• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.6
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• pp.811-819
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• 2023

Understanding the local conditions is a crucial factor in enhancing the success potential of overseas construction projects. This can be achieved through the analysis of news articles of the target market using topic modeling techniques. In this study, the authors aimed to analyze news articles using two topic modeling methods, namely Latent Dirichlet Allocation (LDA) and BERTopic, in order to determine the optimal approach for market condition analysis. To evaluate the alignment between the generated topics and the actual themes of the news documents, the research collected 6,273 BBC news articles, created ground truth data for individual news article topics, and finally compared this ground truth with the results of the topic modeling. The F1 score for LDA was 0.011, while BERTopic achieved a score of 0.244. These results indicate that BERTopic more accurately reflected the actual topics of news articles, making it more effective for understanding the overseas construction market.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.3
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• pp.380-387
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• 2010

For the fault diagnosis of three-phase induction motors, we propose a diagnosis algorithm based on mutual information and linear discriminant analysis (LDA). The experimental unit consists of machinery module for induction motor drive and data acquisition module to obtain the fault signal. As the first step for diagnosis procedure, DFT is performed to transform the acquired current signal into frequency domain. And then, frequency components are selected according to discriminate order calculated by mutual information As the next step, feature extraction is performed by LDA, and then diagnosis is evaluated by k-NN classifier. The results to verify the usability of the proposed algorithm showed better performance than various conventional methods.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.272-274
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• 2013

최근 TV 시청자들의 콘텐츠 소비량이 증가함에 따라 방송사에서 제공하는 TV 프로그램들의 수량이 방대해지고 장르 또한 다양해지고 있기 때문에 시청자가 TV 프로그램을 선택하는 것이 점점 더 어려워지고 있다. 이러한 문제를 해결하기 위해 TV 프로그램 추천이라는 연구가 활발하게 이루어지고 있다. 기존의 연구에서는 시청자를 기반으로 하는 협업 필터링 추천 방법과 아이템을 기반으로 하는 협업 필터링 추천 방법이 제안되었지만 시청자의 시청 의도를 고려하는 연구는 사례는 적다. 이에 본 논문에서는 LDA 모델링을 이용하여 사용자의 시청 의도를 고려한 TV 프로그램 추천 기법을 제안한다. 실험을 통해 시청자의 시청 의도가 반영된 TV 프로그램 추천이 가능하다는 것을 검증했다.

• Journal of the Korean Society for information Management
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• v.39 no.3
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• pp.99-132
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• 2022

The purpose of this study is to propose AET (Augmented and Extended Topics), a novel method of synthesizing both LDA and BERTopic results, and to analyze the recently published LIS articles as an experimental approach. To achieve the purpose of this study, 55,442 abstracts from 85 LIS journals within the WoS database, which spans from January 2001 to October 2021, were analyzed. AET first constructs a WORD2VEC-based cosine similarity matrix between LDA and BERTopic results, extracts AT (Augmented Topics) by repeating the matrix reordering and segmentation procedures as long as their semantic relations are still valid, and finally determines ET (Extended Topics) by removing any LDA related residual subtopics from the matrix and ordering the rest of them by F₁ (BERTopic topic size rank, Inverse cosine similarity rank). AET, by comparing with the baseline LDA result, shows that AT has effectively concretized the original LDA topic model and ET has discovered new meaningful topics that LDA didn't. When it comes to the qualitative performance evaluation, AT performs better than LDA while ET shows similar performances except in a few cases.

• Smart Media Journal
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• v.7 no.4
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• pp.90-98
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• 2018

A Multilinear LDA Method of Tensor Representation for ECG Signal Based Individual Identification Electrocardiogram signals, included in the cardiac electrical activity, are often analyzed and used for various purposes such as heart rate measurement, heartbeat rhythm test, heart abnormality diagnosis, emotion recognition and biometrics. The objective of this paper is to perform individual identification operation based on Multilinear Linear Discriminant Analysis (MLDA) with the tensor feature. The MLDA can solve dimensional aspects of classification problems in high-dimensional tensor, and correlated subspaces can be used to distinguish between different classes. In order to evaluate the performance, we used MPhysionet's MIT-BIH database. The experimental results on this database showed that the individual identification by MLDA outperformed that by PCA and LDA.

• Journal of Korea Multimedia Society
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• v.10 no.7
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• pp.923-933
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• 2007

In this paper, we present a method for performance enhancement of the marker detection system by using SVM(Support Vector Machine) and LDA(Linear Discriminant Analysis). It converts the input image to a binary image and extracts contours of objects in the binary image. After that, it approximates the contours to a list of line segments. It finds quadrangle by using geometrical features which are extracted from the approximated line segments. It normalizes the shape of extracted quadrangle into exact squares by using the warping technique and scale transformation. It extracts feature vectors from the square image by using principal component analysis. It then checks if the square image is a marker image or a non-marker image by using a SVM classifier. After that, it computes feature vectors by using LDA for the extracted marker images. And it calculates the distance between feature vector of input marker image and those of standard markers. Finally, it recognizes the marker by using minimum distance method. Experimental results show that the proposed method achieves enhancement of recognition rate with smaller feature vectors by using LDA and it can decrease false detection errors by using SVM.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.3
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• pp.367-371
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• 2006

Multilayer percepteon has the advantage of learning their optimal parameters and efficiency. However, MLP shows some drawbacks when dealing with high dimensional data within the input space. Also, it Is very difficult to find the optimal parameters when the input data are highly correlated such as large scale face dataset. In this paper, we propose a novel technique for face recognition based on LDA and local MLP. To resolve the main drawback of MLP, we calculate the reduced features by LDA in advance. And then, we construct a local MLP per group consisting of subset of facedatabase to find its optimal learning parameters rather than using whole faces. Finally, we designed the face recognition system combined with the local MLPs. From various experiments, we obtained better classification performance in comparison with the results produced by conventional methods such as PCA and LDA.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.148-151
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• 2006

본 논문에서 얼굴과 홍채 데이터를 사용하여 다중생체시스템에 기반한 스테가노그라피 구현을 제안한다. 이를 위해, 얼굴과 홍채 인식 기반의 다중생체인식을 구성하였다. 여기서, 홍채의 특징벡터는 디지털 워터마킹 기법을 이용하여 얼굴 이미지 안에 숨기게 된다. 얼굴과 홍채의 인식시스템은 퍼지집합 이론과 LDA 기법이 결합하여 확장한 Fuzzy-LDA(Fuzzy-Based Linear Discriminant Analysis)기법을 제안한다. 최종적으로 디지털 워터마킹 기법을 적용하여 얼굴이미지 안에 홍채 정보를 삽입하고 얼굴 데이터와 홍채 데이터를 통한 다중생체인식을 구성하였으며, 최종적으로 생체데이터 인식율의 ROC 곡선을 통해 제안된 워터마킹 기법의 좋은 성능을 확인하였고, 얼굴 인식율을 통해 워터마킹된 얼굴 영상과 원본 얼굴 영상을 비교하였다. 다양한 실험을 통해 제안된 기법이 다중생체시스템을 보호하고 효과적으로 사용 될 수 있음을 확인 할 수 있다.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.157-158
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• 2007

본 논문에서는 산업전반에 걸쳐 널리 사용되는 유도전동기의 고장상태를 검출하기 위해 DFT(Discreet Fourier Transform)와 LDA에 기반을 둔 진단 알고리즘을 제안하고자 한다. 실험에 의해 측정된 전류값을 DFT에 의해 시간공간에서 주파수 공간으로 변환한 후에 LDA기법을 이용하여 특징벡터를 산출한 후 거리 유사도에 의해 진단이 수행된다. 제안된 방법의 타당성을 보이기 위해 여섯 가지의 고장을 대상으로 다양한 조건하에서 실험한 결과 기존 방법에 비교하여 우수한 결과를 나타냈다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.16 no.6
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• pp.37-46
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• 2006

It is known to be hard to apply cryptographic one-way functions to the recognition system using bio-information directly. As one of the solutions about that problem there is a permutation transformation scheme. However, they did not show my algorithmic behavior or any performance analysis of the transformation by experiment. In this paper, by showing the recognition ratio of the transformed scheme by experiment, we prove that that scheme is sound. Also, we adopt their transformation to LDA(Linear Discriminant Analysis) to show the experimental results. In the negative side, we introduce a new type of attack against the permutation transformation schemes. finally, we briefly mention a generalization of the permutation transformation for countermeasure of the attack at the end of this paper.