• Journal of Internet Computing and Services
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• v.7 no.6
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• pp.21-40
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• 2006

As the knowledge-based society hot been constructed, the size of work process that has to be done grows big and the amount of the information that has to be analyzed increases. In the paper business process can be accurately executed by the rule agent to manage precise rules and definitions to be needed by execution of process for management of business process. And individuals con accurately measure and manage personal schedules to execute process through supporting the agent of schedule management. The model is designed and implemented for organization to improve process of control, flexibleness, agility, reliability, and reuse through business process automation and removing process overlapping and for personal to improve measuring personal process capacity and distinguishing process weak points.

• Steel and Composite Structures
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• v.12 no.6
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• pp.523-540
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• 2012

It was clear from the former researches on reinforced concrete filled tubular steel (RCFT) structures that RCFT structures have different performance than concrete filled steel tubular (CFT) structures. However, despite of that, load-sharing ratio of RCFT is evaluating by the formula and range of CFT given by JSCE. Therefore, the aim of this investigation is to study the load-sharing ratio of RCFT columns subjected to axial compressive load by performing numerical simulations of RCFT columns with the nonlinear finite element analysis (FEA) program - ADINA. To achieve this goal, firstly proper material constitutive models for concrete, steel tube and reinforcement are proposed. Then axial compression tests of concrete, RC, CFT, and RCFT columns are carried out to verify proposed material constitutive models. Finally, by the plenty of numerical analysis with small-sized and big-sized columns, load-sharing ratio of RCFT columns was studied, the evaluation formulas and range were proposed, application of the formula was demonstrated, and following conclusions were drawn: The FEA model introduced in this paper can be applied to nonlinear analysis of RCFT columns with reliable results; the load-sharing ratio evaluation formula and range of CFT should not be applied to RCFT; The lower limit for the range of load-sharing ratio of RCFT can be smaller than that of CFT; the proposed formulas for load-sharing ratio of RCFT have practical mean in design of RCFT columns.

• Communications for Statistical Applications and Methods
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• v.28 no.6
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• pp.595-610
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• 2021

Recently a few articles have derived robust first-order rotatable and D-optimal designs for the lifetime response having distributions gamma, lognormal, Weibull, exponential assuming errors that are correlated with different correlation structures such as autocorrelated, intra-class, inter-class, tri-diagonal, compound symmetry. Practically, a first-order model is an adequate approximation to the true surface in a small region of the explanatory variables. A second-order model is always appropriate for an unknown region, or if there is any curvature in the system. The current article aims to extend the ideas of these articles for second-order models. Invariant (free of the above four distributions) robust (free of correlation parameter values) second-order rotatable designs have been derived for the intra-class and inter-class correlated error structures. Second-order rotatability conditions have been derived herein assuming the response follows non-normal distribution (any one of the above four distributions) and errors have a general correlated error structure. These conditions are further simplified under intra-class and inter-class correlated error structures, and second-order rotatable designs are developed under these two structures for the response having anyone of the above four distributions. It is derived herein that robust second-order rotatable designs depend on the respective error variance covariance structure but they are independent of the correlation parameter values, as well as the considered four response lifetime distributions.

• Atmosphere
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• v.29 no.1
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• pp.75-86
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• 2019

In this paper, we introduce a novel Climate & Environment Database System (CEDS). The CEDS is developed by the National Institute of Meteorological Sciences (NIMS) to provide easy and efficient user interfaces and storage management of climate model data, so improves work efficiency. In uploading the data/files, the CEDS provides an option to automatically operate the international standard data conversion (CMORization) and the quality assurance (QA) processes for submission of CMIP6 variable data. This option increases the system performance, removes the user mistakes, and increases the level of reliability as it eliminates user operation for the CMORization and QA processes. The uploaded raw files are saved in a NAS storage and the Cassandra database stores the metadata that will be used for efficient data access and storage management. The Metadata is automatically generated when uploading a file, or by the user inputs. With the Metadata, the CEDS supports effective storage management by categorizing data/files. This effective storage management allows easy and fast data access with a higher level of data reliability when requesting with the simple search words by a novice. Moreover, the CEDS supports parallel and distributed computing for increasing overall system performance and balancing the load. This supports the high level of availability as multiple users can use it at the same time with fast system-response. Additionally, it deduplicates redundant data and reduces storage space.

• Journal of Advanced Navigation Technology
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• v.24 no.6
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• pp.620-626
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• 2020

This research propose and simulate a solar power generation system monitoring system based on Modbus TCP communication using RaspberryPi, an IOT equipment, as a master and an inverter as a slave. In this model, various sensors are added to the RaspberryPi to add necessary information for monitoring solar power plants, and power generation prediction and monitoring information are transmitted to the smart phone through real-time power generation prediction. In addition, information that is continuously generated by the solar power plant is built on the server as big data, and a deep learning model for predicting power generation is trained and updated. As a result of the study, stable communication was possible based on Modbus TCP with the Raspberry Pi in the inverter, and real-time prediction was possible with the deep learning model learned in the Raspberry Pi. The server was able to train various deep learning models with big data, and it was confirmed that LSTM showed the best error with a learning error of 0.0069, a test error of 0.0075, and an RMSE of 0.0866. This model suggested that it is possible to implement a real-time monitoring system that is simpler, more convenient, and can predict the amount of power generation for inverters of various manufacturers.

• The Journal of Korean Association of Computer Education
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• v.14 no.6
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• pp.1-10
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• 2011

Since IT technology grow rapidly, our ethic of consciousness has become big issue with adverse effect. Many scholar has discussed and tired to solve this problem, but it is still helpless to fix. Therefor in this paper, author will not suggest the solution, but will present model list of adverse effects and cases to prevent accidents. The model list of adverse effects, what author will present is about media addiction, harmful content, cyber-violence, right infringement, cyber terror and decision obstacle. This model list is made by primary and secondary survey. This model will show adverse effect of present day, but also will show future adverse effects that can be prevent. Through out this paper, this model list could use for education plan.

• Journal of Soil and Groundwater Environment
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• v.26 no.6
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• pp.165-175
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• 2021

Groundwater is used in many areas in food industry such as food manufacturing, food processing, cooking, and liquor industry etc. in Korea. As groundwater occupies a large portion of food industry, it is necessary to predict deterioration of water quality to ensure the safety of food water since using undrinkable groundwater has a ripple effect that can cause great harm or anxiety to food users. In this study, spatiotemporal data aggregation method was used in order to obtain spatially representative data, which enable prediction of groundwater quality change in a small watershed. In addition, a highly reliable predictive model was developed to estimate long-term changes in groundwater quality by applying a non-parametric segmented regression technique. Two pilot watersheds were selected where a large number of companies use groundwater for food water, and the appropriateness of the model was assessed by comparing the model-produced values with those obtained by actual measurements. The result of this study can contribute to establishing a customized food water management system utilizing big data that respond quickly, accurately, and preemptively to changes in groundwater quality and pollution. It is also expected to contribute to the improvement of food safety management.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1123-1130
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• 2020

This study aims to classify the severity in car crashes using five classification learning models. The dataset used in this study contains 21,013 vehicle crashes, obtained from Korea Expressway Corporation, between the year of 2015-2017 and the LightGBM(Light Gradient Boosting Model) performed well with the highest accuracy. LightGBM, the number of involved vehicles, type of accident, incident location, incident lane type, types of accidents, types of vehicles involved in accidents were shown as priority factors. Based on the results of this model, the establishment of a management strategy for response of highway traffic accident should be presented through a consistent prediction process of accident severity level. This study identifies applicability of Machine Learning Models for Predicting of the Severity of Car Traffic Accidents on a Highway and suggests that various machine learning techniques based on big data that can be used in the future.

• Journal of the Society of Disaster Information
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• v.17 no.2
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• pp.245-253
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• 2021

Purpose: This study develops a risk prediction model that predicts the risk of a fire site by using initial information such as building information and reporter acquisition information, and supports effective mobilization of fire fighting resources and the establishment of damage minimization strategies for appropriate responses in the early stages of a disaster. Method: In order to identify the variables related to the fire damage scale on the fire statistics data, a correlation analysis between variables was performed using a machine learning algorithm to examine predictability, and a learning data set was constructed through preprocessing such as data standardization and discretization. Using this, we tested a plurality of machine learning algorithms, which are evaluated as having high prediction accuracy, and developed a risk prediction model applying the algorithm with the highest accuracy. Result: As a result of the machine learning algorithm performance test, the accuracy of the random forest algorithm was the highest, and it was confirmed that the accuracy of the intermediate value was relatively high for the risk class. Conclusion: The accuracy of the prediction model was limited due to the bias of the damage scale data in the fire statistics, and data refinement by matching data and supplementing the missing values was necessary to improve the predictive model performance.

• Journal of Integrative Natural Science
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• v.8 no.1
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• pp.1-12
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• 2015

CRTh2 receptor is an important mediator of the inflammatory effects and act as beneficial target for the treatment of asthma, COPD, allergic rhinitis and atopic dermatitis. In the present work, Hologram QSAR studies were conducted on a series of 50 training set CRTh2 antagonists (2-(2-(benzylthio)-1H-benzo[d]imidazol-1-yl acetic acids). The best HQSAR model was obtained using atoms, bonds, connections and donor/acceptor as fragment distinction parameter using hologram length 257 and 6 components with fragment size of minimum 7 and maximum 10. Significant cross-validated correlation coefficient ($q^2=0.786$) and non cross-validated correlation coefficients ($r^2=0.954$) were obtained. The model was then used to evaluate the 15 external test compounds which are not included in the training set and the predicted values were in good agreement with the experimental results ($r^2_{pred}=0.739$). Contribution map show that presence of C ring and its substituents makes big contributions for activities. The HQSAR model and analysis from the contribution map could be useful for further design of novel structurally related CRTh2 antagonists.