• Journal of Korean Society of Environmental Engineers
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• v.27 no.10
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• pp.1123-1128
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• 2005

NIB(methylisoborneol) is an earthy/musty odor compound produced as a second metabolite by cyanobacteria and actinomycetes. MIB is not removed by conventional water treatment(coagulation, sedimentation, filtration) and its presence in tap water, even at low ng/L levels, can result in consumer complaints. PAC(powdered activated carbon) can effectively remove MIB when the correct dose is applied. But, since most operators in water treatment plants apply a PAC dose and then adjust that dose depending on direct observation (odor detection) after treatment, the result is often under-dose or eve,-dose. In this study, kinetic and isotherm tests using $^{14}C$-radiolabeled MIB were performed to determine coefficients for the HSDM(homogeneous surface diffusion model), including liquid film mass transfer coefficient($K_f$) and surface diffusion coefficient ($D_s$). The HSDM gave a reasonable fit and allowed prediction with the experimental data. Base on the HSDM, the authors developed an optimum PAC dose prediction program using the Excel spreadsheet. When the developed program was applied at two water treatment plants, the PAC dose based on the experience of operators in the water treatment plant was significantly different from that recommended by the newly developed program. If operators are willing to use the optimum PAC dose prediction program, it should solve dosing problems.

• 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.

• Journal of The Korean Association of Information Education
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• v.25 no.5
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• pp.791-801
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• 2021

In this paper, an educational case that can be applied as artificial intelligence literacy education for preservice teachers and incumbent teachers was studied. To this end, a case of educating the operating principle of an artificial neural network that recognizes images is proposed. This training case focuses on the basic principles of artificial neural network operation and implementation, and applies the method of finding parameter optimization solutions required for artificial neural network implementation in a spreadsheet. In this paper, we focused on the artificial neural network of supervised learning method. First, as an artificial neural network principle education case, an artificial neural network education case for recognizing two types of images was proposed. Second, as an artificial neural network extension education case, an artificial neural network education case for recognizing three types of images was proposed. Finally, the results of analyzing artificial neural network training cases and training satisfaction analysis results are presented. Through the proposed training case, it is possible to learn about the operation principle of artificial neural networks, the method of writing training data, the number of parameter calculations executed according to the amount of training data, and parameter optimization. The results of the education satisfaction survey for preservice teachers and incumbent teachers showed a positive response result of over 70% for each survey item, indicating high class application suitability.

• Journal of Practical Engineering Education
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• v.15 no.2
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• pp.381-388
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• 2023

Through artificial neural network education using spreadsheets, non-major undergraduate students can understand the operation principle of artificial neural networks and develop their own artificial neural network software. Here, training of the operation principle of artificial neural networks starts with the generation of training data and the assignment of correct answer labels. Then, the output value calculated from the firing and activation function of the artificial neuron, the parameters of the input layer, hidden layer, and output layer is learned. Finally, learning the process of calculating the error between the correct label of each initially defined training data and the output value calculated by the artificial neural network, and learning the process of calculating the parameters of the input layer, hidden layer, and output layer that minimize the total sum of squared errors. Training on the operation principles of artificial neural networks using a spreadsheet was conducted for undergraduate non-major students. And image training data and basic artificial neural network development results were collected. In this paper, we analyzed the results of collecting two types of training data and the corresponding artificial neural network SW with small 12-pixel images, and presented methods and execution results of using the collected training data for Orange machine learning model learning and analysis tools.

• Geotechnical Engineering
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• v.6 no.1
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• pp.7-14
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• 1990

The two degree of freedom model is proposed to study the effects of imperfect fixing at the active end of spring-top resonant column apparatus. A computer program using the SYMPHONY spreadsheet is developed to calculate the dimensionless frequency, F, from which modulug can be determined. It is found that the effect of reaction mass through the parameter Tr on dimensionless frequency, F, can not be ignored if Tr$\leq$20. As To increases, the variation of F increases. But for Tr$\geq$ 20, the effect of To becomes small. It is recommended that T. be greater than 20 if single degree of freedom model is rosed to determine modulus of soil. It also is found that damping ratios of specimen and apparatus do not strongly affect the dimensionless frequency, F.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.14 no.2
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• pp.127-133
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• 2015

In this study, an analytical model was developed for one-dimensional finite element analysis (1D FEA) of a spindle system of machine tools and then implemented to automate the FEA as a tool. FEA, with its vibration characteristics such as natural frequencies and modes, was performed using the universal FEA software ANSYS. VBA of EXCEL was used to provide the programming environment for its implementation. This enabled graphic user interfaces (GUIs) to be developed to allow interactions of users with the tool and, in addition, an EXCEL spreadsheet to be linked with the tool for data arrangement. The language of ANSYS was used to develop a code to perform the FEA. It generates an analytical model of the spindle system based on the information at the GUIs and subsequently performs the FEA based on the model. Automation helps identify the near-optimal design of the spindle system with minimum time and efforts.

• The Journal of Information Systems
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• v.20 no.1
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• pp.107-126
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• 2011

Model-based decision support system (DSS) has acted as a crucial role in strengthening the business competitiveness by providing a way of modeling and solving real-world decision problems in a quantitative and scientific manner. It is even more important for trading OTC derivatives, which requires extensive financial-engineering expertise while actively reacting to the continuously changing financial market. This paper proposes a flexible model-based DSS architecture that can support user-friendly interface for executing and analyzing the models and can adapt to the changes of financial market seamlessly. For user-friendliness, we implement the user-interfaces (UIs) using Microsoft Excel, a very widely used spreadsheet program for its great generality and extensibility. Users can utilize the analysis results of DSS or reprocess them for their special needs through the UIs in the form of familiar spreadsheets easily. For adaptiveness to the markets, the proposed architecture is constructed based on the object-oriented concepts, which enables such changes as release of a new financial product can be updated into the system without any delay at the lowest cost. We investigate the practical benefits and limitations of the proposed architecture by a case study on the construction of Model-based Trading Support System (MTSS), performed by a commercial bank in Korea.

• Korean Business Review
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• v.20 no.1
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• pp.105-122
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• 2007

This paper identifies the dimensions of political risk on the basis of the classification between risk and uncertainties to implement the precise identification and assessment of the various types of political risk and develop the sound assessment model to accomplish their practical applications. This paper shows the concrete and detailed processes of deriving the assessment models and applying them with the microsoft excel spreadsheet, confirms the result of Butler and Joaquin(1998), and presents the methods of identifying the various combination effects of the political risk impact and the covariance relationship with the market portfolio return through the sensitivity analysis.

• Journal of Information Science Theory and Practice
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• v.3 no.3
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• pp.75-80
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• 2015

Methods and Material: The PubMed database was used for retrieving data on 'cancer biology.' Articles were downloaded from the years 2000 to 2011. The articles were classified chronologically and transferred to a spreadsheet application for analysis of the data as per the objectives of the study. Statistical Method: To investigate the nature of growth of articles via exponential, linear, and logistics tests. Result: The year wise analysis of the growth of articles output shows that for the years 2000 to 2005 and later there is a sudden increase in output, during the years 2006 to 2007 and 2008 to 2011. The high productivity of articles during these years may be due to their significance in cancer biology literature, having received prominence in research. Conclusion: There is an obvious need for better compilations of statistics on numbers of publications in the years from 2000 to 2011 on various disciplines on a worldwide scale, for informed critical assessments of the amount of new knowledge contributed by these publications, and for enhancements and refinements of present Scientometric techniques (citation and publication counts), so that valid measures of knowledge growth may be obtained. Only then will Scientometrics be able to provide accurate, useful descriptions and predictions of knowledge growth.

• Journal of the Korea Concrete Institute
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• v.11 no.6
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• pp.57-67
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• 1999

Optimization scheme is presented for the design of precast prestressed double-tee beams used as slabs in the parking or market structures. The objective considered is defined by a function that minimizes the hight of the double-tee beam, including the prefabricated element and the concrete topping poured in a second phase. The Sequential Quadratic Programming method is adopted to solve the problem. As an example 12 double-tee beams are designed with the design loads of the current design code of our country. The results from optimization process show that at least 29cm less in overall height than that designed by PCI design handbook. The section determined from the optimization process was refined for practical considerations. A MathCad 7.0 Pro Spreadsheet was prepared to verify all ACI requirements for flexure, shear and deflections. Flexural tests are performed on four full-scale 12.5m prototype models and show that all the specimens are fully comply the flexural strength requirements as specified by ACI 318-95. The present optimization scheme can be used for wider application of the design of precast prestressed double-tee beams with different materials and configurations particularly for in a large scale or for important designs.