• Journal of the Korea Society for Simulation
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• v.19 no.3
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• pp.37-43
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• 2010

In field, one can observe without difficulties that two flocks are intersected or combined with each other. For example, a fish flock in a stream separates into two part by obstacles (e.g. stone) and rejoins behind the obstacles. The dynamics of two flocks guided by their leader were studied in the situation where the flocks cross each other with a crossing angle, ${\theta}$, between their moving directions. Each leader is unaffected by its flock members whereas each member is influenced by its leader and other members. To understand the dynamics, I investigated the order parameter, ${\phi}$, defined by the absolute value of the average unit velocity of the flocks' members. When the two flocks were encountered, the first peak in ${\phi}$ was appeared due to the breaking of the flocks' momentum balance. When the flocks began to separate, the second peak in ${\phi}$ was observed. Subsequently, erratic peaks were emerged by some individuals that were delayed to rejoin their flock. The amplitude of the two peaks, $d_1$ (first) and $d_2$ (second), were measured. Interestingly, they exhibited a synchronized behavior for different ${\theta}$. This simulation model can be a useful tool to explore animal behavior and to develop multi-agent robot systems.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.2
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• pp.343-350
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• 2022

It is worth verifying the effectiveness of data integration between data with different features. This study investigated whether the data integration affects the accuracy of deep neural network (DNN), and which integration method shows the best improvement. This study used two different public datasets. One public dataset was taken in an actual farm in India. And another was taken in a laboratory environment in Korea. Leaf images were selected from two different public datasets to have five classes which includes normal and four different types of plant diseases. DNN used pre-trained VGG16 as a feature extractor and multi-layer perceptron as a classifier. Data were integrated into three different ways to be used for the training process. DNN was trained in a supervised manner via the integrated data. The trained DNN was evaluated by using a test dataset taken in an actual farm. DNN shows the best accuracy for the test dataset when DNN was first trained by images taken in the laboratory environment and then trained by images taken in the actual farm. The results show that data integration between plant images taken in a different environment helps improve the performance of deep neural networks. And the results also confirmed that independent use of plant images taken in different environments during the training process is more effective in improving the performance of DNN.