• Journal of Hospice and Palliative Care
• /
• v.2 no.1
• /
• pp.23-35
• /
• 1999

Purpose : The hospice movement began about 30 years ago in Korea. However, basic studies have seldom been conducted about the general public's knowledge concerning hospice care and their needs for it. The purpose of this study was to investigate the general public's knowledge of and attitude toward hospice, and their needs for hospice care, and to analyze the needs for hospice care in relation to their knowledge and attitude in residents from a specific community. Methods : The survey was conducted with 924 people randomly selected from a district in Seoul. The data were collected through a self-reporting questionnaire constructed by the authors. With 30 items given in the questionnaire, the level of hospice needs showed Cronbach's alpha .89 in a pilot study and .92 in this study and the items were classified into four areas by a factor analysis. The data collected were analyzed by means of t-test and ANOVA. Results : 1) The average age of the respondents was 38. The majority of the respondents were well-educated. 2) Regarding awareness of hospice care, 54%(501 people) indicated they have heard of hospice. About 74% thought that people should be able to prepare for death in advance. About 83% wanted to be informed when they have life threatening illnesses such as terminal cancer. Also, about 63% responded that patients with terminal diseases should be provided with physical, spiritual, and psychological care for minimizing pain and peaceful death. Regarding the attitude toward hospice care, 74% responded that they would use hospice care if needed. The number of the respondents who preferred home visitation by the hospice team to care for the terminally ill ranked first with 34%. Concerning needs for hospice care : 1) By needs area, physical need showed highest mean(M=4.37), followed by social need(M=3.96), emotional need(M=3.87), and the spiritual need(M=3.79). The overall need level showed the mean value of 4.00 which reflects a considerable need for hospice care. 2) By demographic characteristics, people age over 50, the married, and the unemployed indicated higher level of needs for hospice care. Women showed higher level of needs than did men, and Catholics demonstrated higher level of needs than believers of other religion(P<0.0001). 3) As for the knowledge of and attitude toward hospice rare, the level of hospice care needs was significantly higher in the following groups: those who have heard of hospice, those who are aware of death preparation, those who want information on terminal diseases, those who want to use every method to sustain life, and those who are aware of hospice needs(P<0.001). Conclusion : It is assumed that the findings of this study on the knowledge, attitude, and needs for hospice care in the public can contribute to planning a successful hospice care program. Furthermore, the findings of this study will serve as useful data for the promotion of home hospice care to improve the quality of life of community residents, and contribute to the development of hospice care as a whole.

• Journal of Life Science
• /
• v.21 no.4
• /
• pp.509-514
• /
• 2011

Maloactic fermentation (MLF) occurs after completion of alcoholic fermentation and is mediated by lactic acid bacteria (LAB), mainly Oenococcus oeni. Kiwi wine more than commercial grape wine has the problem of high acidity. Therefore, we investigated the optimal MLF conditions for regulating strong acidity and improving the quality properties of wine fermented with Kiwi fruit cultivated in Korea. For alcohol fermentation, industrial wine yeast Saccharomyces cerevisiae KCCM 12650 strains and LAB, known as MLF strains, were used to alleviate wine acidity. First, the various experimental conditions of Kiwi fruit, initial pH (2.5, 3.5, 4.5), fermenting temperature (20, 25, $30^{\circ}C$), and sugar contents (24 $^{\circ}Brix$), were adjusted, and after the fermentation period, we measured the acidity, pH, and the change in organic acid content by the AOAC method and HPLC analysis. The alcohol content of fermented Kiwi wine was 12.75%. Further, total acidity and pH of Kiwi wine were 0.78% and 3.5, respectively. Total sugar and total polyphenol contents of Kiwi wine were 38.72 mg/ml and 60.18 mg/ml, respectively. With regard to organic acid content, the control contained 0.63 mg/ml of oxalic acid, 2.99 mg/ml of malic acid, and 0.71 mg/ml of lactic acid, whereas MLF wine contained 0.69 mg/ml of oxalic acid, 0.06 mg/ml of malic acid, and 3.12 mg/ml of lactic acid. Kiwi wine had lower malic acid values and total acidity than control after MLF processing. In MLF, the optimum initial pH value and fermentation temperature were 3.5 and $25^{\circ}C$, respectively. Therefore, these studies suggest that establishment of optimal MLF conditions could improve the properties of Kiwi wine manufactured in Korea.

• Journal of Intelligence and Information Systems
• /
• v.23 no.1
• /
• pp.95-108
• /
• 2017

Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.

• (The)Study of the Eastern Classic
• /
• no.50
• /
• pp.55-88
• /
• 2013

Gwonwu Hong Chan-yu is one of the modern and contemporary Korean scholars of Sino-Korean literature and one of the literati of his era, so is respected as a guiding light by academic descendants. Gwonwu was a teacher of his era, who experienced all the turbulence of Korean society, such as the Japanese occupation by force, the Korean War, the military dictatorship, and the struggle for democracy, and who educated and led young scholars of his time. However, academia has not payed attention to his life and achievements since his death. This paper is to examine the poetry of Gwonwu Hong Chan-yu, one of the representative modern and contemporary scholar of Sini-Korean literature, which has not yet been discussed by academia. The minimal meaning of this paper is that it is a first work based on his anthology, which has not been discussed by academia, and a first full-scale study on Gwonwu Hongchan-yu. For the reason, this paper aims at the detailed inspection of his poetic pieces recorded in his anthology. Nonetheless, despite such intentions, some limits cannot be avoided here and there in this paper for the insufficient knowledge and academic capability of this paper's writer and for the lack of academic sources. Gwonwu's poetry examined through his anthology shows the characteristic which is that his poems focus on exposing his own internal emotions. Such a characteristic says that his idea of poetic literature payed attention more to individuality, that is exposition of private emotions, than to social utility of poems. Gwonwu's such an idea of poetic literature can be generally affirmed throughout his poetry. Accordingly, Gwonwu preferred classical Chinese poems to archaistic poems, and single poems to serial poems; and avoided writing poems within social relations such as farewell-poems, bestowal-poems, and mourning-poems. When the characteristics of Gwonwu's poetic literature get summarized as such, however, some questions remain. The preferential question is whether the poems in his anthology are the whole poetry of him. Although Gwonwu's poetic pieces that the writer of this paper have checked out till now are all in his anthology, it is very much questionable whether Gwonwu's poetry can be summed up only with these poems. The next question is what is the writing method for taking joy(spice), sentiment, and full-heart into his poems if Gwonwu's poems focus on exposing his internal emotions, and if poems exposing joy and poems exposing sentiment and full-heart appear coherently in various different spaces and circumstances of writing. The final question is what are the meanings of Gwonwu's poems if his poetry checked out through his anthology directly shows either the reality carried in his poems or the reality of a time in his life. The questions listed above are thought to be resolved by the synchronizing process of stereoscopic searches both for Gwonwu as an individual and for the era of his life. Especially, spurring deeper researches toward a new direction regarding Gwonwu's poetry has an important meaning for construction of a complete modern and contemporary history of Sino-Korean literature and for procurement of continuous research on Sino-Korean literature and its history. For the reason, it is thought that more efforts of researchers are required.

• Journal of Intelligence and Information Systems
• /
• v.24 no.1
• /
• pp.183-203
• /
• 2018

News articles are the most suitable medium for examining the events occurring at home and abroad. Especially, as the development of information and communication technology has brought various kinds of online news media, the news about the events occurring in society has increased greatly. So automatically summarizing key events from massive amounts of news data will help users to look at many of the events at a glance. In addition, if we build and provide an event network based on the relevance of events, it will be able to greatly help the reader in understanding the current events. In this study, we propose a method for extracting event networks from large news text data. To this end, we first collected Korean political and social articles from March 2016 to March 2017, and integrated the synonyms by leaving only meaningful words through preprocessing using NPMI and Word2Vec. Latent Dirichlet allocation (LDA) topic modeling was used to calculate the subject distribution by date and to find the peak of the subject distribution and to detect the event. A total of 32 topics were extracted from the topic modeling, and the point of occurrence of the event was deduced by looking at the point at which each subject distribution surged. As a result, a total of 85 events were detected, but the final 16 events were filtered and presented using the Gaussian smoothing technique. We also calculated the relevance score between events detected to construct the event network. Using the cosine coefficient between the co-occurred events, we calculated the relevance between the events and connected the events to construct the event network. Finally, we set up the event network by setting each event to each vertex and the relevance score between events to the vertices connecting the vertices. The event network constructed in our methods helped us to sort out major events in the political and social fields in Korea that occurred in the last one year in chronological order and at the same time identify which events are related to certain events. Our approach differs from existing event detection methods in that LDA topic modeling makes it possible to easily analyze large amounts of data and to identify the relevance of events that were difficult to detect in existing event detection. We applied various text mining techniques and Word2vec technique in the text preprocessing to improve the accuracy of the extraction of proper nouns and synthetic nouns, which have been difficult in analyzing existing Korean texts, can be found. In this study, the detection and network configuration techniques of the event have the following advantages in practical application. First, LDA topic modeling, which is unsupervised learning, can easily analyze subject and topic words and distribution from huge amount of data. Also, by using the date information of the collected news articles, it is possible to express the distribution by topic in a time series. Second, we can find out the connection of events in the form of present and summarized form by calculating relevance score and constructing event network by using simultaneous occurrence of topics that are difficult to grasp in existing event detection. It can be seen from the fact that the inter-event relevance-based event network proposed in this study was actually constructed in order of occurrence time. It is also possible to identify what happened as a starting point for a series of events through the event network. The limitation of this study is that the characteristics of LDA topic modeling have different results according to the initial parameters and the number of subjects, and the subject and event name of the analysis result should be given by the subjective judgment of the researcher. Also, since each topic is assumed to be exclusive and independent, it does not take into account the relevance between themes. Subsequent studies need to calculate the relevance between events that are not covered in this study or those that belong to the same subject.

• Journal of Intelligence and Information Systems
• /
• v.24 no.4
• /
• pp.111-136
• /
• 2018

In this paper, we propose a methodology to extract answer information about queries from various types of unstructured documents collected from multi-sources existing on web in order to expand knowledge base. The proposed methodology is divided into the following steps. 1) Collect relevant documents from Wikipedia, Naver encyclopedia, and Naver news sources for "subject-predicate" separated queries and classify the proper documents. 2) Determine whether the sentence is suitable for extracting information and derive the confidence. 3) Based on the predicate feature, extract the information in the proper sentence and derive the overall confidence of the information extraction result. In order to evaluate the performance of the information extraction system, we selected 400 queries from the artificial intelligence speaker of SK-Telecom. Compared with the baseline model, it is confirmed that it shows higher performance index than the existing model. The contribution of this study is that we develop a sequence tagging model based on bi-directional LSTM-CRF using the predicate feature of the query, with this we developed a robust model that can maintain high recall performance even in various types of unstructured documents collected from multiple sources. The problem of information extraction for knowledge base extension should take into account heterogeneous characteristics of source-specific document types. The proposed methodology proved to extract information effectively from various types of unstructured documents compared to the baseline model. There is a limitation in previous research that the performance is poor when extracting information about the document type that is different from the training data. In addition, this study can prevent unnecessary information extraction attempts from the documents that do not include the answer information through the process for predicting the suitability of information extraction of documents and sentences before the information extraction step. It is meaningful that we provided a method that precision performance can be maintained even in actual web environment. The information extraction problem for the knowledge base expansion has the characteristic that it can not guarantee whether the document includes the correct answer because it is aimed at the unstructured document existing in the real web. When the question answering is performed on a real web, previous machine reading comprehension studies has a limitation that it shows a low level of precision because it frequently attempts to extract an answer even in a document in which there is no correct answer. The policy that predicts the suitability of document and sentence information extraction is meaningful in that it contributes to maintaining the performance of information extraction even in real web environment. The limitations of this study and future research directions are as follows. First, it is a problem related to data preprocessing. In this study, the unit of knowledge extraction is classified through the morphological analysis based on the open source Konlpy python package, and the information extraction result can be improperly performed because morphological analysis is not performed properly. To enhance the performance of information extraction results, it is necessary to develop an advanced morpheme analyzer. Second, it is a problem of entity ambiguity. The information extraction system of this study can not distinguish the same name that has different intention. If several people with the same name appear in the news, the system may not extract information about the intended query. In future research, it is necessary to take measures to identify the person with the same name. Third, it is a problem of evaluation query data. In this study, we selected 400 of user queries collected from SK Telecom 's interactive artificial intelligent speaker to evaluate the performance of the information extraction system. n this study, we developed evaluation data set using 800 documents (400 questions * 7 articles per question (1 Wikipedia, 3 Naver encyclopedia, 3 Naver news) by judging whether a correct answer is included or not. To ensure the external validity of the study, it is desirable to use more queries to determine the performance of the system. This is a costly activity that must be done manually. Future research needs to evaluate the system for more queries. It is also necessary to develop a Korean benchmark data set of information extraction system for queries from multi-source web documents to build an environment that can evaluate the results more objectively.

• Korean Journal of Environment and Ecology
• /
• v.32 no.6
• /
• pp.698-708
• /
• 2018

The purpose of this study was to investigate differences in the breeding call characteristics of cicada species found in urban and forest areas in the central region of Korea by examining the interspecific effects and environmental factors affecting the breeding calls and breeding call patterns. The selected research sites were Gyungnam Apartment in Bangbae-dong, Seoul for the urban area and Chiak Mountain National Park in Wonju for the forest area. The research method for both sites was to record cicada breeding calls for 24 hours with a recorder installed at the site and analyze the results. Data from the Korea Meteorological Administration were used for environmental factors. The research period was from June 19, 2017 to September 30, 2017. As a result of the study, there were differences in the emergence of species between the two research sites: while Platypleura kaempferi, Hyalessa fuscata, Meimuna opalifera, Graptopsaltria nigrofuscata, and Suisha coreana were observed at both sites, Cryptotympana atrata was observed in the urban area and Leptosemia takanonis in the forest area only. The emergence periods of cicadas at the two sites were also different. The activities of P. kaempferi and L. takanonis were noticeable in the forest area. In the urban area, however, L. takanonis was not observed and the duration of activity of P. kaempferi was short. In the urban area, C. atrata appeared and sang for a long period; H. fuscata, M. opalifera, and G. nigrofuscata appeared earlier than in the forest area. S. coreana appeared earlier in the forest area than in the urban area. According to the daily call cycle analysis, even cospecific cicada showed a wide variation in their daily cycle depending on the region and the interspecific effects between different cicadas, and the environmental differences between the urban and forest areas affected the calls of cicadas. The results of correlation analysis between each cicada breeding calls and environmental factors of each site showed positive correlation with average temperature of most cicadas except P. kaempferi and C. atrata. The same species of each site showed positive correlations with more diverse weather factors such as solar irradiance. Logistic regression analysis showed that cicadas with overlapping calling times had significant effects on each other's breeding calls. C. atrata, which appeared only in the urban area, had a positive effect on the calling frequency of H. fuscata, M. opalifera, and G. nigrofuscata, which called in the same period. Additionally, L. takanonis, which appeared only in the forest area, and P. kaempferi had a positive effect on each other, and M. opalifera had a positive effect on the calling frequency of H. fuscata and G. nigrofuscata in the forest area. For the environmental factors, the calling frequency of cicadas was affected by the average temperatures of the urban and forest areas, and cicadas that appeared in the forest area were also affected by the amount of solar radiation. According to the results of statistical analysis, urban cicadas with similar activity periods are influenced by species, especially with respect to urban dominant species, C. atrata. Forest cicadas were influenced by species, mainly M. opalifera, which is a forest dominant species. The results of the meteorological impact analysis were similar to those of the correlation analysis, and were influenced mainly by the temperature, and the influence of the insolation was more increased in the forests.

• Korean Journal of Breeding Science
• /
• v.51 no.3
• /
• pp.263-276
• /
• 2019

'Sinjinbaek' is a bacterial blight (BB)-resistant, mid-late maturing rice cultivar with high grain quality. To diversify the resistance genes and enhance the resistance of Korean rice cultivars against BB, 'Sinjinbaek' was developed from a cross between 'Iksan493' (cultivar name 'Jinbaek') and the F₁ cross between 'Hopum' and 'HR24670-9-2-1' ('HR24670'). 'Jinbaek' is a BB-resistant cultivar with two BB resistance genes, Xa3 and xa5. 'Hopum' is a high grain quality cultivar with the Xa3 resistance gene. 'HR24670' is a near-isogenic line that carries the Xa21 gene, a resistance gene inherited from a wild rice species O. longistaminata, in the genetic background of japonica elite rice line 'Suweon345'. 'Sinjinbaek' was selected through the pedigree method, yield trials, and local adaptability tests. Using bioassay for BB races and DNA markers for resistance genes, three resistance genes, Xa3, xa5, and Xa21, were pyramided in the 'Sinjinbaek' cultivar. 'Sinjinbaek' exhibited high-level and broad-spectrum resistance against BB, including the K3a race, the most virulent race in Korea. 'Sinjinbaek' is a mid-late maturing rice cultivar tolerant to lodging. It has multiple disease resistance against BB, rice blast, and stripe virus. The yield of 'Sinjinbaek' was similar to that of 'Nampyeong'. 'Sinjinbaek' showed excellent grain appearance, good taste of cooked rice, and enhanced milling performance, and we concluded that it could contribute to improving the quality of BB-resistant cultivars. 'Sinjinbaek' was successfully introgressed with the Xa21 gene without the linkage drag negatively affecting its agronomic characteristics. 'Sinjinbaek' improved the resistance of Korean rice cultivars against BB by introgression of a new resistance gene, Xa21, as well as by pyramiding three resistance genes, Xa3, xa5, and Xa21. 'Sinjinbaek' would be suitable for the cultivation in BB-prone areas since it has been used in breeding programs for enhancing plants' resistance to BB (Registration No. 7273).

• Journal of Preventive Medicine and Public Health
• /
• v.32 no.1
• /
• pp.48-59
• /
• 1999

Objectives. This study was conducted to evaluate the association between upper extremity musculoskeletal symptoms and Rapid Upper Limb Assessment(RULA) in vehicle assembly line workers. The goal of this study is to show the feasibility of RULA as a checklist for work related musculoskeletal symptoms (WMSDs) in Korean workers. Methods. The total number of 199 people from the department of assembly and 115 people from the department of Quality Control(QC) in automotive plant were subjects for this cross sectional study. A standard symptom questionnaire survey has been used for the individual characteristics, work history, musculosketal symptoms and non-occupational covariates. The data were obtained by applying one-on-one interview for the all subjects. RULA has been applied for ergonomic work posture analysis and the primary ergonomic risk sure was computed by RULA method. Association between upper extremity musculoskeletal symptoms and RULA were assessed by multiple logistic regression analysis. Results. A total of 314 workers was examined. The prevalence of musculoskeletal symptoms by NIOSH case definition was 62.4%. The distribution of musculoskeletal symptoms by the part of the body turned out to be following; back:41.4%, neck: 32.8%, shoulder: 26.4%, arm: 10.5% and hand:29.3%. The relationship of the individual RULA scores were statistically significant for the prevalence of musculoskeletal symptoms. As the result of the multiple logistic regressioin analysis, grand final score (OR=2.250 95% CI: 1.402-3.612) was associated with musculoskeletal symptoms in any part of the body.; upper arm score(OR=1.786 95% CI: 1.036-3.079) and posture score A(OR=1.634 95% CI: 1.016-2.626) in neck; muscel use score(OR=3.076 95% CI:1.782-5.310) and posture score A(OR=1.798 95% CI: 1.072-3.017) in shoulder; upper arm score(OR=1.715 95% CI: 1.083-2.715) and muscel use score(OR=2.057 95% CI:1.303-3.248) in neck & shoulder; muscle use score(OR=10.662 95% CI: 3.180-35.742) in arm; writst/wist score(OR=2.068 95% CI: 1.130-3.786) and muscle use score(OR=2.215 95% CI: 1.284-3.819) in hand & wrist.; muscle use score of trunk (OR=2.601 95% CI: 1.147-5.901) in back. Conclusions. Musculoskeletal symptoms of the extremities were strongly associated with individual RULA body score. These results show that RULA can be used as a useful assessment tool for the evaluation of musculoskeletal loading which is known to contribute to work-related musculoskeletal disorders. RULA also can be used as a screening tool or incorporated into a wider ergonomic assessment of epidemiological, physical, mental, environmental and organizational factors. As shown in this study, complement of the analysis system for the other risk factors and characterizing between the upper limb and back part will be needed for future work.

• Tuberculosis and Respiratory Diseases
• /
• v.52 no.4
• /
• pp.385-394
• /
• 2002

Background : Flexible fiberoptic bronchoscopy(FFB) has become a widely performed technique for diagnosing and managing pulmonary disease because of its low complication and mortality rate. Since the use of FFB can in patients with severely depressed cardiorespiratory function is increasing and hypoxemia during the FFB can induce significant cardiac arrhythmias, the early detection and adequate management of hypoxemia during FFB is clinically important. Method : To evaluate the necessity of the continuous monitoring of the oxygen saturation($SaO_2$) during the FFB, the $SaO_2$ was continuously monitored from the finger tip using pulse oximetry before, during and after the FFB in 379 patient. The patients were then divided into two groups, those with and without hypoxemia($SaO_2$<90%). The baseline pulmonary function data and the clinical characteristics of the two groups were compared. Results : The mean baseline $SaO_2$ was $96.9{\pm}2.85%$. An $SaO_2$ <90% was recorded at some point in 62(16.4%) out of 379 patients, with 12 out of 62 experiencing this prior to the FFB, in 37 out of 62 during the FFB, and in 13 out of 62 after the FFB. No differences were observed in the smoking and sex distribution between those with and without hypoxemia. The mean age was older in those with hypoxemia than in those without. Significant differences were observed in the mean baseline $SaO_2$ and the mean time for the procedure between the two groups. The $FEV_1$ was significantly lower in those with hypoxemia, and both the FVC and $FEV_1/FVC$ also tended to decrease in this group. Managing hypoxemia included deep breathing in 20 patients, a supplemental oxygen supply in 39 patients, and the abortion of the procedure in 3 patients. Conclusion : These results suggest that the continuous monitoring of the oxygen saturation is necessary during fiberoptic bronchoscopy, and it should be performed in patients with a depressed pulmonay function in order for the early detection and adequate management of hypoxemia.