• Child Health Nursing Research
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• v.13 no.1
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• pp.33-42
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• 2007

Purpose: This study was conducted to investigate the effects of multimodal sensory stimulation on growth and mother-infant interaction in infants with low birth weight (LBW). Method: A non-equivalent control group time-series study design was used. The participants were 38 LBW infants and their mothers (19 in the intervention group and 19 in the control group). The data were collected from September 1, 2003 to March 31, 2004. For the mothers in the intervention group, this researcher instructed mothers in the multimodal sensory stimulation therapy, in turn the mothers used these techniques on their infants once a day during the 4-week research period. The researcher measured weight, length, and head circumference of the LBW infants once a week for 4 weeks and made a film of the mother playing with the infant for 5 minutes in the last week of the research period. Results: Compared to the control group, LBW infants in the intervention group showed significant increases in weekly weight gain (F=3.82, p=.012) and had significantly higher scores for mother-infant interaction (t=3.93, p>.000). Conclusion: The results suggest that multimodal sensory stimulation therapy can be used to increase the growth of LBW infants and improve mother-infant interaction.

• East Asian Journal of Business Economics (EAJBE)
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• v.10 no.2
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• pp.55-69
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• 2022

Purpose - This study is a study to investigate the problem of the selection of means of transportation in international logistics by studying the basics of logistics activities, selection factors of transportation methods, and multimodal transportation. Research design, data, methodology - This study is composed of 5 chapters through literature study. Chapter 1 describes the functions and transportation system of international logistics, Chapter 2 selects transportation, Chapter 3 deals with maritime transportation and multimodal transportation, Chapter 4 describes multimodal transportation in terms of customer service, Chapter 5 addresses the implications and conclusions. Results - When looking at the problem of selecting a means of transportation, it is important that the parties involved in the transportation choose which means of transportation for their convenience and profit during the transportation process. Here, there will be factors to consider, including transportation cost, when selecting a means of transportation, and each means of transportation may have characteristics or advantages and disadvantages. Considering all these points, the adoption of multimodal transportation from a customer service point of view may be the answer. Conclusions - This study pays attention to the academic understanding related to the selection of means of transportation and to how usefully this thesis can be used in the selection of transportation related persons, especially shippers, from a practical level.

• Journal of Korea Society of Industrial Information Systems
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• v.11 no.5
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• pp.170-180
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• 2006

The increase in international logistics market and various customer demands emphasize the importance of Multimodal Transportation, and that market is continuously keeping growing. In order to ensure competitive superiority in a market of such infinite competition, service that can satisfy each individual customer by considering various customer characteristics, has become an issue. Thus, through the aspect of service, in order to improve customer satisfaction, growing factors of Multimodal Transportation Route was studied in this research. For this research, first of all main service factors that affect the growth of Multimodal Transportation were seized by literature survey and positive research. Then, by using these factors a methodology that enables individual customers to assess Multimodal Transportation Route was studied. Through this research, individual customers can acquire objective assessment data and Multimodal Transportation companies can seize what factors are considered as important by their customers.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.251-259
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• 2006

The increase in international logistics market and various customer demands emphasize the importance of Multimodal Transportation, and that market is continuously keep growing. In order to ensure competitive superiority in a market of such infinite competition, service that can satisfy each individual customer by considering various customer characteristics, has become an issue. Thus, through the aspect of service, in order to improve customer satisfaction, growing factors of Multimodal Transportation Route on was studied in this research. For this research, first of all main service factors that affect the growth of Multimodal Transportation were seized by literature survey and positive research. The, by using these factors a methodology that enables individual customers to assess Multimodal Transportation Route was studied. Through this research, individual customers can acquire objective assessment data and Multimodal Transportation companies can seize what factors are considered as important by their customers.

• 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.

• Journal of Broadcast Engineering
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• v.20 no.1
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• pp.140-152
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• 2015

HCI(Human Computer Interaction) enables the interaction between people and computers by using a human-familiar interface called as Modality. Recently, to provide an optimal interface according to various devices and service environment, an advanced HCI method using multiple modalities is intensively studied. However, the multimodal interface has difficulties that modalities have different data formats and are hard to be cooperated efficiently. To solve this problem, a multimodal communicator is introduced, which is based on EMMA(Extensible Multimodal Annotation Markup language) and MMI(Multimodal Interaction Framework) of W3C(World Wide Web Consortium) standards. This standard based framework consisting of modality component, interaction manager, and presentation component makes multiple modalities interoperable and provides a wide expansion capability for other modalities. Experimental results show that the multimodal communicator is facilitated by using multiple modalities of eye tracking and gesture recognition for a map browsing scenario.

• 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 information and communication convergence engineering
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• v.21 no.1
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• pp.75-81
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• 2023

Video platforms, including YouTube, have a structure in which the number of video views is directly related to the publisher's profits. Therefore, video publishers induce viewers by using provocative titles and thumbnails to garner more views. The conventional technique used to limit such harmful videos has low detection accuracy and relies on follow-up measures based on user reports. To address these problems, this study proposes a technique to improve the accuracy of filtering harmful media using thumbnails, titles, and audio data from videos. This study analyzed these three pieces of multimodal information; if the number of harmful determinations was greater than the set threshold, the video was deemed to be harmful, and its upload was restricted. The experimental results showed that the proposed multimodal information extraction technique used for harmfulvideo filtering achieved a 9% better performance than YouTube's Restricted Mode with regard to detection accuracy and a 41% better performance than the YouTube automation system.

• ETRI Journal
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• v.45 no.1
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• pp.105-118
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• 2023

We tested the feasibility of automated discrimination of patients with panic disorder (PD) from healthy controls (HCs) based on multimodal physiological responses using machine learning. Electrocardiogram (ECG), electrodermal activity (EDA), respiration (RESP), and peripheral temperature (PT) of the participants were measured during three experimental phases: rest, stress, and recovery. Eleven physiological features were extracted from each phase and used as input data. Logistic regression (LoR), k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) algorithms were implemented with nested cross-validation. Linear regression analysis showed that ECG and PT features obtained in the stress and recovery phases were significant predictors of PD. We achieved the highest accuracy (75.61%) with MLP using all 33 features. With the exception of MLP, applying the significant predictors led to a higher accuracy than using 24 ECG features. These results suggest that combining multimodal physiological signals measured during various states of autonomic arousal has the potential to differentiate patients with PD from HCs.

• Electronics and Telecommunications Trends
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• v.37 no.1
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• pp.1-9
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• 2022

With the development of artificial intelligence, many studies have focused on evaluating abnormal situations by using various sensors, as industries try to automate some of the surveillance and security tasks traditionally performed by humans. In particular, mobile robots using multimodal sensors are being used for pilot operations aimed at helping security robots cope with various outdoor situations. Multiagent systems, which combine fixed and mobile systems, can provide more efficient coverage (than that provided by other systems), but network bottlenecks resulting from increased data processing and communication are encountered. In this report, we will examine recent trends in object recognition and abnormal-situation determination in various changing outdoor security robot environments, and describe an outdoor security robot platform that operates as a multiagent equipped with a multimodal sensor.