• Journal of Biosystems Engineering
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• v.39 no.3
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• pp.215-226
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• 2014

Recently, spectroscopy has emerged as a potential tool for quality evaluation of numerous food and agricultural products because it provides information regarding both spectral distribution and image features of the sample (i.e., hyperspectral imaging). Spectroscopic techniques reveal hidden information regarding the sample and do so in a non-destructive manner. This review describes the various approaches of spectroscopic modalities, especially hyperspectroscopy and vibrational spectroscopies (i.e., Raman spectroscopy and Fourier transform near infrared spectroscopy) combined with chemometrics for the non-destructive assessment of contaminations and defects in agro-food products.

• The Journal of the Acoustical Society of Korea
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• v.28 no.8
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• pp.832-842
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• 2009

Automatic speech recognition (ASR) has been successfully applied to many real human computer interaction (HCI) applications; however, its performance tends to be significantly decreased under noisy environments. The invention of audio visual speech recognition (AVSR) using an acoustic signal and lip motion has recently attracted more attention due to its noise-robustness characteristic. In this paper, we describe our novel integration scheme for AVSR based on a late integration approach. Firstly, we introduce the robust reliability measurement for audio and visual modalities using model based information and signal based information. The model based sources measure the confusability of vocabulary while the signal is used to estimate the noise level. Secondly, the output probabilities of audio and visual speech recognizers are normalized respectively before applying the final integration step using normalized output space and estimated weights. We evaluate the performance of our proposed method via Korean isolated word recognition system. The experimental results demonstrate the effectiveness and feasibility of our proposed system compared to the conventional systems.

• Journal of radiological science and technology
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• v.39 no.1
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• pp.13-17
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• 2016

In this study, standard model of medical radiation dosage quality control system will be suggested and the useful of this system in clinical field will be reviewed. Radiation dosage information of modalities are gathered from digital imaging and communications in medicine(DICOM) standard data(such as DICOM dose SR and DICOM header) and stored in database. One CT scan, two digital radiography modalities and two mammography modalities in one health promotion center in Seoul are used to derive clinical data for one month. After 1 months research with 703 CT scans, the study shows CT $357.9mGy{\cdot}cm$ in abdomen and pelvic CT, $572.4mGy{\cdot}cm$ in brain without CT, $55.9mGy{\cdot}cm$ in calcium score/heart CT, screening CT at $54mGy{\cdot}cm$ in chest screening CT(low dose screening CT scan), $284.99mGy{\cdot}cm$ in C-spine CT and $341.85mGy{\cdot}cm$ in L-spine CT as health promotion center reference level of each exam. And with 1955 digital radiography cases, it shows $274.0mGy{\cdot}cm2$ and for mammography 6.09 mGy is shown based on 536 cases. The use of medical radiation shall comply with the principles of justification and optimization. This quality management of medical radiation exposure must be performed in order to follow the principle. And the procedure to reduce the radiation exposure of patients and staff can be achieved through this. The results of this study can be applied as a useful tool to perform the quality control of medical radiation exposure.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.9
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• pp.4289-4294
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• 2012

While the incidence of breast cancer (BC) has been relatively low in Asian countries, it has been rising rapidly in Taiwan. Within the last decade, it has replaced cervical cancer as the most diagnosed cancer site for women. Nevertheless, there is a paucity of studies reporting the attitudes and practices of breast cancer screening among Chinese women. The aim of this study is to assess Taiwanese women's knowledge of and attitudes toward BC screening and to identify potential factors that may influence screening behavior. The study population consisted of a sample of 434 Taiwanese women aged 40 and older. Despite access to universal health care for Taiwanese women and the fact that a majority of the women had heard of the breast cancer screening (mammogram, clinical breast exams, etc.), the actual utilization of these screening modalities was relatively low. In the current study, the majority of women had never had mammograms or ultrasound in the past 5 years. The number one most reported barriers were "no time," "forgetfulness," "too cumbersome," and "laziness," followed by the perception of no need to get screened. In addition, the results revealed several areas of misconceptions or incorrect information perceived by study participants. Based on the results from the regression analysis, significant predictors of obtaining repeated screening modalities included age, coverage for screening, barriers, self-efficacy, intention, family/friends diagnosed with breast cancer. The findings from the current study provide the potential to build evidence-based programs to effectively plan and implement policies in order to raise awareness in breast cancer and promote BC screening in order to optimize health outcomes for women affected by this disease.

• Journal of Internet Computing and Services
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• v.13 no.5
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• pp.9-19
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• 2012

Virtual human used as HCI in digital contents expresses his various emotions across modalities like facial expression and body posture. However, few studies considered combinations of such nonverbal multimodal in emotion perception. Computational engine models have to consider how a combination of nonverbal modal like facial expression and body posture will be perceived by users to implement emotional virtual human, This paper proposes the impacts of nonverbal multimodal in design of emotion expressed virtual human. First, the relative impacts are analysed between different modals by exploring emotion recognition of modalities for virtual human. Then, experiment evaluates the contribution of the facial and postural congruent expressions to recognize basic emotion categories, as well as the valence and activation dimensions. Measurements are carried out to the impact of incongruent expressions of multimodal on the recognition of superposed emotions which are known to be frequent in everyday life. Experimental results show that the congruence of facial and postural expression of virtual human facilitates perception of emotion categories and categorical recognition is influenced by the facial expression modality, furthermore, postural modality are preferred to establish a judgement about level of activation dimension. These results will be used to implementation of animation engine system and behavior syncronization for emotion expressed virtual human.

• Smart Media Journal
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• v.12 no.9
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• pp.81-88
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• 2023

Recently, communication through online is increasing due to the spread of non-face-to-face services due to COVID-19. In non-face-to-face situations, the other person's opinions and emotions are recognized through modalities such as text, speech, and images. Currently, research on multimodal emotion recognition that combines various modalities is actively underway. Among them, emotion recognition using speech data is attracting attention as a means of understanding emotions through sound and language information, but most of the time, emotions are recognized using a single speech feature value. However, because a variety of emotions exist in a complex manner in a conversation, a method for recognizing multiple emotions is needed. Therefore, in this paper, we propose a multi-emotion regression model that extracts feature vectors after preprocessing speech data to recognize complex, inherent emotions and takes into account the passage of time.

• The Journal of the Acoustical Society of Korea
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• v.18 no.4
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• pp.31-37
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• 1999

A bimodal speech recognition using visual and audio information has been proposed and researched to improve the performance of ASR(Automatic Speech Recognition) system in noisy environments. The integration method of two modalities can be usually classified into an early integration and a late integration. The early integration method includes a method using a fixed weight of lip parameters and a method using a variable weight according to speech SNR information. The 4 late integration methods are a method using audio and visual information independently, a method using speech optimal path, a method using lip optimal path and a way using speech SNR information. Among these 6 methods, the method using the fixed weight of lip parameter showed a better recognition rate.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.249-252
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• 2000

An experimental multimodal system combining natural input modes such as speech, lip movement, and gaze is proposed in this paper. It benefits from novel human-compute. interaction (HCI) modalities and from multimodal integration for tackling the problem of the HCI bottleneck. This system allows the user to select menu items on the screen by employing speech recognition, lip reading, and gaze tracking components in parallel. Face tracking is a supplementary component to gaze tracking and lip movement analysis. These key components are reviewed and preliminary results are shown with multimodal integration and user testing on the prototype system. It is noteworthy that the system equipped with gaze tracking and lip reading is very effective in noisy environment, where the speech recognition rate is low, moreover, not stable. Our long term interest is to build a user interface embedded in a commercial car navigation system (CNS).

• 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.

• Asia pacific journal of information systems
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• v.33 no.3
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• pp.737-767
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• 2023

With the availability of big customer data and advances in machine learning techniques, the prediction of customer behavior at the session-level has attracted considerable attention from marketing practitioners and scholars. This study aims to predict customer purchase conversion at the session-level by employing customer profile, transaction, and clickstream data. For this purpose, we develop a multimodal deep learning fusion model with dynamic and static features (i.e., DS-fusion). Specifically, we base page views within focal visist and recency, frequency, monetary value, and clumpiness (RFMC) for dynamic and static features, respectively, to comprehensively capture customer characteristics for buying behaviors. Our model with deep learning architectures combines these features for conversion prediction. We validate the proposed model using real-world e-commerce data. The experimental results reveal that our model outperforms unimodal classifiers with each feature and the classical machine learning models with dynamic and static features, including random forest and logistic regression. In this regard, this study sheds light on the promise of the machine learning approach with the complementary method for different modalities in predicting customer behaviors.