• The KIPS Transactions:PartB
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• v.19B no.1
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• pp.19-26
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• 2012

The rapid increase of information imposes new demands of content management. The purpose of automatic audio segmentation and classification is to meet the rising need for efficient content management. With this reason, this paper proposes a high-accuracy algorithm that segments audio signals and classifies them into different classes such as speech, music, silence, and environment sounds. The proposed algorithm utilizes support vector machine (SVM) to detect audio-cuts, which are boundaries between different kinds of sounds using the parameter sequence. We then extract feature vectors that are composed of statistical data and they are used as an input of fuzzy c-means (FCM) classifier to partition audio-segments into different classes. To evaluate segmentation and classification performance of the proposed SVM-FCM based algorithm, we consider precision and recall rates for segmentation and classification accuracy for classification. Furthermore, we compare the proposed algorithm with other methods including binary and FCM classifiers in terms of segmentation performance. Experimental results show that the proposed algorithm outperforms other methods in both precision and recall rates.

• Journal of the Korean Society for Library and Information Science
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• v.44 no.4
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• pp.75-94
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• 2010

This study designed a pilot system in which queries can be expanded through a tag ontology where equivalent, synonymous, or related tags are bound together, in order to improve the retrieval effectiveness of videos. We evaluated the proposed pilot system by comparing it to a tag-based system without tag control, in terms of recall and precision rates. Our study results showed that the mean recall rate in the structured folksonomy-based system was statistically higher than that in the tag-based system. On the other hand, the mean precision rate in the structured folksonomy-based system was not statistically higher than that in the tag-based system. The result of this study can be utilized as a guide on how to effectively use tags as social metadata of digital video libraries.

• English Language & Literature Teaching
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• no.3
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• pp.95-122
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• 1997

The purpose of this research was to investigate the effects of content and formal schema on reading comprehension. Five hundred fiftynine subjects from high school were assigned to one of the following levels and treatment conditions : (1) Higher level & Schema Activation, (2) Higher level & Non-schema Activation, (3) Lower level & Schema Activation, and (4) Lower level & Non-schema Activation. To evaluate the effects of schema activation. two experiments were conducted : one was related to the content schema and the other to the formal schema. To evaluate the effects of content schema, three different types of tests were conducted : (1) cloze test, (2) guessing the meanings of nonsense words, and (3) immediate recall test. To evaluate the effects of formal schema instruction, four kinds of tests were conducted : (1) sorting the sentences according to the importance, (2) identifying the signal words, (3) immediate recall test, and (4) identifying the specific information. For content schema condition, results indicated that the subjects given the titles or pictures before reading in "Content Schema Activation" treatment had better grades than those of the other treatment in all types of tests. regardless of their levels. Schema activation helped the subjects to increase the cognitive predictability of missing words and to participate in the tasks more actively with risk-taking. And it was also shown that good readers tend to process the words meaningfully, while poor readers tend to process the words phonetically or morphologically. Formal schema activation through teaching the text organization also had a significant influence on three types of tests: sorting the sentences according to the importance, identifying the signal words, and immediate recall test, but not on identifying the specific information. The implications from this study can be briefly noted as follows : (l) In teaching reading, the student's background knowledge should be activated as a pre-reading activity. (2) In reading, it is more important to emphasize the student's schema than the features of the text. (3) Various educational interventions should be introduced, especially for the lower level students. (4) Teaching text structures can be a powerful method for the top-down processing strategy.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.2
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• pp.73-78
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• 2020

Musculoskeletal disease is often caused by sitting down for long period's time or by bad posture habits. In order to prevent musculoskeletal disease in daily life, it is the most important to correct the bad sitting posture to the right one through real-time monitoring. In this study, to detect the sitting information of user's without any constraints, we propose posture measurement system based on multi-channel pressure sensor and CNN model for classifying sitting posture types. The proposed CNN model can analyze 5 types of sitting postures based on sitting posture information. For the performance assessment of posture classification CNN model through field test, the accuracy, recall, precision, and F1 of the classification results were checked with 10 subjects. As the experiment results, 99.84% of accuracy, 99.6% of recall, 99.6% of precision, and 99.6% of F1 were verified.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.76-84
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• 2022

Arrhythmia is a condition in which the heart beats abnormally or irregularly, early detection is very important because it can cause dangerous situations such as fainting or sudden cardiac death. However, performance degradation occurs due to personalized differences in ECG signals. In this paper, we propose arrhythmia classification using hybrid combination model of CNN-LSTM. For this purpose, the R wave is detected from noise removed signal and a single bit segment was extracted. It consisted of eight convolutional layers to extract the features of the arrhythmia in detail, used them as the input of the LSTM. The weights were learned through deep learning and the model was evaluated by the verification data. The performance was compared in terms of the accuracy, precision, recall, F1 score through MIT-BIH arrhythmia database. The achieved scores indicate 92.3%, 90.98%, 92.20%, 90.72% in terms of the accuracy, precision, recall, F1 score, respectively.

• Journal of the Korea Society of Computer and Information
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• v.15 no.8
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• pp.141-148
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• 2010

In mobile terminal recommend service system has general information restrictive recommend that individuality considering to user's information find and recommend. Also it has difficult of accurate information recommend bad points user's not offer individuality information preference recommend service. Therefore this paper is propose user's information individuality preference considering by user's individuality preference recommendation system using improved k-means algorithm. Propose method is correlation coefficients using user's information individuality preference when user's individuality preference recommendation using improved k-means algorithm. Restrictive information recommend to fix a problem, information of restrictive general recommend that user's information individuality preference offer to accurate information recommend. Performance experiment is existing service system as compared to evaluating the effectiveness of precision and recall, performance experiment result is appear to precision 85%, recall 68%.

• Science of Emotion and Sensibility
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• v.13 no.3
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• pp.501-510
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• 2010

The knowledge base on emotional information is one of the key elements in the implementation of emotion retrieval systems for contents design of Mobile devices. This study proposed a new approach to the knowledge base implementation by automatically extracting color components from full-color images. In this study, the validity of the proposed method was empirically tested. Database was developed using 100 interior images as visual stimuli and a total of 48 subjects participated in the experiment. In order to test the reliability of the proposed 'emotional information knowledge base', firstly 'recall ratio' that refers to frequencies of correct images from the retrieved images was derived. Secondly, correlation Analysis was performed to compare the ratings by the subjects to what the system calculated. Finally, the rating comparison was used to run a paired-sample t-test. The analysis demonstrated satisfactory recall ration of 62.1%. Also, a significant positive correlation (p<.01) was observed from all the emotion keywords. The paired Sample t-test found that all the emotion keywords except "casual" retrieved the images in the order from more relevant to less relevant images and the difference was statistically significant (t(9)=5.528, p<.05). Findings of this study support that the proposed 'emotional information knowledge base' established only with color information automatically extracted from images can be effectively used for such visual stimuli search tasks as commercial interior images.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.4
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• pp.374-378
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• 2000

in information retrieval, lack of positive examples is a main cause of poor performance. In this case most learning algorithms may not characteristics in the data to low recall. To solve the problem of unbalanced data, we propose a boosting method that uses linear perceptrons as weak learnrs. The perceptrons are trained on local data sets. The proposed algorithm is applied to text filtering problem for which only a small portion of positive examples is available. In the experiment on category crude of the Reuters-21578 document set, the boosting method achieved the recall of 80.8%, which is 37.2% improvement over multilayer with comparable precision.

• The Journal of the Korea Contents Association
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• v.5 no.2
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• pp.229-242
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• 2005

We constructed a full-text retrieval system for the scanned Hangul document images. The system consists of three parts; preprocessing, recognition, and retrieval components. The retrieval algorithm uses recognition results up to k-ranks. The algorithm is not only insensitive to the recognition errors, but also has the advantage of user-controllable recall and precision. For the objective performance evaluation, we used the scanned images of the Journal of Korea Information Science Society provided by KISTI. The system was shown to be practical through theevaluationofrecognitionandretrievalrates.

• The Research Journal of the Costume Culture
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• v.21 no.2
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• pp.206-219
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• 2013

In this study, it is explored how fashion brand slogans are categorized by linguistic characteristics and which linguistic characteristic is effective to improve consumer responses. Only 28% out of 1,346 fashion brands that are investigated are using slogans. Sportswear and men's wear are two product categories more often adopting slogans. A total of 11,113 consumers participated in the experimental study to evaluate slogan characteristics (familiarity, understandability, newness, pleasure), slogan attitude, and brand recall of 30 slogan-brand sets that were categorized by Park's 10 linguistic characteristics. In findings, slogans generating positive attitudes toward slogans and a good rate of brand recall tend to have no brand name in slogan, be written in the second-person view, include a futuristic message, and have information weighted on specialties. Slogan typology suggested based on results may be used for the future research as a basic guideline for the research on fashion brand slogans.