• Child Health Nursing Research
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• 제13권1호
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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.

• 동아시아경상학회지
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• 제10권2호
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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.

• 한국산업정보학회논문지
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• 제11권5호
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• pp.170-180
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• 2006

국제물류 시장의 증가와 다양한 고객의 요구는 복합운송의 중요성을 강조하게 되었고, 그 시장이 지속적으로 성장하고 있다. 이러한 무한 경쟁시장에서 경쟁우위를 확보하기 위해서는 다양한 고객의 특성을 고려하여 개별 고객에게 만족을 줄 수 있는 서비스가 중요하게 고려되고 있다. 이에 본 연구에서는 운송 서비스 측면에서 고객 만족을 더욱 향상시키기 위한 복합운송 선정 요인에 관한 연구를 하였다. 본 연구에서는 먼저 국제복합운송경로 선정에 영향을 미치는 주요 서비스 요인들을 문헌조사와 실증조사를 통해 파악하고, 이 요인을 이용하여 개별 고객이 복합운송경로를 평가할 수 있는 객관적인 자료를 제시하였다. 본 연구를 통하여 개별 고객은 객관적인 평가 자료를 획득할 수 있으며, 복합운송업체는 고객이 어떤 서비스를 중요하게 고려하는지를 파악할 수 있다.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 추계학술대회 논문집(제1권)
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• pp.251-259
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• 2006

국제물류 시장의 증가와 다양한 고객의 요구는 복합운송의 중요성을 강조하게 되었고, 그 시장이 지속적으로 성장하고 있다. 이러한 무한 경쟁시장에서 경쟁우위를 확보하기 위해서는 다양한 고객의 특성을 고려하여 개별 고객에서 만족을 줄 수 있는 서비스가 중요하게 고려되고 있다. 이에 본 연구에서는 운송 서비스 측면에서 고객 만족을 더욱 향상시키기 위한 복합운송 선정 요인에 관한 연구를 하였다. 본 연구에서는 먼저 국제복합운송경로 선정에 영향을 미치는 주요 서비스 요인들을 문헌조사와 실증조사를 통해 파악하고, 이 요인을 이용하여 개별 고객이 복합운송경로를 평가할 수 있는 객관적인 지료를 제시하였다. 본 연구를 통하여 개별 고객은 객관적인 평가 자료를 획득할 수 있으며, 복합운송업체는 고객이 어떤 서비스를 중요하게 고려하는지를 파악할 수 있다.

• Asia pacific journal of information systems
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• 제33권3호
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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.

• 방송공학회논문지
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• 제20권1호
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• pp.140-152
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• 2015

최근 사용자와 컴퓨터간의 양방향 상호작용을 가능하게 하는 HCI(Human Computer Interaction) 연구를 위해 인간의 의사소통 체계와 유사한 인터페이스 기술들이 개발되고 있다. 이러한 인간과의 의사소통 과정에서 사용되는 커뮤니케이션 채널을 모달리티라고 부르며, 다양한 단말기 및 서비스 환경에 따라 최적의 사용자 인터페이스를 제공하기 위해서 두 개 이상의 모달리티를 활용하는 멀티모달 인터페이스가 활발히 연구되고 있다. 하지만, 멀티모달 인터페이스를 사용하기에는 각각의 모달리티가 갖는 정보 형식이 서로 상이하기 때문에 상호 연동이 어려우며 상호 보완적인 성능을 발휘하는데 한계가 있다. 이에 따라 본 논문은 W3C(World Wide Web Consortium)의 EMMA(Extensible Multimodal Annotation Markup language)와 MMI(Multimodal Interaction Framework)표준에 기반하여 복수의 모달리티를 상호연동할 수 있는 멀티모달 커뮤니케이터를 제안한다. 멀티모달 커뮤니케이터는 W3C 표준에 포함된 MC(Modality Component), IM(Interaction Manager), PC(Presentation Component)로 구성되며 국제 표준에 기반하여 설계하였기 때문에 다양한 모달리티의 수용 및 확장이 용이하다. 실험에서는 시선 추적과 동작 인식 모달리티를 이용하여 지도 탐색 시나리오에 멀티모달 커뮤니케이터를 적용한 사례를 제시한다.

• ETRI Journal
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• 제24권1호
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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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• 제21권1호
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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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• 제45권1호
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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.

• 전자통신동향분석
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• 제37권1호
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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.