• Journal of the Korean Society of Propulsion Engineers
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• v.4 no.4
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• pp.18-25
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• 2000

A two-color particle image velocimetry (PIV) has been developed for measuring two dimensional velocity flowfields and applied to a Mach 2.0 supersonic nozzle. This technique is similar to a single-color PIV technique except that two different color laser beams are used to solve the directional ambiguity problem. A green-color laser sheet (532 nm: 2nd harmonic beam of YAG laser) and a red-color laser sheet (619 nm: output beam from YAG pumped Dye laser using Rhodamine 640) are employed to illuminate the seeded particles. A high resolution (3060${\times}$2036) digital color CCD camera is used to record the particle positions. This system eliminates the photographic-film processing time and subsequent digitization time as well as the complexities associated with conventional image shifting techniques for solving directional ambiguity problem. The two-color PIV also has the advantage that velocity distributions in high speed flowfields can be measured simply and accurately by varying the time interval between two different laser beams due to its high signal-to-noise ratio and thereby less requirement of panicle pair numbers for a velocity vector in one interrogation spot. The velocity distribution in the Mach 2.0 supersonic nozzle has been measured and the over-expanded shock cell structure can be predicted by the strain rate field. These results are compared and analyzed with the schlieren photograph for the velocity distributions and shock location.

• Asia pacific journal of information systems
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• v.18 no.1
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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.