• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2007년도 춘계학술대회 논문집
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• pp.113-119
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• 2007

This article deals mainly with two objectives viz, 1) the potentiality of very high-resolution(VHR) multi-spectral and pan chromatic QuickBird satellite data in resources mapping over moderate resolution satellite data (IRS LISS III) and 2) the advantages of using object oriented classification method of eCognition software in land use and land cover analysis over the ISODATA classification method. These VHR data offers widely acceptable metric characteristics for cartographic updating and increase our ability to map land use in geometric detail and improve accuracy of local scale investigations. This study has been carried out in the Sukkalampatti mini-watershed, which is situated in the Eastern Ghats of Tamil Nadu, India. The eCognition object oriented classification method succeeded in most cases to achieve a high percentage of right land cover class assignment and it showed better results than the ISODATA pixel based one, as far as the discrimination of land cover classes and boundary depiction is concerned.

• 터널과지하공간
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• 제11권2호
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• pp.182-189
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• 2001

터널 발파시 발파효율은 암반의 특성에 큰 영향을 받기 때문에 암반 특성을 분석하고 이를 기초로 발파설계를 수행하는 것이 중요하다. 그럼에도 불구하고 현재까지 국내에서의 발파설계는 무결암의 단축압축강도만으로 발파암을 분류한 후 각 발파암의 발파계수를 구하는 방법을 이용하거나 공학적 암반분류법의 하나인 RMR분류를 이용하여 발파암을 분류하되 객관적 근거가 미약한 경험적인 발파계수를 산정하는 방식을 통하여 이루어졌다. 본 연구에서는 절리특성을 고려한 발파설계를 위하여 Ashby의 접근법을 활용하였다. 또한 절리조사 결과를 통한 발파암 분류방법과 발파패턴설계를 추가하여 발파설계 전과정을 수행할 수 있도록 Ashby의 접근법을 응용하였다. 따라서 절리 분포 특성을 고려한 발파암 분류가 가능하고, 절리암반 특성을 고려한 발파설계 를 수행할 수 있을 것으로 기대된다.

• 비파괴검사학회지
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• 제24권6호
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• pp.590-596
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• 2004

신경망 기반의 신호 분류 시스템은 비파괴 검사 시 추출되는 많은 양의 데이터를 처리하기 위한 방법으로 꾸준히 이용되고 있다. 비파괴검사 방법 중, 초음파 탐상법은 용접 지역에서 결함들을 찾기 위하여 비파괴 검사에서 일반적으로 사용되고 있는 추세다. 초음파 탐상법의 중요한 특징은 특정 신호에서 발생하는 불연속성을 판별해내는 능력이다. 지금까지의 보편화되어 있는 기술은 신호를 분류하기 위해 각각의 A-scan 신호를 처리하는 반면 본 논문에서는 이웃하는 A-scan 신호의 정보를 기반으로 하는 2차원 푸리에 변환(Fourier transform)과 주성분 분석(principal component analysis) 기법을 이용하여 특징 벡터를 추출, 분류하는 방법을 제시하고자 한다.

• 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.

• International Journal of Internet, Broadcasting and Communication
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• 제14권4호
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• pp.173-180
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• 2022

In the naked eye observation, the health of livestock can be controlled by the range of activity, temperature, pulse, cough, snot, eye excrement, ears and feces. In order to confirm the health of livestock, this paper uses calf face image data to classify the health status by image shape, color and texture. A series of images that have been processed in advance and can judge the health status of calves were used in the study, including 177 images of normal calves and 130 images of abnormal calves. We used GLCM calculation and Convolutional Neural Networks to extract 6 texture attributes of GLCM from the dataset containing the health status of calves by detecting the image of calves and learning the composite image of Convolutional Neural Networks. In the research, the classification ability of GLCM-CNN shows a classification rate of 91.3%, and the subsequent research will be further applied to the texture attributes of GLCM. It is hoped that this study can help us master the health status of livestock that cannot be observed by the naked eye.

• International Journal of Computer Science & Network Security
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• 제23권12호
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• pp.213-219
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• 2023

In recent years, popularity of deep learning (DL) is increased due to its ability to extract features from Hyperspectral images. A lack of discrimination power in the features produced by traditional machine learning algorithms has resulted in poor classification results. It's also a study topic to find out how to get excellent classification results with limited samples without getting overfitting issues in hyperspectral images (HSIs). These issues can be addressed by utilising a new learning network structure developed in this study.EfficientNet-B4-Based Convolutional network (EN-B4), which is why it is critical to maintain a constant ratio between the dimensions of network resolution, width, and depth in order to achieve a balance. The weight of the proposed model is optimized by Search and Rescue Operations (SRO), which is inspired by the explorations carried out by humans during search and rescue processes. Tests were conducted on two datasets to verify the efficacy of EN-B4, with Indian Pines (IP) and the University of Pavia (UP) dataset. Experiments show that EN-B4 outperforms other state-of-the-art approaches in terms of classification accuracy.

• Journal of Digestive Cancer Research
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• 제7권1호
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• pp.13-17
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• 2019

Stomach cancer is the fifth most common malignancy in the world. The incidence of stomach cancer is declining worldwide, however, gastric cancer still remains the third most common cause of cancer death. The tumor, node, and metastasis (TNM) staging system has been frequently used as a method for cancer staging system and the most important reference in cancer treatment. In 2016, the classification of gastric cancer TNM staging was revised in the 8^th American Joint Committee on Cancer (AJCC) edition. There are several modifications in stomach cancer staging in this edition compared to the 7^th edition. First, the anatomical boundary between esophagus and stomach has been revised, therefore the definition of stomach cancer and esophageal cancer has refined. Second, N3 is separated into N3a and N3b in pathological classification. Patients with N3a and N3b revealed distinct prognosis in stomach cancer, and these results brought changes in pathological staging. Several large retrospective studies were conducted to compare staging between the 7^th and 8^th AJCC editions including prognostic value, stage grouping homogeneity, discriminatory ability, and monotonicity of gradients globally. The main objective of this review is to evaluate the clinical and pathological implications of AJCC 8^th staging classification in the stomach cancer.

• 한국초등과학교육학회지:초등과학교육
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• 제27권1호
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• pp.49-59
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• 2008

The purpose of this study was to invent a quotient equation which could quantitatively evaluate individual's hypothesis evaluating ability. The equation was induced by the analysis of the classification types about hypothesis evaluation knowledges generated by 15 pre-service elementary school teachers and the construction of the quotient equation on hypothesis evaluating ability. The hypothesis evaluation task administered to subjects was dealt with the woodpecker behavior. The task was initiated by generating hypothesis on the following question: 'Why don't woodpecker have brain damage after pecking wood?' Subjects then were asked to design and perform experiments for testing hypothesis. Finally they were asked to evaluate their own hypothesis based on the collected, analyzed and interpreted data. The knowledges generated from their evaluating hypothesis were analyzed by 4 major categories (richness, type, level and accuracy). Then, a general equation which could quantitatively and systematically evaluate individual's hypothesis evaluating ability was invented by an inductive process. After combining all the categories the following quotient equation was proposed; '$VQ\;=\;{\sum}(TE_n\;{\times}\;AE_n)\;{\times}\;LE$'. According to this results, woodpecker task and hypothesis evaluating ability quotient equation (VQ) which invented in this study could be applied to a practical use of measuring students' ability of scientific hypothesis evaluation.

• 대한방사선기술학회지:방사선기술과학
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• 제45권1호
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• pp.41-47
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• 2022

The purpose of this study is to apply a deep learning model that can distinguish lung perfusion and lung ventilation images in nuclear medicine, and to evaluate the image classification ability. Image data pre-processing was performed in the following order: image matrix size adjustment, min-max normalization, image center position adjustment, train/validation/test data set classification, and data augmentation. The convolutional neural network(CNN) structures of VGG-16, ResNet-18, Inception-ResNet-v2, and SE-ResNeXt-101 were used. For classification model evaluation, performance evaluation index of classification model, class activation map(CAM), and statistical image evaluation method were applied. As for the performance evaluation index of the classification model, SE-ResNeXt-101 and Inception-ResNet-v2 showed the highest performance with the same results. As a result of CAM, cardiac and right lung regions were highly activated in lung perfusion, and upper lung and neck regions were highly activated in lung ventilation. Statistical image evaluation showed a meaningful difference between SE-ResNeXt-101 and Inception-ResNet-v2. As a result of the study, the applicability of the CNN model for lung scintigraphy classification was confirmed. In the future, it is expected that it will be used as basic data for research on new artificial intelligence models and will help stable image management in clinical practice.

• 대한원격탐사학회지
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• 제40권1호
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• pp.93-101
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• 2024

Accurate field crop classification is essential for various agricultural applications, yet existing methods face challenges due to diverse crop types and complex field conditions. This study aimed to address these issues by combining support vector machine (SVM) models with multi-seasonal unmanned aerial vehicle (UAV) images, texture information extracted from Gray Level Co-occurrence Matrix (GLCM), and RGB spectral data. Twelve high-resolution UAV image captures spanned March-October 2021, while field surveys on three dates provided ground truth data. We focused on data from August (-A), September (-S), and October (-O) images and trained four support vector classifier (SVC) models (SVC-A, SVC-S, SVC-O, SVC-AS) using visual bands and eight GLCM features. Farm maps provided by the Ministry of Agriculture, Food and Rural Affairs proved efficient for open-field crop identification and served as a reference for accuracy comparison. Our analysis showcased the significant impact of hyperparameter tuning (C and gamma) on SVM model performance, requiring careful optimization for each scenario. Importantly, we identified models exhibiting distinct high-accuracy zones, with SVC-O trained on October data achieving the highest overall and individual crop classification accuracy. This success likely stems from its ability to capture distinct texture information from mature crops.Incorporating GLCM features proved highly effective for all models,significantly boosting classification accuracy.Among these features, homogeneity, entropy, and correlation consistently demonstrated the most impactful contribution. However, balancing accuracy with computational efficiency and feature selection remains crucial for practical application. Performance analysis revealed that SVC-O achieved exceptional results in overall and individual crop classification, while soybeans and rice were consistently classified well by all models. Challenges were encountered with cabbage due to its early growth stage and low field cover density. The study demonstrates the potential of utilizing farm maps and GLCM features in conjunction with SVM models for accurate field crop classification. Careful parameter tuning and model selection based on specific scenarios are key for optimizing performance in real-world applications.