• Journal of the Korean Applied Science and Technology
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• v.33 no.1
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• pp.176-185
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• 2016

Yeonsan Ogae has been known as supporting health and high efficacy of treatment. In recent days, as the efficacy of functional peptides has known, the optimization of oligo peptides production and its characteristics from Ogae legs has been performed. Response surface method was used to perform the optimizaion of enzyme hydrolysis. The range of processes was temperature ( 40, 50 and $60^{\circ}C$), pH( pH 6.0, 7.0 and 8.0 ), and enzyme( 1, 2 and 3% ). The degree of hydrolysis, amino acids, molecular weight of products were analyzed. The optimum process of enzyme hydrolysis were determined as temperature $58^{\circ}C$, pH 7.5, and enzyme concentration 3%. At optimum conditions, the degree of hydrolysis after 2 h reaction was 75-80%. The amino acid and were 168.131 mg/100 g, respectively. The molecular weight of products by using MALDI-TOF was ranged from 300 to 1,000 Da.

• Journal of KIISE:Software and Applications
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• v.31 no.4
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• pp.387-393
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• 2004

DNA computing is technology that applies immense parallel castle of living body molecules into information processing technology, and has used to solve NP-complete problems. However, there are problems which do not look for solutions and take much time when only DNA computing technology solves NP-complete problems. In this paper we proposed an algorithm called ACO(Algorithm for Code Optimization) that can efficiently express DNA sequence and create good codes through composition and separation processes as many as the numbers of reaction by DNA coding method. Also, we applied ACO to Hamiltonian path problem of NP-complete problems. As a result, ACO could express DNA codes of variable lengths more efficiently than Adleman's DNA computing algorithm could. In addition, compared to Adleman's DNA computing algorithm, ACO could reduce search time and biological error rate by 50% and could search for accurate paths in a short time.

• The Journal of the Korea Contents Association
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• v.13 no.9
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• pp.20-27
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• 2013

In recently years, with the growth of social media and the development of mobile devices, the data have been significantly increased. MapReduce is an emerging programming model that processes large amount of data. However, since MapReduce evenly places the data in the dedicated distributed computing environment, it is not suitable to the non-dedicated distributed computing environment. The data replacement algorithms were proposed for performance optimization of MapReduce in the non-dedicated distributed computing environments. However, they spend much time for date replacement and cause the network load for unnecessary data transmission. In this paper, we propose an efficient data replacement algorithm for the performance optimization of MapReduce in the non-dedicated distributed computing environments. The proposed scheme computes the ratio of data blocks in the nodes based on the node availability model and reduces the network load by transmitting the data blocks considering the data placement. Our experimental results show that the proposed scheme outperforms the existing scheme.

• Industrial Engineering and Management Systems
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• v.4 no.1
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• pp.54-67
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• 2005

This paper provides an evaluation of an optimization-based, multiple-input double-output (MIDO) run-to-run (R2R) control scheme for general semiconductor manufacturing processes. The controller in this research, termed adaptive dual response optimizing controller (ADROC), can serve as a process optimizer as well as a recipe regulator between consecutive runs of wafer fabrication. In evaluation, it is assumed that the equipment model could be appropriately described by a pair of second-order polynomial functions in terms of a set of controllable variables. Of practical relevance is to consider a drifting effect in the equipment model since in common semiconductor practice the process tends to drift due to machine aging and tool wearing. We select a typical application of R2R control to chemical mechanical planarization (CMP) in semiconductor manufacturing in this evaluation, and there are five different CMP process scenarios demonstrated, including mean shift, variance increase, and IMA disturbances. For the controller, ADROC, an on-line estimation technique is implemented in a self-tuning (ST) control manner for the adaptation purpose. Subsequently, an ad hoc global optimization algorithm based on the dual response approach, arising from the response surface methodology (RSM) literature, is used to seek the optimum recipe within the acceptability region for the execution of next run. The main components of ADROC are described and its control performance is assessed. It reveals from the evaluation that ADROC can provide excellent control actions for the MIDO R2R situations even though the process exhibits complicated, nonlinear interaction effects between control variables, and the drifting disturbances.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.7
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• pp.2349-2357
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• 2010

This work focuses on the implementation and optimization of the Automated Storage and Retrieval System(AS/RS) which is associated with Logistics Information System(LIS). We survey literature and present a case study for the optimization of Logistics System. In the case study, we examine the adaptation and implementation of AS/RS after we analyzed the processes of the company. Logistics Information System is an important component to keep up with the competitive advantage in the global business environments. The company in the case applies LIS to its efficient physical distribution management. AS/RS leads to some benefits; maximized storage space, increased productivity, reduced labor costs and human error, and improved accuracy. AS/RS is found especially effective when working with narrow aisles and extremely high racks.

• Applied Chemistry for Engineering
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• v.33 no.3
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• pp.272-278
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• 2022

In this study, natural emulsifiers were extracted from Medicago sativa L. and Sapindus saponaria L. The extraction yield using CCD-RSM and the extraction process of foaming stability of the extracted product were optimized and 95% confidence interval was used to confirm the statistical reasonableness of the optimization. Herein, independent parameters were the ethanol volume and extraction temperature, whereas reaction parameters were the extraction yield and foaming stability. Under the condition of 53.5 vol% ethanol and extraction temperature (70.9 ℃), the maximum yield and foaming stability of the extracted product from Medicago sativa L were predicted as 26.2 wt% and 44.5%, respectively. In the case of the extraction from Sapindus saponaria L, the maximum yield and foaming stability were expected to be 31.9 wt% and 47.5% under the optimized conditions including 60.4 vol% of ethanol and extraction temperature (72.4 ℃). The average experimental error for validating the accuracy was about 3.4(± 0.3)% and 5.0(± 0.04)% for the extraction processes from Medicago sativa L. and Sapindus saponaria L., respectively.

• Journal of Information Technology Services
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• v.21 no.2
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• pp.97-107
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• 2022

Recently, interest in smart factories is increasing. Investments to improve intelligence/automation are also being made continuously in manufacturing plants. Facility automation based on sensor data collection is now essential. In addition, we are operating our factories based on data generated in all areas of production, including production management, facility operation, and quality management, and an integrated standard information system. When producing LCD polarizer products, it is most important to link trace information between data generated by individual production processes. All systems involved in production must ensure that there is no data loss and data integrity is ensured. The large-capacity data collected from individual systems is composed of key values linked to each other. A real-time quality analysis processing system based on connected integrated system data is required. In this study, large-capacity data collection, storage, integration and loss prevention methods were presented for optimization of LCD polarizer production. The identification Risk model of inspection products can be added, and the applicable product model is designed to be continuously expanded. A quality inspection and analysis system that maximizes the yield rate was designed by using the final inspection image of the product using big data technology. In the case of products that are predefined as analysable products, it is designed to be verified with the big data knn analysis model, and individual analysis results are continuously applied to the actual production site to operate in a virtuous cycle structure. Production Optimization was performed by applying it to the currently produced LCD polarizer production line.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.7
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• pp.84-90
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• 2022

The purpose of the cover housing component of a car door is to protect the terminals of the plug housing that connects the electric control unit on the door side to the car body. Therefore, for a smooth assembly with the plug housing and to prevent contaminants from penetrating into the gaps that occur after assembly, the warpage of the cover housing should be minimized. In this study, to minimize the warpage of the cover housing, optimization was performed for design factors related to the mold and processes based on the injection molding simulation. These design factors include gate location, gate diameter, injection time, resin temperature, mold temperature, and packing pressure. To optimize the design factors, Taguchi's approach to the design of experiments was adopted. The optimal combination of the design factors and levels that minimize warpage was predicted through L₁₈-orthogonal array experiments and main effects analysis. Moreover, the warpage under the optimal design was estimated by the additive model, and it was confirmed through the simulation experiment that the estimated result was quite consistent with the experimental result. Additionally, it was found that the warpage under the optimal design was significantly improved compared to both the warpage under the initial design and the best warpage among the orthogonal array experimental results, which numerically decreased by 36.9% and 23.4%, respectively.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.4
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• pp.109-117
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• 2022

Most of real-world decision-making processes are used to optimize problems with many objectives of conflicting. Since the betterment of some objectives requires the sacrifice of other objectives, different objectives may not be optimized simultaneously. Consequently, Pareto solution can be considered as candidates of a solution with respect to a multi-objective optimization (MOP). Such problem involves two main procedures: finding Pareto solutions and choosing one solution among them. So-called multi-objective genetic algorithms have been proved to be effective for finding many Pareto solutions. In this study, we suggest a fitness evaluation method based on the achievement level up to the target value to improve the solution search performance by the multi-objective genetic algorithm. Using numerical examples and benchmark problems, we compare the proposed method, which considers the achievement level, with conventional Pareto ranking methods. Based on the comparison, it is verified that the proposed method can generate a highly convergent and diverse solution set. Most of the existing multi-objective genetic algorithms mainly focus on finding solutions, however the ultimate aim of MOP is not to find the entire set of Pareto solutions, but to choose one solution among many obtained solutions. We further propose an interactive decision-making process based on a visualized trade-off analysis that incorporates the satisfaction of the decision maker. The findings of the study will serve as a reference to build a multi-objective decision-making support system.

• Journal of the Korean Institute of Gas
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• v.28 no.1
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• pp.44-52
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• 2024

This study presents a novel approach to hydrogen production by biogas from organic waste without CO₂ removal. A process model was developed to reduce the costs associated with biogas pretreatment and purification processes. Through optimization of heat exchange networks, the simulation aimed to minimize process costs, maximizing hydrogen production and flue gas temperature. The results reveal that the most efficient process model maximizes the flue gas temperature while following the constraint of the number of heat exchangers. These findings hold promise for contributing to the expansion of "Biogas-to-clean hydrogen" energy conversion technology.