• Tunnel and Underground Space
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• v.31 no.1
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• pp.66-81
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• 2021

The effects of directional fracture tensor components and first invariant of fracture tensor on deformation moduli and shear moduli of fractured rock masses is analyzed based on regression analysis performed between 3-D fracture tensor parameters and deformability of DFN blocks. Using one or two deterministic joint sets, a total of 224 3-D discrete fracture network (DFN) cube blocks were generated with various configurations of deterministic density and probabilistic size distribution. The fracture tensor parameters were calculated for each generated DFN systems. Also, deformability moduli with respect to three perpendicular direction of the DFN cube blocks were estimated based on distinct element method. The larger the first invariant of fracture tensor, the smaller the values for the deformability moduli of the DFN blocks. These deformability properties present an asymptotic pattern above the certain threshold. It is found that power-law function describes the relationship between the directional deformability moduli and the corresponding fracture tensor components estimated in same direction.

• Journal of Korea Multimedia Society
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• v.24 no.1
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• pp.40-50
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• 2021

Science museum are dealing with exhibits on field of changing science and technology, and previous research suggested that exhibits replacement should carried out at least every 5 years. In order to efficiently replace exhibits within a limited budget, various studies analyzed visitors' preferences to exhibits. Recently, studies use various technologies to collect the data on visitors' preferences automatically, but almost of studies had a high dependency on their visitors such as visitors needed to carry specific sub-devices in the museums for gathering data. As complementing the limitations of previous research, this study introduces the improved method which is able to automatically collect and quantify visitors' preferences to exhibits using TensorFlow, a deep learning technology. By the proposed analysis method, it was possible to collect 2,520 data of visitors' experience on exhibits in totality. Based on collected data, attraction power and holding power indicating the preference of visitors on exhibits were able to be calculated. The result also confirmed antecedent research conclusion that the attraction power and holding power of the exhibit which consists of 3 dimensional structures work are higher than other exhibits. As a conclusion, the proposed method will provide more convenient data collection method for detecting visitors' preference.

• The Transactions of the Korean Institute of Power Electronics
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• v.9 no.2
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• pp.112-118
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• 2004

Recently, development of rare earth permanent magnet with the high remanence, high coercivity allows the design of brushless motors with very high efficiency over a wide speed range. Cogging torque is produced in a permanent magnet by magnetic attraction between the rotor mounted permanent magnet and the stator teeth. It is an undesired effect that contributes to output ripple, vibration, and noise of machine. This cogging torque can be reduced by variation of magnet arc length, airgap length, magnet thickness, shifting the magnetic pole and varying the radial shoe depth and etc. In this paper, some airgap length and magnet arc that reduce cogging torque are found by finite element method(FEM) and Maxwell stress tensor method. The SPM(Surface Permanent Magnet) type of high-power Brushless DC (BLDC) motor is optimized as a sample model.

• ETRI Journal
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• v.40 no.4
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• pp.421-434
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• 2018

The application of deep neural networks (DNNs) to connect the world with cyber physical systems (CPSs) has attracted much attention. However, DNNs require a large amount of memory and computational cost, which hinders their use in the relatively low-end smart devices that are widely used in CPSs. In this paper, we aim to determine whether DNNs can be efficiently deployed and operated in low-end smart devices. To do this, we develop a method to reduce the memory requirement of DNNs and increase the inference speed, while maintaining the performance (for example, accuracy) close to the original level. The parameters of DNNs are decomposed using a hybrid of canonical polyadic-singular value decomposition, approximated using a tensor power method, and fine-tuned by performing iterative one-shot hybrid fine-tuning to recover from a decreased accuracy. In this study, we evaluate our method on frequently used networks. We also present results from extensive experiments on the effects of several fine-tuning methods, the importance of iterative fine-tuning, and decomposition techniques. We demonstrate the effectiveness of the proposed method by deploying compressed networks in smartphones.

• Proceedings of the KIEE Conference
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• 1994.07a
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• pp.147-149
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• 1994

In designing high efficiency electrical machines, calculation of iron loss is very important. And it is reported that in the induction motor and in the T-joint of 3 phase transformer, there occurred rotational magnetic field and much iron loss is generated owing to this field. In this paper, rotational power loss in the electrical machine under rotational magnetic field is discussed. Until now, loss analysis is based on the magnetic properties under alternating field. And with this one dimensional magnetic propertis, it is difficult to express iron loss under rotational field. In this paper, we used two dimensional magnetic property data for the numerical calculation of rotational power loss. We used finite element method for calculation and the analysis model is two dimensional magnetic property measurement system. We used permeability tensor instead of scalar permeability to present two dimensional magnetic properties. And in this case, we cannot uniquely define energy functional because of the asymmetry of the permeability tensor, so Galerkin method is used for finite element analysis.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.1935-1944
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• 2018

With the increase of the production of energy from renewable, it becomes important to look at techniques to store this energy. Therefore, a single winding bearingless flywheel motor (SWBFM) specially for flywheel energy storage system is introduced. For the control system of SWBFM, coupling between the torque and the suspension subsystems exists inevitably. It is necessary to build a reasonable radial force mathematical model to precisely control SWBFM. However, SWBFM has twelve independently controlled windings which leads to high-order matrix transformation and complex differential calculation in the process of mathematical modeling based on virtual displacement method. In this frame, a Maxwell tensor modeling method which is no need the detailed derivation and complex theoretical computation is present. Moreover, it possesses advantages of universality, accuracy, and directness. The fringing magnetic path is improved from straight and circular lines to elliptical line and the rationality of elliptical line is verified by virtual displacement theory according to electromagnetic torque characteristics. A correction function is taken to increase the model accuracy based on finite element analysis. Simulation and experimental results show that the control system of SWBFM with radial force mathematical model based on Maxwell tensor method is feasible and has high precision.

• Smart Structures and Systems
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• v.19 no.1
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• pp.33-38
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• 2017

This research tries to present a nonlinear thermo-elastic solution for a functionally graded spherical shell subjected to mechanical and thermal loads. Geometric nonlinearity is considered using the Lagrange or finite strain tensor. Non-homogeneous material properties are considered based on a power function. Adomian's decomposition method is used for calculation of nonlinear results. Nonlinear results such as displacement can be evaluated for sphere in terms of different indexes of non-homogeneity. A comprehensive comparison between linear and nonlinear results and evaluation of the percentage of difference between them can be performed in this paper. The obtained results indicate that the improvement of the results due to usage of nonlinear analysis is depending on the non-homogeneous index.

• Journal of Power System Engineering
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• v.19 no.3
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• pp.55-62
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• 2015

This paper discusses a new model for investigating the micro-mechanical behavior of beam-like structures composed of various elastic moduli and complex geometries varying through the cross-sectional directions and also periodically-repeated along the axial directions. The original three-dimensional problem is first formulated in an unified and compact intrinsic form using the concept of decomposition of the rotation tensor. Taking advantage of two smallness of the cross-sectional dimension-to-length parameter and the micro-to-macro heterogeneity and performing homogenization along dimensional reduction simultaneously, the variational asymptotic method is used to rigorously construct an effective zeroth-order beam model, which is similar a generalized Timoshenko one (the first-order shear deformation model) capable of capturing the transverse shear deformations, but still carries out the zeroth-order approximation which can maximize simplicity and promote efficiency. Two examples available in literature are used to demonstrate the consistence and efficiency of this new model, especially for the structures, in which the effects of transverse shear deformations are significant.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.2879-2890
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• 2020

In this paper, we study the estimation of angle, range and polarization parameters of a bistatic polarization sensitive frequency diverse array multiple-input multiple-output (PSFDA-MIMO) radar system. The application of polarization sensitive array in receiver is explored. A signal model of bistatic PSFDA-MIMO radar system is established. In order to utilize the multi-dimensional structure of array signals, the matched filtering radar data can be represented by a third-order tensor model. A joint estimation of the direction-of-departure (DOD), direction-of-arrival (DOA), range and polarization parameters based on parallel factor (PARAFAC) algorithm is proposed. The proposed algorithm does not need to search spectral peaks and singular value decomposition, and can obtain automatic pairing estimation. The method was compared with the existing methods, and the results show that the performance of the method is better. Therefore, the accuracy of the parameter estimation is further improved.

• Journal of Electrical Engineering and Technology
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• v.8 no.2
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• pp.271-281
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• 2013

In order to improve the performance of analysis, it is important to consider the nonlinearity in power system. The Carleman embedding technique (linearization procedure) provides an effective approach in reduction of nonlinear systems. In the approach, a group of differential equations in which the state variables are formed by the original state variables and the vector monomials one can build with products of positive integer powers of them, is constructed. In traditional Carleman linearization technique, the tensor matrix is truncated to form a square matrix, and then regular linear system theory is used to solve the truncated system directly. However, it is found that part of nonlinear information is neglected when truncating the Carleman model. This paper proposes a new approach to solve the problem, by combining the Poincar$\acute{e}$ transformation with the Carleman linearization. Case studies are presented to verify the proposed method. Modal analysis shows that, with traditional Carleman linearization, the calculated contribution factors are not symmetrical, while such problems are avoided in the improved approach.