• Journal of the Korean Wood Science and Technology
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• v.47 no.4
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• pp.385-392
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• 2019

In our previous study, we investigated the use of ensemble models based on LeNet and MiniVGGNet to classify the images of transverse and longitudinal surfaces of five Korean softwoods (cedar, cypress, Korean pine, Korean red pine, and larch). It had accomplished an average F1 score of more than 98%; the classification performance of the longitudinal surface image was still less than that of the transverse surface image. In this study, ensemble methods of two different convolutional neural network models (LeNet3 for smartphone camera images and NIRNet for NIR spectra) were applied to lumber species classification. Experimentally, the best classification performance was obtained by the averaging ensemble method of LeNet3 and NIRNet. The average F1 scores of the individual LeNet3 model and the individual NIRNet model were 91.98% and 85.94%, respectively. By the averaging ensemble method of LeNet3 and NIRNet, an average F1 score was increased to 95.31%.

• Korean Journal of Remote Sensing
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• v.27 no.6
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• pp.637-651
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• 2011

This study presents a rule-based urban image classification method for time series analysis of changes in the vicinity of Asan-si and Cheonan-si in Chungcheongnam-do, using Landsat satellite images (1991-2006). The area has been highly developed through the relocation of industrial facilities, land development, construction of a high-speed railroad, and an extension of the subway. To determine the yearly changing pattern of the urban area, eleven classes were made depending on the trend of development. An algorithm was generalized for the rules to be applied as an unsupervised classification, without the need of training area. The analysis results show that the urban zone of the research area has increased by about 1.53 times, and each correlation graph confirmed the distribution of the Built Up Index (BUI) values for each class. To evaluate the rule-based classification, coverage and accuracy were assessed. When Optimal allowable factor=0.36, the coverage of the rule was 98.4%, and for the test using ground data from 1991 to 2006, overall accuracy was 99.49%. It was confirmed that the method suggested to determine the maximum allowable factor correlates to the accuracy test results using ground data. Among the multiple images, available data was used as best as possible and classification accuracy could be improved since optimal classification to suit objectives was possible. The rule-based urban image classification method is expected to be applied to time series image analyses such as thematic mapping for urban development, urban development, and monitoring of environmental changes.

• Journal of Korean Library and Information Science Society
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• v.23
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• pp.329-362
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• 1995

Newspaper Article Information is an important source of information on social phenomenon with historical value. The development of computer and technology of information communication enables the construction of Newspaper Article Database by CTS and service through computer communication. It made it possible for the peoples to utilize the Newspaper Article Information easily. However, it is very difficult to utilize the currently prevailing system. There are differences in classification system of Newspaper Article Database and the Data Display Format. This survey aims to review the characteristics of Newspaper Article Database and current domestic computer communication service system. By comparing the classification system of Retrieval Menu and Data Display Format, I intended to suggest the standardized way of utilization which enables the users utilize them more easily and conveniently. The results of this survey is as follows : 1. More sub-divided distinction of classification item is required. 2. Separate classification item should be established for the distinction of article form which is very difficult to classify the subject. 3. Data Display Format should be equi n.0, pped with standardized format and signal which enables the users recognize it easily.

• Journal of Korea Game Society
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• v.16 no.6
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• pp.19-28
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• 2016

Digital games are generating various types of virtual landscape and the importance of virtual landscape has been arises. However, there has not been any research done how to design the virtual landscape. To establish virtual landscape design methodology, establishing the classification system and suggesting the representative type of virtual landscape is needed. With this research, I collected the classification standard and established five standards; story, cooperation, interaction level, dimensions and shape of the space. With that system, I classified digital game and could prove the effectiveness. Also by classifying cases through 20 years of timeline, I could come out with three representative types. This result will work as a reference to the future research; establishing design methodology on virtual landscape.

• Journal of Information Management
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• v.43 no.3
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• pp.79-95
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• 2012

This study is designed for library classification of the tourism. Contents studies are summarized as follows. First, an analysis of the current status of classification and items by a key work 'tourism' in KDC, NDC, DDC, UDC, and LCC which are main library classification tables today indicates that they are too limited to travelling and tourism, tourism policy, and tourist attractions etc. Second, we divided areas studies newly based on the theoretical books in order to extract the classification items of the tourism field, and comparatively analyzed main library classification systems today. We divided basic categories of the classification items into four including tourism general, tourists, tourism attraction, tourism media. Third, a new classification was designed for library classification of the tourism field. Basically, the tourism field was assigned to the main four items and the classification items which were weighted or scattered were adjusted.

• Journal of Powder Materials
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• v.15 no.4
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• pp.320-330
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• 2008

On the base of experience in development of Magnetic Powder Composites, and particularly Soft Magnetic Composites, authors are trying to systematize classification and indicate possible development prospective of Magnetic Nanocomposites (MN) technology and their applications in electrical converters. Clear classification and systematization, at an early stage of any materials and technology development, are essential and lead for better understanding and communication between researchers and industry involved. This concern MN as well and it seems to be the right time to make it at present stage of their development. Presented proposal of classification distinguishes various types of MN by their magnetic properties and area of possible applications. It is not a close set of types, and can be extended due to increase of knowledge concern these nanocomposites.

• Journal of Biosystems Engineering
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• v.37 no.4
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• pp.215-224
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• 2012

Purpose: This study was conducted to classify the energy efficiency of 131 tractor models tested during from 2006 to 2010 in Korea. Methods: Four sub-indexes were developed using the fuel consumptions at 60% and 90% of rated speed with partial loads and at pull speeds of 3.0 km/h and 7.5 km/h with maximum drawbar pull. Weighting factors of the sub-indexes were also considered to reflect the characteristics of tractor's actual working hours in Korea. Four sub-indexes were integrated into a classification index. Using the developed classification index, a five-classification system was made on the basis of normal distribution of tractors over the classification range. Percentage of $1^{st}$ grade interval was expected to be close to 15%, $2^{nd}$ grade 20%, $3^{rd}$ grade 30%, $4^{th}$ grade 20%, $5^{th}$ grade 15%. Results: Number of $1^{st}$ grade was 21, $2^{nd}$ grade 23, $3^{rd}$ grade 39, $4^{th}$ grade 33, $5^{th}$ grade 15 among 131 models. Conclusions: Classification index was developed by integrating four sub-indexes. By the classification method using developed index, distribution of classified tractors was acceptable for practical application.

• Journal of the Korean Wood Science and Technology
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• v.47 no.1
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• pp.101-109
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• 2019

This paper examines the classification of five coniferous species, including larch (Larix kaempferi), red pine (Pinus densiflora), Korean pine (Pinus koraiensis), cedar (Cryptomeria japonica), and cypress (Chamaecyparis obtusa), using near-infrared (NIR) spectra. Fifty lumber samples were collected for each species. After air-drying the lumber, the NIR spectra (wavelength = 780-2500 nm) were acquired on the wide face of the lumber samples. Soft independent modeling of class analogy (SIMCA) was performed to classify the five species using their NIR spectra. Three types of spectra (raw, standard normal variated, and Savitzky-Golay $2^{nd}$ derivative) were used to compare the classification reliability of the SIMCA models. The SIMCA model based on Savitzky-Golay $2^{nd}$ derivatives preprocessing was determined as the best classification model in this study. The accuracy, minimum precision, and minimum recall of the best model (PCA models using Savitzky-Golay $2^{nd}$ derivative preprocessed spectra) were evaluated as 73.00%, 98.54% (Korean pine), and 67.50% (Korean pine), respectively.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.89-96
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• 2021

This work is an extension of my work presented a robust and economically efficient method for the Discrimination of four Mung-Beans [1] varieties based on quantitative parameters. Due to the advancement of technology, users try to find the solutions to their daily life problems using smartphones but still for computing power and memory. Hence, there is a need to find the best classifier to classify the Mung-Beans using already suggested features in previous work with minimum memory requirements and computational power. To achieve this study's goal, we take the experiments on various supervised classifiers with simple architecture and calculations and give the robust performance on the most relevant 10 suggested features selected by Fisher Co-efficient, Probability of Error, Mutual Information, and wavelet features. After the analysis, we replace the Artificial Neural Network and Deep learning with a classifier that gives approximately the same classification results as the above classifier but is efficient in terms of resources and time complexity. This classifier is easily implemented in the smartphone environment.

• Journal of the Korean Wood Science and Technology
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• v.49 no.5
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• pp.491-503
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• 2021

Wood identification is regularly performed by observing the wood anatomy, such as colour, texture, fibre direction, and other characteristics. The manual process, however, could be time consuming, especially when identification work is required at high quantity. Considering this condition, a convolutional neural networks (CNN)-based program is applied to improve the image classification results. The research focuses on the algorithm accuracy and efficiency in dealing with the dataset limitations. For this, it is proposed to do the sample selection process or only take a small portion of the existing image. Still, it can be expected to represent the overall picture to maintain and improve the generalisation capabilities of the CNN method in the classification stages. The experiments yielded an incredible F1 score average up to 93.4% for medium sample area sizes (200 × 200 pixels) on each CNN architecture (VGG16, ResNet50, MobileNet, DenseNet121, and Xception based). Whereas DenseNet121-based architecture was found to be the best architecture in maintaining the generalisation of its model for each sample area size (100, 200, and 300 pixels). The experimental results showed that the proposed algorithm can be an accurate and reliable solution.