• Membrane and Water Treatment
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• v.2 no.2
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• pp.105-119
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• 2011

This study presents the tests of an Immersed Membrane BioReactor (IMBR) equipped with a draft tube and focuses on the influence of hydrodynamic conditions on membrane fouling in a pilot-scale using a hollow fiber membrane module of ZW-10 under ambient conditions. In this system, the cross-flow velocities across the membrane surface were induced by a cylindrical draft-tube. The relationship between cross-flow velocity and aeration strength and the influence of the cross-flow on fouling rate (under various hydrodynamic conditions) were investigated using Multi-Dimension Scaling (MDS) analysis. MDS technique is especially suitable for samples with many variables and has relatively few observations, as the data about Membrane Bio-Reactor (MBR) often is. Observations and variables are analyzed simultaneously. According to the results, a specialized form of MDS, CoPlot enables presentation of the results in a two dimensional space and when plotting variables ratio (output/input) rather than original data the efficient units can be visualized clearly. The results indicate that: (i) aeration plays an important role in IMBR performance; (ii) implementing the MDS approach with reference to the variables ratio is consequently useful to characterize performance changes for data classification.

• Journal of Biosystems Engineering
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• v.27 no.2
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• pp.79-88
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• 2002

This study was conducted to develop system for high speed rice transplanting machines. The control system includes a sensor detecting the tilt angle of the seedling bed, a micro-controller and a hydraulic system consisting of a double acting cylinder, a four-way three-position solenoid valve, a relief valve and a hydraulic pump. The levelling system shared the pump with the existing steering control, resulting in a tandem center circuit for the steering and levelling control systems. Using the input signal from the sensor, the micro-controller determined and generated the output signal to control the cylinder through the solenoid valve to keep the seedling bed always parallel to the water surface regardless of soil unevenness during the transplanting operations. Both an ON/OFF and a PWM control schemes were tested. When the flow rate was more than 1 ι/min in the ON/OFF control, the system showed unstable rolling. However, in the PWM control, the system worked stably although the flow rate was more than 1 ι/min. The PWM control showed a better performance when a large difference between the angle and the dead band of the control system occurred. The characteristics of tile system response to given tilt angles were predicted by a computer simulation. Both the ON/OFF and the PWM control systems worked well providing that the operating and waiting times were properly adjusted.

• Computers and Concrete
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• v.24 no.2
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• pp.137-150
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• 2019

In this paper, surrogate models such as multivariate adaptive regression splines (MARS) and M5P model tree (M5P MT) methods have been investigated in order to propose a new formulation for the 28-days compressive strength of self-compacting concrete (SCC) incorporating metakaolin as a supplementary cementitious materials. A database comprising experimental data has been assembled from several published papers in the literature and the data have been used for training and testing. In particular, the data are arranged in a format of seven input parameters covering contents of cement, coarse aggregate to fine aggregate ratio, water, metakaolin, super plasticizer, largest maximum size and binder as well as one output parameter, which is the 28-days compressive strength. The efficiency of the proposed techniques has been demonstrated by means of certain statistical criteria. The findings have been compared to experimental results and their comparisons shows that the MARS and M5P MT approaches predict the compressive strength of SCC incorporating metakaolin with great precision. The performed sensitivity analysis to assign effective parameters on 28-days compressive strength indicates that cementitious binder content is the most effective variable in the mixture.

• Computers and Concrete
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• v.32 no.4
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• pp.373-381
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• 2023

Fly ash, granulated blast furnace slag, marble waste powder, etc. are just some of the by-products of other sectors that the construction industry is looking to include into the many types of concrete they produce. This research seeks to use surrogate machine learning methods to forecast the compressive strength of self-compacting concrete. The surrogate models were developed using Gradient Boosting Machine (GBM), Support Vector Machine (SVM), Random Forest (RF), and Gaussian Process Regression (GPR) techniques. Compressive strength is used as the output variable, with nano silica content, cement content, coarse aggregate content, fine aggregate content, superplasticizer, curing duration, and water-binder ratio as input variables. Of the four models, GBM had the highest accuracy in determining the compressive strength of SCC. The concrete's compressive strength is worst predicted by GPR. Compressive strength of SCC with nano silica is found to be most affected by curing time and least by fine aggregate.

• Geomechanics and Engineering
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• v.35 no.4
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• pp.425-436
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• 2023

An effective tool for researching actual problems in geotechnical and mining engineering is to conduct physical modeling tests using similar materials. A reliable geometric scaled model test requires selecting similar materials and conducting tests to determine physical properties such as the mixing ratio of the mixed materials. In this paper, a method is proposed to determine similar materials that can reproduce target properties using a polynomial model based on experimental results on modeling materials using a gypsum-sand mixture (GSM) to simulate rocks. To that end, a database is prepared using the unconfined compressive strength, elastic modulus, and density of 459 GSM samples as output parameters and the weight ratio of the mixing materials as input parameters. Further, a model that can predict the physical properties of the GSM using this database and a polynomial approach is proposed. The performance of the developed method is evaluated by comparing the predicted and observed values; the results demonstrate that the proposed polynomial model can predict the physical properties of the GSM with high accuracy. Sensitivity analysis results indicated that the gypsum-water ratio significantly affects the prediction of the physical properties of the GSM. The proposed polynomial model is used as a powerful tool to simplify the process of determining similar materials for rocks and conduct highly reliable experiments in a physical modeling test.

• Journal of Digital Convergence
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• v.11 no.5
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• pp.119-133
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• 2013

This study aims to provide power generating plants with eco-efficiency information. To implement the purposes, of study, both DEA(Data, Envelopment Analysis) model and interview were incorporated in terms of methodologies. To analyze the managerial efficiency, total labor cost and number of employees were considered as input factors. CO2, NOx, and water also were considered as input factors to analyze eco-efficiency. Both annual total power product and annual total revenue were used as output factors. CRS(Constant Return to Scale) and VRS(Variable Return to) model were facilitated in this analysis. According to the findings, most of the power plants were evaluated as 'Efficient'' taking into consideration of average value, both 0.928 from CCR model and 0.969 from VRS model. 7 DMUs including DMU3 and DMU12 are efficient out of 35 DMUs relatively, other DMUs are inefficient. For results of inefficient output factors distribution, it was found that inefficiency for NOx was marked relatively higher than CO2. In order to improve the eco-efficiency in the power plants in the long term, the target amount of Co2 as well as NOx reduction needs to be properly proposed in consideration of particularity of power plants. In the long run, renewable energy, alternative fuels should be adapted to reduce the eco-inefficient.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.7C
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• pp.680-690
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• 2006

In this paper, we present a bit allocation scheme based on grouped sub-channels for MIMO-OFDM (Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing) systems using V-BLAST (Vertical-Bell laboratories LAyered Space-Time) detector. A fully adaptive modulation and coding scheme may provide optimal performance in the MIMO-OFDM systems, however it requires excessive feedback information. Instead, SBA (Simplified Bit Allocation) scheme for reduction of feedback overhead, which applies the same modulation and coding to all the good sub-channels, may be considered. The proposed scheme in this paper named SBA-GS (Simplified Bit Allocation based on Grouped Sub-channels) groups sub-channels and assigns the same modulation and coding to the set of selected sub-channel groups. Simulation results show that the proposed scheme achieves comparable bit error rate performance of the conventional SBA scheme, while significantly reducing the feedback overhead in multipath channels with small delay spreads.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.322-327
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• 2003

The water level of a steam generator of pressurized light water nuclear Power generator is known as a subject whose control is difficult because of a shrinking and swelling effect that is been mutually contradictory in a variation of feed water. In this paper, a neural network model selects first coordinative controller by a inappropriate gain of two PI controllers and the selected controller＇s gain is tuned by a fuzzy self-tuner. Model inputs consist of the water level, the feed water, and the stream flow. One controller of both coupling controllers whose gain is handled firstly is decided based upon above data. The proposed method can analyze patterns of signals using the characteristic of neural networks and select one controller that needs to be tuned through the observed result in this paper. If one controller between both the water level controller and the feed water controller is selected by the neural network model then a gain of the PI controller is suitably tuned by the fuzzy self-tuner. Rules of the fuzzy self-tuner drew from the pattern of input and output data. In the summary, the goal of this Paper is to select the suitable controller and tune the control gain of the selected controller suitably through such two processes.

• Korean Journal of Ecology and Environment
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• v.42 no.2
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• pp.242-252
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• 2009

The purpose of this study was to develop a Multi-metric Water Quality Assessment (MWQA) model and apply it to dataset sampled from Paldang and Daechung reservoir in 2008. The various water dataset used to this study included 5 year data sets (2003${\sim}$2007) in Korean reservoirs which were obtained from the Ministry of Environment, Korea. In this study, suggested MWQA model has 4 metrics that were composed of 4 parameters such as chemical, physical, biological, and hydrological variables. And, each of the variables attributed total phosphorus (TP) concentration in water, secchi depth (SD) measure in water, chlorophyll-${\alpha}$(Chl-${\alpha}$) concentration in water and the ratio of inflow of water into lakes and efflux of water from lakes, input/output (I/O). First, we established the criteria for trophic boundaries. The boundary between oligotrophic and mesotrophic categories was defined by the lower third of the cumulative distribution of the values. The mesotrophic-eutrophic boundary was defined by the upper third of the distribution. Second, each metric was given by a point-oligo=1, meso=3, eu=5. And then, obtained total score from each metric was divided 5 grade-Excellent, Good, Fair, Poor, and Very poor. As the results of applying the proposed MWQA model, the Paldang reservoir obtained "Fair" or "Poor" grade and Daechung reservoir obtained "Excellent" or "Good" grade. The suggested MWQA model through these procedures will enable to manage efficiently the reservoir. And, more studies such as metric numbers and attributes should be done for the accurate application of the new model.

• Nuclear Engineering and Technology
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• v.52 no.7
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• pp.1443-1451
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• 2020

The objective of this study is to design a robust power control system for a small pressurized water reactor (PWR) to achieve stable power operations under conditions of external disturbances and internal model uncertainties. For this purpose, the multiple-input multiple-output transfer function models of the reactor core at five power levels are derived from point reactor kinetics equations and the Mann's thermodynamic model. Using the transfer function models, five local reactor power controllers are designed using an H infinity (H_∞) mixed sensitivity method to minimize the core power disturbance under various uncertainties at the five power levels, respectively. Then a multimodel approach with triangular membership functions is employed to integrate the five local controllers into a multimodel robust control system that is applicable for the entire power range. The performance of the robust power system is assessed against 10% of full power (FP) step load increase transients with coolant inlet temperature disturbances at different power levels and large-scope, rapid ramp load change transient. The simulation results show that the robust control system could maintain satisfactory control performance and good robustness of the reactor under external disturbances and internal model uncertainties, demonstrating the effective of the robust power control design.