• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.3
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• pp.200-207
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• 2001

In this paper, we propose an HMM(Hidden Markov modeJ)-MLP(Multi-layer perceptron) hybrid model for recognizing legal words on the English bank check. We adopt an explicit segmentation-based word level architecture to implement an HMM engine with nonscaled and non-normalized symbol vectors. We also introduce an MLP for implicit segmentation-based word recognition. The final recognition model consists of a hybrid combination of the HMM and MLP with a new hybrid probability measure. The main contributions of this model are a novel design of the segmentation-based variable length HMMs and an efficient method of combining two heterogeneous recognition engines. ExperimenLs have been conducted using the legal word database of CENPARMI with encouraging results.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.10
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• pp.159-168
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• 2022

The time series of foreign trade turnover is complex and variable and contains linear and nonlinear information. This paper proposes preprocessing the dataset by the EMD algorithm and combining the linear prediction advantage of the SARIMA model with the nonlinear prediction advantage of the EMD-LSTM model to construct the SARIMA-EMD-LSTM hybrid model by the weight assignment method. The forecast performance of the single models is compared with that of the hybrid models by using MAPE and RMSE metrics. Furthermore, it is confirmed that the weight assignment approach can benefit from the hybrid models. The results show that the SARIMA model can capture the fluctuation pattern of the time series, but it cannot effectively predict the sudden drop in foreign trade turnover caused by special reasons and has the lowest accuracy in long-term forecasting. The EMD-LSTM model successfully resolves the hysteresis phenomenon and has the highest forecast accuracy of all models, with a MAPE of 7.4304%. Therefore, it can be effectively used to forecast the Sino-Russia foreign trade turnover time series post-epidemic. Hybrid models cannot take advantage of SARIMA linear and LSTM nonlinear forecasting, so weight assignment is not the best method to construct hybrid models.

• The Korean Journal of Applied Statistics
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• v.31 no.4
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• pp.447-461
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• 2018

Domestic film industry sales are increasing every year. Theaters are the primary sales channels for movies and the number of audiences using the theater affects additional selling rights. Therefore, the number of audiences using the theater is an important factor directly linked to movie industry sales. In this paper we consider a hybrid model that combines a multiple linear regression model and the Bass model to predict the audience numbers for a specific day. By combining the two models, the predictive value of the regression analysis was corrected to that of the Bass model. In the analysis, three films with different release dates were used. All subset regression method is used to generate all possible combinations and 5-fold cross validation to estimate the model 5 times. In this case, the predicted value is obtained from the model with the smallest root mean square error and then combined with the predicted value of the Bass model to obtain the final predicted value. With the existence of past data, it was confirmed that the weight of the Bass model increases and the compensation is added to the predicted value.

• Journal of information and communication convergence engineering
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• v.21 no.2
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• pp.159-166
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• 2023

The COI gene is a sequence of approximately 650 bp at the 5' terminal of the mitochondrial Cytochrome c Oxidase subunit I (COI) gene. As an effective DeoxyriboNucleic Acid (DNA) barcode, it is widely used for the taxonomic identification and evolutionary analysis of species. We created a CNN-LSTM hybrid model by combining the gene features partially extracted by the Long Short-Term Memory ( LSTM ) network with the feature maps obtained by the CNN. Compared to K-Means Clustering, Support Vector Machines (SVM), and a single CNN classification model, after training 278 samples in a training set that included 15 genera from two orders, the CNN-LSTM hybrid model achieved 94% accuracy in the test set, which contained 118 samples. We augmented the training set samples and four genera into four orders, and the classification accuracy of the test set reached 100%. This study also proposes calculating the cosine similarity between the training and test sets to initially assess the reliability of the predicted results and discover new species.

• 한국지구과학회:학술대회논문집
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• 2005.09a
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• pp.254-262
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• 2005

본 연구에서는 중학교 과학 영재아들이 가지고 있는 조석에 관한 정신모형을 탐색하기 위하여 상황에 따른 개념 유형을 분류하고, 각 유형들을 구성하는 국면들을 분석하였다. 조석 현상에 관해 두 가지 상황으로 구성된 과제 수행을 실시한 후 그 응답 결과를 분석한 결과, 상당수의 학생들이 상황에 따라 서로 다른 개념 유형을 나타내었다. 상황에 따른 개념 유형들을 구성하고 있는 국면을 분석한 결과, 각 유형들은 내용-일반적 국면을 공통적으로 포함하고 있었으나, 내용-특정적 국면과 전략적 국면에서는 많은 차이를 나타내었다. 두 가지 상황에서 나타나는 개념 유형들과 이들 유형을 구성하는 국면들을 조합하여 학생들의 정신모형을 분석한 결과 다음과 같은 4가지 모형으로 나눌 수 있었다: (1) Tide model (2) Force model (3) Phase model (4) Hybrid model. Tide model은 과학적으로 옳은 모형이며, Force model과 Phase model은 옳지 않은 모형이며, Hybrid model은 혼합 모형으로 상황에 따라 나타나는 개념 유형이 서로 부합되지 않는 모형이다. 중학교 과학 영재아들이 조석 현상에 대해 가장 많이 가지고 있는 모형은 Tide model(45.0%)이었으며, 그 다음으로는 Hybrid model(30.0%), Force model(12.5%), Phase model(7.5%) 순으로 나타났다.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2908-2910
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• 1999

In this paper, a fuzzy model is identified and optimized using the hybrid algorithm and HCM clustering method. Here, the hybrid algorithm is carried out as the structure combined with both a genetic algorithm and the improved complex method. The one is utilized for determining the initial parameters of membership function, the other for obtaining the fine parameters of membership function. HCM clustering algorithm is used to determine the confined region of initial parameters and also to avoid overflow phenomenon during auto-tuning of hybrid algorithm. And the standard least square method is used for the identification of optimum consequence parameters of fuzzy model. Two numerical examples are shown to evaluate the performance of the proposed model.

• The Korean Journal of Applied Statistics
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• v.29 no.3
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• pp.525-537
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• 2016

The paper considers a hybrid model to analyze and forecast time series data based on an empirical mode decomposition (EMD) that accommodates complex characteristics of time series such as nonstationarity and nonlinearity. We aggregate IMFs using the concept of cumulative energy to improve the interpretability of intrinsic mode functions (IMFs) from EMD. We forecast aggregated IMFs and residue with a hybrid model that combines the ARIMA model and an exponential smoothing method (ETS). The proposed method is applied to forecast KOSPI time series and is compared to traditional forecast models. Aggregated IMFs and residue provide a convenience to interpret the short, medium and long term dynamics of the KOSPI. It is also observed that the hybrid model with ARIMA and ETS is superior to traditional and other types of hybrid models.

• Journal of the Semiconductor & Display Technology
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• v.10 no.3
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• pp.75-81
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• 2011

This paper presents a novel method for image classification based on a hybrid genetic algorithm (GA) and support vector machine (SVM) approach which can significantly improve the classification performance for content-based image retrieval (CBIR). Though SVM has been widely applied to CBIR, it has some problems such as the kernel parameters setting and feature subset selection of SVM which impact the classification accuracy in the learning process. This study aims at simultaneously optimizing the parameters of SVM and feature subset without degrading the classification accuracy of SVM using GA for CBIR. Using the hybrid GA and SVM model, we can classify more images in the database effectively. Experiments were carried out on a large-size database of images and experiment results show that the classification accuracy of conventional SVM may be improved significantly by using the proposed model. We also found that the proposed model outperformed all the other models such as neural network and typical SVM models.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.27 no.12
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• pp.1744-1749
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• 2003

The purpose of this study is to obtain the information about the development process of GDI spray. To acquire the characteristics of GDI spray, the computational study of hollow cone spray for high-pressure swirl injectors was performed. Several hybrid models using the modified KIVA code have been introduced and compared. WB model and LISA model were used for the primary breakup, and DDB and APTAB models were used for secondary breakup. To compare with the calculated results, the experimental results such as cross-sectional images and SMD distribution were acquired by laser Mie scattering technique and Phase Doppler Analyzer respectively. The results show that LISA+APTAB hybrid model has the best prediction for spray formation process.

• Journal of KSNVE
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• v.10 no.3
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• pp.437-443
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• 2000

The optimal design problem for increasing dynamic stiffness using hybrid model which composed of original detailed BIW(body in white) and impinged beam elements is investigated. Using the characteristics of the beam elements and design sensitivity analysis this approach utilizes an optimization technique to determine the optimal section properties of beam elements. The constraint is to increase the first natural frequency by five percent compared with original one. The results show that the first torsion and bending natural frequencies are increased by five percent using hybrid model and optimization. These results indicate that this optimization method can be employed to enhance the dynamic stiffness of vehicle body structure in design concept stage.