Search results for: Jin-Soo Maeng

Authors: Namsoo Kim, So-Hee Son, Jin-Soo Maeng, Yong-Jin Cho, Chul-Jin Kim, Chong-Tai Kim

Abstract:

Wheat gluten hydrolyzates (WGHs) and anchovy fine powder hydrolyzates (AFPHs) were produced at 300 MPa using combinations of Flavourzyme 500MG (F), Alcalase 2.4L (A), Marugoto E (M) and Protamex (P), and then were compared to those produced at ambient pressure concerning the contents of soluble solid (SS), soluble nitrogen and electrophoretic profiles. The contents of SS in the WGHs and AFPHs increased up to 87.2% according to the increase in enzyme number both at high and ambient pressure. Based on SS content, the optimum enzyme combinations for one-, two-, three- and four-enzyme hydrolysis were determined as F, FA, FAM and FAMP, respectively. Similar trends were found for the contents of total soluble nitrogen (TSN) and TCA-soluble nitrogen (TCASN). The contents of SS, TSN and TCASN in the hydrolyzates together with electrophoretic mobility maps indicates that the high-pressure treatment of this study accelerated protein hydrolysis compared to ambient-pressure treatment.

Keywords: Production, Wheat gluten hydrolyzates, Anchovy fine powder hydrolyzates, Protease combinations.

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Authors: Teklu Urgessa, Wookjae Maeng, Joong Seek Lee

Abstract:

Application of five implementations of three data mining classification techniques was experimented for extracting important insights from tourism data. The aim was to find out the best performing algorithm among the compared ones for tourism knowledge discovery. Knowledge discovery process from data was used as a process model. 10-fold cross validation method is used for testing purpose. Various data preprocessing activities were performed to get the final dataset for model building. Classification models of the selected algorithms were built with different scenarios on the preprocessed dataset. The outperformed algorithm tourism dataset was Random Forest (76%) before applying information gain based attribute selection and J48 (C4.5) (75%) after selection of top relevant attributes to the class (target) attribute. In terms of time for model building, attribute selection improves the efficiency of all algorithms. Artificial Neural Network (multilayer perceptron) showed the highest improvement (90%). The rules extracted from the decision tree model are presented, which showed intricate, non-trivial knowledge/insight that would otherwise not be discovered by simple statistical analysis with mediocre accuracy of the machine using classification algorithms.

Keywords: Classification algorithms; data mining; tourism; knowledge discovery.

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Authors: Maeng-Hwan Hyun, Dae-Man Do, Young-Bok Choi

Abstract:

Around the world, there are frequent incidents of natural disasters, such as earthquakes, tsunamis, floods, and snowstorms, as well as manmade disasters such as fires, arsons, and acts of terror. These diverse and unpredictable adversities have resulted in a number of fatalities and injuries. If disaster occurrence can be assessed quickly and information such as the exact location of the disaster and evacuation routes can be provided, victims can promptly move to safe locations, minimizing losses. This paper proposes a behavior analysis method based on a nine degrees-of-freedom (9-DOF) sensor that is effective for the emergency rescue evacuation support system (ERESS), which is being researched with an objective of providing evacuation support during disasters. Based on experiments performed using the acceleration sensor and the gyroscope sensor in the 9-DOF sensor, data are analyzed for human behavior regarding stationary position, walking, running, and during emergency situation to suggest guidelines for system judgment. Using the results of the experiments performed to determine disaster occurrence, it was confirmed that the proposed method quickly determines whether a disaster has occurred.

Keywords: Behavior Analysis, Nine degrees-of-freedom sensor, Emergency rescue.

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