• Proceedings of the Korea Society for Simulation Conference
• /
• 2002.05a
• /
• pp.33-41
• /
• 2002

This paper deals with the development of simulation model for the container terminal consisting of 3 berths, 8 container cranes, 16 yard blocks with each yard cranes and 90 yard trucks in order to evaluate the various operational rules. The proposed operational rules are 3 ship dispatching rules, 3 berth allocation rules, 2 crane allocation rules, 2 yard allocation rules and 2 yard truck allocation rules and 4 performance measures like ship time in the terminal, ship time in the port, the number of ships processed and the number of containers handled are considered. The simulation result are as follows. 1) no difference among 3 ship dispatching rules, 2) berth allocation rules depends on performance measures 3) dynamic crane allocation is better than fixed policy 4) pooling yard allocation is better than short distance yard allocation rules and 5) fixed yard truck allocation by berth is a little better than pooling policy.

• Journal of the Korea Safety Management & Science
• /
• v.10 no.3
• /
• pp.245-251
• /
• 2008

The research proposes three ANOVA(Analysis of Variance) tests using expected mean square(EMS) algorithms in various split-plot designs. The variance tests consist of Never-Pool test, Sometimes-Pool test and Always-Pool test. This paper also presents two EMS algorithms such as standard method and easy method. These algorithms are useful to make a decision rule for pooling. Numerical examples are illustrated for various split-plot designs such as split-plot designs, split-split-plot designs, repetition split-plot designs, and nested designs. Pragmatically, the results are summarized and compared with popular ANOVA spreadsheets and data model equations.

• Journal of Korean Society of Transportation
• /
• v.3 no.1
• /
• pp.58-75
• /
• 1985

On facing the information society, the multi-faceted information utilization and the establishment of synthetic information management system in the overall industry and business administration have raised considerable attention. Thus, this study aims at the improvement of freight transport management through the establishment of pooling of the road freight transport information system as its effective information supporting system. Especially, it describes the freight transport information network, structure, function, and subject of the freight transport information center, as a basic planning design for the freight transport information system. Furthermore, it deals with the identification of the systems design requirements, working process, and S/W & H/W design specifications. Finally, we expect that this study will be contributed for the improvement of the road freight transport business to meet both the increase of freight transport demands due to the continuing economic growth and the social needs for the establishment of transport-order.

• International journal of advanced smart convergence
• /
• v.5 no.3
• /
• pp.1-7
• /
• 2016

In existing Convolutional Neural Network (CNNs) for object recognition task, there are only few efforts known to reduce the noises from the images. Both convolution and pooling layers perform the features extraction without considering the noises of the input image, treating all pixels equally important. In computer vision field, there has been a study to weight a pixel importance. Seam carving resizes an image by sacrificing the least important pixels, leaving only the most important ones. We propose a new way to combine seam carving approach with current existing CNN model for object recognition task. We attempt to remove the noises or the "unimportant" pixels in the image before doing convolution and pooling, in order to get better feature representatives. Our model shows promising result with CIFAR-10 dataset.

• Applied Microscopy
• /
• v.44 no.3
• /
• pp.83-87
• /
• 2014

Electron tomography (ET) is a useful tool to investigate three-dimensional details based on virtual slices of relative thick specimen, and it requires complicated procedures consisted of image acquisition steps and image processing steps with computer program. Although the complicated step, this technique allows us to overcome some limitations of conventional transmission electron microscopy: (1) overlapping of information in the ultrathin section covering from 30 nm to 90 nm when we observe very small structures, (2) fragmentation of the information when we study larger structures over 100 nm. There are remarkable biological findings with ET, especially in the field of neuroscience, although it is not popular yet. Understanding of behavior of synaptic vesicle, active zone, pooling and fusion in the presynaptic terminal have been enhanced thanks to ET. Some sophisticated models of postsynaptic density with ET and immune labeling are introduced recently. In this review, we introduce principles, practical steps of ET and some recent researches in synapse biology.

• Journal of Korea Port Economic Association
• /
• v.18 no.1
• /
• pp.27-41
• /
• 2002

This paper deals with the development of a simulation model for the container terminal, which consists of 3 berths, 8 container cranes, and 16 yard blocks with each yard crane and 90 yard trucks in order to evaluate the various operational rules. The proposed operational rules are 3 ship-dispatching rules, 3 berth allocation rules, 2 crane allocation rules, 2 yard allocation rules, and 2 yard truck allocation rules. These rules are simulated using 4 performance measures, such as ship time in the terminal, ship time in the port, the number of ships processed, and the number of containers handled. The simulation result is as follows: 1) there is no difference among 3 ship-dispatching rules, 2) berth allocation rules depend on performance measures, 3) dynamic crane allocation is better than fixed policy, 4) pooling yard allocation is better than short distance yard allocation rules, and 5) fixed yard truck allocation by berth is a little better than pooling policy.

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

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2019.01a
• /
• pp.77-78
• /
• 2019

게임엔진을 이용한 FPS 디펜스 게임은 유니티3D 엔진을 사용하여 개발 하였으며 1인칭 시점으로 제한시간동안 몰려오는 적군을 막아내며 목표물을 지키는 게임이다. 많은 오브젝트를 관리하기 위해서 오브젝트 풀링을 사용하여 오브젝트가 생성-제거의 반복시 메모리에 부담을 주게되는 것을 씬 시작시 가용할 오브젝트를 불러온 뒤에 필요시에만 사용 하는 방법으로 메모리의 부담을 적게 하였고 플레이 기록을 랭킹으로 하여 사용자 간에 경쟁심을 유발 할 수 있도록 하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.1
• /
• pp.60-79
• /
• 2022

The U-Net architecture-based segmentation models attained remarkable performance in numerous medical image segmentation missions like skin lesion segmentation. Nevertheless, the resolution gradually decreases and the loss of spatial information increases with deeper network. The fusion of adjacent layers is not enough to make up for the lost spatial information, thus resulting in errors of segmentation boundary so as to decline the accuracy of segmentation. To tackle the issue, we propose a new deep learning-based segmentation model. In the decoding stage, the feature channels of each decoding unit are concatenated with all the feature channels of the upper coding unit. Which is done in order to ensure the segmentation effect by integrating spatial and semantic information, and promotes the robustness and generalization of our model by combining the atrous spatial pyramid pooling (ASPP) module and channel attention module (CAM). Extensive experiments on ISIC2016 and ISIC2017 common datasets proved that our model implements well and outperforms compared segmentation models for skin lesion segmentation.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• fall
• /
• pp.113-116
• /
• 2021

일반적으로 딥러닝 기반의 객체 검출(Object Detection)기법은 합성곱 신경망(Convolutional Neural Network, CNN)을 통해 입력된 영상의 특징(Feature)을 추출하여 이를 통해 객체 검출을 수행한다. 최근 자연어 처리 분야에서 획기적인 성능을 보인 트랜스포머(Transformer)가 영상 분류, 객체 검출과 같은 컴퓨터 비전 작업을 수행하는데 있어 경쟁력이 있음이 드러나고 있다. 본 논문에서는 YOLOv4-CSP의 CSP 블록을 개선한 one-stage 방식의 객체 검출 네트워크를 제안한다. 개선된 CSP 블록은 트랜스포머(Transformer)의 멀티 헤드 어텐션(Multi-Head Attention)과 CSP 형태의 공간 피라미드 풀링(Spatial Pyramid Pooling, SPP) 연산을 기반으로 네트워크의 Backbone과 Neck에서의 feature 학습을 돕는다. 본 실험은 MSCOCO test-dev2017 데이터 셋으로 평가하였으며 제안하는 네트워크는 YOLOv4-CSP의 경량화 모델인 YOLOv4s-mish에 대하여 평균 정밀도(Average Precision, AP)기준 2.7% 향상된 검출 정확도를 보인다.