• Smart Structures and Systems
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• v.26 no.3
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• pp.345-360
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• 2020

Modern swarm intelligence heuristic search methods are widely applied in the field of structural health monitoring due to their advantages of excellent global search capacity, loose requirement of initial guess and ease of computational implementation etc. To this end, a hybrid strategy is proposed based on butterfly optimization algorithm (BOA) and differential evolution (DE) with purpose of effective combination of their merits. In the proposed identification strategy, two improvements including mutation and crossover operations of DE, and dynamic adaptive operators are introduced into original BOA to reduce the risk to be trapped in local optimum and increase global search capability. The performance of the proposed algorithm, hybrid butterfly optimization and differential evolution algorithm (HBODEA) is evaluated by two numerical examples of a simply supported beam and a 37-bar truss structure, as well as an experimental test of 8-story shear-type steel frame structure in the laboratory. Compared with BOA and DE, the numerical and experimental results show that the proposed HBODEA is more robust to detect the reduction of stiffness with limited sensors and contaminated measurements. In addition, the effect of search space, two dynamic operators, population size on identification accuracy and efficiency of the proposed identification strategy are further investigated.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.7
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• pp.972-980
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• 2022

The butterfly species recognition technology based on machine learning using images has the effect of reducing a lot of time and cost of those involved in the related field to understand the diversity, number, and habitat distribution of butterfly species. In order to improve the accuracy and time efficiency of butterfly species classification, various features used as the inputs of machine learning models have been studied. Among them, branch length similarity(BLS) entropy or color intensity entropy methods using the concept of entropy showed higher accuracy and shorter learning time than other features such as Fourier transform or wavelet. This paper proposes a feature extraction algorithm using RGB color intensity entropy for butterfly color images. In addition, we develop butterfly recognition systems that combines the proposed feature extraction method with representative ensemble models and evaluate their performance.

• The Journal of the Korea Contents Association
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• v.17 no.5
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• pp.624-632
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• 2017

Automatic butterfly identification using images is one of the interesting research fields because it helps the related researchers studying species diversity and evolutionary and development process a lot in this field. The performance of the butterfly species identification system is dependent heavily on the quality of selected features. In this paper, we propose color intensity (CI) entropy by using the distribution of color intensities in a butterfly image. We show color intensity entropy can increase the recognition rate by 10% if it is used together with previously suggested branch length similarity entropy. In addition, the performance comparison with other features such as Eigenface, 2D Fourier transform, and 2D wavelet transform is conducted against several well known machine learning methods.

• The Korean Journal of Ecology
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• v.26 no.4
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• pp.199-202
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• 2003

According to the energy hypothesis, the energy input per unit area primarily determines species richness in regions of roughly equal area. Some energy-related ecological research included identification of major climatic variables to determine regional species richness. In this study, the local butterfly species richness was examined to find out whether weather variables affected the local distribution or abundance of butterfly populations. Butterfly monitoring data from May 2001 to April 2002 taken at Mt. Yudal, Mokpo, in the southwestern part of Korea, and six weather variables (monthly mean values of temperature, precipitation, evaporation, wind speed, air pressure, and sunlight) were analyzed. Multiple regression analysis showed that only temperature explained 80% and 70% of the variability of log-transformed number of species and individuals, respectively, indicating that temperature played an important role in local species richness. Furthermore, global warming could affect the abundance and distribution of butterflies regionally as well as locally.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.12
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• pp.1917-1923
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• 2010

This study focuses on model identification and a 2-DOF PID control algorithm for cooling processes; a pneumatic butterfly-type control valve is used for this purpose. The mathematical model is a transfer function composed of a time delay and a second-order delay system. The control valve is identified as a first-order delay system with a time delay and included in the controlled plant. From the experimental data sets for a demo plant, the model parameters are identified, and the 2-DOF PID control gains are analytically derived by Kitamori's method. We show via a computer simulation and an experimental test that the performance of the proposed 2-DOF PID control system is better than that of a conventional 1-DOF PID control system.

• Korean Journal of Environmental Biology
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• v.37 no.1
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• pp.82-87
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• 2019

Global warming has a significant impact on diverse ecosystems including agroecosystem through; changing of phenology, physiology and distribution. Monitoring of biological responses emanating from global warming is required to understand the challenges of biological diversity conservation posed by climate change. The Korean government selected four butterfly species as indicators of climate change in agroecosystem: Papilio xuthus, Pieris rapae, Colias erate, and Eurema mandarina. The aim of this study was to investigate the different monitoring methods of the butterflies in Korea and suggest a suitable monitoring method to track the population trends of butterflies in the agroecosystem. Butterfly monitoring was conducted in eight sites throughout Korea from April to October, 2018 using three survey methods: point census at rice paddy area, point census at the border between rice paddy and hill and line transect along the rice paddy and hill. Each method took approximately 30 min. to count the butterflies. A total of 4,691 butterflies and 92 species were counted: The most dominant species was Pieris rapae with a total count of 1,205 individuals followed by Polygonia c-aureum, Zizeeria maha, Colias erate, Cupido argiades and Papilio xuthus. Among the three census methods, the total number of species and individuals when using line transect method was statistically higher than in the other methods. However, the numbers of the four butterflies indicators showed no difference throughout three census methods. Based on the number of species and the total individuals butterflies in agroecosystem, we advocate for the application of line transect method as it can find more butterflies in agroecosystem. In addition, we advised for the implementation of education programs on the line transect method in butterfly identification to participants of the national monitoring program.