• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.347-353
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• 2023

Measuring the growth of fish in fish farms still uses a laborious method. This method requires a lot of labor and causes stress to the fish, which has a negative impact on mortality. To solve this problem, we propose the Fish Growth Regression System (FGRS), a system to automate the growth of fish. FGRS consists of two modules. The first is a module that detects fish based on Yolo v8, and the second consists of a module that predicts the growth of fish using fish image data and a CNN-based neural network model. As a result of the simulation, the average prediction error before learning was 134.2 days, but after learning, the average error decreased to 39.8 days. It is expected that the system proposed in this paper can be used to predict the growing date and use the growth prediction of fish to contribute to automation in fish farms, resulting in a significant reduction in labor and cost savings.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1530-1537
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• 2016

Recently, solar power generation shows the significant growth in the renewable energy field. Using the short-term prediction, it is possible to control the electric power demand and the power generation plan of the auxiliary device. However, a short-term prediction can be used when you know the weather forecast. If it is not possible to use the weather forecast information because of disconnection of network at the island and the mountains or for security reasons, the accuracy of prediction is not good. Therefore, in this paper, we proposed a system capable of short-term prediction of solar power generation amount by using only the weather information that has been collected by oneself. We used temperature, humidity and insolation as weather information. We have applied a moving average to each information because they had a characteristic of time series. It was composed of min, max and average of each information, differences of mutual information and gradient of it. An artificial neural network, SVM and RBF Network model was used for the prediction algorithm and they were combined by Ensemble method. The results of this suggest that using a moving average during pre-processing and ensemble prediction models will maximize prediction accuracy.

• International Journal of Precision Engineering and Manufacturing
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• v.1 no.2
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• pp.43-47
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• 2000

When estimating fatigue crack growth (FCG) life of structures, the use of crack growth models and knowledge of the values of their corresponding parameters are of vital importance. Inconsistency in using models with appropriate parameters can lead to enormous errors in FCG life prediction. In this paper examples are analyzed and compared with test results to show the possible problems, Consistency checks are necessary for avoiding some pitfalls, and also necessary for verifying the correct performance and accuracy of the used computer program.

• Proceedings of the KWS Conference
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• 2003.05a
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• pp.43-45
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• 2003

The empirical model for predicting the prior austenite grain size in low-alloy steel weld HAZ was developed through examining the effect of alloying element. The test alloys were made by vacuum induction melting. Grain growth behaviors were observed and analyzed by isothermal grain growth test and subsequent metallography. As a result, it was found that the grain growth might be controlled by grain boundary diffusion and the empirical model for grain growth was presented.

• Journal of the Korean Society of Safety
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• v.8 no.4
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• pp.47-53
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• 1993

The growth of short cracks can be well described in terms of the effective stress intensity factor range, which is calculated on the base of crack closure. The relation between the crack opening SIF and crack length is determined from the experimental results. The crack opening SIF of short cracks, Kop, can be predicted from the crack opening SIF at threshold of long crack, Kop.L. The growth rate of short cracks at notch root can be predicted from the crack opening SIF of short cracks, Kop, and the growth equation of long cracks in region II.

• Fire Science and Engineering
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• v.33 no.6
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• pp.20-27
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• 2019

In this study, a new function with higher accuracy for fire heat release rate prediction was developed. The 'αt²' curve, which is the major exponential function currently used for fire engineering calculations, must be improved to minimize the prediction gap that causes fire system engineering inefficiency and lower cost-effectiveness. The newly developed prediction function was designed to cover the initial fire stage that features rapid growth based on logistic function theory, which has a more logical background and graphical similarity compared to conventional exponential function methods for 'αt²'. The new function developed in this study showed apparently higher prediction accuracy over wider range of fire growth durations. With the progress of fire growth pattern studies, the results presented herein will contribute towards more effective fire protection engineering.

• Health Policy and Management
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• v.20 no.3
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• pp.123-137
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• 2010

The main purpose of this article is for deriving functions related to the prediction of the closure of the hospitals, and finding out how the discriminant functions affect the closure of the hospitals. Empirical data were collected from 3 years financial statements of 41 private hospitals closed down from 2000 till 2006 and 62 private hospitals in business till now. As a result, the functions related to the prediction of the closure of the private hospital are 4 indices: Return on Assets, Operating Margin, Normal Profit Total Assets, Interest expenses to Total borrowings and bonds payable. From these discriminant functions predicting the closure, I found that the profitability indices - Return on Assets, Operating Margin, Normal Profit Total Assets - are the significant affecting factors. The discriminant functions predicting the closure of the group of the hospitals, 3 years before the closure were Normal Profit to Gross Revenues, Total borrowings and bonds payable to total assets, Total Assets Turnover, Total borrowings and bonds payable to Revenues, Interest expenses to Total borrowings and bonds payable and among them Normal Profit to Gross Revenues, Total borrowings and bonds payable to total assets, Total Assets Turnover, Total borrowings and bonds payable to Revenues are the significant affecting factors. However 2 years before the closure, the discriminant functions predicting the closure of the hospital were Interest expenses to Total borrowings and bonds payable and it was the significant affecting factor. And, one year before the closure, the discriminant functions predicting the closure were Total Assets Turnover, Fixed Assets Turnover, Growth Rate of Total Assets, Growth Rate of Revenues, Interest expenses to Revenues, Interest expenses to Total borrowings and bonds payable. Among them, Total Assets Turnover, Growth Rate of Revenues, Interest expenses to Revenues were the significant affecting factors.

• Journal of Bio-Environment Control
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• v.32 no.1
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• pp.34-39
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• 2023

Growth modeling in plant factories can not only control stable production and yield, but also control environmental conditions by considering the relationship between environmental factors and plant growth rate. In this study, using the expolinear function, we modeled perilla [Perilla frutescens (L.) Britt.] cultivated in a plant factory. Perilla growth was investigated 12 times until flower bud differentiation occurred after planting under light intensity, photoperiod, and the ratio of mixed light conditions of 130 μmol·m^-2·s^-1, 12/12 h, red:green:blue (7:1:2), respectively. Additionally, modeling was performed to predict dry and fresh weights using the expolinear function. Fresh and dry weights were strongly positively correlated (r = 0.996). Except for dry weight, fresh weight showed a high positive correlation with leaf area, followed by plant height, number of leaves, number of nodes, leaf length, and leaf width. When the number of days after transplanting, leaf area, and plant height were used as independent variables for growth prediction, leaf area was found to be an appropriate independent variable for growth prediction. However, additional destructive or non-destructive methods for predicting growth should be considered. In this study, we created a growth model formula to predict perilla growth in plant factories.

• Genomics & Informatics
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• v.15 no.4
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• pp.162-169
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• 2017

Metal binding proteins or metallo-proteins are important for the stability of the protein and also serve as co-factors in various functions like controlling metabolism, regulating signal transport, and metal homeostasis. In structural genomics, prediction of metal binding proteins help in the selection of suitable growth medium for overexpression's studies and also help in obtaining the functional protein. Computational prediction using machine learning approach has been widely used in various fields of bioinformatics based on the fact all the information contains in amino acid sequence. In this study, random forest machine learning prediction systems were deployed with simplified amino acid for prediction of individual major metal ion binding sites like copper, calcium, cobalt, iron, magnesium, manganese, nickel, and zinc.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.9
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• pp.2271-2279
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• 1993

In the case of life prediction on the structures and machines after long service, it is natural to consider a degradation problems. Most of degradation data form practical structures are isolated data obtained at the time of periodical inspection or repair. From such data, it may be difficult to obtain the degradation curve available and necessary for life prediction. In this paper, for the purpose of obtaining a degradation curves, developed the simulate degradation method and fatigue test and Charpy impact test were conducted on the degraded, simulate degraded and recovered materials. Fatigue life prediction were conducted by using the relationship between fracture transition temperature (DBTT : vTrs) obtained from the Charpy impact test through the degradation process and fatigue crack growth constants of m and C obtained from the fatigue test.