• Journal of KIISE
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• v.43 no.9
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• pp.947-952
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• 2016

Most data centers attempt to consolidate servers using virtualization technology to efficiently utilize limited physical resources. Moreover, virtualized systems have commonly adopted contents-based page sharing mechanism for page deduplication among virtual machines (VMs). However, previous page sharing schemes are limited by the inability to effectively manage accumulated hints which mean sharable pages in stack. In this paper, we propose a priority-based hint management scheme to efficiently manage accumulated hints, which are sent from guest to host for improving page sharing opportunity in virtualized systems. Experimental results show that our scheme removes pages with low sharing potential, as compared with the previous schemes, by efficiently managing the accumulated pages.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1095-1105
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• 2021

The advancement of dam operation is further required due to the upcoming rainy season, typhoons, or torrential rains. Besides, physical models based on specific rules may sometimes have limitations in controlling the release discharge of dam due to inherent uncertainty and complex factors. This study aims to forecast the water level of the nearest station to the dam multi-timestep-ahead and evaluate the availability when it makes a decision for a release discharge of dam based on LSTM (Long Short-Term Memory) of deep learning. The LSTM model was trained and tested on eight data sets with a 1-hour temporal resolution, including primary data used in the dam operation and downstream water level station data about 13 years (2009~2021). The trained model forecasted the water level time series divided by the six lead times: 1, 3, 6, 9, 12, 18-hours, and compared and analyzed with the observed data. As a result, the prediction results of the 1-hour ahead exhibited the best performance for all cases with an average accuracy of MAE of 0.01m, RMSE of 0.015 m, and NSE of 0.99, respectively. In addition, as the lead time increases, the predictive performance of the model tends to decrease slightly. The model may similarly estimate and reliably predicts the temporal pattern of the observed water level. Thus, it is judged that the LSTM model could produce predictive data by extracting the characteristics of complex hydrological non-linear data and can be used to determine the amount of release discharge from the dam when simulating the operation of the dam.

• Journal of Internet Computing and Services
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• v.22 no.2
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• pp.11-17
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• 2021

Software-defined networking provides a programmable and flexible way to manage the network by separating the control plane from data plane. However, the limited switch memory restricts the number of flow entries in the flow table used to forward packets. This leads to flow table overflow and flow entry reinstallation, which severely degrade the network performance. Therefore, this paper proposes a comprehensive policy for timely eviction of inactive flow entries to optimally maintain flow tables usage. In particular, statistics of user datagram protocol flow entries are periodically sampled to enable the inactive entries to be evicted early. Through traffic-based experiments, we found that the proposed system reduces the number of overflow occurrences and flow entries reinstallation compared to the random and FIFO policies.

• Smart Media Journal
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• v.12 no.10
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• pp.63-70
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• 2023

Recently, energy consumption for heating costs, which is 35% of smart farm energy costs, has increased, requiring energy consumption efficiency, and the importance of new and renewable energy is increasing due to concerns about the realization of electricity bills. Renewable energy belongs to hydropower, wind, and solar power, of which solar energy is a power generation technology that converts it into electrical energy, and this technology has less impact on the environment and is simple to maintain. In this study, based on the greenhouse heat storage tank and heat pump data, the factors that affect the heat storage tank are selected and a heat storage tank supply temperature prediction model is developed. It is predicted using Long Short-Term Memory (LSTM), which is effective for time series data analysis and prediction, and XGBoost model, which is superior to other ensemble learning techniques. By predicting the temperature of the heat pump heat storage tank, energy consumption may be optimized and system operation may be optimized. In addition, we intend to link it to the smart farm energy integrated operation system, such as reducing heating and cooling costs and improving the energy independence of farmers due to the use of solar power. By managing the supply of waste heat energy through the platform and deriving the maximum heating load and energy values required for crop growth by season and time, an optimal energy management plan is derived based on this.

• Journal of Digital Contents Society
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• v.3 no.1
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• pp.101-111
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• 2002

Lately development of network around Internet expands range of data traffic to multimedia information, and so for the guarantee of multimedia services end-to-end QoS(Quality of Service) must service because comparing with existing Internet service can not support For satisfying those QoS requirements, network have to guarantee not only network on parameter, such as delay, jitter, throughput but also system resources like CPU utilization, memory usage. Therefore it is urgent to develop QoS based middleware to distribute multimedia data and maximize network utilization in the limited resource environment. And it must be necessary of network to provide end-to-end QoS(Quality of Service) for multimedia applications. Multimedia applications want that QoS which satisfy their own service properties be guaranteed Then, We must analyze those necessary QoS requirements md define QoS parameter which specify as two viewpoint, user's and network's perspective. Therefore network provider supplying network for usual user and university, enterprise must want to find about their own network performance and problem. It is essential for network manager to want to use a tool like this. On the basis of technique about QoS test-bed in the AIM network, We studied on the method of QoS measurement and management about end-to-end connection in the Internet. We measured network status about end-to-end connection and analyze the result of performance.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.75-82
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• 2022

There are various exhibition halls, shopping malls, theme parks around us and analysis of interest in exhibits or contents is mainly done through questionnaires. These questionnaires are mainly depend on the subjective memory of the person being investigated, resulting in incorrect statistical results. Therefore, it is possible to identify an exhibition space with low interest by tracking the movement and counting the number of visitors. Based on this, it can be used as quantitative data for exhibits that need replacement. In this paper, we use deep learning-based artificial intelligence algorithms to recognize visitors, assign IDs to the recognized visitors, and continuously track them to identify the movement path. When visitors pass the counting line, the system is designed to count the number and transmit data to the server for integrated management.

• Journal of Industrial Convergence
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• v.20 no.6
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• pp.117-123
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• 2022

Appearance, which is directly related to competitiveness, has become one of the essential self-care for modern people living in the era of the 4th industrial revolution. For the purpose of producing more practical and convenient wigs for suffering customers, data of research subjects were collected through an information search portal site. The trend of new material development of leading wig companies was analyzed. As a result of the study, it was found that many applications for wig attachment and binding technology were applied before 2005, artificial hair-related manufacturing technology for wigs from 2006 to 2013, and functional-related wig technology after 2014. In addition, both H and M companies showed the development trend of new materials for shape memory materials and nanoskins with their own characteristics. We believe that this study will be provided as basic data for the development of functional wigs that can lead to customer satisfaction while providing customers with a comfortable and convenient fit in the wig industry market.

• Korea Trade Review
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• v.48 no.2
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• pp.27-43
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• 2023

The maritime industry is playing an increasingly vital part in global economic expansion. Specifically, the Baltic Dry Index is highly correlated with global commodity prices. Hence, the importance of BDI prediction research increases. But, since the global situation has become more volatile, it has become methodologically more difficult to predict the BDI accurately. This paper proposes an integrated machine-learning strategy for accurately forecasting BDI trends. This study combines the benefits of a convolutional neural network (CNN) and long short-term memory neural network (LSTM) for research on prediction. We collected daily BDI data for over 27 years for model fitting. The research findings indicate that CNN successfully extracts BDI data features. On this basis, LSTM predicts BDI accurately. Model R2 attains 94.7 percent. Our research offers a novel, machine-learning-integrated approach to the field of shipping economic indicators research. In addition, this study provides a foundation for risk management decision-making in the fields of shipping institutions and financial investment.

• The Journal of Korean society of community based occupational therapy
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• v.3 no.2
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• pp.1-12
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• 2013

Objective : The objective of this study is to present basic data to find health care plans for the elderly by comparing time-management ability and ADL and identifying the relationships between groups with subjects of elderly people living alone and living with family in Daejeon Metropolitan City. Method : A total of 80 elders who lived alone or with family that were aged 65 or older were selected with MMSE-K, 40 people were selected as subjects for each group. For time-management ability, a questionnaire was used. ADL were assessed by using FIM. The study period was May to June 2013. Result : Comparison of scores for time-management ability and FIM of the elderly who live alone or living with family did not show any statistically significant difference. In comparison of detailed scores between groups, there were statistically significant differences between the two groups being social interaction, problem solving and memory in social cognition items among detailed items. Conclusion : Through this study, we understood that social cognitive function of the aged living alone who had less opportunity of interaction compared to that of the aged living with family was lowered. Based on this, development and study on various programs should be made with consideration of sociodemographic characteristics of the elderly within community-based occupational therapy in the future.

• Journal of Korea Spatial Information System Society
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• v.12 no.1
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• pp.18-27
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• 2010

In u-GIS environment, GeoSensor environment requires that dynamic data captured from various sensors and static information in terms of features in 2D or 3D are fused together. GeoSensors, the core of this environment, are distributed over a wide area sporadically, and are collected in any size constantly. As a result, storage space could be exceeded because of restricted memory in DSMS. To solve this kind of problems, a lot of related studies are being researched actively. There are typically 3 different methods - Random Load Shedding, Semantic Load Shedding, and Sampling. Random Load Shedding chooses and deletes data in random. Semantic Load Shedding prioritizes data, then deletes it first which has lower priority. Sampling uses statistical operation, computes sampling rate, and sheds load. However, they are not high accuracy because traditional ones do not consider spatial characteristics. In this paper 'Pre-Filtering based Post Load Shedding' are suggested to improve the accuracy of spatial query and to restrict load shedding in DSMS. This method, at first, limits unnecessarily increased loads in stream queue with 'Pre-Filtering'. And then, it processes 'Post-Load Shedding', considering data and spatial status to guarantee the accuracy of result. The suggested method effectively reduces the number of the performance of load shedding, and improves the accuracy of spatial query.