• Membrane Journal
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• v.33 no.1
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• pp.34-45
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• 2023

Two computational intelligence techniques namely artificial neural networks (ANN) and support vector machine (SVM) are employed to model the permeate flux based on seven input variables including time, transmembrane pressure, rotating velocity, the pore diameter of the membrane, dynamic viscosity, concentration and density of the feed fluid. The best-fit model was selected through the trial-error method and the two statistical parameters including the coefficient of determination (R²) and the average absolute relative deviation (AARD) between the experimental and predicted data. The obtained results reveal that the optimized ANN model can predict the permeate flux with R² = 0.999 and AARD% = 2.245 versus the SVM model with R² = 0.996 and AARD% = 4.09. Thus, the ANN model is found to predict the permeate flux with high accuracy in comparison to the SVM approach.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.145-153
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• 2023

This study emphasizes the essential need in the military for effective measurement and monitoring of soldiers' physical fitness, health, and exercise capabilities to enhance both their overall fitness and combat effectiveness. The effective assessment of physical fitness is considered a core element of management, aligning with principles of modern management. Particularly, preparing soldiers with robust physical fitness is deemed crucial for adapting to dynamic changes on the battlefield. In this research, the RFM (Recency, Frequency, Monetary) customer analysis and clustering methods, validated in e-commerce, are introduced as a basis for applying an AI-driven customer analysis approach to assess military personnel fitness. To achieve this, the study explores the incorporation of the RSC (Reveal, Sustainable, Control) analysis model. This model aims to effectively categorize and monitor military personnel fitness. The application of the RFM technique in the RSC analysis model quantifies and models military fitness, fostering continuous improvement and seeking strategies to enhance the effectiveness of fitness management. Through these methods, the study develops an AI customer analysis technique applied to the RSC clustering analysis method for improving and sustaining military personnel fitness.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.919-925
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• 2023

Modern drones are equipped with miniaturized mission equipment capable of performing various tasks such as surveillance and reconnaissance. Consequently, these mission equipment are exposed to disturbances like wind loads and motor rotations, which can lead to instability in the operation of the Electro-Optical Targeting System (EOTS). Specifically, simple step inputs for changing the line of sight in EOTS can cause abrupt changes in speed, inducing overshoot and potentially creating instability along with other disturbances. To address this, a velocity profile was designed so that the angular velocity moves in a trapezoidal shape when changing the EOTS line of sight. A Double-loop controller was designed to apply this profile as an input to the external loop receiving position feedback. The system's stability was then compared, and the velocity profile was optimized within a stable range by varying maximum speed and acceleration.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.1-18
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• 2016

Owing to the extensive use of Web media and the development of the IT industry, a large amount of data has been generated, shared, and stored. Nowadays, various types of unstructured data such as image, sound, video, and text are distributed through Web media. Therefore, many attempts have been made in recent years to discover new value through an analysis of these unstructured data. Among these types of unstructured data, text is recognized as the most representative method for users to express and share their opinions on the Web. In this sense, demand for obtaining new insights through text analysis is steadily increasing. Accordingly, text mining is increasingly being used for different purposes in various fields. In particular, issue tracking is being widely studied not only in the academic world but also in industries because it can be used to extract various issues from text such as news, (SocialNetworkServices) to analyze the trends of these issues. Conventionally, issue tracking is used to identify major issues sustained over a long period of time through topic modeling and to analyze the detailed distribution of documents involved in each issue. However, because conventional issue tracking assumes that the content composing each issue does not change throughout the entire tracking period, it cannot represent the dynamic mutation process of detailed issues that can be created, merged, divided, and deleted between these periods. Moreover, because only keywords that appear consistently throughout the entire period can be derived as issue keywords, concrete issue keywords such as "nuclear test" and "separated families" may be concealed by more general issue keywords such as "North Korea" in an analysis over a long period of time. This implies that many meaningful but short-lived issues cannot be discovered by conventional issue tracking. Note that detailed keywords are preferable to general keywords because the former can be clues for providing actionable strategies. To overcome these limitations, we performed an independent analysis on the documents of each detailed period. We generated an issue flow diagram based on the similarity of each issue between two consecutive periods. The issue transition pattern among categories was analyzed by using the category information of each document. In this study, we then applied the proposed methodology to a real case of 53,739 news articles. We derived an issue flow diagram from the articles. We then proposed the following useful application scenarios for the issue flow diagram presented in the experiment section. First, we can identify an issue that actively appears during a certain period and promptly disappears in the next period. Second, the preceding and following issues of a particular issue can be easily discovered from the issue flow diagram. This implies that our methodology can be used to discover the association between inter-period issues. Finally, an interesting pattern of one-way and two-way transitions was discovered by analyzing the transition patterns of issues through category analysis. Thus, we discovered that a pair of mutually similar categories induces two-way transitions. In contrast, one-way transitions can be recognized as an indicator that issues in a certain category tend to be influenced by other issues in another category. For practical application of the proposed methodology, high-quality word and stop word dictionaries need to be constructed. In addition, not only the number of documents but also additional meta-information such as the read counts, written time, and comments of documents should be analyzed. A rigorous performance evaluation or validation of the proposed methodology should be performed in future works.

• The Journal of the Acoustical Society of Korea
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• v.35 no.4
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• pp.261-271
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• 2016

In the prediction of response of a pile in soil, numerical approaches such as a finite element method are generally applied due to complicate nonlinear behaviors of soils. However, the numerical methods based on the finite elements require heavy efforts in pile and soil modelling and also take long computing time. So their usage is limited especially in the early design stage in which principal dimensions and properties are not specified and tend to vary. On the contrary, theoretical approaches adopting linear approximations for soils are relatively simple and easy to model and take short computing time. Therefore, if they are validated to be reliable, they would be applicable in predicting responses of a pile in soil, particularly in early design stage. In case of wind turbines regarded in this study, it is required to assess their natural frequencies in early stages, and in this simulation the supporting pile inserted in soil could be replaced with a simplified elastic boundary condition at the bottom end of the wind turbine tower. To do this, analysis for a pile in soil is performed in this study to extract the spring constants at the top end of the pile. The pile in soil can be modelled as a beam on elastic spring by assuming that the soils deform within an elastic range. In this study, it is attempted to predict pile deformations and influence factors for lateral loads by means of the beam-on-spring model. As two example supporting structures for wind turbines, mono pile and suction pile models with different diameters are examined by evaluating their influence factors and validated by comparing them with those reported in literature. In addition, the deflection profiles along the depth and spring constants at the top end of the piles are compared to assess their supporting features.

• Computational Structural Engineering
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• v.7 no.3
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• pp.95-103
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• 1994

The safety related components in the nuclear power plant should be designed to withstand the seismic load. Among these components the integrity of reactor internals under earthquake load is important in stand points of safety and economics, because these are classified to Seismic Class I components. So far the modelling methods of reactor internals have been investigated by many authors. In this paper, the dynamic behaviour of reactor internals of Yong Gwang 1&2 nuclear power plants under SSE(Safe Shutdown Earthquake) load is analyzed by using of the simpled Global Beam Model. For this, as a first step, the characteristic analysis of reactor internal components are performed by using of the finite element code ANSYS. And the Global Beam Model for reactor internals which includes beam elements, nonlinear impact springs which have gaps in upper and lower positions, and hydrodynamical couplings which simulate the fluid-filled cylinders of reactor vessel and core barrel structures is established. And for the exciting external force the response spectrum which is applied to reactor support is converted to the time history input. With this excitation and the model the dynamic behaviour of reactor internals is obtained. As the results, the structural integrity of reactor internal components under seismic excitation is verified and the input for the detailed duel assembly series model could be obtained. And the simplicity and effectiveness of Global Beam Model and the economics of the explicit Runge-Kutta-Gills algorithm in impact problem of high frequency interface components are confirmed.

• Journal of Korean Tunnelling and Underground Space Association
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• v.25 no.4
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• pp.305-330
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• 2023

Unlike NATM tunnels, Shield TBM tunnels have split linings. Therefore, the stress distribution of the lining is different even if the lining is under the same load. Representative methods for analyzing the stress generated in lining in Shield TBM tunnels include Non-joint Mode that does not consider connections and a 2-ring beam-spring model that considers ring-to-ring joints and segment connections. This study is an analysis method by Break-joint Mode. However, we do not consider the structural role of segment lining connections. The effectiveness of the modeling is verified by analyzing behavioral characteristics against vibration loads by modeling with segment connection interfaces to which vertical stiffness and shear stiffness, which are friction components, are applied. Unlike the Non-joint mode, where the greatest stress occurs on the crown for static loads such as earth pressure, the stress distribution caused by contact between segment lining and friction stiffness produced the smallest stress in the crown key segment where segment connections were concentrated. The stress distribution was clearly distinguished based on segment connections. The results of static analysis by earth pressure, etc., produced up to seven times the stress generated in Non-joint mode compared to the stress generated by Break-joint Mode. This result is consistent with the stress distribution pattern of the 2-ring beam-spring model. However, as for the stress value for the train vibration load, the stress of Break-joint Mode was greater than that of Non-joint mode. This is a different result from the static mechanics concept that a segment ring consisting of a combination of short members is integrated in the circumferential direction, resulting in a smaller stress than Non-joint mode with a relatively longer member length.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.101-124
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• 2018

Recently, most of the technologies have been developed in various forms through the advancement of single technology or interaction with other technologies. Particularly, these technologies have the characteristic of the convergence caused by the interaction between two or more techniques. In addition, efforts in responding to technological changes by advance are continuously increasing through forecasting promising convergence technologies that will emerge in the near future. According to this phenomenon, many researchers are attempting to perform various analyses about forecasting promising convergence technologies. A convergence technology has characteristics of various technologies according to the principle of generation. Therefore, forecasting promising convergence technologies is much more difficult than forecasting general technologies with high growth potential. Nevertheless, some achievements have been confirmed in an attempt to forecasting promising technologies using big data analysis and social network analysis. Studies of convergence technology through data analysis are actively conducted with the theme of discovering new convergence technologies and analyzing their trends. According that, information about new convergence technologies is being provided more abundantly than in the past. However, existing methods in analyzing convergence technology have some limitations. Firstly, most studies deal with convergence technology analyze data through predefined technology classifications. The technologies appearing recently tend to have characteristics of convergence and thus consist of technologies from various fields. In other words, the new convergence technologies may not belong to the defined classification. Therefore, the existing method does not properly reflect the dynamic change of the convergence phenomenon. Secondly, in order to forecast the promising convergence technologies, most of the existing analysis method use the general purpose indicators in process. This method does not fully utilize the specificity of convergence phenomenon. The new convergence technology is highly dependent on the existing technology, which is the origin of that technology. Based on that, it can grow into the independent field or disappear rapidly, according to the change of the dependent technology. In the existing analysis, the potential growth of convergence technology is judged through the traditional indicators designed from the general purpose. However, these indicators do not reflect the principle of convergence. In other words, these indicators do not reflect the characteristics of convergence technology, which brings the meaning of new technologies emerge through two or more mature technologies and grown technologies affect the creation of another technology. Thirdly, previous studies do not provide objective methods for evaluating the accuracy of models in forecasting promising convergence technologies. In the studies of convergence technology, the subject of forecasting promising technologies was relatively insufficient due to the complexity of the field. Therefore, it is difficult to find a method to evaluate the accuracy of the model that forecasting promising convergence technologies. In order to activate the field of forecasting promising convergence technology, it is important to establish a method for objectively verifying and evaluating the accuracy of the model proposed by each study. To overcome these limitations, we propose a new method for analysis of convergence technologies. First of all, through topic modeling, we derive a new technology classification in terms of text content. It reflects the dynamic change of the actual technology market, not the existing fixed classification standard. In addition, we identify the influence relationships between technologies through the topic correspondence weights of each document, and structuralize them into a network. In addition, we devise a centrality indicator (PGC, potential growth centrality) to forecast the future growth of technology by utilizing the centrality information of each technology. It reflects the convergence characteristics of each technology, according to technology maturity and interdependence between technologies. Along with this, we propose a method to evaluate the accuracy of forecasting model by measuring the growth rate of promising technology. It is based on the variation of potential growth centrality by period. In this paper, we conduct experiments with 13,477 patent documents dealing with technical contents to evaluate the performance and practical applicability of the proposed method. As a result, it is confirmed that the forecast model based on a centrality indicator of the proposed method has a maximum forecast accuracy of about 2.88 times higher than the accuracy of the forecast model based on the currently used network indicators.

• Korean Chemical Engineering Research
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• v.52 no.5
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• pp.574-580
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• 2014

Storage tanks of Carbon dioxide ($CO_2$) carriers utilized for the purpose of carbon capture and storage (CCS) into subsea strata have to undergo a pre-cooling session before beginning to load cryogenic liquid cargos in order to prevent physical and thermal deterioration of tanks which may result from cryogenic $CO_2$ contacting tank walls directly. In this study we propose dynamic model to calculate the tank inflow of $CO_2$ gas injected for precooling process and its dynamic simulation results under proportional-integral control algorithm. We selected two cases in which each of them had one controlled variable (CV) as either the tank pressure or the tank temperature and discussed the results of that decision-making on the pre-cooling process. As a result we demonstrated that the controlling instability arising from nonlinearity and singularity of the mathematical model could be avoided by choosing tank pressure as CV instead of tank temperature.

• Journal of Soil and Groundwater Environment
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• v.11 no.2
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• pp.45-55
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• 2006

A 2-D unconfined flow model is developed to analyze annual variations of groundwater level and bank filtration rate (BFR) for an experimental riverbank filtration site in Koryeong, Korea. Two types of boundary conditions are examined for the river boundary in the conceptual model: the static head condition that uses the average water level of the river and the dynamic cyclic condition that incorporates annual fluctuation of water level. Simulations show that the estimated BFR ranges $74.3{\sim}87.0%$ annually with the mean of 82.4% for the static head boundary condition and $52.7{\sim}98.1%$ with the mean of 78.5% for the dynamic cyclic condition. The results illustrate that the dynamic cyclic condition should be used for accurate evaluation of BFR. Simulations also show that increase of the distance between the river and the pumping wells slightly decreases BFR up to 4%, and thereby indicate that it is not a critical factor to be accounted for in designing BFR of the bank filtration system. A sensitivity analysis is performed to examine the effects of model parameters such as hydraulic conductivity and specific yield of the aquifer, recharge rate, and pumping rate. The results demonstrate that the average groundwater level and BFR are most sensitive to both the pumping rate and the recharge rate, while the water level of the pumping wells is sensitive to the hydraulic conductivity and the pumping rate.