• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1167-1187
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• 2020

With the development of medical imaging technology, image registration has been widely used in the field of disease diagnosis. The registration between different modal images of brain magnetic resonance (MR) is particularly important for the diagnosis of brain diseases. However, previous registration methods don't take advantage of the prior knowledge of bilateral brain symmetry. Moreover, the difference in gray scale information of different modal images increases the difficulty of registration. In this paper, a multimodal medical image registration method based on image segmentation and symmetric self-similarity is proposed. This method uses modal independent self-similar information and modal consistency information to register images. More particularly, we propose two novel symmetric self-similarity constraint operators to constrain the segmented medical images and convert each modal medical image into a unified modal for multimodal image registration. The experimental results show that the proposed method can effectively reduce the error rate of brain MR multimodal medical image registration with rotation and translation transformations (average 0.43mm and 0.60mm) respectively, whose accuracy is better compared to state-of-the-art image registration methods.

• Journal of the Korean Chemical Society
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• v.61 no.4
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• pp.211-217
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• 2017

The purpose of this study was to examine UK students' use of multimodal representations in science. Students were asked to explain their understandings of the scientific concept and presentation of the multimodal representations in a science Assessing Pupils' Progress (APP) task. Participants of this study were fifty-four Year 7 students taught by the same teacher. Students from one class (27 students) were assigned to the experimental group, and then they received instruction encouraging the using of multimodal representations as evidences to support students' claims. One class (27 students) was assigned to the control group and they received instruction with traditional teaching methods. Both groups performed an APP task for assessment. The samples of APP assessments produced by students both from the experimental and control groups were analyzed using an analysis framework of multimodal representations, embeddedness in evidence and understanding of scientific concepts. Data analysis indicated that the students in the experimental group performed better than that of the control group on embeddedness of multimodal representations in the APP task. In addition, there was a significant difference between the two groups in the evaluation of understand of the scientific concepts.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.170-178
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• 2017

Biometric recognition is one of the major challenging topics which needs high performance of recognition accuracy. Most of existing methods rely on a single source of biometric to achieve recognition. The recognition accuracy in biometrics is affected by the variability of effects, including illumination and appearance variations. In this paper, we propose a new multimodal biometrics recognition using convolutional neural network. We focus on multimodal biometrics from face and periocular regions. Through experiments, we have demonstrated that facial multimodal biometrics features deep learning framework is helpful for achieving high recognition performance.

• Journal of Animal Science and Technology
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• v.63 no.6
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• pp.1443-1452
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• 2021

This study aimed to investigate the effects of the multimodal group intervention that combined animal-assisted therapy (AAT) and integrated play therapy (IPT) on depression and self-esteem in undergraduate university students. The subjects were 40 students attending animal-related and social welfare departments of universities located in a metropolitan city. The multimodal intervention program was conducted for eight sessions (twice a week, 60 min each) in the experimental group. Data analysis was conducted using the independent sample t-test, ANCOVA, and paired sample t-test for pre- and post-test. Therefore, the multimodal intervention program applying AAT and IPT showed positive effects on depression and self-esteem in university students. Based on these results, this study proposed the operation of multidisciplinary education and practical and policy utilization methods to reduce depression among university students and help improve their self-esteem.

• The Journal of Information Systems
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• v.32 no.3
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• pp.1-17
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• 2023

Purpose The proliferation of data on the internet has created a need for innovative methods to analyze user satisfaction data. Traditional survey methods are becoming inadequate in dealing with the increasing volume and diversity of data, and new methods using unstructured internet data are being explored. While numerous comment-based user satisfaction studies have been conducted, only a few have explored user satisfaction through video and audio data. Multimodal sentiment analysis, which integrates multiple modalities, has gained attention due to its high accuracy and broad applicability. Design/methodology/approach This study uses multimodal sentiment analysis to analyze user satisfaction of iPhone and Samsung products through online videos. The research reveals that the combination model integrating multiple data sources showed the most superior performance. Findings The findings also indicate that price is a crucial factor influencing user satisfaction, and users tend to exhibit more positive emotions when content with a product's price. The study highlights the importance of considering multiple factors when evaluating user satisfaction and provides valuable insights into the effectiveness of different data sources for sentiment analysis of product reviews.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.12
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• pp.2529-2531
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• 2009

A simple multimodal fingerprint recognition method based on two types of feature vectors such as minutiae points and directional features is proposed, where Directional Filter Bank (DFB) is used to extract directional features. Experimental results show that the proposed method can effectively combine minutiae- and DFB-based methods and produce a better matching capability in the poor quality fingerprint image.

• International Journal of Highway Engineering
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• v.17 no.2
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• pp.123-134
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• 2015

PURPOSES : The key point of a multimodal LOS (level-of-service) evaluation system is that all of the modes are mutually associated to determine each mode's LOS. For example, the LOS of the bicycle mode is measured based on not only bicycle volumes, but also automobile volumes. However, the Korea Highway Capacity Manual (KHCM) still focuses on the automobile mode in evaluating the LOS of the roads. Additionally, the KHCM's LOS of the other modes, except for the automobile, is not consistent with actual road conditions. The KHCM, therefore, needs to develop and introduce a multimodal LOS system in order to evaluate the service conditions more accurately. METHODS: As a preliminary step to the introduction of multimodal LOS research, in this study the current problem of the KHCM's LOS system through a close review and comparison with other HCMs (highway capacity manuals) was identified. Secondly, a field survey and investigation of the urban streets to apply the HCM's multimodal LOS system was conducted. Finally, a comparison analysis of the results of the HCM and KHCM LOS was performed. RESULTS: In the study, it was found that the results of the LOS for the automobile mode did not show a significant difference between the HCM and KHCM. However, the LOS of the bicycle and pedestrian mode tended to be worse in the multimodal LOS system, which results from considering the effects of the automobile mode. Moreover, it was found that many cases have the potential to improve the overall LOS conditions, while reducing the automobile capacity. CONCLUSIONS: With the introduction of the multimodal LOS system, road diet and complete streets can be easily applied to ans actual road improvement project. Ultimately, the multimodal LOS system should be introduced into the KHCM, which can then be applied to traffic impact studies and other road improvement projects for more accurate evaluations.

• PNF and Movement
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• v.20 no.2
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• pp.203-214
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• 2022

Purpose: We investigated the effects of multimodal vs. stabilization exercises on chronic low back pain. Methods: Study participants were randomly assigned to a multimodal exercise (n = 20) or a stabilization exercise group (n = 20). Participants in the multimodal exercise group performed stabilization, stretching, and endurance exercises, whereas those in the stabilization exercise group performed only stabilization exercises. Participants in both groups performed the exercises for 1 hour thrice a week for 5 weeks. The following outcomes were evaluated: pain intensity (numeric rating scale), disability (the Oswestry Disability Index [ODI] and the Roland-Morris Disability Questionnaire [RMDQ]), pain-induced fear (the Fear of Daily Activities Questionnaire [FDAQ], the Fear-Avoidance Belief Questionnaire [FABQ], and the Tampa scale for kinesiophobia-11 [TSK-11]). Outcome measures were evaluated at baseline and after intervention. Results: Significant post-intervention improvement was observed in pain intensity and the RMDQ and FDAQ scores in both groups (p < 0.01). The post-intervention ODI, FABQ, and TSK-11 scores were improved in the multimodal exercise group (p < 0.01). Additionally, significant differences were observed in pain intensity, as well as in the ODI, FDAQ, and FABQ scores in the multimodal exercise group compared with these findings in the stabilization exercise group (p < 0.01). Conclusion: The multimodal and stabilization exercise programs reduced pain intensity, disability, and pain-induced fear. Compared with stabilization exercises, multimodal exercises more effectively reduced pain intensity, disability, and pain-induced fear. This study highlights that musculoskeletal rehabilitation for people with chronic low back pain should include a multimodal exercise program.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.607-613
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• 2011

This paper reviews search methods of multiple reasonable paths to implement multimodal public transportation network of Seoul. Such a large scale multimodal public transportation network as Seoul, the computation time of path finding algorithm is a key and the result of path should reflect route choice behavior of public transportation passengers. Search method of alternative path is divided by removing path method and deviation path method. It analyzes previous researches based on the complexity of algorithm for large-scale network. Applying path finding algorithm in public transportation network, transfer and loop constraints must be included to be able to reflect real behavior. It constructs the generalized cost function based on the smart card data to reflect travel behavior of public transportation. To validate the availability of algorithm, experiments conducted with Seoul metropolitan public multimodal transportation network consisted with 22,109 nodes and 215,859 links by using the deviation path method, suitable for large-scale network.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.757-758
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• 2008

This paper aims to propose an emergency recognition system using multimodal information extracted by an image processing module, a voice processing module, and a gravity sensor processing module. Each processing module detects predefined events such as moving, stopping, fainting, and transfer them to the multimodal integration module. Multimodal integration module recognizes emergency situation by using the transferred events and rechecks it by asking the user some question and recognizing the answer. The experiment was conducted for a faint motion in the living room and bathroom. The results of the experiment show that the proposed system is robust than previous methods and effectively recognizes emergency situations at various situations.