• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.135-140
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• 2014

In this paper, we describe the contents of noise characteristic enhancement using digital filtering to the motion vector in the pattern noise of optical mouse. The designed circuit is implemented to enhance the smoothing and trembling with filtering and averaging of x, y motion vector before PS2 or USB output. The function is verified by using FPGA and the performance is measured by the fabricated chip using $0.35{\mu}m$ standard CMOS process. The system clock is 6MHz and the motion vector has the range of +6 to -6 per 1/1700sec. It is tested using the Cartesian robot to measure the noise characteristic enhancement.

• The Journal of the Korea Contents Association
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• v.18 no.7
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• pp.536-550
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• 2018

This paper examine how acceptance of AI robots can be achieved according to occupational groups when the discourse on human labor and occupation changes due to the new industrial revolution is spreading steadily. Previous research on the acceptance of new technologies and products has predicted use behavior through subjective beliefs and values that do not change well over the short term. Therefore, this study compares the beliefs, values, and use intention of AI robots according to occupation. As a result, the subjective belief factor for AI robots was classified into belief in rationality(reason) and belief in benevolence(emotion). The value factors were divided into acceptance value(role performance, communication, social comparison) and avoidance value(risk, complexity). There was a significant difference in the effect of these on the use intention of AI robot by occupation and employment types. This result suggests that there are gaps in the occupation group at the rate of technology acceptance, as opposed to the existing prediction that AI robots will be rapidly expanded by professionals.

• Journal of Broadcast Engineering
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• v.28 no.2
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• pp.178-184
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• 2023

Finding texts in general scene images and recognizing their contents is a very important task that can be used as a basis for robot vision, visual assistance, and so on. However, for the low-resolution text images, the degradations, such as noise or blur included in text images, are more noticeable, which leads to severe performance degradation of text recognition accuracy. In this paper, we propose a new Korean text image super-resolution based on a Transformer-based model, which generally shows higher performance than convolutional neural networks. In the experiments, we show that text recognition accuracy for Korean text images can be improved when our proposed text image super-resolution method is used. We also propose a new Korean text image dataset for training our model, which contains massive HR-LR Korean text image pairs.

• Journal of Intelligence and Information Systems
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• v.12 no.3
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• pp.67-82
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• 2006

We propose the HBEM(Hybrid Behavior Evolution Model) composed of rule classification and evolutionary neural network using rule descriptor and link descriptor for evolutionary behavior of virtual robots. In our model, two levels of the knowledge of behaviors were represented. In the upper level, the representation was improved using rule and link descriptors together. And then in the lower level, behavior knowledge was represented in form of bit string and learned adapting their chromosomes by the genetic operators. A virtual robot was composed by the learned chromosome which had the best fitness. The composed virtual robot perceives the surrounding situations and they were classifying the pattern through rules and processing the result in neural network and behaving. To evaluate our proposed model, we developed HBES(Hybrid Behavior Evolution System) and adapted the problem of gathering food of the virtual robots. In the results of testing our system, the learning time was fewer than the evolution neural network of the condition which was same. And then, to evaluate the effect improving the fitness by the rules we respectively measured the fitness adapted or not about the chromosomes where the learning was completed. In the results of evaluating, if the rules were not adapted the fitness was lowered. It showed that our proposed model was better in the learning performance and more regular than the evolutionary neural network in the behavior evolution of the virtual robots.

• 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.