• 대한인간공학회지
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• 제23권2호
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• pp.25-36
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• 2004

The effects of task characteristics on the relative efficiency of visual display dimension were studied using a simulated tele-robotic task. Through a conventional method of task analysis. the tele-robotic task was divided into two categories: the task element requiring focused attention (FA task) and the task element requiring global attention (CA task). Time-ta-completion data were collected for a total of 120 trials involving 10 participants. For the CA task. there was no significant difference between the multiple two-dimensional (20) display and the three-dimensional (3D) monocular display. For the FA task. however. the multiple 20 display was superior to the 3D monocular display. The results suggest that the characteristics of a given task have a considerable effect on the choice of display dimensionality and the multiple 3D display is better for human operators to effectively judge depth if the task requires frequent use of focused attention.

• 한국중재학회지:중재연구
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• 제26권4호
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• pp.43-62
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• 2016

The international arbitration system has been a useful method of settling disputes arising from international transactions. Arbitration provides the opportunity for the parties to choose a fair and neutral forum and to participate in the selection of the decision maker and the rules that will be applied. Because arbitration is a creature of contract, there is no agreement to arbitrate if there is no contract. An arbitration clause should be designed to fit the circumstances of the transaction and the parties' needs. The parties draft an arbitration clause with insufficient attention to the transaction to which it relates. Insufficient attention to arbitration agreement has caused the delay of arbitration procedure or even the inability to arbitrate. Therefore the parties pay sufficient attention to the underlying transaction so that the arbitration clause can be tailored to their particular requirements and to possible disputes that may reasonably be anticipated.

• 로봇학회논문지
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• 제17권4호
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• pp.425-430
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• 2022

As robots are considered one of the mainstream digital transformations, robots with machine vision becomes a main area of study providing the ability to check what robots watch and make decisions based on it. However, it is difficult to find a small object in the image mainly due to the flaw of the most of visual recognition networks. Because visual recognition networks are mostly convolution neural network which usually consider local features. So, we make a model considering not only local feature, but also global feature. In this paper, we propose a detection method of a small marker on the object using deep learning and an algorithm that considers global features by combining Transformer's self-attention technique with a convolutional neural network. We suggest a self-attention model with new definition of Query, Key and Value for model to learn global feature and simplified equation by getting rid of position vector and classification token which cause the model to be heavy and slow. Finally, we show that our model achieves higher mAP than state of the art model YOLOr.

• 한국정보과학회 언어공학연구회:학술대회논문집(한글 및 한국어 정보처리)
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• 한국정보과학회언어공학연구회 2018년도 제30회 한글 및 한국어 정보처리 학술대회
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• pp.361-363
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• 2018

FAQ 시스템은 주어진 질문과 가장 유사한 질의를 찾아 이에 대한 답을 제공하는 시스템이다. 질의 간의 유사도를 측정하기 위해 문장을 벡터로 표현하며 일반적으로 TFIDF, Okapi BM25와 같은 방법으로 계산한 단어 가중치 벡터를 이용하여 문장을 표현한다. 하지만 단어 가중치 벡터는 어휘적 정보를 표현하는데 유용한 반면 단어의 의미적인(semantic) 정보는 표현하기 어렵다. 본 논문에서는 이를 보완하고자 딥러닝을 이용한 문장 임베딩을 구축하고 단어 가중치 벡터와 문장 임베딩을 조합한 문장 유사도 계산 모델을 제안한다. 또한 문장 임베딩 구현 시 self-attention 기법을 적용하여 문장 내 중요한 부분에 가중치를 주었다. 실험 결과 제안하는 유사도 계산 모델은 비교 모델에 비해 모두 높은 성능을 보였고 self-attention을 적용한 실험에서는 추가적인 성능 향상이 있었다.

• Journal of Information Processing Systems
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• 제17권4호
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• pp.754-771
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• 2021

In the task of continuous dimension emotion recognition, the parts that highlight the emotional expression are not the same in each mode, and the influences of different modes on the emotional state is also different. Therefore, this paper studies the fusion of the two most important modes in emotional recognition (voice and visual expression), and proposes a two-mode dual-modal emotion recognition method combined with the attention mechanism of the improved AlexNet network. After a simple preprocessing of the audio signal and the video signal, respectively, the first step is to use the prior knowledge to realize the extraction of audio characteristics. Then, facial expression features are extracted by the improved AlexNet network. Finally, the multimodal attention mechanism is used to fuse facial expression features and audio features, and the improved loss function is used to optimize the modal missing problem, so as to improve the robustness of the model and the performance of emotion recognition. The experimental results show that the concordance coefficient of the proposed model in the two dimensions of arousal and valence (concordance correlation coefficient) were 0.729 and 0.718, respectively, which are superior to several comparative algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권8호
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• pp.2552-2570
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• 2022

Underwater images usually have various problems, such as the color cast of underwater images due to the attenuation of different lights in water, the darkness of image caused by the lack of light underwater, and the haze effect of underwater images because of the scattering of light. To address the above problems, the channel attention mechanism, strengthen-operate-subtract (SOS) boosting mechanism and gated fusion module are introduced in our paper, based on which, an underwater image recovery network is proposed. First, for the color cast problem of underwater images, the channel attention mechanism is incorporated in our model, which can well alleviate the color cast of underwater images. Second, as for the darkness of underwater images, the similarity between the target underwater image after dehazing and color correcting, and the image output by our model is used as the loss function, so as to increase the brightness of the underwater image. Finally, we employ the SOS boosting module to eliminate the haze effect of underwater images. Moreover, experiments were carried out to evaluate the performance of our model. The qualitative analysis results show that our method can be applied to effectively recover the underwater images, which outperformed most methods for comparison according to various criteria in the quantitative analysis.

• 산경연구논집
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• 제15권1호
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• pp.31-39
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• 2024

Purpose: Through an ethical supply chain, brands not only catch the eye but win over a fan base of consumers who prize credibility and consistency in what they purchase. Currently, the ethical supply chain is no longer just a manufacturing process; it has become a compelling story. It draws people's attention and wins their loyalty. This research study will examine the benefits of an ethical supply chain in attracting consumer attention and building brand loyalty. Research design, data and methodology: For this research study, A detailed method was used to search and analyze relevant articles. Initial searches used set terms in certain databases. Screening criteria were the thorough scrutiny of titles and abstracts to decide their relevance to the study at hand. Thus, to enhance the quality of data, duplicate entries were deleted. Results: Based on the analysis of the prior literature, the results highlight the power of ethical saliency, showing that consumers themselves are looking for and rewarding products that meet their ethical standards. This attention to ethically transparent brands, in turn, encourages more interest and interaction with them. Conclusions: Therefore, practitioners must transmit the firm's ethical standards through all channels of communication-investor relations materials and financial reports alike.

• 스마트미디어저널
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• 제13권3호
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• pp.53-61
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• 2024

본 논문은 기존 방법보다 우수한 성능을 달성하는 피부 병변 분할을 위한 새로운 M자 모양 인코더-디코더 아키텍처를 제안한다. 제안된 아키텍처는 왼쪽과 오른쪽 다리를 활용하여 다중 스케일 특징 추출을 가능하게 하고, 스킵 연결 내에서 어텐션 메커니즘을 통합하여 피부 병변 분할 성능을 더욱 향상시킨다. 입력 영상은 네 가지 다른 패치로 분할되어 입력되며 인코더-디코더 프레임워크 내에서 피부 병변 분할 성능의 향상된 처리를 가능하게 한다. 제안하는 방법에서 어텐션 메커니즘을 통해 입력 영상의 특징에 더 많은 초점을 맞추어 더욱 정교한 영상 분할 결과를 도출하는 것이다. 실험 결과는 제안된 방법의 효과를 강조하며, 기존 방법과 비교하여 우수한 정확도, 정밀도 및 Jaccard 지수를 보여준다.

• 한국실내디자인학회논문집
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• 제22권2호
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• pp.194-202
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• 2013

Commercial spaces are very sensitive in terms of trend and uniqueness, and many elements expressing space coexist together with brilliant ideas aiming to attract consumer attention and even induce sudden desire of purchase. Coexistence in space is expressed in various ways and out of those ways, representation by material is especially apparent. The application of material does not individually influence the expression of space, but the characteristics of multiple materials applied contribute to the distinction of space through mutual communication. This study intends to analyze how conversion is centered to express the distinction of space through materials communicated in space. The detailed study results are as follows. First, the conversion expression method of material resulted as 'Substitution', 'Contrast', 'Assimilation', 'Creation'. These four was divided and analyzed into 'Time', 'Space', 'Genre'. As a result of this analysis, such significances were shown as 'Contrast' in 'Time', 'Substitution' in 'Space', and 'Assimilation' in 'Genre'. Second, the conflict due to heterogeneity by conversion of 'Contrast' in 'Time' through past and current materials appeared to induce interest amongst consumers. Third, within 'Space', 'Substitution' of natural/artificial materials was noticeably applied. This is evaluated as a constructive way of expressing natural forms into artificial forms further intending to provoke attention and stimulate emotion. Fourth, in conversion through 'Assimilation' in 'Genre', rather than using materials only from other areas, synchronizing it by combining architectural materials is an effective method. Such results are thought to be a distinctive design method that draws attention of customers by communicating disparate materials in commercial space. Thus, the study results are expected to be utilized as an elementary resource in designing commercial space with character and high satisfaction.

• 한국컴퓨터정보학회논문지
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• 제27권8호
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• pp.1-7
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• 2022

인공지능 기술이 발달하면서 뉴로사이언스 마이닝(NSM: NeuroScience Mining)과 AI를 접목하려는 시도가 증가하고 있다. 나아가 NSM은 뉴로사이언스와 비즈니스 애널리틱스의 결합으로 인해 연구범위가 확장되고 있다. 본 연구에서는 fNIRS 실험을 통해 확보한 뉴로 데이터를 분석하여 비즈니스 문제 해결 창의성(BPSC: business problem-solving creativity)을 예측하고 이를 통해 NSM의 잠재력을 조사한다. BPSC는 비즈니스에서 차별성을 가지게 하는 중요한 요소이지만, 인지적 자원의 하나인 BPSC의 측정 및 예측에는 한계가 존재한다. 본 논문에서는 BPSC 예측 성능을 높이는 방안으로 CNN, BiLSTM 그리고 어텐션 네트워크를 결합한 새로운 NSM 기법을 제안한다. 제안된 NSM 기법을 15만 개 이상의 fNIRS 데이터를 활용하여 유효성을 입증하였다. 연구 결과, 본 논문에서 제안하는 NSM 방법이 벤치마킹한 알고리즘(CNN, BiLSTM)에 비하여 우수한 성능을 가지는 것으로 나타났다.