A Review of Point Set Registration: From Pairwise Registration to Groupwise Registration.

This paper presents a comprehensive literature review on point set registration. The state-of-the-art modeling methods and algorithms for point set registration are discussed and summarized. Special attention is paid to methods for pairwise registration and groupwise registration. Some of the most prominent representative methods are selected to conduct qualitative and quantitative experiments. From the experiments we have conducted on 2D and 3D data, CPD-GL pairwise registration algorithm and JRMPC groupwise registration algorithm seem to outperform their rivals both in accuracy and computational complexity. Furthermore, future research directions and avenues in the area are identified.

Structural health monitoring of a reinforced concrete building during the severe typhoon Vicente in 2012.

The goal of this study is to investigate the structural performance of reinforced concrete building under the influence of severe typhoon. For this purpose, full-scale monitoring of a 22-story reinforced concrete building was conducted during the entire passage process of a severe typhoon "Vicente." Vicente was the eighth tropical storm developed in the Western North Pacific Ocean and the South China Sea in 2012. Moreover, it was the strongest and most devastating typhoon that struck Macao since 1999. The overall duration of the typhoon affected period that lasted more than 70 hours and the typhoon eye region covered Macao for around one hour. The wind and structural response measurements were acquired throughout the entire typhoon affected period. The wind characteristics were analyzed using the measured wind data including the wind speed and wind direction time histories. Besides, the structural response measurements of the monitored building were utilized for modal identification using the Bayesian spectral density approach. Detailed analysis of the field data and the typhoon generated effects on the structural performance are discussed.

Influences of environmental factors on the dissipation of green tides in the Yellow Sea, China.

Green tides attack the Yellow Sea every year since 2007 and have caused substantial financial loss. Based on Haiyang-1C/Coastal zone imager (HY-1C/CZI) and Terra/MODIS satellite images, the temporal and spatial distribution of green tides floating in the Yellow Sea during 2019 was extracted. The relationships between the growth rate of the green tides and the environmental factors including sea surface temperature (SST), photosynthetically active radiation (PAR), sea surface salinity (SSS), nitrate and phosphate during the green tides' dissipation phase has been detected. Based on the maximum likelihood estimation, a regression model that includes SST, PAR and phosphate was recommended to predict the growth rate of the green tides in the dissipation phase (R2 = 0.63), and this model was also examined using Bayesian information criterion and Akaike information criterion. When the average SST in the study area was above 23.6 °C, the coverage of green tides began to decrease with the increase in temperature under the influence of PAR. The growth rate of the green tides was related to SST (R = -0.38), PAR (R = -0.67) and phosphate (R = 0.40) in the dissipation phase. Compared with HY-1C/CZI, the green tide area extracted using Terra/MODIS tended to be underestimated when the green tide patches were smaller than 11.2 km2. Otherwise, the lower spatial resolution of MODIS resulted in larger mixed pixels of water and algae, which would overestimate the total area of the green tides.

Emission Inventories and Particulate Matter Air Quality Modeling over the Pearl River Delta Region.

The Pearl River Delta (PRD) region is located on the southeast coast of mainland China and it is an important economic hub. The high levels of particulate matter (PM) in the atmosphere, however, and poor visibility have become a complex environmental problem for the region. Air quality modeling systems are useful to understand the temporal and spatial distribution of air pollution, making use of atmospheric emission data as inputs. Over the years, several atmospheric emission inventories have been developed for the Asia region. The main purpose of this work is to evaluate the performance of the air quality modeling system for simulating PM concentrations over the PRD using three atmospheric emission inventories (i.e., EDGAR, REAS and MIX) during a winter and a summer period. In general, there is a tendency to underestimate PM levels, but results based on the EDGAR emission inventory show slightly better accuracy. However, improvements in the spatial and temporal disaggregation of emissions are still needed to properly represent PRD air quality. This study's comparison of the three emission inventories' data, as well as their PM simulating outcomes, generates recommendations for future improvements to atmospheric emission inventories and our understanding of air pollution problems in the PRD region.