• Structural Engineering and Mechanics
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• v.7 no.1
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• pp.111-126
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• 1999

The work presented here focuses on the development of suitable discretised formulations, for large-displacement shape and non-shape design sensitivity analysis (DSA), which enable the straightforward incorporation of structural optimisation into established finite element analysis (FEA) codes. For the generalised displacement-based functional the design sensitivity vector has been expressed in terms of displacement sensitivity. The Total Lagrangian formulation is utilised for modelling of large deformation of truss structures. The variational formulation of the sensitivity analysis procedure is discretised by using "pseudo" - finite elements, Results are presented for the sensitivity analysis and optimisation of standard truss structures. For the purposes of this work, the analysis and optimisation procedures outlined below are incorporated into the FEA code ABAQUS.

• Journal of Korean Institute of Industrial Engineers
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• v.17 no.2
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• pp.39-50
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• 1991

The movement of human beings - walking, running, jumping and climbing, etc. - have long been of scientific interest. In particular, the science of human walking is called gait analysis. Various instruments have been developed to assist in the study of human gait. Recently gait analysis techniques are used in medical research to investigate the abnormalities of pathological gait. In this study, we constructed a comprehensive gait analysis system consisting of a walkway, a force platform, foot-switches and an ExpertVision motion analysis system. Time-distance gait parameters and vector diagrams can be analyzed by a special application program called Force Analysis System(FOANAS). Using quantitative discrimination of this system, the gait characteristic parameters of normal and pathological gait is facilitated.

• Journal of Electrical Engineering and Technology
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• v.9 no.3
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• pp.926-931
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• 2014

This paper presents the torque calculation and parametric analysis of synchronous permanent magnet couplings (SPMCs). Based on a magnetic vector potential, we obtained the analytical magnetic field solutions produced by permanent magnets (PMs). Then, the analytical solutions for a magnetic torque were obtained. All analytical results were extensively validated with the non-linear a two-dimensional (2D) finite element analysis (FEA). In particular, test results such as torque measurements are presented that confirm the analysis. Finally, using the derived analytical magnetic torque solutions, we carried out a parametric analysis to determine the influence of the design parameters on the SPMC's behavior.

• Proceedings of the KIEE Conference
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• 2005.07b
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• pp.987-989
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• 2005

Axi-periodic analysis using magnetic vector potential is formulated in time harmonic field and applied to the field analysis for the end region of large turbo generator in this paper. By using axi-periodic analysis, the effect of flux shield, one of the structure placed in the end region of large turbo generator to prevent stator end from thermal damage, is studied, and eddy current loss in the flux shield is estimated for operation conditions. 3D FEA is used for the verification of presented analysis method. Because 3D flux distribution can be calculated with 2D modeling, magnetic field showing 3D distribution can be effectively calculated by axi-periodic analysis comparing with 3D FEA.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.1-25
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• 2020

Sentiment Analysis (SA) is a Natural Language Processing (NLP) task that analyzes the sentiments consumers or the public feel about an arbitrary object from written texts. Furthermore, Aspect-Based Sentiment Analysis (ABSA) is a fine-grained analysis of the sentiments towards each aspect of an object. Since having a more practical value in terms of business, ABSA is drawing attention from both academic and industrial organizations. When there is a review that says "The restaurant is expensive but the food is really fantastic", for example, the general SA evaluates the overall sentiment towards the 'restaurant' as 'positive', while ABSA identifies the restaurant's aspect 'price' as 'negative' and 'food' aspect as 'positive'. Thus, ABSA enables a more specific and effective marketing strategy. In order to perform ABSA, it is necessary to identify what are the aspect terms or aspect categories included in the text, and judge the sentiments towards them. Accordingly, there exist four main areas in ABSA; aspect term extraction, aspect category detection, Aspect Term Sentiment Classification (ATSC), and Aspect Category Sentiment Classification (ACSC). It is usually conducted by extracting aspect terms and then performing ATSC to analyze sentiments for the given aspect terms, or by extracting aspect categories and then performing ACSC to analyze sentiments for the given aspect category. Here, an aspect category is expressed in one or more aspect terms, or indirectly inferred by other words. In the preceding example sentence, 'price' and 'food' are both aspect categories, and the aspect category 'food' is expressed by the aspect term 'food' included in the review. If the review sentence includes 'pasta', 'steak', or 'grilled chicken special', these can all be aspect terms for the aspect category 'food'. As such, an aspect category referred to by one or more specific aspect terms is called an explicit aspect. On the other hand, the aspect category like 'price', which does not have any specific aspect terms but can be indirectly guessed with an emotional word 'expensive,' is called an implicit aspect. So far, the 'aspect category' has been used to avoid confusion about 'aspect term'. From now on, we will consider 'aspect category' and 'aspect' as the same concept and use the word 'aspect' more for convenience. And one thing to note is that ATSC analyzes the sentiment towards given aspect terms, so it deals only with explicit aspects, and ACSC treats not only explicit aspects but also implicit aspects. This study seeks to find answers to the following issues ignored in the previous studies when applying the BERT pre-trained language model to ACSC and derives superior ACSC models. First, is it more effective to reflect the output vector of tokens for aspect categories than to use only the final output vector of [CLS] token as a classification vector? Second, is there any performance difference between QA (Question Answering) and NLI (Natural Language Inference) types in the sentence-pair configuration of input data? Third, is there any performance difference according to the order of sentence including aspect category in the QA or NLI type sentence-pair configuration of input data? To achieve these research objectives, we implemented 12 ACSC models and conducted experiments on 4 English benchmark datasets. As a result, ACSC models that provide performance beyond the existing studies without expanding the training dataset were derived. In addition, it was found that it is more effective to reflect the output vector of the aspect category token than to use only the output vector for the [CLS] token as a classification vector. It was also found that QA type input generally provides better performance than NLI, and the order of the sentence with the aspect category in QA type is irrelevant with performance. There may be some differences depending on the characteristics of the dataset, but when using NLI type sentence-pair input, placing the sentence containing the aspect category second seems to provide better performance. The new methodology for designing the ACSC model used in this study could be similarly applied to other studies such as ATSC.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.237-262
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• 2022

There has been a growing interest in IPOs (Initial Public Offerings) due to the profitable returns that IPO stocks can offer to investors. However, IPOs can be speculative investments that may involve substantial risk as well because shares tend to be volatile, and the supply of IPO shares is often highly limited. Therefore, it is crucially important that IPO investors are well informed of the issuing firms and the market before deciding whether to invest or not. Unlike institutional investors, individual investors are at a disadvantage since there are few opportunities for individuals to obtain information on the IPOs. In this regard, the purpose of this study is to provide individual investors with the information they may consider when making an IPO investment decision. This study presents a model that uses machine learning and text analysis to predict whether an IPO stock price would move up or down after the first 5 trading days. Our sample includes 691 Korean IPOs from June 2009 to December 2020. The input variables for the prediction are three tone variables created from IPO prospectuses and quantitative variables that are either firm-specific, issue-specific, or market-specific. The three prospectus tone variables indicate the percentage of positive, neutral, and negative sentences in a prospectus, respectively. We considered only the sentences in the Risk Factors section of a prospectus for the tone analysis in this study. All sentences were classified into 'positive', 'neutral', and 'negative' via text analysis using TF-IDF (Term Frequency - Inverse Document Frequency). Measuring the tone of each sentence was conducted by machine learning instead of a lexicon-based approach due to the lack of sentiment dictionaries suitable for Korean text analysis in the context of finance. For this reason, the training set was created by randomly selecting 10% of the sentences from each prospectus, and the sentence classification task on the training set was performed after reading each sentence in person. Then, based on the training set, a Support Vector Machine model was utilized to predict the tone of sentences in the test set. Finally, the machine learning model calculated the percentages of positive, neutral, and negative sentences in each prospectus. To predict the price movement of an IPO stock, four different machine learning techniques were applied: Logistic Regression, Random Forest, Support Vector Machine, and Artificial Neural Network. According to the results, models that use quantitative variables using technical analysis and prospectus tone variables together show higher accuracy than models that use only quantitative variables. More specifically, the prediction accuracy was improved by 1.45% points in the Random Forest model, 4.34% points in the Artificial Neural Network model, and 5.07% points in the Support Vector Machine model. After testing the performance of these machine learning techniques, the Artificial Neural Network model using both quantitative variables and prospectus tone variables was the model with the highest prediction accuracy rate, which was 61.59%. The results indicate that the tone of a prospectus is a significant factor in predicting the price movement of an IPO stock. In addition, the McNemar test was used to verify the statistically significant difference between the models. The model using only quantitative variables and the model using both the quantitative variables and the prospectus tone variables were compared, and it was confirmed that the predictive performance improved significantly at a 1% significance level.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.5
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• pp.942-948
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• 2011

This paper deals with characteristic analysis of axial flux permanent magnet (AFPM) machines with axially magnetized PM rotor using quasi-3-D analysis modeling. On the basis of magnetic vector potential and a two-dimensional (2-D) polar-coordinate system, the magnetic field solutions due to various PM rotors are obtained. In particular, 3-D problem, that is, the reduction of magnetic fields near outer and inner radius of the PM is solved by introducing a special function for radial position. And then, the analytical solutions for back-emf and torque are also derived from magnetic field solutions. The predictions are shown in good agreement with those obtained from 3-D finite element analyses (FEA). Finally, it can be judged that analytical solutions for electromagnetic quantities presented in this paper are very useful for the AFPM machines in terms of following items : initial design, sensitivity analysis with design parameters, and estimation of control parameters.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.3
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• pp.632-638
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• 2009

Probabilistic Latent Semantic Analysis has many applications in information retrieval and filtering, natural language processing, machine learning from text, and in related areas. In this paper, we propose an algorithm using weighted Probabilistic Latent Semantic Analysis Model to find the contextual phrases and opinions from documents. The traditional keyword search is unable to find the semantic relations of phrases, Overcoming these obstacles requires the development of techniques for automatically classifying semantic relations of phrases. Through experiments, we show that the proposed algorithm works well to discover semantic relations of phrases and presents the semantic relations of phrases to the vector-space model. The proposed algorithm is able to perform a variety of analyses, including such as document classification, online reputation, and collaborative recommendation.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.27 no.7
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• pp.76-81
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• 2013

This paper presents an effective pedagogical method for nodal analysis in linear circuit. In the proposed method, basic equations are built only for passive elements and independent current sources. And then, the basic equations are modified by considering other sources such as voltage sources and dependent current sources. In the proposed method, the equations are presented in form of a matrix and a vector of which elements are built systematically by considering every element in a circuit one by one. This make the proposed method easy to apply to intricately composed circuit and easy to solve the final simultaneous equations and easy to realize as computer program for nodal analysis and easy to memorize compared to the conventional method.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.3
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• pp.46-51
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• 2002

Recently, High capacity alternator are used fur some special equipments in industry. But, several serious problem are occured, especially, broken coil of rotor, caused by crash with stator on rotating the rotor. Although added coil cover thor protect coil of rotor, coil cover is broken. In this study purpose 2 step for corrected that problems. First, three dimensional finite element method far investigate what is most important point. For that purpose, performed stress analysis of coil and coil cover that modeling and finite analysis by ANSYS software. Second, Apply prestress when winding the coil on stator to modify direction of net force. Vector analysis is used for determine corrected prestress. Result of the analysis and prestress are reviewed.