• Journal of Korea Water Resources Association
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• v.41 no.5
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• pp.517-525
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• 2008

Regional rainfall quantile depends on the identification of hydrologically homogeneous regions. Various variables relevant to precipitation can be used to form regions. Since the type and number of variables may lead to improve the efficiency of partitioning, it is important to select those precipitation related variables, which represent most of the information from all candidate variables. Multivariate analysis techniques can be used for this purpose. Procrustes analysis which can decrease the dimension of variables based on their correlations, are applied in this study. 42 rainfall related variables are decreased into 21 ones by Procrustes analysis. Factor analysis is applied to those selected variables and then 5 factors are extracted. Fuzzy-c means technique classifies 68 stations into 6 regions. As a result, the GEV distributions are fitted to 6 regions while the lognormal and generalized logistic distributions are fitted to 5 regions. For the comparison purpose with previous results, rainfall quantiles based on generalized logistic distribution are estimated by at-site frequency analysis, index flood method, and regional shape estimation method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.8
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• pp.8-17
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• 2017

Infrastructures such as bridges and tunnels are crucial elements of national economic growth, and sudden collapses may lead to great catastrophes with significant social and economic losses, as well as a loss of lives. Hence, an efficient maintenance technique must be applied to guarantee safety, secure budgets to maintain a certain level of service, and prevent maintenance expenditures from being concentrated in a specific time period. Developed countries have experienced rapid increases in maintenance budgets, and maintenance costs now account for about 40% of the total maintenance budget. The level of service in asset management systems is an essential element for setting management goals and making priority decisions. Therefore, this study uses fuzzy theory to develop a new way to assess the level of service.The assessment model was applied to an actual bridge to evaluate the level of service for users.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.345-353
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• 2021

Cloud computing represent a new era of computing that's forms through the combination of service-oriented architecture (SOA), Internet and grid computing with virtualization technology. Virtualization is a concept through which every cloud is enable to provide on-demand services to the users. Most IT service provider adopt cloud based services for their users to meet the high demand of computation, as it is most flexible, reliable and scalable technology. Energy based performance tradeoff become the main challenge in cloud computing, as its acceptance and popularity increases day by day. Cloud data centers required a huge amount of power supply to the virtualization of servers for maintain on- demand high computing. High power demand increase the energy cost of service providers as well as it also harm the environment through the emission of CO₂. An optimization of cloud computing based on energy-performance tradeoff is required to obtain the balance between energy saving and QoS (quality of services) policies of cloud. A study about power usage of resources in cloud data centers based on workload assign to them, says that an idle server consume near about 50% of its peak utilization power [1]. Therefore, more number of underutilized servers in any cloud data center is responsible to reduce the energy performance tradeoff. To handle this issue, a lots of research proposed as energy efficient algorithms for minimize the consumption of energy and also maintain the SLA (service level agreement) at a satisfactory level. VM (virtual machine) consolidation is one such technique that ensured about the balance of energy based SLA. In the scope of this paper, we explore reinforcement with fuzzy logic (RFL) for VM consolidation to achieve energy based SLA. In this proposed RFL based active VM consolidation, the primary objective is to manage physical server (PS) nodes in order to avoid over-utilized and under-utilized, and to optimize the placement of VMs. A dynamic threshold (based on RFL) is proposed for over-utilized PS detection. For over-utilized PS, a VM selection policy based on fuzzy logic is proposed, which selects VM for migration to maintain the balance of SLA. Additionally, it incorporate VM placement policy through categorization of non-overutilized servers as- balanced, under-utilized and critical. CloudSim toolkit is used to simulate the proposed work on real-world work load traces of CoMon Project define by PlanetLab. Simulation results shows that the proposed policies is most energy efficient compared to others in terms of reduction in both electricity usage and SLA violation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.4D
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• pp.451-457
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• 2008

Establishing realistic bus service coverage is needed to build optimum city bus line networks and reasonable bus service coverage areas. The purposes of this study are understanding the characteristics of the present walking time and marginal walking time of small-medium cities and constructing an ANFIS (Adaptive Neuro-Fuzzy Inference System) model to estimate the marginal walking time for certain age and income. The cities of Masan, Chongwon and Jinju are selected for study cities. The 80 percentile of present walking time of bus users of these cities are 10.2-11.1 minutes, thus the values are greater than the 5 minutes of the maximum walking time in USA and the marginal walking times of 21.1-21.8 minutes are much greater. An ANFIS model based on pulled data of the cities are constructed to estimate the marginal walking time of small-medium cities. Analyzing the relationship between marginal walking time and age/income by using the model, the marginal walking time decreases as the age increases, but is near constant from the age of 25 to 35. And the marginal walking time is inversely proportional to the income. In comparing the surveyed and the estimated values, as the statistics of coefficient of determination, MSE and MAE are 0.996, 0.163, 0.333 respectively, it may be judged that the explainability of the model is very high. The technique developed in this study can be applied to other cities.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.199-208
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• 2023

Taguchi method is one of the most popular approaches for design optimization such that performance characteristics become robust to uncontrollable noise variables. However, most previous Taguchi method applications have addressed a single-characteristic problem. Problems with multiple characteristics are more common in practice. The multi-criteria decision making(MCDM) problem is to select the optimal one among multiple alternatives by integrating a number of criteria that may conflict with each other. Representative MCDM methods include TOPSIS(Technique for Order of Preference by Similarity to Ideal Solution), GRA(Grey Relational Analysis), PCA(Principal Component Analysis), fuzzy logic system, and so on. Therefore, numerous approaches have been conducted to deal with the multi-characteristic design problem by combining original Taguchi method and MCDM methods. In the MCDM problem, multiple criteria generally have different measurement units, which means that there may be a large difference in the physical value of the criteria and ultimately makes it difficult to integrate the measurements for the criteria. Therefore, the normalization technique is usually utilized to convert different units of criteria into one identical unit. There are four normalization techniques commonly used in MCDM problems, including vector normalization, linear scale transformation(max-min, max, or sum). However, the normalization techniques have several shortcomings and do not adequately incorporate the practical matters. For example, if certain alternative has maximum value of data for certain criterion, this alternative is considered as the solution in original process. However, if the maximum value of data does not satisfy the required degree of fulfillment of designer or customer, the alternative may not be considered as the solution. To solve this problem, this paper employs the desirability function that has been proposed in our previous research. The desirability function uses upper limit and lower limit in normalization process. The threshold points for establishing upper or lower limits let us know what degree of fulfillment of designer or customer is. This paper proposes a new design optimization technique for multi-characteristic design problem by integrating the Taguchi method and our desirability functions. Finally, the proposed technique is able to obtain the optimal solution that is robust to multi-characteristic performances.

• Journal of Sensor Science and Technology
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• v.6 no.1
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• pp.6-15
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• 1997

This paper presents the multi-objective optimal design of thermopile sensor having beam or membrane structure. The thermopile sensor is composed of $Si_{3}N_{4}/SiO_{2}$ dielectric membrane, Al-polysilicon thermocouples and $RuO_{2}$ thin film for black body. The sensing method is based on the Seebeck effect which is originated from the temperature difference of the two positions, black body and silicon rim. The objective functions of the presented design are sensitivity, detectivity and thermal time constant. The modelling of the sensor is proposed including the package. The multi-objective optimization technique is applied to the design of the sensor not only inspecting the modelling equation but also simulating mathematical programming method. Especially, fuzzy optimization technique is adapted to get the optimal solution which enables the designer to reach the more practical solution. The design constraint of the voltage output originated from the change of the environmental temperature is included for practical use.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.2
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• pp.191-196
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• 2005

Although the technology for emotion recognition is important one which was demanded in various fields, it still remains as the unsolved problems. Especially, there is growing demand for emotion recognition technology based on racial image. The facial image based emotion recognition system is complex system comprised of various technologies. Therefore, various techniques such that facial image analysis, feature vector extraction, pattern recognition technique, and etc, are needed in order to develop this system. In this paper, we propose new emotion recognition system based un previously studied facial image analysis technique. The proposed system recognizes the emotion by using the fuzzy classifier. The facial image database is built up and the performance of the proposed system is verified by using built database.

• The Journal of the Acoustical Society of Korea
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• v.18 no.8
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• pp.84-93
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• 1999

Determination of grain size of heat-treated stainless steel based fm digital ultrasonic signal processing technique is presented. This techniques consist in evidence accumulation with multiple feature parameters, difference absolute mean value(DAMV), variance(VAR), mean frequency (MEANF), auto regressive model coefficient(ARC) and linear cepstrum coefficient(LCC). Feature parameters were extracted from ultrasonic echo signal of heat-treated metals. It was found that a few parameters might not be sufficient to exactly evaluate the grain size of heat-treated metals. The determination of grain size of heat-treated metals was carried out through the evidence accumulation procedure using the distances measured with reference parameters. A fuzzy mapping function is designed to transform the distances for the application of the evidence accumulation method. In the work presented, heat-treated stainless steel samples with various grain sizes are examined. The processed experimental results supports the feasibility of the grain size determination technique presented.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.9
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• pp.697-706
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• 2020

The relationship between education and work is of the greatest concern to individuals and society because they are the key drivers of growth and development. In the context of Industry 4.0, labor and educators are facing the challenges of big changes in the workplace. How to prepare undergraduate students for the world of employment has become the most important mission of higher education providers. This paper explored the competency gap in the labor market in Vietnam from the perspective of employees who have been dissatisfied with the current status. First, a qualitative method with the Delphi technique was applied to confirm this consensus in an employees' competency model. Then, the satisfaction level for each competency criterion was explored by applying the advance quantitative method, namely, best non-fuzzy performance approach. Lifelong learning was ranked first, followed by creativity and innovation, foreign languages, expertise and digitalization, adaptability, and finally, organizing and managing ability. Critical thinking and problem-solving were perceived to have the biggest gap. The order of competency satisfaction is useful in explaining the mismatch between education quality and labor market demand. The findings provide valuable guidelines for education managers who seek to bridge the competency gap and improve education quality.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.5
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• pp.183-188
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• 2013

A variety of cluster analysis techniques prerequisite to cluster objects having similar characteristics in data mining. But the clustering of those algorithms have lots of difficulties in dealing with categorical data within the databases. The imprecise handling of uncertainty within categorical data in the clustering process stems from the only algebraic logic of rough set, resulting in the degradation of stability and effectiveness. This paper proposes a information-theoretic rough entropy(RE) by taking into account the dependency of attributes and proposes a technique called min-mean-mean roughness(MMMR) for selecting clustering attribute. We analyze and compare the performance of the proposed technique with K-means, fuzzy techniques and other standard deviation roughness methods based on ZOO dataset. The results verify the better performance of the proposed approach.