• Journal of the Korea Computer Graphics Society
• /
• v.23 no.3
• /
• pp.95-103
• /
• 2017

Horizon correction is a crucial stage for image composition enhancement. In this paper, we propose a deep learning based method for estimating the slanted angle of a photograph and correcting it. To estimate and correct the horizon direction, existing methods use hand-crafted low-level features such as lines, planes, and gradient distributions. However, these methods may not work well on the images that contain no lines or planes. To tackle this limitation and robustly estimate the slanted angle, we propose a convolutional neural network (CNN) based method to estimate the slanted angle by learning more generic features using a huge dataset. In addition, we utilize multiple adaptive spatial pooling layers to extract multi-scale image features for better performance. In the experimental results, we show our CNN-based approach robustly and accurately estimates the slanted angle of an image regardless of the image content, even if the image contains no lines or planes at all.

• Proceedings of the Korean Vacuum Society Conference
• /
• 2014.02a
• /
• pp.306.1-306.1
• /
• 2014

정보화 시대의 발전에 따라 점점 더 많은 정보를 더욱 빠르게 처리할 수 있는 기기들이 요구되고 있다. 메모리는 그 중에서 핵심적인 부품으로써 소자의 고집적화와 고속화가 계속 진행되면서 기존의 메모리 소자들은 집적화에서 그 한계에 도달하고 있다. 기존 소자들의 집적화의 한계를 극복하기 위하여 새로운 비휘발성 메모리 소자들이 제안되었다. 그 중 resistive switching random access memory(ReRAM)은 저항의 변화특성을 사용하는 메모리로 간단한 구조를 가지고 있기 때문에 집적화에 유리하다는 장점을 가지고 있다. 그 외에도 빠른 동작 속도와 낮은 전압에서의 동작이 가능하여 차세대 메모리로써 각광받고 있는 추세이다. 본 연구실에서는 이미 nitride 물질을 기반으로 한 여러 ReRAM 소자들을 제안해 왔다. 그 중 AlN-based ReRAM 소자는 빠른 동작 속도와 좋은 내구성을 보인 바 있다. 하지만 상업화를 위해서 해결해야 할 문제점들이 아직 존재하고 있다. 대표적으로 소자의 배열에서 각 소자의 균일한 동작이 보증되어야 하기 때문에 소자의 셋/리셋 전압의 산포를 줄이고 동작 전류 레벨을 낮추어야 할 필요성이 존재한다. 이러한 ReRAM의 이슈를 해결하기 위해, 본 실험에서는 기존의 AlN-based ReRAM 소자에 Ti를 도핑 방법을 이용하여 소자의 동작 전압 및 전류의 산포를 줄이기 위한 연구를 진행 하였다. 본 실험은 co-sputtering 방법을 이용하여 Ti가 도핑된 AlN을 저항변화 물질로 사용하여 그 특성을 살펴보았다. Ti의 도핑 효과로 소자의 신뢰성 향상 및 동작 전압의 감소 등의 효과를 얻을 수 있었다. 이는 nitride 기반 물질에서 Ti dopant에 의해 형성된 TiN의 효과로 설명된다. TiN는 metallic한 특성을 지니고 있기에 저항변화물질 내에서 일종의 metallic particle의 역할을 수행할 수 있다. 따라서 conducting path의 형성과정에서 이러한 particle 들이 전계를 유도하여 좀 더 균일한 set/reset 특성을 나타내게 된다.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.4
• /
• pp.213-221
• /
• 2021

In this paper, we suggest the Deep Neural Network Model System for predicting results of the match of 'League of Legends (LOL).' The model utilized approximately 26,000 matches of the LOL game and Keras of Tensorflow. It performed an accuracy of 93.75% without overfitting disadvantage in predicting the '2020 League of Legends Worlds Championship' utilizing the real data in the middle of the game. It employed functions of Sigmoid, Relu and Logcosh, for better performance. The experiments found that the four variables largely affected the accuracy of predicting the match --- 'Dragon Gap', 'Level Gap', 'Blue Rift Heralds', and 'Tower Kills Gap,' and ordinary users can also use the model to help develop game strategies by focusing on four elements. Furthermore, the model can be applied to predicting the match of E-sports professional leagues around the world and to the useful training indicators for professional teams, contributing to vitalization of E-sports.

• The Journal of the Acoustical Society of Korea
• /
• v.42 no.6
• /
• pp.603-616
• /
• 2023

In this study, machine learning-based clustering and classification of residential noise in apartment buildings was conducted using frequency and temporal characteristics. First, a residential noise source dataset was constructed . The residential noise source dataset was consisted of floor impact, airborne, plumbing and equipment noise, environmental, and construction noise. The clustering of residential noise was performed by K-Means clustering method. For frequency characteristics, L_eq and L_max values were derived for 1/1 and 1/3 octave band for each sound source. For temporal characteristics, Leq values were derived at every 6 ms through sound pressure level analysis for 5 s. The number of k in K-Means clustering method was determined through the silhouette coefficient and elbow method. The clustering of residential noise source by frequency characteristic resulted in three clusters for both L_eq and L_max analysis. Temporal characteristic clustered residential noise source into 9 clusters for L_eq and 11 clusters for L_max. Clustering by frequency characteristic clustered according to the proportion of low frequency band. Then, to utilize the clustering results, the residential noise source was classified using three kinds of machine learning. The results of the residential noise classification showed the highest accuracy and f1-score for data labeled with L_eq values in 1/3 octave bands, and the highest accuracy and f1-score for classifying residential noise sources with an Artificial Neural Network (ANN) model using both frequency and temporal features, with 93 % accuracy and 92 % f1-score.