• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.541-551
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• 2021

Among the numerous duties of a librarian in a library, the work of arranging books is a job that the librarian has to do one by one. Thus, the cost of labor and time is large. In order to solve this problem, the interest in book-arranging robots based on artificial intelligence has recently increased. In this paper, we propose the K-ACO algorithm, which is the shortest path algorithm for multi-stops that can be applied to the library book arrangement robots. The proposed K-ACO algorithm assumes multiple robots rather than one robot. In addition, the K-ACO improves the ANT algorithm to create K clusters and provides the shortest path for each cluster. In this paper, the performance analysis of the proposed algorithm was carried out from the perspective of book arrangement time. The proposed algorithm, the K-ACO algorithm, was applied to a university library and compared with the current book arrangement algorithm. Through the simulation, we found that the proposed algorithm can allocate fairly, without biasing the work of arranging books, and ultimately significantly reduce the time to complete the entire work. Through the results of this study, we expect to improve quality services in the library by reducing the labor and time costs required for arranging books.

• 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.