• International conference on construction engineering and project management
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• 2022.06a
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• pp.1226-1236
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• 2022

Because students majoring in construction-related fields must develop a broad repository of knowledge and skills, effective transferal of these is the primary focus of most academic programs. While inculcation of this body of knowledge is certainly critical, actual construction projects are complicated ventures that involve levels of risk and uncertainty, such as resistant neighboring communities, unforeseen weather conditions, escalating material costs, labor shortages and strikes, accidents on jobsites, challenges with emerging forms of technology, etc. Learning how to develop a level of discernment about potential ways to handle such uncertainty often takes years of costly trial-and-error in the proverbial "school of hard knocks." There is therefore a need to proactively expedite the development of a sharpened intuition when making decisions. The AGC Education and Research Foundation case study committee was formed to address this need. Since its inception in 2011, 14 freely downloadable case studies have thus far been jointly developed by an academics and industry practitioners to help educators elicit varied responses from students about potential ways to respond when facing an actual project dilemma. AGC case studies are typically designed to focus on a particular concern and topics have thus far included: ethics, site logistics planning, financial management, prefabrication and modularization, safety, lean practices, preconstruction planning, subcontractor management, collaborative teamwork, sustainable construction, mobile technology, and building information modeling (BIM). This session will include an overview of the history and intent of the AGC case study program, as well as lively interactive demonstrations and discussions on how case studies can be used both by educators within a typical academic setting, as well as by industry practitioners seeking a novel tool for their in-house training programs.

• Journal of Veterinary Clinics
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• v.30 no.4
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• pp.268-272
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• 2013

The purpose of this study was to assess the effects of reconstruction kernel, and slice thickness on the accuracy of spiral CT-based volume assessment over a range of object sizes typical of synthetic simulated tumor. Spiral CT scanning was performed at various reconstruction kernels (soft tissue, standard, bone), and slice thickness (1, 2, 3 mm) using a phantom made of gelatin and 10 synthetic simulated tumors of different sizes (diameter 3.0-12.0 mm). Three-dimensional volume assessments were obtained using an automated software tool. Results were compared with the reference volume by calculating the percentage error. Statistical analysis was performed using ANOVA and setting statistical significance at P < 0.05. In general, smaller slice thickness and larger sphere diameters produced more accurate volume assessment than larger slice thickness and smaller sphere diameter. The measured volumes were larger than the actual volumes by a common factor depending on slice thickness; in 100HU simulated tumors that had statistically significant, 1 mm slice thickness produced on average 27.41%, 2 mm slice thickness produced 45.61%, 3 mm slice thickness produced 93.36% overestimates of volume. However, there was no statistically significant difference in volume error for spiral CT scans taken with techniques where only reconstruction kernel was changed. These results supported that synthetic simulated tumor size, slice thickness were significant parameters in determining volume measurement errors. For an accurate volumetric measurement of an object, it is critical to select an appropriate slice thickness and to consider the size of an object.

• International journal of advanced smart convergence
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• v.4 no.1
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• pp.45-53
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• 2015

This paper is proposed mathematical load modelling based on system identification approach of energy consumption of residential air conditioning. Due to air conditioning is one of the significant equipment which consumes high energy and cause the peak load of power system especially in the summer time. The demand response is one of the solutions to decrease the load consumption and cutting peak load to avoid the reservation of power supply from power plant. In order to operate this solution, mathematical modelling of air conditioning which explains the behaviour is essential tool. The four type of linear model is selected for explanation the behaviour of this system. In order to obtain model, the experimental setup are performed by collecting input and output data every minute of 9,385 BTU/h air-conditioning split type with $25^{\circ}C$ thermostat setting of one sample house. The input data are composed of solar radiation ($W/m^2$) and ambient temperature ($^{\circ}C$). The output data are power and energy consumption of air conditioning. Both data are divided into two groups follow as training data and validation data for getting the exact model. The model is also verified with the other similar type of air condition by feed solar radiation and ambient temperature input data and compare the output energy consumption data. The best model in term of accuracy and model order is output error model with 70.78% accuracy and $17^{th}$ order. The model order reduction technique is used to reduce order of model to seven order for less complexity, then Kalman filtering technique is applied for remove white Gaussian noise for improve accuracy of model to be 72.66%. The obtained model can be also used for electrical load forecasting and designs the optimal size of renewable energy such photovoltaic system for supply the air conditioning.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.12
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• pp.1279-1284
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• 2012

Micro structures which are widely used at various fields are commonly fabricated by lithograph, etching and laser methods. Recently, with the emergence of micro tools and ultra-precision machine tools, fabrication of the micro structures obtained using end-milling are studied. However, there are some problems due to the diameter of the micro end-mill getting smaller below $100{\mu}m$. The micro run-out resulted from miniaturization of end-mills have influence seriously on accuracy of micro structures. The error of run-out with a tooling jig showed a decrease of about $9.3{\mu}m$. Furthermore, micro structures with width of $30{\mu}m$ could be applied through experiments of slot machining obtained using 30 and $50{\mu}m$ end-mill. Also, narrow angle structures with $30^{\circ}$ angle could be applied through analysis of machining acute angle structures. Based on basic experiments, micro fluidics channels and spiral patterns for air bearing were machined.

• Atmosphere
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• v.22 no.4
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• pp.415-427
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• 2012

$CO_2$ is the most important trace gas related to climate change. Therefore, understanding surface carbon sources and sinks is important when seeking to estimate the impact of $CO_2$ on the environment and climate. CarbonTracker, developed by NOAA, is an inverse modeling system that estimates surface carbon fluxes using an ensemble Kalman filter with atmospheric $CO_2$ measurements as a constraint. In this study, to investigate the capability of CarbonTracker as an analysis tool for estimating surface carbon fluxes in Asia, an experiment with a nesting domain centered in Asia is performed. In general, the results show that setting a nesting domain centered in Asia region enables detailed estimations of surface carbon fluxes in Asia. From a rank histogram, the prior ensemble spread verified at observational sites located in Asia is well represented with a relatively flat rank histogram. The posterior flux in the Eurasian Boreal and Eurasian Temperate regions is well analyzed with proper seasonal cycles and amplitudes. On the other hand, in tropical regions of Asia, the posterior flux does not differ greatly from the prior flux due to fewer $CO_2$ observations. The root mean square error of the model $CO_2$ calculated by the posterior flux is less than the model $CO_2$ calculated by the prior flux, implying that CarbonTracker based on the ensemble Kalman filter works appropriately for the Asia region.

• Journal of IKEEE
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• v.24 no.1
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• pp.25-32
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• 2020

In this paper, we propose an image generator for OLED panel test that can compensate for color coordinates and luminance by using panel defect inspection and optical measurement while displaying images on OLED panel. The proposed image generator consists of two processes: the image generation process and the process of compensating color coordinates and luminance using optical measurement. In the image generating process, the panel is set to receive the panel information to drive the panel, and the image is output by adjusting the output setting of the image generator according to the panel information. The output form of the image is configured by digital RGB method. The pattern generation algorithm inside the image generator outputs color and gray image data by transmitting color data to a 24-bit data line based on a synchronization signal according to the resolution of the panel. The process of compensating color coordinates and luminance using optical measurement outputs an image to an OLED panel in an image generator, and compensates for a portion where color coordinates and luminance data measured by an optical module differ from reference data. To evaluate the accuracy of the image generator for the OLED panel test proposed in this paper, Xilinx's Spartan 6 series XC6SLX25-FG484 FPGA was used and the design tool was ISE 14.5. The output of the image generation process was confirmed that the target setting value and the simulation result value for the digital RGB output using the oscilloscope matched. Compensating the color coordinates and luminance using optical measurements showed accuracy within the error rate suggested by the panel manufacturer.

• The Journal of Korean Society for Radiation Therapy
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• v.16 no.2
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• pp.33-41
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• 2004

Purpose : Film has been the primary tool in coincidence testing between the light field and the radiation field, which constitutes the quality assurance list of a linear accelerator. But there is a great chance of errors being different among the observer when using film. Thus this study set out to use the BIS(Beam Image System) in addition to film in comparing and evaluating coincidence results between the two fields and in searching for the improvement measures. Materials & Methods : Photon beam of 6 and 15MV was exposed to film and the BIS using a linear accelerator. The light and radiation fields were each $50{\times}50,\;100{\times}100,\;and\;200{\times}200mm^2$. The gantry angle was $0^{\circ}$ when using film and $0^{\circ}\;and\;270^{\circ}$ when using the BIS. The devices adopted to test coincidence between the two fields were a ruler and film scanner when using film. With the BIS, the width of the scanned light and radiation fields was measured for errors with setting the X and Y axis. Results : The visual measurements of the observer with film resulted that the radiation field was bigger than the light field and that their maximum error was 1.9mm. The results were the same with the measurements using the film scanner except for the average error, which was less than 1.9mm. On the contrary, the measurements using the BIS showed that the light field was bigger than the radiation field at the gantry angle of $0^{\circ}\;and\;270^{\circ}$. The maximum error was 0.96mm, and the error range was $<{\pm}2mm$ both in the X and Y axis. The average error of ${\Delta}X$, Y was the smallest in the order of the visual film measurements, film scanner measurements, and BIS measurements Conclusion . This requires a careful measurement for accurate quality assurance since errors are much different according to each observer that tests coincidence between visual fields with film. And an observer needs to use another image device or develop a measuring device of his own if it seems necessary for accurate measurements.

• Progress in Medical Physics
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• v.21 no.2
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• pp.192-200
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• 2010

Cyberknife with small field size is more difficult and complex for dosimetry compared with conventional radiotherapy due to electronic disequilibrium, steep dose gradients and spectrum change of photons and electrons. The purpose of this study demonstrate the usefulness of Geant4 as verification tool of measurement dose for delivering accurate dose by comparing measurement data using the diode detector with results by Geant4 simulation. The development of Monte Carlo Model for Cyberknife was done through the two-step process. In the first step, the treatment head was simulated and Bremsstrahlung spectrum was calculated. Secondly, percent depth dose (PDD) was calculated for six cones with different size, i.e., 5 mm, 10 mm, 20 mm, 30 mm, 50 mm and 60 mm in the model of water phantom. The relative output factor was calculated about 12 fields from 5 mm to 60 mm and then it compared with measurement data by the diode detector. The beam profiles and depth profiles were calculated about different six cones and about each depth of 1.5 cm, 10 cm and 20 cm, respectively. The results about PDD were shown the error the less than 2% which means acceptable in clinical setting. For comparison of relative output factors, the difference was less than 3% in the cones lager than 7.5 mm. However, there was the difference of 6.91% in the 5 mm cone. Although beam profiles were shown the difference less than 2% in the cones larger than 20 mm, there was the error less than 3.5% in the cones smaller than 20 mm. From results, we could demonstrate the usefulness of Geant4 as dose verification tool.

• Journal of Construction Engineering and Project Management
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• v.5 no.1
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• pp.11-19
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• 2015

This paper presents optimized artificial neural networks (ANNs) claims prediction and decision awareness framework that guides owner organizations in their pre-bid construction project decisions to minimize claims. The framework is composed of two genetic optimization ANNs models: a Claims Impact Prediction Model (CIPM), and a Decision Awareness Model (DAM). The CIPM is composed of three separate ANNs that predict the cost and time impacts of the possible claims that may arise in a project. The models also predict the expected types of relationship between the owner and the contractor based on their behavioral and technical decisions during the bidding phase of the project. The framework is implemented using actual data from international projects in the Middle East and Egypt (projects owned by either public or private local organizations who hired international prime contractors to deliver the projects). Literature review, interviews with pertinent experts in the Middle East, and lessons learned from several international construction projects in Egypt determined the input decision variables of the CIPM. The ANNs training, which has been implemented in a spreadsheet environment, was optimized using genetic algorithm (GA). Different weights were assigned as variables to the different layers of each ANN and the total square error was used as the objective function to be minimized. Data was collected from thirty-two international construction projects in order to train and test the ANNs of the CIPM, which predicted cost overruns, schedule delays, and relationships between contracting parties. A genetic optimization backward analysis technique was then applied to develop the Decision Awareness Model (DAM). The DAM combined the three artificial neural networks of the CIPM to assist project owners in setting optimum values for their behavioral and technical decision variables. It implements an intelligent user-friendly input interface which helps project owners in visualizing the impact of their decisions on the project's total cost, original duration, and expected owner-contractor relationship. The framework presents a unique and transparent hybrid genetic algorithm-ANNs training and testing method. It has been implemented in a spreadsheet environment using MS Excel$^{(R)}$ and EVOLVERTM V.5.5. It provides projects' owners of a decision-support tool that raises their awareness regarding their pre-bid decisions for a construction project.