• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.6_2
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• pp.521-527
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• 2013

Rule set is an important step which impacts significantly on accuracy of object-oriented classification result. Therefore, this paper proposes a rule set to extract land cover from Landsat Thematic Mapper (TM) imagery acquired in Donganh, Hanoi, Vietnam. The rules were generated to distinguish five classes, namely river, pond, residential areas, vegetation and paddy. These classes were classified not only based on spectral characteristics of features, but also indices of water, soil, vegetation, and urban. The study selected five indices, including largest difference index max.diff; length/width; hue, saturation and intensity (HSI); normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) based on membership functions of objects. Overall accuracy of classification result is 0.84% as the rule set is used in classification process.

• Explosives and Blasting
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• v.21 no.4
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• pp.1-10
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• 2003

The classification of weak rocks is normally connected with the rippability classifications. The excavation of rock is frequently carried out by blasting. A classification of the weak rocks by test blasting with small quantity of explosives was attempted in the present study. The crater ratio and blasting constant that resoled from test blasting were used as a e parameter of the classification. The seismic velocity of rock mass and Protodyakonov's index were also applied for the also rock classification.

• Journal of the Korean Operations Research and Management Science Society
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• v.33 no.2
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• pp.1-16
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• 2008

The purpose of this paper is to propose a new binary classification method for predicting corporate failure based on genetic algorithm, and to validate its prediction power through empirical analysis. Establishing virtual companies representing bankrupt companies and non-bankrupt ones respectively, the proposed method measures the similarity between the virtual companies and the subject for prediction, and classifies the subject into either bankrupt or non-bankrupt one. The values of the classification variables of the virtual companies and the weights of the variables are determined by the proper model to maximize the hit ratio of training data set using genetic algorithm. In order to test the validity of the proposed method, we compare its prediction accuracy with ones of other existing methods such as multi-discriminant analysis, logistic regression, decision tree, and artificial neural network, and it is shown that the binary classification method we propose in this paper can serve as a premising alternative to the existing methods for bankruptcy prediction.

• Steel and Composite Structures
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• v.7 no.3
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• pp.185-200
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• 2007

Tubular construction is widely used in a range of civil and structural engineering applications. To date, the principal product range has comprised square, rectangular and circular hollow sections. However, hot-rolled structural steel elliptical hollow sections have been recently introduced and offer further choice to engineers and architects. Currently though, a lack of fundamental structural performance data and verified structural design guidance is inhibiting uptake. Of fundamental importance to structural metallic design is the concept of cross-section classification. This paper proposes slenderness parameters and a system of cross-section classification limits for elliptical hollow sections, developed on the basis of laboratory tests and numerical simulations. Four classes of cross-sections, namely Class 1 to 4 have been defined with limiting slenderness values. For the special case of elliptical hollow sections with an aspect ratio of unity, consistency with the slenderness limits for circular hollow sections in Eurocode 3 has been achieved. The proposed system of cross-section classification underpins the development of further design guidance for elliptical hollow sections.

• Journal of Electrical Engineering and Technology
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• v.9 no.1
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• pp.170-177
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• 2014

This paper presents a new method of classification of the induction machine faults using Time Frequency Representation, Particle Swarm Optimization and artificial neural network. The essence of the feature extraction is to project from faulty machine to a low size signal time-frequency representation (TFR), which is deliberately designed for maximizing the separability between classes, a distinct TFR is designed for each class. The feature vectors size is optimized using Particle Swarm Optimization method (PSO). The classifier is designed using an artificial neural network. This method allows an accurate classification independently of load level. The introduction of the PSO in the classification procedure has given good results using the reduced size of the feature vectors obtained by the optimization process. These results are validated on a 5.5-kW induction motor test bench.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.4
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• pp.333-340
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• 2008

This paper shows that the use of prior probabilities of the involved classes improve the accuracy of classification in case of poor separability between classes. Three cases of experiments are designed with two LiDAR datasets while considering three different classes (building, tree, and flat grass area). Moreover, random sampling method with human interpretation is used to achieve the approximate prior probabilities in this research. Based on the experimental results, Bayesian classification with the appropriate prior probability makes the improved classification results comparing with the case of non-prior probability when the ratio of prior probability of one class to that of the other is significantly different to 1.0.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.6
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• pp.1027-1035
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• 1994

This paper aims to make an artificial arm control strategy. For this, we propose a new feature extraction method and design artificial neural network for the functional classification of myoelectric signal(MES). We first transform the two channel myoelectric signals (MES) for biceps and triceps into frequency domain using fast Fourier transform (FFT). And features were obtained by comparing the magnitudes of ensemble spectrum data and used as inputs to the three-layer neural network for the learning. By changing the number of units in hidden layer of neural network we observed the improvement of classification performance. To observe the effeciency of the proposed scheme we performed experiments for classification of six arm functions to the three subjects. And we obtained on average 94[%] the ratio of classification.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.6 no.2
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• pp.42-52
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• 1988

The main object of this study is to prove the effectiveness of Landsat data in detecting the stressed areas in forest by extracting these areas. And also to choose the effective bands for this type of survey and to reduce the effect of shadow in forest to improve the accuracy of classification are the other objects. In this study Landsat-5 TM data is used and image processing techniques such as spatial filtering and ratio are taken to reduce the effect of shadow and to improve the classification accuracy. As a result following conclusions are obtained. First, Landsat TM data is useful to detect the stressed areas in forest. Second, when detecting the stressed area, band 4 and 5 are the most effective. Third, spatial filtering and ratio are useful to reudce the effect of shadow and improve the classification accuracy. Especially, ratio has great effect on improving the classification accuracy between forest and other areas.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.6
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• pp.7-12
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• 2014

It has good features for social networking with friends SNS is maintained. However, various enterprises, individuals invading the inconvenience spammers have exposure to a number of users to tweet spam. The study was conducted in the existing research on these spam tweets. However, the results showed a more accurate classification and detection is difficult because of the lack of precision and different causes. In this paper, we describe how to classify the characteristics of spammers, classification criteria. Also has a link rate and difference between followers and following, these features were present classification criteria for spammers account. This experiment was performed according to the criteria. Randomized trial of spam and non-spam accounts were selected and account type was conducted according to the criteria 68% of the link ratio of spam accounts. Followers / Following ratio was 27581.5. Non-spam accounts was 6.12%. Followers / Following ratio was 1.26.

• The Korean Journal of Applied Statistics
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• v.32 no.2
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• pp.291-300
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• 2019

This study investigates the characteristics of evaluation measures for classification models on a binary response variable in order to evaluate their suitability for use. Six measures are considered: Accuracy, Sensitivity, Specificity, Precision, F-measure, and the Heidke's skill score (HSS). Evaluation measures are reformulated using x(ratio of actually 1), y(ratio predicted by 1), z(ratio of both actual and predicted by 1) from the confusion matrix. We suggest two necessary conditions to assess the suitability of the evaluation measures. The first condition is that the measure function is constant for x and y in the case of a random model. The second condition is that the measure function is increasing for z and decreasing for x and y. Since only HSS satisfies the two conditions, that is always appropriate as an evaluation measure for the classification model on the binary response variable, and the other measures should be used within a limited range.