주어진 시스템에서 정보와 정보흐름에 대한 패턴인식을 하기 위해서는, 정보를 내포하고 있는 문맥이 내용에 따라서 다른 단어나 다른 정보를 추론하여 원래의미를 전달함에 있어 오도할 수 있기 때문에, 문맥의 분해에서 정보 조각의 묶음 형태로 전환하는 작업에서부터 연구는 시작되어야만 한다. 많은 연구자들이 정보의 저장, 재표현, 부호화, 검색 등에 관해 효과적인 방법론을 찾고자 노력해 오고 있다. 유사한 노력의 일환으로 본 논문에서는 군이론과 상황이론을 응용해

To understand the pattern recognition from dataset, a study should be started from the decomposition process of context into a collection of data pieces because the context may infer different words or information. Many researchers have been focused on finding an effective methodology for data storage, retrieval, representation, and discovery. As a similar endeavor, this paper proposes a new modeling method using group theory and situation theory. This paper provides how to construct a semi-group as a modeling method for pattern recognition from huge dataset. This process of construction of semi‐groups can be used as a retrieval tool for the decomposed information if necessary.

Traditionally the steady-state central section of the vowel length has been assumed to characterize the vowel quality. However, since Peterson and Barney (1952), this position has been challenged especially for American English monophthongal vowels. In this paper, introduced are low-ordered 12 mel-scale frequency cepstral coefficients (MFCC), which can characterize the shape of the oral cavity filter for monophthongal vowel production in the mel-scale domain. Four pattern recognition classification models are fitted to the measurements of spectral and cepstral parameters at multiple sections of the vowel duration along with F0, Gender and Duration for the AE vowel signals in the hVd syllable in Hillenbrand et al. (1995). It turns out that pattern recognition classifiers with the cepstral properties outperform those with spectral properties, reaching the perception level of American English listeners’.