• International Journal of Computer Science & Network Security
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• 제21권5호
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• pp.256-266
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• 2021

The Convolutional Neural Network (CNN) has recently made potential improvements in face verification applications. In fact, different models based on the CNN have attained commendable progress in the classification rate using a massive amount of data in an uncontrolled environment. However, the enormous computation costs and the considerable use of storage causes a noticeable problem during training. To address these challenges, we focus on relevant data trained within the CNN model by integrating a lifting method for a better tradeoff between the data size and the computational efficiency. Our approach is characterized by the advantage that it does not need any additional space to store the features. Indeed, it makes the model much faster during the training and classification steps. The experimental results on Labeled Faces in the Wild and YouTube Faces datasets confirm that the proposed CNN framework improves performance in terms of precision. Obviously, our model deliberately designs to achieve significant speedup and reduce computational complexity in deep CNNs without any accuracy loss. Compared to the existing architectures, the proposed model achieves competitive results in face recognition tasks

• 천문학회보
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• 제44권1호
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• pp.51.2-51.2
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• 2019

Multi-wavelength observations become very popular in astronomy. Even though there are some correlations among different sensor images, it is not easy to translate from one to the other one. In this study, we apply a deep learning method for image-to-image translation, based on conditional generative adversarial networks (cGANs), to solar images. To examine the validity of the method for scientific data, we consider several different types of pairs: (1) Generation of SDO/EUV images from SDO/HMI magnetograms, (2) Generation of backside magnetograms from STEREO/EUVI images, (3) Generation of EUV & X-ray images from Carrington sunspot drawing, and (4) Generation of solar magnetograms from Ca II images. It is very impressive that AI-generated ones are quite consistent with actual ones. In addition, we apply the convolution neural network to the forecast of solar flares and find that our method is better than the conventional method. Our study also shows that the forecast of solar proton flux profiles using Long and Short Term Memory method is better than the autoregressive method. We will discuss several applications of these methodologies for scientific research.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권10호
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• pp.4214-4230
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• 2020

This paper introduces a pilot allocation scheme for massive MIMO systems based on deep convolutional neural network (CNN) learning. This work is an extension of a prior work on the basic deep learning framework of the pilot assignment problem, the application of which to a high-user density nature is difficult owing to the factorial increase in both input features and output layers. To solve this problem, by adopting the advantages of CNN in learning image data, we design input features that represent users' locations in all the cells as image data with a two-dimensional fixed-size matrix. Furthermore, using a sorting mechanism for applying proper rule, we construct output layers with a linear space complexity according to the number of users. We also develop a theoretical framework for the network capacity model of the massive MIMO systems and apply it to the training process. Finally, we implement the proposed deep CNN-based pilot assignment scheme using a commercial vanilla CNN, which takes into account shift invariant characteristics. Through extensive simulation, we demonstrate that the proposed work realizes about a 98% theoretical upper-bound performance and an elapsed time of 0.842 ms with low complexity in the case of a high-user-density condition.

• 천문학회보
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• 제41권1호
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• pp.75.4-76
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• 2016

DEEP-South (Deep Ecliptic patrol of the Southern sky) is one of the secondary science projects of KMTNet (Korea Microlensing Telescope Network). The objective of this project is twofold, the physical characterization and the discovery of small Solar System bodies, focused on NEOs (Near Earth objects). In order to achieve the goals, we are implementing a software package to detect and report moving objects in the $18k{\times}18k$ mosaic CCD images of KMTNet. In this paper, we present preliminary results of the moving object detection experiments using the prototype MODP (Moving Object Detection Program). We utilize multiple images that are being taken at three KMTNet sites, towards the same target fields (TFs) obtained at different epochs. This prototype package employs existing softwares such as SExtractor (Source-Extracto) and SCAMP (Software for Calibrating Astrometry and Photometry); SExtractor generates catalogs, while SCAMP conducts precision astrometric calibration, then MODP determines if a point source is moving. We evaluated the astrometric accuracy and efficiency of the current version of MODP. The plan for upgrading MODP will also be mentioned.

• 한국멀티미디어학회논문지
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• 제23권8호
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• pp.927-939
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• 2020

Gait recognition can identify people's identity from a long distance, which is very important for improving the intelligence of the monitoring system. Among many human features, gait features have the advantages of being remotely available, robust, and secure. Traditional gait feature extraction, affected by the development of behavior recognition, can only rely on manual feature extraction, which cannot meet the needs of fine gait recognition. The emergence of deep convolutional neural networks has made researchers get rid of complex feature design engineering, and can automatically learn available features through data, which has been widely used. In this paper,conduct feature metric learning in the three-dimensional space by combining the three-dimensional convolution features of the gait sequence and the Siamese structure. This method can capture the information of spatial dimension and time dimension from the continuous periodic gait sequence, and further improve the accuracy and practicability of gait recognition.

• 대한임베디드공학회논문지
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• 제19권1호
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• pp.47-55
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• 2024

We spend a lot of time in indoor space, and the space has a huge impact on our lives. Interior design plays a significant role to make an indoor space attractive and functional. However, it should consider a lot of complex elements such as color, pattern, and material etc. With the increasing demand for interior design, there is a growing need for technologies that analyze these design elements accurately and efficiently. To address this need, this study suggests a deep learning-based design analysis system. The proposed system consists of a semantic segmentation model that classifies spatial components and an image classification model that classifies attributes such as color, pattern, and material from the segmented components. Semantic segmentation model was trained using a dataset of 30000 personal indoor interior images collected for research, and during inference, the model separate the input image pixel into 34 categories. And experiments were conducted with various backbones in order to obtain the optimal performance of the deep learning model for the collected interior dataset. Finally, the model achieved good performance of 89.05% and 0.5768 in terms of accuracy and mean intersection over union (mIoU). In classification part convolutional neural network (CNN) model which has recorded high performance in other image recognition tasks was used. To improve the performance of the classification model we suggests an approach that how to handle data that has data imbalance and vulnerable to light intensity. Using our methods, we achieve satisfactory results in classifying interior design component attributes. In this paper, we propose indoor space design analysis system that automatically analyzes and classifies the attributes of indoor images using a deep learning-based model. This analysis system, used as a core module in the A.I interior recommendation service, can help users pursuing self-interior design to complete their designs more easily and efficiently.

• Journal of Information Processing Systems
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• 제16권5호
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• pp.1001-1007
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• 2020

In recent years, machine learning algorithms are continuously being used and expanded in various fields, such as facial recognition, signal processing, personal authentication, and stock prediction. In particular, various algorithms, such as deep learning, reinforcement learning, and Q-learning, are continuously being improved. Among these algorithms, the expansion of deep learning is rapidly changing. Nevertheless, machine learning algorithms have not yet been applied in several fields, such as personal authentication technology. This technology is an essential tool in the digital information era, walking recognition technology as promising biometrics, and technology for solving state-space problems. Therefore, algorithm technologies of deep learning, reinforcement learning, and Q-learning, which are typical machine learning algorithms in various fields, such as agricultural technology, personal authentication, wireless network, game, biometric recognition, and image recognition, are being improved and expanded in this paper.

• 천문학회보
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• 제44권1호
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• pp.63.2-63.2
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• 2019

Eight hundred thousand asteroids in the solar system have been identified so far under extensive sky surveys. Kilometer to sub-km sized asteroids, however, are still waiting for discovery, and their size and orbital distribution will provide a better understanding of the collisional and dynamical evolution of the solar system. In order to study the number of asteroids which is detectable with 1.6 m telescope and their orbital distribution, we conducted a small observation campaign as a part of Deep Ecliptic Patrol of the Southern Sky (DEEP-South) project, which is an asteroid survey in the southern hemisphere with Korea Microlensing Telescope Network (KMTNet). We observed the ecliptic plane near opposition ($2^{\circ}{\times}2^{\circ}$ field of view centering on ${\alpha}=22h40m31s$, ${\delta}=-08^{\circ}22^{\prime}58^{{\prime}{\prime}}$) in August 2018, and identified 464 moving objects by visual inspection. As a result, 266 of 464 moving objects turn out to be previously unknown asteroids, and their signal to noise ratio is below two on numerous occasions. Most of the newly detected objects are main belt asteroids (MBAs), while three Hildas, one Jupiter trojan, and two Hungarias are also identified. In this meeting, we report the differences in the orbital distributions between the previously known asteroids and newly discovered ones using statistical methods. We also talk about the observational bias of this survey and suggest future works.

• Journal of Advanced Research in Ocean Engineering
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• 제3권3호
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• pp.136-148
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• 2017

The paper presents dual arm ROV manipulation using deep reinforcement learning. The purpose of this underwater manipulator is to investigate and excavate natural resources in ocean, finding lost aircraft blackboxes and for performing other extremely dangerous tasks without endangering humans. This research work emphasizes on a self-learning approach using Deep Reinforcement Learning (DRL). DRL technique allows ROV to learn the policy of performing manipulation task directly, from raw image data. Our proposed architecture maps the visual inputs (images) to control actions (output) and get reward after each action, which allows an agent to learn manipulation skill through trial and error method. We have trained our network in simulation. The raw images and rewards are directly provided by our simple Lua simulator. Our simulator achieve accuracy by considering underwater dynamic environmental conditions. Major goal of this research is to provide a smart self-learning way to achieve manipulation in highly dynamic underwater environment. The results showed that a dual robotic arm trained for a 3DOF movement successfully achieved target reaching task in a 2D space by considering real environmental factor.

• 천문학회보
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• 제41권1호
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• pp.55.2-56
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• 2016

The spin states of asteroids is regarded as an important clue to understand not only the physical property of an individual object but also the dynamical evolution of the of the population as a whole. Single asteroids can be broadly classified into two separate groups according to their rotational states; Principal Axis (PA) and Non-Principal Axis (NPA) rotators. To date, lightcurve observations have been carried out mostly for PA asteroids. However, discovery of NPA objects has recently been increased due to new observing techniques, and this is the reason why rotational properties of NPA rotators became an issue. As a DEEP-South pilot study for NPA, we selected two targets, 5247 Krolv (1982 UP6) and 14764 Kilauea (7072 P-L) considering their Principal Axis Rotation (PAR) code and visibility. Observations were made between Jan. and Feb. 2016 for 17 nights employing Korea Microlensing Telescope Network (KMTNet) 1.6 m telescopes installed at SSO and SAAO using DEEP-South TO (Target of Opportunity) mode. To obtain lightcurves, we conducted time-series photometry using Johnson-Cousins R-filter. Multi-band photometry was also made with BVRI filters at the same time, for taxonomy. Their preliminary lightcurves and approximate mineralogy will be presented.