• Journal of Applied Reliability
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• v.17 no.4
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• pp.332-342
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• 2017

Purpose: In this study, the lifetime distribution of a k-out-of-n system with heterogeneous components is suggested as Markov model, and the time-to-failure (TTF) distribution of each component is considered as phase-type distribution (PHD). Furthermore, based on the model, a redundancy allocation problem with a mix of components (RAPMC) is proposed. Methods: The lifetime distribution model for the system is formulated by the structured Markov chain. From the model, the various information on the system lifetime can be ascertained by the matrix-analytic (or-geometric) method. Conclusion: By the generalization of TTF distribution (PHD) and the consideration of heterogeneous components, the lifetime distribution model can delineate many real systems and be exploited for developing system operation policies such as preventive maintenance, warranty. Moreover, the effectiveness of the proposed RAPMC is verified by numerical experiments. That is, under the equivalent design conditions, it presented a system with higher reliability than RAP without component mixing (RAPCM).

• The Journal of the Acoustical Society of Korea
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• v.19 no.3
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• pp.35-44
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• 2000

In this paper, we propose a new trajectory model for characterizing segmental features and their interaction based upon a general framework of hidden Markov models. Each segment, a sequence of vectors, is represented by a trajectory of observed sequences. This trajectory is obtained by applying a new design matrix which includes transitional information on contiguous frames, and is characterized as a polynomial regression function. To apply the trajectory to the segmental HMM, the frame features are replaced with the trajectory of a given segment. We also propose the likelihood of a given segment and the estimation of trajectory parameters. The obervation probability of a given segment is represented as the relation between the segment likelihood and the estimation error of the trajectories. The estimation error of a trajectory is considered as the weight of the likelihood of a given segment in a state. This weight represents the probability of how well the corresponding trajectory characterize the segment. The proposed model can be regarded as a generalization of a conventional HMM and a parametric trajectory model. The experimental results are reported on the TIMIT corpus and performance is show to improve significantly over that of the conventional HMM.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.4
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• pp.343-350
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• 2000

In this paper, we propose the Multi-FPNN(Fuzzy Polynomial Neural Networks) model based on FNN and PNN(Polyomial Neural Networks) for optimal system identifacation. Here FNN structure is designed using fuzzy input space divided by each separated input variable, and urilized both in order to get better output performace. Each node of PNN structure based on GMDH(Group Method of Data handing) method uses two types of high-order polynomials such as linearane and quadratic, and the input of that node uses three kinds of multi-variable inputs such as linear and quadratic, and the input of that node and Genetic Algorithms(GAs) to identify both the structure and the prepocessing of parameters of a Multi-FPNN model. Here, HCM clustering method, which is carried out for data preproessing of process system, is utilized to determine the structure method, which is carried out for data preprocessing of process system, is utilized to determance index with a weighting factor is used to according to the divisions of input-output space. A aggregate performance inddex with a wegihting factor is used to achieve a sound balance between approximation and generalization abilities of the model. According to the selection and adjustment of a weighting factor of this aggregate abjective function which it is acailable and effective to design to design and optimal Multi-FPNN model. The study is illustrated with the aid of two representative numerical examples and the aggregate performance index related to the approximation and generalization abilities of the model is evaluated and discussed.

• The Journal of Information Systems
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• v.16 no.1
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• pp.159-178
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• 2007

As generalization of using PC and the Internet information technology (IT) users in organizations are not passive clients but active producers of information services. One of the reasons for the change of users' role is social interactions with other users and IT department staffs. That is, users can find and develop new functions and usefulness of IT, especially, Web-based ones through communication with other users or IT department staffs. The objective of this study is to investigate the relationship between IT department service quality and social interactions with other users. For this purpose, we suggest a research model based on adaptive structuration theory (AST), which is to explain the changes in social structure (rules and resources) of IT by social interactions, as well as service quality theory. Our model's exogenous variable is service quality which is a second-order factor consisting of reliability, responsiveness, assurance, and empathy. As endogenous variables, we adopt two variables for appropriateness of using IT, faithfulness of appropriation and consensus on appropriation. Finally, dependent variables of ow model are usefulness and ease of use which can be considered as attitude on IT as well as other variables for appropriateness. For empirical test our model is applied to users of groupware and ERP in organizations and analyzed by partial least square. In result, all hypotheses suggested in our model are supported statistically.

• Journal of Navigation and Port Research
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• v.36 no.7
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• pp.553-559
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• 2012

It is extremely important to predict the logistics requirements in a scientific and rational way. However, in recent years, the improvement effect on the prediction method is not very significant and the traditional statistical prediction method has the defects of low precision and poor interpretation of the prediction model, which cannot only guarantee the generalization ability of the prediction model theoretically, but also cannot explain the models effectively. Therefore, in combination with the theories of the spatial economics, industrial economics, and neo-classical economics, taking city of Erdos as the research object, the study identifies the leading industry that can produce a large number of cargoes, and further predicts the static logistics generation of the Erdos and hinterlands. By integrating various factors that can affect the regional logistics requirements, this study established a logistics requirements potential model from the aspect of spatial economic principles, and expanded the way of logistics requirements prediction from the single statistical principles to an new area of special and regional economics.

• Applied Chemistry for Engineering
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• v.19 no.3
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• pp.277-286
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• 2008

This paper deals with the theoretical derivation of the modified Haldane model of the substrate inhibition in the microbial growth processes. Based on the biological concepts of substrate-receptor complex working mechanisms, a new microbial kinetics of N-fold multiplex substrate inhibition and its generalization has been considered theoretically, which is natural expansion of the simple substrate inhibition mechanism in the enzyme reaction. As a result, the modified Haldane model of the substrate inhibition turns out to be a well-designed four-parameter kinetic model with a biological constant of the total substrate inhibition concentration.

• Annual Conference of KIPS
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• 2023.05a
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• pp.706-708
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• 2023

In this study, we embark on a journey to uncover the essence of emotions by exploring the depths of transfer learning on three pre-trained transformer models. Our quest to classify five emotions culminates in discovering the KLUE (Korean Language Understanding Evaluation)-BERT (Bidirectional Encoder Representations from Transformers) model, which is the most exceptional among its peers. Our analysis of F1 scores attests to its superior learning and generalization abilities on the experimental data. To delve deeper into the mystery behind its success, we employ the powerful SHAP (Shapley Additive Explanations) method to unravel the intricacies of the KLUE-BERT model. The findings of our investigation are presented with a mesmerizing text plot visualization, which serves as a window into the model's soul. This approach enables us to grasp the impact of individual tokens on emotion classification and provides irrefutable, visually appealing evidence to support the predictions of the KLUE-BERT model.

• Structural Engineering and Mechanics
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• v.7 no.2
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• pp.225-240
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• 1999

This paper presents a mechanistic approach to uniaxial viscoelastic constitutive modeling of asphalt concrete that accounts for damage evolution under cyclic loading conditions. An elasticviscoelastic correspondence principle in terms of pseudo variables is applied to separately evaluate viscoelasticity and time-dependent damage growth in asphalt concrete. The time-dependent damage growth in asphalt concrete is modeled by using a damage parameter based on a generalization of microcrack growth law. Internal state variables that describe the hysteretic behavior of asphalt concrete are determined. A constitutive equation in terms of stress and pseudo strain is first established for controlled-strain mode and then transformed to a controlled-stress constitutive equation by simply replacing physical stress and pseudo strain with pseudo stress and physical strain. Tensile uniaxial fatigue tests are performed under the controlled-strain mode to determine model parameters. The constitutive equations in terms of pseudo strain and pseudo stress satisfactorily predict the constitutive behavior of asphalt concrete all the way up to failure under controlled-strain and -stress modes, respectively.

• ETRI Journal
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• v.22 no.2
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• pp.1-10
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• 2000

Pattern recognition is one of the most common problems encountered in engineering and scientific disciplines, which involves developing prediction or classification models from historic data or training samples. This paper introduces a new approach, called the Representational Capability (RC) algorithm, to handle pattern recognition problems using radial basis function (RBF) models. The RC algorithm has been developed based on the mathematical properties of the interpolation and design matrices of RBF models. The model development process based on this algorithm not only yields the best model in the sense of balancing its parsimony and generalization ability, but also provides insights into the design process by employing a design parameter (${\delta}$). We discuss the RC algorithm and its use at length via an illustrative example. In addition, RBF classification models are developed for heart disease diagnosis.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1830-1835
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• 2003

A novel index representing burden distribution form in the blast furnace is developed and index estimation model is built with an empirical modeling method to monitor inner condition of the furnace without expensive sensors. To find the best combination of index and modeling method, two candidates for the index and four modeling methods have been examined. Results have shown that 3-D index have more resolution in describing the distribution form than 1-D index and ANN model produces smallest RMSE due to nonlinearity between the indices and charging mode. Although ANN has shown the best prediction accuracy in this study, PLS can be a good alternative due to its advantages in generalization capability, consistency, simplicity and training time. The second best result of PLS in the prediction results supports this fact.