• Korean Journal of Environment and Ecology
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• v.29 no.3
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• pp.410-420
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• 2015

In order to estimate the influence of environmental factors on zooplankton communities in lentic freshwater ecosystems, 20 reservoirs and wetlands were monitored by season in 2013. A total of 109 species of zooplankton were identified during the study period. Zooplankton assemblage showed a different distribution in its density and diversity in accordance with the seasons. In particular, the density of zooplankton (98 species and 603ind. L-1) was the most in autumn when compared to the other seasons. In order to effectively analyze zooplankton distribution that are affected by various environmental factors, a Self-Organizing Map (SOM) was used, which extracts information through competitive and adaptive properties. A total of 11 variables (8 environment factors and 3 groups of zooplankton) were patterned on to the SOM. Based on a U-matrix, four clusters were identified from the model. Among zooplankton communities, rotifer displayed a positive relationship with water temperature, and cladocerans and copepod were positively related to conductivity, chlorophyll a, and nutrient factor (i. e. TN and TP). In contrast, high dissolved oxygen appeared to have a negative effect on zooplankton distribution. Consequently, the SOM results depicted a clear pattern of zooplankton density clusters partitioned by environmental factors, which play a key role in determining the seasonal distribution of zooplankton groups in lentic freshwater ecosystem.

• Smart Media Journal
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• v.13 no.3
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• pp.18-26
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• 2024

Road surface monitoring is essential for maintaining road environment safety through managing risk factors like rutting and crack detection. Using autonomous driving-based UGVs with high-performance 2D laser sensors enables more precise measurements. However, the increased energy consumption of these sensors is limited by constrained battery capacity. In this paper, we propose a fusion sensor system for efficient surface monitoring with UGVs. The proposed system combines color information from cameras and depth information from line laser sensors to accurately detect surface displacement. Furthermore, a dynamic sampling algorithm is applied to control the scanning frequency of line laser sensors based on the detection status of monitoring targets using camera sensors, reducing unnecessary energy consumption. A power consumption model of the fusion sensor system analyzes its energy efficiency considering various crack distributions and sensor characteristics in different mission environments. Performance analysis demonstrates that setting the power consumption of the line laser sensor to twice that of the saving state when in the active state increases power consumption efficiency by 13.3% compared to fixed sampling under the condition of λ=10, µ=10.