• Journal of Korean Academy of Nursing
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• v.38 no.6
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• pp.822-830
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• 2008

Purpose: The purpose of this study was to analyze effects of a community-based case management program for clients with hypertension living in the community. Methods: The research design was a one group pre and post-test design with 30 participants with hypertension who agreed to participate in the 8-12 week case management program provided by case managers from the National Health Insurance Corporation in 2002. Data were collected three times, before and after the case management services, and 6 months later. Outcomes included changes in blood pressure, knowledge of hypertension and daily life practices, including alcohol consumption, smoking, exercise, and medication adherence. Results: Repeated-measures ANOVA and post-hoc tests of means revealed significant differences before and after service for systolic blood pressure, daily life practices (monitoring body weight and BP, low salt and cholesterol and high vegetable diet, and stress-relief practices), and exercise. The goal for medication adherence was attained after service. Significant improvements from baseline to 6 months after service were observed in measures of salt and vegetables in diet. There were no significant differences on hypertension knowledge, alcohol consumption or smoking behavior between before service and after, and at 6 months. Conclusion: The findings provide preliminary evidence that case management intervention can have positive outcomes on BP control, daily life practices, exercise, and medication adherence for clients with hypertension. However, additional interventions are needed to sustain long-term effects.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.28 no.1
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• pp.108-116
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• 2018

Objectives: This study investigated to the analytical work environment, analyst's expert and status of analytical instrument in the designated organization's laboratory for measuring work environment, and carried out to ensure reliability of analytical results. Methods: This study was conducted by 114 analysts who work in designated organization's laboratory for measuring work environment. Information on the working environment and personal characteristics of the analysts were collected using a self-reported questionnaire and were analyzed using the SPSS program through analysis of frequency and t-test. Results: The speciality of subjects was occupational health(57.0%), environmental health(38.6%) and environmental engineering(4.4%), and they had a higher level of academic ability than workers in other industries. Analysts had to handle a large number of sample analysis and many tasks other than analytical work. The analysts answered that it was difficult to analyze organic substances than inorganic substances, and the difficult parts were the analytical methods setting of new substances(55.3%), instrument analysis(24.6%) and principle of analysis(23.7%). Analytical instruments mainly have legally required instruments. The difficulty of the analysis is solved from the senior analyst in the laboratory and analytical information is mainly exchanged through seminar organized by the Association of Occupational Health Analysts. The analysts who are planning to move or considering the company were 48.2%, and the reasons for moving the company were difficult to work(14.0%), low salary(9.6%), employment type(8.8%) and job stress(7.0%). Conclusions: The conclusions of our study were that it was possible to secure reliability by solving the problems such as implementing professional education to improve expertise of analysts, strengthening analytical instruments through institutional improvement and improving work environment.

• Journal of Hospice and Palliative Care
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• v.14 no.4
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• pp.197-203
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• 2011

Purpose: Pain is the most common and influential symptom in cancer patients. Few studies concerning pain intensity in the terminally ill cancer patients have been done. This study aimed to identify factors related with more than moderate pain. Methods: This study used secondary data of 162 terminal cancer inpatients at the palliative ward of six training hospitals in Korea. Physician-assessed pain assessment was by 10 point numeric rating scale. Substantial pain was defined more than moderate intensity by the Korean National Guideline for cancer pain. The Korean version of the MD Anderson Symptom Inventory was self-administered to assess symptoms. Survival prediction was estimated by the attending physicians at the time of admission. Results: Less than six weeks of predicted survival and more than numeric rating of six for worst drowsiness in the previous 24 h were significantly related to substantial pain (P=0.012 and P=0.046, respectively). The dose of opioid analgesics was positively related to substantial pain (P=0.004). Conclusion: Factors positively related to substantial pain were less than six weeks of predicted survival and considerable drowsiness. Careful monitoring and active preparation for pain are required in terminal cancer patients having those factors.

• Korean Journal of Remote Sensing
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• v.21 no.3
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• pp.189-211
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• 2005

This paper makes an effort to compare the recently evolved soft classification method based on Linear Spectral Mixture Modeling (LSMM) with the traditional hard classification methods based on Iterative Self-Organizing Data Analysis (ISODATA) and Maximum Likelihood Classification (MLC) algorithms in order to achieve appropriate results for mapping, monitoring and preserving valuable coastal wetland ecosystems of southern India using Indian Remote Sensing Satellite (IRS) 1C/1D LISS-III and Landsat-5 Thematic Mapper image data. ISODATA and MLC methods were attempted on these satellite image data to produce maps of 5, 10, 15 and 20 wetland classes for each of three contrast coastal wetland sites, Pitchavaram, Vedaranniyam and Rameswaram. The accuracy of the derived classes was assessed with the simplest descriptive statistic technique called overall accuracy and a discrete multivariate technique called KAPPA accuracy. ISODATA classification resulted in maps with poor accuracy compared to MLC classification that produced maps with improved accuracy. However, there was a systematic decrease in overall accuracy and KAPPA accuracy, when more number of classes was derived from IRS-1C/1D and Landsat-5 TM imagery by ISODATA and MLC. There were two principal factors for the decreased classification accuracy, namely spectral overlapping/confusion and inadequate spatial resolution of the sensors. Compared to the former, the limited instantaneous field of view (IFOV) of these sensors caused occurrence of number of mixture pixels (mixels) in the image and its effect on the classification process was a major problem to deriving accurate wetland cover types, in spite of the increasing spatial resolution of new generation Earth Observation Sensors (EOS). In order to improve the classification accuracy, a soft classification method based on Linear Spectral Mixture Modeling (LSMM) was described to calculate the spectral mixture and classify IRS-1C/1D LISS-III and Landsat-5 TM Imagery. This method considered number of reflectance end-members that form the scene spectra, followed by the determination of their nature and finally the decomposition of the spectra into their endmembers. To evaluate the LSMM areal estimates, resulted fractional end-members were compared with normalized difference vegetation index (NDVI), ground truth data, as well as those estimates derived from the traditional hard classifier (MLC). The findings revealed that NDVI values and vegetation fractions were positively correlated ($r^2$= 0.96, 0.95 and 0.92 for Rameswaram, Vedaranniyam and Pitchavaram respectively) and NDVI and soil fraction values were negatively correlated ($r^2$ =0.53, 0.39 and 0.13), indicating the reliability of the sub-pixel classification. Comparing with ground truth data, the precision of LSMM for deriving moisture fraction was 92% and 96% for soil fraction. The LSMM in general would seem well suited to locating small wetland habitats which occurred as sub-pixel inclusions, and to representing continuous gradations between different habitat types.

• Journal of the Korean Geophysical Society
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• v.5 no.1
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• pp.9-18
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• 2002

We performed a long-term monitoring of self-potential(SP) using the Cu-CuSO₄non-polarizable electrode and copper-clad electrodes(CCE) in a test site in order to analyze the effects of surrounding environmental noises such as temperature, rainfall and soil moisture content on the electrodes. Analysis of the temperature dependence of the non-polarizable electrodes showed that is temperature coefficient was about +0.5 mV/°Fwhen its end was exposed to atmosphere while it was less than +0.5 mV/℃ when submerged into the subsurface, which reflects that there exists an 8 to 11 hour lag between temperatures at the depth of 15 cm and atmosphere. CCE was independent of atmospheric temperature in subsurface but showed temperature coefficient of 1.0 mV/℃ when exposed to atmosphere. Drifts of 1 to 2 mV recorded with the non-polarizable electrode directly related to the soil moisture content when it was buried in subsurface. Drift with CCE also showed similar trend to the soil moisture content, and 5 mV drift was recorded according to 5% of daily variation. The soil moisture content had strong effects on the measurement with CCE in rainfall since the flow potential is generated on the surface of the electrode.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.531-540
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• 2021

As cyber attacks and crimes increase exponentially and hacking attacks become more intelligent and advanced, hacking attack methods and routes are evolving unpredictably and in real time. In order to reinforce the enemy's responsiveness, this study aims to propose a method for developing an artificial intelligence-based security control platform by building a next-generation security system using artificial intelligence to respond by self-learning, monitoring abnormal signs and blocking attacks.The artificial intelligence-based security control platform should be developed as the basis for data collection, data analysis, next-generation security system operation, and security system management. Big data base and control system, data collection step through external threat information, data analysis step of pre-processing and formalizing the collected data to perform positive/false detection and abnormal behavior analysis through deep learning-based algorithm, and analyzed data Through the operation of a security system of prevention, control, response, analysis, and organic circulation structure, the next generation security system to increase the scope and speed of handling new threats and to reinforce the identification of normal and abnormal behaviors, and management of the security threat response system, Harmful IP management, detection policy management, security business legal system management. Through this, we are trying to find a way to comprehensively analyze vast amounts of data and to respond preemptively in a short time.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.4
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• pp.1-14
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• 2021

This study examined the techniques of creating gardens overseas using native plants from the Korean peninsula, focusing on the case of 'Das Dritte Land', an art garden created in Berlin, Germany. While Korean garden artists are recognized worldwide and are planning to globalize Korean gardens, the purpose of this study is to share information so that Korean gardeners can expand their activities and rediscover the utilization and value of plants native to the Korean peninsula. The work began as part of a project to mark the 30th anniversary of the collapse of the Berlin Wall. To realize the landscape of Korea with the motif of Inwang Jesaekdo, the geographical shape of the Baekdu-Daegan trail was reproduced with black stone, and the naturalization of Korean peninsula species was utilized in the creation of a garden Berlin. It is a surreal bio-top utopia that blooms with the bio-groups of the Korean peninsula. This study examined the process of plant survey analysis, transportation and stabilization, planting planning, composition and monitoring, and targeting the self-growth of the Korean peninsula, which is a symbol of harmony between the South and the North. The planting of Korea's native plants in overseas gardens symbolizes the uniting of the ecosystems on the Korean peninsula. The process of the Korean peninsula's young plants taking root, flowering, and spreading along Germany's previously divided border metaphorically conveys the desire for the unification of the Korean peninsula. In addition, various art programs in the garden space suggest a foundation for cultural dialogue and communication between the two Koreas. Moreover, creating gardens overseas implies that the cooperation of plant research institutes plays an important role in the transfer of plants and the maintenance of life, while the advancement of Korean gardens overseas plays an essential role in the spread of garden culture in our country.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.669-683
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• 2022

The frequency and damage of forest fires have tended to increase over the past 20 years. In order to effectively respond to forest fires, information on forest fire damage should be well managed. However, information on the extent of forest fire damage is not well managed. This study attempted to present a method that extracting information on the area of forest fire in real time and quasi-real-time using visible infrared imaging radiometer suite (VIIRS) images. VIIRS data observing the Korean Peninsula were obtained and visualized at the time of the East Coast forest fire in March 2022. VIIRS images were classified without supervision using iterative self-organizing data analysis (ISODATA) algorithm. The results were reclassified using the relationship between the burned area and the location of the flame to extract the extent of forest fire. The final results were compared with verification and comparison data. As a result of the comparison, in the case of large forest fires, it was found that classifying and extracting VIIRS images was more accurate than estimating them through forest fire occurrence data. This method can be used to create spatial data for forest fire management. Furthermore, if this research method is automated, it is expected that daily forest fire damage monitoring based on VIIRS will be possible.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.211-227
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• 2022

Illegal gambling through online gambling sites has become a significant social problem. The development of Internet technology and the spread of smartphones have led to the proliferation of illegal gambling sites, so now illegal online gambling has become accessible to anyone. In order to mitigate its negative effect, the Korean government is trying to detect illegal gambling sites by using self-monitoring agents or reporting systems such as 'Nuricops.' However, it is difficult to detect all illegal sites due to limitations such as a lack of staffing. Accordingly, several scholars have proposed intelligent illegal gambling site detection techniques. Xu et al. (2019) found that fake or illegal websites generally have unique features in the HTML tag structure. It implies that the HTML tag structure can be important for detecting illegal sites. However, prior studies to improve the model's performance by utilizing the HTML tag structure in the illegal site detection model are rare. Against this background, our study aimed to improve the model's performance by utilizing the HTML tag structure and proposes Tag2Vec, a modified version of Doc2Vec, as a methodology to vectorize the HTML tag structure properly. To validate the proposed model, we perform the empirical analysis using a data set consisting of the list of harmful sites from 'The Cheat' and normal sites through Google search. As a result, it was confirmed that the Tag2Vec-based detection model proposed in this study showed better classification accuracy, recall, and F1_Score than the URL-based detection model-a comparative model. The proposed model of this study is expected to be effectively utilized to improve the health of our society through intelligent technology.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.101-106
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• 2023

In this busy world actually stress is continuously grow up in research and monitoring social websites. The social interaction is a process by which people act and react in relation with each other like play, fight, dance we can find social interactions. In this we find social structure means maintain the relationships among peoples and group of peoples. Its a limit and depends on its behavior. Because relationships established on expectations of every one involve depending on social network. There is lot of difference between emotional pain and physical pain. When you feel stress on physical body we all feel with tensions, stress on physical consequences, physical effects on our health. When we work on social network websites, developments or any research related information retrieving etc. our brain is going into stress. Actually by social network interactions like watching movies, online shopping, online marketing, online business here we observe sentiment analysis of movie reviews and feedback of customers either positive/negative. In movies there we can observe peoples reaction with each other it depends on actions in film like fights, dances, dialogues, content. Here we can analysis of stress on brain different actions of movie reviews. All these movie review analysis and stress on brain can calculated by machine learning techniques. Actually in target oriented business, the persons who are working in marketing always their brain in stress condition their emotional conditions are different at different times. In this paper how does brain deal with stress management. In software industries when developers are work at home, connected with clients in online work they gone under stress. And their emotional levels and stress levels always changes regarding work communication. In this paper we represent emotional intelligence with stress based analysis using machine learning techniques in social networks. It is ability of the person to be aware on your own emotions or feeling as well as feelings or emotions of the others use this awareness to manage self and your relationships. social interactions is not only about you its about every one can interacting and their expectations too. It about maintaining performance. Performance is sociological understanding how people can interact and a key to know analysis of social interactions. It is always to maintain successful interactions and inline expectations. That is to satisfy the audience. So people careful to control all of these and maintain impression management.