• Nuclear Medicine and Molecular Imaging
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• v.41 no.2
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• pp.71-77
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• 2007

Advance of neuroimaging technique has greatly influenced recent brain research field. Among various neuroimaging modalities, positron emission tomography has played a key role in molecular neuroimaging though functional MRI has taken over its role in the cognitive neuroscience. As the analysis technique for PET data is more sophisticated, the complexity of the method is more increasing. Despite the wide usage of the neuroimaging techniques, the assumption and limitation of procedures have not often been dealt with for the clinician and researchers, which might be critical for reliability and interpretation of the results. In the current paper, steps of voxel-based statistical analysis of PET including preprocessing, intensity normalization, spatial normalization, and partial volume correction will be revisited in terms of the principles and limitations. Additionally, new image analysis techniques such as surface-based PET analysis, correlational analysis and multimodal imaging by combining PET and DTI, PET and TMS or EEG will also be discussed.

• The Transactions of the Korea Information Processing Society
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• v.13 no.4
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• pp.174-180
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• 2024

In this paper, we explore an emotion classification method through multimodal learning utilizing wav2vec 2.0 and KcELECTRA models. It is known that multimodal learning, which leverages both speech and text data, can significantly enhance emotion classification performance compared to methods that solely rely on speech data. Our study conducts a comparative analysis of BERT and its derivative models, known for their superior performance in the field of natural language processing, to select the optimal model for effective feature extraction from text data for use as the text processing model. The results confirm that the KcELECTRA model exhibits outstanding performance in emotion classification tasks. Furthermore, experiments using datasets made available by AI-Hub demonstrate that the inclusion of text data enables achieving superior performance with less data than when using speech data alone. The experiments show that the use of the KcELECTRA model achieved the highest accuracy of 96.57%. This indicates that multimodal learning can offer meaningful performance improvements in complex natural language processing tasks such as emotion classification.

• Earthquakes and Structures
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• v.12 no.3
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• pp.271-284
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• 2017

This paper presents a multimodal adaptive nonlinear static method of analysis that, differently from the nonlinear static methods suggested in seismic codes, does not require the definition of the equivalent Single-Degree-Of-Freedom (SDOF) system to evaluate the seismic response of structures. First, the proposed method is formulated for the assessment of r.c. plane frames and then it is extended to 3D framed structures. Furthermore, the proposed nonlinear static approach is re-elaborated as a displacement-based design method that does not require the use of the behaviour factor and takes into account explicitly the plastic deformation capacity of the structure. Numerical applications to r.c. plane frames and to a 3D framed structure with inplan irregularity are carried out to illustrate the attractive features as well as the limitations of the proposed method. Furthermore, the numerical applications evidence the uncertainty about the suitability of the displacement demand prediction obtained by the nonlinear static methods commonly adopted.

• Proceedings of the Korean Vacuum Society Conference
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• 2015.08a
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• pp.51.2-51.2
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• 2015

Understanding interfacial phenomena has been one of the main research issues not only in semiconductors but only in life sciences. I have been trying to meet the atomic scale surface and interface analysis challenges from semiconductor industries and furthermore to extend the application scope to biomedical areas. Optical imaing has been most widely and successfully used for biomedical imaging but complementary ion beam imaging techniques based on mass spectrometry and ion scattering can provide more detailed molecular specific and nanoscale information In this presentation, I will review the 27 years history of medium energy ion scattering (MEIS) development at KRISS and DGIST for nanoanalysis. A electrostatic MEIS system constructed at KRISS after the FOM, Netherland design had been successfully applied for the gate oxide analysis and quantitative surface analysis. Recenlty, we developed time-of-flight (TOF) MEIS system, for the first time in the world. With TOF-MEIS, we reported quantitative compositional profiling with single atomic layer resolution for 0.5~3 nm CdSe/ZnS conjugated QDs and ultra shallow junctions and FINFET's of As implanted Si. With this new TOF-MEIS nano analysis technique, details of nano-structured materials could be measured quantitatively. Progresses in TOF-MEIS analysis in various nano & bio technology will be discussed. For last 10 years, I have been trying to develop multimodal nanobio imaging techniques for cardiovascular and brain tissues. Firstly, in atherosclerotic plaque imaging, using, coherent anti-stokes raman scattering (CARS) and time-of-flight secondary ion mass spectrometry (TOF-SIMS) multimodal analysis showed that increased cholesterol palmitate may contribute to the formation of a necrotic core by increasing cell death. Secondly, surface plasmon resonance imaging ellipsometry (SPRIE) was developed for cell biointerface imaging of cell adhesion, migration, and infiltration dynamics for HUVEC, CASMC, and T cells. Thirdly, we developed an ambient mass spectrometric imaging system for live cells and tissues. Preliminary results on mouse brain hippocampus and hypotahlamus will be presented. In conclusions, multimodal optical and mass spectrometric imaging privides overall structural and morphological information with complementary molecular specific information, which can be a useful methodology for biomedical studies. Future challenges in optical and mass spectrometric imaging for new biomedical applications will be discussed.

• Phonetics and Speech Sciences
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• v.3 no.2
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• pp.71-78
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• 2011

In this paper, we propose an approach which constructs classifier ensembles of various channel compensation and feature enhancement methods. CMN and CMVN are used as channel compensation methods. PCA, kernel PCA, greedy kernel PCA, and kernel multimodal discriminant analysis are used as feature enhancement methods. The proposed ensemble system is constructed with the combination of 15 classifiers which include three channel compensation methods (including 'without compensation') and five feature enhancement methods (including 'without enhancement'). Experimental results show that the proposed ensemble system gives highest average speaker identification rate in various environments (channels, noises, and sessions).

• International Journal of Contents
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• v.11 no.2
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• pp.50-56
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• 2015

The area of discourse analysis has long neglected the value of images as a semiotic resource in communication. This paper suggests that like language, images are rich in meaning potential and are governed by visual grammar structures which can be utilized to decode the meanings of images. Employing a theoretical framework in visual communication, two digital images are examined for their representational and interactive dimensions and the dimensions' relation to the magazine advertisement genre. The results show that the framework identified narrative and conceptual processes, relations between participants and viewers, and symbolic attributes of the images, which all contribute to the sociological interpretations of the images. The identities and relationships between viewers and participants suggested in the images signify desirable qualities that may be associated to the product of the advertiser. The findings support the theory of visual grammar and highlight the potential of images to convey multi-layered meanings.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.1
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• pp.132-141
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• 2015

There might have been weakness in securing user authentication or verification with real time service approach, while existing unimodal biometric authentication has been used mainly for user identification and recognition. Accordingly, it is essential to research and develop ways that upgrade security performance with multi biometric based real time authentication and verification technology. This paper focused to suggest binding assignment and strategy for developing multi biometric authentication technology through investigation of advanced study and patents. Description includes introduction, technology outline, technology trend, patent analysis, and conclusion.

• Journal of Korean Society of Transportation
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• v.28 no.3
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• pp.85-98
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• 2010

As transit networks are becoming more multimodal, the concept of connectivity of transit networks becomes important. This study aims to develop a quantitative model for measuring the connectivity of multimodal transit networks. To that end, we select, as evaluation measures of a transit line, its length, capacity, and speed. We then define the connecting power of a transit line as the product of those measures. The degree centrality of a node, which is a widely used centrality measure in social network analysis, is employed with appropriate modifications suited for transit networks. Using the degree centrality of a transit stop and the connecting powers of transit lines serving the transit stop, we develop an index quantifying the level of connectivity of the transit stop. From the connectivity indexes of transit stops, we derive the connectivity index of a transit line as well as an area of a multimodal transit network. In addition, we present a method to evaluate the connectivity of a transfer center using the connectivity indexes of transit stops and passenger acceptance rate functions. A case study shows that the connectivity evaluation model developed in this study takes well into consideration characteristics of multimodal transit networks, adequately measures the connectivity of transit stops, lines, and areas, and furthermore can be used in determining the level of service of transfer centers.

• ETRI Journal
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• v.24 no.1
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• pp.1-11
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• 2002

A video summary abstracts the gist from an entire video and also enables efficient access to the desired content. In this paper, we propose a novel method for summarizing news video based on multimodal analysis of the content. The proposed method exploits the closed caption data to locate semantically meaningful highlights in a news video and speech signals in an audio stream to align the closed caption data with the video in a time-line. Then, the detected highlights are described using MPEG-7 Summarization Description Scheme, which allows efficient browsing of the content through such functionalities as multi-level abstracts and navigation guidance. Multimodal search and retrieval are also within the proposed framework. By indexing synchronized closed caption data, the video clips are searchable by inputting a text query. Intensive experiments with prototypical systems are presented to demonstrate the validity and reliability of the proposed method in real applications.

• Journal of The Korean Association For Science Education
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• v.31 no.6
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• pp.931-941
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• 2011

This study aimed to examine the effects of argument-based chemistry laboratory investigations using the Science Writing Heuristic (SWH) approach on students' use and embedding of multimodal representations in summary writing. Participants of this study were thirty-nine freshman students majoring in science education at a National University in Korea. Argument-based chemistry laboratory investigations using the SWH approach were implemented for twenty-three students enrolled in one cohort, and the traditional chemistry laboratory teaching was implemented for 16 students enrolled in the other cohort. Summary writing samples were collected from students before and after the implementation. Summary writing samples produced by students were examined using an analysis framework for examining the use and embeddedness of multimodal representations. Summary writing was categorized into one of verbal mode, symbolic mode, and visual mode. With regard to the embedding of multi-modal representations, summary writing samples were analyzed in terms of 'constructing understanding,' 'integrating multiple modes,' 'providing valid claims and evidence,' and 'representing multiple modes.' Data analysis shows that the students of the SWH group were better at utilizing and embedding multimodal representations in summary writing as they provided evidence supporting their claims. This study provides important implications on pre-service science teacher education.