• Journal of Institute of Control, Robotics and Systems
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• v.19 no.10
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• pp.907-914
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• 2013

A FastSLAM is an algorithm for SLAM (Simultaneous Localization and Mapping) using a Rao-Blackwellized particle filter and its performance is known to degenerate over time due to the loss of particle diversity, mainly caused by the particle depletion problem in the resampling phase. In this paper, the GeSPIR (Geographically Stratified Particle Information-based Resampling) technique is proposed to solve the particle depletion problem. The proposed algorithm consists of the following four steps : the first step involves the grouping of particles divided into K regions, the second obtaining the normal weight of each region, the third specifying the protected areas, and the fourth resampling using regional equalization weight. Simulations show that the proposed algorithm obtains lower RMS errors in both robot and feature positions than the conventional FastSLAM algorithm.

• The Journal of Korea Robotics Society
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• v.4 no.2
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• pp.97-104
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• 2009

Recently, simultaneous localization and mapping (SLAM) approaches employing Rao-Blackwellized particle filter (RBPF) have shown good results. However, due to the usage of the accurate sensors, distinct particles which compensate one another are attenuated as the RBPF-SLAM continues. To avoid this particle depletion, we propose the strategic games to assign the particle's payoff which replaces the importance weight in the current RBPF-SLAM framework. From simulation works, we show that RBPF-SLAM with the strategic games is inconsistent in the pessimistic way, which is different from the existing optimistic RBPF-SLAM. In addition, first, the estimation errors with applying the strategic games are much less than those of the standard RBPF-SLAM, and second, the particle depletion is alleviated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.4
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• pp.2038-2057
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• 2017

Gaussian learning is a new technology in the computational intelligence area. However, this technology weakens the learning ability of a particle swarm and achieves a lack of diversity. Thus, this paper proposes a vector Gaussian learning strategy and presents an effective approach, named particle swarm optimization based on vector Gaussian learning. The experiments show that the algorithm is more close to the optimal solution and the better search efficiency after we use vector Gaussian learning strategy. The strategy adopts vector Gaussian learning to generate the Gaussian solution of a swarm's optimal location, increases the learning ability of the swarm's optimal location, and maintains the diversity of the swarm. The method divides the states into normal and premature states by analyzing the state threshold of the swarm. If the swarm is in the premature category, the algorithm adopts an inertia weight strategy that decreases linearly in addition to vector Gaussian learning; otherwise, it uses a fixed inertia weight strategy. Experiments are conducted on eight well-known benchmark functions to verify the performance of the new approach. The results demonstrate promising performance of the new method in terms of convergence velocity and precision, with an improved ability to escape from a local optimum.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.6
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• pp.619-625
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• 2009

FastSLAM which uses the Rao-Blackwellized particle filter is one of the famous solutions to SLAM (Simultaneous Localization and Mapping) problem that estimates concurrently a robot's pose and surrounding environment. However, the particle depletion problem arises from the loss of the particle diversity in the resampling process of FastSLAM. Then, the performance of FastSLAM degenerates over the time. In this work, DIR (Density Information-based Resampling) technique is proposed to solve the particle depletion problem. First, the cluster is constructed based on the density of each particle, and the density of each cluster is computed. After that, the number of particles to be reserved in each cluster is determined using a linear method based on the distance between the highest density cluster and each cluster. Finally, the resampling process is performed by rejecting the particles which are not selected to be reserved in each cluster. The performance of the DIR proposed to solve the particle depletion problem in FastSLAM was verified in computer simulations, which significantly reduced both the RMS position error and the feature error.

• Asian Journal of Atmospheric Environment
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• v.4 no.3
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• pp.115-140
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• 2010

Great concerns about atmospheric aerosols are attributed to their multiple roles to atmospheric processes. For example, atmospheric aerosols influence global climate, directly by scattering or absorbing solar radiations and indirectly by serving as cloud condensation nuclei. They also have a significant impact on human health and visibility. Many of these effects depend on the size and composition of atmospheric aerosols, and thus detailed information on the physicochemical properties and the distribution of airborne particles is critical to accurately predict their impact on the Earth's climate as well as human health. A single particle analysis technique, named low-Z particle electron probe X-ray microanalysis (low-Z particle EPMA) that can determine the concentration of low-Z elements such as carbon, nitrogen and oxygen in a microscopic volume has been developed. The capability of quantitative analysis of low-Z elements in individual particle allows the characterization of especially important atmospheric particles such as sulfates, nitrates, ammonium, and carbonaceous particles. Furthermore, the diversity and the complicated heterogeneity of atmospheric particles in chemical compositions can be investigated in detail. In this review, the development and methodology of low-Z particle EPMA for the analysis of atmospheric aerosols are introduced. Also, its typical applications for the characterization of various atmospheric particles, i.e., on the chemical compositions, morphologies, the size segregated distributions, and the origins of Asian dust, urban aerosols, indoor aerosols in underground subway station, and Arctic aerosols, are illustrated.

• Journal of Korean Society on Water Environment
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• v.33 no.6
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• pp.728-735
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• 2017

Many restoration projects are underway to revive disturbed streams. In order to achieve successful stream restoration, a variety of microhabitats should be created to promote biological diversity. Research on biological classification of microhabitats is essential for biological diversity. However, research on classification using only physical environmental factors has been carried out. The purpose of this study is to classify and quantify the microhabitat of the stream by using macroinvertebrates systematically. In this study, eight wadeable streams and four non-wadeable streams were surveyed to identify the benthic macroinvertebrates in these various microhabitats. Among the physical environmental factors (current velocity, water depth, substrate), the particle size of the substrate was the most influential factor in the emergence of the Habitat Orientaion Groups. Among the HOGs, clinger and burrower were highly correlated with physical environment factors and showed the opposite tendency. The distribution of clinger and burrower according to the physical environmental factors showed two tendencies based on the current velocity (0.3 m/s) and water depth (0.4 m). In addition, the particle size of the substrate showed three trends (${\leq}-5.0$, -5.0 < mean diameter ${\leq}-2.0$, > -2.0). Based on the abundance tendency of these two HOGs, the microhabitats were classified into nine types, from a eupotamic microhabitat to a lentic microhabitat. Classification of the microhabitats using HOGs can be employed for creating microhabitats to promote biological diversity in future stream restoration plans.

• Journal of Information Processing Systems
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• v.18 no.3
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• pp.332-343
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• 2022

To address the problems of the gravitational search algorithm (GSA) in which the population is prone to converge prematurely and fall into the local solution when solving the single-objective optimization problem, a sine map jumping gravity search algorithm based on asynchronous learning is proposed. First, a learning mechanism is introduced into the GSA. The agents keep learning from the excellent agents of the population while they are evolving, thus maintaining the memory and sharing of evolution information, addressing the algorithm's shortcoming in evolution that particle information depends on the current position information only, improving the diversity of the population, and avoiding premature convergence. Second, the sine function is used to map the change of the particle velocity into the position probability to improve the convergence accuracy. Third, the Levy flight strategy is introduced to prevent particles from falling into the local optimization. Finally, the proposed algorithm and other intelligent algorithms are simulated on 18 benchmark functions. The simulation results show that the proposed algorithm achieved improved the better performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.5972-5989
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• 2019

With the onset of the big data age, data is growing exponentially, and the issue of how to optimize large-scale data processing is especially significant. Large-scale global optimization (LSGO) is a research topic with great interest in academia and industry. Spark is a popular cloud computing framework that can cluster large-scale data, and it can effectively support the functions of iterative calculation through resilient distributed datasets (RDD). In this paper, we propose a hybrid mechanism of particle swarm optimization (PSO) and differential evolution (DE) algorithms based on Spark (SparkPSODE). The SparkPSODE algorithm is a parallel algorithm, in which the RDD and island models are employed. The island model is used to divide the global population into several subpopulations, which are applied to reduce the computational time by corresponding to RDD's partitions. To preserve population diversity and avoid premature convergence, the evolutionary strategy of DE is integrated into SparkPSODE. Finally, SparkPSODE is conducted on a set of benchmark problems on LSGO and show that, in comparison with several algorithms, the proposed SparkPSODE algorithm obtains better optimization performance through experimental results.

• Ocean and Polar Research
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• v.39 no.4
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• pp.245-255
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• 2017

There are a number of pieces of evidences that suggest a link between marine diatoms and microorganisms, but knowledge about related microbial communities is greatly lacking. The present study investigated the microbial community structures related to the growth of the marine diatom Cyclotella meneghiniana. We collected free-living bacteria (FLB) and particle-associated bacteria (PAB) at each growth stage (e.g., lag, exponential, stationary and death) of the diatom, and analyzed their bacterial 16S rDNA using pyrosequencing. Metagenomics analysis showed that community structures of FLB and PAB differed considerably with the progress of growth stages. FLB showed higher diversity than PAB, but variation in the different growth stages of C. meneghiniana was more evident in PAB. The proportion of the genus Hoeflea, belonging to the order Rhizobiales, was dominant in both FLB and PAB, and it gradually increased with the growth of C. meneghiniana. However, Enhydrobacter clade tended to considerably decrease in PAB. In addition, Marinobacter decreased steadily in FLB, but first increased and then decreased in PAB. These results suggest that Hoeflea, Enhydrobacter, and Marinobacter may be closely related to the growth of diatom C. meneghiniana.

• Journal of Ecology and Environment
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• v.30 no.4
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• pp.347-356
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• 2007

The structure and dynamics of Korean red pine stands established in the riparian zone were studied in the Tsang stream in Mt. Seorak National Park, in east-central Korea. Pine stands were classified into four successional stages, the initial, establishing, competitive, and stabilizing stages, based on the age distribution of a dominant tree, Pinus densiflora, the vegetation stratification, and the microtopography of the riverine environment. The stages usually corresponded to disturbance frequencies, depending on the horizontal and vertical distances from the watercourse. Stands of the initial and establishing stages lacked tree or subtree layers, or both. As stands progressed through the developmental stages, soil particle size became finer and moisture retention capacity was improved. The stand ordination reflected the developmental stage, and the species ordination differentiated species specializing in relatively dry and wet habitats. The results of the analysis of vegetation dynamics provided ecological information which will be useful for understanding the developmental processes of vegetation established in riparian zones. Species diversity indices usually increased across developmental stages, following the typical pattern for successional processes. We discuss the importance and necessity of riparian vegetation in Korea, where most riparian forests have disappeared due to excessive human land use.