• Proceedings of KOSOMES biannual meeting
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• 2008.05a
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• pp.137-141
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• 2008

9 kinds of nanoparticle used for this study was a particle with the size of less than 100 nm of diameter, and Artemia sp. cyst examined what kind a influence to have upon the process hatched out in nauplius. 82% hatched in nauplius at the opposition ward where a nanoparticle wasn't added after 24 time course. AGZ020, Nano silver, P-25, Sb and SnO nanoparticle showed hatching rate of 18%, 20%, 13%, 50% and 0% respectively by the 20mg/L density, and it became clear that a harmful effect is big, but I had a harmful effect compared with the opposition ward by 75%, 60%, 73% and 73% respectively by Ag-$TiO_2$, In, Sn and Zn nanoparticle, but a feeble thing was known relatively compared with AGZ020, Nano silver, P-25, Sb and SnO nanoparticle. The difference was mused this with the ingredient a nanoparticle has. Ag is included 2% and AGZ020, Nano silver and P-25 nanoparticle are used widely as anti-fungus agent, and the SnO nanoparticle which became combination is a light catalyst pill, and oxygen is used for a Sn particle. This and others, a possibility that use is generalized and flows into aquatic environment in sequence the home electronics, functionality cosmetics, anti-fungus agent and a light catalyst pill at present becomes high for nanoparticles and others. The anxiety which has an influence on the ecology world in the water with this can be generated, so I'd have to study the potential danger a nanoparticle has continuously.

• Journal of Korean Medicine for Obesity Research
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• v.13 no.1
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• pp.1-9
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• 2013

Objectives: This study was performed to analyze the recent trend of the studies about obesity in medicine and to provide background for futher studies. Methods: All of the article were selected in "The Korean Journal of Obesity". Search were conducted through "http://kosso.or.kr" with the search word "obesity". Collected articles were classified into clinical study, experimental study, literary study, case report. Results: One hundred eighty four studies were included and analyzed in terms of study design, subject, intervention, period, obesity index and so on. The numbers of clinical studies, literary studies, experimental studies, case reports were respectively 143 (77.7%), 26 (14.1%), 2 (1.1%) and 7 (3.8%). In clinical studies, observational studies were 112 (78.3%) and intervention studies were 31 (21.7%). And most of sample sizes were more than 100 and less than 499. Body mass index, waist circumstance, body fat percent were major criteria of clinical study. Most of the subjects on obesity were about relation with another disease or experimental results and diagnosis. According to classification by the kinds of intervention, diet, exercise, drug, behavior were respectively 22, 18, 8, and 8. More than fourty percent of intervention studies were studied for 12 weeks. Conclusions: To improve the quality of Korean Medicine study for obesity, we need to recruiting big sample size and activate randomized clinical trial.

• Journal of Broadcast Engineering
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• v.26 no.6
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• pp.680-691
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• 2021

Due to the recent developments of attaining 3D contents by using devices such as 3D scanners, the diversity of the contents being used in AR(Augmented Reality)/VR(Virutal Reality) fields is significantly increasing. There are several ways to represent 3D data, and using point clouds is one of them. A point cloud is a cluster of points, having the advantage of being able to attain actual 3D data with high precision. However, in order to express 3D contents, much more data is required compared to that of 2D images. The size of data needed to represent dynamic 3D point cloud objects that consists of multiple frames is especially big, and that is why an efficient compression technology for this kind of data must be developed. In this paper, a motion estimation and compensation method for dynamic point cloud objects using 3D DCT is proposed. This will lead to switching the 3D video frames into I frames and P frames, which ensures higher compression ratio. Then, we confirm the compression efficiency of the proposed technology by comparing it with the anchor technology, an Intra-frame based compression method, and 2D-DCT based V-PCC.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.

• The Journal of the Korea Contents Association
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• v.19 no.1
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• pp.141-151
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• 2019

A network of graph data structure is used in many applications to represent interactions between entities. Recently, as the size of the network to be processed due to the development of the big data technology is getting larger, it becomes more difficult to handle it in one server, and thus the necessity of distributed processing is also increasing. In this paper, we propose a distributed processing system for efficiently performing subgraph and stores. To reduce unnecessary searches, we use statistical information of the data to determine the search order through probabilistic scoring. Since the relationship between the vertex and the degree of the graph network may show different characteristics depending on the type of data, the search order is determined by calculating a score to reduce unnecessary search through a different scoring method for a graph having various distribution characteristics. The graph is sequentially searched in the distributed servers according to the determined order. In order to demonstrate the superiority of the proposed method, performance comparison with the existing method was performed. As a result, the search time is improved by about 3 ~ 10% compared with the existing method.

• Journal of Navigation and Port Research
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• v.42 no.6
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• pp.539-545
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• 2018

In recent years, research on shipping market forecasting with the employment of non-linear AI models has attracted significant interest. In previous studies, input variables were selected with reference to past papers or by relying on the intuitions of the researchers. This paper attempts to address this issue by applying the stepwise regression model and the random forest model to the Cape-size bulk carrier market. The Cape market was selected due to the simplicity of its supply and demand structure. The preliminary selection of the determinants resulted in 16 variables. In the next stage, 8 features from the stepwise regression model and 10 features from the random forest model were screened as important determinants. The chosen variables were used to test both models. Based on the analysis of the models, it was observed that the random forest model outperforms the stepwise regression model. This research is significant because it provides a scientific basis which can be used to find the determinants in shipping market forecasting, and utilize a machine-learning model in the process. The results of this research can be used to enhance the decisions of chartering desks by offering a guideline for market analysis.

• Journal of Korean Society of Forest Science
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• v.108 no.3
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• pp.372-381
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• 2019

This study was conducted to find proof for the hypothesis that the God tree of Chosun has been misrepresented in Chosun-Gersu-Nosu-Myungmokji (CGNM). The following results were obtained. First, it was established that 64 species and 3170 trees were recorded in CGNM. An old, big tree is classified as a God tree if linked to it there are testimonies and legends about divine elements, and it is classified as a Noble tree if linked to it there are testimonies and legends of historical elements. In total, 2632 trees of eight species were analyzed, from the Zelkova serrata, which has the greatest number of trees, to the eighth most frequent, Abies holophylla. The means of diameter at breast height (DBH), height, and age of the God and the Noble trees were calculated for each of the eight species. In seven out of eight species, the DBH and age of the Noble tree were more than those of the God tree. In addition, the height of the Noble tree was more than that of the God tree in six out of eight species. The fact that the God tree is smaller than the Noble tree, contrary to the common expectation that the Noble tree is a small size tree, was confirmed. This hypothesis was proved by the data gathered. Second, the Japanese Government-General of Korea has pursued a policy to defeat the village ritual based on the God tree being linked with superstition. For such a policy, the God tree should be small and unattractive, and it would have been good for the tree to be superstitious. The CGNM was created as explanatory material or evidence for distorting the sacredness of the God tree of Chosun. Third, CGNM compiled a chronological order of DBH data to make it easy to explain the fabricated facts that the God tree of Chosun is smaller and dwarfed compared to the Noble tree.

• Journal of Korea Port Economic Association
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• v.35 no.1
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• pp.57-76
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• 2019

This study deals with the determinants of employment productivity of transportation labor, who are the main agents of the transportation industry that has made significant contributions to our country's industrial development. The study selected the determinants of employment productivity using the Korea Labor and Income Panel Study data, and analyzed the effects of various factors using panel logistic regression, panel OLS model, and panel robust regression. The results were as follows. First, a more positive effect was shown when employees held a regular job, had a "high level of education", "joining the labor union" and "experiencing vocational training". Second, in the case of job security, having a "high level of education" and "joining the labor union" showed a more positive effect; further, job security was higher for employees who worked in a "big company" or were "married". Third, in the case of higher income productivity, higher values of "age", "academic ability" and "company size" had a more positive effect, whereas larger values of "education" and "health condition except job training" had a negative one. Fourth, in the case of job satisfaction, "female", "joining the labor union" and having a higher "income" or "job security" led to higher satisfaction and a better "health condition compared to an average person". Further, a higher "overall life satisfaction" and "economic level" led to lower job satisfaction. The analysis of the determinants of employment productivity of transportation business and seeking for improvement plan is expected to improve the employment productivity in the transportation business.

• The Journal of the Korea Contents Association
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• v.19 no.5
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• pp.48-63
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• 2019

We tried new analysis including social network analysis(SNA) on the transaction network centered on electronic companies using more than 50 thousand company transaction data obtained from Korean enterprise data (KED) for the year of 2015. We found 97 clusters having more than 10 firms and remarkable 13 clusters having more than 90% sales of the electronic industry in Korea. Clusters are the groups of companies having most of their transactions in the clusters they belong to. We found 5 clusters have 83% of sales in the electronic industry. Most of clusters have main single firms having most of the sales in each clusters except a few clusters. However, we found a few firms to have high rear production linkage effect and found the firms with high linkage effect specially for the small and medium size enterprise (SME). The companies with high production linkage (specially on SMEs) should be managed in terms of (SME) growth policy. The last firm group consisting of the small clusters with less than 10 firms has high employment coefficients. The clusters or company having high production linkage effect on this last firm group should be noted in the terms of employment policy. We also note that there exist the firms with the high value of betweenness coefficients meaning high potential of technology development. They should be managed carefully in terms of technology development policy.

• Journal of the Korean Society for information Management
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• v.38 no.4
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• pp.1-23
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• 2021

The purpose of this study is to analyze the effects of the bestseller ranks on the book circulations in public libraries. To achieve this goal, the weekly data sets of 179 books' library circulation and bestseller list from January 1, 2018 to December 29, 2019 were constructed based on the data collected from BigData MarketC and YES24. Three methods for analyzing panel data including linear regression, fixed-effect, and random effect models were compared, and it turned out that fixed-effect model was better than other methods. The results show that the average ranks of bestsellers were associated with their public library circulations visually. Also, the analysis of fixed-effect model showed that the single rank decline of a book on the bestseller list decreases its average circulation of 0.108 while the size of effect varied depending on subject of books. The study empirically demonstrated the impact of a bestseller list on people's book circulation behavior, suggesting that public libraries need to reference sociocultural context as well as bestseller book lists to predict library user needs and to formulate collection development policy.