the interactive effects of adjacent buildings on their seismic performance are not frequently considered in seismic design. the adjacent buildings, however, are interrelated through the soil during seismic ground motions. the seismic energy is redistributed in the neighboring buildings through multiple structure-soil-structure interactions (sssi). for example, in an area congested with many nea...

The inhibitory effect on human and rat intestinal disaccharidase by the extractive from the leaves of Morus alba (ELM) containing 0.24 % 1-deoxynojirimycin equivalent and its inhibitory activities were investigated by the modified Dahlqvist method. In the presence of 1000-fold diluted ELM solution, the sucrase activity of four human samples was inhibited by 96 % and that of maltase and isomalta...

Extreme learning machine (ELM) as an emergent technology has shown its good performance in regression applications as well as in large dataset (and/or multi-label) classification applications. The ELM theory shows that the hidden nodes of the ‘‘generalized’’ single-hidden layer feedforward networks (SLFNs), which need not be neuron alike, can be randomly generated and the universal classificati...

Abstract: This paper discusses short-term electricity-load forecasting using an extreme learning machine (ELM) with automatic knowledge representation from a given input-output data set. For this purpose, we use a Takagi-Sugeno-Kang (TSK)-based ELM to develop a systematic approach to generating if-then rules, while the conventional ELM operates without knowledge information. The TSK-ELM design ...

As a new machine learning approach, the extreme learning machine (ELM) has received much attention due to its good performance. However, when directly applied to hyperspectral image (HSI) classification, the recognition rate is low. This is because ELM does not use spatial information, which is very important for HSI classification. In view of this, this paper proposes a new framework for the s...

Compared with conventional weighted voting methods, class-specific soft voting (CSSV) system has several advantages. On one hand, it not only deals with the soft class probability outputs but also refines the weights from classifiers to classes. On the other hand, the class-specific weights can be used to improve the combinative performance without increasing much computational load. This paper...