This paper presents a new pattern recognition approach for enhancing the selectivity of gas sensor arrays for clustering intelligent odor detection. The aim of this approach was to accurately classify an odor using pattern recognition in order to enhance the selectivity of gas sensor arrays. This was achieved using an odor monitoring system with a newly developed neural-genetic classification algorithm (NGCA). The system shows the enhancement in the sensitivity of the detected gas. Experiments showed that the proposed NGCA delivered better performance than the previous genetic algorithm (GA) and artificial neural networks (ANN) methods. We also used PCA for data visualization. Our proposed system can enhance the reproducibility, reliability, and selectivity of odor sensor output, so it is expected to be applicable to diverse environmental problems including air pollution, and monitor the air quality of clean-air required buildings such as a kindergartens and hospitals.

At room temperature or in the refrigerator, foods such as beef, fish and shellfish decompose very quickly and such decomposed foods are highly likely to cause food poisoning. In particularly, food kept in the refrigerator can be decomposed unknowingly to the user, and the decomposition can produce harmful gases such as volatile fatty acid, CO₂ and methane to cause new diseases. In this study, an odor monitoring system using a recognition module integrating 8 gas sensors and differentiated pattern recognition algorithm built by the research institute is developed in order to measure the degree of food decomposition. The problem of low selectivity, a common characteristic of sensor arrays, was solved through pattern recognition technology using the proposed Genetic Artificial Neural network (GANN) algorithm. The GANN algorithm evaluates the similarity of pattern using ANN in the similarity and fitness evaluation of GA in order to enhance the reliability and selectivity of pattern extraction from harmful gas. We analyzed the characteristics of a pattern for each input gas, and evaluated the results of matching with DB data built up for identifying output odorous substance. In the evaluation, the proposed GANN algorithm was compared with existing ANN and GA, and the proposed GANN showed the highest recognition rate with 97% matching. Through further studies, this idea can be applied not only to real-time monitoring of indoor fire, gas leaking, etc. but also to various prediction and forecasting systems of observed data in diverse areas including environment, medicine, survey, safety/security, chemistry and food.

There are many types of respiratory infectious diseases caused by germs, virus, mycetes and parasites. Researchers recently have tried to develop mathematical models to predict the epidemic of infectious diseases. However, with the development of ground transportation system in modern society, the spread of infectious diseases became faster and more complicated in terms of the speed and the pathways. The route of infectious diseases during Vancouver Olympics was predicted based on the Susceptible-Infectious-Recovered (SIR) model. In this model only the air traffic as an essential factor for the intercity migration of infectious diseases was involved. Here, we propose a multi-city transmission model to predict the infection route during 2018 Winter Olympics in Korea based on the pre-existing SIR model. Various types of transportation system such as a train, a car, a bus, and an airplane for the interpersonal contact in both inter- and intra-city are considered. Simulation is performed with assumptions and scenarios based on realistic factors including demographic, transportation and diseases data in Korea. Finally, we analyze an economic profit and loss caused by the variation of the number of tourists during the Olympics.