• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.5
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• pp.969-976
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• 2007

A fuzzy classifier which needs various analyses of features using genetic algorithms is proposed. The fuzzy classifier has a simple structure, which contains a classification part based on fuzzy logic theory and a rule generation part using genetic algorithms. The rule generation part determines optimal fuzzy membership functions and inclusion or exclusion of each feature in fuzzy classification rules. We analyzed recognition rate of a specific object, then added finer features repetitively, if necessary, to the object which has large misclassification rate. And we introduce repetitive analyses method for the minimum size of string and population, and for the improvement of recognition rates. This classifier is applied to two examples of the recognition of iris data and the recognition of Thyroid Gland cancer cells. The fuzzy classifier proposed in this paper has recognition rates of 98.67% for iris data and 98.25% for Thyroid Gland cancer cells.

• Journal of the Korea Society of Computer and Information
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• v.15 no.9
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• pp.19-24
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• 2010

The number of automobiles are continuously increasing in Korea since 1990's and it causes frustrating commuting traffic and holyday traffic. Meanwhile, the obsolete traffic signal control system is still under static control based on the aggregated traffic statistics thus it is not sufficiently adaptive in real world traffic situation that changes in real time. Thus, in this paper, we propose an adaptive signal control system using fuzzy control technology that can react to real time traffic situations. The method computes the priority of signal phases based on the number of waiting automobiles and occupying time on intersection using fuzzy membership functions. The phase with highest priority obtains "proceed" signal. Also, the duration of this "proceed" signal is determined based on the ratio of number of waiting automobiles of given phase and total number of waiting automobiles on intersection. In experiment, we show that the proposed fuzzy control system is better than the static control system for all sorts of traffic congestion situations by simulation.

• Journal of the Korean earth science society
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• v.40 no.1
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• pp.9-23
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• 2019

Super-refraction of radar beams tends to occur primarily under a particular vertical structure of temperature and water vapor pressure profiles. A quality control process for the removal of anomalous propagation (AP) ehcoes are required because APs are easily misidentified as precipitation echoes. For this purpose, we collected X-band polarimetric radar parameters (differential reflectivity, cross-correlation coefficient, and differential phase) only including non-precipitation echoes (super-refraction and clear-sky ground echoes) and precipitation echoes, and compared the echo types regarding the relationships among radar reflectivities, polarimetric parameters, and the membership functions. We developed a removal algorithm for the non-precipitation echoes using the texture approach for the polarimetric parameters. The presented algorithm is qualitatively validated using the S-band Jindo radar in Jeollanam-do. Our algorithm shows the successful identification and removal of AP echoes.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.17 no.4
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• pp.131-136
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• 2007

Ad hoc networks enable efficient resource aggregation in decentralized manner, and are inherently scalable and fault-tolerant since they do not depend on any centralized authority. However, lack of a centralized authority prompts many security-related challenges. Moreover, the dynamic topology change in which network nodes frequently join and leave adds a further complication in designing effective and efficient security mechanism. Security services for ad hoc networks need to be provided in a scalable and fault-tolerant manner while allowing for membership change of network nodes. In this paper, we investigate distributed certification mechanisms using a threshold cryptography in a way that the functions of a CA(Certification Authority) are distributed into the network nodes themselves and certain number of nodes jointly issue public key certificates to future joining nodes. In the process, we summarize one interesting report [5] in which the recently proposed RSA-based ad hoc signature scheme, called URSA, contains unfortunate yet serious security flaws. We then propose new scheme by fixing their security flaws.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.13 no.3
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• pp.135-144
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• 2003

An intrusion detection system shows different result over overall detection area according to its detection characteristics of inner detection algorithms or techniques. To expand detection areas, we requires an integrated detection which can be archived both by deploying a few detection systems which detect different detection areas and by combining their results. In addition to expand detection areas, we need to decrease the workload of security managers by false alarms and improve the correctness by minimizing false alerts which happen during the process of integration. In this paper, a method for aggregation detection use fuzzy inference to integrate a vague detection results which imply the characteristics of detection systems. Their analyzed detection characteristics are expressed as fuzzy membership functions and fuzzy rule bases which are applied through the process of fuzzy control. And, it integrate a vague decision results and minimize the number of false alerts by reflecting the characteristics of detection systems. Also it does minimize inference objects by applying thresholds decided through several experiments.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.751-758
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• 2021

Munitions must be analyzed to identify any risks for quality assurance in development and mass production. Risk identification for parts, compositions, and systems is carried out through failure mode effects analysis (FMEA) as one of the most reliable methods. FMEA is a design tool for the failure mode of risk identification and relies on the RPN (risk priority number). FMEA has disadvantages because its severity, occurrence, and detectability are rated at the same level. Fuzzy FMEA applies fuzzy logic to compensate for the shortcomings of FMEA. The fuzzy logic of Fuzzy FMEA is to express uncertainties about the phenomenon and provides quantitative values. In this paper, Fuzzy FMEA is applied to the failure mode of a rotorcraft landing system. The Fuzzy rule and membership functions were conducted in the Fuzzy model to study the RPN in the failure mode of a landing system. This method was selected to demonstrate crisp values of severity, occurrence, and detectability. In addition, the RPN was obtained. The results of Fuzzy FMEA for the landing system were analyzed for the RPN and ranking by fuzzy logic. Finally, Fuzzy FMEA confirmed that it could use the data in quality assurance activities for rotorcraft.

• Journal of the Korea Society of Computer and Information
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• v.14 no.12
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• pp.55-62
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• 2009

This paper proposes a decision method of fuzzy weights by utilizing degrees of contribution in order to classify insect footprint patterns having difficulties to classify species clearly. Insect footprints revealed delicately in the form of scattered spots since they are very small. Therefore it is not easy to define shape of footprints unlike other species, and there are lots of noises in the footprint patterns so that it is difficult to distinguish those from correct data. For these reasons, the extracted feature set has obvious feature values with some uncertain feature values, so we estimate weights according to degrees of contribution. If the one of feature values has distinct difference enough to decide a class among other classes, high weight is assigned to make classification. A calculated weight determines the membership values by fuzzy functions and objects are classified into the class having a superior value.atu present experimental resultseighrontribution. Iinsect footprints with noises by the proposed method.

• Journal of the Korea Institute of Building Construction
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• v.23 no.1
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• pp.103-111
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• 2023

Accurate cost estimation in the early stages of a construction project is critical to the successful execution of the project. In this study, an ANFIS model was presented to predict construction costs in the early stages of a construction project. To increase the usability of the model, open construction cost data was used, and a model using limited information in the early stage of the project was presented. We analyzed existing studies related to ANFIS to identify recent trends, and after reviewing the basic structure of ANFIS, presented an ANFIS model for predicting conceptual construction costs. The variation in prediction performance depending on the type and number of membership functions of the ANFIS model was analyzed, the model with the best performance was presented, and the prediction accuracy of representative machine learning models was compared and analyzed. Through comparing the ANFIS model with other machine learning models, it was found to show equal or better performance, and it is concluded that it can be applied to predicting construction costs in the early stage of a project.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.304-311
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• 2022

Construction inspection is a crucial stage that ensures that all contractual requirements of a construction project are verified. The construction inspection capabilities among state highway agencies have been greatly affected due to budget reduction. As a result, efficient inspection practices such as risk-based inspection are required to optimize the use of limited resources without compromising inspection quality. Automated prioritization of textual requirements according to their criticality would be extremely helpful since contractual requirements are typically presented in an unstructured natural language in voluminous text documents. The current study introduces a novel model for predicting the risk level of requirements using machine learning (ML) algorithms. The ML algorithms tested in this study included naïve Bayes, support vector machines, logistic regression, and random forest. The training data includes sequences of requirement texts which were labeled with risk levels (such as very low, low, medium, high, very high) using the fuzzy logic systems. The fuzzy model treats the three risk factors (severity, probability, detectability) as fuzzy input variables, and implements the fuzzy inference rules to determine the labels of requirements. The performance of the model was examined on labeled dataset created by fuzzy inference rules and three different membership functions. The developed requirement risk prediction model yielded a precision, recall, and f-score of 78.18%, 77.75%, and 75.82%, respectively. The proposed model is expected to provide construction inspectors with a means for the automated prioritization of voluminous requirements by their importance, thus help to maximize the effectiveness of inspection activities under resource constraints.

• Information Systems Review
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• v.22 no.4
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• pp.135-161
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• 2020

With the growth of blockchain and cryptocurrency-related markets, cryptocurrency exchanges are growing as a new industry. However, as the legal and regulatory definitions of cryptocurrencies are still in progress, unlike existing industrial groups, they are not under the supervision of regulatory agencies. As a result, users (i.e., cryptocurrency investors) have suffered two types of damage that could occur from hacking and other accidents on the exchanges. One type of the damage is the loss of assets caused by the extortion of personal information or account and the other is the damage from users who might be involved in external frauds. Both are analyzed in comparison with existing operators whose functions are like the exchanges. The results of this study show that membership (KYC: Know Your Client), log-in, and additional authentication in transactions are on the similar level to those of the operators while the fraud detection system (FDS) and anti-money laundering (AML) of fiat currencies and cryptocurrencies need rapid improvement.