• The Transactions of the Korea Information Processing Society
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• v.6 no.2
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• pp.522-533
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• 1999

This paper proposes speaker recognition algorithm which includes both the pitch parameter and the fuzzy inference. This study proposes a pitch detection method PVPF(peak and valley pitch detection fuction) by means of comparing spectra which utilizes the transform characteristics between time and frequency. In this paper, makes reference pattern using membership function and performs vocal tract recognition of common character using fuzzy pattern matching in order to include time variation width for non-linear utterance time.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.699-701
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• 2018

본 연구는 인공 신경망 '추론'과정에서 연산량을 줄이는 아이디어를 고안했고, 이를 구현하여 기존 알고리즘과 성능을 비교 분석하였다. 특정 데이터 셋에 대한 실험을 통해 ReLU(Rectified Linear Unit) 함수의 결과를 분석했고, 그 결과를 통해 ReLU 함수의 결과가 예측가능함을 확인했다. 또한 인공 신경망 알고리즘에 ReLU 함수의 결과 예측 기법을 적용하여 인공 신경망 추론과정을 최적화했다. 이 아이디어를 기반으로 구현된 인공 신경망은 기존 아이디어로 구현된 인공 신경망에 비해 약 3배 빠른 성능을 보였다.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2005.07a
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• pp.566-567
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• 2005

Until now variable pattern classification methods have been introduced. So, variable methods in PD source classification were applied. NN(neural network) the most used scheme as a PD(partial discharge) source classification. But in recent year another method were developed. These methods is present superior to NN in the field of image and signal process function of classification. In this paper, it is show classification result in PD source using three methods; that is, BP(back-propagation), ANFIS(adaptive neuro-fuzzy inference system), PCA-LDA(principle component analysis-linear discriminant analysis).

• Ocean Systems Engineering
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• v.9 no.2
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• pp.179-190
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• 2019

This paper deals with the problem of the global stabilization for a class of ocean structure systems. It is well known that, in general, the global asymptotic stability of the ocean structure subsystems does not imply the global asymptotic stability of the composite closed-loop system. The classical fuzzy inference methods cannot work to their full potential in such circumstances because given knowledge does not cover the entire problem domain. However, requirements of fuzzy systems may change over time and therefore, the use of a static rule base may affect the effectiveness of fuzzy rule interpolation due to the absence of the most concurrent (dynamic) rules. Designing a dynamic rule base yet needs additional information. In this paper, we demonstrate this proposed methodology is a flexible and general approach, with no theoretical restriction over the employment of any particular interpolation in performing interpolation nor in the computational mechanisms to implement fitness evaluation and rule promotion.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.949-952
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• 1993

As well-known, fuzzy control has been recognized to be of great usefulness in many engineering fields. However, the present design methods of fuzzy control systems depend on trial and error the thing that limits its usefulness. Therefore, an effective and convenient support tools for design and evaluation are greatly needed as well as the establishment of the design methods and guidling. From these backgrounds, we have developed a fuzzy control simulator[1, 2] which has various fuzzy control methods such as "direct method", "indirect method" and "fuzzy-PID method". This paper deals especially with the "direct method" function of the simulator. The simulator was developed for personal computers and programed in C language.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.9
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• pp.4049-4054
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• 2014

Background: Race and ethnicity are significant factors in predicting survival time of breast cancer patients. In this study, we applied advanced statistical methods to predict the survival of White non-Hispanic female breast cancer patients, who were diagnosed between the years 1973 and 2009 in the United States (U.S.). Materials and Methods: Demographic data from the Surveillance Epidemiology and End Results (SEER) database were used for the purpose of this study. Nine states were randomly selected from 12 U.S. cancer registries. A stratified random sampling method was used to select 2,000 female breast cancer patients from these nine states. We compared four types of advanced statistical probability models to identify the best-fit model for the White non-Hispanic female breast cancer survival data. Three model building criterion were used to measure and compare goodness of fit of the models. These include Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC). In addition, we used a novel Bayesian method and the Markov Chain Monte Carlo technique to determine the posterior density function of the parameters. After evaluating the model parameters, we selected the model having the lowest DIC value. Using this Bayesian method, we derived the predictive survival density for future survival time and its related inferences. Results: The analytical sample of White non-Hispanic women included 2,000 breast cancer cases from the SEER database (1973-2009). The majority of cases were married (55.2%), the mean age of diagnosis was 63.61 years (SD = 14.24) and the mean survival time was 84 months (SD = 35.01). After comparing the four statistical models, results suggested that the exponentiated Weibull model (DIC= 19818.220) was a better fit for White non-Hispanic females' breast cancer survival data. This model predicted the survival times (in months) for White non-Hispanic women after implementation of precise estimates of the model parameters. Conclusions: By using modern model building criteria, we determined that the data best fit the exponentiated Weibull model. We incorporated precise estimates of the parameter into the predictive model and evaluated the survival inference for the White non-Hispanic female population. This method of analysis will assist researchers in making scientific and clinical conclusions when assessing survival time of breast cancer patients.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.4
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• pp.499-505
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• 2015

Trend Impact Analysis(: TIA) is an advanced forecasting tool used in futures studies for identifying, understanding and analyzing the consequences of unprecedented events on future trends. An adaptive neuro-fuzzy inference system is a kind of artificial neural network that integrates both neural networks and fuzzy logic principles, It is considered to be a universal estimator. In this paper, we propose an advanced mechanism to generate more justifiable estimates to the probability of occurrence of an unprecedented event as a function of time with different degrees of severity using Adaptive Neuro-Fuzzy Inference System(: ANFIS). The key idea of the paper is to enhance the generic process of reasoning with fuzzy logic and neural network by adding the additional step of attributes simulation, as unprecedented events do not occur all of a sudden but rather their occurrence is affected by change in the values of a set of attributes. An ANFIS approach is used to identify the occurrence and severity of an event, depending on the values of its trigger attributes. The trigger attributes can be calculated by a stochastic dynamic model; then different scenarios are generated using Monte-Carlo simulation. To compare the proposed method, a simple simulation is provided concerning the impact of river basin drought on the annual flow of water into a lake.

• Journal of Digital Convergence
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• v.17 no.1
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• pp.239-247
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• 2019

An accurate prediction of emotion is a very important issue for the sake of patient-centered medical device development and emotion-related psychology fields. Although there have been many studies on emotion prediction, no studies have applied the heart rate variability and neuro-fuzzy approach to emotion prediction. We propose ANFEP(Adaptive Neuro Fuzzy System for Emotion Prediction) HRV. The ANFEP bases its core functions on an ANFIS(Adaptive Neuro-Fuzzy Inference System) which integrates neural networks with fuzzy systems as a vehicle for training predictive models. To prove the proposed model, 50 participants were invited to join the experiment and Heart rate variability was obtained and used to input the ANFEP model. The ANFEP model with STDRR and RMSSD as inputs and two membership functions per input variable showed the best results. The result out of applying the ANFEP to the HRV metrics proved to be significantly robust when compared with benchmarking methods like linear regression, support vector regression, neural network, and random forest. The results show that reliable prediction of emotion is possible with less input and it is necessary to develop a more accurate and reliable emotion recognition system.

• Journal of the Korea Society of Computer and Information
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• v.14 no.5
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• pp.183-193
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• 2009

Both On-Line Analytical Processing (OLAF) data cubes and Statistical Databases (SDBs) deal with multidimensional data sets. and both are concerned with statistical summarizations over the dimensions of the data sets. However, there is a distinction between the two that can be made. While SDBs are usually derived from other base data, OLAF data cubes often represent directly the base data. In other word, the base data of SDBs are the macro-data, whereas the core cubiod data in OLAF data cubes are the micro-data. The base table in OLAF is used to populate the data cube with values of the measure attribute, and each record in the base tables is used to populate a cell of the core cuboid. The fact that OLAF data cubes mostly represent the micro-data may make some records be absent in the base table. Some cells of the core cuboid remain empty, if corresponding records are absent in the base table. Wang and others proposed a method for securing OLAF data cubes against privacy breaches. They assert that the proposed method does not depend on specific types of aggregation functions. In this paper, however, it is found that their assertion on aggregate functions is wrong whenever any cell of the core cuboid remains empty. The objective of this study is to design an inference control process in OLAF data cubes which rectifying Wang's error.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.12
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• pp.519-524
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• 2023

In hashing-based image retrieval, the hash code of a manipulated image is different from the original image, making it difficult to search for the same image. This paper proposes and evaluates a self-supervised deephashing model that generates perceptual hash codes from feature information such as texture, shape, and color of images. The comparison models are autoencoder-based variational inference models, but the encoder is designed with a fully connected layer, convolutional neural network, and transformer modules. The proposed model is a variational inference model that includes a SimAM module of extracting geometric patterns and positional relationships within images. The SimAM module can learn latent vectors highlighting objects or local regions through an energy function using the activation values of neurons and surrounding neurons. The proposed method is a representation learning model that can generate low-dimensional latent vectors from high-dimensional input images, and the latent vectors are binarized into distinguishable hash code. From the experimental results on public datasets such as CIFAR-10, ImageNet, and NUS-WIDE, the proposed model is superior to the comparative model and analyzed to have equivalent performance to the supervised learning-based deephashing model. The proposed model can be used in application systems that require low-dimensional representation of images, such as image search or copyright image determination.