• Smart Structures and Systems
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• v.4 no.5
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• pp.629-640
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• 2008

A new algorithm is proposed to determine optimal accelerometer locations (OAL) when a structure is identified by frequency domain system identification (SI) method. As a result, a guideline is presented for selecting OAL which can reflect modal response of a structure properly. The guideline is to provide a minimum number of necessary accelerometers with the variation in the number of measurable target modes. To determine OAL for SI applications effectively, the modal sensitivity effective independence distribution vector (MS-EIDV) is developed with the likelihood function of measurements. By maximizing the likelihood of the occurrence of the measurements relative to the predictions, Fisher Information Matrix (FIM) is derived as a function of mode shape sensitivity. This paper also proposes a statistical approach in determining the structural parameters with a presumed parameter error which reflects the epistemic paradox between the determination of OAL and the application of a SI scheme. Numerical simulations have been carried out to examine the proposed OAL algorithm. A two-span multi-girder bridge and a two-span truss bridge were used for the simulation studies. To overcome a rank deficiency frequently occurred in inverting a FIM, the singular value decomposition scheme has been applied.

• Journal of Korean Ophthalmic Optics Society
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• v.6 no.2
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• pp.121-127
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• 2001

While SVD and Gaussian elimination method were applied to the additive damped least squares(DLS), the convergence and the stability of the optimization process were examined in a triplet-type camera lens-system where the condition number is well conditioned. DLS with SVD method generated a suitable merit function but this merit function may be trapped in a local minimum by the nonlinearity of error function. Therefore, the least camera lens-system was further designed by the global optimization method is grid method, and this method is adopted to get merit function that convergent to global minimum without local minimum trapping.

• Bulletin of the Korean Mathematical Society
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• v.51 no.6
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• pp.1841-1850
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• 2014

It is known that the ranks of the semigroups $\mathcal{SOP}_n$, $\mathcal{SPOP}_n$ and $\mathcal{SSPOP}_n$ (the semigroups of orientation preserving singular self-maps, partial and strictly partial transformations on $X_n={1,2,{\ldots},n}$, respectively) are n, 2n and n + 1, respectively. The idempotent rank, defined as the smallest number of idempotent generating set, of $\mathcal{SOP}_n$ and $\mathcal{SSPOP}_n$ are the same value as the rank, respectively. Idempotent can be seen as a special case (with m = 1) of m-potent. In this paper, we investigate the m-potent ranks, defined as the smallest number of m-potent generating set, of the semigroups $\mathcal{SOP}_n$, $\mathcal{SPOP}_n$ and $\mathcal{SSPOP}_n$. Firstly, we characterize the structure of the minimal generating sets of $\mathcal{SOP}_n$. As applications, we obtain that the number of distinct minimal generating sets is $(n-1)^nn!$. Secondly, we show that, for $1{\leq}m{\leq}n-1$, the m-potent ranks of the semigroups $\mathcal{SOP}_n$ and $\mathcal{SPOP}_n$ are also n and 2n, respectively. Finally, we find that the 2-potent rank of $\mathcal{SSPOP}_n$ is n + 1.

• Journal of Multimedia Information System
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• v.2 no.2
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• pp.215-222
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• 2015

Epileptic Seizure is a popular brain disease in the world. It affects the nervous system and the activities of brain function that make a person who has seizure signs cannot control and predict his actions. Based on the Electroencephalography (EEG) signals which are recorded from human or animal brains, the scientists use many methods to detect and recognize the abnormal activities of brain. Tucker model is investigated to solve this problem. Tucker decomposition is known as a higher-order form of Singular Value Decomposition (SVD), a well-known algorithm for decomposing a matric. It is widely used to extract good features of a tensor. After decomposing, the result of Tucker decomposition is a core tensor and some factor matrices along each mode. This core tensor contains a number of the best information of original data. In this paper, we used Tucker decomposition as a way to obtain good features. Training data is primarily applied into the core tensor and the remained matrices will be combined with the test data to build the Tucker base that is used for testing. Using core tensor makes the process simpler and obtains higher accuracies.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.4
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• pp.387-397
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• 1992

The spatial domain design of 2-dimensional separable denominator digital filters(SDDF) based on the reduced dimensional decomposition can be realized when the given 2-D impulse response specifications are decomposed into a pair of 1-D specifications via singular value decompositions(SVD). Because of use of the balaned approximation and equivalent transform as 1-D design algorithm, 2-D design algorithm retains the advantage that is numerically stable and can minimize quantization errors. In this paper in order to analyze and reduce these errors, minimum comfficient quantization realization is directly derived from impulse response specification. And using the equivalent trans form relation between mininum coefficient quantization error and minimum roundoff error realizations, we optimally realize a SDDF. This algorithm is analyzed by the simulation, which shows that it is superior to direct or balanced realization in quantization errors.

• Proceedings of the KSRS Conference
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• v.2
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• pp.582-585
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• 2006

Augustine volcano is an active stratovolcano located southwest of Anchorage, Alaska. Augustine volcano experienced seven significantly explosive eruptions in 1812, 1883, 1908, 1935, 1963, 1976, and 1986, and a minor eruption in January 2006. To measure ground surface deformation of Augustine volcano, we applied satellite radar interferometry with ERS-1/2 and ENVISAT SAR images acquired from three descending and three ascending satellite tracks. Multiple interferograms are stacked to reduce artifacts due to changes in atmospheric condition and retrieve temporal deformation sequence. For this, we used Least Square (LS) method for reducing atmospheric effects and Singular Value Decomposition (SVD) method for the retrieval of a temporal deformation sequence. Interferograms before 2006 eruption show about 3 cm/year subsidence by contraction of pyroclastic flow deposits from the 1986 eruption. Interferograms during 2006 eruption do not show significant deformation around volcano crater. Interferograms after 2006 eruption show again a several cm subsidence by compaction and contraction of pyroclastic flow deposits for a few months. This study demonstrates that satellite radar interferometry can monitor deformation of Augustine volcano to help understand the magma plumbing system driving surface deformation.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2331-2336
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• 2005

This paper proposes a new haptic device using a parallel mechanism with gimbal type actuators. This device has three legs actuated by 2-DOF gimbal mechanisms, which make the device simple and light by fixing all the actuators to the base. Three extra sensors are placed at passive joints to obtain a unique solution of the forward kinematics problem. The proposed haptic device is developed for an operator to use it on a desktop in due consideration of the size of an average Korean. The proposed haptic device has a small workspace for on operator to use it on a desktop and more sensitivity than a serial type haptic device. Therefore, the motors of the proposed haptic device are fixed at the base plate so that the proposed haptic device has a better dynamic bandwidth due to a low moving inertia. With this conceptual design, optimization of the design parameters is carried out. The objective function is defined by the fuzzy minimum of the global design indices, global force/moment isotropy index, global force/moment payload index, and workspace. Each global index is calculated by a SVD (singular value decomposition) of the force and moment parts of the jacobian matrix. Division of the jacobian matrix assures a consistency of the units in the matrix. Due to the nonlinearity of this objective function, Genetic algorithms are adopted for a global optimization.

• The Journal of Bigdata
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• v.2 no.1
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• pp.41-48
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• 2017

Recently, the popularity of the recommendation system and the evaluation of the performance of the algorithm of the recommendation system have become important. In this study, we used modeling and RMSE to verify the effectiveness of various algorithms in movie data. The data of this study is based on user-based collaborative filtering using Pearson correlation coefficient, item-based collaborative filtering using cosine correlation coefficient, and item-based collaborative filtering model using singular value decomposition. As a result of evaluating the scores with three recommendation models, we found that item-based collaborative filtering accuracy is much higher than user-based collaborative filtering, and it is found that matrix recommendation is better when using matrix decomposition.

• ETRI Journal
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• v.28 no.1
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• pp.59-66
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• 2006

Learning behaviors of a radial basis function network (RBFN) using a singular value decomposition (SVD) and stochastic gradient (SG) algorithm, together named RBF-SVD-SG, for odor sensing systems are analyzed, and a fast training method is proposed. RBF input data is from a conducting polymer sensor array. It is revealed in this paper that the SG algorithm for the fine-tuning of centers and widths still shows ill-behaving learning results when a sufficiently small convergence coefficient is not used. Since the tuning of centers in RBFN plays a dominant role in the performance of RBFN odor sensing systems, our analysis is focused on the center-gradient variance of the RBFN-SVD-SG algorithm. We found analytically that the steadystate weight fluctuation and large values of a convergence coefficient can lead to an increase in variance of the center-gradient estimate. Based on this analysis, we propose to use the least mean square algorithm instead of SVD in adjusting the weight for stable steady-state weight behavior. Experimental results of the proposed algorithm have shown faster learning speed and better classification performance.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.26 no.1
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• pp.97-103
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• 2016

The sensitivity of interior noise that the passengers perceive is comparatively high in the train, and structure-borne and air-borne types of noises come into the train. In this paper, to analyze contributions of these noise sources operational transfer path analysis(OTPA) is used. OTPA has some advantages of executing the contribution rates of several sources simultaneously, and in this work, 29 points are measured while running. Transfer functions between reference measurement points and response measurement points are calculated by the singular value decomposition(SVD) and Principal component analysis(PCA) method, and the frequency characteristics of the noise source are successfully derived. Also the interior noise is predicted and compared with measurement data to show the reliability.