Multi-Hazard Evaluation Using Cluster Analysis—For Designated Evacuation Centers of Yokohama

Abstract

Hazard maps are usually prepared for each disaster, including seismic hazard maps, flood hazard maps, and landslide hazard maps. However, when the general public attempts to check their own disaster risk, most are likely not aware of the specific types of disaster. So, first of all, we need to know what kinds of hazards are important. However, the information that integrates multiple hazards is not well maintained, and there are few such studies. On How to cite this paper: Ochiai, T. and Enomoto, T. (2021) Multi-Hazard Evaluation Using Cluster Analysis-For Designated Evacuation Centers of Yokohama.

Hybrid Algorithm for English Translation Speech Recognition Based on Deep Learning Model and Clustering

“…When the collected speech raw data samples are detected by the speech recognition system, more than half of the recognition errors are due to endpoints. erefore, the noisiness of the real environment in which the original signal is collected directly affects the difficulty of accurate endpoint detection of the speech signal [13]. erefore, during speech recognition, it is necessary to cut off the silence at the beginning and end of the collected speech samples so as not to affect the later detection of speech recognition.…”