• Journal of the Korean Society of Radiology
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• v.81 no.6
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• pp.1305-1333
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• 2020

Deep learning has recently achieved remarkable results in the field of medical imaging. However, as a deep learning network becomes deeper to improve its performance, it becomes more difficult to interpret the processes within. This can especially be a critical problem in medical fields where diagnostic decisions are directly related to a patient's survival. In order to solve this, explainable artificial intelligence techniques are being widely studied, and an attention mechanism was developed as part of this approach. In this paper, attention techniques are divided into two types: post hoc attention, which aims to analyze a network that has already been trained, and trainable attention, which further improves network performance. Detailed comparisons of each method, examples of applications in medical imaging, and future perspectives will be covered.

• Journal of Information Processing Systems
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• v.19 no.4
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• pp.427-438
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• 2023

Plenty of works have indicated that single image super-resolution (SISR) models relying on synthetic datasets are difficult to be applied to real scene text image super-resolution (STISR) for its more complex degradation. The up-to-date dataset for realistic STISR is called TextZoom, while the current methods trained on this dataset have not considered the effect of multi-scale features of text images. In this paper, a multi-scale and attention fusion model for realistic STISR is proposed. The multi-scale learning mechanism is introduced to acquire sophisticated feature representations of text images; The spatial and channel attentions are introduced to capture the local information and inter-channel interaction information of text images; At last, this paper designs a multi-scale residual attention module by skillfully fusing multi-scale learning and attention mechanisms. The experiments on TextZoom demonstrate that the model proposed increases scene text recognition's (ASTER) average recognition accuracy by 1.2% compared to text super-resolution network.

• Toxicological Research
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• v.26 no.4
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• pp.237-243
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• 2010

Endocrine disrupting chemicals (EDCs) have been shown to interfere with physiological systems, i.e., adversely affecting hormone balance (endocrine system), or disrupting normal function, in the female and male reproductive organs. Although endocrine disruption is a global concern for human health, its impact and significance and the screening strategy for detecting these synthetic or man-made chemicals are not clearly understood in female and male reproductive functions. Thus, in this review, we summarize the interference of environmental EDCs on reproductive development and function, and toxicological mechanism(s) of EDCs in in vitro and in vivo models of male and female reproductive system. In addition, this review highlights the effect of exposure to multiple EDCs on reproductive functions, and brings attention to their toxicological mechanism(s) through estrogen receptors.

• Journal of the Semiconductor & Display Technology
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• v.17 no.4
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• pp.6-10
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• 2018

Organic light emitting devices have attracted the attention of many people because of their high potential for self-emission and flexible display devices. However, due to limitations in device efficiency and lifetime, partial commercialization is underway. In this paper, we have investigated the electrical conduction mechanism of the organic light emitting device by the temperature and the thickness of the light emitting layer through the current - voltage characteristics with respect to the conduction mechanism directly affecting the efficiency and lifetime of the organic light emitting device. Through the study, it was found that the conduction in the low electric field region is caused by the movement of the heat excited charge in the ohmic region and the tunneling of the electric charge due to the high electric field in the high electric field region.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.1192-1201
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• 2021

This study focused on how to analyze the ICT policy formation process using social big data. Specifically, in this study, a method for quantifying variables that influenced policy formation using the concept of a policy triggering mechanism and elements necessary to present the analysis results were proposed. For the analysis of the ICT policy triggering mechanism, variables such as 'Scope', 'Duration', 'Interactivity', 'Diversity', 'Attention', 'Preference', 'Transmutability' were proposed. In addition, 'interpretation of results according to data level', 'presentation of differences between collection and analysis time points', and 'setting of garbage level' were suggested as elements necessary to present the analysis results.

• Journal of Construction Engineering and Project Management
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• v.3 no.1
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• pp.22-27
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• 2013

Inter-organizational Relationships (IORs) governance is one of the emerging research areas that have been studied in different contexts (e.g. economics, strategy, organization, and management). This view, particularly, attracted rising attention from academics and practitioners in the context of construction projects, due to the complex forms of IORs in terms of inter-firm exchanges (e.g. engineering, procurement, finance, construction, and operation) in these projects. The focus of IORs governance is to control Inter-organizational relationships among two or more cooperative parties to alleviate conflict and achieve mutual gains. One of the mechanisms that have been identified in the related literature is formal governance mechanism. Although many empirical studies have been conducted using formal governance terms and indicators, there isn't yet a consensual definition of this mechanism and its components that may cause misinterpretation of research results and also impede future research. This paper makes contribution to the concept of IORs governance by clarifying the meaning of formal governance mechanism and identifying different indicators of this mechanism that have been used and identified in previous studies. This provides an innovative and useful framework to understand formal governance mechanism and its application in construction projects.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.321-327
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• 2013

Inter-organizational Relationships (IORs) governance is one of the emerging research areas that have been studied in different contexts (e.g. economics, strategy, organization, and management). This view, particularly, attracted rising attention from academics and practitioners in the context of construction projects, due to the complex forms of IORs in terms of inter-firm exchanges (e.g. engineering, procurement, finance, construction, and operation) in these projects. The focus of IORs governance is to control Inter-organizational relationships among two or more cooperative parties to alleviate conflict and achieve mutual gains. One of the mechanisms that have been identified in the related literature is formal governance mechanism. Although many empirical studies have been conducted using formal governance terms and indicators, there isn't yet a consensual definition of this mechanism and its components that may cause misinterpretation of research results and also impede future research. This paper makes contribution to the concept of IORs governance by clarifying the meaning of formal governance mechanism and identifying different indicators of this mechanism that have been used and identified in previous studies. This provides an innovative and useful framework to understand formal governance mechanism and its application in construction projects.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.300-302
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• 2022

Palm vein identification has attracted attention due to its distinct characteristics and excellent recognition accuracy. However, many contactless palm vein identification systems suffer from the issue of having low-quality palm images, resulting in degradation of recognition accuracy. This paper proposes the use of U-Net architecture to correctly segment the vascular blood vessel from palm images. Attention gate mechanism and residual block are also utilized to effectively learn the crucial features of a specific segmentation task. The experiments were conducted on CASIA dataset. Hessian-based Jerman filtering method is applied to label the palm vein patterns from the original images, then the network is trained to segment the palm vein features from the background noise. The proposed method has obtained 96.24 IoU coefficient and 98.09 dice coefficient.

• Korean Journal of Cognitive Science
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• v.18 no.4
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• pp.399-415
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• 2007

Many objects are always present in a visual scene. Since the visual system has limited capacity to process multiple stimuli at a time, how to cope with this informational overload is one of the important problems to solve in visual perception. This study investigated the suppressive interactions among multiple stimuli when attention was directed to either one of the stimuli or all of them. The results indicate that suppressive interactions among multiple circles were reduced in V4 when subjects paid attention to one of the four locations, as compared to the unattended condition. However, suppressive interactions were not reduced when they paid attention to all four items as a set, in order to compute their mean size. These results suggest that whereas focused attention serves to later out irrelevant information, distributed attention provides an average representation of multiple stimuli.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.16-37
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• 2022

Accurate liver segment segmentation based on radiological images is indispensable for the preoperative analysis of liver tumor resection surgery. However, most of the existing segmentation methods are not feasible to be used directly for this task due to the challenge of exact edge prediction with some tiny and slender vessels as its clinical segmentation criterion. To address this problem, we propose a novel deep learning based segmentation model, called Boundary-Aware Dual Attention Liver Segment Segmentation Model (BADA). This model can improve the segmentation accuracy of liver segments with enhancing the edges including the vessels serving as segment boundaries. In our model, the dual gated attention is proposed, which composes of a spatial attention module and a semantic attention module. The spatial attention module enhances the weights of key edge regions by concerning about the salient intensity changes, while the semantic attention amplifies the contribution of filters that can extract more discriminative feature information by weighting the significant convolution channels. Simultaneously, we build a dataset of liver segments including 59 clinic cases with dynamically contrast enhanced MRI(Magnetic Resonance Imaging) of portal vein stage, which annotated by several professional radiologists. Comparing with several state-of-the-art methods and baseline segmentation methods, we achieve the best results on this clinic liver segment segmentation dataset, where Mean Dice, Mean Sensitivity and Mean Positive Predicted Value reach 89.01%, 87.71% and 90.67%, respectively.