• Journal of electromagnetic engineering and science
• /
• v.18 no.3
• /
• pp.206-211
• /
• 2018

In this paper, we proposed a two-level feature vector fusion technique to improve the performance of target classification. The proposed method combines feature vectors of the early-time region and late-time region in the first-level fusion. In the second-level fusion, we combine the monostatic and bistatic features obtained in the first level. The radar cross section (RCS) of the 3D full-scale model is obtained using the electromagnetic analysis tool FEKO, and then, the feature vector of the target is extracted from it. The feature vector based on the waveform structure is used as the feature vector of the early-time region, while the resonance frequency extracted using the evolutionary programming-based CLEAN algorithm is used as the feature vector of the late-time region. The study results show that the two-level fusion method is better than the one-level fusion method.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.19 no.1
• /
• pp.109-118
• /
• 2023

Data-based analysis methods have become used more for estimating or predicting housing prices, and neural network models and decision trees in the field of big data are also widely used more and more. Neural network models are often evaluated to be superior to existing statistical models in terms of estimation or prediction accuracy. However, there is ambiguity in determining the input feature of the input layer of the neural network model, that is, the type and number of input features, and decision trees are sometimes used to overcome these disadvantages. In this paper, we evaluate the existing methods of using decision trees and propose the method of using decision trees to prioritize input feature selection in neural network models. This can be a complementary or combined analysis method of the neural network model and decision tree, and the validity was confirmed by applying the proposed method to house price estimation. Through several comparisons, it has been summarized that the selection of appropriate input characteristics according to priority can increase the estimation power of the model.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.8
• /
• pp.2178-2198
• /
• 2024

Graph-based document models have good capabilities to reveal inter-dependencies among unstructured text data. Natural language processing (NLP) systems that use such models as an intermediate representation have shown good performance. This paper proposes a novel fuzzy graph-based document model and to demonstrate its effectiveness by applying fuzzy logic tools for text summarization. The proposed system accepts a text document as input and identifies some of its sentence level features, namely sentence position, sentence length, numerical data, thematic word, proper noun, title feature, upper case feature, and sentence similarity. The fuzzy membership value of each feature is computed from the sentences. We also propose a novel algorithm to construct the fuzzy graph as an intermediate representation of the input document. The Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metric is used to evaluate the model. The evaluation based on different quality metrics was also performed to verify the effectiveness of the model. The ANOVA test confirms the hypothesis that the proposed model improves the summarizer performance by 10% when compared with the state-of-the-art summarizers employing alternate intermediate representations for the input text.

• KIPS Transactions on Software and Data Engineering
• /
• v.8 no.1
• /
• pp.37-44
• /
• 2019

With the advance of digital IT technology, the performance of the printing and scanning devices is improved and their price becomes cheaper. As a result, the public can easily access these devices for crimes such as forgery of official and private documents. Therefore, if we can identify which printing device is used to print the documents, it would help to narrow the investigation and identify suspects. In this paper, we propose a deep learning model for printer identification. A convolutional neural network model based on local features which is widely used for identification in recent is presented. Then, another model including a step to calculate global features and hence improving the convergence speed and accuracy is presented. Using 8 printer models, the performance of the presented models was compared with previous feature-based identification methods. Experimental results show that the presented model using local feature and global feature achieved 97.23% and 99.98% accuracy respectively, which is much better than other previous methods in accuracy.

• International journal of advanced smart convergence
• /
• v.13 no.3
• /
• pp.312-320
• /
• 2024

Healthcare services converged with ICT technology are improving quality of life and satisfaction through various customized services. In ICT-based medical services, data interchange between medical services is important, and HL7 FHIR, a medical data standard, enables efficient medical data interchange. FHIR-based medical information services using wireless data broadcasting can efficiently support massive clients. This paper proposes a function point model to evaluate the implementation cost of FHIR-based health information services using wireless data broadcasting. The proposed cost evaluation model can effectively evaluate the development cost by applying the complexity of converting medical data into FHIR format and the complexity of organizing indexes to efficiently support massive clients. The comparison of the proposed feature point evaluation model with simple feature points shows the efficiency and suitability of the proposed cost evaluation model.

• International Journal of CAD/CAM
• /
• v.5 no.1
• /
• pp.39-47
• /
• 2005

Different CAx systems are being utilized throughout the product lifecycle due to the practical reasons in the supply chain and design processes. One of the major problems facing enterprises of today is how to share and exchange data among heterogeneous applications. Since different software applications use different terminologies, it is difficult to share and exchange the product data with internal and external partners. This paper presents a method to enhance the CAD model interoperability based on feature ontology. The feature ontology has been constructed based on the feature definition of modeling commands of CAD systems. A method for integration of semantic data has been proposed, implemented, and tested with two commercial CAD systems.

• Journal of Ship and Ocean Technology
• /
• v.9 no.3
• /
• pp.33-42
• /
• 2005

As the networks connect the world, enterprises tend to move manufacturing activities into virtual spaces. Since different software applications use different data terminology, it becomes a problem to interoperate, interchange, and manage electronic data among heterogeneous systems. It is said that approximately one billion dollar has been being spent yearly in USA for product data exchange and interoperability. As commercial CAD systems have brought in the concept of design feature for the sake of interoperability, terminologies of design features need to be harmonized. In order to define design feature terminology for integration, knowledge about feature definitions of different CAD systems should be considered. STEP standard have attempted to solve this problem, but it defines only syntactic data representation so that semantic data integration is not possible. This paper proposes a methodology for integrating modeling features of CAD systems. We utilize the ontology concept to build a data model of design features which can be a semantic standard of feature definitions of CAD systems. Using feature ontology, we implement an integrated virtual database and a simple system which searches and edits design features in a semantic way.

• Journal of Korea Multimedia Society
• /
• v.20 no.2
• /
• pp.126-133
• /
• 2017

The conventional hand-crafted features used to track objects have limitations in object representation. Convolutional neural networks, which show good performance results in various areas of computer vision, are emerging as new ways to break through the limitations of feature extraction. CNN extracts the features of the image through layers of multiple layers, and learns the kernel used for feature extraction by itself. In this paper, we use the feature map extracted from the convolution layer of the convolution neural network to create an outline model of the object and use it for tracking. We propose a method to adaptively update the outline model to cope with various environment change factors affecting the tracking performance. The proposed algorithm evaluated the validity test based on the 11 environmental change attributes of the CVPR2013 tracking benchmark and showed excellent results in six attributes.

• Journal of Advanced Marine Engineering and Technology
• /
• v.23 no.5
• /
• pp.622-629
• /
• 1999

This paper presents the development of an interactive knowledge-based CAM system for design-ing and manufacturing the mold. The system is composed of two functional parts. One is the geo-metric modeller that uses the feature-based models. The models include base plate step, hole, pocket, boss and slot, These are designed by interactive user interface. The other is the expert sys-tem module with inference engine and knowledge database of workpiece material tools manufac-turing machines process an working conditions. With two parts the final mold shape is generated with manufacturing information for effective production.

• The Journal of Korea Robotics Society
• /
• v.18 no.3
• /
• pp.330-336
• /
• 2023

In this paper, a novel line-based simultaneous localization and mapping (SLAM) using a loss function of vanishing point measurements is proposed. In general, the Huber norm is used as a loss function for point and line features in feature-based SLAM. The proposed loss function of vanishing point measurements is based on the unit sphere model. Because the point and line feature measurements define the reprojection error in the image plane as a residual, linear loss functions such as the Huber norm is used. However, the typical loss functions are not suitable for vanishing point measurements with unbounded problems. To tackle this problem, we propose a loss function for vanishing point measurements. The proposed loss function is based on unit sphere model. Finally, we prove the validity of the loss function for vanishing point through experiments on a public dataset.