• Tuberculosis and Respiratory Diseases
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• v.41 no.4
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• pp.354-362
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• 1994

Objectives and Methods : The presence of aneuploidy or high proliferative activity in cytologic specimens is considered as complementary for the diagnosis of malignancy. To evaluate the diagnostic usefulness of DNA ploidy and cell cycle analysis in lung cancer, we compared the diagnostic yielding rates of DNA ploidy test by brushing specimens using flow cytometry with bronchoscopic forceps biopsy and brushing cytology. Results : Of the seventy-six cases, 55 cases proved to have malignant diseases(squamous cell cancer: 27, adenocarcinoma: 7, large cell cancer: 1, undifferentiated: 4 and small cell cancer: 16). The incidence of aneuploidy in lung cancer patients was 32.7%(18/55), as opposed to no cases in benign disease. And the proportion of high proliferative activity(S+G2M>22%) in lung cancer patients was 42.9%(15/35), but none in benign diseases. In fifty-six of 75 cases(74.7%), cytology of brushing specimens and DNA analysis(either aneuploidy or high proliferative activity vs. diploidy and low proliferative activity) were in concordance. The sensitivity with only brushing cytology was 41.8%(23/55), but with the addition of DNA analysis, it was increased to 56.4%(31/55), without decreasing the specificity(100%). And there was a case whose clue for malignancy was absent except aneuploidy, and he was confirmed to have squamous cell cancer following open thoracotomy. There were no differences in the frequency of aneuploidy or high proliferative activity between histologic subtypes of bronchogenic malignancy. Conclusions : The diagnostic detection rate of lung cancer was improved with the addition of DNA ploidy and cell cycle analysis, and the presence of aneuploidy or high proliferative activity was a relatively specific indicator of malignant disease. It would be useful to test DNA ploidy and cell cycle analysis with brushing specimen for the diagnosis of bronchogenic malignancy particularly in patients whose biopsy specimen could not be obtainable.

• Journal of Korean Home Economics Education Association
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• v.19 no.3
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• pp.61-76
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• 2007

The purpose of the study is to identify and understand problems existing in the middle school home economics practice rooms in Jeju Special Self-Governing Province. The findings are based on the examination and the analysis of the indoor environment and the condition of the facilities and equipment. Study method employs on-site research and a survey. The on-site research was conducted about temperature, humidity, intensity of illumination, and status of teaching instrument in 10 out of 41 middle schools in Jeju Special Self-Governing Province from August 16 to September 30, 2006. Meanwhile, the survey was implemented by mail for 95 teachers in charge of manual training and home economics education in 41 middle schools in Jeju from November 1 to 23, 2005. 64 questionnaires out of total 95 were collected, including those collected during the period of on-site research. Finally, 61 questionnaires which were effective among the answered ones were used for analysis. Collected materials were analyzed with the SPSS Win.12.0 program for frequency, percentile analysis. In conclusion, the study determines that the condition of the home economics practice rooms of the middle school in JSSGP in terms of temperature, humidity, lighting and ventilation is very inadequate. The structure of the practice room represents an inefficient work flow pattern. Further, the facilities and equipment are in a very poor condition because the facilities are old and the retention rate of teaching tools is low. Therefore, to address these problems, the study suggests that improvements on the facilities and equipment should be made and teaching tools should be replenished in accordance with the industry standard.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.25-38
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• 2019

Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.191-207
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• 2021

Up to this day, mobile communications have evolved rapidly over the decades, mainly focusing on speed-up to meet the growing data demands of 2G to 5G. And with the start of the 5G era, efforts are being made to provide such various services to customers, as IoT, V2X, robots, artificial intelligence, augmented virtual reality, and smart cities, which are expected to change the environment of our lives and industries as a whole. In a bid to provide those services, on top of high speed data, reduced latency and reliability are critical for real-time services. Thus, 5G has paved the way for service delivery through maximum speed of 20Gbps, a delay of 1ms, and a connecting device of 106/㎢ In particular, in intelligent traffic control systems and services using various vehicle-based Vehicle to X (V2X), such as traffic control, in addition to high-speed data speed, reduction of delay and reliability for real-time services are very important. 5G communication uses high frequencies of 3.5Ghz and 28Ghz. These high-frequency waves can go with high-speed thanks to their straightness while their short wavelength and small diffraction angle limit their reach to distance and prevent them from penetrating walls, causing restrictions on their use indoors. Therefore, under existing networks it's difficult to overcome these constraints. The underlying centralized SDN also has a limited capability in offering delay-sensitive services because communication with many nodes creates overload in its processing. Basically, SDN, which means a structure that separates signals from the control plane from packets in the data plane, requires control of the delay-related tree structure available in the event of an emergency during autonomous driving. In these scenarios, the network architecture that handles in-vehicle information is a major variable of delay. Since SDNs in general centralized structures are difficult to meet the desired delay level, studies on the optimal size of SDNs for information processing should be conducted. Thus, SDNs need to be separated on a certain scale and construct a new type of network, which can efficiently respond to dynamically changing traffic and provide high-quality, flexible services. Moreover, the structure of these networks is closely related to ultra-low latency, high confidence, and hyper-connectivity and should be based on a new form of split SDN rather than an existing centralized SDN structure, even in the case of the worst condition. And in these SDN structural networks, where automobiles pass through small 5G cells very quickly, the information change cycle, round trip delay (RTD), and the data processing time of SDN are highly correlated with the delay. Of these, RDT is not a significant factor because it has sufficient speed and less than 1 ms of delay, but the information change cycle and data processing time of SDN are factors that greatly affect the delay. Especially, in an emergency of self-driving environment linked to an ITS(Intelligent Traffic System) that requires low latency and high reliability, information should be transmitted and processed very quickly. That is a case in point where delay plays a very sensitive role. In this paper, we study the SDN architecture in emergencies during autonomous driving and conduct analysis through simulation of the correlation with the cell layer in which the vehicle should request relevant information according to the information flow. For simulation: As the Data Rate of 5G is high enough, we can assume the information for neighbor vehicle support to the car without errors. Furthermore, we assumed 5G small cells within 50 ~ 250 m in cell radius, and the maximum speed of the vehicle was considered as a 30km ~ 200 km/hour in order to examine the network architecture to minimize the delay.