• Transactions on Control, Automation and Systems Engineering
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• v.1 no.1
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• pp.8-15
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• 1999

One of the main reason advocating redundant manipulators' superiority in application is that they can afford to optimize a dexterity measure, for example the manipulability measure. However, to obtain the gradient of the manipulability is not an easy task in case of general manipulator with high degrees of redundancy. This article proposes a method to compute the gradient of the manipulability, based on recursive algorithm to compute the Jacobian and its derivative using Denavit-Hartenberg parameters only. To characterize the null motion of redundant manipulators, the null space matrix using square minors of the Jacobian is also proposed. With these capabilities, the inverse kinematics of a redundant manipulator system can be done automatically. The result is easily extended to dual manipulator system using the relative kinematics.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.9
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• pp.2854-2865
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• 1996

The concept of variable-geometry truss structure(VGTS) is introduced as a class of actively controlled adaptive structure. VGTS can purposefully vary its geometric configurations by changing the lengths of some members of the structure. General kinematics and inverse kinematics of a statically determinate VGTS(variable geometry truss structure) are studied. The solution technique is based on the Jacobian matrix obtained via joint equilibrium equations. Pseudoinverse control method is applied to resolve the redundancy of a large VGTS. two types of actuator layout of octahedral type VGTS, VG truss and Stewart platform, are compared. Introducing the concept of performance index, Stewart platform based layout was found to has less consumption energy and manipulation time. A functional VGTS model with 3 octahedral modules is designed and manufactured for the labaratory demonstration. Six vertically located length-variable members are used to create general 6 d.o.f. motions.

• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.884-892
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• 2011

The analysis of the tourist industry in relation to the general industries is of high use as a means to measure an economic effectiveness as the interest in the policy of service industry increases. From the Input-Output Tables of both 2007 and 2008, Inverse Matrix Coefficients, Imports Requirement Coefficients, and Value Added Requirement Coefficients have been derived. As a result of analysis, the main indexes of the industry-related analysis have almost no differences as compared with those of the 1980s. In spite of the reduction in the scope of the tourist industry in this paper, it is estimated that the reason why the above-mentioned result has been derived is that the influence of today's tourist industry grows bigger than that of the past. In the future studies, the agreement on the classification of tourist industry is requested. In addition, all kinds of calculations have to be derived in general, and the general parts of the tourist industry have to be analyzed in details.

• Journal of Electrical Engineering and Technology
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• v.9 no.4
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• pp.1283-1289
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• 2014

The state transition matrix are obtained by solving state equations in terms of Laplace inverse transformation and Cayley-Hamilton theorem, and an establishment of a precise discrete-iterative mapping of the voltage-fed buck-boost converter operating in discontinuous conduction mode is made. On the basis of the mapping, the converter bifurcation diagrams and Lyapunov exponent diagrams with the input voltage, the resistance, the inductance and the capacitance as the bifurcation parameters are obtained, and the effect of the parameters on the system stability is deeply studied. The results obtained show that they have a great influence on the stability of the system, and the general trend is that the increase of either the voltage-fed coefficient, input voltage or the load resistance, or the decrease of the filtering inductance, capacitance will make the system stability become poorer, and that all the parameters have a critical value, and when they are greater or less than the values, the system will go through stable 1T orbits, stable 2T orbits, 4T orbits, 8T orbits and eventually approaches chaos.

• The Korean Journal of Applied Statistics
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• v.32 no.2
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• pp.265-276
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• 2019

The document-term frequency matrix is a general data of objects in text mining. In this study, we introduce a traditional term weighting scheme TF-IDF (term frequency-inverse document frequency) which is applied in the document-term frequency matrix and used for text classifications. In addition, we introduce and compare TF-IDF-ICSDF and TF-IGM schemes which are well known recently. This study also provides a method to extract keyword enhancing the quality of text classifications. Based on the keywords extracted, we applied support vector machine for the text classification. In this study, to compare the performance term weighting schemes, we used some performance metrics such as precision, recall, and F1-score. Therefore, we know that TF-IGM scheme provided high performance metrics and was optimal for text classification.

• Geophysics and Geophysical Exploration
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• v.7 no.3
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• pp.207-212
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• 2004

This article reviews recent developments in three-dimensional (3-D) magntotelluric (MT) imaging. The inversion of MT data is fundamentally ill-posed, and therefore the resultant solution is non-unique. A regularizing scheme must be involved to reduce the non-uniqueness while retaining certain a priori information in the solution. The standard approach to nonlinear inversion in geophysis has been the Gauss-Newton method, which solves a sequence of linearized inverse problems. When running to convergence, the algorithm minimizes an objective function over the space of models and in the sense produces an optimal solution of the inverse problem. The general usefulness of iterative, linearized inversion algorithms, however is greatly limited in 3-D MT applications by the requirement of computing the Jacobian(partial derivative, sensitivity) matrix of the forward problem. The difficulty may be relaxed using conjugate gradients(CG) methods. A linear CG technique is used to solve each step of Gauss-Newton iterations incompletely, while the method of nonlinear CG is applied directly to the minimization of the objective function. These CG techniques replace computation of jacobian matrix and solution of a large linear system with computations equivalent to only three forward problems per inversion iteration. Consequently, the algorithms are efficient in computational speed and memory requirement, making 3-D inversion feasible.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.4
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• pp.295-303
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• 2008

In general, transform domain adaptive filters show faster convergence speed than the time domain adaptive filters, but the amount of calculation increases dramatically as the filter order increases. This problem can be solved by making use of the subband structure in transform domain adaptive filters. In this paper, to increase the convergence speed on the generalized subband decomposition FIR adaptive filters, a structure of the adaptive filter with subfilter of dyadic sparsity factor in wavelet transform domain is designed. And, in this adaptive filter, the equivalent input in transform domain is derived and, by using the input, the convergence properties for the LMS algorithm is analyzed and evaluated. By using this sub band adaptive filter, the inverse system modeling and the periodic noise canceller were designed, and, by computer simulation, the convergence speeds of the systems on LMS algorithm were compared with that of the subband adaptive filter using DFT(discrete Fourier transform).

• Structural Engineering and Mechanics
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• v.28 no.2
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• pp.129-152
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• 2008

This paper considers the solution of the stochastic differential equations (SDEs) with random operator and/or random excitation using the spectral SFEM. The random system parameters (involved in the operator) and the random excitations are modeled as second order stochastic processes defined only by their means and covariance functions. All random fields dealt with in this paper are continuous and do not have known explicit forms dependent on the spatial dimension. This fact makes the usage of the finite element (FE) analysis be difficult. Relying on the spectral properties of the covariance function, the Karhunen-Loeve expansion is used to represent these processes to overcome this difficulty. Then, a spectral approximation for the stochastic response (solution) of the SDE is obtained based on the implementation of the concept of generalized inverse defined by the Neumann expansion. This leads to an explicit expression for the solution process as a multivariate polynomial functional of a set of uncorrelated random variables that enables us to compute the statistical moments of the solution vector. To check the validity of this method, two applications are introduced which are, randomly loaded simply supported reinforced concrete beam and reinforced concrete cantilever beam with random bending rigidity. Finally, a more general application, randomly loaded simply supported reinforced concrete beam with random bending rigidity, is presented to illustrate the method.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.4
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• pp.393-398
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• 2015

A very important part in vibration analysis is to calculate the frequency response function (FRF). In general, a large sized or/and complicated structure has many thousands to millions of degrees. Therefore, the FRF cannot be calculated by the traditional analysis method using an inverse matrix. This paper presents a new FRF calculation method of a superstructure by synthesizing sub-structure modes, of which the DOF can be deduced by partitioning into some sub-structures. To confirm its analysis results, the method was applied to an assembled plate ($B300{\times}L900{\times}t5mm$) with three diagonal sub-plates($B300{\times}L300{\times}t5mm$) in series and compared with the measured data. The test results have were comparable those of the calculated ones with an error less than 5%.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.1-18
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• 2015

Collaborative filtering(CF) algorithm has been popularly used for recommender systems in both academic and practical applications. A general CF system compares users based on how similar they are, and creates recommendation results with the items favored by other people with similar tastes. Thus, it is very important for CF to measure the similarities between users because the recommendation quality depends on it. In most cases, users' explicit numeric ratings of items(i.e. quantitative information) have only been used to calculate the similarities between users in CF. However, several studies indicated that qualitative information such as user's reviews on the items may contribute to measure these similarities more accurately. Considering that a lot of people are likely to share their honest opinion on the items they purchased recently due to the advent of the Web 2.0, user's reviews can be regarded as the informative source for identifying user's preference with accuracy. Under this background, this study proposes a new hybrid recommender system that combines with users' review mining. Our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and his/her text reviews on the items when calculating similarities between users. In specific, our system creates not only user-item rating matrix, but also user-item review term matrix. Then, it calculates rating similarity and review similarity from each matrix, and calculates the final user-to-user similarity based on these two similarities(i.e. rating and review similarities). As the methods for calculating review similarity between users, we proposed two alternatives - one is to use the frequency of the commonly used terms, and the other one is to use the sum of the importance weights of the commonly used terms in users' review. In the case of the importance weights of terms, we proposed the use of average TF-IDF(Term Frequency - Inverse Document Frequency) weights. To validate the applicability of the proposed system, we applied it to the implementation of a recommender system for smartphone applications (hereafter, app). At present, over a million apps are offered in each app stores operated by Google and Apple. Due to this information overload, users have difficulty in selecting proper apps that they really want. Furthermore, app store operators like Google and Apple have cumulated huge amount of users' reviews on apps until now. Thus, we chose smartphone app stores as the application domain of our system. In order to collect the experimental data set, we built and operated a Web-based data collection system for about two weeks. As a result, we could obtain 1,246 valid responses(ratings and reviews) from 78 users. The experimental system was implemented using Microsoft Visual Basic for Applications(VBA) and SAS Text Miner. And, to avoid distortion due to human intervention, we did not adopt any refining works by human during the user's review mining process. To examine the effectiveness of the proposed system, we compared its performance to the performance of conventional CF system. The performances of recommender systems were evaluated by using average MAE(mean absolute error). The experimental results showed that our proposed system(MAE = 0.7867 ~ 0.7881) slightly outperformed a conventional CF system(MAE = 0.7939). Also, they showed that the calculation of review similarity between users based on the TF-IDF weights(MAE = 0.7867) leaded to better recommendation accuracy than the calculation based on the frequency of the commonly used terms in reviews(MAE = 0.7881). The results from paired samples t-test presented that our proposed system with review similarity calculation using the frequency of the commonly used terms outperformed conventional CF system with 10% statistical significance level. Our study sheds a light on the application of users' review information for facilitating electronic commerce by recommending proper items to users.