• YAKHAK HOEJI
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• v.49 no.1
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• pp.74-79
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• 2005

Metabonomic analysis has been recognized as a powerful approach for characterizing metabolic changes in biofluids due to toxicity, disease process or environmental influences. To investigate the possibility of relating metabolic changes with $^{1}H-NMR$ spectra, urine samples from Sprague-Dawley rats treated with various dietary restrictions or toxic substances (nicotine) were analysed using $^{1}H-NMR$ spectroscopy and pattern recognition techniques. Dietary restrictions-given to male rats were normal diet and high fat diet and fasting. The nicotine urine samples were collected from SD rats administered with nicotine (25 mg/kg) at the various time intervals. $^{1}H-NMR$ spectra of all urine samples were acquired at 400 MHz on a VARIAN spectrometer. To establish the presence of any intrinsic class-related patterns or clusters in each NMR data, methods of PCA (principal component analysis) and soft independent modeling of class analogy (SIMCA) analysis were used, and the results from these analyses were compared to each other. In all cases of dietary conditions and nicotine treatment, SIMCA analysis gave better results for the discrimination of NMR spectra of urine samples than PCA.

• Cross-Cultural Studies
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• v.39
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• pp.83-95
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• 2015

Every country has a dark history in the process of transition to a modern state. Many countries have until the 21st century, especially in East Asia, colonialism, has experienced conflict influenced, racial discrimination, the trauma of such massacre. Such a dark history in many countries are also still in progress. Dark History of China, which maintains the proletarian dictatorship is the 'Cultural Revolution(Wenhua da Geming)'. 'Cultural Revolution' is neither the outer aspect of the ideological struggle, but in fact it was not even class struggle ideology and class struggle. Put an end to the feudal intellectuals in China in the course of the tragedy stood and lead to build a new China suffered the humiliation of being betrayed from state power. Chinese writers after the 'Cultural Revolution' ended, was created in the process of creation reflects the tragedy of the 'Cultural Revolution' in the country(national memory) is suffering from the pain and suffering the same growth process as it saw this novel growth experienced in the personal growth process. "Road leaving at 18 years" of WiHua has ruled out the pain of growing national attention wholly to personal growth and pain. Such "Road leaving at 18 years" in the sense suggests the possibility of a typical growth story in China Contemporary Literature.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4426-4442
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• 2020

Since scene segmentation is becoming a hot topic in the field of autonomous driving and medical image analysis, researchers are actively trying new methods to improve segmentation accuracy. At present, the main issues in image semantic segmentation are intra-class inconsistency and inter-class indistinction. From our analysis, the lack of global information as well as macroscopic discrimination on the object are the two main reasons. In this paper, we propose a Densely connected residual Attention network (DA-Res2Net) which consists of a dense residual network and channel attention guidance module to deal with these problems and improve the accuracy of image segmentation. Specifically, in order to make the extracted features equipped with stronger multi-scale characteristics, a densely connected residual network is proposed as a feature extractor. Furthermore, to improve the representativeness of each channel feature, we design a Channel-Attention-Guide module to make the model focusing on the high-level semantic features and low-level location features simultaneously. Experimental results show that the method achieves significant performance on various datasets. Compared to other state-of-the-art methods, the proposed method reaches the mean IOU accuracy of 83.2% on PASCAL VOC 2012 and 79.7% on Cityscapes dataset, respectively.

• Korean Journal of Remote Sensing
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• v.23 no.4
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• pp.257-272
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• 2007

This paper analyzed relationships between various features from SAR data with multiple acquisition dates and mode (frequency, polarization and incidence angles), and land-cover classes. Two typical types of features were extracted by considering acquisition conditions of currently available SAR data. First, coherence, temporal variability and principal component transform-based features were extracted from multi-temporal and single mode SAR data. C-band ERS-1/2, ENVISAT ASAR and Radarsat-1, and L-band JERS-1 SAR data were used for those features and different characteristics of different SAR sensor data were discussed in terms of land-cover discrimination capability. Overall, tandem coherence showed the best discrimination capability among various features. Long-term coherence from C-band SAR data provided a useful information on the discrimination of urban areas from other classes. Paddy fields showed the highest temporal variability values in all SAR sensor data. Features from principal component transform contained particular information relevant to specific land-cover class. As features for multiple mode SAR data acquired at similar dates, polarization ratio and multi-channel variability were also considered. VH/VV polarization ratio was a useful feature for the discrimination of forest and dry fields in which the distributions of coherence and temporal variability were significantly overlapped. It would be expected that the case study results could be useful information on improvement of classification accuracy in land-cover classification with SAR data, provided that the main findings of this paper would be confirmed by extensive case studies based on multi-temporal SAR data with various modes and ground-based SAR experiments.

• Journal of KIISE:Software and Applications
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• v.28 no.9
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• pp.681-687
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• 2001

In order to raise a class discrimination power by combining multiple classifiers under the Bayesian decision theory, the upper bound of a Bayes error rate bounded by the conditional entropy of a class variable and decision variables obtained from training data samples should be minimized. Wang and Wong proposed a tree dependence first-order approximation scheme of a high order probability distribution composed of the class and multiple feature pattern variables for minimizing the upper bound of the Bayes error rate. This paper presents an extended high order product approximation scheme dealing with higher order dependency more than the first-order tree dependence, based on the minimization of the upper bound of the Bayes error rate. Multiple recognizers for unconstrained handwritten numerals from CENPARMI were combined by the proposed approximation scheme using the Bayesian formalism, and the high recognition rates were obtained by them.

• The Journal of the Korea Contents Association
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• v.10 no.12
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• pp.84-90
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• 2010

In this paper, we propose a quadratic discriminant analysis based approach for improving the discriminating strength of weak classifiers based on simple Haar-like features that were used in the Viola-Jones object detection framework. Viola and Jones built a strong classifier using a boosted ensemble of weak classifiers. However, their single threshold (or decision boundary) based weak classifier is sub-optimal and too weak for efficient discrimination between object class and background. A quadratic discriminant analysis based approach is presented which leads to hyper-quadric boundary between the object class and background class, thus realizing multiple thresholds based weak classifiers. Experiments carried out for car detection using 1000 positive and 3000 negative images for training, and 500 positive and 500 negative images for testing show that our method yields higher classification performance with fewer classifiers than single threshold based weak classifiers.

• International Journal of Control, Automation, and Systems
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• v.6 no.6
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• pp.828-835
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• 2008

We present a novel method for eye location by means of a two-level classifier scheme. Locating the eye by machine-inspection of an image or video is an important problem for Computer Vision and is of particular value to applications in biomedical imaging. Our method aims to overcome the significant challenge of an eye-location that is able to maintain high accuracy by disregarding highly variable changes in the environment. A first level of computational analysis processes this image context. This is followed by object detection by means of a two-class discrimination classifier(second algorithmic level).We have tested our eye location system using FERET and BioID database. We compare the performance of two-level classifier with that of non-level classifier, and found it's better performance.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.336-342
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• 2001

One of the most important problems on rule induction methods is that they cannot extract rules, which plausibly represent experts decision processes. On one hand, rule induction methods induce probabilistic rules, the description length of which is too short, compared with the experts rules. On the other hand, construction of Bayesian networks generates too lengthy rules. In this paper, the characteristics of experts rules are closely examined and a new approach to extract plausible rules is introduced, which consists of the following three procedures. First, the characterization of decision attributes (given classes) is extracted from databases and the classes are classified into several groups with respect to the characterization. Then, two kinds of sub-rules, characterization rules for each group and discrimination rules for each class in the group are induced. Finally, those two parts are integrated into one rule for each decision attribute. The proposed method was evaluated on a medical database, the experimental results of which show that induced rules correctly represent experts decision processes.

• Speech Sciences
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• v.8 no.2
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• pp.61-71
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• 2001

Speech material, which is collected from ARS(Automatic Response System), was analyzed and classified into disease and non-disease state. The material include 11 different kinds of diseases. Along with ARS speech, DAT(Digital Audio Tape) speech is collected in parallel to give the bench mark. To analyze speech material, analysis tools, which is developed local laboratory, are used to provide an improved and robust performance to the obtained parameters. To classify speech into disease and non-disease class, multi-layered neural network was used. Three different combinations of 3, 6, 12 parameters are tested to obtain the proper network size and to find the best performance. From the experiment, the classification rate of 92.5% was obtained.

• Communications for Statistical Applications and Methods
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• v.31 no.4
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• pp.365-375
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• 2024

Diverse methods to evaluate the prediction model of a time to event have been proposed in the context of right censored data where all subjects are subject to be susceptible. A time-dependent AUC (area under curve) measures the predictive ability of a marker based on case group and control one which are varying over time. When a substantial portion of subjects are event-free, a population consists of a susceptible group and a cured one. An uncertain curability of censored subjects makes it difficult to define both case group and control one. In this paper, our goal is to propose a time-dependent AUC for a cure rate model when a censoring distribution is related with covariates. A class of inverse probability of censoring weighted (IPCW) AUC estimators is proposed to adjust the possible sampling bias. We evaluate the finite sample performance of the suggested methods with diverse simulation schemes and the application to the melanoma dataset is presented to compare with other methods.