• Journal of Power Electronics
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• v.19 no.2
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• pp.443-453
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• 2019

As the core component of transmission systems, converters are very prone to failure. To improve the accuracy of fault diagnosis for wind power converters, a fault feature extraction method combined with a wavelet transform and compressed sensing theory is proposed. In addition, an improved AdaBoost-SVM is used to diagnose wind power converters. The three-phase output current signal is selected as the research object and is processed by the wavelet transform to reduce the signal noise. The wavelet approximation coefficients are dimensionality reduced to obtain measurement signals based on the theory of compressive sensing. A sparse vector is obtained by the orthogonal matching pursuit algorithm, and then the fault feature vector is extracted. The fault feature vectors are input to the improved AdaBoost-SVM classifier to realize fault diagnosis. Simulation results show that this method can effectively realize the fault diagnosis of the power transistors in converters and improve the precision of fault diagnosis.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.778-790
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• 2023

Although density peak clustering can often easily yield excellent results, there is still room for improvement when dealing with complex, high-dimensional datasets. One of the main limitations of this algorithm is its reliance on geometric distance as the sole similarity measurement. To address this limitation, we draw inspiration from the information bottleneck theory, and propose a novel density peak clustering algorithm that incorporates this theory as a similarity measure. Specifically, our algorithm utilizes the joint probability distribution between data objects and feature information, and employs the loss of mutual information as the measurement standard. This approach not only eliminates the potential for subjective error in selecting similarity method, but also enhances performance on datasets with multiple centers and high dimensionality. To evaluate the effectiveness of our algorithm, we conducted experiments using ten carefully selected datasets and compared the results with three other algorithms. The experimental results demonstrate that our information bottleneck-based density peaks clustering (IBDPC) algorithm consistently achieves high levels of accuracy, highlighting its potential as a valuable tool for data clustering tasks.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.47-73
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• 2020

KTX rolling stocks are a system consisting of several machines, electrical devices, and components. The maintenance of the rolling stocks requires considerable expertise and experience of maintenance workers. In the event of a rolling stock failure, the knowledge and experience of the maintainer will result in a difference in the quality of the time and work to solve the problem. So, the resulting availability of the vehicle will vary. Although problem solving is generally based on fault manuals, experienced and skilled professionals can quickly diagnose and take actions by applying personal know-how. Since this knowledge exists in a tacit form, it is difficult to pass it on completely to a successor, and there have been studies that have developed a case-based rolling stock expert system to turn it into a data-driven one. Nonetheless, research on the most commonly used KTX rolling stock on the main-line or the development of a system that extracts text meanings and searches for similar cases is still lacking. Therefore, this study proposes an intelligence supporting system that provides an action guide for emerging failures by using the know-how of these rolling stocks maintenance experts as an example of problem solving. For this purpose, the case base was constructed by collecting the rolling stocks failure data generated from 2015 to 2017, and the integrated dictionary was constructed separately through the case base to include the essential terminology and failure codes in consideration of the specialty of the railway rolling stock sector. Based on a deployed case base, a new failure was retrieved from past cases and the top three most similar failure cases were extracted to propose the actual actions of these cases as a diagnostic guide. In this study, various dimensionality reduction measures were applied to calculate similarity by taking into account the meaningful relationship of failure details in order to compensate for the limitations of the method of searching cases by keyword matching in rolling stock failure expert system studies using case-based reasoning in the precedent case-based expert system studies, and their usefulness was verified through experiments. Among the various dimensionality reduction techniques, similar cases were retrieved by applying three algorithms: Non-negative Matrix Factorization(NMF), Latent Semantic Analysis(LSA), and Doc2Vec to extract the characteristics of the failure and measure the cosine distance between the vectors. The precision, recall, and F-measure methods were used to assess the performance of the proposed actions. To compare the performance of dimensionality reduction techniques, the analysis of variance confirmed that the performance differences of the five algorithms were statistically significant, with a comparison between the algorithm that randomly extracts failure cases with identical failure codes and the algorithm that applies cosine similarity directly based on words. In addition, optimal techniques were derived for practical application by verifying differences in performance depending on the number of dimensions for dimensionality reduction. The analysis showed that the performance of the cosine similarity was higher than that of the dimension using Non-negative Matrix Factorization(NMF) and Latent Semantic Analysis(LSA) and the performance of algorithm using Doc2Vec was the highest. Furthermore, in terms of dimensionality reduction techniques, the larger the number of dimensions at the appropriate level, the better the performance was found. Through this study, we confirmed the usefulness of effective methods of extracting characteristics of data and converting unstructured data when applying case-based reasoning based on which most of the attributes are texted in the special field of KTX rolling stock. Text mining is a trend where studies are being conducted for use in many areas, but studies using such text data are still lacking in an environment where there are a number of specialized terms and limited access to data, such as the one we want to use in this study. In this regard, it is significant that the study first presented an intelligent diagnostic system that suggested action by searching for a case by applying text mining techniques to extract the characteristics of the failure to complement keyword-based case searches. It is expected that this will provide implications as basic study for developing diagnostic systems that can be used immediately on the site.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.21 no.7
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• pp.882-890
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• 1997

A buoyant jet flow impinging on a circular cylinder is investigated including heat conduction through the cylinder. Temperature and flow fields are obtained by an iterative method, and the effects of the non-uniform wall temperature on the flow and heat transfer are analyzed. Effects of three-dimensionality and the traversing of the jet are also included. Nusselt number over the cylinder surface for the conjugate case is relatively small as compared with the constant wall temperature case due to the small temperature gradient. As the conductivity of the cylinder becomes lower, Nusselt number decreases due to the reduced temperature gradient. Increasing jet traversing speed causes the surface temperature of the cylinder to decrease, which increases local Nusselt number over the surface.

• Journal of the Korean Society of Costume
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• v.66 no.5
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• pp.16-32
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• 2016

This study examines music visualization characteristics appearing in Giorgio Armani's S/S 2000 Collection and Genny's F/W 2000 Collection, which elicited and reinterpreted the characteristics through an analysis of Kandinsky's paintings. Study results are as follows. First, music visualization characteristics were extracted through an analysis of Kandinsky's works to examine music visualization that appears in contemporary fashion. Further analysis of Kandinsky's works were done in regards to music visualization (Impression, Improvisation, and Composition), and music visualization characteristics were categorized into 'spatial element', 'mobility', and 'overlap'. Second, the analysis of contemporary fashion with a spatial component showed that space was often clothed through color contrasts that highlighted concise and playful effects. Emphasis on line and three-dimensional effects were shown by overlapping lines and costume pleats with exposure expressed by semiotic forms and fabric character4istics. Third, the analysis of clothes that express mobility shows that they commonly express mobility through free color arrangements and a shading of colors with playfulness. The effects of emphasis, uniformity, and exposure were shown through the gloss and transformation of fabric that emphasized fabric characteristics; in addition, the effect of simplicity, three-dimensionality, and uniformity were expressed by adopting the forms of geometric shapes. Fourth, the analysis of clothes that manifest the overlap showed a predominant overlapping of colors and fabric. The esthetic effects of playfulness and exposure were emphasized through colors, shapes, and lines.

• 한국전산유체공학회:학술대회논문집
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• 2001.10a
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• pp.84-90
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• 2001

Secondary instability of flow past a circular cylinder is examined using direct numerical simulation at Reynolds number 220 and 250. The higher-order finite difference scheme is employed for the spatial distributions along with the second order Adams-Bashforth and the first order backward-Euler time integration. In x-y plane, the convection term is applied by the 5th order upwind scheme, and the pressure and viscosity terms are applied by the 4th order central difference. In spanwise, Navier-Stokes equation is distributed using Spectral Method. The critical Reynolds number for this instability is found to be about Re=190. The secondary instability leads re three-dimensionality with a spanwise wavelength about 4 cylinder diameters at onset (A-mode). Results of three-dimensional effect in wake of a circular cylinder are represented with spanwise and streamwise vorticity contours as Reynolds numbers.

• The Korean Journal of Applied Statistics
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• v.30 no.4
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• pp.555-566
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• 2017

For multivariate datasets with large number of variables, classical dimensional reduction methods such as principal component analysis may not be effective for data visualization. The underlying reason is that the dimensionality of the space of variables is often larger than two or three, while the visualization to the human eye is most effective with two or three dimensions. This paper proposes a working procedure which first partitions the variables into several "latent" clusters, explores individual data subsets, and finally integrates findings. We use R pakacage "ClustOfVar" for partitioning variables around latent dimensions and the principal component biplot method to visualize within-cluster patterns. Additionally, we use the technique for embedding supplementary variables to figure out the relationships between within-cluster variables and outside variables.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.1
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• pp.143-151
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• 2015

In this paper, we investigate how the power consumption of a heat pump dryer depends on various factors in the drying process by analyzing variables that affect the power consumption. Since there are in general many variables that affect the power consumption, for a feasible analysis, we utilize the principal component analysis to reduce the number of variables (or dimensionality) to two or three. We find that the first component is correlated positively to the entrance temperature of various devices such as compressor, expander, evaporator, and the second, negatively to condenser. We then model the power consumption as a multiple regression with two and/or three transformed variables of the selected principal components. We find that fitted value from the multiple regression explains 80~90% of the observed value of the power consumption. This results can be applied to a more elaborate control of the power consumption in the heat pump dryer.

• Publications of The Korean Astronomical Society
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• v.30 no.2
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• pp.481-483
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• 2015

Multi-dimensionality in the inner working of core-collapse supernovae has long been considered one of the most important ingredients to understand the explosion mechanism. We perform a series of numerical experiments to explore how rotation impacts the 3-dimensional hydrodynamics of core-collapse supernova. We employ a light-bulb scheme to trigger explosions and a three-species neutrino leakage scheme to treat deleptonization effects and neutrino losses from the neutron star interior. We find that the rotation can help the onset of neutrino-driven explosions for models in which the initial angular momentum is matched to that obtained from recent stellar evolutionary calculations (${\sim}0.3-3rad\;s^{-1}$ at the center). For models with larger initial angular momenta, a shock surface deforms to be oblate due to larger centrifugal force. This makes a gain region, in which matter gains energy from neutrinos, more concentrated around the equatorial plane. As a result, the preferred direction of the explosion in 3-dimensional rotating models is perpendicular to the spin axis, which is in sharp contrast to the polar explosions around the axis that are often obtained from 2-dimensional simulations.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.82-87
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• 2023

<Deep Sea> is an 3D animated film that stands out for its exceptional special effects and distinctive artistic style. The film employs a multitude of dazzling and vibrant ink particles, creating a strong sense of three-dimensionality and weightlessness, while simultaneously portraying a dreamlike and elegant representation of a deep sea ink painting. Furthermore, through the utilization of fragmented stream of consciousness narrative technique, the film establishes a unique artistic effect infused with a Chinese atmosphere. This paper by analyzing the unique particle ink art style and color and stream of consciousness narrative methods in film, this paper discusses the innovative art style generated by traditional ink art style combined with three-dimensional technology, and the integration of traditional ink art ideas and artistic conception in animated films. The objective is to cultivate a new ink art style and prove the importance of traditional cultural expression in animated films, while providing new perspectives for the future application of traditional art in animation.