• 한국음향학회지
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• 제21권2호
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• pp.160-166
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• 2002

본 논문에서는 CV (Consonant Vowel), VCCV (Vowel Consonant Consonant Vowel), VC (Vowel Consonant) 인식 단위를 이용한 새로운 어휘 독립 음성인식 시스템을 구현하였다. 이 인식 단위는 음절의 안정된 모음 구간에서 분할하여 구성했기 때문에 분할이 용이하다. VCCV단위가 존재하지 않을 경우에는 VC와 CV 반음절 모델을 결합하여 대체모델을 구성하였다. 모음군 군집화 (clustering)와 VCCV 모델이 존재하지 않을 경우 대체모델에 결합규칙을 적용하여 제 1후보에서 90.4% (모델 A)에서 95.6% (모델 C)로 5.2%의 인식 성능향상을 가져왔다. 인식실험결과 제 2후보에서 98.8%의 인식률로 제안된 방법이 효율적임을 확인하였다.

• 산업경영시스템학회지
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• 제40권2호
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• pp.92-98
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• 2017

Data clustering determines a group of patterns using similarity measure in a dataset and is one of the most important and difficult technique in data mining. Clustering can be formally considered as a particular kind of NP-hard grouping problem. K-means algorithm which is popular and efficient, is sensitive for initialization and has the possibility to be stuck in local optimum because of hill climbing clustering method. This method is also not computationally feasible in practice, especially for large datasets and large number of clusters. Therefore, we need a robust and efficient clustering algorithm to find the global optimum (not local optimum) especially when much data is collected from many IoT (Internet of Things) devices in these days. The objective of this paper is to propose new Hybrid Simulated Annealing (HSA) which is combined simulated annealing with K-means for non-hierarchical clustering of big data. Simulated annealing (SA) is useful for diversified search in large search space and K-means is useful for converged search in predetermined search space. Our proposed method can balance the intensification and diversification to find the global optimal solution in big data clustering. The performance of HSA is validated using Iris, Wine, Glass, and Vowel UCI machine learning repository datasets comparing to previous studies by experiment and analysis. Our proposed KSAK (K-means+SA+K-means) and SAK (SA+K-means) are better than KSA(K-means+SA), SA, and K-means in our simulations. Our method has significantly improved accuracy and efficiency to find the global optimal data clustering solution for complex, real time, and costly data mining process.