• The Korean Journal of Zoology
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• v.30 no.2
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• pp.140-153
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• 1987

나이제리아의 middle belt지역의 2개 저수지와 3개 하천에서 Aspatharia sinuata의 생장양상을 조사하였다. 가장 생장이 빠른 곳은 Oyun저수지와 Agbuur강이었으며, 가장 느린 곳은 Asa저수지와 Oyun강이었다. Walford plot에 의한 분석결과에 의하면 이 조개의 이론적 최대 길이는 Asa저수지가 7.39, Oyun저수지 9.65, Oyun강 6.75, Odo-Otin강 7.60, Agbuur강 9.86이었다. 일반적으로 생장은 초기 2년 사이가 빨랐고, 그 후는 생장속도가 낮아졌다. Asa저수지에서의 방사실험 결과에 의하면 패각에는 주생장선이 1년에 하나씩 형성되는 것으로 나타났다. 그리고 연례적인 수위저하와 그로 인한 하면 및 투명도의 감소가 이 조개의 생장을 억제하는 주요인이 되는 것으로 보였다.

• Journal of Oriental Neuropsychiatry
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• v.14 no.2
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• pp.35-42
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• 2003

Objective : This study was performed to investigate the childpsychiatric diseases in oriental medicine. Method : Several main testbooks and paper of oriental medicine in Korea and China were selected and investigated for this study. And then the results of research were analyzed, and compared to DSM-IV. Results and Conclusion : Some childpsychiatric diseases in oriental medicine like oyun(五軟), ojie(五遲), ogyung(五硬), yaje(夜啼), kaego(客?), kueji(鬼持), kibyung(?病) and so on, were revealed. It seems that they are analogous to mental disorders of western psychiatry.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.280-282
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• 2005

A ribonucleic acid (RNA) is one of the two types of nucleic acids found in living organisms. An RNA molecule represents a long chain of monomers called nucleotides. The sequence of nucleotides of an RNA molecule constitutes its primary structure, and the pattern of pairing between nucleotides determines the secondary structure of an RNA. Non-coding RNA genes produce transcripts that exert their function without ever producing proteins. Predicting the secondary structure of non-coding RNAs is very important for understanding their functions. We focus on Nussinov's algorithm as useful techniques for predicting RNA secondary structures. We introduce a new traceback matrix and scoring table to improve above algorithm. And the improved algorithm provides better levels of performance than the originals.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.11a
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• pp.15-18
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• 2005

A ribonucleic acid (RNA) is one of the two types of nucleic acids found in living organisms. An RNA molecule represents a long chain of monomers called nucleotides. The sequence of nucleotides of an RNA molecule constitutes its primary structure, and the pattern of pairing between nucleotides determines the secondary structure of an RNA. Non-coding RNA genes produce transcripts that exert their function without ever producing proteins. Predicting the secondary structure of non-coding RNAs is very important for understanding their functions. We focus on Nussinov's algorithm as useful techniques for predicting RNA secondary structures. We introduce a new traceback matrix and scoring table to improve above algorithm. And the improved prediction algorithm provides better levels of performance than the originals.

• Journal of Information Processing Systems
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• v.14 no.5
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• pp.1254-1271
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• 2018

Keyphrase extraction is one of fundamental natural language processing (NLP) tools to improve many text-mining applications such as document summarization and clustering. In this paper, we propose to use two novel techniques on the top of the state-of-the-art keyphrase extraction methods. First is the anti-patterns that aim to recognize non-keyphrase candidates. The state-of-the-art methods often used the rich feature set to identify keyphrases while those rich feature set cover only some of all keyphrases because keyphrases share very few similar patterns and stylistic features while non-keyphrase candidates often share many similar patterns and stylistic features. Second one is to use the dependency graph instead of the word co-occurrence graph that could not connect two words that are syntactically related and placed far from each other in a sentence while the dependency graph can do so. In experiments, we have compared the performances with different settings of the graphs (co-occurrence and dependency), and with the existing method results. Finally, we discovered that the combination method of dependency graph and anti-patterns outperform the state-of-the-art performances.

• Environmental Engineering Research
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• v.25 no.3
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• pp.267-273
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• 2020

This Hydrokinetic energy system is the process of extracting energy from rivers, canals and others sources to generate small scale electrical energy for decentralized usage. This study investigates the application of Soil and Water Assessment Tool (SWAT) in Geographical Information System (GIS) environment to evaluate the theoretical hydrokinetic energy potentials of selected Rivers (Asa, Awun and Oyun) all in Asa watershed, Kwara state, Nigeria. SWAT was interfaced with an open source GIS system to predict the flow and other hydrological parameters of the sub-basins. The model was calibrated and validated using observed stream flow data. Calibrated flow results were used in conjunction with other parameters to compute the theoretical hydrokinetic energy potentials of the Rivers. Results showed a good correlation between the observed flow and the simulated flow, indicated by ash Sutcliffe Efficiency (NSE) and R² of 0.76 and 0.85, respectively for calibration period, and NSE and R² of 0.70 and 0.74, respectively for the validation period. Also, it was observed that highest potential of 154.82 MW was obtained along River Awun while the lowest potential of 41.63 MW was obtained along River Asa. The energy potentials obtained could be harnessed and deployed to the communities around the watershed for their energy needs.

• Journal of Information Processing Systems
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• v.18 no.5
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• pp.637-649
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• 2022

Investors aim to increase their profitability by investing in the stock market. An adroit strategy for minimizing related risk lies through diversifying portfolio operationalization. In this paper, we propose a six-step stocks portfolio selection model. This model is based on data mining clustering techniques that reflect the ensuing impact of the political, economic, legal, and corporate governance in Mongolia. As a dataset, we have selected stock exchange trading price, financial statements, and operational reports of top-20 highly capitalized stocks that were traded at the Mongolian Stock Exchange from 2013 to 2017. In order to cluster the stock returns and risks, we have used k-means clustering techniques. We have combined both k-means clustering with Markowitz's portfolio theory to create an optimal and efficient portfolio. We constructed an efficient frontier, creating 15 portfolios, and computed the weight of stocks in each portfolio. From these portfolio options, the investor is given a choice to choose any one option.

• Journal of Korea Multimedia Society
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• v.23 no.3
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• pp.402-411
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• 2020

Power line noise in electrocardiogram signals makes it difficult to diagnose cardiovascular disease. ECG signals without power line noise are needed to increase the accuracy of diagnosis. In this paper, it is proposed DNN(Deep Neural Network) model to remove the power line noise in ECG. The proposed model is learned with noisy ECG, and clean ECG. Performance of the proposed model were performed in various environments(varying amplitude, frequency change, real-time amplitude change). The evaluation used signal-to-noise ratio and root mean square error (RMSE). The difference in evaluation metrics between the noisy ECG signals and the de-noising ECG signals can demonstrate effectiveness as the de-noising model. The proposed DNN model learning result was a decrease in RMSE 0.0224dB and a increase in signal-to-noise ratio 1.048dB. The results performed in various environments showed a decrease in RMSE 1.7672dB and a increase in signal-to-noise ratio 15.1879dB in amplitude changes, a decrease in RMSE 0.0823dB and a increase in signal-to-noise ratio 4.9287dB in frequency changes. Finally, in real-time amplitude changes, RMSE was decreased 0.3886dB and signal-to-noise ratio was increased 11.4536dB. Thus, it was shown that the proposed DNN model can de-noise power line noise in ECG.

• Journal of Information Processing Systems
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• v.9 no.1
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• pp.31-40
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• 2013

In this paper, a novel method is proposed to build an ensemble of classifiers by using a feature selection schema. The feature selection schema identifies the best feature sets that affect the arrhythmia classification. Firstly, a number of feature subsets are extracted by applying the feature selection schema to the original dataset. Then classification models are built by using the each feature subset. Finally, we combine the classification models by adopting a voting approach to form a classification ensemble. The voting approach in our method involves both classification error rate and feature selection rate to calculate the score of the each classifier in the ensemble. In our method, the feature selection rate depends on the extracting order of the feature subsets. In the experiment, we applied our method to arrhythmia dataset and generated three top disjointed feature sets. We then built three classifiers based on the top-three feature subsets and formed the classifier ensemble by using the voting approach. Our method can improve the classification accuracy in high dimensional dataset. The performance of each classifier and the performance of their ensemble were higher than the performance of the classifier that was based on whole feature space of the dataset. The classification performance was improved and a more stable classification model could be constructed with the proposed approach.

• Journal of Information Processing Systems
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• v.8 no.4
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• pp.575-588
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• 2012

Exploiting unlabeled text data with a relatively small labeled corpus has been an active and challenging research topic in text mining, due to the recent growth of the amount of biomedical literature. Biomedical named-entity recognition is an essential prerequisite task before effective text mining of biomedical literature can begin. This paper proposes an Active Co-Training (ACT) algorithm for biomedical named-entity recognition. ACT is a semi-supervised learning method in which two classifiers based on two different feature sets iteratively learn from informative examples that have been queried from the unlabeled data. We design a new classification problem to measure the informativeness of an example in unlabeled data. In this classification problem, the examples are classified based on a joint view of a feature set to be informative/non-informative to both classifiers. To form the training data for the classification problem, we adopt a query-by-committee method. Therefore, in the ACT, both classifiers are considered to be one committee, which is used on the labeled data to give the informativeness label to each example. The ACT method outperforms the traditional co-training algorithm in terms of f-measure as well as the number of training iterations performed to build a good classification model. The proposed method tends to efficiently exploit a large amount of unlabeled data by selecting a small number of examples having not only useful information but also a comprehensive pattern.