• Journal of Information Processing Systems
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• v.15 no.2
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• pp.305-319
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• 2019

The prediction of the sum of container is very important in the field of container transport. Many influencing factors can affect the prediction results. These factors are usually composed of many variables, whose composition is often very complex. In this paper, we use gray relational analysis to set up a proper forecast index system for the prediction of the sum of containers in foreign trade. To address the issue of the low accuracy of the traditional prediction models and the problem of the difficulty of fully considering all the factors and other issues, this paper puts forward a prediction model which is combined with a back-propagation (BP) neural networks and the support vector machine (SVM). First, it gives the prediction with the data normalized by the BP neural network and generates a preliminary forecast data. Second, it employs SVM for the residual correction calculation for the results based on the preliminary data. The results of practical examples show that the overall relative error of the combined prediction model is no more than 1.5%, which is less than the relative error of the single prediction models. It is hoped that the research can provide a useful reference for the prediction of the sum of container and related studies.

• Food Science and Preservation
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• v.20 no.3
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• pp.289-295
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• 2013

The capacity of a pressure fan can be designed based on the air flow resistance of containers packed with fruits and vegetables in a pressure cooling system. This study was conducted to develop an air flow resistance model that was dependent on changes in the air flow rate and the method of stacking containers. The air flow resistance of a container packed with uniformly shaped balls was 1.5 times greater than the sum of the air flow resistance of a vacant container and that of a wire net container packed with only balls. In addition, the air flow resistance increased exponentially as the width of the stacks increased; however, the air flow resistance did not increase greatly as the length and height of the stacks increased, which indicates that the air flow resistance is primarily influenced by the width of the stack in the air flow direction. The air flow resistance in two lines of stacking was up to 17% less than that of the width of the stack. It was also possible to determine the air flow resistance using a function of the air flow resistance through a single container and develop a prediction model. A prediction model of air flow resistance that is dependent on the stacking method and the air flow resistance of a single container was developed.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.1094-1095
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• 2023

우리나라는 지리적 여건 상 대외무역에 대한 의존도가 높기 때문에, 해상운송에서의 물동량을 예측하여 항만시설을 개발하는 것이 매우 중요하다. 한편 우리나라 컨테이너 운송의 75%는 부산항을 통해 운송되고 있기 때문에 경기 회복을 위해서는 부산항의 경쟁력 강화가 급선무이다. [1] 물동량은 경제적 수입 뿐만 아니라, 지속가능성을 예측하는 측면에서도 가치가 있다. 본 연구에서는 물동량, 경제지수, 기후정보 등 다양한 입력변수와 LSTM 모델을 이용하여 보다 정확한 부산항 컨테이너 물동량 딥러닝 예측모델을 구현하였다.