of maximizing only one function. When all three functions were considered simultaneously, timber production offset the carbon storage and water conservation, and carbon storage and water conservation interacted synergistically. However, when considering only two of the three functions, all combinations of functions demonstrated tradeoffs with one other. Therefore, we discovered that by considering all three functions equally, rather than only one or two functions, the economic and public values of the study area can be maximized.

U-Net Cloud Detection for the SPARCS Cloud Dataset from Landsat 8 Images (Landsat 8 기반 SPARCS 데이터셋을 이용한 U-Net 구름탐지)

  • Kang, Jonggu;Kim, Geunah;Jeong, Yemin;Kim, Seoyeon;Youn, Youjeong;Cho, Soobin;Lee, Yangwon
    • Korean Journal of Remote Sensing
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    • v.37 no.5_1
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    • pp.1149-1161
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    • 2021
  • With a trend of the utilization of computer vision for satellite images, cloud detection using deep learning also attracts attention recently. In this study, we conducted a U-Net cloud detection modeling using SPARCS (Spatial Procedures for Automated Removal of Cloud and Shadow) Cloud Dataset with the image data augmentation and carried out 10-fold cross-validation for an objective assessment of the model. Asthe result of the blind test for 1800 datasets with 512 by 512 pixels, relatively high performance with the accuracy of 0.821, the precision of 0.847, the recall of 0.821, the F1-score of 0.831, and the IoU (Intersection over Union) of 0.723. Although 14.5% of actual cloud shadows were misclassified as land, and 19.7% of actual clouds were misidentified as land, this can be overcome by increasing the quality and quantity of label datasets. Moreover, a state-of-the-art DeepLab V3+ model and the NAS (Neural Architecture Search) optimization technique can help the cloud detection for CAS500 (Compact Advanced Satellite 500) in South Korea.

A Qualitative Case Study on the Application of Spatial Design in the One-Person Housing Space by Combining BIM Design Technology (BIM 설계 기술을 융합한 1인 주거공간디자인 사례연구)

  • Kim, Ji Eun;Park, Eun Soo
    • Korea Science and Art Forum
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    • v.37 no.2
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    • pp.101-112
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    • 2019
  • Regardless of the size, the role and meaning of the space required for human daily life are the same. Especially, as the spatial design is small and the size is small, the careful design and problem solving are needed to enable comfortable and convenient life even in a narrow space. The purpose of this study is to design a convergence model that utilizes the advantages of BIM, which can simulate actual design based on a new one-person housing space design plan optimized for one person. This study applied the BIM design technology to one-person housing 2D design, and the suitability examination and the space optimization design of the interior design were carried out. As a result of the study, utility of space improvement, consideration of housing environment, interference check, application of eco-friendly housing system, and MEP design item were derived. Therefore, BIM space design in interior space has been confirmed as a way to overcome limit and inefficiency of 2D design which is applied to actual space by various space design elements. Based on the results of this study, the One-person housing space model, which is applied to the study, is a pure creation designed based on various one-person housing of social and cultural peculiarities derived from previous research. This design example was applied to BIM technology to confirm the detailed and practical design possibility.

Ultrasound-assisted Extraction of Total Flavonoids from Wheat Sprout: Optimization Using Central Composite Design Method (밀싹으로부터 플라보노이드성분의 초음파 추출 : 중심합성계획모델을 이용한 최적화)

  • Lee, Seung Bum;Wang, Xiaozheng;Hong, In Kwon
    • Applied Chemistry for Engineering
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    • v.29 no.6
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    • pp.663-669
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    • 2018
  • The process of extracting active ingredients from wheat sprout using ultrasound assisted method was optimized with a central composite design model. The response value of the central composite design model established the extraction yield and the total flavonoids content, main effects and interactive effects were analyzed depending on independent variables such as the extraction time, volume ratio of ethanol to ultrapure water, and ultrasonic irradiation power. The volume ratio of ethanol to ultrapure water and ultrasonic irradiation power were relatively large for the extraction yield and the extraction time was most significantly affected the total flavonoids, Considering both the extraction yield and total flavonoids content, the optimal extraction conditions were as follows: the extraction time of 17.00 min, volume ratio of ethanol to ultrapure water of 50.25 vol%, ultrasonic irradiation power of 551.70 W. In this case, the extraction yield and total flavonoids content were 28.43 wt% and $29.99{\mu}g\;QE/mL\;dw$, respectively. The actual experimental extraction yield and total flavonoids content under this condition were 8.73 wt% and $29.65{\mu}g\;QE/mL\;dw$, respectively with respective error rates of 1.05 and 1.13%.

Introduction of Inverse Analysis Model Using Geostatistical Evolution Strategy and Estimation of Hydraulic Conductivity Distribution in Synthetic Aquifer (지구통계학적 진화전략 역산해석 기법의 소개 및 가상 대수층 수리전도도 분포 예측에의 적용)

  • Park, Eungyu
    • Economic and Environmental Geology
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    • v.53 no.6
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    • pp.703-713
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    • 2020
  • In many geological fields, including hydrogeology, it is of great importance to determine the heterogeneity of the subsurface media. This study briefly introduces the concept and theory of the method that can estimate the hydraulic properties of the media constituting the aquifer, which was recently introduced by Park (2020). After the introduction, the method was applied to the synthetic aquifer to demonstrate the practicality, from which various implications were drawn. The introduced technique uses a global optimization technique called the covariance matrix adaptation evolution strategy (CMA-ES). Conceptually, it is a methodology to characterize the aquifer heterogeneity by assimilating the groundwater level time-series data due to the imposed hydraulic stress. As a result of applying the developed technique to estimate the hydraulic conductivity of a hypothetical aquifer, it was confirmed that a total of 40000 unknown values were estimated in an affordable computational time. In addition, the results of the estimates showed a close numerical and structural similarity to the reference hydraulic conductivity field, confirming that the quality of the estimation by the proposed method is high. In this study, the developed method was applied to a limited case, but it is expected that it can be applied to a wider variety of cases through additional development of the method. The development technique has the potential to be applied not only to the field of hydrogeology, but also to various fields of geology and geophysics. Further development of the method is currently underway.

A Study on the Optimal Setting of Large Uncharged Hole Boring Machine for Reducing Blast-induced Vibration Using Deep Learning (터널 발파 진동 저감을 위한 대구경 무장약공 천공 장비의 최적 세팅조건 산정을 위한 딥러닝 적용에 관한 연구)

  • Kim, Min-Seong;Lee, Je-Kyum;Choi, Yo-Hyun;Kim, Seon-Hong;Jeong, Keon-Woong;Kim, Ki-Lim;Lee, Sean Seungwon
    • Explosives and Blasting
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    • v.38 no.4
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    • pp.16-25
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    • 2020
  • Multi-setting smart-investigation of the ground and large uncharged hole boring (MSP) method to reduce the blast-induced vibration in a tunnel excavation is carried out over 50m of long-distance boring in a horizontal direction and thus has been accompanied by deviations in boring alignment because of the heavy and one-directional rotation of the rod. Therefore, the deviation has been adjusted through the boring machine's variable setting rely on the previous construction records and expert's experience. However, the geological characteristics, machine conditions, and inexperienced workers have caused significant deviation from the target alignment. The excessive deviation from the boring target may cause a delay in the construction schedule and economic losses. A deep learning-based prediction model has been developed to discover an ideal initial setting of the MSP machine. Dropout, early stopping, pre-training techniques have been employed to prevent overfitting in the training phase and, significantly improved the prediction results. These results showed the high possibility of developing the model to suggest the boring machine's optimum initial setting. We expect that optimized setting guidelines can be further developed through the continuous addition of the data and the additional consideration of the other factors.

A study on the 3-step classification algorithm for the diagnosis and classification of refrigeration system failures and their types (냉동시스템 고장 진단 및 고장유형 분석을 위한 3단계 분류 알고리즘에 관한 연구)

  • Lee, Kangbae;Park, Sungho;Lee, Hui-Won;Lee, Seung-Jae;Lee, Seung-hyun
    • Journal of the Korea Convergence Society
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    • v.12 no.8
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    • pp.31-37
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    • 2021
  • As the size of buildings increases due to urbanization due to the development of industry, the need to purify the air and maintain a comfortable indoor environment is also increasing. With the development of monitoring technology for refrigeration systems, it has become possible to manage the amount of electricity consumed in buildings. In particular, refrigeration systems account for about 40% of power consumption in commercial buildings. Therefore, in order to develop the refrigeration system failure diagnosis algorithm in this study, the purpose of this study was to understand the structure of the refrigeration system, collect and analyze data generated during the operation of the refrigeration system, and quickly detect and classify failure situations with various types and severity . In particular, in order to improve the classification accuracy of failure types that are difficult to classify, a three-step diagnosis and classification algorithm was developed and proposed. A model based on SVM and LGBM was presented as a classification model suitable for each stage after a number of experiments and hyper-parameter optimization process. In this study, the characteristics affecting failure were preserved as much as possible, and all failure types, including refrigerant-related failures, which had been difficult in previous studies, were derived with excellent results.

Cloud Detection from Sentinel-2 Images Using DeepLabV3+ and Swin Transformer Models (DeepLabV3+와 Swin Transformer 모델을 이용한 Sentinel-2 영상의 구름탐지)

  • Kang, Jonggu;Park, Ganghyun;Kim, Geunah;Youn, Youjeong;Choi, Soyeon;Lee, Yangwon
    • Korean Journal of Remote Sensing
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    • v.38 no.6_2
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    • pp.1743-1747
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    • 2022
  • Sentinel-2 can be used as proxy data for the Korean Compact Advanced Satellite 500-4 (CAS500-4), also known as Agriculture and Forestry Satellite, in terms of spectral wavelengths and spatial resolution. This letter examined cloud detection for later use in the CAS500-4 based on deep learning technologies. DeepLabV3+, a traditional Convolutional Neural Network (CNN) model, and Shifted Windows (Swin) Transformer, a state-of-the-art (SOTA) Transformer model, were compared using 22,728 images provided by Radiant Earth Foundation (REF). Swin Transformer showed a better performance with a precision of 0.886 and a recall of 0.875, which is a balanced result, unbiased between over- and under-estimation. Deep learning-based cloud detection is expected to be a future operational module for CAS500-4 through optimization for the Korean Peninsula.