• The Bulletin of The Korean Astronomical Society
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• v.41 no.2
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• pp.29.4-29.4
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• 2016

The relations between star formation and properties of molecular clouds are studied based on a sample of star forming regions in the Galactic Plane. Sources were selected by having radio recombination lines to provide identification of associated molecular clouds and dense clumps. Radio continuum and mid-infrared emission were used to determine star formation rates, while 13CO and submillimeter dust continuum emission were used to obtain masses of molecular and dense gas, respectively. We test whether total molecular gas or dense gas provides the best predictor of star formation rate. We also test two specific theoretical models, one relying on the molecular mass divided by the free-fall time, the other using the free-fall time divided by the crossing time. Neither is supported by the data. The data are also compared to those from nearby star forming regions and extragalactic data. The star formation "efficiency," defined as star formation rate divided by mass, spreads over a large range when the mass refers to molecular gas; the standard deviation of the log of the efficiency decreases by a factor of three when the mass of relatively dense molecular gas is used rather than the mass of all the molecular gas.

• The Korean Journal of Ceramics
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• v.6 no.1
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• pp.1-5
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• 2000

Particle packing properties are important to develop high technology products in the field of cement and concrete. Two types of particle packing models for aggregates with sand and cement were introduced: the loose and the dense aggregate packing. Aggregate packing models with randomly generated sand and cement particles in the interstices of aggregates fit the Furnas model very well. Different aggregate models show different packing properties with the experimental results. Main reason for the difference with the experimental results is due to sand rearrangement in the loose aggregate packing model and to aggregate relaxation in the dense aggregate packing model. In the experimental situation, aggregates seem to be more disordered and have a relaxed packing structure in the dense packing, and sands seem to have a more rearranged packing structure in the loose packing model.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.171-174
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• 2003

In this paper, we propose the method of stereo images composition using adaptive dense disparity estimation. For the correct composition of stereo image and 3D virtual object, we need correct marker position and depth information. The existing algorithms use position information of markers in stereo images for calculating depth of calibration object. But this depth information may be wrong in case of inaccurate marker tracking. Moreover in occlusion region, we can't know depth of 3D object, so we can't composite stereo images and 3D virtual object. In these reasons, the proposed algorithm uses adaptive dense disparity estimation for calculation of depth. The adaptive dense disparity estimation is the algorithm that use pixel-based disparity estimation and the search range is limited around calibration object.

• Journal of Mechanical Science and Technology
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• v.19 no.4
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• pp.1027-1035
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• 2005

Thermophoresis in dense gases is studied by using a multi-scale approach and Born- Yvon­Green (BYG) equation. The problem of a particle movement in an ambient dense gas under temperature gradient is divided into inter and outer ones. The pressure gradient in the inner region is obtained from the solutions of BYG equation. The velocity profile is derived from the conservation equations and calculated using the pressure gradient, which provides the particle velocity in the outer problem. It is shown that the temperature gradient applied to the quiescent ambient gas induces some pressure gradient and thus flow tangential to the particle surface in the interfacial region. The mechanism that induces the flow may be the dominant source of the thermophretic particle movement in dense gases. It is also shown that the particle velocity has a nonlinear relationship with the applied temperature gradient and decreases with increasing temperature.

• International journal of advanced smart convergence
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• v.9 no.2
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• pp.58-67
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• 2020

In this paper, we propose a data-driven-based beam selection scheme for massive multiple-input and multiple-output (MIMO) systems in ultra-dense networks (UDN), which is capable of addressing the problem of high computational cost of conventional coordinated beamforming approaches. We consider highly dense small-cell scenarios with more small cells than mobile stations, in the millimetre-wave band. The analog beam selection for hybrid beamforming is a key issue in realizing millimetre-wave UDN MIMO systems. To reduce the computation complexity for the analog beam selection, in this paper, two deep neural network models are used. The channel samples, channel gains, and radio frequency beamforming vectors between the access points and mobile stations are collected at the central/cloud unit that is connected to all the small-cell access points, and are used to train the networks. The proposed machine-learning-based scheme provides an approach for the effective implementation of massive MIMO system in UDN environment.

• Journal of Korea Multimedia Society
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• v.21 no.4
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• pp.478-484
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• 2018

The choice of the motion features influences the result of the human action recognition method directly. Many factors often influence the single feature differently, such as appearance of the human body, environment and video camera. So the accuracy of action recognition is restricted. On the bases of studying the representation and recognition of human actions, and giving fully consideration to the advantages and disadvantages of different features, the Dense Trajectories(DT) algorithm is a very classic algorithm in the field of behavior recognition feature extraction, but there are some defects in the use of optical flow images. In this paper, we will use the improved Dense Trajectories(iDT) algorithm to optimize and extract the optical flow features in the movement of human action, then we will combined with Support Vector Machine methods to identify human behavior, and use the image in the KTH database for training and testing.

• Proceedings of the KSME Conference
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• 2003.11a
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• pp.899-904
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• 2003

A new model for dense gas dispersion is formulated within the Lagrangian framework. In several accidental released situations, denser-than-air vapour clouds are formed which exhibit dispersion behavior markedly different from that observed for passive atmospheric pollutants. For relevant prediction of dense gas dispersion, the gravity and entrainment effects need to implemented. The model deals with negative buoyancy which is affected by gravity. Also, the model is subjected to entrainment. The mean downward motion of each particle was accounted for by considering the Langevin equation with buoyancy correction term.

• Journal of the Korea Society of Computer and Information
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• v.24 no.3
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• pp.19-27
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• 2019

This paper proposes a new method using multi-layer overlapped windows to improve the performance of YOLO network which is vulnerable to detect small dense objects. In particular, the proposed method uses the YOLO Network based on the multi-layer overlapped windows to track small dense vehicles that approach from long distances. The method improves the detection performance for location and size of small vehicles. It allows crossing area of two multi-layer overlapped windows to track moving vehicles from a long distance to a short distance. And the YOLO network is optimized so that GPU computation time due to multi-layer overlapped windows should be reduced. The superiority of the proposed algorithm has been proved through various experiments using captured images from road surveillance cameras.

• Journal of Korea Multimedia Society
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• v.22 no.6
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• pp.665-675
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• 2019

In this paper, we compare and analyze the classification performance of deep learning algorithm Convolutional Neural Network(CNN) ac cording to ensemble generation and combining techniques. We used several CNN models(VGG16, VGG19, DenseNet121, DenseNet169, DenseNet201, ResNet18, ResNet34, ResNet50, ResNet101, ResNet152, GoogLeNet) to create 10 ensemble generation combinations and applied 6 combine techniques(average, weighted average, maximum, minimum, median, product) to the optimal combination. Experimental results, DenseNet169-VGG16-GoogLeNet combination in ensemble generation, and the product rule in ensemble combination showed the best performance. Based on this, it was concluded that ensemble in different models of high benchmarking scores is another way to get good results.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.06a
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• pp.272-275
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• 2018

본 논문에서는 기존 신경망 기반의 이미지 압축에 많이 사용되었던 신경망인 ResNet 을 대신하여 더 적은 개수의 파라미터를 사용하여 좋은 성능을 낼 수 있는 신경망 구조인 DenseNet 을 이미지 압축에 사용한다. 이미지 압축을 위해 사용되는 신경망 구조는 일반적으로 오토 인코더 구조인데, 병목 층에서 정보 손실이 상당히 많이 발생한다. 따라서 이미지 압축에서 신경망 내에서의 정보 전달은 상당히 중요하다. 기존의 논문에서는 이를 위해 이전의 정보를 그대로 뒤로 전달해주는 구조인 ResNet 을 사용하여 깊은 층에 대해서도 수렴이 잘 되는 결과를 보여주었다. 그러나 많은 수의 파라미터를 사용하는 단점을 해결하기 위해 본 논문에서는 DenseNet 을 이미지 압축에 사용하였고, 병목 층에서의 정보 손실로 인해 이미지의 고주파수 성분이 사라지는 현상을 해결하기 위해 원래 이미지와 JPEG2000 으로 압축한 이미지와의 차이를 추가 입력으로 넣어주어서 주관적인 화질을 개선하였다.