• 대한원격탐사학회지
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• 제27권4호
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• pp.435-444
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• 2011

고해상도 위성영상의 다중분광자료만을 이용하여 토지 피복도를 제작할 경우, 낮은 분광해상도와 단일 토지 피복 내에 존재하는 불균질성으로 인해 분류 결과의 정확도가 저하되는 문제가 발생한다. 특히 식생 클래스의 경우 단일 토지 피복임에도 불구하고 절감 특성에 따라 해당 영역 안에 산림, 초지, 농업지역 등이 함께 분류되는 문제가 두드러진다. 본 연구에서는 이러한 문제를 개선하기 위해 광학 영상 기반의 사전분류를 수행한 후 식생으로 분류된 영역에 대해 고해상도 위성영상의 다중분광정보와 SAR 영상 산란 정보를 통합하고 식생을 세분류하였다. 사전 분류와 식생분류는 최대우도 감독분류를 통해 수행되었으며 식생 세분류 결과와 사전 분류결과 중 비식생 클래스의 융합을 통해 계층적 분류 방법을 제안하였다. 제안 기법은 SAR 영상이나 GLCM 질감 정보를 영상 전체에 걸쳐 단순 통합한 분류결과뿐만 아니라 GLCM 질감 정보를 식생 지역에 적용한 계층적 분류결과에 비해 높은 정확도를 보였으며 특히 식생과 비식생의 분류 정확도가 모두 높게 나타났다.

• 한국측량학회:학술대회논문집
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• 한국측량학회 2010년 춘계학술발표회 논문집
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• pp.387-388
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• 2010

There is no generalized and systematic method yet to data pre-processing for point cloud data classification even if there have been lots of previous studies such as local maxima filter, morphology filter, slope based filter and so on. Main focus of this study is to present classification method for bare ground information from LiDAR data for the mountainous area.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 G
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• pp.2905-2907
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• 1999

The meaningful speech sound block classification provides very important information in the speech recognition. The following technique of the classification is based on the DWT (discrete wavelet transform), which will provide a more fast algorithm and a useful, compact solution for the pre-processing of speech recognition. The algorithm is implemented to the unvoiced/voiced classification and the denoising.

• 대한의용생체공학회:의공학회지
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• 제43권5호
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• pp.299-307
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• 2022

In this paper, we propose a pre-processing method that converts to Spider Chart image data for classification of gripping movement using EMG (electromyography) sensors and Convolution Neural Networks (CNN) deep learning. First, raw data for six hand gestures are extracted from five test subjects using an 8-channel armband and converted into Spider Chart data of octagonal shapes, which are divided into several sliding windows and are learned. In classifying six hand gestures, the classification performance is compared with the proposed pre-processing method and the existing methods. Deep learning was performed on the dataset by dividing 70% of the total into training, 15% as testing, and 15% as validation. For system performance evaluation, five cross-validations were applied by dividing 80% of the entire dataset by training and 20% by testing. The proposed method generates 97% and 94.54% in cross-validation and general tests, respectively, using the Spider Chart preprocessing, which was better results than the conventional methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권2호
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• pp.420-437
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• 2024

There are some problems in network traffic classification (NTC), such as complicated statistical features and insufficient training samples, which may cause poor classification effect. A NTC architecture based on one-dimensional Convolutional Neural Network (CNN) and transfer learning is proposed to tackle these problems and improve the fine-grained classification performance. The key points of the proposed architecture include: (1) Model classification--by extracting normalized rate feature set from original data, plus existing statistical features to optimize the CNN NTC model. (2) To apply transfer learning in the classification to improve NTC performance. We collect two typical network flows data from Youku and YouTube, and verify the proposed method through extensive experiments. The results show that compared with existing methods, our method could improve the classification accuracy by around 3-5%for Youku, and by about 7 to 27% for YouTube.

• 한국컴퓨터정보학회논문지
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• 제11권6호
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• pp.1-8
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• 2006

많은 연구자들이 자동 염색체 핵형 분류와 해석을 연구하고 있다. 현미경상의 이미지를 개개의 염색체로 자동 분류하기 위해서는 이미지 전처리 핵형 분류기 구현 등의 세부 절차가 필요하다. 이미지 전처리에서는 개개의 염색체 분리, 잡음 제거, 특징 파라미터 추출을 진행한다. 추출된 형태학적 특징 파라미터는 동원체 지수, 상대 길이비, 상대 면적비이다. 본 논문에서는 인간 염색체 핵형 분류를 위하여 퍼지 분류기가 사용되어졌다. 추출된 형태학적 특징 파라미터가 퍼지 분류기의 입력 파라미터로 사용되었다. 우리는 개개의 염색체 그룹에 대한 최적 퍼지 분류기를 위하여 멤버쉽 함수를 선택하는 것을 연구하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권4호
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• pp.1442-1462
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• 2014

Packet classification is a key technology of the Internet for routers to classify the arriving packets into different flows according to the predefined rulesets. Previous packet classification algorithms have mainly focused on search speed and memory usage, while overlooking update performance. In this paper, we propose PreCuts, which can drastically improve the update speed. According to the characteristics of IP field, we implement three heuristics to build a 3-layer decision tree. In the first layer, we group the rules with the same highest byte of source and destination IP addresses. For the second layer, we cluster the rules which share the same IP prefix length. Finally, we use the heuristic of information entropy-based bit partition to choose some specific bits of IP prefix to split the ruleset into subsets. The heuristics of PreCuts will not introduce rule duplication and incremental update will not reduce the time and space performance. Using ClassBench, it is shown that compared with BRPS and EffiCuts, the proposed algorithm not only improves the time and space performance, but also greatly increases the update speed.

• 산업경영시스템학회지
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• 제44권3호
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• pp.33-38
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• 2021

Recently, transfer learning techniques with a base convolutional neural network (CNN) model have widely gained acceptance in early detection and classification of crop diseases to increase agricultural productivity with reducing disease spread. The transfer learning techniques based classifiers generally achieve over 90% of classification accuracy for crop diseases using dataset of crop leaf images (e.g., PlantVillage dataset), but they have ability to classify only the pre-trained diseases. This paper provides with an evaluation scheme on selecting an effective base CNN model for crop disease transfer learning with regard to the accuracy of trained target crops as well as of untrained target crops. First, we present transfer learning models called CDC (crop disease classification) architecture including widely used base (pre-trained) CNN models. We evaluate each performance of seven base CNN models for four untrained crops. The results of performance evaluation show that the DenseNet201 is one of the best base CNN models.

• 청소년시설환경
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• 제17권1호
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• pp.131-141
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• 2019

The purpose of this study is to derive and type utilization indexes by comparing public design indicators with preceding studies related to closed school facilities, and to derive preliminary classification systems through a correlation review between indicators. Pre-research was conducted in the scope of academic papers, academic journals, research reports, and special act for promoting the utilization of closed school assets. Public design indicators were set in the scope of domestic design guidelines, the Seoul city public design assessment system, the 'Good Building' designation system, and the UK Design Quality Index (DQI). and the design review of the British architects. First of all, the research method looked at laws, procedures and utilization of closed schools, and reviewed the preceding study and domestic and international public design indicators sequentially. Next, the association was reviewed through a comparison between the preceding study and the public design indicators, and a preliminary classification system for the use of closed schools was derived from this.

• Journal of Information Processing Systems
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• 제19권5호
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• pp.673-687
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• 2023

Convolutional neural network-based deep learning technology is the most commonly used in image identification, but it requires large-scale data for training. Therefore, application in specific fields in which data acquisition is limited, such as in the military, may be challenging. In particular, the identification of ground weapon systems is a very important mission, and high identification accuracy is required. Accordingly, various studies have been conducted to achieve high performance using small-scale data. Among them, the ensemble method, which achieves excellent performance through the prediction average of the pre-trained models, is the most representative method; however, it requires considerable time and effort to find the optimal combination of ensemble models. In addition, there is a performance limitation in the prediction results obtained by using an ensemble method. Furthermore, it is difficult to obtain the ensemble effect using models with imbalanced classification accuracies. In this paper, we propose a transfer learning-based feature fusion technique for heterogeneous models that extracts and fuses features of pre-trained heterogeneous models and finally, fine-tunes hyperparameters of the fully connected layer to improve the classification accuracy. The experimental results of this study indicate that it is possible to overcome the limitations of the existing ensemble methods by improving the classification accuracy through feature fusion between heterogeneous models based on transfer learning.