• International conference on construction engineering and project management
• /
• 2005.10a
• /
• pp.935-940
• /
• 2005

This paper presents the results of a statistical analysis using historical data of cost contingency. As a result, a model that predicts and estimates an accurate cost contingency value using the least squares estimation method was developed. Data such as original contract amounts, estimated contingency amounts set by maximum funding limits, and actual contingency amounts, were collected and used for model development. The more effective prediction model was selected from the two developed models based on its prediction capability. The model would help guide project managers making financial decisions when the determination of the cost contingency amounts for multiple projects is necessary.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.5
• /
• pp.1356-1376
• /
• 2023

Software effort estimation is one of the most difficult tasks in software development whereas predictability is also of equal importance for strategic management. Accurate prediction of the actual cost that will be incurred in software development can be very beneficial for the strategic management. This study discusses the latest trends in software estimation focusing on analogy-based techniques to show how they have improved the accuracy for software effort estimation. It applies the standard deviation technique to the expected value of analogy-based estimates to improve accuracy. In more than 60 percent cases the applied technique of this study helped in improving the accuracy of software estimation by reducing the Magnitude of Relative Error (MRE). The technique is simple and it calculates the expected value of cost or time and then uses different confidence levels which help in making more accurate commitments to the customers.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2020.06a
• /
• pp.44-45
• /
• 2020

Korea has the fourth highest CO₂ emission among OECD countries in 2018, As of 2019, total greenhouse gas emissions per capita increased by about 98.2% in comparison to 1990. Korea has promised a 37% reduction in greenhouse gas emissions in 2030 from the projected Paris Climate Change Accord. Currently, many countries use the emissions trading system(ETS) for international carbon management. In 2015, ETS has been implemented in Korea, and the importance of calculating CO₂ emissions from construction machinery has increased. So, we require an accurate calculation of the environmental charges through the allocated CERs. Using the CER price and related search keywords, this paper derive about prediction models of CER price and compare and focus on more accurate prediction about CER price. By this method, the budget needed to establish the initial construction process plan can be calculated based on more accurate predicted CER price.

• Journal of Korean Institute of Industrial Engineers
• /
• v.33 no.1
• /
• pp.1-10
• /
• 2007

The capabilities ofBusiness Process Management Systems (BPMS's) are continuously extended to increase theeffectiveness of the management and enactment of business processes. This paper identifies the challenge ofcase prediction, which for a specific case under the control of a BPMS deals with the estimation of the remaining time until it is completed. An accurate case prediction facility is a valuable tool for the operationalcontrol of business processes, as it enables the pre-active monitoring of time violations. Little research has beencarried out in this area and few commercial tools support case prediction. This paper lists the requirements onsuch a facility and sketches sonae directions to reach a solution. To illustrate the depth of the problem, a smallaspect of the problem is treated in more detail. It involves the complex relations between tasks and resources inbusiness processes, which makes an exact analytical approach mfeasible.

• Journal of computational fluids engineering
• /
• v.11 no.3 s.34
• /
• pp.59-63
• /
• 2006

Accurate prediction of a supersonic missile base drag continues to defy even well-rounded CFD codes. In an effort to address the accuracy and predictability of the base drags, the influence of grid system and competitive turbulence models on the base drag is analyzed. Characteristics of some turbulence models is reviewed through incompressible turbulent flow over a flat plate, and performance for the base drag prediction of several turbulence models such as Baldwin-Loman(B-L), Spalart-Allmaras(S-A), k-$\varepsilon$, k-$\omega$ model is assessed. When compressibility correction is injected into the S-A model, prediction accuracy of the base drag is enhanced. The NSWC wind tunnel test data are utilized for comparison of CFD and semi-empirical codes on the accuracy of base drag predictability: they are about equal, but CFD tends to perform better. It is also found that, as angle of attack of a missile with control fins increases, even the best CFD analysis tool we have lacks the accuracy needed for the base drag prediction.

• Proceedings of the Korean Society for Bioinformatics Conference
• /
• 2005.09a
• /
• pp.237-242
• /
• 2005

Predicting the cellular location of an unknown protein gives a valuable information for inferring the possible function of the protein. For more accurate prediction system, we need a good feature extraction method that transforms the raw sequence data into the numerical feature vector, minimizing information loss. In this paper, we propose new methods of extracting underlying features only from the sequence data by computing pairwise sequence alignment scores. In addition, we use composition based features to improve prediction accuracy. To construct an SVM ensemble from separately trained SVM classifiers, we propose specificity based weighted majority voting. The overall prediction accuracy evaluated by the 5-fold cross-validation reached 88.53% for the eukaryotic animal data set. By comparing the prediction accuracy of various feature extraction methods, we could get the biological insight on the location of targeting information. Our numerical experiments confirm that our new feature extraction methods are very useful for predicting subcellular localization of proteins.

• Journal of the Korean Society for Precision Engineering
• /
• v.34 no.1
• /
• pp.23-27
• /
• 2017

This paper aims to develop a prediction model for the product quality of a casting process. Prediction of the product quality utilizes an artificial neural network (ANN) in order to renovate the manufacturing technology of the root industry. Various aspects of the research on the prediction algorithm for the casting process using an ANN have been investigated. First, the key process parameters have been selected by means of a statistics analysis of the process data. Then, the optimal number of the layers and neurons in the ANN structure is established. Next, feed-forward back propagation and the Levenberg-Marquardt algorithm are selected to be used for training. Simulation of the predicted product quality shows that the prediction is accurate. Finally, the proposed method shows that use of the ANN can be an effective tool for predicting the results of the casting process.

• Transactions of Materials Processing
• /
• v.6 no.6
• /
• pp.482-488
• /
• 1997

New carbon equivalent equation for the better prediction for the better prediction of roll force in a continuous hot strip mill has been formulated by applying a neural network method. In predicting roll force of steel strip, carbon equivalent equation which normalize the effects of various alloying elements by a carbon equivalent content is very critical for the accurate prediction of roll force. To overcome the complex relationships between alloying elements and operational variables such as temperature, strain, strain rate and so forth, a neural network method which is effective for multi-variable analysis was adopted in the present work as a tool to determine a proper carbon equivalent equation. The application of newly formulated carbon equivalent equation has increased prediction accuracy of roll force significantly and the effectiveness of neural network method is well confirmed in this study.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.4 no.1
• /
• pp.31-40
• /
• 1998

Through the combination of existing tidal prediction model and numerical tidal model, the efficient tidal prediction system was formulated and applied to the neighboring area of Pusan port. Because all tidal constituents for tidal prediction (69 tidal constituents are normally used) couldn't be considered due to the physical limits on computing process, some errors between the observed and predicted values were inevitably occurred. But it was confirmed that the computed values with maximum 10% of relative errors can be obtained if four major tidal constituents(M2, S2, K1, O1) are used. Thus, if other constituents than four major tidal constituents are additionally used, more accurate values will be obtained. Furthermore, if the database for harmonic constants in coastal waters is made in advance, using the numerical tidal model, prompt tidal prediction can be achieved at any time when it is required.

• International Journal of Computer Science & Network Security
• /
• v.22 no.1
• /
• pp.55-60
• /
• 2022

Churn prediction is a critical long-term problem for many business like music, games, magazines etc. The churn probability can be used to study many aspects of a business including proactive customer marketing, sales prediction, and churn-sensitive pricing models. It is quite challenging to design machine learning model to predict the customer churn accurately due to the large volume of the time-series data and the temporal issues of the data. In this paper, a parallel artificial neural network is proposed to create a highly-accurate customer churn model on a large customer dataset. The proposed model has achieved significant improvement in the accuracy of churn prediction. The scalability and effectiveness of the proposed algorithm is also studied.