• Journal of Korean Institute of Industrial Engineers
• /
• v.32 no.2
• /
• pp.133-140
• /
• 2006

Large signal type data sets are difficult to classify, especially if the data sets are non-stationary. In this paper, large signal type and non-stationary data sets are wavelet transformed so that distinct features of the data are extracted in wavelet domain rather than time domain. For the classification of the data, a few wavelet coefficients representing class properties are employed for statistical classification methods : Linear Discriminant Analysis, Quadratic Discriminant Analysis, Neural Network etc. The application of our wavelet-based feature selection method to a mass spectrometry data set for ovarian cancer diagnosis resulted in 100% classification accuracy.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.467-469
• /
• 2003

This research aimed to develop forest type classification technique for the mixed forest with coniferous and broad-leaved species using the high resolution satellite data. QuickBird data was used as satellite data. The method of this research was to extract satellite data for every single tree crown using image segmentation technique, then to evaluate the accuracy of classification by changing grouping criteria such as tree species, families, coniferous or broad-leaved species, and timber prices. As a result, the classification of tree species and families level was inaccurate, on the other hand, coniferous or broad-leaved species and timber price level was high accurate.

• Fashion & Textile Research Journal
• /
• v.13 no.5
• /
• pp.734-742
• /
• 2011

This study has been undertaken as a basic research for automatic pattern design for baseball uniforms manufactured under custom-MTM system, propose building up of a system whereby various partial patterns are combined under an automatic design system and develop a multi-combination type pattern searching algorithm which allows development of a various designs. As a result of this, type classification based on pattern design elements includes side, open, collar, facing and panel type. Design have been divided into coarse classification ranging from level 1 to 7 according to pattern design elements, based on a design distribution chart. Out of 7 such levels, 3 major types determining design which are, more specifically, level 1 sleeve type, level 2 open type and level 3 collar type, have been taken and combined to determine a total of 12 types to be used for design classification codes. Respective name of style and patterns have been coded using alphabet and numerals. Totally, pattern searching algorithm of multi-combination type has been developed whereby combination of patterns belonging to a specific style can be retrieved automatically once that style name is designated on the automatic pattern design system.

• Journal of the Korean Society of Safety
• /
• v.34 no.5
• /
• pp.119-131
• /
• 2019

Among the themes for CSR(Corporate Social Responsibility), consumer issues include protecting the health and safety of consumers who purchase and use the products. In particular, ensuring product safety is a major theme of consumer issues for corporate social responsibility. Currently, the government implements the Electrical Appliances and Consumer Products Safety Control Act for product safety management and selects products that may harmful to consumers as safety control items, and manages the products by designating them as 4 types of safety certification, safety confirmation, supplier conformity verification, and safety standard compliance. In this paper, we propose management plans for the establishment of a more reasonable classification type of safety management target for 48 items of consumer products to be controlled by the act, and confirm the validity of the plan. First, we perform cluster analysis using data for CISS (Consumer Injury Surveillance System) to derive a new classification type of the safety management target. Next, we compare the results of the cluster analysis with the classification type of the act and the existing scenario classification method RAS (Risk Assessment by Scenario) and the causal network method RAMP (Risk Assessment Method based on Probability). Based on these results, we propose two new plans of safety management target classification and verify its validity.

• Journal of Korean Academy of Oral and Maxillofacial Radiology
• /
• v.24 no.2
• /
• pp.227-236
• /
• 1994

The purpose of this study was to observe mandibular condyle shape in an asymptomatic population. In order to carry out this study, 96 temporomandibular joints in 48 adults(22 males, 26 females), who were asymptomatic for temporomandibular disturbances and had no history of prosthodontic or orthodontic treatments, were selected, and radiographed using the Sectograph(Denar Co., U.S.A.) for lateral and frontal individualized corrected TMJ tomograph and submentovertex radiograph. Mandibular condyles were classified morphologically, and measured medioateral and anteroposterior dimensions and condylar angulation. The obtained results were as follows. 1. In the classification of condyle shape on lateral tomographs, 94.8% were convex type and 5.2% were angled type. 2. In the classification of condyle shape on frontal tomographs, 45.3% were convex type, 32.0% were round type, 16.0% were flat type, and 6.7% were angled type. 3. In the classification of condyle shape on submentovertex radiographs, 34.5% were flat-convex type, 22.9% were flat-flat type, 20.8% were concave-convex type, 19.8% were convex-convex type, and 1.0% were concave-flat type and convex-flat type. Concave-concave type, convex-concave type, and flat-concave type were not observed. 4. The average mediolateral legth of the condyle was 19.3㎜ and the average anteroposterior length was 9.4㎜. The average angle between the long axis of condyle and the coronal plane made on submentovertex view was 19.6 degrees.

• Geomechanics and Engineering
• /
• v.37 no.3
• /
• pp.263-277
• /
• 2024

Artificial Neural Networks (ANNs) are artificial learning algorithms that provide successful results in solving many machine learning problems such as classification, prediction, object detection, object segmentation, image and video classification. There is an increasing number of studies that use ANNs as a prediction tool in soil classification. The aim of this research was to understand the role of hyperparameter optimization in enhancing the accuracy of ANNs for soil type classification. The research results has shown that the hyperparameter optimization and hyperparamter optimized ANNs can be utilized as an efficient mechanism for increasing the estimation accuracy for this problem. It is observed that the developed hyperparameter tool (HyperNetExplorer) that is utilizing the Covariance Matrix Adaptation Evolution Strategy (CMAES), Genetic Algorithm (GA) and Jaya Algorithm (JA) optimization techniques can be successfully used for the discovery of hyperparameter optimized ANNs, which can accomplish soil classification with 100% accuracy.

• Journal of Korean Society for Geospatial Information Science
• /
• v.17 no.2
• /
• pp.55-60
• /
• 2009

By applying NDVI(Normalized Difference Vegetation Index) and TCT(Tasseled-Cap Transformation) image algorithm on the basis of KOMSAP-2 MSC(Multi Spectral Camera) image(Jun. 12, 2007) for Naenam-myeon, Gyeongju city in this study, DN distribution map was drawn up. Discrimination analysis of image algorithm for the accuracy improvement of forest type classification was conducted through the comparative analysis between the distribution maps of NDVI and TCT DN, and forest field surveying data, and finally, the accuracy of the forest type classification was verified through the overlay analysis with the forest field surveying data. Through this study, it is thought that low cost and high efficiency will be able to be expected in the process of the examination for the automation practicality of the forest type classification and of the production of the accurate forest type classification map by using KOMPSAT-2 MSC image.

• The Journal of the Acoustical Society of Korea
• /
• v.13 no.4
• /
• pp.5-11
• /
• 1994

In this paper, the ciassification of Korean character type using multi neural network and fuzzy inference based on block partition is studied. For the effective classification of a consonant and a vowel, block partition method which devide the region of a consonant and a vowel for each type in the character is proposed. And the partitioned block can be changed according to the each type adaptively. For the improvement of classification rate, the multi neural network with a whole and a part neural network is consisted, and the character type by using fuzzy inference is decided. To verify the validity of the proposed method, computer simulation is accomplished, and from the classification rate $92.6\%$, the effectivity of the method is confirmed.

• Aerospace Engineering and Technology
• /
• v.6 no.1
• /
• pp.213-221
• /
• 2007

This paper showed how to analysis the object-based classification for wildfire fuel type map using Hyperion hyperspectral remote sensing data acquired in April, 2002 and compared the results of the object-based classification with the results of the pixel-based classification. Our methodological approach for wildfire fuel type map firstly processed correcting abnormal pixels and atypical bands and also calibrating atmospheric noise for enhanced image quality. Fuel type map is characterized by the results of the spectral mixture analysis(SMA). Object-based approach was based on segment-based endmember selection, while pixel-based method used standard SMA. To validate and compare, we used true-color high resolution orthoimagery.

• International Journal of Highway Engineering
• /
• v.16 no.4
• /
• pp.127-134
• /
• 2014

PURPOSES : To operate more efficient traffic management system, it is utmost important to detect the change in congestion level on a freeway segment rapidly and reliably. This study aims to develop classification method of congestion change type. METHODS: This research proposes two classification methods to capture the change of the congestion level on freeway segments using the dedicated short range communication (DSRC) data and the vehicle detection system (VDS) data. For developing the classification methods, the decision tree models were employed in which the independent variable is the change in congestion level and the covariates are the DSRC and VDS data collected from the freeway segments in Korea. RESULTS : The comparison results show that the decision tree model with DSRC data are better than the decision tree model with VDS data. Specifically, the decision tree model using DSRC data with better fits show approximately 95% accuracies. CONCLUSIONS : It is expected that the congestion change type classified using the decision tree models could play an important role in future freeway traffic management strategy.