• Steel and Composite Structures
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• v.49 no.1
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• pp.65-79
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• 2023

This paper presents a development of empirical evaluations, which can be used to evaluate the damage of fiber-reinforced concrete composites (FRC) wall subjected to close-in blast loads. For this development, a combined application of numerical simulation and machine learning approaches are employed. First, finite element modeling of FRC wall under blast loading is developed and verified using experimental data. Numerical analyses are then carried out to investigate the dynamic behavior of the FRC wall under blast loading. In addition, a data set of 384 samples on the damage of FRC wall due to blast loads is then produced in order to develop machine learning models. Second, three robust machine learning models of Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) are employed to propose empirical evaluations for predicting the damage of FRC wall. The proposed empirical evaluations are very useful for practical evaluation and design of FRC wall subjected to blast loads.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.697-702
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• 1997

Control system of electric machine systems is often required to provide the good control performance even in the presence of various variable loads. In this study, time delay control technique is adopted to overcome such variable loads. Also, in this research a new approach of avoiding saturation by varying the reference model for the time delay control based systems subject to the step changes in reference inputs. These schemes are verified by applications to the position controls of the AC servo motor system and the engine throttle actuator.

• Wind and Structures
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• v.36 no.6
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• pp.355-366
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• 2023

Although machine learning (ML) techniques have been widely used in various fields of engineering practice, their applications in the field of wind engineering are still at the initial stage. In order to evaluate the feasibility of machine learning algorithms for prediction of wind loads on high-rise buildings, this study took the exposure category type, wind direction and the height of local wind force as the input features and adopted four different machine learning algorithms including k-nearest neighbor (KNN), support vector machine (SVM), gradient boosting regression tree (GBRT) and extreme gradient (XG) boosting to predict wind force coefficients of CAARC standard tall building model. All the hyper-parameters of four ML algorithms are optimized by tree-structured Parzen estimator (TPE). The result shows that mean drag force coefficients and RMS lift force coefficients can be well predicted by the GBRT algorithm model while the RMS drag force coefficients can be forecasted preferably by the XG boosting algorithm model. The proposed machine learning based algorithms for wind loads prediction can be an alternative of traditional wind tunnel tests and computational fluid dynamic simulations.

• International Journal of Precision Engineering and Manufacturing
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• v.4 no.5
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• pp.27-33
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• 2003

G-code, another name of ISO6983, has been a popular commanding language for operating machine tools. This G-code, however, limits the usage of today's fast evolving high-performance hardware. For intelligent machines, the communications between machine and CAD/CAM departments become important, but the loss of information during generating G-code makes the production department isolated. The new standard for operating machine tools, named STEP-NC is just about to be standardized as ISO14649. As this new standard stores CAD/CAM information as well as operation commands of CNC machines, and this characteristic makes this machine able to exchange information with other departments. In this research, the new CNC machine operated by STEP-NC was built and tested. Unlike other prototypes of STEP-NC milling machines, this system uses the STEP-NC file in XML file form as data input. This machine loads information from XML file and deals with XML file structure. It is possible for this machine to exchange information to other databases using XML. The STEP-NC milling machines in this research loads information from the XML file, makes tool paths for two5D features with information of STEP-NC, and machines automatically without making G-code. All software is programmed with Visual $C^{++}$, and the milling machine is built with table milling machine, step motors, and motion control board for PC that can be directly controlled by Visual $C^{++}$ commands. All software and hardware modules are independent from each other; it allows convenient substitution and expansion of the milling machine. Example 1 in ISO14649-11 having the full geometry and machining information and example 2 having only the geometry and tool information were used to test the automatic machining capability of this system.

• KEPCO Journal on Electric Power and Energy
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• v.7 no.1
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• pp.171-177
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• 2021

In terms of distribution planning, accurate electric load prediction is one of the most important factors. The future load prediction has manually been performed by calculating the maximum electric load considering loads transfer/switching and multiplying it with the load increase rate. In here, the risk of human error is inherent and thus an automated maximum electric load forecasting system is required. Although there are many existing methods and techniques to predict future electric loads, such as regression analysis, many of them have limitations in reflecting the nonlinear characteristics of the electric load and the complexity due to Photovoltaics (PVs), Electric Vehicles (EVs), and etc. This study, therefore, proposes a method of predicting future electric loads on distribution lines by using Machine Learning (ML) method that can reflect the characteristics of these nonlinearities. In addition, predictive models were developed based on actual data collected at KEPCO's existing distribution lines and the adequacy of developed models was verified as well. Also, as the distribution planning has a direct bearing on the investment, and amount of investment has a direct bearing on the maximum electric load, various baseline such as maximum, lowest, median value that can assesses the adequacy and accuracy of proposed ML based electric load prediction methods were suggested.

• Journal of the Korean Society for Precision Engineering
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• v.24 no.4 s.193
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• pp.84-92
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• 2007

Dynamic process planning requires not only more flexible capabilities of a CAPP system but also higher utility of the generated process plans. In order to meet the requirements, this paper develops an algorithm that can select machines for the machining operations by calculating the machine loads. The developed algorithm is based on the multi-objective genetic algorithm that gives rise to a set of optimal solutions (in general, known as the Pareto-optimal solutions). The objective is to satisfy both the minimization number of part movements and the maximization of machine utilization. The algorithm is characterized by a new and efficient method for nondominated sorting through K-means algorithm, which can speed up the running time, as well as a method of two stages for genetic operations, which can maintain a diverse set of solutions. The performance of the algorithm is evaluated by comparing with another multiple objective genetic algorithm, called NSGA-II and branch and bound algorithm.

• Nuclear Engineering and Technology
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• v.55 no.8
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• pp.2747-2756
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• 2023

Reinforced Concrete (RC) shear walls are one of the civil structures in nuclear power plants to resist lateral loads such as earthquakes and wind loads effectively. Risk-informed and performance-based regulation in the nuclear industry requires considering possible accidents and determining desirable performance on structures. As a result, rather than predicting only the ultimate capacity of structures, the prediction of performances on structures depending on different damage states or various accident scenarios have increasingly needed. This study aims to develop machine-learning models predicting drifts of the RC shear walls according to the damage limit states. The damage limit states are divided into four categories: the onset of cracking, yielding of rebars, crushing of concrete, and structural failure. The data on the drift of shear walls at each damage state are collected from the existing studies, and four regression machine-learning models are used to train the datasets. In addition, the bagging ensemble method is applied to improve the accuracy of the individual machine-learning models. The developed models are to predict the drifts of shear walls consisting of various cross-sections based on designated damage limit states in advance and help to determine the repairing methods according to damage levels to shear walls.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.61 no.1
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• pp.29-34
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• 2012

Due to the increasing of nonlinear loads such as converters and inverters connected to the electric power distribution system, and extensive application of harmonic generation sources with power electronic devices, disturbance of the electric power system and its influences on industries have been continuously increasing. Thus, it is difficult to construct accurate load model for active and reactive power in environments with harmonics. In this research, we develop a load modeling method based on Extreme Learning Machine(ELM) with fast learning procedure for residential loads. Using data sets acquired from various residential loads, the proposed method has been intensively tested. As the experimental results, we confirm that the proposed method makes it possible to effective estimate active and reactive powers than conventional methods.

• Journal of the Korean Geotechnical Society
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• v.36 no.11
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• pp.157-165
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• 2020

At present, there are no clearly stated criteria or theories in calculating additional vertical dynamic loads that occur at the machine foundation due to vibration and reflecting them in the design at home and abroad. According to the domestic standard, although it is not a serious vibration condition, the additional dynamic load due to vibration is considered up to 100% of the static load. This is an extremely conservative design. The purpose of this study is to propose a model test method for evaluating the quantitative magnitude of additional dynamic loads that are generated at certain static loads due to vertical mechanical vibrations. As preliminary basic tests for the model tests, the test for evaluating the effects of reflective wave that may occur within a limited size soil box and the test for estimating the natural frequency of the devised model soil-foundation system were carried out. From the analysis of results for basic tests, a method to minimize the influence of the reflected wave was prepared, and the effect of the resonance of the model system was minimized during the model tests. After the basic tests, the main model tests were conducted. Through the proposed main test, the quantitative magnitude of additional dynamic loads caused by machine vibration on a shallow foundation for machine on medium dense sand foundations were evaluated. From the results of the model test, the feasibility of design applied at home and abroad was reviewed.

• Structural Engineering and Mechanics
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• v.71 no.3
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• pp.317-327
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• 2019

The aim of this paper is to present the most important issues on the implementation, operation and maintenance of foundation for machines. The article presents the newest solutions both in terms of technology implementation as well as materials used in construction of such structures. Foundations for machines are special building structures used to transfer loads from an operating machine to the subsoil. The purpose of these foundations is not just to transfer loads, but also to reduce vibrations occurring during operation of the machine, i.e. their damping and preventing redistribution to other elements of the building. It should be noted that foundations for machines (particularly foundations for hammers) are the most dynamically loaded building structures. For these reasons, they require precise static and dynamic calculations, accuracy in their implementation and care for them after they have been made. Therefore, the paper in detail present the guidelines regarding: design, construction and maintenance of structures of this type. Furthermore, the most important parameters and characteristics of materials used for the construction of these foundations are described. As a result of the conducted analyzes, it was found that the concrete mix, in foundations for machines, should have a low water/binder ratio. For its execution, it is necessary to use broken aggregates from igneous rocks and binders modified with mineral additives and chemical admixtures. On the other hand, the reinforcement of composites should contain a large amount of structural reinforcement to prevent shrinkage cracks.