• Journal of the Korea Convergence Society
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• v.8 no.12
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• pp.49-53
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• 2017

Breast cancer is the sccond most female cancer patient in the entire female cancer patient, and has emerged as the highest contributor to female cancer deaths. If breast cancer id detected early, the cure rate is 92 percent. However, if early detection fails, breast cancer has a very high rate of metastasis. The transition from cancer to cancer has become more successful as cancer progresses. Early diagnosis of cancer is an important factor in improving quality of life. Examples of breast cancer include Mammograph, ultrasound, and Momotome. Mommography is not only painful for the examiner, but also for easy access to breast cancer exam inations. In this paper, breast cancer diagnosis data mammograph data was used. In addition, the Neural Network were classified for early diagnosis of breast cancer early using NEWFM. After learning of data using NEWFM, the accuracy of the breast cancer data classification was 84.4391%.

• Journal of Convergence for Information Technology
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• v.8 no.5
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• pp.131-135
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• 2018

The heart disease taking the second place of the cause of the death of modern people is a terrible disease that makes sudden death without noticing. To judge the aortic valve disease of heart diseases a name of disease was diagnosed using psychological data provided from physioNet. Aortic valve is a valve of the area that blood is spilled from left ventricle to aorta. Aortic stenosis of heart troubles is a disease when the valve does not open appropriately in contracting the left ventricle to aorta due to narrowed aortic valve. In this paper, 3126 samples of cardiac sound data were used as an experiment data composed of 180 characteristics including normal people and aortic valve stenosis patients. To diagnose normal and aortic valve stenosis patients, NEWFM was utilized. By using an average method of weight as an feature selection method of NEWFM, the result shows 91.0871% accuracy.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.3
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• pp.95-103
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• 1998

This paper deals with a problem occuring in recognition of tactile images due to the effects of imposed force at a me urement moment. Tactile image of a contact surface, used for recognition of the surface type, varies depending on the forces imposed so that a false recognition may result in. This paper fuzzifies two parameters of the contour of a tactile image with the membership function formed by considering the imposed force. Two fuzzifed paramenters are fused by the average Minkowski's dist; lnce. The proposed algorithm was implemented on the multisensor system cnmposed of an optical tact le sensor and a 6 axes forceltorque sensor. By the experiments, the proposed algorithm has shown average recognition ratio greater than 869% over all imposed force ranges and object models which is about 14% enhancement comparing to the case where only the contour information is used. The pro- ~oseda lgorithm can be used for end-effectors manipulating a deformable or fragile objects or for recognition of 3D objects by implementing on multi-fingered robot hand.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.20-26
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• 2008

As they have more and more intelligence robots or computers these days, so the interaction between intelligence robot(computer) - human is getting more and more important also the emotion recognition and expression are indispensable for interaction between intelligence robot(computer) - human. In this paper, firstly we extract emotional features at speech signal and facial image. Secondly we apply both BL(Bayesian Learning) and PCA(Principal Component Analysis), lastly we classify five emotions patterns(normal, happy, anger, surprise and sad) also, we experiment with decision fusion and feature fusion to enhance emotion recognition rate. The decision fusion method experiment on emotion recognition that result values of each recognition system apply Fuzzy membership function and the feature fusion method selects superior features through SFS(Sequential Forward Selection) method and superior features are applied to Neural Networks based on MLP(Multi Layer Perceptron) for classifying five emotions patterns. and recognized result apply to 2D facial shape for express emotion.

• Journal of Intelligence and Information Systems
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• v.8 no.2
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• pp.1-13
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• 2002

In this paper, we propose an object tracking system which can be convinced of moving area shaped on objects through color sequential images, decided moving directions of foot messengers or vehicles of image sequences. In static camera, we suggests a new evaluating method extracting co-occurrence matrix with feature vectors of RGB after analyzing and blocking difference images, which is accessed to field of camera view for motion. They are energy, entropy, contrast, maximum probability, inverse difference moment, and correlation of RGB color vectors. we describe how to analyze and compute corresponding relations of objects between adjacent frames. In the clustering, we apply an algorithm of FCM(fuzzy c means) to analyze matching and clustering problems of adjacent frames of the featured vectors, energy and entropy, gotten from previous phase. In the matching phase, we also propose a method to know correspondence relation that can track motion each objects by clustering with similar area, compute object centers and cluster around them in case of same objects based on membership function of motion area of adjacent frames.

• The KIPS Transactions:PartB
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• v.12B no.2 s.98
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• pp.123-130
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• 2005

In this paper, fingerprint identification algorithm using pixel direction factor in blocks is proposed to minimize false acceptance ratio and to apply security system. The proposed algorithm is that a fingerprint image is divided by 16 blocks, then feature parameters which have direct factors of $0^{\circ},\;45^{\circ},\;90^{\circ}\;and\;135^{\circ}$ is extracted for each block. Membership function of a reference fingerprint and an input fingerprint for the extracted parameters is calculated, then identification of two fingerprint is distinguished using fuzzy inference. False acceptance ratio is evaluated about different fingerprints of In kinds regardless of sex and shape which are obtained from adults, and false rejection ratio is evaluated about fingerprints which are obtained by adding fingerprints of 10 kinds on different fingerprints of 100 kinds. The experiment results is that false acceptance ratio is average $0.34\%$ about experiment of 4,950 times, and false rejection ratio is average $3.7\%$ about experiment of 1,000 times. The proposed algerian is excellent for recognition rate and security.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2023.04a
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• pp.38-38
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• 2023

Sorghum (Sorghum bicolor L.) is a promising biomass crop with a high lignocellulose content. This study aimed to select high salt-tolerance sorghum lines for cultivation on reclaimed land. Using 7-day seedlings of the sorghum population consisted of 71 radiation-derived mutants (M₂ to M₆) and 33 genetic resources, survival rate (SR), plant height (PH), root length (RL), fresh weight (FW), and chlorophyll content (CC) were measured for two weeks after 102 mM (0.6%) NaCl treatment. Furthermore, the characteristics of the sorghum population were confirmed using correlation analysis, PCA (principal component analysis), and the FCE (fuzzy comprehensive evaluation) method. Under 102 mM NaCl conditions, SR ranged from 4.9 (IS645-200-6) to 82.4% (KLSo79125-200-1), with an average of 49.9%. PH varied from 7.5 (Mesusu-100-2) to 33.2 cm (DINE-A-MITE-100-2-10), with an average of 20.4 cm. RL ranged from 1.0 (IS645-200-1) to 17.0 cm (30-100-2), with an average of 7.7 cm. FW varied from 0.1 (IS645-200-6) to 4.5 g/plant (DINE-A-MITE-100-2-10), with an average of 2.1 g/plant. CC ranged from 0.9 (DINE-A-MITE-100-2-2) to 3.1 mg/g (IS12937), with an average of 1.7 mg/g. An overall positive correlation, with SR and FW (r = 0.86, P < 0.01), and FW and CC (r = 0.79, P < 0.01), was shown by correlation analysis. Among the five traits, two principal components were extracted by PCA analysis. PC1 was significantly associated with FW, while PC2 was highly involved with RL. To evaluate the salt tolerance level of the sorghum population when an FCE based on trait data was performed, MFV (membership function value) was 0.68. As a result of compiling the MFV of each line, eight lines with MFV > 0.68 were selected. Ultimately, the radiation-derived mutant lines, DINE-A-MITE-100-2-10 and DINE-A-MITE-100-2-12 were selected as salt-tolerant sorghum lines. The results are expected to inform salt-tolerant sorghum breeding programs, and the high salt-tolerance sorghum lines might be advantageous for cultivation on reclaimed land.