• Journal of Korea Water Resources Association
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• v.55 no.2
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• pp.111-120
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• 2022

The concept of soil moisture memory was used as a method for quantifying the function of soil to control water flow, which evaluates the average residence time of precipitation. In order to characterize the soil moisture memory, a new measurement index called stored precipitation fraction (F_p(f)) was used by tracking the increments in soil moisture by the precipitation event. In this study, the temporal and spatial distribution of soil moisture memory was evaluated along with the slope and soil characteristics of the surface (0~5 cm) soil by using satellite- and model-based precipitation and soil moisture in the Korean peninsula, from 2019 to 2020. The spatial deviation of the soil moisture memory was large as the stored precipitation fraction in the soil decreased preferentially along the mountain range at the beginning (after 3 hours), and the deviation decreased overall after 24 hours. The stored precipitation fraction in the soil clearly decreased as the slope increased, and the effect of drainage of water in the soil according to the composition ratio of the soil particle size was also shown. In addition, average soil moisture contributed to the increase and decrease of hydraulic conductivity, and the rate of rainfall transfer to the depths affected the stored precipitation fraction. It is expected that the results of this study will greatly contribute in clarifying the relationship between soil moisture memory and surface characteristics (slope, soil characteristics) and understanding spatio-temporal variation of soil moisture.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.9
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• pp.595-602
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• 2013

WiFi fingerprinting is well known as an effective localization technique used for indoor environments. However, this technique requires a large amount of pre-built fingerprint maps over the entire space. Moreover, due to environmental changes, these maps have to be newly built or updated periodically by experts. As a way to avoid this problem, crowd-sourced fingerprint mapping attracts many interests from researchers. This approach supports many volunteer users to share their WiFi fingerprints collected at a specific environment. Therefore, crowd-sourced fingerprinting can automatically update fingerprint maps up-to-date. In most previous systems, however, individual users were asked to enter their positions manually to build their local fingerprint maps. Moreover, the systems do not have any principled mechanism to keep fingerprint maps clean by detecting and filtering out erroneous fingerprints collected from multiple users. In this paper, we present the design of a crowd-sourced fingerprint mapping and localization(CMAL) system. The proposed system can not only automatically build and/or update WiFi fingerprint maps from fingerprint collections provided by multiple smartphone users, but also simultaneously track their positions using the up-to-date maps. The CMAL system consists of multiple clients to work on individual smartphones to collect fingerprints and a central server to maintain a database of fingerprint maps. Each client contains a particle filter-based WiFi SLAM engine, tracking the smartphone user's position and building each local fingerprint map. The server of our system adopts a Gaussian interpolation-based error filtering algorithm to maintain the integrity of fingerprint maps. Through various experiments, we show the high performance of our system.

• Korean Journal of Ecology and Environment
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• v.33 no.3 s.91
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• pp.187-196
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• 2000

This paper describes a simple method that uses differences among Carlson's (1977) trophic state index (TSI) values based on total phosphorus (TP), chlorophyll a (CHL) and Secchi depth (SD) to draw inferences regarding the factors that are limiting to phytoplankton growth and the composition of lake seston. Examples are provided regarding seasonal and spatial patterns in a large subtropical lake (Lake Okeechobee, Florida, USA) and inter- and intra-lake variations from a multilake data set developed from published studies. Once an investigator has collected routine water quality data and established TSI values based on TP, CHL, and SD, a number of inferences can be made. Additional information can be provided where it also is possible to calculate a TSI based on total nitrogen (TN). Where TSI (CHL)<>TSI (SD), light attenuating particles are large (large filaments or colonies of algae), and the phytoplankton may be limited by zooplankton grazing. Other limiting conditions are inferred by different relationships between the TSI values. Results of this study indicate that the analysis is quite robust, and that it generally gives good agreement with conclusions based on more direct methods (e.g., nutrientaddition bioassays, zooplankton size data, zooplankton removal experiments). The TSI approach, when validated periodically with these more costly and time-intensive methods, provides an effective, low cost method for tracking long-term changes in pelagic structure and function with potential value in monitoring lake ecology and responses to management.