• KIPS Transactions on Computer and Communication Systems
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• v.1 no.3
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• pp.143-150
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• 2012

A simulation methodology and corresponding program based on it is to be discussed for analyzing the effects of the networking operation of existing DHC system in connection with CHP system on-site. The practical simulation for arbitrary areas with various building compositions is carried out for the analysis of operational features in both systems, and the various aspects of thermal energy grids with connecting operation are highlighted through the detailed assessment of predicted results. The intrinsic operational features of CHP prime movers, gas engine, gas turbine etc., are effectively implemented by realizing the performance data, i.e. actual operation efficiency in the full and part loads range. For the sake of simplicity, a simple mathematical correlation model is proposed for simulating various aspects of change effectively on the existing DHC system side due to the connecting operation, instead of performing cycle simulations separately. The empirical correlations are developed using the hourly based annual operation data for a branch of the Korean District Heating Corporation (KDHC) and are implicit in relation between main operation parameters such as fuel consumption by use, heat and power production. In the simulation, a variety of system configurations are able to be considered according to any combination of the probable CHP prime-movers, absorption or turbo type cooling chillers of every kind and capacity. From the analysis of the thermal network operation simulations, it is found that the newly proposed methodology of mathematical correlation for modelling of the existing DHC system functions effectively in reflecting the operational variations due to thermal energy grids with connecting operation. The effects of intrinsic features of CHP prime-movers, e.g. the different ratio of heat and power production, various combinations of different types of chillers (i.e. absorption and turbo types) on the overall system operation are discussed in detail with the consideration of operation schemes and corresponding simulation algorithms.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.1-19
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• 2018

Since stock movements forecasting is an important issue both academically and practically, studies related to stock price prediction have been actively conducted. The stock price forecasting research is classified into structured data and unstructured data, and it is divided into technical analysis, fundamental analysis and media effect analysis in detail. In the big data era, research on stock price prediction combining big data is actively underway. Based on a large number of data, stock prediction research mainly focuses on machine learning techniques. Especially, research methods that combine the effects of media are attracting attention recently, among which researches that analyze online news and utilize online news to forecast stock prices are becoming main. Previous studies predicting stock prices through online news are mostly sentiment analysis of news, making different corpus for each company, and making a dictionary that predicts stock prices by recording responses according to the past stock price. Therefore, existing studies have examined the impact of online news on individual companies. For example, stock movements of Samsung Electronics are predicted with only online news of Samsung Electronics. In addition, a method of considering influences among highly relevant companies has also been studied recently. For example, stock movements of Samsung Electronics are predicted with news of Samsung Electronics and a highly related company like LG Electronics.These previous studies examine the effects of news of industrial sector with homogeneity on the individual company. In the previous studies, homogeneous industries are classified according to the Global Industrial Classification Standard. In other words, the existing studies were analyzed under the assumption that industries divided into Global Industrial Classification Standard have homogeneity. However, existing studies have limitations in that they do not take into account influential companies with high relevance or reflect the existence of heterogeneity within the same Global Industrial Classification Standard sectors. As a result of our examining the various sectors, it can be seen that there are sectors that show the industrial sectors are not a homogeneous group. To overcome these limitations of existing studies that do not reflect heterogeneity, our study suggests a methodology that reflects the heterogeneous effects of the industrial sector that affect the stock price by applying k-means clustering. Multiple Kernel Learning is mainly used to integrate data with various characteristics. Multiple Kernel Learning has several kernels, each of which receives and predicts different data. To incorporate effects of target firm and its relevant firms simultaneously, we used Multiple Kernel Learning. Each kernel was assigned to predict stock prices with variables of financial news of the industrial group divided by the target firm, K-means cluster analysis. In order to prove that the suggested methodology is appropriate, experiments were conducted through three years of online news and stock prices. The results of this study are as follows. (1) We confirmed that the information of the industrial sectors related to target company also contains meaningful information to predict stock movements of target company and confirmed that machine learning algorithm has better predictive power when considering the news of the relevant companies and target company's news together. (2) It is important to predict stock movements with varying number of clusters according to the level of homogeneity in the industrial sector. In other words, when stock prices are homogeneous in industrial sectors, it is important to use relational effect at the level of industry group without analyzing clusters or to use it in small number of clusters. When the stock price is heterogeneous in industry group, it is important to cluster them into groups. This study has a contribution that we testified firms classified as Global Industrial Classification Standard have heterogeneity and suggested it is necessary to define the relevance through machine learning and statistical analysis methodology rather than simply defining it in the Global Industrial Classification Standard. It has also contribution that we proved the efficiency of the prediction model reflecting heterogeneity.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.4
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• pp.1-15
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• 2023

This research intended to reveal the special characteristics of the vegetation structure and the tendency of change of -landfill slope districts, which are reclaimed land, through an investigationsinto the presently existent vegetation and plant community structure of the World Cup Park landfill slope district. For the analysis of changes in vegetation, this study compared the results of field surveys in 1999, 2003, 2005, 2007, 2008, 2012, 2016, and 2021. For the investigation into the plant community structure, a field investigation was carried out in 2021 with six fixed investigation districts designated in 1999 as subjects. To analyze the change in the plant community structure, the past data on the population, the number of the species, and the species diversity by the layer in 2021 were compared and analyzed in the landfill slope district, which is reclaimed land. The changes of the vegetation distribution and the power had been affected by typhoons (Kompasu). Above the plantation foundation, which had been dry and poor, Salix koreensis, marsh woody plants that had formed the community, decreased greatly. The Robinia pseudoacacia community, after the typhoon in 2010, decreased in the number of species and population. Afterward, it showed a tendency to rebound. Regarding the Ailanthus altissima-Robinia pseudoacacia-Paulownia tomentosa community, the number of the species and the population had shown a change similar to the Robinia pseudoacacia community. The Paulownia tomentosa and the Ailanthus altissima have been culled. The slope was predicted as a Future Robinia pseudoacacia forest. The Salix pseudolasiogyne community has been transitioning to a Robinia pseudoacacia forest. Only some enumeration districts, the Robinia pseudoacacia forests and the Salix pseudolasiogyne, had been growing. However, most had been in been declining. It was predicted that this community will be maintained as a Robinia pseudoacacia forest in the future. As these vegetation communities are the representative vegetation of the landfill slope districts, which is reclaimed land, there is a need to understand the ecosystem changes of the community through continuous monitoring. The results of this research can be utilized as a basic material for the vegetation restoration of reclaimed land.

• Korean Journal of Environmental Biology
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• v.41 no.4
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• pp.505-518
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• 2023

The key to invasive pest management lies in preemptive action. However, most current research using species distribution models is conducted after an invasion has occurred. This study modeled the potential distribution of the globally notorious sweet potato pest, the sweet potato weevil(Cylas formicarius), that has not yet invaded Korea using MaxEnt. Using global occurrence data, bioclimatic variables, and topsoil characteristics, MaxEnt showed high explanatory power as both the training and test areas under the curve exceeded 0.9. Among the environmental variables used in this study, minimum temperature in the coldest month (BIO06), precipitation in the driest month (BIO14), mean diurnal range (BIO02), and bulk density (BDOD) were identified as key variables. The predicted global distribution showed high values in most countries where the species is currently present, with a significant potential invasion risk in most South American countries where C. formicarius is not yet present. In Korea, Jeju Island and the southwestern coasts of Jeollanam-do showed very high probabilities. The impact of climate change under shared socioeconomic pathway (SSP) scenarios indicated an expansion along coasts as climate change progresses. By applying the 10th percentile minimum training presence rule, the potential area of occurrence was estimated at 1,439 km² under current climate conditions and could expand up to 9,485 km² under the SSP585 scenario. However, the model predicted that an inland invasion would not be serious. The results of this study suggest a need to focus on the risk of invasion in islands and coastal areas.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.

• Journal of the Korean Geophysical Society
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• v.5 no.4
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• pp.293-303
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• 2002

Applying elastic plate model, we estimated elastic thickness and rigidity of the lithosphere in southern part of the Korean Peninsula($332km{\times}332km$ area of which center is $36.5^{\circ}N$ in latitude and $127.5^{\circ}E$ in longitude) by analysing terrain data and gravity data measured up to 2002. We tried to exclude the East Sea in choosing the study area because it has different tectonic environment. The mean Moho depth was estimated to be 30 km by power spectrum analysis of gravity data in the study area, Assuming one layer crust and applying elastic plate model, the loads with wavelengths of greater than 300 km are locally compensated, loads with wavelengths in the range 80-300km are partially supported by the strength of the lithosphere, and loads with wavelengths of less than 80km are almost completely supported by lithospheric strength. Assuming crustal model and rigidity, we calculated predicted coherence and compared it with observed coherence. As a result, we wert able to estimate the effective elastic thickness to be of 15 km(corresponding flexural rigidity is $3.0{\times}10^{22}Nm$). This indicates that the crust of the study area is relatively weaker than other old and stable continental regions but is similar to continental margins or oceanic area. The low rigidity could be explained by many tectonic and thermal activities such as orogenic activities, magmatic intrusions, volcanic activities, foldings, faultings, etc.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.37 no.8
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• pp.781-788
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• 2013

Predicting the engine component temperature is a basic step to conduct structural safety evaluation in medium-speed diesel engine design. Recent trends such as increasing power density and performance necessitate more effective thermal management of the engine for achieving the desired durability and reliability. In addition, the local temperatures of several engine components must be maintained in the proper range to avoid problems such as low- or high-temperature corrosion. Therefore, it is very important to predict the temperature distribution of each engine part accurately in the design stage. In this study, the temperature of an engine component is calculated by using steady-state conjugate heat transfer analysis. A proper approach to determine the thermal load distribution on the thermal boundary area is suggested by using 1D engine system analysis, 3D transient CFD results, and previous experimental data from another developed engine model. A Hyundai HiMSEN engine having 250-mm bore size was chosen to validate the analysis procedure. The predicted results showed a reasonable agreement with experimental results.

• Journal of Aerospace System Engineering
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• v.11 no.3
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• pp.1-7
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• 2017

The applications of high-frequency induction heating has recently been studied in various industrial fields. In this study, induction heating is applied to a SCM440 specimen that is widely used in industry. The specimen was made up of a cylinder 20 mm in diameter and 160 mm long. An induction heating power supply module was used to generate heat in the cylinder at a high frequency (approximately 85 kHz) for 50 seconds. The temperature of the specimen was measured at the 150 mm length in 5 second intervals. Results such as joule heat and temperature are compared with the numerical model analysis using an electromagnetic-thermal co-simulation technique. The analytical model of the cylinder was modeled by considering the skin effect. The median measured temperature after induction heating was conducted for 50 seconds was $57.65^{\circ}C$, compared to a predicted analytical value of $57.27^{\circ}C$. Thus, the analytical results are in good agreement with the experimental results, and this model can predict the induction heating phenomenon numerically.

• Applied Chemistry for Engineering
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• v.26 no.4
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• pp.495-504
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• 2015

This work investigated the characteristics of a packed-bed plasma reactor system and the performances of the plasma reactors connected in series or in parallel for the decomposition of ethylene. Before the discharge ignition, the effective capacitance of the ${\gamma}$-alumina packed-bed plasma reactor was larger than that of the reactor without any packing, but after the ignition the effective capacitance was similar to each other, regardless of the packing. The energy of electrons created by plasma depends mainly on the electric field intensity, and was not significantly affected by the gas composition in the range of 0~20% (v/v) oxygen (nitrogen : 80~100% (v/v)). Among the various reactive species generated by plasma, ground-state atomic oxygen and ozone are understood to be primarily involved in oxidation reactions, and as the electric field intensity increases, the amount of ground-state atomic oxygen relatively decreases while that of nitrogen atom increases. Even though there are many parameters affecting the performance of the plasma reactor such as a voltage, discharge power, gas flow rate and residence time, all parameters can be integrated into a single parameter, namely, specific input energy (SIE). It was experimentally confirmed that the performances of the plasma reactors connected in series or in parallel could be treated as a function of SIE alone, which simplifies the scale-up design procedure. Besides, the ethylene decomposition results can be predicted by the calculation using the rate constant expressed as a function of SIE.

• Proceedings of the Korea Society for Energy Engineering kosee Conference
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• 2002.11a
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• pp.171-184
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• 2002

Approx. 200,000 bpd vacuum residue oil is produced from oil refineries in Korea. These are supplying to use asphalt, high sulfur fuel oil, and upgrading at the residue hydro-desulfurization unit. Vacuum residue oil has high energy content, however high sulfur content and high concentration of heavy metals represent improper low grade fuel. To meet growing demand for effective utilization of vacuum residue oil from refineries, recently some of the oil refinery industries in Korea, such as SK oil refinery and LG Caltex refinery, have already proceeded feasibility study to construct 435-500 MWe IGCC power plant and hydrogen production facilities. Recently, KIER(Korea Institute of Energy Research) are studing on the Vacuum Residue gasification process using an oxygen-blown entrained-flow gasifier. The experiment runs were evaluated under the reaction temperature : 1,100~1,25$0^{\circ}C$, reaction pressure : 1~6kg/$\textrm{cm}^2$G, oxygen/V.R ratio : 0.8~0.9 and steam/V.R ratio : 0.4-0.5. Experimental results show the syngas composition(CO+H$_2$) : 85~93%, syngas flow rate : 50~110Mm$^3$/hr, heating value : 2,300~3,000 ㎉/Nm$^3$, carbon conversion : 65~92, cold gas efficiency : 60~70%. Also equilibrium modeling was used to predict the vacuum residue gasification process and the predicted values were compared reasonably well with experimental data.