• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권1호
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• pp.1-14
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• 2024

Time-series forecasting is extensively used in the actual world. Recent research has shown that Transformers with a self-attention mechanism at their core exhibit better performance when dealing with such problems. However, most of the existing Transformer models used for time series prediction use the traditional encoder-decoder architecture, which is complex and leads to low model processing efficiency, thus limiting the ability to mine deep time dependencies by increasing model depth. Secondly, the secondary computational complexity of the self-attention mechanism also increases computational overhead and reduces processing efficiency. To address these issues, the paper designs an efficient multi-layer attention-based time-series forecasting model. This model has the following characteristics: (i) It abandons the traditional encoder-decoder based Transformer architecture and constructs a time series prediction model based on multi-layer attention mechanism, improving the model's ability to mine deep time dependencies. (ii) A cross attention module based on cross attention mechanism was designed to enhance information exchange between historical and predictive sequences. (iii) Applying a recently proposed sparse attention mechanism to our model reduces computational overhead and improves processing efficiency. Experiments on multiple datasets have shown that our model can significantly increase the performance of current advanced Transformer methods in time series forecasting, including LogTrans, Reformer, and Informer.

• Communications for Statistical Applications and Methods
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• 제28권1호
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• pp.39-57
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• 2021

Time series prediction is an area of great interest to many people. Algorithms for time series prediction are widely used in many fields such as stock price, temperature, energy and weather forecast; in addtion, classical models as well as recurrent neural networks (RNNs) have been actively developed. After introducing the attention mechanism to neural network models, many new models with improved performance have been developed; in addition, models using attention twice have also recently been proposed, resulting in further performance improvements. In this paper, we consider time series prediction by introducing attention twice to an RNN model. The proposed model is a method that introduces H-attention and T-attention for output value and time step information to select useful information. We conduct experiments on stock price, temperature and energy data and confirm that the proposed model outperforms existing models.

• 한국실내디자인학회논문집
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• 제20권4호
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• pp.101-108
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• 2011

When observing an object in a space, a part of it is remembered into our perception in the time for paying attention or conscious observation and it reaches to our visual understanding. In this study, it examined characteristics by each subject through the process of visual understanding by changes in such observation time. The results from this study are summarized as belows: First, through analysis of the observation data focused on the distance between the observed points, it was able to apply those visual theories organized before to the analysis of characteristics of the time for understanding by each subject. Second, there showed big differences in the time for visual understanding by each subject according to changes in the observation time so that it was found that there were big differences according to the characteristics of subject's intention or purpose of the observation of a space. Third, as the number of continuous observation gives an important clue in judgement of how well the space was understood, it was able to compare and organize the mutual characteristics of the time the attention was concentrated, the time observed intentionally and the time understood visually. Fourth, it was found that the shorter subjects gave the intentional observation in observing a space, the longer they spent the time for paying attention, while the less they could understand it visually.

• ETRI Journal
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• 제42권1호
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• pp.101-107
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• 2020

For Korean language processing, morphological analysis is a critical component that requires extensive work. This morphological analysis can be conducted in an end-to-end manner without requiring a complicated feature design using a sequence-to-sequence model. However, the sequence-to-sequence model has a time complexity of O(n²) for an input length n when using the attention mechanism technique for high performance. In this study, we propose a linear-time Korean morphological analysis model using a local monotonic attention mechanism relying on monotonic alignment, which is a characteristic of Korean morphological analysis. The proposed model indicates an extreme improvement in a single threaded environment and a high morphometric F1-measure even for a hard attention model with the elimination of the attention mechanism formula.

• 디지털융복합연구
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• 제11권10호
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• pp.731-736
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• 2013

아동들의 주의력 결핍으로 인한 여러 가지 사회적 문제가 야기되고 있다. 주의력 결핍이 심각한 아동들을 위한 아동 친화적인 주의력 향상 방법이 제공될 필요성이 있다. 최근 아동들은 3D 게임을 매우 좋아하며 자발적인 참여를 하고 있다. 그러므로 아동들이 좋아하는 3D 게임을 이용하여 아동들의 주의력을 향상시키고자 3D 게임을 통한 주의력을 측정하는 방법에 대한 연구를 수행하였다. 기존 의학계에서 사용하는 주의력 측정 방법들을 고찰하고 게임내에서 주의력을 측정할 수 있는 변수들을 설정하였다. 누락 오류, 오경보 오류, 정반응 시간 평균, 정반응 시간 표준편차가 설정된 변수들인데, 일반아동과 주의력이 부족한 아동간에 대하여 시각 주의력에 대한 실험을 통해, 이들 변수들 간에 값의 차이가 있음을 알 수 있었다.

• 품질경영학회지
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• 제49권4호
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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.

• 한국소음진동공학회논문집
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• 제27권2호
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• pp.155-161
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• 2017

When a military vehicle is driven on a cross-country road, the occupants are exposed to vibration at a body resonance. In case that the exposure continues for too long period, the attention ability of the occupant could be affected by the vibration exposure. In the study, it was experimentally tried to find if there is a correlation between degradation of attention and vibration exposure. Two kinds of test among various psychological attention tests were employed, which were selected with considering a situation of carrying out military mission on vehicle. At the result, the searching test for controlled attention showed significant degradation in the accuracy and performance time in case of exposure at the vibration. And the attention degradation appeared to be greater when the vibration exposure increases. The dual task test for divided attention showed the difference between vibration and non-vibration condition, but showed it is insignificant for the attention to degrade by increasing exposure time.

• 한국초등과학교육학회지:초등과학교육
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• 제39권1호
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• pp.133-144
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• 2020

The purpose of this study is to identify the effect of Teacher Participation-oriented Education Program (TPEP) for Elementary School Teachers to Improve Class Expertise in Science Classes with a focus on visual attention. The participants were two elementary school teachers in Seoul and taught science subjects. The lesson topic applied to this study were 'Structure and Function of Our Body' in the second semester of fifth grade and 'Volcano and Earthquake' in the second semester of fourth grade. The mobile eye tracker SMI's ETG 2w, which is a binocular tracking system was used in this study. In this study, the actual practice time, participant's visual attention, visual intake time average, and visual intake time average were analyzed by class phase. The results of the study are as follows. First, as a result of analyzing the actual class execution time, the actual class execution time was almost in line with the lesson plan after the TPEP application. Second, visual attention in the areas related to teaching and learning activities was high after applying TPEP. Factors affecting the progress of the class and cognitive burdens were identified quantitatively and objectively through visual attention. Third, as a result of analyzing the visual intake time average of participants, there was a statistically significant difference in all classes. Fourth, as a result of analyzing the visual intake time average of participants, the results were statistically significant in the introduction(video), activity 1, activity 2, and activity 3 stages in the lecture type class. The Teacher Participation-oriented Education Program (TPEP) for Elementary School Teachers to Improve Class Expertise in Science Classes can extend elementary science class expertise such as self-class analysis, eye tracking, linguistic, gesture, and class design beyond traditional class analysis and consulting.

• 한국실내디자인학회논문집
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• 제24권6호
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• pp.145-153
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• 2015

For this research with the objects of spaces in two department stores which can be referred to as representative facility of commercial space, observation test has carried out to estimate how much visitors rivet their eyes to the display of shops. In addition, to find out what effect the difference among the department types has on the selective attention to space element, the observation time was applied as a medium for estimation. The followings are the result from analyzing the observation frequency and the observation intensity feature of each section where the characteristics of design could be found at attention. First, both images of A and B had concentrative dominant-observation at left shops. In case of Image A, Customers began to observe the right shops very attentively after 25 seconds, and with Image B, the attentive observation at right and left took place alternatively after 35 seconds. In other words, regardless of the characteristics of shop displays, the left shops were observed first while in case of the observation after the early and middle time-frame the characteristics of shops were found to have effects on observation. Second, the normal observation showed some difference among attention sections over time while on the whole both images of A and B had the same highly attentive observation at the middle space. Accordingly, it could be concluded that the middle space was playing a faithful role as background for commercial spaces. Third, the ignorant observation, which is the opposite to the attentive observation, was found different between the images of A and B. When the ignorant observation is considered to have intentionality, it will be possible to set up the display which may attract the attention aggressively by the process of figuring out the characteristics of ignored shops.

• 한국산학기술학회논문지
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• 제14권10호
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• pp.5003-5011
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• 2013

본 논문은 정서-주의 태스크를 수행할 때의 신경학적, 행동학적 반응이 정서조절 능력을 반영할 수 있는지를 측정하였다. 이를 위해, 건강한 성인 남녀 19명을 대상으로 3일에 걸쳐 1일 1회씩 동일 시간대에 정서-주의 상호작용이 요구되는 실험을 수행하였다. 이 실험을 통해 정서를 유발하는 비디오를 보여주기 전과 후, 정서-주의 태스크를 수행할 때의 EEG와 반응시간이 측정되었다. ERP P100과 P300 성분 간의 밀접한 상관관계가 나타났으며, P100 진폭이 높을수록 P300 잠재기가 길게 나타났다. 또한 P300 잠재기가 길수록 반응시간이 길게 나타났다. 또한, 정서-주의 태스크를 수행할 때 ERP 성분과 반응시간에 개인별 차이가 존재하였다. 정서유발을 위해 사용된 비디오 자극의 유형에 관계없이 ERP 성분의 낮은 진폭과 빠른 잠재기를 나타낸 집단이 더 빠른 반응시간을 나타내었다. 이러한 반응특성은 목표인식에 대한 성급한 반응으로 해석될 수 있으며, 낮은 정서 조절력과 관련이 있다고 할 수 있다.