• Journal of Korean Society of Industrial and Systems Engineering
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• v.34 no.2
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• pp.9-18
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• 2011

Vender prioritization process is one of the most critical tasks of production and logistics management for many companies. Determining the most critical criteria for vender prioritization process is a vital means for a purchasing company to improve its supply chain productivity. This study discuss the use of a Fuzzy analytic network process (Fuzzy ANP) model which is an efficient tool to handle the fuzziness of the data involved in deciding the preferences of different criteria which are not independent. Also, the comparison of classical ANP and Fuzzy ANP is described using simulation with triangular distribution random number generation. It is shown that Fuzzy ANP model possesses some attractive properties and could be used as an alternative to the known vender prioritization methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2333-2353
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• 2020

This paper proposes a novel data prioritization and transmission mechanism to minimize the number of packets transmitted and reduce network overload using the constrained application protocol (CoAP) in resource-constrained networks. The proposed scheme adopts four classification parameters to classify and prioritize data from a sensor. With the packet prioritization scheme, the sensed data having the lowest priority is only delivered using the proposed keep-alive message notification to decrease the number of packets transmitted. The performance evaluation demonstrates that the proposed scheme shows the improvement of resource utilization in energy consumption, and bandwidth usage compared with the existing CoAP methods. Furthermore, the proposed scheme supports quality-of-service (QoS) per packet by differentiating transmission delays regarding priorities.

• Journal of Information Technology Applications and Management
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• v.16 no.3
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• pp.29-43
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• 2009

The advancement of emerging technologies can create more national value, and this motivates many nations to invest their resources in the emerging technologies. However, due to limited financial and human resources, even a wealthy nation cannot afford to randomly invest its resource in all profitable technologies. Therefore, cautious appraisal and prioritization of the competitive technologies should be conducted first, and then concentrated investment should be done for only the selected technologies. In this study, we propose a quantitative criterion for prioritizing the targeted electronic device technologies. The prioritization scheme devised in this study consists of a growth layer, a profitability layer, a vitality layer, and an influence layer. The proposed model forecasts the most promising technologies by applying the revised version of the Analytic Hierarchy Process (AHP). We performed empirical experiments on 12 emerging electronic device technologies to analyze the practical applicability of our study. The experimental data was obtained from 70 experts in high-tech industry as a part of the 2004 Prioritization and Selection project that was carried out in South Korea. As a result, the proposed scheme was able to present the most promising areas for investment in the field of electronic device technology.

• The Journal of the Korea Contents Association
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• v.15 no.1
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• pp.264-275
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• 2015

While the method of prioritization has been practicing in the community needs-based programs to improve the effectiveness and efficiency of community health planning, it has not been systematically used. This study aims to suggest more sophisticated method of prioritizing. Based on the analysis of 81 community health plans which adopt prioritizing method, this study tried to examine their methods and criteria and evaluate their adequacy. In the prioritization process, projects themselves, rather than health problems, were commonly adopted for the subject of the analysis. The most used was the Basic priority rating, followed by the Prioritization matrix. Looking at the size of health problem among the prioritization criteria, the prevalence for chronic diseases and the proportion of people with health problems for health behaviors, mainly were used as indicators. Along with the size of health problem, other factors such as the degree of seriousness of health problem, and the effectiveness of intervention have been used as the criteria of prioritizing, not fully supported by objective data base and the clear standard of scoring. In the prioritization, the analysis need to be limited only to health problems, and the scoring criteria for each health problem area be presented.

• Genomics & Informatics
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• v.11 no.4
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• pp.191-199
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• 2013

High-throughput next-generation sequencing (NGS) technology produces a tremendous amount of raw sequence data. The challenges for researchers are to process the raw data, to map the sequences to genome, to discover variants that are different from the reference genome, and to prioritize/rank the variants for the question of interest. The recent development of many computational algorithms and programs has vastly improved the ability to translate sequence data into valuable information for disease gene identification. However, the NGS data analysis is complex and could be overwhelming for researchers who are not familiar with the process. Here, we outline the analysis pipeline and describe some of the most commonly used principles and tools for analyzing NGS data for disease gene identification.

• Journal of Convergence for Information Technology
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• v.11 no.11
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• pp.10-16
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• 2021

As the implementation of smart factories spreads widely, the need for research to improve data efficiency is raised by prioritizing massive amounts of big data using IoT devices in terms of relevance and quality. The purpose of this study is to investigate whether prioritizing big data in management accounting decisions such as cost volatility estimation and recipe optimization can improve smart solution performance and decision-making effectiveness. Based on the survey answers of 84 decision makers at domestic small and medium-sized manufacturers who operate smart solutions such as ERP and MES that link manufacturing data in real time, empirical research was conducted. As a result, it was analyzed that setting prioritization of big data has a positive effect on decision-making in management accounting. became In addition, it was found that big data prioritization has a mediating effect that indirectly affects smart solution performance by using big data in management accounting decision making. Through the research results, it will be possible to contribute as a prior research to develop a scale to evaluate the correlation between big data in the process of business decision making.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.1
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• pp.515-524
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• 2014

In this paper, The investment prioritization model was investigated for keeping the service level of mooring facilities more than the target level of management with a limited budget in the right time. Network level prioritization criteria of the national scale was developed to take into criteria index and quantitative evaluation, management authority's opinion. Delphi method was conducted maintenance exports of mooring facilities over twice for verifying the validity and adequacy. In order to improve the objectivity of criteria, the criteria for evaluating the utilization of port facilities is presented form yearly facility traffic/facility's length, the result of the analysis of facility traffic data. The investment prioritization criteria of mooring facilities is expected to be utilized for more efficient and national budget distribution applied to the maintenance budget plan of nationwide.

• Journal of Internet Computing and Services
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• v.17 no.5
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• pp.89-95
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• 2016

In a software testing domain, test case prioritization techniques improve the performance of regression testing, and arrange test cases in such a way that maximum available faults be detected in a shorter time. User-sessions and cookies are unique features of web applications that are useful in regression testing because they have precious information about the application state before and after making changes to software code. This approach is in fact a user-session based technique. The user session will collect from the database on the server side, and test cases are released by the small change configuration of a user session data. The main challenges are the effectiveness of Average Percentage Fault Detection rate (APFD) and time constraint in the existing techniques, so in this paper developed an intelligent framework which has three new techniques use to manage and put test cases in group by applying useful criteria for test case prioritization in web application regression testing. In dynamic weighting approach the hybrid criteria which set the initial weight to each criterion determines optimal weight of combination criteria by evolutionary algorithms. The weight of each criterion is based on the effectiveness of finding faults in the application. In this research the priority is given to test cases that are performed based on most common http requests in pages, the length of http request chains, and the dependency of http requests. To verify the new technique some fault has been seeded in subject application, then applying the prioritization criteria on test cases for comparing the effectiveness of APFD rate with existing techniques.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.3
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• pp.917-926
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• 2013

This study presents the feasibility of fuzzy multi-criteria decision making (MCDM) techniques for the robust prioritization of projects. It is applied to water resources planning problem. Results from weighted sum method (WSM), analytic hierarchy process (AHP), revised analytic hierarchy process (R-AHP), and TOPSIS are compared with those from Fuzzy WSM, Fuzzy, AHP, Fuzzy R-AHP, and Fuzzy TOPSIS. For the calculation, all weights on criteria and the normalized data were obtained from the same investigation. As a result, the rankings from four MCDM techniques are slightly different while those from fuzzy MCDM show the comparatively consistent ranking. Therefore, it is desirable to use fuzzy MCDM technique when MCDM is used for the prioritization problem, since fuzzy MCDM can include the uncertain variability of input data and weighting values on criteria.

• Proceedings of the Korean Nuclear Society Conference
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• 1995.10a
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• pp.185-190
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• 1995

Conventional alarm system has many difficulties in the operator's identifying the plant status during special situations such as design basis accidents. To solve the shortcomings, an on-line alarm annunciator system, called dynamic alarm console (DAC), was developed. In the DAC, a signal is generated as alarm by the use of an adaptive setpoint check strategy based on operating mode, and time delay technique is used not to generate nuisance alarms. After alarm generation, if activated alarm is a level precursor alarm or a consequencial alarm, it would be suppressed, and the residual alarms go through dynamic prioritization which provide the alarms with pertinent priorities to the current operating mode. Dynamic prioritization is achieved by going through the system- and mode-oriented prioritization. The DAC has the alarm hierarchical structure based on the physical and functional importance of alarms. Therefore the operator can perceive alarm impacts on the safety or performance of the plant with the alarm propagation from equipment level to plant functional level. In order to provide the operator with the most possible cause of the event and quick cognition of the plant status even without recognizing the individual alarms, reactor trip status tree (RTST) was developed. The DAC and the RTST have been simulated with on-line data obtained from the full-scope simulator for several abnormal cases. The results indicated that the system can provide the operator with useful and compact information fur the earlier termination and mitigation of an abnormal state.