• KIPS Transactions on Software and Data Engineering
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• v.7 no.9
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• pp.351-360
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• 2018

The LWR (Locally Weighted Regression) model, which is traditionally a lazy learning model, is designed to obtain the solution of the prediction according to the input variable, the query point, and it is a kind of the regression equation in the short interval obtained as a result of the learning that gives a higher weight value closer to the query point. We study on an incremental ensemble learning approach for LWR, a form of lazy learning and memory-based learning. The proposed incremental ensemble learning method of LWR is to sequentially generate and integrate LWR models over time using a genetic algorithm to obtain a solution of a specific query point. The weaknesses of existing LWR models are that multiple LWR models can be generated based on the indicator function and data sample selection, and the quality of the predictions can also vary depending on this model. However, no research has been conducted to solve the problem of selection or combination of multiple LWR models. In this study, after generating the initial LWR model according to the indicator function and the sample data set, we iterate evolution learning process to obtain the proper indicator function and assess the LWR models applied to the other sample data sets to overcome the data set bias. We adopt Eager learning method to generate and store LWR model gradually when data is generated for all sections. In order to obtain a prediction solution at a specific point in time, an LWR model is generated based on newly generated data within a predetermined interval and then combined with existing LWR models in a section using a genetic algorithm. The proposed method shows better results than the method of selecting multiple LWR models using the simple average method. The results of this study are compared with the predicted results using multiple regression analysis by applying the real data such as the amount of traffic per hour in a specific area and hourly sales of a resting place of the highway, etc.

• The Korean Journal of Pain
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• v.6 no.2
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• pp.192-198
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• 1993

In 1984, a magnetic resonance spectrometer(magnetic resonance analyser, MRA $I_{TM}$) was developed by Sigrid Lipsett and Ronald J. Weinstock in the USA, Biomedical applications of the spectrometer have been examined by Dr. Hoang Van Duc(pathologist, USC), and Nakamura, et al(Japan). From their theoretical views, the biophysical functions of this machine are to analyse and synthesize a healthy tissue and organ resonance pattern, and to detect and correct an abnormal tissue and organ resonance pattern. All of the above functions are based on Quantum physics. The healthy tissue and organ resonance patterns are predetermined as standard magnetic resonance patterns by digitizing values based on peak resonance emissions(response levels or high pitched echo-sounds amplified via human body). In clinical practice, a counter or neutralizing resonance pattern calculated by the spectrometer can correct a phase-shifted resonance pattern(response levels or low pitched echo-sounds) of a diseased tissue and organ. By administering the counter resonance pattern into the site of pain and trigger point, it is possible to readjust the phase-shifted resonance pattern and then to alleviate pain through regulation of the neurotransmitter function of the nervous system. For assessing clinical effectiveness of pain relief with MRA $I_{TM}$ this study was designed to estimate pain intensity by the patient's subjective verbal rating scale(VRS such as graded to no pain, mild, moderate and severe) before application of it, to evaluate an amount of pain relief as applied the spectrometer by the patients subjective pain relief scale(visual analogue scale, VAS, 0~100%), and then to observe a continuation of pain relief following its application for managing acute and chronic pain in the 102 patients during an 8 months period beginning March, 1993. An application time of the spectrometer ranged from 15 to 30 minutes daily in each patient at or near the site of pain and trigger point when the patient wanted to be treated. The subjects consisted of 54 males and 48 females, with the age distribution between 23~40 years in 29 cases, 41~60 years in 48 cases and 61~76 years in 25 cases respectively(Table 1). The kinds of diagnosis and the main site of pain, the duration of pain before the application, and the frequency of it's application were recorded on the Table 2, 3 and 4. A distinction between acute and chronic pain was defined according to both of the pain intervals lasting within and over 3 months. The results of application of the spectrometer were noted as follows; In 51 cases of acute pain before the application, the pain intensities were rated mild in 10 cases, moderate in 15 cases and severe in 26 cases. The amounts of pain relief were noted as between 30~50% in 9 cases, 51~70% in 13 cases and 71~95% in 29 cases. The continuation of pain relief appeared between 6~24 hours in two cases, 2~5 days in 10 cases, 6~14 days in 4 cases, 15 days in one case, and completely relived of pain in 34 cases(Table 5~7). In 51 cases of chronic pain before the application, the pain intensities were rated mild in 12 cases, moderate in l8 cases and severe in 21 cases. The amounts of pain relief were noted as between 0~50% in 10 cases, 51~70% in 27 cases and 71~90% in 14 cases. The continuation of pain relief appeared to have no effect in two cases. The level of effective duration was between 6~12 hours in two cases, 2~5 days in 11 cases, 6~14 days in 14 cases, 15~60 days in 9 cases and in 13 cases the patient was completely relieved of pain(Table 5~7). There were no complications in the patients except a mild reddening and tingling sensation of skin while applying the spectrometer. Total amounts of pain relief in all of the subjects were accounted as poor and fair in 19(18.6%) cases, good in 40(39.2%) cases and excellent in 43(42.2%) cases. The clinical effectiveness of MRA $I_{TM}$ showed variable distributions from no improvements to complete relief of pain by the patient's assessment. In conclusion, we suggest that MRA $I_{TM}$ may be successful in immediate and continued pain relief but still requires several treatments for continued relief and may be gradually effective in pain relief while being applied repeatedly.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.81-96
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• 2013

Video information retrieval has become a very important issue because of the explosive increase in video data from Web content development. Meanwhile, content-based video analysis using visual features has been the main source for video information retrieval and browsing. Content in video can be represented with content-based analysis techniques, which can extract various features from audio-visual data such as frames, shots, colors, texture, or shape. Moreover, similarity between videos can be measured through content-based analysis. However, a movie that is one of typical types of video data is organized by story as well as audio-visual data. This causes a semantic gap between significant information recognized by people and information resulting from content-based analysis, when content-based video analysis using only audio-visual data of low level is applied to information retrieval of movie. The reason for this semantic gap is that the story line for a movie is high level information, with relationships in the content that changes as the movie progresses. Information retrieval related to the story line of a movie cannot be executed by only content-based analysis techniques. A formal model is needed, which can determine relationships among movie contents, or track meaning changes, in order to accurately retrieve the story information. Recently, story-based video analysis techniques have emerged using a social network concept for story information retrieval. These approaches represent a story by using the relationships between characters in a movie, but these approaches have problems. First, they do not express dynamic changes in relationships between characters according to story development. Second, they miss profound information, such as emotions indicating the identities and psychological states of the characters. Emotion is essential to understanding a character's motivation, conflict, and resolution. Third, they do not take account of events and background that contribute to the story. As a result, this paper reviews the importance and weaknesses of previous video analysis methods ranging from content-based approaches to story analysis based on social network. Also, we suggest necessary elements, such as character, background, and events, based on narrative structures introduced in the literature. We extract characters' emotional words from the script of the movie Pretty Woman by using the hierarchical attribute of WordNet, which is an extensive English thesaurus. WordNet offers relationships between words (e.g., synonyms, hypernyms, hyponyms, antonyms). We present a method to visualize the emotional pattern of a character over time. Second, a character's inner nature must be predetermined in order to model a character arc that can depict the character's growth and development. To this end, we analyze the amount of the character's dialogue in the script and track the character's inner nature using social network concepts, such as in-degree (incoming links) and out-degree (outgoing links). Additionally, we propose a method that can track a character's inner nature by tracing indices such as degree, in-degree, and out-degree of the character network in a movie through its progression. Finally, the spatial background where characters meet and where events take place is an important element in the story. We take advantage of the movie script to extracting significant spatial background and suggest a scene map describing spatial arrangements and distances in the movie. Important places where main characters first meet or where they stay during long periods of time can be extracted through this scene map. In view of the aforementioned three elements (character, event, background), we extract a variety of information related to the story and evaluate the performance of the proposed method. We can track story information extracted over time and detect a change in the character's emotion or inner nature, spatial movement, and conflicts and resolutions in the story.

• Journal of Chest Surgery
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• v.39 no.4 s.261
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• pp.261-268
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• 2006

Background: Current vascular prostheses are still inadequate for reconstruction of small-diameter vessels. Autologous pericardium can be a good alternative for this purpose as it already possesses good blood compatibility and shows a mechanical behavior similar to that of natural arteries. However, the clinical use of autologous pericardial tissue as a small-diameter vascular graft has limitations due to mixed outcomes from uncertain biological behavior and difficulty to gain reliable patency results in animal experiments. To study this issue, we implanted fresh and glutaraldehyde-treated autologous pericardium as small-diameter arterial grafts in dogs, and compared their time-related changes histologically. Material and Method: As a form of 5mm-diameter arterial graft, one pair of autologous pericardial tissue was used for comparison between the glutaraldehyde-treated and the glutaraldehyde-untreated grafts in the bilateral carotid arteries in the same dog. The patency of the grafts were evaluated at regular intervals with Doppler ultrasonography. After the predetermined periods of 3 days, 2 weeks, 1 month, 3 months and 6 months, the grafts in each animal were explanted. The retrieved grafts were processed for light and electron microscopic analyses following gross observation. Result: Of 7 animals, 2 were excluded from the study because one died postoperatively due to bleeding and the other was documented as one side of the grafts being obstructed. All 10 grafts in the remaining 5 dogs were patent. Grossly, a variable degree of thromboses were observed in the luminal surfaces of the grafts at 3 days and 2 weeks, despite good patency. Pseudointimal smooth blood-contacting surfaces were developed in the grafts at f month and later. By light microscopy, mesothelial cell layers of the pericardial tissue were absent in all explanted grafts. Newly formed endothelial cell layers on the blood-contacting surface were observed in both the glutaraldehyde-treated and fresh grafts at 3 months and later. The collagen fibers became degraded by fragmentation in the fresh graft at 1 month and In the glutaraldehyde-treated graft at 3 months. At 6 months, the collagen layers were no longer visible in either the glutaraldehyde-treated or fresh grafts. By electron microscopy, a greater amount of coarse fibrin fibers were observed in the fresh grafts than in the glutaraldehyde-treated grafts and, more compact and well-arrayed layers were observed in the glutaraldehyde-treated grafts than in the fresh grafts. Conclusion: The glutaraldehyde-treated small-diameter pericardial arterial grafts showed a better endothelialization of the blood-contacting surface and a slower fragmentation of the collagen layers than the fresh grafts, although it has yet to be proven whether these differences are so significant as to affect the patency results between the groups.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.125-148
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• 2018

Recently, as the demand for big data analysis increases, cases of analyzing unstructured data and using the results are also increasing. Among the various types of unstructured data, text is used as a means of communicating information in almost all fields. In addition, many analysts are interested in the amount of data is very large and relatively easy to collect compared to other unstructured and structured data. Among the various text analysis applications, document classification which classifies documents into predetermined categories, topic modeling which extracts major topics from a large number of documents, sentimental analysis or opinion mining that identifies emotions or opinions contained in texts, and Text Summarization which summarize the main contents from one document or several documents have been actively studied. Especially, the text summarization technique is actively applied in the business through the news summary service, the privacy policy summary service, ect. In addition, much research has been done in academia in accordance with the extraction approach which provides the main elements of the document selectively and the abstraction approach which extracts the elements of the document and composes new sentences by combining them. However, the technique of evaluating the quality of automatically summarized documents has not made much progress compared to the technique of automatic text summarization. Most of existing studies dealing with the quality evaluation of summarization were carried out manual summarization of document, using them as reference documents, and measuring the similarity between the automatic summary and reference document. Specifically, automatic summarization is performed through various techniques from full text, and comparison with reference document, which is an ideal summary document, is performed for measuring the quality of automatic summarization. Reference documents are provided in two major ways, the most common way is manual summarization, in which a person creates an ideal summary by hand. Since this method requires human intervention in the process of preparing the summary, it takes a lot of time and cost to write the summary, and there is a limitation that the evaluation result may be different depending on the subject of the summarizer. Therefore, in order to overcome these limitations, attempts have been made to measure the quality of summary documents without human intervention. On the other hand, as a representative attempt to overcome these limitations, a method has been recently devised to reduce the size of the full text and to measure the similarity of the reduced full text and the automatic summary. In this method, the more frequent term in the full text appears in the summary, the better the quality of the summary. However, since summarization essentially means minimizing a lot of content while minimizing content omissions, it is unreasonable to say that a "good summary" based on only frequency always means a "good summary" in its essential meaning. In order to overcome the limitations of this previous study of summarization evaluation, this study proposes an automatic quality evaluation for text summarization method based on the essential meaning of summarization. Specifically, the concept of succinctness is defined as an element indicating how few duplicated contents among the sentences of the summary, and completeness is defined as an element that indicating how few of the contents are not included in the summary. In this paper, we propose a method for automatic quality evaluation of text summarization based on the concepts of succinctness and completeness. In order to evaluate the practical applicability of the proposed methodology, 29,671 sentences were extracted from TripAdvisor 's hotel reviews, summarized the reviews by each hotel and presented the results of the experiments conducted on evaluation of the quality of summaries in accordance to the proposed methodology. It also provides a way to integrate the completeness and succinctness in the trade-off relationship into the F-Score, and propose a method to perform the optimal summarization by changing the threshold of the sentence similarity.