• International Journal of Naval Architecture and Ocean Engineering
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• v.8 no.4
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• pp.344-359
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• 2016

Due to the increasing occurrence of maritime accidents and high-level requirements and modernization of naval wars, the concept of ship environment adaptability becomes more and more important. Therefore, it is of great importance to carry out an evaluation system for ship environment adaptability, which contributes to both ship design and classification. This paper develops a comprehensive evaluation system for ship environment adaptability based on fuzzy mathematics theory. An evaluation index system for ship environment adaptability is elaborately summarized first. Then the analytic hierarchy process (AHP) and entropy weighting methods are applied to aggregate the evaluations of criteria weights for each criterion and the corresponding subcriteria. Next, the multilevel fuzzy comprehensive evaluation method is applied to assess the ship integrative environment adaptability. Finally, in order to verify the proposed approach, an illustrative example for optimization and evaluation of five ship alternatives is adopted. Moreover, the influence of criteria weights, membership functions and fuzzy operators on the results is also analyzed.

• Journal of Biomedical Engineering Research
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• v.21 no.5
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• pp.527-535
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• 2000

For a reliable patient set-up verification, better portal films are needed to track relevant features. Simulator films are compared with portal films as a reference image in radiotherapy planning. This shows some possibilities of the use of image information of simulator images for enhancement and restorations of portal images which are very poor in quality compared with the simulator images. This paper present an approach that combine an associative memory, a kind of artificial neural networks with fuzzy image enhancement technique using genetic algorithm which determines the fuzzy region of membership function by the use of maximum entropy principles. A higher portal image quality than conventional technique is achieved.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.1
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• pp.36-41
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• 2012

We tested a single pendulum simulation and observed the influence of several situation space segmentation types in reinforcement learning processes in order to propose a new adaptive automation for situation space segmentation. Its segmentation is performed by the Contraction Algorithm and the Cell Division Approach. Also, its automation is performed by "entropy," which is defined on action values’ distributions. Simulation results were shown to demonstrate the influence and adaptability of the proposed method.

• Journal of Korea Multimedia Society
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• v.17 no.3
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• pp.312-323
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• 2014

Voice activity detection is very important process that find voice activity from noisy speech signal for noise cancelling and speech enhancement. Over the past few years, many studies have been made on voice activity detection, it has poor performance for speech signal of sentence form in a low SNR environment. In this paper, it proposed new voice activity detection algorithm that has beginning VAD process using entropy and main VAD process using fuzzy membership shifted c-means clustering. We conduct an experiment in various SNR environment of white noise to evaluate performance of the proposed algorithm and confirmed good performance of the proposed algorithm.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.3
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• pp.97-104
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• 2002

In this paper we describe the method which optimizes the partition of the input space by means of measure of fuzziness for fuzzy neural network. It covers its generation of fuzzy rules for input sub space. It verifies the performance of the system depended on the various time interval of the input. This method divides the input space into several fuzzy regions and assigns a degree of each of the generated rules for the partitioned subspaces from the given data using the Shannon function and fuzzy entropy function generating the optimal knowledge base without the irrelevant rules. In this scheme the basic idea of the fuzzy neural network is to realize the fuzzy rule base and the process of reasoning by neural network and to make the corresponding parameters of the fuzzy control rules be adapted by the steepest descent algorithm. According to the input interval the proposed inference procedure proves that the fast convergence of root mean square error (RMSE) owes to the optimal partition of the input space

• Journal of the Korea Convergence Society
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• v.4 no.3
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• pp.35-41
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• 2013

Grouping problem with similarities between DNA sequences are studied. The similaritymeasure and the distance measure showed the complementary characteristics. Distance measure can be obtained by complementing similarity measure, and vice versa. Similarity measure is derived and proved. Usefulness of the proposed similarity measure is applied to grouping problem of 25 cockroach DNA sequences. By calculation of DNA similarity, 25 cockroaches are clustered by four groups, and the results are compared with the previous neighbor-joining method.

• Journal of Korean Society for Quality Management
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• v.36 no.3
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• pp.1-12
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• 2008

Obtaining the accurate final importance of each customer attribute (CA) is very important in the house of quality(HOQ), because it is deployed to the quality of the final product or service through the quality function deployment(QFD). The final importance is often calculated by the multiplication of the relative importance rate and the competitive priority rate. Traditionally, the sample mean is used for estimating two rates but the dispersion is ignored. This paper proposes a new approach that incorporates statistical significance to consider the dispersion of rates in determining the final importance of CA. The approach is illustrated with a design of car door for each case of crisp and fuzzy numbers.

• Journal of Navigation and Port Research
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• v.45 no.5
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• pp.252-258
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• 2021

Given the cyclicality, seasonality, and capital-intensiveness, the development of the shipping industry has long been contingent on corporate financing activities. As such, there have been a growing number of cities in East Asia pursuing a global maritime financial center in order to support their domestic shipping industry. However, it is widely accepted that financial services relevant to shipping in East Asia are quite under-developed compared to those of other leading maritime financial centers in Europe and North America. In this regard, this paper aimed to construct an evaluation index of maritime financial centers in terms of financial ecological environment for the purpose of highlighting the current status of development and suggesting future directions. Furthermore, this paper examined the development of shipping finance in Qingdao as a numerical example using the fuzzy comprehensive evaluation and compared results with those of Shanghai.

• Nuclear Engineering and Technology
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• v.51 no.2
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• pp.453-462
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• 2019

Mental workload (MWL) is a major consideration for the user interface design in nuclear power plants (NPPs). However, each MWL evaluation method has its advantages and limitations, thus the evaluation and control methods based on multi-index methods are needed. In this study, fuzzy comprehensive evaluation (FCE) theory was adopted for assessment of interface designs in NPP based on operators' MWL. An evaluation index system and membership functions were established, and the weights were given using the combination of the variation coefficient and the entropy method. The results showed that multi-index methods such as performance measures (speed of task and error rate), subjective rating (NASA-TLX) and physiological measure (eye response) can be successfully integrated in FCE for user interface design assessment. The FCE method has a correlation coefficient compared with most of the original evaluation indices. Thus, this method might be applied for developing the tool to quickly and accurately assess the different display interfaces when considering the aspect of the operators' MWL.

• Journal of KIISE:Databases
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• v.28 no.4
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• pp.545-557
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• 2001

In this paper we propose a hierarchical classification algorithm based on rough membership function which can reason a new object approximately. We use the fuzzy reasoning method that substitutes fuzzy membership value for linguistic uncertainty and reason approximately based on the composition of membership values of conditional sttributes Here we use the rough membership function instead of the fuzzy membership function It can reduce the process that the fuzzy algorithm using fuzzy membership function produces fuzzy rules In addition, we transform the information system to the understandable minimal decision information system In order to do we, study the discretization of continuous valued attributes and propose the discretization algorithm based on the rough membership function and the entropy of the information theory The test shows a good partition that produce the smaller decision system We experimented the IRIS data etc. using our proposed algorithm The experimental results with IRIS data shows 96%~98% rate of classification.