• 한국컴퓨터정보학회논문지
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• 제21권9호
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• pp.11-18
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• 2016

In this paper, we propose a gas classification method using combined features for an electronic nose system that performs well even when some loss occurs in measuring data samples. We first divide the entire measurement for a data sample into three local sections, which are the stabilization, exposure, and purge; local features are then extracted from each section. Based on the discrimination analysis, measurements of the discriminative information amounts are taken. Subsequently, the local features that have a large amount of discriminative information are chosen to compose the combined features together with the global features that extracted from the entire measurement section of the data sample. The experimental results show that the combined features by the proposed method gives better classification performance for a variety of volatile organic compound data than the other feature types, especially when there is data loss.

• International Journal of Advanced Culture Technology
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• 제11권4호
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• pp.393-405
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• 2023

Accurate prediction of wind speed and power is vital for enhancing the efficiency of wind energy systems. Numerous solutions have been implemented to date, demonstrating their potential to improve forecasting. Among these, deep learning is perceived as a revolutionary approach in the field. However, despite their effectiveness, the noise present in the collected data remains a significant challenge. This noise has the potential to diminish the performance of these algorithms, leading to inaccurate predictions. In response to this, this study explores a novel feature engineering approach. This approach involves altering the data input shape in both Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) and Autoregressive models for various forecasting horizons. The results reveal substantial enhancements in model resilience against noise resulting from step increases in data. The approach could achieve an impressive 83% accuracy in predicting unseen data up to the 24th steps. Furthermore, this method consistently provides high accuracy for short, mid, and long-term forecasts, outperforming the performance of individual models. These findings pave the way for further research on noise reduction strategies at different forecasting horizons through shape-wise feature engineering.

• International Journal of CAD/CAM
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• 제6권1호
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• pp.89-97
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• 2006

This paper presents a novel approach for non-iterative surface smoothing with feature preservation on arbitrary meshes. Laplacian operator is performed in a global way over the mesh. The surface smoothing is formulated as a quadratic optimization problem, which is easily solved by a sparse linear system. The cost function to be optimized penalizes deviations from the global Laplacian operator while maintaining the overall shape of the original mesh. The features of the original mesh can be preserved by adding feature constraints and barycenter constraints in the system. Our approach is simple and fast, and does not cause surface shrinkage and distortion. Many experimental results are presented to show the applicability and flexibility of the approach.

• Journal of information and communication convergence engineering
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• 제14권2호
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• pp.129-135
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• 2016

In this paper, we present a novel algorithm for image-based maritime obstacle detection using global sparsity potentials (GSPs), in which "global" refers to the entire sea area. The horizon line is detected first to segment the sea area as the region of interest (ROI). Considering the geometric relationship between the camera and the sea surface, variable-size image windows are adopted to sample patches in the ROI. Then, each patch is represented by its texture feature, and its average distance to all the other patches is taken as the value of its GSP. Thereafter, patches with a smaller GSP are clustered as the sea surface, and patches with a higher GSP are taken as the obstacle candidates. Finally, the candidates far from the mean feature of the sea surface are selected and aggregated as the obstacles. Experimental results verify that the proposed approach is highly accurate as compared to other methods, such as the traditional feature space reclustering method and a state-of-the-art saliency detection method.

• 한국멀티미디어학회논문지
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• 제23권2호
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• pp.127-134
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• 2020

In this paper, we propose a map alignment method for maps generated by point-line monocular SLAM. In the proposed method, the information of feature lines as well as feature points extracted from multiple maps are fused into a single map. To this end, the proposed method first searches for similar areas between maps via Bag-of-Words-based image matching. Thereafter, it calculates the similarity transformation between the maps in the corresponding areas to align the maps. Finally, we merge the overlapped information of multiple maps into a single map by removing duplicate information from similar areas. Experimental results show that maps created by different users are combined into a single map, and the accuracy of the fused map is similar with the one generated by a single user. We expect that the proposed method can be utilized for fast imagery map generation.

• 대한전자공학회논문지SP
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• 제46권6호
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• pp.58-67
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• 2009

대응점 정합은 컴퓨터 비전에서 중요한 작업 중에 하나지만 스케일, 조명, 시점이 변한 환경에서 대응점을 찾는 과정은 매우 어렵다. 대응점 정합 알고리즘인 SURF(Speeded Up Robust Features) 기법은 SIFT(Scale Invariant Feature Transform) 기법에 비해 정합 속도가 매우 빠르고 비슷한 정합 성능을 보여 널리 사용되고 있다. 하지만 SURF 기법은 흑백 영상과 지역 공간정보를 사용하기 때문에 유사한 패턴이 존재하는 영상에서 대응점의 정합 성능이 매우 떨어진다. 이런 문제점을 해결하기 위해 본 논문에서는 강인한 컬러 특징 정보와 광역적 특징 정보를 이용하는 확장 SURF 알고리즘을 제안한다. 제안하는 알고리즘은 비슷한 패턴이 존재하더라도 색상정보과 광역 공간 정보를 추가로 사용되기 때문에 대응점 매칭 성능을 크게 향상시킨다. 본 논문에서는 제안하는 방법의 우수성을 조명과 시점이 변화하고 유사한 패턴들을 갖는 영상들에 적용하여 기존의 방법들과 비교 실험함으로서 입증하였다.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제8권1호
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• pp.37-44
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• 2019

디지털 IT 기술의 발달로 인하여 프린터와 스캐너의 성능이 향상되고 가격이 저렴해지면서 일반인들도 쉽게 접할 수 있게 되었다. 그러나 이에 따른 부작용으로 공문서 및 사문서 위조 등의 범죄들이 쉽게 이루어질 수 있다. 따라서 해당 문서가 어떤 프린터를 사용하여 출력 되었는가를 특정할 수 있다면 수사 범위를 줄이고 용의자를 판별하는데 도움이 된다. 본 논문에서는 프린터 장치 판별을 위하여 딥러닝 모델을 제안한다. 먼저 최근 인식 등에서 범용적으로 활용되는 지역 특징 기반의 컨볼루셔널 뉴널 네트워크를 이용한 프린터 장치 판별 모델을 제안하고, 전역 특징 기반의 처리 과정을 네트워크 모델에 도입함으로 인하여 수렴 속도 및 정확도를 향상한 기법을 제안한다. 제안한 모델의 성능은 8개의 프린터 장치를 활용하여 기존 프린터 판별을 위한 특징 기반 기술과 비교를 수행하였다. 그 결과 제안하는 지역 특징 기반의 모델과 전역 특징 기반의 모델이 각각 97.23% 및 99.98%의 높은 판별 정확도를 달성하였고, 기존 기술들에 비하여 높은 정확도를 갖는 우수성을 보였다.

• 한국멀티미디어학회논문지
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• 제21권12호
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• pp.1473-1480
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• 2018

The extraction and recognition of human motion characteristics need to combine biometrics to determine and judge human behavior in the movement and distinguish individual identities. The so-called biometric technology, the specific operation is the use of the body's inherent biological characteristics of individual identity authentication, the most noteworthy feature is the invariance and uniqueness. In the past, the behavior recognition technology based on the single characteristic was too restrictive, in this paper, we proposed a mixed feature which combined global silhouette feature and local optical flow feature, and this combined representation was used for human action recognition. And we will use the KTH database to train and test the recognition system. Experiments have been very desirable results.

• 한국군사과학기술학회지
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• 제16권5호
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• pp.583-589
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• 2013

In this paper, we proposed a system that efficiently provides support maps which includes the grid based terrain analysis information. To do this, we use the FDB which is defined as a GIS database that contains features with attributes attached to the features. The FDB is composed of a number of features and feature classes. In order to create support maps, it is necessary to classify feature classes that are associated with each support map and to search them in a grid map. The proposed system use a ontology model to classify semantically feature classes and the quad-tree data structure to find them in a grid map quickly. Therefore, our system is expected to be utilized for global path planning of UGV. In this paper, we show the possibility through an experimental implementation.

• 로봇학회논문지
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• 제17권4호
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• pp.425-430
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• 2022

As robots are considered one of the mainstream digital transformations, robots with machine vision becomes a main area of study providing the ability to check what robots watch and make decisions based on it. However, it is difficult to find a small object in the image mainly due to the flaw of the most of visual recognition networks. Because visual recognition networks are mostly convolution neural network which usually consider local features. So, we make a model considering not only local feature, but also global feature. In this paper, we propose a detection method of a small marker on the object using deep learning and an algorithm that considers global features by combining Transformer's self-attention technique with a convolutional neural network. We suggest a self-attention model with new definition of Query, Key and Value for model to learn global feature and simplified equation by getting rid of position vector and classification token which cause the model to be heavy and slow. Finally, we show that our model achieves higher mAP than state of the art model YOLOr.