• Journal of the Korea Society of Computer and Information
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• v.16 no.12
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• pp.257-263
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• 2011

Conventional middleware is software providing services between clients and servers efficiently, but it is not applicable to RFID systems because of low consistency due to the absence of context awareness function, and problems in the management of meaning, security system, etc. Accordingly, we need a quality selection process and a quality evaluation method for selecting RFID middleware based on new criteria. This Paper proposed a new selection process based on international standard ISO/IEC 14598, and extracted and selected optimal quality factors through the proposed process. The selected quality factors were mapped to the quality characteristics of standard quality model ISO/IEC 9126, and to quality factors of RFID middleware of SUN, Microsoft, EPCglobal, IBM, etc. The results of these works showed that the quality factors extracted and selected through the proposed process were fair and adequate for evaluating the quality of RFID middleware.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.2
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• pp.111-120
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• 2017

This paper proposes an improved standard genetic algorithm (GA) of making a near optimal schedule for integrated process planning and scheduling problem (IPPS) considering tool flexibility and tool related constraints. Process planning involves the selection of operations and the allocation of resources. Scheduling, meanwhile, determines the sequence order in which operations are executed on each machine. Due to the high degree of complexity, traditionally, a sequential approach has been preferred, which determines process planning firstly and then performs scheduling independently based on the results. The two sub-problems, however, are complicatedly interrelated to each other, so the IPPS tend to solve the two problems simultaneously. Although many studies for IPPS have been conducted in the past, tool flexibility and capacity constraints are rarely considered. Various meta-heuristics, especially GA, have been applied for IPPS, but the performance is yet satisfactory. To improve solution quality against computation time in GA, we adopted three methods. First, we used a random circular queue during generation of an initial population. It can provide sufficient diversity of individuals at the beginning of GA. Second, we adopted an inferior selection to choose the parents for the crossover and mutation operations. It helps to maintain exploitation capability throughout the evolution process. Third, we employed a modification of the hybrid scheduling algorithm to decode the chromosome of the individual into a schedule, which can generate an active and non-delay schedule. The experimental results show that our proposed algorithm is superior to the current best evolutionary algorithms at most benchmark problems.

• Economic and Environmental Geology
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• v.52 no.3
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• pp.243-250
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• 2019

The purpose of this study is to pro-actively respond to changes in government R&D policy and start to supplement the limitations of previous KIGAM R&D planning process. We looked out through the existing literature for a variety of R&D planning process, and analyzed the R&D planning process characteristics of each institution through the interview. As a result, we can be derived conclusions and implications from three sides, environmental analysis, demand excavation methods, R&D project configuration and selection method. In the case of environmental analysis and the overall need to enhance the skills and mega trend analysis by market trend analysis. And in the demand side, the institute need to establish challenging and specific R&D goals. In addition, in case of configuration and selection of R&D projects we derived several implications, such as convergence, SME support, resource analysis, selection of long-term project.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.449-455
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• 2002

This paper considers that advanced planning and scheduling (APS) in manufacturing and the efficient purchasing where each customer order has its due date and multi-suppliers exit We present a Make-To­Order Supply Chan (MTOSC) model of efficient purchasing process from multi-suppliers and APS with outsourcing in a supply chain, which requires the absolute due date and minimized total cost. Our research has included two states. One is for efficient purchasing from suppliers: (a) selection of suppliers for required parts; (b) optimum part lead­time of selected suppliers. Supplier selection process has received considerable attention in the business­management literature. Determining suitable suppliers in the supply chain has become a key strategic consideration. However, the nature of these decisions usually is complex and unstructured. These influence factors can be divided into quantitative and qualitative factors. In the first level, linguistic values are used to assess the ratings for the qualitative factors such as profitability, relationship closeness and quality. In the second level a MTOSC model determines the solutions (supplier selection and order quantity) by considering quantitative factors such as part unit price, supplier's lead-time, and storage cost, etc. The other is for APS: (a) selection of the best machine for each operation; (b) deciding sequence of operations; (c) picking out the operations to be outsourcing; and (d) minimizing makespan under the due date of each customer's order. To solve the model, a genetic algorithm (GA)-based heuristic approach is developed. From the numerical experiments, GA­based approach could efficiently solve the proposed model, and show the best process plan and schedule for all customers' orders.

• Journal of Korean Society for Geospatial Information Science
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• v.14 no.1 s.35
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• pp.29-36
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• 2006

In the process of location selection, it is assumed that transformer substation socially recognized as a dangerous facility require especially more rigorous and resonable process. This paper implements suitability analysis for optimum location selection of transformer substation in southern Gyeonggi province using AHP(Analytic Hierarchy Process) and spatial analysis of GIS in terms of safety, national land use, economical efficiency and environment preservation. To do this, necessary data from 1/5,000 digital map are extracted a s raster format for suitability analysis and a field investigation also was done. In the procedure of site selection, three candidate zones with 1.5km radius were selected for the whole research area and then through field survey low transformer sites were selected from candidate zones. In the last the appropriateness of selected sites was evaluated. The results of the suitability analysis showed that the first candidate site satisfied the location condition best and other candidate sites generally showed good location condition.

• Journal of Korea Technology Innovation Society
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• v.15 no.4
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• pp.815-836
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• 2012

Corporate Laboratories have been a key driver of Korea's economic growth through attaining technology competitiveness. The study is focused on selecting outstanding small and medium corporate laboratories with high R&D capability and fostering the selected laboratories. The main topics of this study are to establish logics regarding the selection process and to propose the implementation schemes of the process. For the selection of outstanding laboratories, this study presents a evaluation indicators based on logic model and verification of the validity of following evaluation indicators through a Pilot test. The evaluation indicators from this study are expected to be in practical use as a reference for support policies of outstanding laboratories' R&D activities. For corporations, these indicators can be used to examine their R&D capability. This study also suggests differentiated policy support measures using the findings to maximize the effectiveness of the selection process.

• Knowledge Management Research
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• v.20 no.3
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• pp.155-171
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• 2019

In the entertainment industry which has great uncertainty, it is essential to predict public preference first. Thanks to various mass media channels such as cable TV and internet-based streaming services, the reality audition program has been getting big attention every day and it is being used as a new window to new entertainers' debut. This phenomenon means that it is changing from a closed selection process to an open selection process, which delegates selection rights to the public. This is characterized by the popularity of the public being reflected in the selection process. Therefore, this study aims to implement a machine learning model which predicts the winner of , which has recently been popular in South Korea. By doing so, this study is to extend the research method in the cultural industry and to suggest practical implications. We collected the data of winners from the 1st, 2nd, and 3rd seasons of the Produce 101 and implemented the predictive model through the machine learning method with the accumulated data. We tried to develop the best predictive model that can predict winners of by using four machine learning methods such as Random Forest, Decision Tree, Support Vector Machine (SVM), and Neural Network. This study found that the audience voting and the amount of internet news articles on each participant were the main variables for predicting the winner and extended the discussion by analyzing the precision of prediction.

• Proceedings of the Korea Society for Simulation Conference
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• 1998.10a
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• pp.199-203
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• 1998

Process planning and scheduling are traditionally regarded as separate tasks performed sequentially. But if two tasks are performed concurrently, greater performance can be achieved. In this study, we propose new approach to integration of process planning and scheduling. We propose new process planning combinations selection method using simulation based genetic algorithms. Computational experiments show that proposed method yield better performance when compared with existing methods.

• Geomechanics and Engineering
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• v.25 no.1
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• pp.1-16
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• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1773-1793
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• 2023

Automated machine learning, often referred to as "AutoML," is the process of automating the time-consuming and iterative procedures that are associated with the building of machine learning models. There have been significant contributions in this area across a number of different stages of accomplishing a data-mining task, including model selection, hyper-parameter optimization, and preprocessing method selection. Among them, preprocessing method selection is a relatively new and fast growing research area. The current work is focused on the recommendation of preprocessing methods, i.e., feature subset selection (FSS) algorithms. One limitation in the existing studies regarding FSS algorithm recommendation is the use of a single learner for meta-modeling, which restricts its capabilities in the metamodeling. Moreover, the meta-modeling in the existing studies is typically based on a single group of data characterization measures (DCMs). Nonetheless, there are a number of complementary DCM groups, and their combination will allow them to leverage their diversity, resulting in improved meta-modeling. This study aims to address these limitations by proposing an architecture for preprocess method selection that uses ensemble learning for meta-modeling, namely AutoFE-Sel. To evaluate the proposed method, we performed an extensive experimental evaluation involving 8 FSS algorithms, 3 groups of DCMs, and 125 datasets. Results show that the proposed method achieves better performance compared to three baseline methods. The proposed architecture can also be easily extended to other preprocessing method selections, e.g., noise-filter selection and imbalance handling method selection.