• Structural Engineering and Mechanics
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• v.67 no.3
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• pp.245-254
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• 2018

In this paper, a Cloud Model based Fruit Fly Optimization Algorithm (CMFOA) is presented for structural damage identification, which is a global optimization algorithm inspired by the foraging behavior of fruit fly swarm. It is assumed that damage only leads to the decrease in elementary stiffness. The differences on time-domain structural acceleration data are used to construct the objective function, which transforms the damaged identification problem of a structure into an optimization problem. The effectiveness, efficiency and accuracy of the CMFOA are demonstrated by two different numerical simulation structures, including a simply supported beam and a cantilevered plate. Numerical results show that the CMFOA has a better capacity for structural damage identification than the basic Fruit Fly Optimization Algorithm (FOA) and the CMFOA is not sensitive to measurement noise.

• Structural Engineering and Mechanics
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• v.56 no.3
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• pp.409-422
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• 2015

In this paper, the Multi-Swarm Fruit Fly Optimization Algorithm (MFOA) is presented for structural damage identification using the first several natural frequencies and mode shapes. We assume damage only leads to the decrease of element stiffness. The differences on natural frequencies and mode shapes of damaged and intact state of a structure are used to establish the objective function, which transforms a damage identification problem into an optimization problem. The effectiveness and accuracy of MFOA are demonstrated by three different structures. Numerical results show that the MFOA has a better capacity for structural damage identification than the original Fruit Fly Optimization Algorithm (FOA) does.

• International Journal of Industrial Entomology and Biomaterials
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• v.29 no.2
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• pp.198-206
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• 2014

The olive fly Bactrocera oleae (Rossi) is the key pest for olive cultivation worldwide. Substantial effort has been invested in the development of the sterile insect technique (SIT) to control this pest. One of the limitations to develop SIT technology for olive fruit fly is the low ability of wild females to lay eggs in other medium than olive fruits, and their slow adaptation to oviposition in artificial substrates. In the present study, fruit grapes were used as an alternative egg collection medium to harvest eggs and young larvae from freshly colonized wild strains originating from France, Italy, Spain and Croatia. The larvae were allowed to develop into the fruits until the second instar, before they were extracted out and further reared on a standard artificial diet. Furthermore, F1 to F4 female flies were alternatively offered wax bottles to oviposit. Finally, the performance of hybrid strains created from crosses between wild and long colonised flies was assessed. The results showed that females of all 4 wild strains readily oviposited eggs in grapes and from the F2 generation onward, females from all strains were adapted to laying eggs in wax bottles. No difference was observed in eggs and pupae production among all strains tested. The findings are discussed for their implications on SIT application against olive fruit fly.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2005.09a
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• pp.61-61
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• 2005

• Korean journal of applied entomology
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• v.57 no.3
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• pp.185-190
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• 2018

Monitoring exotic fruit flies is essential for quarantine procedure. Wax formulation containing fruit fly lures is relatively long-lasting in field conditions and has been applicable to monitor the fruit flies. This study was performed to extend the application of wax formulation against different fruit flies. The wax formulation containing lures was tested in Thailand, at which various exotic fruit flies inhabited. Captured flies were identified to be Bactrocera dorsalis, Zeugodacus cucurbitae, and Zeugodacus caudata by molecular diagnosis technique.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.3
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• pp.199-203
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• 2016

A brain-computer interface or BCI provides an alternative method for acting on the world. Brain signals can be recorded from the electrical activity along the scalp using an electrode cap. By analyzing the EEG, it is possible to determine whether a person is thinking about his/her hand or foot movement and this information can be transferred to a machine and then translated into commands. However, we do not know which information relates to motor imagery and which channel is good for extracting features. A general approach is to use all electronic channels to analyze the EEG signals, but this causes many problems, such as overfitting and problems removing noisy and artificial signals. To overcome these problems, in this paper we used a new optimization method called the Fruit Fly optimization algorithm (FOA) to select the best channels and then combine them with CSP method to extract features to improve the classification accuracy by linear discriminant analysis. We also used particle swarm optimization (PSO) and a genetic algorithm (GA) to select the optimal EEG channel and compared the performance with that of the FOA algorithm. The results show that for some subjects, the FOA algorithm is a better method for selecting the optimal EEG channel in a short time.

• Korean Journal of Agricultural and Forest Meteorology
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• v.9 no.4
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• pp.217-227
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• 2007

The western cherry fruit fly, Rhagoletis indifferens Curran (Diptera:Tephritidae), is the most important pest of cultivated cherries in the Pacific Northwest area of the United States, being widely distributed throughout Oregon, Washington, Montana, Utah, Idaho, Colorado and parts of Nevada. The control of R. indifferens has been based on calendar sprays after its first emergence because of their zero tolerance for quarantine. Therefore, a good prediction model is needed for the spray timing. This study was conducted to obtain the empirical population dynamic information of R. indifferens after overwintering in the major cherry growing area of the Pacific Northwest of the United States, where the information is critically needed to develop and validate the prediction model of the fruit fly. Adult fly populations were monitored by using yellow sticky and emergence traps. Larvae growth and density in fruits were observed by fruit sampling and the pupal growth and density were monitored by pupal collection traps. The first adult was emerged around mid May and a large number of adults were caught in early June. A fruit had more than one larva from mid June to early July. A large number of pupae were caught in early July. The pupae were collected in various period of time to determine the effect of pupation timing and the soil moisture content during the winter. A series of population density data collected in each of the developmental stage were analyzed and organized to provide more reliable validation information for the population dynamic models.

• Korean journal of applied entomology
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• v.57 no.1
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• pp.51-52
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• 2018

Out of 60 quarantine insect pests in Korea, 42 species are classified into tephritid fruit flies. Most of these fruit flies are attracted to two natural products, methyl eugenol (ME) or raspberry ketone. Paraffin wax has been devised to formulate these lures. The formulated lures were applied to field test in Taiwan to attract quarantine fruit flies. Wax-ME formulation was installed in Delta trap and could attract 60-80 males of B. dorsalis per day during late August, while a wax formulation of Cue-lure (a methylated compound of raspberry ketone) attracted both B. cucurbitae and B. tau. These wax formulations can be applied to monitor these three quarantine species in Korea.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3534-3549
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• 2020

The traditional method of smoke image recognition has low accuracy. For this reason, we proposed an algorithm based on the good group of IMFOA which is GMFOA to optimize the parameters of SVM. Firstly, we divide the motion region by combining the three-frame difference algorithm and the ViBe algorithm. Then, we divide it into several parts and extract the histogram of oriented gradient and volume local binary patterns of each part. Finally, we use the GMFOA to optimize the parameters of SVM and multiple kernel learning algorithms to Classify smoke images. The experimental results show that the classification ability of our method is better than other methods, and it can better adapt to the complex environmental conditions.

• Journal of Korean Elementary Science Education
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• v.21 no.1
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• pp.135-142
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• 2002

This paper aims to compare the effects of two teaching methods, the discovery-observational(DO) and the expository-observational(EO) instructions, on students learning interest in the life cycle of fruit fly. The subjects, 463 third-graders from two elementary schools in Changwon City, were divided into two groups, the DO group and the EO group. After the instruction on the life of the flies in two different teaching ways, a questionnaire with 13 items was devised regarding the students' interest, and the subjects were asked to respond to it. The results reveal that the general mean score of the DO group is higher than that of the EO group. Also, the DO group obtains the higher mean score in each item, except two items about knowledge learning. The differences of the mean scores of the two types, general as well as item-individual, between the two groups are statistically significant. This suggests that the class about the life cycle of living creatures easily getatable and observable, such as fruit flies, should be student-centered investigatory one, where students themselves collect them and observe the process of their growth and whole cycle.