• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.161-166
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• 2018

In this paper, we propose a method for the random selection of pooling operations for the regularization and reduction of cross validation in convolutional neural networks. The pooling operation in convolutional neural networks is used to reduce the size of the feature map and for its shift invariant properties. In the existing pooling method, one pooling operation is applied in each pooling layer. Because this method fixes the convolution network, the network suffers from overfitting, which means that it excessively fits the models to the training samples. In addition, to find the best combination of pooling operations to maximize the performance, cross validation must be performed. To solve these problems, we introduce the probability concept into the pooling layers. The proposed method does not select one pooling operation in each pooling layer. Instead, we randomly select one pooling operation among multiple pooling operations in each pooling region during training, and for testing purposes, we use probabilistic weighting to produce the expected output. The proposed method can be seen as a technique in which many networks are approximately averaged using a different pooling operation in each pooling region. Therefore, this method avoids the overfitting problem, as well as reducing the amount of cross validation. The experimental results show that the proposed method can achieve better generalization performance and reduce the need for cross validation.

• Journal of Navigation and Port Research
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• v.32 no.5
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• pp.369-377
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• 2008

Interest in high productivity and streamlined operation is growing amid a heated competition between ports and mega size vessel oriented operation. It becomes clear that terminals that operate YT(Yard Tractor) for transportation in container yard have less efficiency in operation and cost comparing terminals functioning AGV(Automated Guided Vehicle) system. To offset disadvantages arising out of YT operations, continual efforts (e,g YT pooling operation) were taken place for the last decade, but the result has not been fruitful; YT pooling operation still has a problem in that it cannot read individual YT's actual location. This study suggests 'YT dynamic operation model'- a practical application of RTLS(Real Time Location System) technology which will solve YT pooling operation problems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.9
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• pp.1158-1165
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• 2021

This paper proposes a CNN accelerator which is optimized Pooling layer operation incorporated in Multiplication And Accumulation(MAC) to reduce the memory size. For optimizing memory and data path circuit, the quantized 8bit integer weights are used instead of 32bit floating-point weights for pre-training of MNIST data set. To reduce chip area, the proposed CNN model is reduced by a convolutional layer, a 4*4 Max Pooling, and two fully connected layers. And all the operations use specific MAC with approximation adders and multipliers. 94% of internal memory size reduction is achieved by simultaneously performing the convolution and the pooling operation in the proposed architecture. The proposed accelerator chip is designed by using TSMC65nmGP CMOS process. That has about half size of our previous paper, 0.8*0.9 = 0.72mm². The presented CNN accelerator chip achieves 94% accuracy and 77us inference time per an MNIST image.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.7
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• pp.2603-2609
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• 2010

Currently at port, various trials about the improvement of operating method for raising operation efficiency of transferring equipment are made, but if the delay is occurred to quay crane due to individual team method in YT operation, the problem which even arranged YT should stop the work. Therefore, this study installed wireless location determining device within yard and measured the location of YT with real-time. Also, in order to raise the location determination effect, we converted operating method into pooling operation method and arranged YT. Through this study, we can trace the movement of YT with real-time and the work control method which anlayzes and evaluates congestion of yard objectively is prepared.

• Journal of Navigation and Port Research
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• v.33 no.6
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• pp.401-407
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• 2009

The improvement in the terminal productivity plays a key role for container terminals to be more competitive. The productivity of yard tractors(YT) is one of the most important factor accelerating the terminal productivity. Thus, YT pooling system is newly introduced in container terminals for increasing the YT productivity. Recently, the terminals in Korea tend to adopt YT pooling system. This paper proposes the important decision factors for YT pooling work space and several types of formulations according to states of container terminals.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.23 no.1
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• pp.120-125
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• 2015

Passengers and Airlines wish neither delay nor cancellation due to aircraft defects. However, about 1 delay or cancellation case occurs out of 100 departures worldwide whereas 1 quarter case does in Korean domestic industry. Independent LCC carriers in Korea have almost double case. Most cases are recovered by replacing aircraft components. Airlines have prepared the spare components based on the reliability data by manufacturers to rectify defects or perform preventive maintenances. The total value for initial spares including engine is 40% of the aircraft price when they operate less than 5 aircraft. The more airlines operate the aircraft, the less the ratio of the investment for spares reflecting the economy of scale. This study intends to suggest how to improve the efficiencies as well as the safety of LCC throughout parts pooling including engines.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.29 no.1
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• pp.377-382
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• 2005

This paper deals with productivity improvement of stevedoring system by pooling opertaions of transport vehicle at automated container terminal. Usually, in traditional container terminals, grouping operations of transport vehicle are applied for container crane because vehicle routing path is simple and vehicle assignment is easy. But this static assignment(SA) operation that arrsign vehicles to container crane ar apron reduces flexibility of vehicles. Therefore, This paper presented 4 dynamic assignment(DA) method to improve efficiency of vehicles. These 4 dynamic assignment method consider present situations of container crane such as sequence(Se), queue time(Qt), productivity(Pr), numeric of vehicle assignment(Nv), numeric of buffer(Nb) at vehicles assignment. At the results, dynamic assignment operation to consider Qt, Nv, Nb is most efficient and by next time, dynamic assignment operation to consider Se is superior more than static assignment operation. but, dynamic assignment operation to consider Pr or Qt of container crane only is inefficient than static assignment operation.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.2
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• pp.175-180
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• 2018

In this paper, a feature extraction and object recognition algorithm using only morphological operations is proposed. The morphological operations used in feature extraction are erosion and dilation, opening and closing combining erosion and dilation, and morphological edge and skeleton detection operation. In the process of recognizing an object based on features, a pooling operation is applied to reduce the dimension. Among various structuring elements, $3{\times}3$ rhombus, $3{\times}3$ square, and $5{\times}5$ circle are arbitrarily selected in morphological operation process. It has confirmed that the proposed algorithm can be applied in object recognition fields through experiments using Internet images.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.10
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• pp.935-943
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• 2017

CNN (Convolution neural network), which is used for image classification and speech recognition among neural networks learning based on positive data, has been continuously developed to have a high performance structure to date. There are many difficulties to utilize in an embedded system with limited resources. Therefore, we use GPU (General-Purpose Computing on Graphics Processing Units), which is used for general-purpose operation of GPU to solve the problem because we use pre-learned weights but there are still limitations. Since CNN performs simple and iterative operations, the computation speed varies greatly depending on the thread allocation and utilization method in the Single Instruction Multiple Thread (SIMT) based GPGPU. To solve this problem, there is a thread that needs to be relaxed when performing Convolution and Pooling operations with threads. The remaining threads have increased the operation speed by using the method used in the following feature maps and kernel calculations.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.6
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• pp.567-572
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• 2019

In this paper, we propose an algorithm to perform convolution, pooling, and ReLU operations in CNN using binary image and binary kernel. It decomposes 256 gray-scale images into 8 bit planes and uses a binary kernel consisting of -1 and 1. The convolution operation of binary image and binary kernel is performed by addition and subtraction. Logically, it is a binary operation algorithm using the XNOR and comparator. ReLU and pooling operations are performed by using XNOR and OR logic operations, respectively. Through the experiments to verify the usefulness of the proposed algorithm, We confirm that the CNN operation can be performed by converting it to binary logic operation. It is an algorithm that can implement deep running even in a system with weak computing power. It can be applied to a variety of embedded systems such as smart phones, intelligent CCTV, IoT system, and autonomous car.