• 한국추진공학회지
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• 제4권4호
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• pp.18-25
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• 2000

유동장의 2차원 평면 속도 분포를 측정하기 위하여 two-color PIV 기법을 개발하였고, 마하 2.0 초음속 노즐에 적용하여 보았다 이 기법은 single-color PIV 기법과 유사하나 서로 다른 색의 두 레이저 빔을 사용하여 방향성의 문제를 해결하는 차이점을 갖는다. 녹색의 레이저 평면광 (532 nm)과 적색의 레이저 평면광 (619 nm)이 주입된 입자를 조사하기 위하여 사용되었고, 입자 위치가 고해상도 (3060${\times}$2036) 디지털 칼라 CCD 카메라에 기록되었다. 이러한 디지털 칼라 CCD 카메라론 이용한 two-color PIV 시스템은 사진 필름 현상 시간과 이에 따른 디지털화하는 시간 그리고 방향성의 문제론 해결하기 위해 사용되는 일반적인 image shifting 기법과 관련된 어려움을 제거해 준다. 또한 고속 유동장에서는 알맞은 입자 밀도의 주입이 어려워지는데, two-color PIV는 높은 신호 대 잡음비로 인하여 속도 벡터론 얻기 위해서 조사영역에 존재해야 하는 벡터쌍의 수가 줄어들게 된다. 따라서 다른 색의 두레이저 빔의 시간 간격을 조절함으로써 고속 유동장의 속도 분포를 쉽고 정확하게 측정할 수 있게 된다. 마하 2.0 초음속 노즐에서의 속도 분포가 측정되었으며, 속도장으로부터 변형률장을 구하여 과팽창 충격파 구조를 예측해 보았다. Two-color PIV에 의해 얻어진 속도 분포와 충격파의 위치 결과는 schlieren 사진과 비교 분석해 보았다.

• Asia pacific journal of information systems
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• 제18권1호
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• pp.79-96
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• 2008

One of the roles of the Semantic Web services is to execute dynamic intra-organizational services including the integration and interoperation of business processes. Since different organizations design their processes differently, the retrieval of similar semantic business processes is necessary in order to support inter-organizational collaborations. Most approaches for finding services that have certain features and support certain business processes have relied on some type of logical reasoning and exact matching. This paper presents our approach of using imprecise matching for expanding results from an exact matching engine to query the OWL(Web Ontology Language) MIT Process Handbook. MIT Process Handbook is an electronic repository of best-practice business processes. The Handbook is intended to help people: (1) redesigning organizational processes, (2) inventing new processes, and (3) sharing ideas about organizational practices. In order to use the MIT Process Handbook for process retrieval experiments, we had to export it into an OWL-based format. We model the Process Handbook meta-model in OWL and export the processes in the Handbook as instances of the meta-model. Next, we need to find a sizable number of queries and their corresponding correct answers in the Process Handbook. Many previous studies devised artificial dataset composed of randomly generated numbers without real meaning and used subjective ratings for correct answers and similarity values between processes. To generate a semantic-preserving test data set, we create 20 variants for each target process that are syntactically different but semantically equivalent using mutation operators. These variants represent the correct answers of the target process. We devise diverse similarity algorithms based on values of process attributes and structures of business processes. We use simple similarity algorithms for text retrieval such as TF-IDF and Levenshtein edit distance to devise our approaches, and utilize tree edit distance measure because semantic processes are appeared to have a graph structure. Also, we design similarity algorithms considering similarity of process structure such as part process, goal, and exception. Since we can identify relationships between semantic process and its subcomponents, this information can be utilized for calculating similarities between processes. Dice's coefficient and Jaccard similarity measures are utilized to calculate portion of overlaps between processes in diverse ways. We perform retrieval experiments to compare the performance of the devised similarity algorithms. We measure the retrieval performance in terms of precision, recall and F measure? the harmonic mean of precision and recall. The tree edit distance shows the poorest performance in terms of all measures. TF-IDF and the method incorporating TF-IDF measure and Levenshtein edit distance show better performances than other devised methods. These two measures are focused on similarity between name and descriptions of process. In addition, we calculate rank correlation coefficient, Kendall's tau b, between the number of process mutations and ranking of similarity values among the mutation sets. In this experiment, similarity measures based on process structure, such as Dice's, Jaccard, and derivatives of these measures, show greater coefficient than measures based on values of process attributes. However, the Lev-TFIDF-JaccardAll measure considering process structure and attributes' values together shows reasonably better performances in these two experiments. For retrieving semantic process, we can think that it's better to consider diverse aspects of process similarity such as process structure and values of process attributes. We generate semantic process data and its dataset for retrieval experiment from MIT Process Handbook repository. We suggest imprecise query algorithms that expand retrieval results from exact matching engine such as SPARQL, and compare the retrieval performances of the similarity algorithms. For the limitations and future work, we need to perform experiments with other dataset from other domain. And, since there are many similarity values from diverse measures, we may find better ways to identify relevant processes by applying these values simultaneously.