• Journal of information and communication convergence engineering
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• v.16 no.2
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• pp.106-113
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• 2018

Although deep learning methods of convolutional neural networks (CNNs) and long-/short-term memory (LSTM) are widely used for text categorization, they still have certain shortcomings. CNNs require that the text retain some order, that the pooling lengths be identical, and that collateral analysis is impossible; In case of LSTM, it requires the unidirectional operation and the inputs/outputs are very complex. Against these problems, we thus improved these traditional deep learning methods in the following ways: We created collateral CNNs accepting disorder and variable-length pooling, and we removed the input/output gates when creating bidirectional LSTMs. We have used four benchmark datasets for topic and sentiment classification using the new methods that we propose. The best results were obtained by combining LTSM regional embeddings with data convolution. Our method is better than all previous methods (including deep learning methods) in terms of topic and sentiment classification.

• Journal of Navigation and Port Research
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• v.35 no.3
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• pp.235-241
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• 2011

The shuttle that currently connects Busan New Port to the logistics companies in the Hinterland has the following companies: first, resources are consumed redundantly as each logistics company has independent transport vehicles. Second, the companies are not taking advantage of geographical merits of clustered complexes because different vehicles are used each time due to irregular schedules. In this respect, this study had the following purposes to realize these solutions: first, heuristic approach was made for operation scheduling and real-time operating rules to configure the best possible dynamic plan. Second, the reduction of consumption of resources with the shuttle and the efficiency were examined through a simulation of pooling and dual cycling applied to logistics companies' shipping plans.

• Journal of Navigation and Port Research
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• v.33 no.8
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• pp.555-562
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• 2009

At container terminals, a major measurement of productivity can be work-efficiency in quay-side. At the apron, containers are loaded onto the ship and unloaded to apron by Q/C(Quay Crane). For improving the productivity of quay crane, the more efficient Y/T(Yard Tractor) operation method is necessary in container terminals. Between quay-side and yard area, current transferring methods is single-cycling which doesn't start loading unless it finishes unloading. Dual-cycling is a technique that can be used to improve the productivity of quay-side and utility of yard tractor by ship loading and unloading simultaneously. Using the dual-cycling at terminals only necessitates an operational change without purchasing extra equipment. Exactly, Y/T operation method has to be changed the dedicate system to pooling system. This paper presents an efficient ship loading and unloading plan in container terminals, which use the dual-cycling. We propose genetic and tabu search algorithm for this problem.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.1
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• pp.41-49
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• 2023

A convolutional neural network (CNN) is a representative algorithm for implementing artificial neural networks. CNNs have improved on the issues of rapid increase in calculation amount and low object classification rates, which are associated with a conventional multi-layered fully-connected neural network (FNN). However, because of the rapid development of IT devices, the maximum resolution of images captured by current smartphone and tablet cameras has reached 108 million pixels (MP). Specifically, a traditional CNN algorithm requires a significant cost and time to learn and process simple, high-resolution images. Therefore, this study proposes an improved CNN algorithm for implementing an object classification learning model for simple, high-resolution images. The proposed method alters the adjacency matrix value of the pooling layer's max pooling operation for the CNN algorithm to reduce the high-resolution image learning model's creation time. This study implemented a learning model capable of processing 4, 8, and 12 MP high-resolution images for each altered matrix value. The performance evaluation result showed that the creation time of the learning model implemented with the proposed algorithm decreased by 36.26% for 12 MP images. Compared to the conventional model, the proposed learning model's object recognition accuracy and loss rate were less than 1%, which is within the acceptable error range. Practical verification is necessary through future studies by implementing a learning model with more varied image types and a larger amount of image data than those used in this study.

• Journal of Navigation and Port Research
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• v.36 no.1
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• pp.69-74
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• 2012

Recent global supply chain, improving the efficiency of container shipping process is very important. In the overseas shipping of goods, the voyage of super containership is common to overcome amount of increasing cargo. Thus, container terminal managers make an experiment on the double cycle and dual cycle operation, which ship loading and unloading were carried out simultaneously, for maximizing the productivity of quay side. Yard Tractors(YTs) pooling methods also are introduced for increasing the efficiency of assignment of YTs. In this paper, we analyzed the efficiency of dual cycling through comparing existing pooling methods with the modified method for the dual cycling. We developed a simulation model using simulation analysis software, Arena. The result of experiment shown that the more dual cycling don't always increase the gross crane rate(GCR), which means productivity of quay cranes(QCs) per hour.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.9
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• pp.2056-2063
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• 2012

In a port terminal, containers are stored and transshipped by yard tractors and crane vehicles. For operation efficiency of the terminal, location information of these vehicles is an essential factor. However, most of port terminals try to estimate location of these assets using indirect methods such as event tracking of shipping or unshipping containers. Because these kinds of events are rarely occurred, location of the event includes seriously locating error compared to a real location of vehicle. In this paper, we propose a real-time asset tracking system to obtain accurate and reliable location of terminal assets. The proposed system overcomes a location estimation error caused by container stacks which interrupt wireless communication. In order to mitigate uncertainty and increase accuracy of location estimation, we designed hardwares and multi-step locating system to resolve additional preblems. We implemented system components, and installed these at a port environment for evaluation. The result shows superiority of the system that the accuracy is approximately 5.87 meters (CEP).

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.141-147
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• 2021

In this paper, in order to improve the utilization of hyperspectral large-capacity data feature information by reducing complex computations by dimension reduction of neural network inputs in embedded systems, the band selection algorithm is applied in each subset. Among feature extraction and feature selection techniques, the feature selection aim to improve the optimal number of bands suitable for datasets, regardless of wavelength range, and the time and performance, more than others algorithms. Through this experiment, although the time required was reduced by 1/3 to 1/9 times compared to the others band selection technique, meaningful results were improved by more than 4% in terms of performance through the K-neighbor classifier. Although it is difficult to utilize real-time hyperspectral data analysis now, it has confirmed the possibility of improvement.

• Journal of Distribution Science
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• v.13 no.6
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• pp.97-104
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• 2015

Purpose - The distribution of agricultural products is changing due to recent shifts in environmental free trade. Specifically, the competitiveness of domestic agricultural products has weakened as a result of the Korea-China Financial Trade Agreement. Agricultural producers are faced with increasing difficulties and organized production centers are growing in importance daily. To overcome this crisis, agricultural producer organizations are vying for environment-friendly agricultural certifications, Good Agriculture Practices (GAP) and Hazard Analysis and Critical Control Point (HACCP). In particular, as consumer demand for higher safety grows, farmers are increasing their certification rates. Therefore, this certification system is expected to help strengthen the competitiveness of agricultural producer organizations. Research design/data/methodology - Organized production centers are classified by certification. A survey was conducted with 91 organizations using factor analysis and logistic regression analysis for the examination. The factor analysis results are as follows. Raw material procurement, education·specialization, marketing, joint business, organizing ability, business management, effectiveness, certification, and larger organizations were classified as the nine types of factors. These factors affect the organized production centers and are used in the logistic regression analysis. The purpose of such research and analysis is to suggest a direction for future production center policies. Results - The basic statistical results are as follows: analysis of the producer organizations of 91 sites, average number of members per site of 1,624, and average sales of 25,961 million won. Additionally, the average income per farmer is 175 million won, and the pooling system rate is 53.5%. The factor analysis results are as follows. Factor 1 consists of contract cultivation, ongoing shipment, selection subdivision, traceability, and major retailer management. Factor 2 consists of manual cultivation, specialty selection, education program, and R&D. Factor 3 consists of advertising, various dealers, various sales strategies, and a unified sales counter. Factor 4 consists of agricultural materials co-purchase, policy support, co-shipment, and incentives. Factor 5 consists of the co-selection and pooling system. Factor 6 consists of co-branding and operating by the organization's article. Factor 7 consists of the buy-sell ratio and rate of operation of the agriculture promotion center. Factor 8 consists of bargaining power in volume and participation rate of farmer certification. Factor 9 consists of increasing new subscribers. The logistic regression analysis results are as follows. Considering the results by type of certification, the environment-friendly agricultural certification type and the GAP certification type have a (+) influence. GAP and HACCP certification types affecting the education·specialization factor have a (+) influence. Considering the results for each type of certification, the environment-friendly agricultural certification types on the effectiveness factor have (-) influence; the HACCP certification types on the organizing ability and effectiveness factor have a (-) influence. Conclusions - Agricultural producer organizations should develop plans as follows: The organizations need to secure education for agricultural production; increase the pooling system ratio for sustainable organizational development; and, finally, expand the number of agricultural producer organizations.

• Management Science and Financial Engineering
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• v.6 no.2
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• pp.47-67
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• 2000

It is discussed how to assign delivery tasks to automated guided vehicles (AGVs) for multiple container cranes in automated container terminals. The primary goal of dispatching AGVs is to complete all the lading and discharging operations as early as possible, and the secondary goal is to minimize the total travel distance of AGVs. It is assumed that AGVs are not dedicated to a specific container crane but shared among multiple cranes. A mathematical formulation is developed and a heuristic algorithm is suggested to obtain a near optimal solution with a reasonable amount of computational time. The single-cycle and the dual-cycle operations in both the seaside and the landside operations are analyzed. The effects of pooling AGVs for multiple container cranes on the performance of an entire AGV system are also analyze through a numerical experiment.

• Journal of Digital Convergence
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• v.11 no.1
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• pp.211-223
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• 2013

In Korea, the location of yard tractors is figured out in real time by using RFID system in container terminals. However, even though the location recognition of RFID system works fine when transfer crane is in yard operation, there are some problems when container crane is in ship operation. That is because yard tractors come one by one to each transfer crane in an order, but yard tractors come in 4 lanes to the container crane, which makes the system impossible to recognize each yard tractor separately. Therefore, we developed the infrared-ray communication system which can recognize yard tractors accurately in not only in the yard operation of transfer crane but also in the ship operation of container crane in same way in this study. The result in this study showed constant number of recognition, and the range of recognition measures 5.7m in 25m distance. The range of recognition shown in this study is enough to recognize each yard tractor passing under container crane separately.