• Advances in concrete construction
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• v.14 no.1
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• pp.15-34
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• 2022

The use of waste rubber in concrete can reduce natural aggregate consumption and improve some technical properties of concrete. Although there are several equations for estimating the mechanical properties of concrete containing waste rubber, limited numbers of machine learning-based models have been proposed to predict the mechanical properties of rubbercrete. In this study, an extensive database of the mechanical properties of rubbercrete was gathered from a comprehensive survey of the literature. To model the mechanical properties of rubbercrete, M5P tree and linear gene expression programming (LGEP) methods as two machine learning techniques were employed to achieve reliable mathematical equations. Two procedures of input variable selection were considered in this study. The crucial component ratios of rubbercrete and concrete age were assumed as the input variables in the first procedure. In contrast, the volumes of the coarse and fine waste rubber and the compressive strength of concrete without waste rubber were considered the second procedure of the input variables. The results show that the models obtained by LGEP are more accurate than those achieved by the M5P model tree and existing traditional equations. Besides, the volumes of the coarse and fine waste rubber and the compressive strength of concrete without waste rubber are better predictors of the mechanical properties of rubbercrete compared to the first procedure of input variable selection.

• Journal of the Korean Society of Safety
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• v.30 no.6
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• pp.34-39
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• 2015

The study is aimed to develop fuzzy logic system that has overcurrent and saturation time as input variable and possibility of electrical fire as output variable by making bad conductor area with physical damage to indoor wiring. Most previous studies focused on thermal characteristics depending on the current size and no study considered the current size and saturation time at the same time. Therefore, the paper made into account current value and saturation time together. To this end, it created bad conductor area half the size of IV conductor (1.6 mm) on purpose and transmit electrical current from 10A to 60A by unit of 2A to find out the thermal characteristics and saturation time for current. Based on the data that came out, the study applied fuzzy logic and established the current and saturation time as input variable and chance of fire as output variable. As a result, the center of area of the system that depended only on the existing current value was 75 while the system that applied both current and saturation time presented the chance of fire at 92. It is found that the chance of bad conductor area and deteriorated insulation of electrical wire had current and saturation time as important variables. The data can be used as basic data like deteriorated wire insulation or operation features of circuit breaker in investigating the cause of electrical fire.

• Proceedings of the Korean Nuclear Society Conference
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• 1998.05b
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• pp.659-664
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• 1998

The importance of input wariables of real-time accident consequence assessment model has been analyzed. Partial correlation coefficients of input variables related to the plume and the ingestion exposure have been estimated using latino hypercube sampling technique. It is known that wind speed and growth dilution rate are the most important variable in plume and ingestion exposure, respectively.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.3
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• pp.9-19
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• 2015

Internal air temperature of greenhouse is an important variable that can be influenced by the complex interaction between outside weather and greenhouse inside climate. This paper focuses on a data-based model approach to predict internal air temperature of the greenhouse. External air temperature, solar radiation, wind speed and wind direction were measured next to an experimental greenhouse supported by the Electronics and Telecommunications Research Institute and used as input variables for the model. Internal air temperature was measured at the center of three sections of the greenhouse and used as an output variable. The proposed model consisted of a transfer function including the four input variables and tested the prediction accuracy according to the sampling interval of the input variables, the orders of model polynomials and the time delay variable. As a result, a second-order model was suitable to predict the internal air temperature having the predictable time of 20-30 minutes and average errors of less than ${\pm}1K$. Afterwards mechanistic interpretation was conducted based on the energy balance equation, and it was found that the resulting model was considered physically acceptable and satisfied the physical reality of the heat transfer phenomena in a greenhouse. The proposed data-based model approach is applicable to any input variables and is expected to be useful for predicting complex greenhouse microclimate involving environmental control systems.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.6
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• pp.1167-1172
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• 2017

Desktop computers, which were the main means of Internet use and information activity, were pushed out by smart devices that emphasized mobility and simplicity. The recent information production and consumption activities are performed through smart devices, but there is no input device for smart devices that can fully replace traditional input devices such as full-size PC compatible keyboards. Because of the small size of the virtual keyboard that uses the touch screen of the smart device, typographical error occurs at a high rate. In this paper, we propose a variable virtual keyboard that minimizes the typographical errors of the conventional virtual keyboards. The proposed method minimizes the user 's input error by adjusting the size of each key of the virtual keyboard based on accumulated dataset of position and pressure of the user' s input error even though there is no difference in the key position arrangement of the conventional virtual keyboards.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.824-826
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• 1999

In this paper, we design an Fuzzy-Neural Networks(FNN) by means of divisions of fuzzy input space with multi-input variables. Fuzzy input space of Yamakawa's FNN is divided by each separated input variable, but that of the proposed FNN is divided by mutually combined input variables. The membership functions of the proposed FNN use both triangular and gaussian membership types. The parameters such as apexes of membership functions, learning rates, momentum coefficients, weighting value, and slope are adjusted using genetic algorithms. Also, an aggregate objective function(performance index) with weighting value is utilized to achieve a sound balance between approximation and generalization abilities of the model. To evaluate the performance of the proposed model, we use the data of sewage treatment process.

• Journal of Welding and Joining
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• v.32 no.1
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• pp.108-111
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• 2014

Low arc weldability of aluminum alloy is enhanced by applying variable polarity TIG and the result is theoretically investigated to figure out the mechanism. Conventionally, it is well known fact that DCEP (reverse polarity) arc is effective on aluminum welding. The reason is due to oxide layer removal by plasma ion bombardment and therefore it is named as cleaning effect. Another fact of polarity characteristic is that DCEN shows higher heat input efficiency therefore conventional variable polarity arc used to apply DCEP portion as small as possible. However, higher DCEP portion shows bigger weldment in this research and it is explained by adopting a theory of arc concentration on oxide layer with tunneling effect which was not clearly mentioned before in several variable polarity TIG welding research. Disagreement between variable polarity TIG welding result and conventional arc polarity theory is rationally explained for the first time with help of electron emission theory.

• The KIPS Transactions:PartA
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• v.8A no.1
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• pp.56-62
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• 2001

This paper presents a neural network based variable structure control scheme for nonlinear systems. In this scheme, a set of local variable structure control laws are designed on the basis of the linear models about preselected representative points which cover the range of the system operation of interest. From the combination of the set of local variable structure control laws, neural networks infer the approximate control input in between the operating points. The neural network based variable structure control alleviates the effects of model uncertainties, which cannot be compensated by the control techniques using feedback linearization. It also relaxes the discontinuity in the system’s behavior that appears when the control schemes based on the family of the linear models are applied to nonlinear systems. Simulation results of a ball and beam system, to which feedback linearization cannot be applied, demonstrate the feasibility of the proposed method.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.10
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• pp.1259-1263
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• 1999

In this paper, a method to guarantee system continuity is developed, which can be applied to discontinuity problem in the time domain of restructurable control system. This method can be summarized as input alternation using weight change considering convergence speed of system mode. Input is changed from 'system continuity guarantee input,' which is defined as a input that minimizes the change of state variables, to 'alternative controller input,' which is selected by Neil's PI/EAM[3]. AIDC aircraft model is used for simulation. By showing the waveform of system input and state variable, we can sure that this method is effective for depression of system shock like jerk.

• Journal of the Korea Safety Management & Science
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• v.11 no.1
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• pp.121-130
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• 2009

This paper considers a six sigma project for reducing the cost copper of the cable materials in a electric wire company. The project follows a disciplined process of five macro phases: define, measure, analyze, improve, and control (DMAIC). A process map is used to identify process input variables. Three key process input variables are selected by using an input variables are selected by using an input variable evaluation table: large cable, plating, and a twisted pair. DOE is utilized for finding the optimal process conditions of the three key process input variables. The implementing result of this six sigma project is enable for reducing of the 2.8% copper materials.