• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.4
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• pp.15-26
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• 2009

Bass diffusion model have played a central role in studying the diffusion of the new products since 1969, the year of publication of Bass model. Almost 750 publications based on the Bass diffusion model have explored extensions and applications. Extension models can be divided into two types. One is the model containing marketing-mix variables and the other is the model containing additional parameters. This paper presents another extension model of the latter type. Our model allows the time varying coefficients of innovation and imitation. Two pieces approximation of time varying coefficients is introduced and it's parameters are estimated based on NLS(Non-Linear Mean Square) method. Empirical studies are performed and the results show that our model is superior to the basic Bass model and the NUI(Non-Uniform Influence) model which is the well-known extension of the Bass model. The model developed in this paper is, also, transformed into the Bass model with the ready potential adopters in order to enhance the descriptive power.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.2
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• pp.151-157
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• 2008

A non-fragile guaranteed cost control (GCC) problem is presented for a class of discrete time-delay nonlinear systems described by Takagi-Sugeno (T-S) fuzzy model. The systems are assumed to have norm-bounded time-varying uncertainties in the matrices of state, delayed state and control gains. Sufficient conditions are first obtained which guarantee that the closed-loop system is asymptotically stable and the closed-loop cost function value is not more than a specified upper bound. Then the design method of the non-fragile guaranteed cost controller is formulated in terms of the linear matrix inequality (LMI) approach. A numerical example is given to illustrate the effectiveness of the proposed design method.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.15 no.5
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• pp.72-78
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• 2006

The paper proposes reference model error feedback control scheme for motion control system with hard non-linear components as like saturation and dead-zone in plant input part. Additionally, the plant has the system uncertainty effected by plant model parameter deviation and disturbance. The control algorithm uses the reference model to apply additional feedback loop with the error between reference model output and actual output effected by disturbance and non-linear components. And the stability evaluation based on Popov stability and controller design method are formulated to be performed. The effectiveness of the proposed scheme is examined by simulations. The results are proven by reasonable performances following reference model responses with good disturbance rejection performance without over-tuning of controller.

• Korean Journal of Computational Design and Engineering
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• v.5 no.3
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• pp.242-254
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• 2000

This paper describes an efficient solid modeling method for thin-walled plastic or sheet metal parts, based on the non-manifold offsetting operations. Since the previous methods for modeling and converting a sheet into a solid have adopted the boundary representations for solid object as their topological framework, it is difficult to represent the exact adjacency relationship between topological entities of a sheet model and a mixture of wireframe and sheet models that can appear in the meantime of modeling procedure, and it is hard to implement topological operations for sheet modeling and transformation of a sheet into a solid. To solve these problems, we introduce a non-manifold B-rep and propose a sheet conversion method based on a non-manifold offset algorithm. Because the non-manifold offset aigorithm based on mathematical definitions results in an offset solid with tubular and spherical thickness-faces we modify it to generate the ruled or planar thickness-faces that are mostly shown in actual plastic or sheet metal parts. In addition, in order to accelerate the Boolean operations used the offset algorithm, we also develope an efficient face-face intersection algorithm using topological adjacency information.

• 한국연소학회:학술대회논문집
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• 2012.11a
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• pp.141-143
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• 2012

The present study is aiming at numerically analyze the soot formation processes coupled with gas reaction mechanism in turbulent non-premixed and partially premixed flames. In order to realistically represent turbulence-chemistry interactions with detailed chemical kinetics and soot formation behaviour related to the turbulent non-premixed and partially premixed flames, the transient flamelet[1] and flamelet based level-set approach[2] are coupled with soot formation based on the two equation model [3] and DQMOM (Direct Quadrature Method of Moment)[4].

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4310-4330
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• 2020

With the continuous emergence of new applications and cyberattacks and their frequent updates, the need for automatic protocol reverse engineering is gaining recognition. Although several methods for automatic protocol reverse engineering have been proposed, each method still faces major limitations in extracting clear specifications and in its universal application. In order to overcome such limitations, we propose an automatic protocol reverse engineering method using a two-pathway model based on a contiguous sequential pattern (CSP) algorithm. By using this model, the method can infer both command-oriented protocols and non-command-oriented protocols clearly and in detail. The proposed method infers all the key elements of the protocol, which are syntax, semantics, and finite state machine (FSM), and extracts clear syntax by defining fine-grained field types and three types of format: field format, message format, and flow format. We evaluated the efficacy of the proposed method over two non-command-oriented protocols and three command-oriented protocols: the former are HTTP and DNS, and the latter are FTP, SMTP, and POP3. The experimental results show that this method can reverse engineer with high coverage and correctness rates, more than 98.5% and 99.1% respectively, and be general for both command-oriented and non-command-oriented protocols.

• Health Policy and Management
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• v.14 no.2
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• pp.78-98
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• 2004

The purpose of this study was to categorize the contribution evasion and develop the expected models for contribution arrears in National Health Care System. The modified logistic regression model in non-payments was used as logistic regression model based on the statistical method. By using this model, we arranged non-payment types and typical branches those are appeared by statistical technique. First fact, sex and age branches those are able to take a part in economy had effect mostly. Also they had difference in non-payment probability by existence of their incomes and property. Especially people who didn't have their own house and car were appeared in high non-payment probability, disease and reduction characteristic(rare diseases, reduction of seniors, handicaps, numbers of medical treatments) didn't effect much in probability. The reason for some characteristic of non-payment which is higher than the correct threshold value of Logistic Regression Model (a suggested model for predicting non-payment)'s distribution of probability was mostly moral hazard. Living difficulty was the bigger reason for non-payment, but moral slackening was the bigger reason for non-payment. But it is careless to decide that moral hazard is just the reason, there is a necessity to examine on the side of sociology based in family. By the reason, the member's non-payment reason can be classified by economy, population, and psychology, but there was a comprehension that losing of work desire could be one reason. So we analyzed informations for composition of family of members. In conclusion, we grasped that family conflict makes non-payment and conversion of member in the National Basic Livelihood Protection System difficult.

• Asia-Pacific Journal of Business
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• v.15 no.1
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• pp.145-157
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• 2024

Purpose - In this study, we propose an empirical model for predicting seasoned equity offering (SEO here after) using machine learning methods. Design/methodology/approach - The models utilize the random forest method based on decision trees that considers non-linear relationships, as well as the gradient boosting tree model. SEOs incur significant direct and indirect costs. Therefore, CEOs' decisions of seasoned equity issuances are made only when the benefits outweigh the costs, which leads to a non-linear relationship between SEOs and a determinant of them. Particularly, a variable related to market timing effectively exhibit such non-linear relations. Findings - To account for these non-linear relationships, we hypothesize that decision tree-based random forest and gradient boosting tree models are more suitable than the linear methodologies due to the non-linear relations. The results of this study support this hypothesis. Research implications or Originality - We expect that our findings can provide meaningful information to investors and policy makers by classifying companies to undergo SEOs.

• Smart Structures and Systems
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• v.15 no.1
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• pp.81-97
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• 2015

Recently, a new output-only modal identification method based on time-frequency independent component analysis (ICA) has been developed by the authors and shown to be useful for even highly-damped structures. In many cases, it is of interest to identify the complex modes of structures with non-proportional damping. This study extends the time-frequency ICA based method to a complex ICA formulation for output-only modal identification of non-proportionally-damped structures. The connection is established between complex ICA model and the complex-valued modal expansion with sparse time-frequency representation, thereby blindly separating the measured structural responses into the complex mode matrix and complex-valued modal responses. Numerical simulation on a non-proportionally-damped system, laboratory experiment on a highly-damped three-story frame, and a real-world highly-damped base-isolated structure identification example demonstrate the capability of the time-frequency complex ICA method for identification of structures with complex modes in a straightforward and efficient manner.

• International journal of advanced smart convergence
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• v.9 no.2
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• pp.20-27
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• 2020

Sound event detection in real-world environments suffers from the interference of non-stationary and time-varying noise. This paper presents an adaptive noise reduction method for sound event detection based on non-negative matrix factorization (NMF). In this paper, we proposed a deep learning model that integrates Convolution Neural Network (CNN) with Non-Negative Matrix Factorization (NMF). To improve the separation quality of the NMF, it includes noise update technique that learns and adapts the characteristics of the current noise in real time. The noise update technique analyzes the sparsity and activity of the noise bias at the present time and decides the update training based on the noise candidate group obtained every frame in the previous noise reduction stage. Noise bias ranks selected as candidates for update training are updated in real time with discrimination NMF training. This NMF was applied to CNN and Hidden Markov Model(HMM) to achieve improvement for performance of sound event detection. Since CNN has a more obvious performance improvement effect, it can be widely used in sound source based CNN algorithm.