• The KIPS Transactions:PartC
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• v.19C no.2
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• pp.135-144
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• 2012

All the servers in a traditional server cluster environment are kept On. If the request load reaches to the maximum, we exploit its maximum possible performance, otherwise, we exploit only some portion of maximum possible performance so that the efficiency of server power consumption becomes low. We can improve the efficiency of power consumption by controlling power mode of servers according to load situation, that is, by making On only minimum number of servers needed to handle current load while making Off the remaining servers. In the existing power mode control method, they used a static policy to decide server power mode at a fixed time interval so that it cannot adapt well to the dynamically changing load situation. In order to improve the existing method, we propose a dynamic server power control algorithm. In the proposed method, we keep the history of server power consumption and, based on it, predict whether power consumption increases in the near future. Based on this prediction, we dynamically change the time interval to decide server power mode. We performed experiments with a cluster of 30 PCs. Experimental results show that our proposed method keeps the same performance while reducing 29% of power consumption compared to the existing method. In addition, our proposed method allows to increase the average CPU utilization by 66%.

• The Korea Journal of Herbology
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• v.25 no.2
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• pp.129-136
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• 2010

Objective : Sinapis alba L. (SA) is a korean traditional herbal medicine that is usually used to prevent or treat inflammatory diseases, such as respiratory infection and rheumatoid arthritis. However, the effects of SA supplementation in vitro on serum antibody levels, splenocyte and peritoneal macrophage immune responses have not yet been determined. In this study, we examined the effect of SA on the production of Th1/Th2 cytokines. Methods : Splenocytes were isolated from naive C57BL/6 mice. Cells were enriched for CD4+ cell populations by first staining the cells with anti-CD4 (BD PharMingen, Calif, USA). CD4+ T cells were selected on a (CS) column, and the flow-through was collected as CD4+ T cells. Isolated cells were activated by overnight incubation on 24-well plates coated with $1{\mu}g/mL$ anti-CD3, $1{\mu}g/mL$ anti-CD28 and with SA ($100{\mu}g/mL$). Primary macrophages were collected from the peritoneal cavities of mice (8-week-old female C57BL/6). The peritoneal macrophages were washed and plated with RPMI-1640 overnight for the experiments. After 48-hours cultures, samples were centrifuged at 2000 rpm for 10 minutes, and the supernatants were stored at $-80^{\circ}C$. Mouse IL-4, IFN-$\gamma$ and TNF-$\alpha$ were quantified using ELISA kits (BioSource International, Camarillo, Calif, USA) according to the manufacturer's protocols. Results : SA at 100ug/ml decreased the generation of Th1 cytokine (IFN-$\gamma$) by 0.5-fold. However, SA has no effect on Th2 (IL-4) production. Conclusions : These results suggest that SA may play an important role in the control of T-cell-mediated autoimmunity by down-regulation of Th1 cytokine (especially IFN-$\gamma$, TNF-$\alpha$). These data may contribute to the design of new immunomodulating treatments for a group of autoimmune diseases.

• Journal of Periodontal and Implant Science
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• v.35 no.2
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• pp.461-474
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• 2005

The ultimate goal of periodontal therapy is the regeneration of periodontal tissue and the repair of function. For more than a decade there have been many efforts to develop materials and methods of treatment to promote periodontal tissue regeneration. Recently many efforts are concentrated on the regeneration potential of material used in traditional medicine. Safflower(Carthamus tinctorius L.) seed extract(SSE) have long clinically used in Korea to promote bone formation and prevent osteoporosis. The purpose of this study was to examine the effects of SSE on bone formation in human osteoblastic cell line. Human fetal osteoblastic cell line(hFOB 1.19) was cultured with DMEM and SSE($1{\mu}g/ml$, $10{\mu}g/ml$, $100{\mu}g/ml$, $1mg/ml$) at $34^{\cdot}C$ with 5% $CO_2$ in 100% humidity. The proliferation, differentiation of the cell was evaluated by several experiments. Cell proliferation was significantly increased at $10{\mu}g/ml$, $100{\mu}g/ml$, 1mg/ml of SSE after 3 and 7 days incubation(p<0.05). Cell spreading assay was significantly increased at $100{\mu}g/ml$ of SSE after 3 days and $1{\mu}g/ml$, $10{\mu}g/ml$, $100{\mu}g/ml$, 1mg/ml of SSE after 7 days(p<0.05). Alkaline Phosphatase(ALP) level was significantly increased in $10{\mu}g/ml$, $100{\mu}g/ml$, 1mg/ml of SSE(p<0.05). Collagen synthesis was significantly increased at $10{\mu}g/ml$, $100{\mu}g/ml$, 1mg/ml of SSE(p<0.05). A quantified calcium accumulation was significantly increased at $10{\mu}g/ml$, $100{\mu}g/ml$ of SSE(p<0.05). ALP and osteocalcin mRNA was expressed in $100{\mu}g/ml$ of SSE by RT-PCR. These results indicate that SSE are capable of increasing osteoblasts mineralization and may play an important role in bone formation.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.8
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• pp.181-188
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• 2016

Recently, as the amount of spatial information increases, an interest in the study of spatial information processing has been increased. Spatial database systems extended from the traditional relational database systems are difficult to handle large data sets because of the scalability. SpatialHadoop extended from Hadoop system has a low performance, because spatial computations in SpationHadoop require a lot of write operations of intermediate results to the disk, resulting in the performance degradation. In this paper, Spatial Computation Spark(SC-Spark) is proposed, which is an in-memory based distributed processing framework. SC-Spark is extended from Spark in order to efficiently perform the spatial operation for large-scale data. In addition, SC-Spark based on the GPGPU is developed to improve the performance of the SC-Spark. SC-Spark uses the advantage of the Spark holding intermediate results in the memory. And GPGPU-based SC-Spark can perform spatial operations in parallel using a plurality of processing elements of an GPU. To verify the proposed work, experiments on a single AMD system were performed using SC-Spark and GPGPU-based SC-Spark for Point-in-Polygon and spatial join operation. The experimental results showed that the performance of SC-Spark and GPGPU-based SC-Spark were up-to 8 times faster than SpatialHadoop.

• Journal of Intelligence and Information Systems
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• v.14 no.1
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• pp.1-17
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• 2008

Data mining is defined as the process of discovering meaningful and useful pattern in large volumes of data. In particular, finding associations rules between items in a database of customer transactions has become an important thing. Some data structures and algorithms had been proposed for storing meaningful information compressed from an original database to find frequent itemsets since Apriori algorithm. Though existing method find all association rules, we must have a lot of process to analyze association rules because there are too many rules. In this paper, we propose a new data structure, called a Frequent Pattern Network (FPN), which represents items as vertices and 2-itemsets as edges of the network. In order to utilize FPN, We constitute FPN using item's frequency. And then we use a clustering method to group the vertices on the network into clusters so that the intracluster similarity is maximized and the intercluster similarity is minimized. We generate association rules based on clusters. Our experiments showed accuracy of clustering items on the network using confidence, correlation and edge weight similarity methods. And We generated association rules using clusters and compare traditional and our method. From the results, the confidence similarity had a strong influence than others on the frequent pattern network. And FPN had a flexibility to minimum support value.

• The Transactions of the Korea Information Processing Society
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• v.6 no.12
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• pp.3447-3455
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• 1999

Database correctness is guaranteed by standard transaction scheduling schemes like two-phase locking for the context of concurrent execution environment in which short-lived ones are normally mixed with long-lived ones. Traditional syntax-oriented serializability notions are considered to be not enough to handle in particular various types of transaction in terms of duration of execution. To deal with this situation, altruistic locking has attempted to reduce delay effect associated with lock release moment by use of the idea of donation. An improved form of altruism has also been deployed in extended altruistic locking in a way that scope of data to be early released is enlarged to include even data initially not intended to be donated. In this paper, we first of all investigated limitations inherent in both altruistic schemes from the perspective of alleviating starvation occasions for transactions in particular of short-lived nature. The idea of two-way donation locking(2DL) has then been experimented to see the effect of more than single donation in distributed database systems. Simulation experiments shows that 2DL outperforms the conventional two-phase locking in terms of the degree of concurrency and average transaction waiting time under the circumstances that the size of long-transaction is in between 5 and 9.

• Journal of Intelligence and Information Systems
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• v.11 no.2
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• pp.59-79
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• 2005

Export containers in a container terminal are usually classified into a few weight groups and those belonging to the same group are placed together on a same stack. The reason for this stacking by weight groups is that it becomes easy to have the heavier containers be loaded onto a ship before the lighter ones, which is important for the balancing of the ship. However, since the weight information available at the time of container arrival is only an estimate, those belonging to different weight groups are often stored together on a same stack. This becomes the cause of extra moves, or rehandlings, of containers at the time of loading to fetch out the heavier containers placed under the lighter ones. In this paper, we use machine learning techniques to derive a classifier that can classify the containers into the weight groups with improved accuracy. We also show that a more useful classifier can be derived by applying a cost-sensitive learning technique, for which we introduce a scheme of searching for a good cost matrix. Simulation experiments have shown that our proposed method can reduce about 5$\sim$7% of rehandlings when compared to the traditional weight grouping method.

• Korean Journal of Weed Science
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• v.13 no.1
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• pp.7-13
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• 1993

Experiments were carried out to investigate the utilization of paraquat and diquat as desiccants for harvest-aid of sesame. When paraquat and diquat were sprayed at 3days or 3hours before cutting of sesame plants, moisture content in treated plots was evenly rapidly decreased and sesame yeild was not significantly different as compared with that in untreated plot. Percentage of germination of sesame seed in treated plots was similar to that in untreated check. Residual amount in sesame seed was not detectable when these chemicals were treated with 250ppm at 3days before cutting of sesame plant. Percentage of thrashing was high in sesame plants treated with paraquat and diauat with the lapse of time after cutting. Required labor in thrashing of sesame was reduced when applied with these chemicals. Harvest efficiency of sesame after spray of paraquat and diquat was good by comparison with the traditional practice.

• The Journal of Internal Korean Medicine
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• v.41 no.3
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• pp.339-349
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• 2020

Objective: This study aimed to evaluate anti-inflammatory and antitussive expectoration effects of Friltillariae Thunbergii Bulbus (FTB) in a mouse model. Materials and Methods: To evaluate the anti-inflammatory effects of the FTB, we conducted in vitro experiments using RAW264.7 cells. An MTT assay and enzyme-linked immunosorbent assay (ELISA) were carried out to examine the anti-inflammatory effects of FTB. The expectorant effect on phenol red secretion, the antitussive effect on cough induced by ammonia solution, and leukocyte increased inhibition effects in acute airway inflammation in the animal model were confirmed. Results: FTB did not show cytotoxicity in the experimental group at 10, 30, 50, 100, 300, or 500 ㎍/ml and significantly inhibited the increase of NO, TNF-α and IL-6 in the experimental groups at 30, 50, 100, 300, and 500 ㎍/ml concentrations. In sputum, cough, and acute airway inflammation animal models, FTB significantly increased phenol red secretion in the 400 mg/kg administration group. FTB significantly reduced the number of coughs and significantly increased cough delay time in both 200 and 400 mg/kg dose groups. FTB decreased the white blood cell count in BALF (bronchoalveolar lavage fluid) in the 400 mg/kg administration group. Conclusion: Our study revealed that FTB elicits antitussive and expectorant effects by inhibiting inflammatory cytokines, increasing sputum secretion, suppressing cough, and reducing inflammatory cells. We concluded that FTB is a highly promising agent for respiratory tract infection with therapeutic opportunities.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.53-59
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• 2016

Recently, huge stream data have been generated in real time from various applications such as wireless sensor networks, Internet of Things services, and social network services. For this reason, to develop an efficient method have become one of significant issues in order to discover useful information from such data by processing and analyzing them and employing the information for better decision making. Since stream data are generated continuously and rapidly, there is a need to deal with them through the minimum access. In addition, an appropriate method is required to analyze stream data in resource limited environments where fast processing with low power consumption is necessary. To address this issue, the sliding window model has been proposed and researched. Meanwhile, one of data mining techniques for finding meaningful information from huge data, pattern mining extracts such information in pattern forms. Frequency-based traditional pattern mining can process only binary databases and treats items in the databases with the same importance. As a result, frequent pattern mining has a disadvantage that cannot reflect characteristics of real databases although it has played an essential role in the data mining field. From this aspect, high utility pattern mining has suggested for discovering more meaningful information from non-binary databases with the consideration of the characteristics and relative importance of items. General high utility pattern mining methods for static databases, however, are not suitable for handling stream data. To address this issue, sliding window based high utility pattern mining has been proposed for finding significant information from stream data in resource limited environments by considering their characteristics and processing them efficiently. In this paper, we conduct various experiments with datasets for performance evaluation of sliding window based high utility pattern mining algorithms and analyze experimental results, through which we study their characteristics and direction of improvement.