• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.181-199
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• 2010

As rapid progress of population aging and strong interest in health, the demand for new healthcare service is increasing. Until now healthcare service has provided post treatment by face-to-face manner. But according to related researches, proactive treatment is resulted to be more effective for preventing diseases. Particularly, the existing healthcare services have limitations in preventing and managing metabolic syndrome such a lifestyle disease, because the cause of metabolic syndrome is related to life habit. As the advent of ubiquitous technology, patients with the metabolic syndrome can improve life habit such as poor eating habits and physical inactivity without the constraints of time and space through u-healthcare service. Therefore, lots of researches for u-healthcare service focus on providing the personalized healthcare service for preventing and managing metabolic syndrome. For example, Kim et al.(2010) have proposed a healthcare model for providing the customized calories and rates of nutrition factors by analyzing the user's preference in foods. Lee et al.(2010) have suggested the customized diet recommendation service considering the basic information, vital signs, family history of diseases and food preferences to prevent and manage coronary heart disease. And, Kim and Han(2004) have demonstrated that the web-based nutrition counseling has effects on food intake and lipids of patients with hyperlipidemia. However, the existing researches for u-healthcare service focus on providing the predefined one-way u-healthcare service. Thus, users have a tendency to easily lose interest in improving life habit. To solve such a problem of u-healthcare service, this research suggests a u-healthcare recommender system which is based on collaborative filtering principle and social network. This research follows the principle of collaborative filtering, but preserves local networks (consisting of small group of similar neighbors) for target users to recommend context aware healthcare services. Our research is consisted of the following five steps. In the first step, user profile is created using the usage history data for improvement in life habit. And then, a set of users known as neighbors is formed by the degree of similarity between the users, which is calculated by Pearson correlation coefficient. In the second step, the target user obtains service information from his/her neighbors. In the third step, recommendation list of top-N service is generated for the target user. Making the list, we use the multi-filtering based on user's psychological context information and body mass index (BMI) information for the detailed recommendation. In the fourth step, the personal information, which is the history of the usage service, is updated when the target user uses the recommended service. In the final step, a social network is reformed to continually provide qualified recommendation. For example, the neighbors may be excluded from the social network if the target user doesn't like the recommendation list received from them. That is, this step updates each user's neighbors locally, so maintains the updated local neighbors always to give context aware recommendation in real time. The characteristics of our research as follows. First, we develop the u-healthcare recommender system for improving life habit such as poor eating habits and physical inactivity. Second, the proposed recommender system uses autonomous collaboration, which enables users to prevent dropping and not to lose user's interest in improving life habit. Third, the reformation of the social network is automated to maintain the quality of recommendation. Finally, this research has implemented a mobile prototype system using JAVA and Microsoft Access2007 to recommend the prescribed foods and exercises for chronic disease prevention, which are provided by A university medical center. This research intends to prevent diseases such as chronic illnesses and to improve user's lifestyle through providing context aware and personalized food and exercise services with the help of similar users'experience and knowledge. We expect that the user of this system can improve their life habit with the help of handheld mobile smart phone, because it uses autonomous collaboration to arouse interest in healthcare.

• Journal of the Korean Applied Science and Technology
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• v.34 no.4
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• pp.874-882
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• 2017

As the interest on the air-pollution is gradually rising up at home and abroad, automotiv e and fuel researchers have been working on the exhaust emission reduction from vehicles through a lot of approaches, which consist of new engine design, innovative after-treatment systems, using clean (eco-friendly alternative) fuels and fuel quality improvement. This research has brought forward three main issues : evaporative, performance, air pollution. In addition, researcher studied the environment problems of the bio-ethanol, bio-butanol, bio-ETBE (Ethyl Tertiary Butyl Ether), MTBE (Methyl Tert iary Butyl Ether) fuel contained in the fuel as octane number improver. The researchers have many dat a about the health effects of ingestion of octane number improver. However, the data support the con clusion that octane number improver is a potential human carcinogen at high doses. Based on the bio-fuel and octane number improver types (bio-ethanol, bio-butanol, bio-ETBE, MTBE), this paper dis cussed the influence of gasoline fuel properties on the evaporative emission characteristics. Also, this p aper assessed the acceleration and power performance of gasoline vehicle for the bio-fuel property. As a result of the experiment, it was found that all the test fuels meet the domestic exhaust gas standards, and as a result of measurement of the vapor pressure of the test fuels, the bio - ethanol : 15 kPa and the biobutanol : 1.6 kPa. thus when manufacturing E3 fuel, Increasing the biobutanol content reduces evaporation gas and vapor pressure. In addition, Similar accelerating and powering performance was shown for the type of biofuel and when bio-butanol and bio-ethanol were compared accelerated perf ormance was improved by about 3.9% and vehicle power by 0.8%.

• Journal of Bio-Environment Control
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• v.28 no.4
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• pp.438-446
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• 2019

The interest in mini watermelon (Citrullus lanatus) with small fruits weighing 2-3 kg has increased by the increasing trend in one-person households and consequent tendency to consume small meals. Watermelon grafting onto cucurbit rootstocks is a very effective way to control soil-borne diseases, such as Fusarium wilt; however, this practice negatively impacts the fruit quality. This study was conducted to investigate the growth, fruit set, and fruit quality of mini watermelon grafted onto wild watermelon accessions (Citrullus spp.) resistant to Fusarium wilt. Five watermelon accessions (Galactica, IT 208441, PI 482322, PI 500303, and PI 593358) were evaluated as rootstocks for the mini watermelon "Ministar". Non-grafted "Ministar" and "Ministar" grafted onto "Shintozwa" (Cucurbita maxima D. C. moschata D.) or "Bullojangsaeng" (Lagenaria leucantha) were used as controls. The roots of the transplants grafted onto "PI 593358" and "Shintozwa" weighed more than those on other rootstocks. Additionally, the transplants on "PI 593358" showed better growth and fruit set in the field than the other transplants. However, the total soluble solid contents and fruit quality indices of the transplants on "PI 593358" and "Shintozwa" were lower, whereas the total fruit quality index of those on "PI 482322" was higher. Thus, the wild watermelon accessions tested can potentially be used as basic germplasm for developing watermelon rootstocks instead of cucurbit rootstocks. The most promising accession for this purpose was found to be "PI 482322".

• Korean Journal of Soil Science and Fertilizer
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• v.44 no.5
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• pp.808-813
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• 2011

This study was conducted to reduce the dependability of farmers on chemical fertilizers for rice cultivation. Soil chemical and biological properties were monitored before experiment and at the time of harvesting. The results showed that EC, available $SiO_2$, and exchangeable $Ca^{2+}$ were decreased at the time of harvesting while pH, OM, and exchangeable $K^+$ and $Mg^{2+}$ were remain unchanged, compared with soil before experiment. Population of aerobic bacteria, Bacillus sp., and fungi were also increased at the time of harvesting in the paddy field, compared with before fertilization, in the treatment of 50% soil-testing fertilizer+ 50% compost. Concentrations of N, P, and K in rice leaves increased with the fertilizers application, maximum increase was recorded in 50% soil-testing fertilizer+ 50% compost. Non-significant difference was observed in the morphological parameters of rice among the treatments. The chlorophyll contents of rice leaf increased in a similar fashion up to 60 days, thereafter, sharp decrease was observed in all the treatments. Maximum yield (per 10a) was recorded in the field treated with 50% soil-testing fertilizer+ 50% compost followed by standard applied fertilizer, 70% soil-testing fertilizer+ 30% compost, soil-testing fertilizer and unfertilized plot. Amylose content showed non-significant difference within the treatments. Protein content increased with the use of fertilizers and best protein content was recorded in the treatment of 50% soil-testing fertilizer+ 50% compost. It was concluded that the amount of the chemical fertilizer used was directly proportional to the protein content of rice grain. However, the palatability of rice grown in unfertilized field was better than the treatments but minimum yield was obtained. Hence, the treatment of 50% soil-testing fertilizer+ 50% compost, was the best among the fertilizer combinations for rice cultivation as supported by the yield, protein and palatability index.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.139-161
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• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.5
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• pp.1-11
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• 2021

The purpose of this study is to find out the main factors affecting air quality in urban physical space factors, and provide clues for environmental improvement. Nine monitoring stations in China's industrial city, Changchun, collected AQI concentration data from January 1, 2018 to December 31, 2019. This paper analyzes the types and distribution characteristics of urban physical facilities within a radius of 300m with the detection station as the center. The monitoring station is divided into three groups, and the difference in floating dust concentration among the three groups in different seasons is analyzed. The results show that AQI concentration is the highest in spring and winter, followed by summer, and the lowest in autumn. The place with the highest concentrations of AQI in spring are F (93.00), D (91.10), I (89.20), in summer are D (69.05), A (67.89), B (84.44), in autumn are I (62.80), G (60.84), D (53.27), D (53.27), in winter are I (95.82), H (95.60), f (94.04). Through SPSS analysis, it shows that the air index in a space with a diameter of 600 meters is related to forest land, grassland, bare land, water space, tree height, building area (average value), and building volume (average value). According to the statistical analysis results of spring and winter with the most serious pollution, forest land area (43,637m², 15.44%) and water surface area (18,736m², 6.63%) accounted for the majority, and group 1 (A, B, C) with the least average building area (448m², 0.17%) and average building volume (10,201m²) had the lowest pollution concentration. On the contrary, group 2 (D, E, F) had the highest AQI concentration, with less or no woodland (1,917m², 0.68%) and water surface area (0m², 0%), and the highest average building area (1,056m², 0.37%) and average building volume (17,470m³). It is confirmed that the characteristics of the area with the highest AQI concentration are that the more the site ratio of tree height above 12m, the smaller the site ratio of bare land, and the lower the pollution degree. On the contrary, the larger the area of bare land, the higher the pollution degree. By analyzing the characteristics of nine monitoring stations in Changchun, it can be seen that the air quality brought by the physical characteristics of urban space is closely related to the above factors.

• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1159-1172
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• 2020

This study applied deep convolution neural network based on U-Net and SegNet using long period weather radar data to very short-term rainfall prediction. And the results were compared and evaluated with the translation model. For training and validation of deep neural network, Mt. Gwanak and Mt. Gwangdeoksan radar data were collected from 2010 to 2016 and converted to a gray-scale image file in an HDF5 format with a 1km spatial resolution. The deep neural network model was trained to predict precipitation after 10 minutes by using the four consecutive radar image data, and the recursive method of repeating forecasts was applied to carry out lead time 60 minutes with the pretrained deep neural network model. To evaluate the performance of deep neural network prediction model, 24 rain cases in 2017 were forecast for rainfall up to 60 minutes in advance. As a result of evaluating the predicted performance by calculating the mean absolute error (MAE) and critical success index (CSI) at the threshold of 0.1, 1, and 5 mm/hr, the deep neural network model showed better performance in the case of rainfall threshold of 0.1, 1 mm/hr in terms of MAE, and showed better performance than the translation model for lead time 50 minutes in terms of CSI. In particular, although the deep neural network prediction model performed generally better than the translation model for weak rainfall of 5 mm/hr or less, the deep neural network prediction model had limitations in predicting distinct precipitation characteristics of high intensity as a result of the evaluation of threshold of 5 mm/hr. The longer lead time, the spatial smoothness increase with lead time thereby reducing the accuracy of rainfall prediction The translation model turned out to be superior in predicting the exceedance of higher intensity thresholds (> 5 mm/hr) because it preserves distinct precipitation characteristics, but the rainfall position tends to shift incorrectly. This study are expected to be helpful for the improvement of radar rainfall prediction model using deep neural networks in the future. In addition, the massive weather radar data established in this study will be provided through open repositories for future use in subsequent studies.

• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.33 no.1
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• pp.20-25
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• 2022

Background and Objectives Globus pharyngeus is one of the most common symptoms of patients visiting otorhinolaryngology out-patient clinic, and usually long-lasting, difficult to treat, and frequently recurrent. Mucomyst^®, N-acetyl cysteine is an inhalation agent mainly used for mucolysis and reducing inflammation in airway via antioxidative effect. The purpose of this study was to evaluate the efficacy of inhaled Mucomyst^® treatment in patients with globus pharyngeus refractory to proton pump inhibitor (PPI). Materials and Method We prospectively evaluated the efficacy of Mucomyst^® in relieving symptoms of globus pharyngeus refractory to PPI in nine medical centers. Three hundred and three patients enrolled and finally 229 patients finished the inhaled Mucomyst^® therapy for 8 weeks. We analyzed the change of Reflux Symptom Index (RSI), Reflux Finding Score (RFS), Visual Analogue Scale (VAS) for globus, and Globus Pharyngeus Symptom Scale (GPS) after use of Mucomyst^® for 4 and 8 weeks. Results The GPS, RSI, RFS, and VAS score significantly decreased serially in patients who finished 8 week-inhalation treatment. The GPS improvement gap was significantly correlated with initial GPS (p<0.001) in multiple regression analysis. Conclusion Inhaled Mucomyst^® therapy was effective for the reduction of both subjective and objective findings in refractory globus patients. This study might suggest new treatment option for patients with globus. However, further thorough studies would be needed to assess the real effect of inhaled Mucomyst^® treatment as a standard treatment for globus.

• Journal of the Korean earth science society
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• v.43 no.3
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• pp.367-385
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• 2022

This study evaluates the temperature and precipitation results in East Asia simulated from the Hadley Centre Global Environmental Model version 3 regional climate model (HadGEM3-RA) developed by the UK Met Office. The HadGEM3-RA is conducted in the Coordinated Regional climate Downscaling Experiment-East Asia (CORDEX-EA) Phase II domain for 15 year (2000-2014). The spatial distribution of rainbands produced from the HadGEM3-RA by the summer monsoon is in good agreement with the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of water resources (APRODITE) data over the East Asia. But, precipitation amount is overestimated in Southeast Asia and underestimated over the Korean Peninsula. In particular, the simulated summer rainfall and APRODITE data show the least correlation coefficient and the maximum value of root mean square error in South Korea. Prediction of temperature in Southeast Asia shows underestimation with a maximum error during winter season, while it appears the largest underestimation in South Korea during spring season. In order to evaluate local predictability, the time series of temperature and precipitation compared to the ASOS data of the Seoul Meteorological Station is similar to the spatial average verification results in which the summer precipitation and winter temperature underestimate. Especially, the underestimation of the rainfall increases when the amounts of precipitation increase in summer. The winter temperature tends to underestimate at low temperature, while it overestimates at high temperature. The results of the extreme climate index comparison show that heat wave is overestimated and heavy rainfall is underestimated. The HadGEM3-RA simulated with a horizontal resolution of 25 km shows limitations in the prediction of mesoscale convective system and topographic precipitation. This study indicates that improvement of initial data, horizontal resolution, and physical process are necessary to improve predictability of regional climate model.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.6
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• pp.27-36
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• 2021

At a time when the equitable use of urban parks is gradually emerging as a social issue, this study was initiated to expand the influence of urban parks by improving the quality of park services, thereby resolving areas not covered by urban park services. This study targeted the Hangang Park in Seoul, where the qualitative service of parks shows the greatest difference. The influence relationship between the qualitative services of the park and the user's sphere of influence, which indicates the distribution of park users, was proposed to assess the influence of improvements in the quality of service. As a research method, the top three districts and the bottom three districts were selected through the Han River Park user satisfaction survey conducted from 2017 to 2019, and a qualitative service evaluation was carried out. It was derived using the data acquired in September. Afterward, by performing a spatial autocorrelation analysis on the user's sphere of influence, additional verification of the user's sphere of influence was performed numerically and visually. As a result of the study, the user influence in the top three districts, with high-quality service, was stronger and wider than that of the lower three districts. It was confirmed that the quality of service of the park affects the user influence. This shows that to realize park equity, it is necessary to improve the quality of services through continuous management and improvement of individual parks and the creation of new parks. This study has significance in that it recognizes the limitations of research on park services from a supplier's point of view and evaluates the qualitative services of parks from the perspective of actual park users. We propose an alternative to deal with the lower the park deprivation index.