• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.2977-2979
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• 2000

A Fuzzy Hybrid-Multilayer Perceptron (FH-MLP) Structure is proposed in this paper. proposed FH-MLP is not a fixed architecture. that is to say. the number of layers and the number of nodes in each layer of FH-MLP can be generated to adapt to the changing environment. FH-MLP consists of two parts. one is fuzzy nodes which each node is operated as a small fuzzy system with fuzzy implication rules. and its fuzzy system operates with Gaussian or Triangular membership functions in premise part and constants or regression polynomial equation in consequence part. the other is polynomial nodes which several types of high-order polynomial such as linear. quadratic. and cubic form are used and is connected as various kinds of multi-variable inputs. To demonstrate the effectiveness of the proposed method. time series data for gas furnace process has been applied.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2005년도 추계 학술대회 발표논문집
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• pp.159-162
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• 2005

Identification is the process automatically identify who is speaking on the basis of information obtained from speech waves. In training phase, each speaker models are trained using each speaker's speech data. GMMs (Gaussian Mixture Models), which have been successfully applied to speaker modeling in text-independent speaker identification, are not efficient in insufficient training data environment. This paper proposes speaker modeling method using MLLR (Maximum Likelihood Linear Regression) method which is used for speaker adaptation in speech recognition. We make SD-like model using MLLR adaptation method instead of speaker dependent model (SD). Proposed system outperforms the GMMs in small training data environment.

• 한국군사과학기술학회지
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• 제25권1호
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• pp.72-81
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• 2022

Maritime patrol aircraft is an efficient solution for detecting submarines at sea. The aircraft can only detect submarines by sonobuoy, but the number of buoy is limited. In this paper, we present the online sonobuoy deployment method for estimating the location of submarines. We use Gaussian process regression to estimate the submarine existence probability map, and Bayesian optimization to decide the next best position of sonobuoy. Further, we show the performance of the proposed method by simulation.

• 천문학회보
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• 제44권2호
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• pp.59.2-59.2
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• 2019

In this talk, I will explain the implications of a rapid appearance of dark energy between the redshifts ($z$) of one and two on the expansion rate and growth of perturbations. Using both Gaussian process regression and a parametric model, I show that this is the preferred solution to the current set of low-redshift ($z<3$) distance measurements if $H_0=73~\rm km\,s^{-1}\,Mpc^{-1}$ to within 1\% and the high-redshift expansion history is unchanged from the $\Lambda$CDM inference by the Planck satellite. Dark energy was effectively non-existent around $z=2$, but its density is close to the $\Lambda$CDM model value today, with an equation of state greater than $-1$ at $z<0.5$. If sources of clustering other than matter are negligible, we show that this expansion history leads to slower growth of perturbations at $z<1$, compared to $\Lambda$CDM, that is measurable by upcoming surveys and can alleviate the $\sigma_8$ tension between the Planck CMB temperature and low-redshift probes of the large-scale structure.

• 천문학회보
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• 제45권1호
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• pp.49.1-49.1
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• 2020

In this talk, I will discuss a few different techniques to reconstruct different cosmological functions, such as the primordial power spectrum and the expansion history. These model independent techniques are useful because they can discover surprising results in a way that nested modeling cannot. For instance, we can use the modified Richardson Lucy algorithm to reconstruct a novel primordial power spectra from the Planck data that can resolve the "Hubble tension". This novel primordial power spectrum has regular oscillatory features that would be difficult to find using parametric methods. Further, we can use Gaussian process regression to reconstruct the expansion history of the Universe from low-redshift distance datasets. We can also this technique to test if these datasets are consistent with one another, which essentially allows for this technique to serve as a systematics finder.

• International Journal of Computer Science & Network Security
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• 제24권3호
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• pp.107-112
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• 2024

Nowadays the deadly virus famous as COVID-19 spread all over the world starts from the Wuhan China in 2019. This disease COVID-19 Virus effect millions of people in very short time. There are so many symptoms of COVID19 perhaps the Identification of a person infected with COVID-19 virus is really a difficult task. Moreover it's a challenging task to identify whether a person or individual have covid test positive or negative. We are developing a framework in which we used machine learning techniques..The proposed method uses DecisionTree, KNearestNeighbors, GaussianNB, LogisticRegression, BernoulliNB , RandomForest , Machine Learning methods as the classifier for diagnosis of covid ,however, 5-fold and 10-fold cross-validations were applied through the classification process. The experimental results showed that the best accuracy obtained from Decision Tree classifiers. The data preprocessing techniques have been applied for improving the classification performance. Recall, accuracy, precision, and F-score metrics were used to evaluate the classification performance. In future we will improve model accuracy more than we achieved now that is 93 percent by applying different techniques

• 대한토목학회논문집
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• 제40권6호
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• pp.547-553
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• 2020

노후화 시설물의 증가에 따라 선제적 유지관리의 중요성은 점차 증대되고 있다. 선제적 유지관리는 시설물의 응답 계측으로부터 시작되기 때문에 높은 정밀도를 가지는 응답을 획득하는 것이 중요하다. 국부적인 응답 중 변형률은 균열 감지 및 피로 진전 예측 등에 활용가능하다. 변형률 센서는 크게 이산형 및 분포형 센서로 구분된다. 이산형 센서의 대표적인 예가 광섬유 브래그 격자(FBG)와 전기 저항식 게이지이다. 이산형 센서는 높은 정확성과 재현성(고 정밀)을 가지지만, 측정점이 제한된다는 한계를 가진다. 브릴루앙 산란 기반 광섬유 변형률 계측 시스템 중 하나인 Brillouin Optical Correlation Domain Analysis (BOCDA)은 대표적인 분포형 센서이며, 5 cm 라는 높은 공간 분해능을 가진다. BOCDA는 투영된 광원에서 발생하는 산란파를 이용하여 광섬유 전 구간의 변형률을 계측한다. 측정점이 많아지는 장점이 있으나, 이산형 센서에 낮은 정확도와 재현성을 가진다. 본 연구에서는 고 정밀 데이터(이산형 센서)와 저 정밀 데이터(분포형 센서) 각각의 장점을 융합하는 후처리 기법을 제안하였으며, 이에 대한 가능성을 검증 실험을 통해 확인했다.

• 한국시뮬레이션학회논문지
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• 제30권2호
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• pp.23-31
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• 2021

개체단위 M&S의 교전 결과에 대한 정확도를 높이기 위해서는 신뢰성 있는 무기효과지수를 바탕으로 피해 정도가 모의되어야 한다. 무기효과지수는 특정 교전 환경에서 무기체계와 표적에 대한 명중확률(Ph)과 살상확률(Pk)을 지수화한 값으로, 주로 JMEM 데이터나 JMEM 방법론에 따라 생산된 데이터가 활용되고 있다. 그러나 JMEM 방법론은 표적 중심부를 기준으로 원형공산오차를 통과하는 파편이나 탄의 격자 통과량으로 살상확률을 계산하기 때문에 지형, 대기, 장애물 등의 추가적인 환경 요소를 고려하기 위해서는 전문가에 의한 보정이 요구된다. 따라서 본 논문에서는 다수의 교전 및 환경요소가 반영된 무기효과지수 생산을 위해 공학급 무기체계 모델을 활용하여 몬테카를로 시뮬레이션을 수행하고, 그 결과 데이터를 바탕으로 메타모델을 생성하였다. 명중확률과 살상확률 메타모델로 로지스틱 회귀모델과 가우시안 프로세스 회귀모델이 각각 생성되었으며, 예시 시나리오에 적용하여 모델 적합도를 관찰하였다. 본 연구에서 제시한 절차를 따르면 개체단위 M&S의 입력자료를 효율적으로 생산할 수 있을 것으로 기대한다.

• Computers and Concrete
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• 제33권1호
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• pp.55-75
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• 2024

The present study introduces an optimum performance soft computing model for predicting the compressive strength of high-performance concrete (HPC) by comparing models based on conventional (kernel-based, covariance function-based, and tree-based), advanced machine (least square support vector machine-LSSVM and minimax probability machine regressor-MPMR), and deep (artificial neural network-ANN) learning approaches using a common database for the first time. A compressive strength database, having results of 1030 concrete samples, has been compiled from the literature and preprocessed. For the purpose of training, testing, and validation of soft computing models, 803, 101, and 101 data points have been selected arbitrarily from preprocessed data points, i.e., 1005. Thirteen performance metrics, including three new metrics, i.e., a20-index, index of agreement, and index of scatter, have been implemented for each model. The performance comparison reveals that the SVM (kernel-based), ET (tree-based), MPMR (advanced), and ANN (deep) models have achieved higher performance in predicting the compressive strength of HPC. From the overall analysis of performance, accuracy, Taylor plot, accuracy metric, regression error characteristics curve, Anderson-Darling, Wilcoxon, Uncertainty, and reliability, it has been observed that model CS4 based on the ensemble tree has been recognized as an optimum performance model with higher performance, i.e., a correlation coefficient of 0.9352, root mean square error of 5.76 MPa, and mean absolute error of 4.1069 MPa. The present study also reveals that multicollinearity affects the prediction accuracy of Gaussian process regression, decision tree, multilinear regression, and adaptive boosting regressor models, novel research in compressive strength prediction of HPC. The cosine sensitivity analysis reveals that the prediction of compressive strength of HPC is highly affected by cement content, fine aggregate, coarse aggregate, and water content.

• International Journal of Computer Science & Network Security
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• 제24권7호
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• pp.108-117
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• 2024

The selection and recommendation of a suitable job applicant from the pool of thousands of applications are often daunting jobs for an employer. The recommendation and selection process significantly increases the workload of the concerned department of an employer. Thus, Resume Classification System using the Natural Language Processing (NLP) and Machine Learning (ML) techniques could automate this tedious process and ease the job of an employer. Moreover, the automation of this process can significantly expedite and transparent the applicants' selection process with mere human involvement. Nevertheless, various Machine Learning approaches have been proposed to develop Resume Classification Systems. However, this study presents an automated NLP and ML-based system that classifies the Resumes according to job categories with performance guarantees. This study employs various ML algorithms and NLP techniques to measure the accuracy of Resume Classification Systems and proposes a solution with better accuracy and reliability in different settings. To demonstrate the significance of NLP & ML techniques for processing & classification of Resumes, the extracted features were tested on nine machine learning models Support Vector Machine - SVM (Linear, SGD, SVC & NuSVC), Naïve Bayes (Bernoulli, Multinomial & Gaussian), K-Nearest Neighbor (KNN) and Logistic Regression (LR). The Term-Frequency Inverse Document (TF-IDF) feature representation scheme proven suitable for Resume Classification Task. The developed models were evaluated using F-Score_M, Recall_M, Precission_M, and overall Accuracy. The experimental results indicate that using the One-Vs-Rest-Classification strategy for this multi-class Resume Classification task, the SVM class of Machine Learning algorithms performed better on the study dataset with over 96% overall accuracy. The promising results suggest that NLP & ML techniques employed in this study could be used for the Resume Classification task.