• Journal of the Korean Wood Science and Technology
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• v.36 no.5
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• pp.49-55
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• 2008

This study was carried to investigate the effects of steaming and four different drying methods on the longitudinal liquid permeability of Korean pine (Pinus koraiensis Sieb.et Zucc.) board. Four drying methods were air drying, conventional kiln drying, microwave-vacuum drying and high temperature drying. Darcy equation was used for calculating the specific permeability of the small sapwood specimens taken from the treated boards while capillary rising method was used for the heartwood specimens. The sapwood specimens were extracted with water and benzene-alcohol solution to examine the mechanism of liquid flow in treated wood. No significant correlation was found between specific permeability and the number of resin canals of the sapwood specimens. Extraction decreased the differences of specific permeabilities of the sapwood specimens between the five treatment methods. The effects of extraction on the longitudinal permeability are different between five treatments. The fluid path in heartwood was observed by dynamic observation method.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.443-450
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• 2005

In this paper, we propose an intelligent model lot extraction of the web user's preference and present the results of evaluation. For this purpose, we analyze shortcomings of current information retrieval engine being used and reflect preference weights on learner. As it doesn't depend on frequency of each word but intelligently learns patterns of user behavior, the mechanism Provides the appropriate set of results about user's questions. Then, we propose the concept of preference trend and its considerations and present an algorithm for extracting preference with examples. Also, we design an intelligent model for extraction of behavior patterns and propose HTML index and process of intelligent learning for preference decision. Finally, we validate the proposed model by comparing estimated results(after applying the Preference) of document ranking measurement.

• Ocean Systems Engineering
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• v.7 no.2
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• pp.157-177
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• 2017

This research is based on the concept of safety airbag to design a self-rescue system for the autonomous underwater vehicle (AUV) using micro inertial sensing module. To reduce the possibility of losing the underwater vehicle and the difficulty of searching and rescuing, when the AUV self-rescue system (ASRS) detects that the AUV is crashing or encountering a serious collision, it can pump carbon dioxide into the airbag immediately to make the vehicle surface. ASRS consists of 10-DOF sensing module, sensing attitude algorithm and air-pumping mechanism. The attitude sensing modules are a nine-axis micro-inertial sensor and a barometer. The sensing attitude algorithm is designed to estimate failure attitude of AUV properly using sensor calibration and extended Kalman filter (SCEKF), feature extraction and backpropagation network (BPN) classify. SCEKF is proposed to be used subsequently to calibrate and fuse the data from the micro-inertial sensors. Feature extraction and BPN training algorithms for classification are used to determine the activity malfunction of AUV. When the accident of AUV occurred, the ASRS will immediately be initiated; the airbag is soon filled, and the AUV will surface due to the buoyancy. In the future, ASRS will be developed successfully to solve the problems such as the high losing rate and the high difficulty of the rescuing mission of AUV.

• Mass Spectrometry Letters
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• v.4 no.3
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• pp.41-46
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• 2013

The goal of this study is to find an experimental condition which enables us to perform enzymatic studies on the cellular behavior of PTEN (phosphatase and tensine homolog) through identification of molecular species of phosphatidylinositol 3,4,5-trisphosphates and their quantitative analysis in a mammalian cell line using mass spectrometry. We initially exployed a two-step extraction process using HCl for extraction of phosphatidylinositol 3,4,5-trisphosphates from two mammalian cell lines and further analyzed the extracted phosphatidylinositol 3,4,5-trisphosphates using tandem mass spectrometry for the identification of them. We finally quantified the concentration of phosphatidylinositol 3,4,5-trisphosphates using internal standard calibration. From these observation, we found that HEK 293-T cells is a good model to examine the enzymatic behavior of PTEN in a cell, and the minimum amount of phosphatidylinositol 3,4,5-trisphosphates is more than 50 pmol for quantification in a mass spectrometer. These results suggest that the well-optimized experimental conditions are required for the investigation of the cellular PTEN in terms of the catalytic mechanism and further for the detailed identification of cellular substrates.

• The KIPS Transactions:PartB
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• v.11B no.3
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• pp.257-266
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• 2004

This paper proposes caricature generation system that uses shading mechanism that extracts textural features of face. Using this method, we can get more realistic caricature. Since this system If vector-based, the generated character's face has no size limit and constraint. so it is available to transform the shape freely and to apply various facial expressions to 2D face. Moreover, owing to the vector file's advantage, It can be used in mobile environment as small file size This paper presents methods that generate vector-based face, create shade and synthesize the shade with the vector face.

• Journal of Multimedia Information System
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• v.6 no.2
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• pp.49-60
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• 2019

Plants are very crucial for life on Earth. There is a wide variety of plant species available, and the number is increasing every year. Species knowledge is a necessity of various groups of society like foresters, farmers, environmentalists, educators for different work areas. This makes species identification an interdisciplinary interest. This, however, requires expert knowledge and becomes a tedious and challenging task for the non-experts who have very little or no knowledge of the typical botanical terms. However, the advancements in the fields of machine learning and computer vision can help make this task comparatively easier. There is still not a system so developed that can identify all the plant species, but some efforts have been made. In this study, we also have made such an attempt. Plant identification usually involves four steps, i.e. image acquisition, pre-processing, feature extraction, and classification. In this study, images from Swedish leaf dataset have been used, which contains 1,125 images of 15 different species. This is followed by pre-processing using Gaussian filtering mechanism and then texture and color features have been extracted. Finally, classification has been done using Multiclass-support vector machine, which achieved accuracy of nearly 93.26%, which we aim to enhance further.

• The Korean Journal of Food And Nutrition
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• v.35 no.5
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• pp.313-323
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• 2022

To investigate antioxidant effects of tea tree root extracts using various extraction methods, cytotoxicity, DPPH and ABTS radical scavenging, SOD, nitrite scavenging activity and inhibitory activity of lipid peroxidation, reducing power, ferrous ion chelating activity were measured. Cytotoxicity for RAW 264.7 cells was not observed at concentrations treated with below 90 ㎍/mL in all extracts. The maximum DPPH radical, nitrite scavenging, SOD activity and inhibitory activity of lipid peroxidation were obtained at the ethylacetate and 70% ethanol extract. The maximum ABTS radical scavenging activity was obtained at the ethylacetate and hot water extract. However, in the case of reducing power and ferrous ion chelating activity, they were obtained at 70% ethanol and hexane extract, respectively. Nitrate scavenging activity showed the most excellent scavenging ability of 59.6% at 90 ㎍/mL of ethylacetate. The hexane extract had the highest ferrous ion chelating activity, showing 61.05% at 50 ㎍/mL, 66.07% at 70 ㎍/mL and 76.81% at 90 ㎍/mL, respectively. The results of this research show that the ethylacetate and 70% ethanol extracts of tea tree root can be used as a natural material for scavenging the radicals. However, future study is necessary to understand the mechanism of antioxidant activity by identification of substances.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.504-519
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• 2023

Heart disease is becoming the top reason of death all around the world. Diagnosing cardiac illness is a difficult endeavor that necessitates both expertise and extensive knowledge. Machine learning (ML) is becoming gradually more important in the medical field. Most of the works have concentrated on the prediction of cardiac disease, however the precision of the results is minimal, and data integrity is uncertain. To solve these difficulties, this research creates an Integrated Accurate-Secure Heart Disease Prediction (IAS) Model based on Deep Convolutional Neural Networks. Heart-related medical data is collected and pre-processed. Secondly, feature extraction is processed with two factors, from signals and acquired data, which are further trained for classification. The Deep Convolutional Neural Networks (DCNN) is used to categorize received sensor data as normal or abnormal. Furthermore, the results are safeguarded by implementing an integrity validation mechanism based on the hash algorithm. The system's performance is evaluated by comparing the proposed to existing models. The results explain that the proposed model-based cardiac disease diagnosis model surpasses previous techniques. The proposed method demonstrates that it attains accuracy of 98.5 % for the maximum amount of records, which is higher than available classifiers.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.49-62
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• 2023

There has been a rapid increase in the creation and alteration of new malware samples which is a huge financial risk for many organizations. There is a huge demand for improvement in classification and detection mechanisms available today, as some of the old strategies like classification using mac learning algorithms were proved to be useful but cannot perform well in the scalable auto feature extraction scenario. To overcome this there must be a mechanism to automatically analyze malware based on the automatic feature extraction process. For this purpose, the dynamic analysis of real malware executable files has been done to extract useful features like API call sequence and opcode sequence. The use of different hashing techniques has been analyzed to further generate images and convert them into image representable form which will allow us to use more advanced classification approaches to classify huge amounts of images using deep learning approaches. The use of deep learning algorithms like convolutional neural networks enables the classification of malware by converting it into images. These images when fed into the CNN after being converted into the grayscale image will perform comparatively well in case of dynamic changes in malware code as image samples will be changed by few pixels when classified based on a greyscale image. In this work, we used VGG-16 architecture of CNN for experimentation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.12
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• pp.2697-2704
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• 2011

In tissue engineering area, researchers observe symbiotic relationship such as proliferation, interaction, division apoptosis with time between cells in process of the 3D cell culture in hydrogels. The 3D cell culture process can be taken photographs into sliced images using confocal microscope. Symbiotic mechanism and changes of cell behaviors can be observed and analyzed from the images acquired by confocal microscope. In this paper, we proposed and developed graded noise elimination method and cluster boundary extraction method to extract boundaries information from sliced confocal images acquired in process of the 3D cell culture in hydrogels. The experiment based algorithm showed excellent performance for eliminating noises that have very small millet-shaped size. It is also showed to extract exact boundaries information for even complex clusters.