• Journal of Korean Society for Quality Management
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• v.49 no.4
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• pp.569-579
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• 2021

Purpose: The purpose of this study is the time series analysis for predicting the yield of crops applicable to each farm using environmental variables measured by smart farms cultivating tomato. In addition, it is intended to confirm the influence of environmental variables using a deep learning model that can be explained to some extent. Methods: A time series analysis was performed to predict production using environmental variables measured at 75 smart farms cultivating tomato in two periods. An LSTM-based encoder-decoder model was used for cases of several farms with similar length. In particular, Dual Attention Mechanism was applied to use environmental variables as exogenous variables and to confirm their influence. Results: As a result of the analysis, Dual Attention LSTM with a window size of 12 weeks showed the best predictive power. It was verified that the environmental variables has a similar effect on prediction through wieghtss extracted from the prediction model, and it was also verified that the previous time point has a greater effect than the time point close to the prediction point. Conclusion: It is expected that it will be possible to attempt various crops as a model that can be explained by supplementing the shortcomings of general deep learning model.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.9
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• pp.391-398
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• 2022

As non face-to-face classes carry on discussion about learner's attention continues. To improve learning effect learner's attention is important whether non face-to-face classes or face-to-face classes. In this study a mobile application prototype inducing learner attention is developed taking account of learner emotion that is one of the factors affecting learner attention. When learner selects one of the four emotions displayed in the application, it shows the activity inducing the learner's attention related to the selected emotion. In order to evaluate the usability of the developed application, 32 middle and high school students are asked to run the application and then conduct a survey using 5 point Likert scale. The survey result indicates that there is a possibility that the developed application in this study induces learner attention as showing that the result point of 'I can pay attention' and 'I feel psychological stable' is respectively 3.56 and the result point of 'I feel useless thought disappear' is 3.6.

• Journal of Advanced Navigation Technology
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• v.27 no.2
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• pp.182-189
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• 2023

Depth estimation is a key technology in 3D map generation for autonomous driving of vehicles, robots, and drones. The existing sensor-based method has high accuracy but is expensive and has low resolution, while the camera-based method is more affordable with higher resolution. In this study, we propose self-attention-based unsupervised monocular depth estimation for UAV camera system. Self-Attention operation is applied to the network to improve the global feature extraction performance. In addition, we reduce the weight size of the self-attention operation for a low computational amount. The estimated depth and camera pose are transformed into point cloud. The point cloud is mapped into 3D map using the occupancy grid of Octree structure. The proposed network is evaluated using synthesized images and depth sequences from the Mid-Air dataset. Our network demonstrates a 7.69% reduction in error compared to prior studies.

• ETRI Journal
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• v.43 no.5
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• pp.775-786
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• 2021

To translate in real time, a simultaneous translation system should determine when to stop reading source tokens and generate target tokens corresponding to a partial source sentence read up to that point. However, conventional attention-based neural machine translation (NMT) models cannot produce translations with adequate latency in online scenarios because they wait until a source sentence is completed to compute alignment between the source and target tokens. To address this issue, we propose a reinforced learning (RL)-based attention mechanism, the reinforced attention mechanism, which allows a neural translation model to jointly train the stopping criterion and a partial translation model. The proposed attention mechanism comprises two modules, one to ensure translation quality and the other to address latency. Different from previous RL-based simultaneous translation systems, which learn the stopping criterion from a fixed NMT model, the modules can be trained jointly with a novel reward function. In our experiments, the proposed model has better translation quality and comparable latency compared to previous models.

• Cartoon and Animation Studies
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• s.6
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• pp.172-190
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• 2002

Flash animation occupied public attention since . It started to by payed attention through the internet first, and it spread into the off line. Flash animation becomes one of animation genre, as it tooks the place at the international animation festival. Also, as it is produced for tv, and the cinema, it has an infinite of possibilities. In this point, we have to notice that the domestic flash animation is the most successful in the world. Therefore, this paper will show accomplishment point and distinction of flash animation. Chapter 1, 2, will talk about basic outline and practical use of flash. Chapter 3 will look at a growth industry and process of flash animation, focused on Mashmaro, Zolaman, and Woobyboy. Chapter 4, will discuss about accomplishment point and distinction of flash animation compare with existing animation. Also it will conclude with a reflection on recommendations for further improvement.

• Korean Journal of Mathematics
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• v.29 no.3
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• pp.455-466
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• 2021

Coupled fixed point results have attracted much attention among the researchers in recent times specially in the field of fuzzy metric spaces. In this paper we established a coupled fixed point result for weakly compatible mappings in G-fuzzy metric spaces. We have deduced a corollary to our main theorem. Our result also supported by examples.

• Journal of Korea Multimedia Society
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• v.24 no.2
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• pp.328-335
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• 2021

In the era of 'Untact', virtual reality-based job training platforms are actively being used as part of non-face-to-face education for students with developmental disabilities. Because the people with developmental disabilities may lack sufficient cognitive abilities, it is difficult to conduct untact training seamlessly without the help of a third party. Therefore, it is necessary for training programs to identify the right timing to provide help so that the training can be continued. This research analyzed the visual attention of students with developmental disabilities in virtual reality-based job training program in order to determine the point of time when an intervention is required by the trainee. Results showed that students who completed the mission tended to have intense visual attention on a small number of objects for a certain period of time; the visual attention of the students who failed tended to shift erratically among multiple objects.

• Proceedings of the Korea Society of Design Studies Conference
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• 2001.10a
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• pp.7.2-7
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• 2001

• International Journal of Reliability and Applications
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• v.2 no.3
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• pp.199-207
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• 2001

The failure rate functions between successive failures are of concatenated form. We allow the parameters of failure rate function change after a certain failure and its fixing. We confine out attention to a model wherein the interfailure times are described by its failure rate function. We suggest an adaptive failure rate function with a change-point under the assumption that interfailure times are record value statistics from a Weibull distribution. The proposed model will be applied through a practical example of software failure data.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.21 no.1
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• pp.3-10
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• 2010

Objectives ： The current review aimed to describe the major findings of the NIMH Collaborative Multisite Multimodal Treatment Study of Children with Attention-Deficit Hyperactivity Disorder (MTA) with regard to the treatment of children with attention-deficit hyperactivity disorder (ADHD). Methods ： We performed a general review of the literature regarding the efficacy of the MTA's proposed treatments. Results ： There is a large and still increasing body of evidence regarding the MTA's treatment outcomes. We present and discuss details of the findings at each follow-up point. Conclusion ： Currently, findings regarding the MTA's treatments suggest children with combined-type ADHD exhibit significant impairment in adolescence, despite their initial symptom improvement. Further studies, using innovative treatment approaches and targeting specific areas of adolescent impairment, are needed in order to enhance ADHD treatment outcomes.