• 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.

• Applied Science and Convergence Technology
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• v.25 no.2
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• pp.36-41
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• 2016

Similarity and diversity of various quantum ring structures are investigated by classifying energy dispersions of three different structures: an electrostatic quantum ring, a magnetic quantum ring, and a magnetic-electric quantum ring. The wave functions and the eigenenergies of a single electron in the quantum ring structures are calculated by solving the Schrdinger equation without any electron-electron interaction. Magnetoconductance is studied by calculating a two-terminal conductance while taking into account the backscattering via the resonance through the states of the quantum rings at the center of a quasi-one dimensional conductor. It is found that the energy spectra for the various quantum ring structures are sensitive to additional electrostatic potentials as well as to the effects of a nonuniform magnetic field. There are also characteristics of similarity and diversity in the energy dispersions and in the single-channel magnetoconductance.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1415-1421
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• 2015

This paper presents a new algorithm called Quantum-behaved Electromagnetism-like Mechanism Algorithm which is used to solve economic load dispatch of power system. Electromagnetism-like mechanism algorithm simulates attraction and repulsion mechanism for particles in the electromagnetic field. Every solution is a charged particle, and it move to optimum solution according to certain criteria. Quantum-behaved electromagnetism-like mechanism algorithm merges quantum computing theory with electromagnetism-like mechanism algorithm. Superposition characteristic of quantum methodology can make a single particle present several states, and the characteristic potentially increases population diversity. Probability representation of quantum methodology is to make particle state be presented according to a certain probability. And the quantum rotation gates are used to realize update operation of particles. The algorithm is tested for 13-generator system and 40-generator system, which validates it can effectively solve economic load dispatch problem. Through performance comparison, it is obvious the solution is superior to other optimization algorithm.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.1000-1013
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• 2017

Clustering is a NP-hard problem that is used to find the relationship between patterns in a given set of patterns. It is an unsupervised technique that is applied to obtain the optimal cluster centers, especially in partitioned based clustering algorithms. On the other hand, cat swarm optimization (CSO) is a new meta-heuristic algorithm that has been applied to solve various optimization problems and it provides better results in comparison to other similar types of algorithms. However, this algorithm suffers from diversity and local optima problems. To overcome these problems, we are proposing an improved version of the CSO algorithm by using opposition-based learning and the Cauchy mutation operator. We applied the opposition-based learning method to enhance the diversity of the CSO algorithm and we used the Cauchy mutation operator to prevent the CSO algorithm from trapping in local optima. The performance of our proposed algorithm was tested with several artificial and real datasets and compared with existing methods like K-means, particle swarm optimization, and CSO. The experimental results show the applicability of our proposed method.

• 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.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.6
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• pp.15-29
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• 2018

This paper proposes an enhanced object tracking algorithm to compensate the lack of temporal information in existing particle swarm optimization based object trackers using the trajectory of the target object. The proposed scheme also enables the tracking and documentation of the location of an online updated set of distractions. Based on the trajectories information and the distraction set, a rule based approach with adaptive parameters is utilized for occlusion detection and determination of the target position. Compare to existing algorithms, the proposed approach provides more comprehensive use of available information and does not require manual adjustment of threshold values. Moreover, an effective weight adjustment function is proposed to alleviate the diversity loss and pre-mature convergence problem in particle swarm optimization. The proposed weight function ensures particles to search thoroughly in the frame before convergence to an optimum solution. In the existence of multiple objects with similar feature composition, this algorithm is tested to significantly reduce convergence to nearby distractions compared to the other existing swarm intelligence based object trackers.

• Industrial Engineering and Management Systems
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• v.8 no.3
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• pp.171-180
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• 2009

Population learning algorithm (PLA) is a population-based method that was inspired by the similarities to the phenomenon of social education process in which a diminishing number of individuals enter an increasing number of learning stages. The study aims to develop a framework that repeatedly applying the PLA to solve the discrete resource constrained project scheduling problem with two objectives: minimizing project makespan and renewable resource availability, which are two most common concerns of management when a project is being executed. The PLA looping framework will provide a number of near Pareto optimal schedules for the management to make a choice. Different improvement schemes and learning procedures are applied at different stages of the process. The process gradually becomes more and more sophisticated and time consuming as there are less and less individuals to be taught. An experiment with ProGen generated instances was conducted, and the results demonstrated that the looping framework using PLA outperforms those using genetic local search, particle swarm optimization with local search, scatter search, as well as biased sampling multi-pass algorithm, in terms of several performance measures of proximity. However, the diversity using spread metric does not reveal any significant difference between these five looping algorithms.

• 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.