• KCID journal
• /
• v.17 no.2
• /
• pp.17-27
• /
• 2010

In order to manage the water quality in reservoir, it is necessary to understand the temporal and spatial variation of reservoirs and to classify the reservoirs. In this research, agricultural reservoirs are classified according to physical characteristics (depth, residence time, shape of the reservoir etc) and water quality using multivatriate analysis (PCA and CA). CA (Cluster Analysis) method classify reservoirs into several groups as a similarity of the reservoirs, but it is difficult to indicate a full list to the one table. In case of PCA (Principle Component Analysis) method, it has the advantage for the classification on the reservoirs depending on the water quality similarity and also it is useful to analyze the relationship between related factors through correlation analysis. However PCA is limited to classify into several groups based on the characteristics of the reservoirs and each user should be classified as randomly subjective according to the relative position of the reservoir in the figure. In conclusions, compared to conventional reservoirs classification methods, both CA and PCA methods are considered to be a classification method that describes the nature of the reservoir well, but classification results has a restriction on use, so further research will be needed to complement.

• The Journal of the Acoustical Society of Korea
• /
• v.19 no.3
• /
• pp.20-25
• /
• 2000

This paper proposes the method of surface classification and threshold value selection for surface classification of the three-dimensional object recognition. The processings of three-dimensional image processing system consist of three steps, i.e, acquisition of range data, feature extraction and matching process. This paper proposes the method of shape feature extraction from the acquired rage data in the entire three-dimensional image processing system. In order to achieve these goals, firstly, this article proposes the surface classification method by using the distribution characteristics of sign value from range values. Also pre-existing method which uses the K-curvature and K-curvature has limitation in the practical threshold value selection. To overcome this, this article proposes the selection of threshold value for surface classification. Finally, the effectiveness of this article is demonstrated by the several experiments.

• Journal of Sasang Constitutional Medicine
• /
• v.17 no.2
• /
• pp.46-51
• /
• 2005

1. Objectives The purpose of this study is to objectify the diagnosis of Sasang Constitution. Using the methods of this study, it will improve to classificate Sasang Constitution. 2. Methods 1) Automatic feature extraction of human frontal faces for Sasang Constitution classification. 2) Color feature extraction of human frontal faces (1)Erosion filtering (skin-white, the other-black) (2) Median median 3. Results and Conclusions Observing a person's shape has been the major method for Sasang Constitution classification, which usually has been dependent upon doctor's intuition as of these days. We are developing an automatic system which provides objective basic data for Sasang Constitution classification. For this, in this paper, firstly, the signal processing techniques are applied to automatic feature extraction of human frontal faces for Sasang Constitution classification. The experiment is conducted to verify the effectiveness of the proposed system.

• Korean Journal of Materials Research
• /
• v.16 no.10
• /
• pp.633-638
• /
• 2006

Silica powders with spherical shape and narrow size distribution were prepared by large-scale ultrasonic spray pyrolysis applying the droplet classification apparatus. On the other hand, silica powders prepared by large-scale ultrasonic spray pyrolysis without droplet classification apparatus had broad size distribution. Droplet classification apparatus used in this paper applied the principles of cyclone and dispersion plate with small holes. The droplets formed from the ultrasonic spray generator applying the droplet classification apparatus had narrow size distribution. The droplets with fine and large sizes were eliminated by droplet classification apparatus. The optimum flow rate of the carrier gas and diameter of the hole of the dispersion plate were studied to reduce the size distribution of the silica powders prepared by large-scale ultrasonic spray pyrolysis. The size distribution of the silica powders prepared by large-scale ultrasonic spray pyrolysis at the optimum preparation conditions was 0.76.

• International Journal of Computer Science & Network Security
• /
• v.22 no.10
• /
• pp.271-281
• /
• 2022

Manual seed classification challenges can be overcome using a reliable and autonomous seed purity identification and classification technique. It is a highly practical and commercially important requirement of the agricultural industry. Researchers can create a new data mining method with improved accuracy using current machine learning and artificial intelligence approaches. Seed classification can help with quality making, seed quality controller, and impurity identification. Seeds have traditionally been classified based on characteristics such as colour, shape, and texture. Generally, this is done by experts by visually examining each model, which is a very time-consuming and tedious task. This approach is simple to automate, making seed sorting far more efficient than manually inspecting them. Computer vision technologies based on machine learning (ML), symmetry, and, more specifically, convolutional neural networks (CNNs) have been widely used in related fields, resulting in greater labour efficiency in many cases. To sort a sample of 3000 seeds, KNN, SVM, CNN and CNN-SVM hybrid classification algorithms were used. A model that uses advanced deep learning techniques to categorise some well-known seeds is included in the proposed hybrid system. In most cases, the CNN-SVM model outperformed the comparable SVM and CNN models, demonstrating the effectiveness of utilising CNN-SVM to evaluate data. The findings of this research revealed that CNN-SVM could be used to analyse data with promising results. Future study should look into more seed kinds to expand the use of CNN-SVMs in data processing.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.17 no.2
• /
• pp.121-135
• /
• 2014

Object-based classification methods provide a valid alternative to traditional pixel-based methods. This study reports the results of an object-based classification to examine land cover in the demilitarized zones(DMZs) of Inje-gun. We used land cover classes(7 classes for main category and 13 classes for sub-category) selected from the criteria by Korea Ministry of Environment. The average and standard deviation of the spectrum values, and homogeneity of GLCM were chosen to map land cover types in an hierarchical approach using the nearest neighborhood method. We then identified the distributional characteristics of land cover by considering 3 topographic characteristics (altitude, slope gradient, distance from the Southern Limited Line(SLL)) within the DMZs. The results showed that scale 72, shape 0.2, color 0.8, compactness 0.5 and smoothness 0.5 were the optimum weight values while scale, shape and color were most influenced parameters in image segmentation. The forests (92%) were main land cover type in the DMZs; the grassland(5%), the urban area (2%) and the forests (broadleaf forest: 44%, mixed forest: 42%, coniferous forest: 6%) also occupied mostly in land cover classes for sub-category. The results also showed that facilities and roads had higher density within 2 km from the SLL, while paddy, field and bare land were distributed largely outside 6 km from the SLL. In addition, there was apparent distinction in land cover by topographic characteristics. The forest had higher density at above altitude 600m and above slope gradient $30^{\circ}$ while agriculture, bare land and grass land were distributed mainly at below altitude 600m and below slope gradient $30^{\circ}$.

• Journal of Fashion Business
• /
• v.12 no.2
• /
• pp.87-99
• /
• 2008

This study aims to classify lower trunk body types of Chinese men in their middle age (30s and 40s) and suggest the standard for them to design slacks pattern. Mollison's relation deviations were used to analyze the direct measurement items in this research compared to those of Size Korea 2004. Though Korean middle-aged men were higher in most items than Chinese counterparts, all items except mid-thigh circumference and side hip length were merely within the range of ${\pm}1\sigma$. According to the results of size classification by absolute values, factor analysis extracted to 2 factors(horizontal size of lower body and vertical size of lower body), and cluster analysis brought about 3 types(type1: tall and thin trunk (36.9%), type 2 : normal height and thick trunk(45.5%), and type 3 : short and normal trunk(17.6%) with some significant differences among them. Also, the results of shape classification by index values, they were extracted 4 factors (waist-hip flatness, waist-hip cross section, vertical waist-groin and hip-surface length) by factor analysis and revealed 3 types(type 1: different waist-hip width, thick, long waist, long upper hip, and short hip-surface length(27.8%), type 2 : different waist-hip width, flat, short upper hip, high hip and groin, and average hip-surface length(29.4%), and type 3: small waist-hip width, thick, average upper hip, and short hip-surface length (42.8%)) with significant differences among them by cluster analysis. The results of standard body types by shape-size combination, 19subjects(10.16%) under these values are regarded as standard body types. Significance was not seen in all items in the t-test results between the total group and the standard group. The latter had lower variation coefficients and smaller individual differences than the former. However, in-depth research is required for generalization since this research is limited to a small number of subjects in Ningbo of Zhejiang.

• Journal of Electrical Engineering and information Science
• /
• v.3 no.2
• /
• pp.131-138
• /
• 1998

We derive a back-propagation learning algorithm of fuzzy neural networks using fuzzy operations, which preserves the shapes of fuzzy numbers, in order to utilize fuzzy if-then rules as well as numerical data in the learning of neural networks for classification problems and for fuzzy control problems. By introducing the shape preseving fuzzy operation into a neural network, the proposed network simplifies fuzzy arithmetic operations of fuzzy numbers with exact result in learning the network. And we illustrate our approach by computer simulations on numerical examples.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2004.10a
• /
• pp.280-285
• /
• 2004

In this study, we made a comparative study of backpropagation neural network and probabilistic neural network and bayesian classifier and perceptron as shape recognition algorithm of welding flaws. For this purpose, variables are applied the same to four algorithms. Here, feature variable is composed of time domain signal itself and frequency domain signal itself. Through this process, we comfirmed advantages/disadvantages of four algorithms and identified application methods of four algorithms.

• Journal of Fashion Business
• /
• v.8 no.1
• /
• pp.76-90
• /
• 2004

The purpose of this study was to classify women's face types and to analyze the measurement of face types. For study, 180 adult women(aged between 20 and 29) in Pusan and Ulsan area was sampled to be measured for facial types. Data were analyzed by Frequencies, Means, Duncan's Multiple Range Test, Distinction analysis. The major results were as followed. Women's face types were classified by 6 types and there were round shape(29.4%), oblong shape(18.9%), inverted triangle shape(16.1%), square shape(13.9%), egg shape(11.7%), diamond shape(10.0%) in the subject. Phyiognomic facial height was 182.38mm, the upper face length was 59.82mm, the middle face length 60.82mm, the lower face length 61.76mm, and the index of face length to face breadth was 1.35. The face width was 134.90mm, interocular distance 34.75mm, the nose width 33.93mm, and mouth width was 43.87mm. And also, differences from those measurements like forehead breadth, face length/bizygion breadth, forehead slopper, bigonion breadth, bignathion breadth, bignathion slopper.