• 해양환경안전학회지
• /
• 제21권3호
• /
• pp.253-258
• /
• 2015

The purpose of this study is to find suitable probability distribution function of complex distribution data like multimodal. Normal distribution is broadly used to assume probability distribution function. However, complex distribution data like multimodal are very hard to be estimated by using normal distribution function only, and there might be errors when other distribution functions including normal distribution function are used. In this study, we experimented to find fit probability distribution function in multimodal area, by using AIS(Automatic Identification System) observation data gathered in Mokpo port for a year of 2013. By using chi-squared statistic, gaussian mixture model(GMM) is the fittest model rather than other distribution functions, such as extreme value, generalized extreme value, logistic, and normal distribution. GMM was found to the fit model regard to multimodal data of maritime traffic flow distribution. Probability density function for collision probability and traffic flow distribution will be calculated much precisely in the future.

• 한국항만경제학회지
• /
• 제22권1호
• /
• pp.175-198
• /
• 2006

This paper presents a network capacity model that can be used as an analytical tool for strategic planning and resource allocation for multimodal transportation systems. In the context of freight transportation, the multimodal network capacity problem (MNCP) is formulated as a mathematical model of nonlinear bi-level optimization problem. Given network configuration and freight demand for multiple origin-destination pairs, the MNCP model is designed to determine the maximum flow that the network can accommodate. To solve the MNCP, a heuristic solution algorithm is developed on the basis of a linear approximation method. A hypothetical exercise shows that the MNCP model and solution algorithm can be successfully implemented and applied to not only estimate the capacity of multimodal network, but also to identify the capacity gaps over all individual facilities in the network, including intermodal facilities. Transportation agencies and planners would benefit from the MNCP model in identifying investment priorities and thus developing sustainable transportation systems in a manner that considers all feasible modes as well as low-cost capacity improvements.

• 한국항해항만학회:학술대회논문집
• /
• 한국항해항만학회 2004년도 춘계학술대회 논문집
• /
• pp.421-426
• /
• 2004

다국적기업의 증가는 물류환경을 국제복합운송체계로 이끌고 있다. 기반시설이 잘 갖추어진 유럽의 경우에는 제2의 실크로드를 개발하기위한 노력이 끊임없이 행해지고 있다. 본 논문에서는 국제복합운송의 중요성에 대해 설명하고, 유럽 복합운송 체계에 대한 분석을 통해 국내 및 동북아 복합운송체계를 제안한다. 이를 위해 유럽 복합운송 사례를 분석하고 TAR의 북부 노선을 통해 한반도 남단을 기점으로 하는 노선의 경제적 효과와 이를 철도와 항만 및 공항으로 연결하는 복합운송 모델을 개발한다. 실질적으로 본 연구의 실효성을 증명할 수치적 근거가 부족하나, 본 논문에서는 기존에 제시되지 않았던 구체적인 복합운송 모델을 개발 및 제안함으로써, ESCAP에서 수행하게 될 시범 컨테이너 전용열차 운행 프로젝트를 통해 노선별 거리, 시간, 비용, 세금, 통관절차, 환적시설 등의 정보를 차후 경제성평가 연구에 활용할 수 있을 것으로 기대한다.

• 한국시스템다이내믹스연구
• /
• 제11권1호
• /
• pp.59-83
• /
• 2010

The Korean Government decided to reduce 30% of carbon emissions as of 2020, tightening regulations to reduce greenhouse gas in the international society. Therefore it will burden Korean logistics industry that overland trucking freight covers 70~80% of all, to lower emissions. As known, rail and coast(feeder) transport systems can be substituted for road transport but there are many problems to solve in Korean multimodal (intermodal) transport system such as time, cost, etc. Because of this, multimodal transport system should be improved systematically. For the reason, it aims to study a conceptual model with Thinking Process of TOC(theory of constraints) and System Dynamics to help improve the existing multimodal transport system for green logistics.

• Journal of Information Processing Systems
• /
• 제16권6호
• /
• pp.1250-1260
• /
• 2020

This study proposes a novel deep neural network model that can accurately detect objects and their relationships in an image and represent them as a scene graph. The proposed model utilizes several multimodal features, including linguistic features and visual context features, to accurately detect objects and relationships. In addition, in the proposed model, context features are embedded using graph neural networks to depict the dependencies between two related objects in the context feature vector. This study demonstrates the effectiveness of the proposed model through comparative experiments using the Visual Genome benchmark dataset.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2000년도 하계종합학술대회 논문집(4)
• /
• pp.85-87
• /
• 2000

In pattern classification, the Bhattacharyya distance has been used as a class separability measure and provides useful information for feature selection and extraction. In this paper, we propose a method to predict the classification error for multimodal data based on the Bhattacharyya distance. In our approach, we first approximate the pdf of multimodal distribution with a Gaussian mixture model and find the bhattacharyya distance and classification error. Exprimental results showed that there is a strong relationship between the Bhattacharyya distance and the classification error for multimodal data.

• 정보처리학회논문지:소프트웨어 및 데이터공학
• /
• 제12권12호
• /
• pp.525-534
• /
• 2023

가스 누출 감지 시스템은 가스의 폭발성과 독성으로 인한 인명 피해를 최소화할 핵심적인 장치이다. 누출 감지 시스템은 대부분 단일 센서를 활용한 방식으로, 가스 센서나 열화상 카메라를 통한 검출 방식으로 진행되고 있다. 이러한 단일 센서 활용의 가스 누출감지 시스템 성능을 고도화하기 위하여, 본 연구에서는 가스 센서와 열화상 이미지 데이터에 멀티모달형 딥러닝을 적용한 연구를 소개한다. 멀티모달 공인 데이터셋인 MultimodalGasData를 통해 기존 논문과의 성능을 비교하였고, 가스 센서와 열화상 카메라의 단일모달 모델을 기반하여 네 가지 멀티모달 모델을 설계 및 학습하였다. 이를 통해 가스 센서와 열화상 카메라는 각각 1D CNN, GasNet 모델이 96.3%와 96.4%의 가장 높은 성능을 보였다. 앞선 두 단일모달 모델을 기반한 Early Fusion 형식의 멀티모달 모델 성능은 99.3%로 가장 높았으며, 또한 기존 논문의 멀티모달 모델 대비 3.3% 높았다. 본 연구의 높은 신뢰성을 갖춘 가스 누출 감지 시스템을 통해 가스 누출로 인한 추가적인 피해가 최소화되길 기대한다.

• 대한임베디드공학회논문지
• /
• 제18권6호
• /
• pp.285-292
• /
• 2023

With the wide spread of smart farms and the advancements in IoT technology, it is easy to obtain additional data in addition to crop images. Consequently, deep learning-based crop disease diagnosis research utilizing multimodal data has become important. This study proposes a crop disease diagnosis method using multimodal supervised contrastive learning by expanding upon the multimodal self-supervised learning. RandAugment method was used to augment crop image and time series of environment data. These augmented data passed through encoder and projection head for each modality, yielding low-dimensional features. Subsequently, the proposed multimodal supervised contrastive loss helped features from the same class get closer while pushing apart those from different classes. Following this, the pretrained model was fine-tuned for crop disease diagnosis. The visualization of t-SNE result and comparative assessments of crop disease diagnosis performance substantiate that the proposed method has superior performance than multimodal self-supervised learning.

• 아동학회지
• /
• 제34권4호
• /
• pp.163-177
• /
• 2013

Digital technology has advanced rapidly and it is anticipated that multimodal ways of meaning-making will become increasingly important. Consequently, teaching multimodal literacies is becoming a major issue in education. This study focuses on the use of picture diaries as a means of teaching multimodal literacies. Picture diaries are one of the basic and unique multimodal texts used in lower elementary level classes in Korea. A further advantage is that it is a promising text model which can be taught in unplugged ways. In order to explore the educational implications of using such picture diaries, this study sought to analyze the ways in which twenty four $1_{st}$ graders in an elementary school constructed meaning with written language and pictures in composing picture diaries. 251 picture diaries composed during several months of their $1_{st}$ grade period were analyzed based on the constant comparative method. The results indicated that the students utilized both written language and pictures in diverse and creative ways to provide their audience with more comprehensive meaning. These results indicate that teachers need to consider their students as active multimodal meaning-makers and provide their students with more opportunities to practice multimodal meaning-making and share their experiences.

• 한국항해항만학회지
• /
• 제39권5호
• /
• pp.409-415
• /
• 2015

According to the globalization of world economy on distribution and sales, logistics and transportation parts are playing an important role. Especially, they have to decide what is the key factor of route choice model and how to choose the right transport route in multimodal transport system. By considering the key factors in rote choice model for freight forwarders between Mongolia and Korea, this paper propose 4 main factors: Cost, Delivery time, Freight and Logistics service with 13 sub factors. The importance of factors is surveyed base on AHP through interview with freight forwarders. In results, the empirical insights about current status of Mongolian forwarders are provided with different factors between transportation modes. Expecially, the Time factor is a role factor to choose transport route for air transportation forwarders.