• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.3
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• pp.135-152
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• 2018

The purpose of this study is to propose an optimal fusion method of aerial multi - sensor data to improve the accuracy of land cover classification. Recently, in the fields of environmental impact assessment and land monitoring, high-resolution image data has been acquired for many regions for quantitative land management using aerial multi-sensor, but most of them are used only for the purpose of the project. Hyperspectral sensor data, which is mainly used for land cover classification, has the advantage of high classification accuracy, but it is difficult to classify the accurate land cover state because only the visible and near infrared wavelengths are acquired and of low spatial resolution. Therefore, there is a need for research that can improve the accuracy of land cover classification by fusing hyperspectral sensor data with multispectral sensor and aerial laser sensor data. As a fusion method of aerial multisensor, we proposed a pixel ratio adjustment method, a band accumulation method, and a spectral graph adjustment method. Fusion parameters such as fusion rate, band accumulation, spectral graph expansion ratio were selected according to the fusion method, and the fusion data generation and degree of land cover classification accuracy were calculated by applying incremental changes to the fusion variables. Optimal fusion variables for hyperspectral data, multispectral data and aerial laser data were derived by considering the correlation between land cover classification accuracy and fusion variables.

• The Journal of the Korea Contents Association
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• v.18 no.8
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• pp.178-186
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• 2018

Subject classification of thesis units is essential to serve scholarly information deliverables. However, to date, there is a journal-based topic classification, and there are not many article-level subject classification services. In the case of academic papers among domestic works, subject classification can be a more important information because it can cover a larger area of service and can provide service by setting a range. However, the problem of classifying themes by field requires the hands of experts in various fields, and various methods of verification are needed to increase accuracy. In this paper, we try to classify topics using the unsupervised learning algorithm to find the correct answer in the unknown state and compare the results of the subject classification algorithms using the coherence and perplexity. The unsupervised learning algorithms are a well-known Hierarchical Dirichlet Process (HDP), Latent Dirichlet Allocation (LDA) and Latent Semantic Indexing (LSI) algorithm.

• Journal of Korea Technology Innovation Society
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• v.11 no.4
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• pp.592-612
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• 2008

The convergence of technology as a major breakthrough in technology innovation has been generated and developed. With the advent of the 21st century characterized by the knowledge-based economy, it is happening even more frequently and vigorously than ever. However, study on classification system or definition of notions is necessary as the convergence of technology is still in the early stage. The existing classification system has limited application to the convergence of technology. With no standard available for the classification of the technology convergence, there has been much concern for duplicative R&D investment. On this ground, a new and reformed classification system for the convergence of technology and definition of notions are needed. Previous studies regard base technologies used in the technology convergence and new convergence technologies as important. However, this paper views convergence of technology as a dynamic phenomenon and puts an emphasis on the cause of technology convergence, not on the new technology itself and examines whether base technologies go back to their original state after the completion of technology convergence. This kind of approach will classify technology convergence by characteristics. Although more researches including quantitative analysis are necessary, this paper expects to offer help with further researches on classification system.

• Journal of The Korean Digital Architecture Interior Association
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• v.11 no.3
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• pp.69-77
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• 2011

This study considered the characteristic through the present state of domestic literature museum and grouping by type to help the understanding for domestic literature museum. And conducted a case study on Gyeongsang-do region literature museum to grasp the space form composition characteristic of literature museum. The result gained through these studies is as follows. First, grouping domestic literature museum by type, we can conduct the classification founded on location character, an exhibition writer, and the main body of erection and maintenance management. Second, the classification founded on location character of literature museum is able to be divided into the type of the house of writer's birth, a literary work, writing, and etc. Third, the classification founded on the number of exhibition writers can be divided into the type of independence, an individual pavilion, and integration. Fourthly, the classification founded on the main body of erection and management can be divided into the case in which a local self-governing body is wholly in charge of erection and management, a local government is in charge of erection but entrusts management to a corporate body, etc., a corporate body is in charge of erection and management, and a private person is in charge of erection and management. Fifthly, speaking of the characteristic by type of the Gyeongsang-do region literature museum, the classification founded on location has the type of the house of writer's birth the most, the classification founded on the number of exhibition writers has the type of independence the most, and the classification founded on the main body of erection and management has the most the type in which a local self-governing body is in charge of erection and management. Also, for the characteristic by space form, the case which expresses the character of Korean traditional architecture by form is many the most, and there are pieces of work to pursue shape beauty through the articulation of mass or molding manipulation and the change by space form through the proper combination of concreteness and abstraction as well.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.401-410
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• 2021

Popular in discriminant classification analysis, k-nearest neighbor classification methods have limitations that do not reflect the local characteristic of the data, considering only the number of fixed neighbors. Considering the local structure of the data, the adaptive nearest neighbor method has been developed to select the number of neighbors. In the analysis of high-dimensional data, it is common to perform dimension reduction such as random projection techniques before using k-nearest neighbor classification. Recently, an ensemble technique has been developed that carefully combines the results of such random classifiers and makes final assignments by voting. In this paper, we propose a novel discriminant classification technique that combines adaptive nearest neighbor methods with random projection ensemble techniques for analysis on high-dimensional data. Through simulation and real-world data analyses, we confirm that the proposed method outperforms in terms of classification accuracy compared to the previously developed methods.

• Journal of Biomedical Engineering Research
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• v.42 no.6
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• pp.268-276
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• 2021

Functional near-infrared spectroscopy-based brain-computer interface (fNIRS-based BCI) has been receiving much attention. However, we are practically constrained to obtain a lot of fNIRS data by inherent hemodynamic delay. For this reason, when employing machine learning techniques, a problem due to the high-dimensional feature vector may be encountered, such as deteriorated classification accuracy. In this study, we employ an elastic net-based feature selection which is one of the embedded methods and demonstrate the utility of which by analyzing the results. Using the fNIRS dataset obtained from 18 participants for classifying brain activation induced by mental arithmetic and idle state, we calculated classification accuracies after performing feature selection while changing the parameter α (weight of lasso vs. ridge regularization). Grand averages of classification accuracy are 80.0 ± 9.4%, 79.3 ± 9.6%, 79.0 ± 9.2%, 79.7 ± 10.1%, 77.6 ± 10.3%, 79.2 ± 8.9%, and 80.0 ± 7.8% for the various values of α = 0.001, 0.005, 0.01, 0.05, 0.1, 0.2, and 0.5, respectively, and are not statistically different from the grand average of classification accuracy estimated with all features (80.1 ± 9.5%). As a result, no difference in classification accuracy is revealed for all considered parameter α values. Especially for α = 0.5, we are able to achieve the statistically same level of classification accuracy with even 16.4% features of the total features. Since elastic net-based feature selection can be easily applied to other cases without complicated initialization and parameter fine-tuning, we can be looking forward to seeing that the elastic-based feature selection can be actively applied to fNIRS data.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.99-104
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• 2022

Artificial intelligence is getting a crucial part of our lives with its incredible benefits. Machines outperform humans in recognizing objects in images, particularly in classifying people into correct age and gender groups. In this respect, age and gender classification has been one of the hot topics among computer vision researchers in recent decades. Deployment of deep Convolutional Neural Network(: CNN) models achieved state-of-the-art performance. However, the most of CNN based architectures are very complex with several dozens of training parameters so they require much computation time and resources. For this reason, we propose a new CNN-based classification algorithm with significantly fewer training parameters and training time compared to the existing methods. Despite its less complexity, our model shows better accuracy of age and gender classification on the UTKFace dataset.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2709-2729
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• 2016

Representation based classification, kernel method and sparse representation have received much attention in the field of face recognition. In this paper, we proposed an improved kernel principal component analysis method based on sparse representation to improve the accuracy and robustness for face recognition. First, the distances between the test sample and all training samples in kernel space are estimated based on collaborative representation. Second, S training samples with the smallest distances are selected, and Kernel Principal Component Analysis (KPCA) is used to extract the features that are exploited for classification. The proposed method implements the sparse representation under ℓ₂ regularization and performs feature extraction twice to improve the robustness. Also, we investigate the relationship between the accuracy and the sparseness coefficient, the relationship between the accuracy and the dimensionality respectively. The comparative experiments are conducted on the ORL, the GT and the UMIST face database. The experimental results show that the proposed method is more effective and robust than several state-of-the-art methods including Sparse Representation based Classification (SRC), Collaborative Representation based Classification (CRC), KCRC and Two Phase Test samples Sparse Representation (TPTSR).

• Journal of the Korea Institute of Military Science and Technology
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• v.10 no.4
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• pp.88-96
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• 2007

This paper discusses the fast modulation classifiers capable of classifying both analog and digital modulation signals in wireless communications applications. A total of 7 statistical signal features are extracted and used to classify 9 modulated signals. In this paper, we investigate the performance of the two types of fast modulation classifiers (i.e. 2 nearest neighbor classifiers and 2 minimum distance classifiers) and compare the performance of these classifiers with that of the state of the art for the existing classification methods such as SVM Classifier. Computer simulations indicate good performance on an AWGN channel, even at low signal-to-noise ratios, in case of minimum distance classifiers (MDC for short) and k nearest neighbor classifiers (kNNC for short). Besides a good performance, these type classifiers are considered as ideal candidate to adapt real-time software radio because of their fast modulation classification capability.

• Journal of Satellite, Information and Communications
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• v.11 no.4
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• pp.52-57
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• 2016

The backscatter signals are very weak so they can be easily interfered by signal interferences and channels. In this paper, we propose a two-level classification algorithm for backscatter communications which chooses the idle frequency channel based on cognitive radio systems. The two-level classification algorithm provides an optimal idle frequency channel by obtaining informations about idle frequencies, fading of the channels, and the channels' usage state by primary users. Our simulation results show the improvement of BER and received power performance in backscatter communications by using the proposed algorithm, and the improvement of the algorithm's performance in backscatter communications.