Miwa Tobita, Hamed Eskandari and Tetsuji Matsuo
The authors derive a nonlinear MOR based on the Cauer ladder network (CLN) representation, which serves as an application of the parameterized MOR. Two parametrized CLN…
Abstract
Purpose
The authors derive a nonlinear MOR based on the Cauer ladder network (CLN) representation, which serves as an application of the parameterized MOR. Two parametrized CLN representations were developed to handle the nonlinear magnetic field. Simulations using the parameterized CLN were also conducted using an iron-cored inductor model under the first-order approximation.
Design/methodology/approach
This work studies the effect of parameter variations on reduced systems and aims at developing a general formulation for parametrized model order reduction (MOR) methods with the dynamical transition of parameterized state.
Findings
Terms including time derivatives of basis vectors appear in nonlinear state equations, in addition to the linear network equations of the CLN method. The terms are newly derived by an exact formulation of the parameterized CLN and are named parameter variation terms in this study. According to the simulation results, the parameter variation terms play a significant role in the nonlinear state equations when reluctivity is used, while they can be neglected when differential reluctivity is used.
Practical implications
The computational time of nonlinear transient analyses can be greatly reduced by applying the parameterized CLN when the number of time steps is large.
Originality/value
The authors introduced a general representation for the dynamical behavior of the reduced system with time-varying parameters, which has not been theoretically discussed in previous studies. The effect of the parameter variations is numerically given as a form of parameter variation terms by the exact derivation of the nonlinear state equations. The influence of parameter variation terms was confirmed by simulation.
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Kou Takenouchi, Shingo Hiruma, Takeshi Mifune and Tetsuji Matsuo
The purpose of this study is to apply the topology and parameter optimization (TPO) to interior permanent magnet (IPM) motors to obtain the optimized shape with higher torque…
Abstract
Purpose
The purpose of this study is to apply the topology and parameter optimization (TPO) to interior permanent magnet (IPM) motors to obtain the optimized shape with higher torque, lower ripple and sufficient mechanical strength.
Design/methodology/approach
The constraints regarding the maximum stress, connectivity and mesh quality were considered to achieve not only high electrical performance but also high mechanical strength. To enhance the accuracy of the finite element analysis of the elastic analysis, this paper used body-fitted mesh adaptation technique to avoid the stress concentration.
Findings
The proposed method in this study resulted in feasible shapes with sufficiently high strength compared to previous studies. It is also shown that TPO yielded IPM motors with higher torque compared to topology optimization (TO) with fixed parameters.
Practical implications
Different from the existing studies on topology optimization of IPM motors, the mechanical strength is even considered by evaluating the stress values. Therefore, in the practical phase, geometries can be designed that are less likely to be damaged due to deformation, even in the high-speed rotation range.
Originality/value
This paper performed TO and parameter optimization (PO) simultaneously, considering not only the electrical performance but also the mechanical strength. Furthermore, the mechanical strength was evaluated more precisely by devising the elastic analysis conditions and mesh generation.
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Yuji Shindo, Akihisa Kameari and Tetsuji Matsuo
This paper aims to discuss the relationship between the continued fraction form of the analytical solution in the frequency domain, the orthogonal function expansion and their…
Abstract
Purpose
This paper aims to discuss the relationship between the continued fraction form of the analytical solution in the frequency domain, the orthogonal function expansion and their circuit realization to derive an efficient representation of the eddy-current field in the conducting sheet and wire/cylinder. Effective frequency ranges of representations are analytically derived.
Design/methodology/approach
The Cauer circuit representation is derived from the continued fraction form of analytical solution and from the orthogonal polynomial expansion. Simple circuit calculations give the upper frequency bounds where the truncated circuit and orthogonal expansion are applicable.
Findings
The Cauer circuit representation and the orthogonal polynomial expansions for the magnetic sheet in the E-mode and for the wire in the axial H-mode are derived. The upper frequency bound for the Cauer circuit is roughly proportional to N4 with N inductive elements, whereas the frequency bound for the finite element eddy-current analysis with uniform N elements is roughly proportional to N2.
Practical implications
The Cauer circuit representation is expected to provide an efficient homogenization method because it requires only several elements to describe the eddy-current field over a wide frequency range.
Originality/value
The applicable frequency ranges are analytically derived depending on the conductor geometry and on the truncation types.
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Tetsuji Matsuo, Jun Kawahara, Tomohiro Shimoi and Takeshi Mifune
The purpose of this paper is to examine the numerical stability of a space-time finite integration (FI) method. A symmetric correction is proposed to give an accurate constitutive…
Abstract
Purpose
The purpose of this paper is to examine the numerical stability of a space-time finite integration (FI) method. A symmetric correction is proposed to give an accurate constitutive relation at the subgrid connections.
Design/methodology/approach
A scheme for the numerical stability analysis of the space-time FI method is presented, where the growth rate of instability is evaluated by a numerical eigenvalue analysis formulated from an explicit time-marching scheme.
Findings
The 3D and 4D subgrid schemes using the space-time FI method are conditionally stable, where a symmetric correction does not induce numerical instability. The staircase-type 4D space-time subgrid allows a larger time-step than the straight-type subgrid.
Originality/value
The numerical stability of space-time FI method is proven by an eigenvalue analysis, which provides 3D and 4D stable subgrid schemes.
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Shuhei Yamamoto, Kei Wakabayashi, Tetsuji Satoh, Yuri Nozaki and Noriko Kando
The purpose of this paper is to clarify the characteristics of growth users over a long time to strategically collect a large amount of specific users’ tweets. Twitter reflects…
Abstract
Purpose
The purpose of this paper is to clarify the characteristics of growth users over a long time to strategically collect a large amount of specific users’ tweets. Twitter reflects events and trends in users’ real lives because many of them post tweets related to their experiences. Many studies have succeeded in detecting events along with real-life information from a large amount of tweets by assuming users as social sensors. To collect a large amount of tweets based on specific users for successful Twitter studies, the authors have to know the characteristics of users who are active over long periods of time.
Design/methodology/approach
The authors explore the status of users who were active in 2012, and classify users into three statuses of Dead, Lock and Alive. Based on the differences between the numbers of tweets in 2012 and 2016, the authors further classify Alive users into three types of Eraser, Slumber and Growth. The authors analyze the characteristic feature values observed in each user behavior and provide interesting findings with each status/type based on Gaussian mixture model clustering and point-wise mutual information.
Findings
From their sophisticated experimental evaluations, the authors found that active users more easily dropped out than inactive users, and users who engaged in reciprocal communications often became Growth type. Also, the authors found that active users and users who were not retweeted by other users often became Eraser type. The authors’ proposed methods effectively predicted Growth/Eraser-type users compared with the logistic regression model. From these results, the authors clarified the effectiveness of five feature values per active hour to detect intended Twitter user growth for strategically collecting a large amount of tweets.
Originality/value
The authors focus on user growth prediction. To appropriately estimate users who have potential for growth, they collect a large amount of users and explore their status and growth after three years. The research quantitatively clarifies the characteristics of growth users by clustering using robust feature values and provides interesting findings obtained by analysis. After that, the authors propose an effective prediction method for growth users and evaluate the effectiveness of their proposed method.
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Shuhei Yamamoto and Tetsuji Satoh
This paper aims to propose a multi-label method that estimates appropriate aspects against unknown tweets using the two-phase estimation method. Many Twitter users share daily…
Abstract
Purpose
This paper aims to propose a multi-label method that estimates appropriate aspects against unknown tweets using the two-phase estimation method. Many Twitter users share daily events and opinions. Some beneficial comments are posted on such real-life aspects as eating, traffic, weather and so on. Such posts as “The train is not coming” are categorized in the Traffic aspect. Such tweets as “The train is delayed by heavy rain” are categorized in both the Traffic and Weather aspects.
Design/methodology/approach
The proposed method consists of two phases. In the first, many topics are extracted from a sea of tweets using Latent Dirichlet Allocation (LDA). In the second, associations among many topics and fewer aspects are built using a small set of labeled tweets. The aspect scores for tweets were calculated using associations based on the extracted terms. Appropriate aspects are labeled for unknown tweets by averaging the aspect scores.
Findings
Using a large amount of actual tweets, the sophisticated experimental evaluations demonstrate the high efficiency of the proposed multi-label classification method. It is confirmed that high F-measure aspects are strongly associated with topics that have high relevance. Low F-measure aspects are associated with topics that are connected to many other aspects.
Originality/value
The proposed method features two-phase semi-supervised learning. Many topics are extracted using an unsupervised learning model called LDA. Associations among many topics and fewer aspects are built using labeled tweets.