• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2414-2434
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• 2015

Among numerous localization schemes proposed specifically for Wireless Sensor Network (WSN), the range-free localization algorithms based on the received signal strength indication (RSSI) have attracted considerable research interest for their simplicity and low cost. As a typical range-free algorithm, Approximate Point In Triangulation test (APIT) suffers from significant estimation errors due to its theoretical defects and RSSI inaccuracy. To address these problems, a novel localization method called FIAPIT, which is a combination of an improved APIT (IAPIT) and a fuzzy logic system, is proposed. The proposed IAPIT addresses the theoretical defects of APIT in near (it's defined as a point adjacent to a sensor is closer to three vertexes of a triangle area where the sensor resides simultaneously) and far (the opposite case of the near case) cases partly. To compensate for negative effects of RSSI inaccuracy, a fuzzy system, whose logic inference is based on IAPIT, is applied. Finally, the sensor's coordinates are estimated as the weighted average of centers of gravity (COGs) of triangles' intersection areas. Each COG has a different weight inferred by FIAPIT. Numerical simulations were performed to compare four algorithms with varying system parameters. The results show that IAPIT corrects the defects of APIT when adjacent nodes are enough, and FIAPIT is better than others when RSSI is inaccuracy.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.7
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• pp.907-913
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• 2004

The major interest of general mobile robot is making a route and following a maked route. But, In the case of robot that is in need of movement of partial high speed, the condition of dynamic limitation is exist, and in these conditions, it demands controlling against movements we want. In this paper, in respect of the following a route at the situation that don't have the environmental map, that is, unknown environments, to prevent the slide of moving robot or the overturn that can happen for it moves fast, we organize the dynamic condition of limitation using the fuzzy logic, and we obtain more safe and fast route tracing ability by changing the standard velocity. Especially, by modeling the line tracing mobile robot, we design the tracing controller against a realtime changing target, and using the fuzzy optimized velocity limitation controller, we confirm that our robot shows its stable tracing ability by limiting its velocity intelligently against the continuously changing line.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.9
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• pp.837-843
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• 2001

In this work, we propose a new method of extracting and weighting representative keywords(RKs) from a few documents that might interest a user. In order to extract RKs, we first extract candidate terms and them choose a number of terms called initial representative keywords (IRKs) from them through fuzzy inference. Then, by expanding and reweighting IRKs using term co-occurrence similarity, the final RKs are obtained. Performance of our approach is heavily influenced by effectiveness of selection method of IRKs so that we choose fuzzy inference because it is more effective in handling the uncertainty inherent in selecting representative keywords of documents. The problem addressed in this paper can be viewed as the one of calculating center of document vectors. So, to show the usefulness of our approach, we compare with two famous methods - Rocchio and Widrow-Hoff - on a number of documents collections. The result show that our approach outperforms the other approaches.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.6
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• pp.439-444
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• 2016

The definition of quality continues to evolve. In recent years, there has been growing interest in how to satisfy customers' potential needs with an emphasis on customer-oriented quality. Two-dimensional quality proposed by Kano provides a useful framework for discovering quality attributes critical to customer satisfaction and it is widely employed for product and service development. In Kano model, quality attributes are classified into attractive, one-dimensional, must-be, indifferent, and reverse ones. Finding attractive elements among them is important for achieving customer satisfaction effectively. However, Kano's classification method has limitations in dealing with customers' ambiguous and complex ideas. The customer response itself includes uncertainty and incompleteness. To overcome this problem, fuzzy methods are incorporated with Kano's classification in this paper. According to numerical comparisons, it is shown that the fuzzy Kano method is useful for accommodating various response of customer and is helpful to identify potential needs.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.207-213
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• 2022

Recently, with the improvement of the performance of IoT technology and AI, automation and unmanned work are progressing in a wide range of fields, and interest in image processing, which is the basis of automation such as object recognition and object classification, is increasing. Image noise removal is an important process used as a preprocessing step in an image processing system, and various studies have been conducted. However, in most cases, it is difficult to preserve detailed information due to the smoothing effect in high-frequency components such as edges. In this paper, we propose an algorithm to restore damaged images in AWGN(additive white Gaussian noise) using fuzzy weights based on Gaussian distribution. The proposed algorithm switched the filtering process by comparing the filtering mask and the noise estimate with each other, and reconstructed the image by calculating the fuzzy weights according to the low-frequency and high-frequency components of the image.

• Journal of Digital Convergence
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• v.19 no.8
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• pp.319-325
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• 2021

With the advancement of wireless technology and the rapid growth of the infrastructure of mobile communication technology, systems applying AI-based platforms are drawing attention from users. In particular, the system that understands users' tastes and interests and recommends preferred items is applied to advanced e-commerce customized services and smart homes. However, there is a problem that these recommendation systems are difficult to reflect in real time the preferences of various users for tastes and interests. In this research, we propose a Fuzzy-AHP-based movies recommendation system using the Gated Recurrent Unit (GRU) language model to address a problem. In this system, we apply Fuzzy-AHP to reflect users' tastes or interests in real time. We also apply GRU language model-based models to analyze the public interest and the content of the film to recommend movies similar to the user's preferred factors. To validate the performance of this recommendation system, we measured the suitability of the learning model using scraping data used in the learning module, and measured the rate of learning performance by comparing the Long Short-Term Memory (LSTM) language model with the learning time per epoch. The results show that the average cross-validation index of the learning model in this work is suitable at 94.8% and that the learning performance rate outperforms the LSTM language model.

• Information Systems Review
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• v.10 no.3
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• pp.155-183
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• 2008

Current trend of audit is to check the physical aspects of developed information system, such as checking the budget constraints, time constraints or functional fluency etc. However, ultimate goal of information system is to help the organization to achieve the competency over their competitors. Also, there are three different interest groups in system auditing, like audit requesting group, audited group and audit group, who may have different points of interests in auditing. Current auditing process, however, ignores this point, and so does not check the differences between three groups. This study tries to develop new auditing method to cure these two problems. Contributions of this study may be summarized as follows. First, Introduce the new indexes that can check the possibility that the information system may contribute the competency of organization. Also check the feasibility of indexes through Fuzzy AHP. Second, Divide the audit related person into three groups, and their different needs toward the information system was analyzed. Third, Analyze and compare the main interests of three groups, and weights of each groups to each indexes were calculated. Fourth, Fuzzy theory was applied to quantify the qualitative answers, which may minimize the ambiguity of questionnaire replies.

• Journal of Digital Convergence
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• v.15 no.11
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• pp.425-432
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• 2017

The current recommendation systems have problems including the difficulty of figuring out whether they recommend items that actual users have preference for or have simple interest in, the scarcity of data to recommend proper items due to the extremely small number of users, and the cold-start issue of the dropping system performance to recommend items that can satisfy users according to the influx of new users. In an effort to solve these problems, this study implemented a movie recommendation system to ensure user satisfaction by using the Fuzzy-Analytic Hierarchy Process, which can reflect uncertain situations and problems, and the data partition algorithm to group similar items among the given ones. The data of a survey on movie preference with 61 users was applied to the system, and the results show that it solved the data scarcity problem based on the Fuzzy-AHP and recommended items fit for a user with the data partition algorithm even with the influx of new users. It is thought that research on the density-based clustering will be needed to filter out future noise data or outlier data.

• The Korean Journal of Applied Statistics
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• v.10 no.2
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• pp.261-274
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• 1997

Most of the data sets to which the conventional discriminant rules have been applied contain only those which belong to one and only one class among the classes of interest. However the extension of the bivalence to multivlaence like Fuzzy concepts strongly influence the traditional view that an object must belong to only class. Thus the goal of this paper is to develop new discriminant rules which can handle the data each object of which may belong to moer than two classes with certain degrees of belongings. A calibration model is used for the relationship between the feature vector of an object and the degree of belongings and a Bayesian inference is made with the Metropolis algorithm on the degree of belongings when a feature vector of an object whose membership is unknown is given. An evalution criterion is suggested for the rules developed in this paper and comparision study is carried using two training data sets.

• Journal of Electrical Engineering and Technology
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• v.12 no.5
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• pp.1719-1728
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• 2017

Recent concerns about environmental conditions have triggered the growing interest in using green energy resources. These sources of energy, however, bring new challenges mainly due to their uncertainty and intermittency. In order to alleviate the concerns on the penetration of intermittent energy resources, this paper investigates impacts of realizing demand-side potentials. Among different demand-side management programs, this paper considers demand response wherein consumers change their consumption pattern in response to changing prices. The research studies demand response potentials from different load sectors on generation system well-being. Consumers' sensitivity to time-varying prices is captured via self and cross elasticity coefficients. In the calculation of well-being indices, sequential Monte Carlo simulation approach is accompanied with fuzzy logic. Finally, IEEE-RTS is used as the test bed to conduct several simulations and the associated results are thoroughly discussed.