• Journal of the Korean Society for Heat Treatment
• /
• v.36 no.6
• /
• pp.396-401
• /
• 2023

Aus-tempering heat treatment is suitable for thin and small-sized in precision parts. However, the heat treatment process relies on the experience and skill of the operator, making it challenging to produce precision parts due to the cold forging process. The aims of this study is to explore suitable machine learning models using data from the aus-tempering heat treatment process and analyze the factors that significantly impact the mechanic properties (e.g. hardness). As a result, the study analyzed, from a machine learning perspective, how hardness prediction varies based on the quenching temperature, carbon (C), and copper (Cu) contents.

• Journal of Industrial Convergence
• /
• v.20 no.1
• /
• pp.23-33
• /
• 2022

The study was conducted to develop an implementation model for digital transformation (DX) of manufacturing companies. To this end, previous studies on the process of management innovation and digital transformation were reviewed. The DX process model was derived based on the NEBIC theory and innovation theory applied in the innovation process of the Internet business. In addition, a research model including the factors of the will of the top management class (TMT) was constructed and confirmed through empirical data. The research hypothesis were verified based on data collected from members of mid-sized manufacturing companies promoting digital transformation. Through regression analysis, the influence relationship of each stage of the research model (technical knowledge, TK → opportunity perception, OR → performace expectation, PE and → Intention to execute, IE) was confirmed. Hierarchical regression analysis was conducted to understand the mediating effect of the members' perception of the top management's willingness to promote DX in the process. As a result of checking the Sobel test, it was confirmed that the management's perception of DX promotion partially mediated the relationship at each stage. This study is meaningful in that it presented a model applicable to the digital transformation of the mid-sized manufacturing industry. It is also valuable in providing an empirical basis for innovative research and NEBIC expansion. Longitudinal studies are required to overcome the limitations of empirical data for process models with dynamic characteristics whereas extended empirical studies are required in various fields other than manufacturing to generalize research results.

• KSBB Journal
• /
• v.8 no.2
• /
• pp.178-184
• /
• 1993

Extractive fermentations with the extract of Jerusalem artichoke in an aqueous-two-phase-system of polyethyleneglycol(PEG) and dextran(Dx) were investigated to obtain the effects of composition of PEG and Dx on both fermentation ,characteristics and partition ratio of alcohol. The specific growth rate of K. Fraglis CBS 1555 increased with a decrease of concentration of PEG and Dx. It augmented along with concentration of initial sugar up to 80g/l but decreased thereafter. The specific production rate of alcohol showed a rising tendency up to 100g/lof initial sugar, whereafter represented a decreasing trend. The partition ratio of alcohol between two phases augmented according to decrease of Dx comic. and increase of PEG cone. regardless of initial sugar concentrations. The ratio, however, decreased with Increment of initial sugar concentration at constant composition of PEG and Dx. The partition coefficient of alcohol had an ascending effect to the increase of PEG cone, but it had little effect on the changes of concentrations of Dx and initial sugar. The present study suggests that the optimum composition of PEG and Dx in the aqueous-two-phase-system by extractive fermentation were around 6.5%(w/v) of PEG and 3%(w/v) of Dx in considerations of emulsion state, sedimentation and separation of two phases, alcohol partition ratio, and specific growth rate.

• Transactions of Materials Processing
• /
• v.32 no.6
• /
• pp.329-334
• /
• 2023

The process of heat treatment in cold forging is an essential role in enhancing mechanical properties. However, it relies heavily on the experience and skill of individuals. The aim of this study is to predict hardness using machine learning to optimize production efficiency in cold forging manufacturing. Random Forest (RF), Gradient Boosting Regressor (GBR), Extra Trees (ET), and ADAboosting (ADA) models were utilized. In the result, the RF, GBR, and ET models show the excellent performance. However, it was observed that GBR and ET models leaned significantly towards the influence of temperature, unlike the RF model. We suggest that RF model demonstrates greater reliability in predicting hardness due to its ability to consider various variables that occur during the cold forging process.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.36 no.4
• /
• pp.71-76
• /
• 2013

For a die casting mold, generally, the casting layout design should be considered based on the relation among injection system, casting condition, gate system, and cooling system. Also, the extent or the location of product defects was differentiated according to the various relations of the above conditions. In this research, in order to optimize the casting layout design of an automotive Oil Pan_DX2E, Computer Aided Engineering (CAE) simulation was performed with two layout designs by using the simulation software (AnyCasting). The simulation results were analyzed and compared carefully in order to apply them into the production die-casting mold. During the filling process with two models, internal porosities caused by air entrapments were predicted and also compared with the modification of the gate system and overflow. With the solidification analysis, internal porosities occurring during the solidification process were predicted and also compared with the modified gate system.

• KIPS Transactions on Software and Data Engineering
• /
• v.9 no.8
• /
• pp.229-234
• /
• 2020

This paper presents the design and the implementation of CRESTIVE-DX, a concolic testing tool that distribute the concolic testing process over the embedded target system and the host system for efficient test generation of a target embedded program. CRESTIVE-DX conducts the execution of a target program on the target embedded system to consider possible machine-dependent behaviors of a target program execution, and conducts machine-independent parts, such as search-strategy heuristics, constraint solving, on host systems with high-speed computation unit, and coordinates their concurrent executions. CRESTIVE-DX is implemented by extending an existing concolic testing tool for C programs CREST. We conducted experiments with a test bed that consists of an embedded target system in the Arm Cortex A54 architecture and host systems in the x86-64 architecture. The results of experiments with Unix utility programs Grep, Busybox Awk, and Busybox Ed show that test input generation of CRESTIVE-DX is 1.59 to 2.64 times faster than that of CREST.

• KSBB Journal
• /
• v.6 no.2
• /
• pp.175-180
• /
• 1991

The quantitative effects of molecular weight and concentrations of two phase-forming polymers-polyethylene glycol and crude dextran on the two phase extractive ethanol fermentation were investigated using a Box-Wilson central composite protocol. The regression model obtained was used in order to determine optimum compositions of aqueous two phase system. In the aqueous two phase extractive ethanol fermentation of Kluyueromyces fragilis CBS 1555 with Jerusalem artichoke juice, it was found from the regression model that the variables influenlcing on ethanol fermentation were PEG concentration, time, Dx concentration, and PEG molecular weight strongly in order. The interaction of PEG concentration and PEG molecular weight was also found, and the effect of PEG concentration decreased with increase in molecular weight of PEG. The ethanol concentration incresed with increase in molecular weight of PEG, and with decrease in concentration of PEG. In conolusion, maximum concentration of ethanol produced was obtained at the following compositions; PEG MW 20000, Dx concentration ranged from 4% to 5%, and PEG concentration ranged from 3% to 7%.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2023.05a
• /
• pp.204-205
• /
• 2023

This study studied a prediction model for sending collision alarms for small fishing boats based on semi-supervised learning(SSL). The supervised learning (SL) method requires a large number of labeled data, but the labeling process takes a lot of resources and time. This study used service data collected through a data pipeline linked to 'intelligent maritime traffic information service' and data collected from real-sea experiment. The model accuracy was improved as a result of learning not only real-sea experiment data with labeling determined based on actual user satisfaction but also service data without label determined together.

• Bulletin of the Korean Mathematical Society
• /
• v.55 no.5
• /
• pp.1463-1481
• /
• 2018

Let $X=\{X_t\}_{t{\geq}0}$ be a $L{\acute{e}}vy$ process in ${\mathbb{R}}^d$ and ${\Omega}$ be an open subset of ${\mathbb{R}}^d$ with finite Lebesgue measure. The quantity $H_{\Omega}(t)={\int_{\Omega}}{\mathbb{P}}^x(X_t{\in}{\Omega})$ dx is called the heat content. In this article we consider its generalized version $H^{\mu}_g(t)={\int_{\mathbb{R}^d}}{\mathbb{E}^xg(X_t){\mu}(dx)$, where g is a bounded function and ${\mu}$ a finite Borel measure. We study its asymptotic behaviour at zero for various classes of $L{\acute{e}}vy$ processes.

• Journal of the Chungcheong Mathematical Society
• /
• v.23 no.1
• /
• pp.37-48
• /
• 2010

In this paper we use a generalized Brownian motion process to defined an analytic operator-valued generalized Feynman integral. We then obtain explicit formulas for the analytic operatorvalued generalized Feynman integrals for functionals of the form $$F(x)=f\({\int}^T_0{\alpha}_1(t)dx(t),{\cdots},{\int}_0^T{\alpha}_n(t)dx(t)\)$$, where x is a continuous function on [0, T] and {${\alpha}_1,{\cdots},{\alpha}_n$} is an orthonormal set of functions from ($L^2_{a,b}[0,T]$, ${\parallel}{\cdot}{\parallel}_{a,b}$).