To develop coal gasfication system, many studies have been actively conducted to describe the simulation of steady state. Now, it is necessary to study the gasification system not only in steady state but also in dynamic state to elucidate abnormal condition such as start-up, shut-down, disturbance, and develop control logic. In this study, a model was proposed with process simulation in dynamic state being conducted using a chemical process simulation tool, where a heat and mass transfer model in the gasifier is incorporated, The proposed model was verified by comparison of the results of the simulation with those available from NETL (National Energy Technology Laboratory) report under steady state condition. The simulation results were that the coal gas efficiency was 80.7%, gas thermal efficiency was 95.4%, which indicated the error was under 1 %. Also, the compositions of syngas were similar to those of the NETL report. Controlled variables of the proposed model was verified by increasing oxygen flow rate to gasifier in order to validate the dynamic state of the system. As a result, trends of major process variables were resonable when oxygen flow rate increased by 5% from the steady state value. Coal flow rate to gasifier and quench gas flow rate were increased, and flow rate of liquid slag was also increased. The proposed model in this study is able to be used for the prediction of gasification of various coals and dynamic analysis of coal gasification.
The Wonyenaejang is equivalent to the (senile)cataract in western medicine. The word cataract is used to describe the natural lens that has turned cloudy. As the natural lens of the eye becomes cloudy, it does not allow light to pass through it. Cataracts usually start as a slight cloudiness that progressively grows more opaque. As the cataract becomes more mature(increasingly opaque and dense), the retina receives less and less light. The light that does reach the retina becomes increasingly blurred and distorted. This causes gradual impairment of vision. If left untreated, cataracts can cause needless blindness. Although there are many kinds of cataracts, a senile cataract is the most common one. We chose the oriental medicine textbooks and the oriental medicine journals that were dealing with the symptoms, etiology, and internal/external treatments. The results were as follows : 1. The main causes of this disease are weak liver and kidney, burning up of the wind and heat in the liver and gall, weak spleen and stomach. 2. As the internal treatment of the Cataract, Geegukjihwangtang is mostly prescribed. 3. As the external treatment of the Cataract, (l) In the field of medicine for external application is commonly prescribed (2) In the field of drug action, frequently used treatments are as follows. emission of the evil, alleviation of fever, removal of lump of blood, and the medicine for external applications. (3) In the field of four Qi, cold medicine is commonly prescribed. (4) In the field of five tastes, bitter/hot/sweet mdicine are commonly prescribed. (5) In the field of toxicity, non-togic medicine is commonly prescribed. (6) In the field of channel distribution, most of the medicine belong to liver channel.
This study was investigate the characteristics of GHGs (
Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used