• Journal of the Korea Society of Computer and Information
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• v.3 no.3
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• pp.73-81
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• 1998

In this paper suggest for methods disease region feature extraction of medical image using wavelet. In the preprocessing, the shape informations of medical image are selected by performing the discrete wavelet transform(DWT) with four level coefficient matrix. In this approach, based on the characteristics of the coefficient matrix, 96 feature parameters are calculated as follows: Firstly. obtaining 32 feature parameters which have the characteristics of low frequency from the parameters according to the horizontal high frequency are calculated from the coefficient matrix of horizontal high frequency. In the third place, 16 vertical feature parameters are also calculated using the same kind of procedure with respect to the vertical high frequency. Finally, 32 feature parameters of diagonal high frequency are obtained from the coefficient matrix of diagonal high frequency. Consequently, 96 feature aprameters extracted. Using suggest algorithm in this paper will, implamentation can automatic recognition system, increasing efficiency of picture achieve communication system.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.1
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• pp.39-44
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• 2013

Trees are important ground objects that cause oxygen and reduce carbon dioxide in urban areas. For management of the trees, many studies using LIDAR data have been conducted. But, they rely on overseas developed LIDAR data processing software applications because there is a lack of domestically developed software applications. Therefore, this work was intended to propose an automation process that helps to extract trees automatically from LIDAR data. The proposed process has the function to classify LIDAR data and to extract building regions and trees automatically. It was applied to a study place in Yongin to conduct a test. As a result, about 88% of trees were extracted from the automation process.

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1135-1148
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• 2017

Speech recognition is one of the fascinating fields in the area of Computer science. Accuracy of speech recognition system may reduce due to the presence of noise present in speech signal. Therefore noise removal is an essential step in Automatic Speech Recognition (ASR) system and this paper proposes a new technique called combined thresholding for noise removal. Feature extraction is process of converting acoustic signal into most valuable set of parameters. This paper also concentrates on improving Mel Frequency Cepstral Coefficients (MFCC) features by introducing Discrete Wavelet Packet Transform (DWPT) in the place of Discrete Fourier Transformation (DFT) block to provide an efficient signal analysis. The feature vector is varied in size, for choosing the correct length of feature vector Self Organizing Map (SOM) is used. As a single classifier does not provide enough accuracy, so this research proposes an Ensemble Support Vector Machine (ESVM) classifier where the fixed length feature vector from SOM is given as input, termed as ESVM_SOM. The experimental results showed that the proposed methods provide better results than the existing methods.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.10
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• pp.1-7
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• 2020

In this study, a multichannel gas-liquid microextraction system is designed by integrating the automatic elution of extraction line and multichannel gas-purging liquid phase microextraction. The system uses an injection pump and inert gas to push the extraction solvent to a sample bottle of a gas-phase color autosampler and then implements multichannel gas-liquid microextraction and gas chromatography-mass spectrometry. The system also employs a three-way integrated micro-high-temperature heater, syringe pump, and microflow controller to realize the simultaneous processing of multiple groups of samples, thus improving the sample pretreatment speed and reproducibility and reducing human error. Autoinjection experiments were implemented with polycyclic aromatic hydrocarbon standard samples. The experiments show that the average recovery rate of the system exceeds 70%, and the relative standard among the channels is less than 15%.

• Journal of the Korean Society for Library and Information Science
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• v.56 no.3
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• pp.241-264
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• 2022

Bibliographic metadata can help researchers effectively utilize essential publications that they need and grasp academic trends of their own fields. With the manual creation of the metadata costly and time-consuming. it is nontrivial to effectively automatize the metadata construction using rule-based methods due to the immoderate variety of the article forms and styles according to publishers and academic societies. Therefore, this study proposes a two-step extraction process based on rules and deep neural networks for generating bibliographic metadata of scientific articlles to overcome the difficulties above. The extraction target areas in articles were identified by using a deep neural network-based model, and then the details in the areas were analyzed and sub-divided into relevant metadata elements. IThe proposed model also includes a model for generating reference summary information, which is able to separate the end of the text and the starting point of a reference, and to extract individual references by essential rule set, and to identify all the bibliographic items in each reference by a deep neural network. In addition, in order to confirm the possibility of a model that generates the bibliographic information of academic papers without pre- and post-processing, we conducted an in-depth comparative experiment with various settings and configurations. As a result of the experiment, the method proposed in this paper showed higher performance.

• Journal of the Korea Society of Computer and Information
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• v.15 no.1
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• pp.149-157
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• 2010

As the use of the Internet has recently increased, the demand for opinion information posted on the Internet has grown. However, such resources only exist on the website. People who want to search for information on the Internet find it inconvenient to visit each website. This paper focuses on the opinion information extraction and analysis system through Web mining that is based on statistics collected from Web contents. That is, users' opinion information which is scattered across several websites can be automatically analyzed and extracted. The system provides the opinion information search service that enables users to search for real-time positive and negative opinions and check their statistics. Also, users can do real-time search and monitoring about other opinion information by putting keywords in the system. Proposed technologies proved to have outstanding capabilities in comparison to existing ones through tests. The capabilities to extract positive and negative opinion information were assessed. Specifically, test movie review sentence testing data was tested and its results were analyzed.

• Journal of the Regional Association of Architectural Institute of Korea
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• v.21 no.4
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• pp.15-26
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• 2019

In recent years, researchers are increasingly trying to use BIM-based 3D models for BIM nD design such as 4D (3D + Time) and 5D (4D + Cost). However, there are still many problems in efficiently using process management based on the BIM information created at each design stage. Therefore, this study proposes a method to automate 4D and 5D design support in each design stage by using BIM-based Dynamo algorithm. To do this, I implemented an algorithm that can automatically input the process information needed for 4D and 5D by using Revit's Add-in program, Dynamo. In order to support the 4D design, the algorithm was created to enable automatic process simulation by synchronizing process simulation information (Excel file) through the Navisworks program, BIM software. The algorithm was created to automatically enable process simulation. And to support the 5D design, the algorithm was developed to enable automatic extraction of the information needed for mass production from the BIM model by utilizing the dynamo algorithm. Therefore, in order to verify the 4D and 5D design support algorithms, we verified the applicability through consultation with related workers and experts. As a result, it has been demonstrated that it is possible to manage information about process information and to quickly extract information from design and design changes. In addition, BIM data can be used to manage and input the necessary process information in 4D and 5D, which is advantageous for shortening construction time and cost. This study will make it easy to improve design quality and manage design information, and will be the foundation for future building automation research.

• Journal of Korea Multimedia Society
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• v.15 no.3
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• pp.291-298
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• 2012

In automatic target recognition(ATR) systems, target extraction techniques are very important because ATR performance depends on segmentation result. So, this paper proposes a multi-sensor image fusion method based on adaptive weights. To incorporate the FLIR image and CCD image, we used information such as the bi-modality, distance and texture. A weight of the FLIR image is derived from the bi-modality and distance measure. For the weight of CCD image, the information that the target's texture is more uniform than the background region is used. The proposed algorithm is applied to many images and its performance is compared with the segmentation result using the single image. Experimental results show that the proposed method has the accurate extraction performance.

• The KIPS Transactions:PartB
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• v.16B no.6
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• pp.451-462
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• 2009

The spread of satellite image increases various services using it. Especially, 3D visualization services of the whole earth such as $Google\;Earth^{TM}$ and $Virtual\;Earth^{TM}$ or 3D GIS services for several cities provide realistic geometry information of buildings and terrain of wide areas. These service can be used in the various fields such as urban planning, improvement of roads, entertainment, military simulation and emergency response. The research about extracting the building and terrain information effectively from the high-resolution satellite image is required. In this paper, presents a system for effective extraction of the building model from a single high-resolution satellite image, after examine requirements for building model extraction. The proposed system utilizes geometric features of satellite image and the geometric relationship among the building, the shadow of the building, the positions of the sun and the satellite to minimize user interaction. Finally, after extracting the 3D building, the fact that effective extraction of the model from single high-resolution satellite will be show.

• Journal of KIISE
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• v.43 no.9
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• pp.983-997
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• 2016

Many available online product reviews for any given product makes it difficult for a consumer to locate the helpful reviews. The purpose of this study was to investigate automatic helpfulness evaluation of online product reviews according to review information types based on the target of information. The underlying assumption was that consumers find reviews containing specific information related to the product itself or the reliability of reviewers more helpful than peripheral information, such as shipping or customer service. Therefore, each sentence was categorized by given information types, which reduced the semantic space of review sentences. Subsequently, we extracted specific information from sentences by using a topic-based representation of the sentences and a clustering algorithm. Review ranking experiments indicated more effective results than other comparable approaches.