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This paper applied an adaptive neuro-fuzzy inference system (ANFIS) based on a geographic information system (GIS) environment using landslide-related factors and location for landslide susceptibility mapping. Landslide-related factors such as slope, soil texture, wood type, lithology and density of lineament were extracted from topographic, soil, forest and lineament maps. Landslide locations were identified from interpretation of aerial photographs and field surveys. Landslide-susceptible areas were analyzed by the ANFIS method and mapped using occurrence factors. In particular, we applied various membership functions (MFs), and analysis results were verified by using the landslide location data. The predictive maps using triangular, trapezoidal, and polynomial MFs were the best individual MFs for modeling landslide susceptibility maps (84.96% accuracy), proving that ANFIS could be very effective in modeling landslide susceptibility mapping.

Ground subsidence susceptibility at a coal mine by integration of L-band SAR measurements and a subsidence hazard model incorporated in GIS was estimated. A subsidence hazard map was constructed using JERS-1 SAR data from the early 1990s and the subsidence hazard model. A certainty factor analysis was employed for estimating the relative weights of four control factors influencing coal mine subsidence. The relative weight of each factor was then integrated to generate a subsidence hazard index (SHI) by a fuzzy combination operator. The hazard map was validated by comparison with subsidence observed by ALOS PALSAR interferometry in 2007–2008. The results showed a good agreement between the predicted locations vulnerable to subsidence and the actual subsidence occurrences with an accuracy of about 72.5%. These results showed that the map produced by integration of InSAR and GIS can be used to predict and monitor coal mine subsidence hazards, especially in remote regions.

Biotechnology is the generic technology in the 21st century. It has a strategic importance for the development of companies, industries and nations in the near future. Therefore, many countries have been making a great effort to promote and develop biotechnology. U.S. has been the leading country in the world in promoting biotechnology. However, U.S. biotechnology policy are very diverse, so that there have been no comprehensive and systematic studies on it. In our paper, therefore, we will discuss the U.S. biotechnology policy in detail. For the effective analysis, we will rely on the concept of national innovation system, which emphasize the institutional settings of innovation actors and their interaction. This paper deals with U.S. national innovation system for biotechnology. We will analyze the role of major actors, academia, public research institutes, and industry and their interaction. According to our study, the U.S. biotechnological innovation system is composed of diverse actors and there are lots of start-up companies in biotechnology industry. In addition, there are many diverse policy programs for promoting biotechnology. In the analysis of U.S. biotechnological system and policy, we would like to maintain the Korean perspectives. Because of country-specific frame conditions, Korea and USA have different institutional settings and policies for promoting biotechnology. Therefore, our paper will render meaningful implications for Korea and USA. We also think that this paper will be of interest for international readers.

A load model is very important to improve the accuracy of stability analysis and load flow study in power systems. A power system bus is composed of various loads, and these loads have different power consumptions to voltage/frequency changes. Thus, the effect of voltage/frequency changes must be considered in load modeling. In this research, an ANN was used to construct a component load model for more accurate load modeling. A typical residential load was selected, and experimented on voltage/frequency changes. Acquired data used to construct component ANN models, and an aggregation method of the component load model are presented based on component load model and composition rate. Furthermore, the transformation method used in traditional power system analysis software is also presented.