• ALGAE
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• v.23 no.4
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• pp.289-294
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• 2008

To estimate seaweed biomass or standing crop, a nondestructive sampling can be beneficial because of not much destroying living plants and saving time in field works. We suggest a methodological procedure to estimate seaweed biomass per unit area in marine benthic habitats by using species-specific regression equations. Percent cover data are required from the field samplings for most species to convert them to weight data. However, for tall macroalgae such as kelps we need density data and their size (e.g., size class for subtidal kelps) of individuals. We propose that the field sampling should be done with 5 replicates of 50 cm x 50 cm quadrat at three zones of intertidals (upper, middle, lower) and three depth points (1, 5, 10 m) in subtidals. To obtain a reliable regression equation for a species, a substantial number of replicate is necessary from destructive samplings. The regression equation of a species can be further specified by different locality and different season, especially for the species with variable morphology temporally and spatially. Example estimation carried out in Onpyung, Jeju Island, Korea is provided to compare estimated values with real weight data.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.58 no.3
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• pp.272-280
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• 2022

The first step is to determine the principal dimensions of the design ship, such as length between perpendiculars, beam, draft and depth when accomplishing the design of a new vessel. To make this process easier, a database with a large amount of existing ship data and a regression analysis technique are needed. Recently, deep learning, a branch of artificial intelligence (AI) has been used in regression analysis. In this paper, deep learning neural networks are used for regression analysis to find the regression function between the input and output data. To find the neural network structure with the highest accuracy, the errors of neural network structures with varying the number of the layers and the nodes are compared. In this paper, Python TensorFlow Keras API and MATLAB Deep Learning Toolbox are used to build deep learning neural networks. Constructed DNN (deep neural networks) makes helpful in determining the principal dimension of the ship and saves much time in the ship design process.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.04a
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• pp.105-110
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• 2004

This paper deals with the study of feasibility of rotary carbide tools in the machining of aluminium alloy. A rotary tool holder was designed and manufactured for this work. Experiments were performed using Taguchi methods and regression analysis to analyse the influence of various factors and their interactions on the cutting characteristics of rotary carbide tools during machining. The cutting force is influenced the most featly at the inclination angle. The surface roughness is influenced distinctly at depth of cut. It deduced an equation to predict cutting force and surface roughness. Hence, it could be concluded here that the proposed model agrees with the experimental data satisfactorily.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.4
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• pp.23-29
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• 2004

In this study, four kinds of $Si_3N_4$-based ceramic cutting tools with different sintering time were fabricated to investigate the relation among mechanical properties, grain size and tool life. They were used to turn gray cast iron at a cutting speed of 330m/min and depth of cut of 0.5mm and 1mm in dry, continuos cutting conditions. Multiple linear regression model was used to determine the relations among the mechanical property, grain size and the density. It was found that the combination of hardness and fracture toughness showed a good relation with tool life. It was also shown that hardness was the most important single element for the tool life.

• Proceedings of the KSRS Conference
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• v.2
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• pp.547-550
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• 2006

This study is to suggest a methodology to produce landslide hazard map by combining LRA (Logistic Regression Analysis) and AHP (Analytic Hierarchy Program) Approach. Topographic factors (slope, aspect, elevation), soil drain, soil depth and land use were adopted to classify landslide hazard areas. The method was applied to a 520 $km^2$ region located in the middle of South Korea which have occurred 39 landslides during 1999 and 2003. The suggested method showed 58.9 % matching rate for the real landslide sites comparing with the classified areas of high-risk landslide while LRA and AHP showed 46.1 % and 48.7 % matching rates respectively.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.4 no.4
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• pp.14-20
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• 2005

This paper deals with the study of feasibility of rotary carbide tools in the machining of aluminium alloy. A rotary tool holder was designed and manufactured for this work. Experiments were performed using Taguchi methods and regression analysis to analyse the influence of various factors and their interactions on the cutting characteristics of rotary carbide tools during machining. The cutting force is influenced the most greatly at the inclination angle. The surface roughness is influenced distinctly at depth of cut. It deduced an equation to predict cutting force and surface roughness. Hence, it could be concluded here that the proposed model agrees with the experimental data satisfactorily.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.11
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• pp.96-102
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• 2002

This study is on the estimation method of toot temperature for different tool nose radius and cutting conditions in turning. Experimental analysis has been performed in different cutting conditions such as cutting speed, feed rate, and depth of cut for the tool nose radius, 0.4R, 0.8R using SMC workpiece materials. Tool temperature is measured using a thermo-couple which is embedded in the insert tip. Using multiple linear regression method, the tool temperature can be determined as an exponential equation with cutting variables and tool nose diameters for the different tool materials. The equations determined in this study show a good correlation for the cutting conditions and can be used for a tool temperature estimation technique. The result indicates that the tool temperature decreases for increasing the tool nose radius in general. Also, nose radius hardly influences on the tool temperature compared with cutting speed, feed rate and depth of cut. This method will be useful for the estimation of tool life and temperature using limited experimental data for given cutting conditions.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.3
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• pp.90-96
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• 2018

This paper proposed data driven techniques to forecast the time point of water management of the water reservoir without measuring manganese concentration with the empirical data as Juam Dam of years of 2015 and 2016. When the manganese concentration near the surface of water goes over the criteria of 0.3mg/l, the water management should be taken. But, it is economically inefficient to measure manganese concentration frequently and regularly. The water turnover by the difference of water temperature make manganese on the floor of water reservoir rise up to surface and increase the manganese concentration near the surface. Manganese concentration and water temperature from the surface to depth of 20m by 5m have been time plotted and exploratory analyzed to show that the water turnover could be used instead of measuring manganese concentration to know the time point of water management. Two models for forecasting the time point of water turnover were proposed and compared as follow: The regression model of CR20, the consistency ratio of water temperature, between the surface and the depth of 20m on the lagged variables of CR20 and the first lag variable of max temperature. And, the Box-Jenkins model of CR20 as ARIMA (2, 1, 2).

• Journal of Applied Reliability
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• v.16 no.1
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• pp.48-55
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• 2016

Purpose: The purpose of this study is to identify the object-oriented metrics which have strong impact on the reliability and fault-proneness of software products. The reliability and fault-proneness of software product is closely related to the design properties of class diagrams such as coupling between objects and depth of inheritance tree. Methods: This study has empirically validated the object-oriented metrics to determine which metrics are the best to predict fault-proneness. We have tested the metrics using logistic regressions and artificial neural networks. The results are then compared and validated by ROC curves. Results: The artificial neural network models show better results in sensitivity, specificity and correctness than logistic regression models. Among object-oriented metrics, several metrics can estimate the fault-proneness better. The metrics are CBO (coupling between objects), DIT (depth of inheritance), LCOM (lack of cohesive methods), RFC (response for class). In addition to the object-oriented metrics, LOC (lines of code) metric has also proven to be a good factor for determining fault-proneness of software products. Conclusion: In order to develop fault-free and reliable software products on time and within budget, assuring quality of initial phases of software development processes is crucial. Since object-oriented metrics can be measured in the early phases, it is important to make sure the key metrics of software design as good as possible.

• Journal of Welding and Joining
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• v.30 no.2
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• pp.59-64
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• 2012

Welding is an essential process in the automotive industry. Most welding processes that are used for auto body are spot welding and $CO_2$ welding are used in a small part. In production field, $CO_2$ welding process is decreased and spot welding process is increased due to welding quality is poor and defects are occurred in $CO_2$ welding process frequently. But $CO_2$ welding process should be used at robot interference parts and closed parts where spot welding couldn't. Because of the 0.65mm ~ 2.0mm thickness steel sheet were used in the automotive industry, poor quality of welding area such as burn through and under fill were happened frequently in $CO_2$ process. In this paper, we will study about the penetration depth which gives a huge impact on burn through changing a degree of base metal, welding position and torch angle. Voltage, current and welding speed were fixed but degree of base metal, welding position and torch angle were changed. And Cold- Rolled(CR) steel sheet was used. Penetration depth was analysed by multiple regression analysis to derive approximate calculations. And reliability of approximate calculations were confirmed through additional experiments. As the results of this research, we confirmed the effect of torch and plate angle to bead shape. And we present a possibility that can simulate more accurate to weld geometry, as deduced the verification equations that has tolerance of less than 21.69%.