• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.3
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• pp.67-75
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• 2016

This paper investigates how artificial individuals with behavioral memory circuits adaptively evolve with respect to given environments on a cell-level simulation framework simulating artificial individuals. This makes it possible for us to analyse the advantages of artificial individuals with behavioral memory circuits against the simple artificial individuals that can do only simple reactions with respect to the environments and to know which advanced reactions are possible. In order to do this analysis, we experimented various tests on a specific prey pattern and examined the results. As a first experiment, we tested that artificial individuals with four memory steps competed against from those without memory step to those with three memory steps. Experimental results showed that the artificial individuals with four memory steps were superior to most others. However, artificial individuals with two memory steps were better than those with four memory steps. This was caused that the artificial individuals with two memory steps could evolve faster than those of four memory steps. In a second experiment that all types of artificial individuals are simultaneously evolved, the artificial individuals with two memory steps also showed the best result in the experiment. We could conclude that the artificial individuals with memory was better than those without memory and the best memory steps of artificial individuals were depended on the complexity of prey patterns.

• The Korean Journal of Applied Statistics
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• v.33 no.4
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• pp.511-526
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• 2020

Single-cell RNA-sequencing (scRNA-seq) data consists of each cell's RNA expression extracted from large populations of cells. One main purpose of using scRNA-seq data is to identify inter-cellular heterogeneity. However, scRNA-seq data pose statistical challenges when applying traditional clustering methods because they have many missing values and high level of noise due to technical and sampling issues. In this paper, motivated by analyzing scRNA-seq data, we propose a novel spectral-based clustering method by imposing different weights on genes when computing a similarity between cells. Assigning weights on genes and clustering cells are performed simultaneously in the proposed clustering framework. We solve the proposed non-convex optimization using an iterative algorithm. Both real data application and simulation study suggest that the proposed clustering method better identifies underlying clusters compared with existing clustering methods.

• Journal of Korean Society on Water Environment
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• v.31 no.2
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• pp.181-195
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• 2015

Algal blooms in lakes are one of major environmental issues in Korea. A three-dimensional, hydrodynamic and water quality model was developed and tested in Lake Euiam to assess the performance and limitations of numerical modeling with multiple algal groups using field data commonly collected for algal management. In this study, EFDC was adopted as the basic model framework. Simulated vertical profiles of water temperature, dissolved oxygen and nutrients monitored at five water quality monitoring stations from March to October 2013, which are closely related to algal dynamics simulation, showed good agreement with those of observed data. The overall spatio-temporal variations of three algal groups were reasonably simulated against the chlorophyll-a levels of those estimated from the limited monitoring data (chlorophyll-a level and cell numbers of algal species) with the RMSEs ranging from 2.6 to $17.5mg/m^3$. Also, note that $PO_4-P$ level in the water column was a key limiting factor controlling the growth of three algal groups during most of simulation period. However, the algal modeling results were not fully attainable to the levels of observation during short periods of time showing abrupt increase in algae throughout the lake. In particular, the green algae/cyanobacteria and diatom simulations were underestimated in late June to early July and early October, respectively. The results shows that better understanding of internal algal processes, neglected in most algal modeling studies, is necessary to predict the sudden algal blooms more accurately because the concentrations of external $PO_4-P$ and specific algal groups originated from the tributaries (mainly, dam water releases) during the periods were too low to fully capture the sharp rise of internal algal levels. In this respect, this study suggests that future modeling efforts should be focused on the quantification of internal cycling processes including vertical movement of algal species with respect to changes in environmental conditions to enhance the modeling performance on complex algal dynamics.