• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.05a
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• pp.235-239
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• 1999

This thesis is concerned with the development of useful engineering techniques to detect and analyze nonlinearities in mechanical systems. The methods developed are based on the concepts of higher order spectra, in particular the bispectrum. The study of higher order statistics has been dominated by work on the bispectrum. The bispectrum can be viewed as a decomposition of the third moment(skewness) of a signal over frequency and as such is blind to symmetric nonlinearities. Initially auto higher order spectra are studied in detail with particular attention being paid to normalization method. Traditional method based on the bicoherence are studied. Under certain conditions, notably narrow band signals, the above normalization method is shown to fail and so a new technique based on prewhitening the signal in the time domain is developed.

• Journal of Internet Computing and Services
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• v.9 no.5
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• pp.85-97
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• 2008

Recently, service-oriented systems have been issued by their properties of reducing software development time and effort by reusing functional service units. The reusability of services can effectively promote through loose coupling between services and loose coupling between services depends on component-based system. That is, the component-based system is designed by grouping the tightly coupled classes of the object-oriented system and the service-oriented system is designed by the component-based system. Therefore, to design the component-based system and service-oriented system efficiently, a metric to measure the coupling between classes accurately needs. In this paper, we propose a coupling metric between classes applying a structural property, a dynamic property, and the normalized value by 0-1. We prove the theoretical soundness of the proposed metric by the axioms of briand et al, and suggest the accuracy and practicality through a case study. We suggest the evaluation results of the proposed metric through a comparison with the conventional metrics.

• Journal of Navigation and Port Research
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• v.41 no.1
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• pp.1-8
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• 2017

The estimation of non-linear ship motion is one of the most important issues in recent studies of ship stability. In this paper, bispectral analysis and bicoherence analysis were introduced in order to analyze the non-linear ship motion. In addition to the previously observed non-linear pitching motion in following seas, this study observed the non-linear phase coupling of pitching motion in following & quartering seas, and starboard beam seas. By comparing phase coupling between each frequency quantitatively via the bicoherence analysis, it was confirmed that non-linear phase coupling was much stronger in frequency regions other than the peak frequencies of a power spectrum. Furthermore, it was found out that the results of bicoherence calculation were analagous to each other, although the different normalization methods were applied.

• Annual Conference of KIPS
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• 2022.11a
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• pp.85-87
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• 2022

고품질 코드를 위한 정적 분석은 아직도 매우 필요한 영역이며, 또한 코드의 가시화는 개발자들에게 코드의 복잡한 모듈에 대한 가이드에 필요하다. 기존의 코드 가시화는 정적 분석의 코드 내부 정보들을 DB 테이블화 및 품질 지표(CK Metrics, Coupling, # function Calls, Bed smell) 질의어화, 그리고 추출된 정보를 가시화하는 것에만 초점을 두었다. 문제는 코드 내부 정보(Class, method, parameters, etc) 테이블들에 대한 join 연산 시 엄청난 시간과 리소스가 소모된다. 이 문제를 해결하기 위해, 우리는 테이블 설계의 정규화를 제안한다. 또한 필요한 품질 지표의 질의를 통해 코드 내부 정보 추출하여 데이터 및 제어 복잡 모듈을 식별하여 refactoring 를 가이드 한다. 앞으로는 이 부분의 AI learning 을 통해 bad/good program 을 식별을 기대한다.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.5
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• pp.199-206
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• 2023

The current software becomes the huge size of source codes. Therefore it is increasing the importance and necessity of static analysis for high-quality product. With static analysis of the code, it needs to identify the defect and complexity of the code. Through visualizing these problems, we make it guild for developers and stakeholders to understand these problems in the source codes. Our previous visualization research focused only on the process of storing information of the results of static analysis into the Database tables, querying the calculations for quality indicators (CK Metrics, Coupling, Number of function calls, Bad-smell), and then finally visualizing the extracted information. This approach has some limitations in that it takes a lot of time and space to analyze a code using information extracted from it through static analysis. That is since the tables are not normalized, it may occur to spend space and time when the tables(classes, functions, attributes, Etc.) are joined to extract information inside the code. To solve these problems, we propose a regularized design of the database tables, an extraction mechanism for quality metric indicators inside the code, and then a visualization with the extracted quality indicators on the code. Through this mechanism, we expect that the code visualization process will be optimized and that developers will be able to guide the modules that need refactoring. In the future, we will conduct learning of some parts of this process.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.1
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• pp.114-123
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• 2018

This paper concerns a design and learning method of softmax function neural networks based on K-means clustering. The partial discharge data Information is preliminarily processed through simulation using an Epoxy Mica Coupling sensor and an internal Phase Resolved Partial Discharge Analysis algorithm. The obtained information is processed according to the characteristics of the pattern using a Motor Insulation Monitoring System program. At this time, the processed data are total 4 types that void discharge, corona discharge, surface discharge and slot discharge. The partial discharge data with high dimensional input variables are secondarily processed by principal component analysis method and reduced with keeping the characteristics of pattern as low dimensional input variables. And therefore, the pattern classifier processing speed exhibits improved effects. In addition, in the process of extracting the partial discharge data through the MIMS program, the magnitude of amplitude is divided into the maximum value and the average value, and two pattern characteristics are set and compared and analyzed. In the first half of the proposed partial discharge pattern classifier, the input and hidden layers are classified by using the K-means clustering method and the output of the hidden layer is obtained. In the latter part, the cross entropy error function is used for parameter learning between the hidden layer and the output layer. The final output layer is output as a normalized probability value between 0 and 1 using the softmax function. The advantage of using the softmax function is that it allows access and application of multiple class problems and stochastic interpretation. First of all, there is an advantage that one output value affects the remaining output value and its accompanying learning is accelerated. Also, to solve the overfitting problem, L2-normalization is applied. To prove the superiority of the proposed pattern classifier, we compare and analyze the classification rate with conventional radial basis function neural networks.