• Journal of the Korean Institute of Gas
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• v.22 no.6
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• pp.90-103
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• 2018

This paper presents the development of two ANN models which can predict an optimum production rate by controlling choke size in oil well, and gas injection rate in gas-lift well. The input data was solution gas-oil ratio, water cut, reservoir pressure, and choke size or gas injection rate. The output data was wellhead pressure and production rate. Firstly, a range of each parameters was decided by conducting sensitive analysis of input data for onshore oil well. In addition, 1,715 sets training data for choke size decision model and 1,225 sets for gas injection rate decision model were generated by nodal analysis. From the results of comparing between the nodal analysis and the ANN on the same reservoir system showed that the correlation factors were very high(>0.99). Mean absolute error of wellhead pressure and oil production rate was 0.55%, 1.05% with the choke size model, respectively. And the gas injection rate model showed the errors of 1.23%, 2.67%. It was found that the developed models had been highly accurate.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.3
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• pp.91-98
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• 2021

Today's information and communication technology is rapidly developing, the security of IT infrastructure is becoming more important, and at the same time, cyber attacks of various forms are becoming more advanced and sophisticated like intelligent persistent attacks (Advanced Persistent Threat). Early defense or prediction of increasingly sophisticated cyber attacks is extremely important, and in many cases, the analysis of network-based intrusion detection systems (NIDS) related data alone cannot prevent rapidly changing cyber attacks. Therefore, we are currently using data generated by intrusion detection systems to protect against cyber attacks described above through Host-based Intrusion Detection System (HIDS) data analysis. In this paper, we conducted a comparative study on machine learning algorithms using LID-DS (Leipzig Intrusion Detection-Data Set) host-based intrusion detection data including thread information, metadata, and buffer data missing from previously used data sets. The algorithms used were Decision Tree, Naive Bayes, MLP (Multi-Layer Perceptron), Logistic Regression, LSTM (Long Short-Term Memory model), and RNN (Recurrent Neural Network). Accuracy, accuracy, recall, F1-Score indicators and error rates were measured for evaluation. As a result, the LSTM algorithm had the highest accuracy.

• The Journal of Engineering Geology
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• v.32 no.2
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• pp.221-239
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• 2022

Unknown geotechnical characteristics are key challenges in the design of piles for the plant, civil and building works. Although the N-values which were read through the standard penetration test are important, those N-values of the whole area are not likely acquired in common practice. In this study, the N-value is predicted by means of regression analysis with artificial intelligence (AI). Big data is important to improve learning performance of AI, so circular augmentation method is applied to build up the big data at the current study. The optimal model was chosen among applied AI algorithms, such as artificial neural network, decision tree and auto machine learning. To select optimal model among the above three AI algorithms is to minimize the margin of error. To evaluate the method, actual data and predicted data of six performed projects in Poland, Indonesia and Malaysia were compared. As a result of this study, the AI prediction of this method is proven to be reliable. Therefore, it is realized that the geotechnical characteristics of non-boring points were predictable and the optimal arrangement of structure could be achieved utilizing three dimensional N-value distribution map.

• Korea Journal of Hospital Management
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• v.11 no.3
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• pp.19-41
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• 2006

The Objective of this study was to design the model which predict the future cash flow of hospitals and on the basis of designed model to support sound hospital management by the prediction of future cash flow. The five cash flow measurement variables discussed in financial accrual part were used as variables and these variables were defined as NI, NIDPR, CFO, CFAI, CC. To measure the cash flow B/S related variables, P/L related variables and financial ratio related variables were utilized in this study. To measure cash flow models were designed and to estimate the prediction ability of five cash flow models, the martingale model and the market model were utilized. To estimate relative prediction outcome of cash flow prediction model and simple market model, MAE and MER were used to compare and analyze relative prediction ability of the cash flow model and the market model and to prove superiority of the model of the cash flow prediction model, 32 Regional Public Hospital's cross-section data and 4 year time series data were combined and pooled cross-sectional time series regression model was used for GLS-analysis. To analyze this data, Firstly, each cash flow prediction model, martingale model and market model were made and MAE and MER were estimated. Secondly difference-test was conducted to find the difference between MAE and MER of cash flow prediction model. Thirdly after ranking by size the prediction of cash flow model, martingale model and market model, Friedman-test was evaluated to find prediction ability. The results of this study were as follows: when t-test was conducted to find prediction ability among each model, the error of prediction of cash flow model was smaller than that of martingale and market model, and the difference of prediction error cash flow was significant, so cash flow model was analyzed as excellent compare with other models. This research results can be considered conductive in that present the suitable prediction model of future cash flow to the hospital. This research can provide valuable information in policy-making of hospital's policy decision. This research provide effects as follows; (1) the research is useful to estimate the benefit of hospital, solvency and capital supply ability for substitution of fixed equipment. (2) the research is useful to estimate hospital's liqudity, solvency and financial ability. (3) the research is useful to estimate evaluation ability in hospital management. Furthermore, the research should be continued by sampling all hospitals and constructed advanced cash flow model in dimension, established type and continued by studying unified model which is related each cash flow model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.2
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• pp.185-191
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• 2018

This study examined the type and extent of interruption between induced emotion and succeeding arithmetic operation. The experiment was carried out to determine the influence of the induced emotions (anger, joy, and sorrow) and stimulus types (picture and sentence) on the cognitive process load that may block the interactions among the constituents of working memory. The study subjects were 32 undergraduates who were similar with respect to age and education parameters and were especially instructed to attend to induced emotion by imitation of facial expression and to make a correct decision during the remainder calculation task. In the results, the stimulus types did not exhibit any difference but there was a significant difference among the induced emotion types. The difference was observed in slower response time at positive emotion(joy condition) as compared with other emotions(anger and sorrow). More specifically, error and delayed correct response rate for emotion types were analysed to determine which phase the slower response was associated with. Delayed responses of the joy condition by sentence-inducing stimulus were identified with the error rate difference, and those by picture-inducing stimulus with the delayed correct response rate. These findings not only suggest that induced positive emotion increased response time compared to negative emotions, but also imply that picture-inducing stimulus easily affords arithmetic fluctuation whereas sentence-inducing stimulus results in arithmetic failure.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.7
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• pp.297-306
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• 2017

Citizens want more accurate forecast information using Bus Information System. However, most bus information systems that use an average based short-term prediction algorithm include many errors because they do not consider the effects of the traffic flow, signal period, and halting time. In this paper, we try to improve the precision of forecast information by analyzing the influencing factors of the error, thereby making the convenience of the citizens. We analyzed the influence factors of the error using BIS data. It is shown in the analyzed data that the effects of the time characteristics and geographical conditions are mixed, and that effects on halting time and passes speed is different. Therefore, the halt time is constructed using Generalized Additive Model with explanatory variable such as hour, GPS coordinate and number of routes, and we used Hidden Markov Model to construct a pattern considering the influence of traffic flow on the unit section. As a result of the pattern construction, accurate real-time forecasting and long-term prediction of route travel time were possible. Finally, it is shown that this model is suitable for travel time prediction through statistical test between observed data and predicted data. As a result of this paper, we can provide more precise forecast information to the citizens, and we think that long-term forecasting can play an important role in decision making such as route scheduling.

• Spatial Information Research
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• v.12 no.3
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• pp.285-298
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• 2004

Transportation digital map has built based on NGIS (national geography institute's 1 :5000 digital database) which derived from the aerial photo materials. Transportation digital map is a part of National Transportation Database Building Project carried out by the Korea Transport Institute and Ministry of Construction and Transportation. Transportation digital map for the purpose of transportation plan and investment has been updated and corrected the NGIS database especially for road network. Transportation digital map database is essential basic data fully applied for transportation policy and planning. The database must be reliable and objective to be applied for national transportation policy decision and transportation analysis. In addition, it needs accuracy and currentness to reflect the road network for the survey year. To satisfy the purpose of the database, following steps are necessary first, data Production and building has to be done by guideline of survey and database building. Secondly, geometric and logical errors which can occur during the survey and database building should be carefully detected. Thirdly, sectional guideline for database examination and procedure needs to be set up systematically and coherently This study is about examination guidelines for section and procedure on nodes and links which are essential object in transportation digital map database. According to the type of error, consistent and systematic error examination can lead to quality guarantee for objective and reliable database.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.6
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• pp.1-9
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• 2007

Recently, as a number of media are fused into a phone, the requirement of security of service provided on a mobile phone is increasing. For this, conventional cryptographic key based on password and security card is used in the mobile phone, but it has the characteristics which is easy to be vulnerable and to be illegally stolen. To overcome such a problem, the researches to generate key based on biometrics have been done. However, it has also the problem that biometric information is susceptible to the variation of environment, whereas conventional cryptographic system should generate invariant cryptographic key at any time. So, we propose new method of producing cryptographic key based on "Biometric matching-based key release" instead of "Biometric-based key generation" by using both face and iris information in order to overcome the unstability of uni-modal biometries. Also, by using mega-pixel camera embedded on mobile phone, we can provide users with convenience that both face and iris recognition is possible at the same time. Experimental results showed that we could obtain the EER(Equal Error Rate) performance of 0.5% when producing cryptographic key. And FAR was shown as about 0.002% in case of FRR of 25%. In addition, our system can provide the functionality of controlling FAR and FRR based on threshold.

• The Korean Journal of Applied Statistics
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• v.25 no.1
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• pp.89-99
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• 2012

This study investigates the Granger-causality between economic growth and demand for electricity in Korea, using two quarterly time-series data (real GDP and electricity consumption) for 1970:Q1 through 2009:Q4. We apply Hsiao's sequential procedure to identify a vector autoregressive model to a decision of the optimal lags in the vector error-correction model because the two time-series data contain unit roots respectively and they are cointegrated. According to the empirical results in this study, we find that Hsiao's approach to the Granger-causality indicates a bidirectional causal relation between economic growth and demand for electricity in Korea. Following the Granger and Engle's approach, we also find the statistical evidence on (1) short-run bidirectional causality between real GDP and electricity consumption, (2) bidirectional strong causality between them, and (3) long-run unidirectional causality running from demand for electricity to economic growth. Our results show an inconsistency with the existing studies on Korea's case; however, the results appear to provide more meaningful policy implications for the Korean economy and its strategy of sustainable growth.

• Journal of the Korean Association of Geographic Information Studies
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• v.6 no.3
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• pp.95-106
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• 2003

This study evaluated the architectural density of urban district using airborne laser scanning(ALS) that is a method used in urban planning, water resources and disaster prevention with high interest recently. First, digital elevation model(DEM) and digital surface model(DSM) was constructed from Light detection and ranging(LiDAR). For getting the height of building, ZONALMEAN filter was used in DEM and ZONALMAJORITY filter was used in DSM. This study compared the floor from filtering with the floor from survey and got standard error, which is ${\pm}0.199$ floor. Also, through the overlay and statistical analysis of total-area layer and zone layer, we could present floor area ratio by zone. As a result of comparison with floor area ratio between airborne laser scanning data and survey data, the standard error of floor area ratio shows ${\pm}2.68%$. Therefore, we expect that airborne laser scanning data can be a very efficient source to decision makers who set up landuse plan in near future.