• Communications for Statistical Applications and Methods
• /
• v.16 no.5
• /
• pp.781-790
• /
• 2009

This paper deals with a rank transformation method and a Theil's method based on an ${\alpha}$-level set of a fuzzy number to construct a fuzzy linear regression model. The rank transformation method is a simple procedure where the data are merely replaced with their corresponding ranks, and the Theil's method uses the median of all estimates of the parameter calculated from selected pairs of observations. We also consider two numerical examples to evaluate effectiveness of the fuzzy regression model using the proposed method and of another fuzzy regression model using the least square method.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.11
• /
• pp.3723-3737
• /
• 2022

Google Play is one of the largest Android phone app markets and it contains both free and paid apps. It provides a variety of categories for every target user who has different needs and purposes. The customer's rate every product based on their experience of apps and based on the average rating the position of an app in these arch varies. Fraudulent behaviors emerge in those apps which incorporate search rank maltreatment and malware proliferation. To distinguish the fraudulent behavior, a novel framework is structured that finds and uses follows left behind by fraudsters, to identify both malware and applications exposed to the search rank fraud method. This strategy correlates survey exercises and remarkably joins identified review relations with semantic and behavioral signals produced from Google Play application information, to distinguish dubious applications. The proposed model accomplishes 90% precision in grouping gathered informational indexes of malware, fakes, and authentic apps. It finds many fraudulent applications that right now avoid Google Bouncers recognition technology. It also helped the discovery of fake reviews using the reviewer relationship amount of reviews which are forced as positive reviews for each reviewed Google play the android app.

• KIEAE Journal
• /
• v.16 no.2
• /
• pp.17-22
• /
• 2016

Purpose: Sensitivity analysis offers a good guideline for designing energy conscious buildings, which is fitted to a specific building configuration. Sensitivity analysis is, however, still too expensive to be a part of regular design process. The One-at-a-time (OAT) is the most common and simplest sensitivity analysis method. This study aims to propose a reasonable ground that the OAT can be an alternative method for the variance-based method in some early design scenarios, while the variance-based method is known adequate for dealing with nonlinear response and the effect of interactions between input variables, which are most cases in building energy simulations. Method: A test model representing the early design phase is built in the DOE2 energy simulations. Then sensitivity ranks between the OAT and the Variance-based methods are compared at three U.S. sites. Result: Parameters in the upper rank by the OAT do not much differ from those by the Main effect index. Considering design practices that designers would chose the most energy saving design option first, this rank similarity between two methods seems to be acceptable in the early design phase.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.160-160
• /
• 2023

Generally, Global Climate Models (GCM) cannot be used directly due to their inherent error arising from over or under-estimation of climate variables compared to the observed data. Several bias correction methods have been devised to solve this problem. Most of the traditional bias correction methods are one dimensional as they bias correct the climate variables separately. One such method is the Quantile Mapping method which builds a transfer function based on the statistical differences between the GCM and observed variables. Laux et al. introduced a copula-based method that bias corrects simulated climate data by employing not one but two different climate variables simultaneously and essentially extends the traditional one dimensional method into two dimensions. but it has some limitations. This study uses objective functions to address specifically, the limitations of Laux's methods on the Quantile Mapping method. The objective functions used were the observed rank correlation function, the observed moment function and the observed likelihood function. To illustrate the performance of this method, it is applied to ten GCMs for 20 stations in South Korea. The marginal distributions used were the Weibull, Gamma, Lognormal, Logistic and the Gumbel distributions. The tested copula family include most Archimedean copula families. Five performance metrics are used to evaluate the efficiency of this method, the Mean Square Error, Root Mean Square Error, Kolmogorov-Smirnov test, Percent Bias, Nash-Sutcliffe Efficiency and the Kullback Leibler Divergence. The results showed a significant improvement of Laux's method especially when maximizing the observed rank correlation function and when maximizing a combination of the observed rank correlation and observed moments functions for all GCMs in the validation period.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.17 no.7
• /
• pp.1564-1570
• /
• 2013

In this paper, we propose a robust pupil detection method using rank order filter and cross-correlation. Potential pupil candidates are detected using rank order filter. Eye region is binarized using variable threshold to find eyebrow, and pupil candidates at the eyebrow are removed. The positions of pupil candidates are corrected, the pupil candidates are grouped into pairs based on geometric constraints. A similarity measure is obtained for two eye of each pair using cross-correlation, we select a pair with the largest similarity measure as a final pupil. The experiments have been performed for 500 images of the BioID face database. The results show that it achieves the high detection rate of 96.8% and improves about 11.6% than existing method.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.12
• /
• pp.5888-5903
• /
• 2019

The Relational ranking method applies authority-based ranking in relational dataset that can be modeled as graphs considering also their tuples' values. Authority directions from tuples that contain the given keywords and transfer to their corresponding neighboring nodes in accordance with their values and semantic connections. From our previous work, ObjectRank extends to ValueRank that also takes into account the value of tuples in authority transfer flows. In a maked difference from ObjectRank, which only considers authority flows through relationships, it is only valid in the bibliographic databases e.g. DBLP dataset, ValueRank facilitates the estimation of importance for any databases, e.g. trading databases, etc. A relational keyword search paradigm Object Summary (denote as OS) is proposed recently, given a set of keywords, a group of Object Summaries as its query result. An OS is a multilevel-tree data structure, in which node (namely the tuple with keywords) is OS's root node, and the surrounding nodes are the summary of all data on the graph. But, some of these trees have a very large in total number of tuples, size-l OSs are the OS snippets, have also been investigated using ValueRank.We evaluated the real bibliographical dataset and Microsoft business databases to verify of our proposed approach.

• Journal of the Korea Society of Computer and Information
• /
• v.20 no.1
• /
• pp.29-37
• /
• 2015

Nowadays, the user-centric application based web 2.0 has replaced the web 1.0. The users gain and provide information by interactive network applications. As a result, traditional approaches that only extract and analyze users' local document operating behavior and network browsing behavior to build the users' preference profile cannot fully reflect their interests. Therefore this paper proposed a preference analysis and indicating approach based on the users' communication information from MicroBlog, such as reading, forwarding and @ behavior, and using the improved PersonalRank method to analyze the importance of a user to other users in the network and based on the users' communication behavior to update the weight of the items in the user preference. Simulation result shows that our proposed method outperforms the ontology model, TREC model, and the category model in terms of 11SPR value.

• Journal of Digital Contents Society
• /
• v.9 no.4
• /
• pp.625-633
• /
• 2008

Recently, the participation, opening and joint ownership of the users are important issue. The users want professional and accurate information from web. But users often suffer from retrieving accurate information. Even though the users find information they want, it is not guaranteed that the information is reliable since there are too much information placed on the web. Thus, we propose the novel rank metric to promote reliability and efficiency in information retrieval. In order to verify our approach, we implement a web site based on the proposed rank metric for nonofficial medical science information. The proposed rank metric based on user's level. This is to give score of text through differential rate depending on the user's level. The proposed rank metric enhances the reliability of text which is reflecting the user's mental factor. Thus, this method can be used for enhancing the reliability of text.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.49 no.4
• /
• pp.54-61
• /
• 2012

This paper proposes a fusion method of the queen-bee evolution into the rank-based control of mutation probability for improving the performances of genetic algorithms. The rank-based control of mutation probability which showed some performance improvements than the original method was a method that prevented individuals of genetic algorithms from falling into local optimum areas and also made it possible for the individuals to get out of the local optimum areas if they fell into there. This method, however, showed not good performances at the optimization problems that had a global optimum located in a small area regardless of the number of local optimum areas. We think that this is because the method is insufficient in the convergence into the global optimum, so propose a fusion method of the queen-bee evolution into this method in this paper. The queen-bee evolution inspired by reproduction process of queen-bee is a method that can strengthen the convergency of genetic algorithms. From the extensive experiments with four function optimization problems in order to measure the performances of proposed method we could find that the performances of proposed method was considerably good at the optimization problems whose global optimum is located in a small area as we expected. Our method, however, showed not good performances at the problems whose global optima were distributed in broad ranges and even showed bad performances at the problems whose global optima were located far away. These results indicate that our method can be effectively used at the problems whose global optimum is located in a small area.

• The Korean Journal of Applied Statistics
• /
• v.24 no.1
• /
• pp.137-143
• /
• 2011

The classification of subjects with unknown distribution in a small sample size often involves order-restricted constraints in multivariate parameter setups. Those problems make the optimality of a conventional likelihood ratio based statistical inferences not feasible. Fortunately, Roy (1953) introduced union-intersection principle(UIP) which provides an alternative avenue. Multivariate linear rank statistics along with that principle, yield a considerably appropriate robust testing procedure. Furthermore, conditionally distribution-free test based upon exact permutation theory is used to generate p-values, even in a small sample. Applications of this method are illustrated in a real microarray data example (Lobenhofer et al., 2002).