• Korean Journal of Construction Engineering and Management
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• v.5 no.1 s.17
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• pp.124-132
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• 2004

A human resource management of the general contractors is to mostly deal with daily input by each subcontractor in construction fields. However, this way has some limitations; the identification of proper human-input and productivity, preventive activities or efforts for minimizing schedule delay. The reason why these limitations are that few systematic efforts through a coordinated field administration with the construction schedule planning and human resources. Therefore, on the basis of the construction schedule planning, human resource management of subcontractors is necessary to make for an improvement in construction schedule control. Daily Scheduling Check System(DSCS), as the linked human resources on an existed CPM scheduling software, was developed and this paper then verified validity and effectiveness of using the DSCS for the framework of some actual apartment construction projects

• The KIPS Transactions:PartA
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• v.14A no.7
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• pp.451-456
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• 2007

In this paper, we regard computer systems as heterogeneous multi-server systems and propose a cumulative fair scheduling scheme that pursues long-term fairness. GPS(generalized processor sharing)-based scheduling algorithms, which are usually employed in single-server systems, distribute available capacity in an instantaneous manner. However, applying them to heterogeneous multi-server systems may cause unfairness, since they may not prevent the accumulation of scheduling delays and the extra capacities are distributed in an instantaneous manner. In our scheme, long-term fairness is pursued by proper distribution of extra capacities while guaranteeing reserved capacities. A reference capacity model to determine the ideal progresses of applications is derived from long-term observations, and the scheduler makes the applications gradually follow the ideal progresses while guaranteeing their reserved capacities. A heuristic scheduling algorithm is proposed and the scheme is examined by simulation.

• Journal of the Korea Institute of Building Construction
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• v.24 no.4
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• pp.447-458
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• 2024

This study investigates the effectiveness of Building Information Modeling(BIM) software, specifically SketchUp and Revit, in reducing errors during quantity take-off(QTO) for complex building elements. While 3D modeling offers advantages, existing software may not fully account for manufacturing discrepancies, such as variations in concrete cover thickness and reinforcing bar radius. To address this limitation, this research proposes a BIM-based QTO method for high-insulation wall panels with intricate details. The method utilizes a BIM family library, focusing on key parameters like concrete cover thickness and inner radius of shear reinforcement. A case study compared the cross-sectional details of a wall panel modeled in Revit with the actual manufactured specimen. The analysis revealed a 12% reduction in modeled concrete cover thickness and a 1.27 times larger modeled inner radius of the shear bar compared to the real-world values. The proposed method incorporates these manufacturing variations into the Revit model of the high-insulation wall panel. Software like Navisworks facilitates the identification and correction of any material interferences arising from these adjustments. Furthermore, the method employs a unit wall concept(1m²) to account for the volume of various materials, including insulation and splice sleeves at joints. This allows for the identification of a similar existing family within the BIM library(e.g., "Double RC wall with embedded insulation") that reflects the actual material quantities used in the wall panel. By incorporating these manufacturing-induced variations, the proposed method offers a more accurate QTO process for complex high-insulation wall panels. The "Double RC wall with embedded insulation" family within the Revit program serves as a valuable tool for material quantity estimation in such scenarios.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.3
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• pp.1255-1262
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• 2012

Look into the nuclear power plant of Wolsong currently, it is controlled in order to required operating pressure with PI controller. PI controller has a simple structure and satisfy design requirements to gain setting. However, It is difficult to control without changing the gain from produce changes in parameters such as loss of the valves and the pipes. To solve these problems, the dynamic change of the PI controller gain, or to compensate for the PI controller output is desirable to configure the controller. The aim of this research and development in the parameter variations can be controlled to a stable controller design which is reduced an error and a vibration. Proposed PI/NN control techniques is the PI controller and the neural network controller that combines a parallel and the neural network controller part is compensated output of the controller for changes in the parameters were designed to be robust. To directly evaluate the controller performance can be difficult to test in real processes to reflect the characteristics of the process. Therefore, we develope the simulator model using the real process data and simulation results when compared with the simulated process characteristics that showed changes in the parameters. As a result the PI/NN controller error and was confirmed to reduce vibrations.

• Applied Chemistry for Engineering
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• v.28 no.5
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• pp.559-564
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• 2017

In this study, the optimization process was carried out by using the central composite model of the response surface methodology in waste cooking oil based biodiesel production process. The acid value, reaction time, reaction temperature, methanol/oil molar ratio, and catalyst amount were selected process variables. The response was evaluated by measuring the FAME content (more than 96.5%) and kinematic viscosity (1.9~5.5 cSt). Through basic experiments, the range of optimum operation variables for the central composite model, such as reaction time, reaction temperature and methanol/oil molar ratio, were set as between 45 and 60 min, between 50 and $60^{\circ}C$, and between 8 and 12, respectively. The optimum operation variables, such as biodiesel production reaction time, temperature, and methanol/oil molar ratio deduced from the central composite model were 55.2 min, $57.5^{\circ}C$, and 10, respectively. With those conditions the results deduced from modeling were as followings: the predicted FAME content of the biodiesel and the kinematic viscosity of 97.5% and 2.40 cSt, respectively. We obtained experimental results with deduced operating variables mentioned above as followings: the FAME content and kinematic viscosity of 97.7% and 2.41 cSt, respectively. Error rates for the FAME content and kinematic viscosity were 0.23 and 0.29%, respectively. Therefore, the low error rate could be obtained when the central composite model among surface reaction methods was applied to the optimized production process of waste cooking oil raw material biodiesel.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.4
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• pp.380-386
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• 2019

In this paper, in order to compare the mixing ratio according to the feeding method, the input error of the main material and the sub material was measured and analyzed for 100 cycles using raw material having the same viscosity. As a result of the piston pump method, the input error of main material and sub material varied greatly from 0g to 3g, and the maximum error ratio was 10.3%. In the dual-screw rotation method, the input error varied from 0.01g to 0,4g, and the maximum error ratio was 0.41%, and almost no input error occurred. As the process cycle increased, it was found that the feed was almost uniform. The dual-screw rotary two-component mixing system was used to measure and analyze the inputs of the main and sub materials for 100 using three types of liquid silicones with different viscosities of the raw materials. As a result, the average error was 0.75g and the error rate was less than 1% regardless of the viscosity of the applied raw materials. When rae materials having the same viscosity were used, the average error ratio of the piston pump method was 4.09%.

• KSBB Journal
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• v.16 no.5
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• pp.452-458
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• 2001

Concentrations of ammonia in biological processes were controlled by PID controllers and also neural network based controllers (NN controllers). A flow injection analysis system has been to on-line monitor the concentrations of ammonia in a bioreactor. The effect of the analysis error and the residence time of samples on the control performance were studied. The optimal neural network structure was investigated by using computer simulation and found to be a 3(input layer)-2(hidden layer)-1(output layer). The NN controller is often time consuming, but it has advantage over the PID controller in sensitivity. The 3-2-1 NN controller has been applied to control the ammonia concentrations in a simulated bioprocess and also a real cultivation process of yeast. The good control performance showed that the 3-2-1 NN controller based on the FIA system can be used to control the concentration of substrates in biological processes very well.

• Korean Chemical Engineering Research
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• v.46 no.6
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• pp.1087-1094
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• 2008

This article presents a multi-scale simulation approach starting from the molecular level for the adsorption process development, specifically, in n-hexane adsorption on activated carbon. A grand canonical Monte-Carlo(GCMC) method is used for the prediction of adsorption isotherms of n-hexane on activated carbon at the molecular level. Geometric effects and hydrodynamic properties of the adsorption column are examined by means of the two dimensional CFD(computational fluid dynamics) simulation. The adsorption isotherms from the molecular simulation and the axial diffusivity from the CFD simulation are exploited for the process simulation where the elution curve of n-hexane is obtained. For the first moment(mean residence time) of the pulse-response with respect to temperature and flowrate, the process simulation results obtained from this three-steps multiscale simulation approach show a good agreement with experimental data within 20% of maximum difference. The multi-scale simulation approach addressed in this study will be useful to accelerate the adsorption process development, while reducing the number of experiments required.

• Korean Journal of Food Science and Technology
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• v.27 no.4
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• pp.525-531
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• 1995

Three quality factors of bread during baking process were measured to develop neural network models for bread baking process. Firstly, volume and browning changes during bread baking process were measured using image processing technique and temperature changes inside the bread during process were measured by K-type thermocouples. Relationships among them showed nonlinearity. Secondly, multilayer perception structure with error back propagation learning was used to construct neural network models. Three neural network models for volume, browning, and bread temperature were developed respectively. Developed models showed good performance with predictive error of 4.62% for volume and browning changes after 30 seconds, 7.38% for volume and browning changes after 2 minutes, and 1.09% for temperature change inside the bread respectively.

• Journal of Internet of Things and Convergence
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• v.9 no.3
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• pp.61-67
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• 2023

In order to optimize the pulse electroforming copper process, a double hidden layer BP (Back Propagation) neural network is constructed. Through sample training, the mapping relationship between electroforming copper process conditions and target properties is accurately established, and the prediction of microhardness and tensile strength of the electroforming layer in the pulse electroforming copper process is realized. The predicted results are verified by electrodeposition copper test in copper pyrophosphate solution system with pulse power supply. The results show that the microhardness and tensile strength of copper layer predicted by "3-4-3-2" structure double hidden layer neural network are very close to the experimental values, and the relative error is less than 2.32%. In the parameter range, the microhardness of copper layer is between 100.3~205.6MPa and the tensile strength is between 112~485MPa.When the microhardness and tensile strength are optimal,the corresponding process conditions are as follows: current density is 2A-dm-2, pulse frequency is 2KHz and pulse duty cycle is 10%.