• ALGAE
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• v.31 no.2
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• pp.155-165
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• 2016

Green microalgae from the class Chlorophyceae represent a major biodiversity component of eukaryotic algae in continental water. Identification and classification of this group through morphology is a hard task, since it may present cryptic species and phenotypic plasticity. Despite the increasing use of molecular methods for identification of microorganisms, no single standard barcode marker is yet established for this important group of green microalgae. Some available studies present results with a limited number of chlorophycean genera or using markers that require many different primers for different groups within the class. Thus, we aimed to find a single marker easily amplified and with wide coverage within Chlorophyceae using only one pair of primers. Here, we tested the universality of primers for different genes (tufA, ITS, rbcL, and UCP4) in 22 strains, comprising 18 different species from different orders of Chlorophyceae. The ITS primers sequenced only 3 strains and the UCP primer failed to amplify any strain. We tested two pairs of primers for rbcL and the best pair provided sequences for 10 strains whereas the second one provided sequences for only 7 strains. The pair of primers for the tufA gene presented good results for Chlorophyceae, successfully sequencing 21 strains and recovering the expected phylogeny relationships within the class. Thus, the tufA marker stands out as a good choice to be used as molecular marker for the class.

• Journal of the Korean earth science society
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• v.37 no.1
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• pp.21-28
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• 2016

Mountains have significant influences on the atmospheric environment. The Korean Peninsula consists of approximately 70% mountainous area with numerous mountain ranges and peaks. The initial classification of mountains in Korea was made by a Japanese scientist from 1900 to 1902. In fact, the Japanese study created too many names of mountains to maintain, which led to confusions. The purpose of this study aims to simplify the previous names and classification of mountains in the Korean Peninsula so that they can be utilized for educational and general purpose of the society and educational institutions. Through this study, we name various mountains as one name "Korea Mountains" which is classified as the secondary world-mountain class stretching from the Korean Peninsula to northeast China (southern Manchuria). The Korea Mountains connect the third class regional medium-scale mountains of Jirin, Hamkyoung, Taebaek, and the fourth mountain class, 8 small-scale ranges including the Liaoning, Yaenbaen, Hambeuk, Pyoungbeuk, Whanghae, Charyoung, Kyoungsang and Namhae Mountains. The major mountains in the Korean Peninsula are normally influenced by the general circulation of the atmosphere of the world. The atmospheric conditions are modified on the up-stream and down-stream sides; there is a need for continuous monitoring of the atmospheric environment which impacts the ecosystem and human society.

• Journal of the Korean Institute of Navigation
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• v.20 no.2
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• pp.51-58
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• 1996

For the smooth flow of traffic vehicles and its effective management, it is necessary to have an exact information on traffic condition, i.e., the volume of traffic, velocity, occupancy and classification of vehicles. In particular, for classification of vehicles, there has been only image processing method using camera, where the method can obtain much information but rather expensive. In this paper, an algorithm for the measurement of velocity and total length of vehicles has been proposed to develop a general traffic management system, which is necessary to discriminate the class of vehicles. In order to realize the proposed algorithm, we have developed an ultrasonic spatial filtering method, which has better performance than that of using the traditional vehicle detector. To have this system to be constructed, we have introduced three sets of ultrasonic devices where each has one transmitter and two receivers which are arranged to obtain the spatial difference of objects. The velocity of vehicles can be measured by analyzing the occurrence time of pulses and their time differences. The total length of vehicles can be given by multiplying velocity with time interval of pulses sequence. To confirm the effectiveness of this measuring system, the experiment by the spatial filtering method using the ultrasonic sensors has been carried out. As the results, it is found that the proposed method can be used as one of measurement tools in the general traffic management system.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.1060-1063
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• 2011

인간은 목적지를 향하여 가는 방법의 선택에 있어서 가고자 하는 목적, 목적지, 출발 시간 등에 영향을 받는다. 그러나 이러한 매개변수들과 더불어 중요하게 고려되는 것은 바로 인간의 습관이다. 다시 말해 인간이 목적지로 가는 방법을 선택하는데 습관이라는 매개변수와 밀접한 영향이 있다는 것이다. 이를 미루어 볼 때, 인간의 이동은 습관으로 인해 대부분 특정한 범주 안에서 이동을 할 것이라는 추측할 수 있다. 나아가, 사람들이 흔히 들고 다니는 GPS장치에서 측정된 데이터가 추측한 속성으로 인해 범주를 벗어나는 이상현상을 검출하는 것으로 확장을 할 수 있다. 즉, GPS장치에서 측정된 데이터는 개인별로 클래스화(Classification)가 가능함을 추론할 수 있다. 본 논문에서는 실제 사람이 이동한 좌표를 바탕으로 시간당 변화량을 계산하여 좌표에 사상시켰다. 그리고, 단일 클래스 서포트 백터 머신(OCSVM)을 가지고 클래스화 했으며, OCSVM의 커널 함수 내의 변수인에 따라 클래스의 크기 혹은 클래스 내부의 밀도에 영향을 받음을 알 수 있었으며, 그 둘 사이에는 적절한 교환(Tradeoff)이 발생하였다는 결론이 나왔다.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.57-62
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• 2005

Microarray gene expression profiling technology is one of the most important research topics in clinical diagnosis of disease. Given thousands of genes, only a small number of them show strong correlation with a certain phenotype. To identify such an optimal subset from thousands of genes is intractable, which plays a crucial role when classify multiple-class genes express models from tumor samples. This paper proposes an efficient classifier design method to simultaneously select the most relevant genes using an intelligent genetic algorithm (IGA) and design an accurate classifier using Support Vector Machine (SVM). IGA with an intelligent crossover operation based on orthogonal experimental design can efficiently solve large-scale parameter optimization problems. Therefore, the parameters of SVM as well as the binary parameters for gene selection are all encoded in a chromosome to achieve simultaneous optimization of gene selection and the associated SVM for accurate tumor classification. The effectiveness of the proposed method IGA/SVM is evaluated using four benchmark datasets. It is shown by computer simulation that IGA/SVM performs better than the existing method in terms of classification accuracy.

• Proceedings of the KSRS Conference
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• v.2
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• pp.706-709
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• 2006

Cancer is one of the major causes of death; however, the survival rate can be increased if discovered at an early stage for timely treatment. According to the statistics of the World Health Organization of 2002, breast cancer was the most prevalent cancer for all cancers occurring in women worldwide, and it account for 16.8% of entire cancers inflicting Korean women today. In order to classify the type of breast cancer whether it is benign or malignant, this study was conducted with the use of the discriminant analysis and the decision tree of data mining with the breast cancer data disclosed on the web. The discriminant analysis is a statistical method to seek certain discriminant criteria and discriminant function to separate the population groups on the basis of observation values obtained from two or more population groups, and use the values obtained to allow the existing observation value to the population group thereto. The decision tree analyzes the record of data collected in the part to show it with the pattern existing in between them, namely, the combination of attribute for the characteristics of each class and make the classification model tree. Through this type of analysis, it may obtain the systematic information on the factors that cause the breast cancer in advance and prevent the risk of recurrence after the surgery.

• Korean Journal of Cognitive Science
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• v.6 no.2
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• pp.27-45
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• 1995

In this paper, we suggest the "Fuzzy Behavior Knowledge Space(FBKS)" and explain how to utilize the FBKS when aggregating decisions of individual classifiers. The concept of "Behavior Knowledge Space(BKS)" is known to be the best method in the context that each classifier offers only one class label as its decision. However. the BKS does not considers measurement value of class label. Furthermore, it does not allow the heuristic knowledge of human experts to be embedded when combining multiple decisions. The FBKS eliminates such drawbacks of the BKS by adapting the fwzy concepts. Our method applies to the classification results that contain both class labels and associated measurement values. Experimental results confirm that the FBKS could be a very promising tool in pattern recognition areas.

• Journal of Korean Elementary Science Education
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• v.27 no.3
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• pp.287-295
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• 2008

Elementary students' behaviors classifying leaves have been analyzed according to the material types provided for the classification class. 199 sixth grade students were participated in the task classifying the leaves of various plants for the research. The three types of materials provided to them for the class were real leaves, photos of the leaves and explanation cards including the photos of leaves. One of the research findings was that the only material made students handle in the observed behaviors was the real leave of the material types given as classifying. Three were differences between groups in the time required and the number of using criteria for the class. The numbers of criteria had been applied to analyzing their behaviors as classifying the real leaves which were less than those with photo materials. The amount of taken time to classify the real leaves and photo materials were less than those of another material. Finally, the contents of criteria did not differ between groups except appearing properties presented to the task with photo and explanation materials. It is expected that the research can be contributed for elementary school teachers and for curriculum developers to choose appropriate instructional materials as constructing curriculum contents for elementary science to make elementary school students acquire classifying skill in science classes.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.7
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• pp.1088-1097
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• 2021

Vibration data of mechanical equipment inevitably have noise. This noise adversely af ects the maintenance of mechanical equipment. Accordingly, the performance of a learning model depends on how effectively the noise of the data is removed. In this study, the noise of the data was removed using the Denoising Auto Encoder (DAE) technique which does not include the characteristic extraction process in preprocessing time series data. In addition, the performance was compared with that of the Wavelet Transform, which is widely used for machine signal processing. The performance comparison was conducted by calculating the failure detection rate. For a more accurate comparison, a classification performance evaluation criterion, the F-1 Score, was calculated. Failure data were detected using the One-Class SVM technique. The performance comparison, revealed that the DAE technique performed better than the Wavelet Transform technique in terms of failure diagnosis and error rate.

• Resources Recycling
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• v.27 no.1
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• pp.84-91
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• 2018

In this study, a novel soft information based most probable classification scheme is proposed for sorting recyclable metal alloys with laser induced breakdown spectroscopy (LIBS). Regression analysis with LIBS captured spectrums for estimating concentrations of common elements can be efficient for classifying unknown arbitrary metal alloys, even when that particular alloy is not included for training. Therefore, partial least square regression (PLSR) is employed in the proposed scheme, where spectrums of the certified reference materials (CRMs) are used for training. With the PLSR model, the concentrations of the test spectrum are estimated independently and are compared to those of CRMs for finding out the most probable class. Then, joint soft information can be obtained by assuming multi-variate normal (MVN) distribution, which enables to account the probability measure or a prior information and improves classification performance. For evaluating the proposed schemes, MVN soft information is evaluated based on PLSR of LIBS captured spectrums of 9 metal CRMs, and tested for classifying unknown metal alloys. Furthermore, the likelihood is evaluated with the radar chart to effectively visualize and search the most probable class among the candidates. By the leave-one-out cross validation tests, the proposed scheme is not only showing improved classification accuracies but also helpful for adaptive post-processing to correct the mis-classifications.