• International conference on construction engineering and project management
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• 2009.05a
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• pp.203-211
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• 2009

Korean public owners who order public multi-family housing construction projects have yet to gain access to a model for predicting construction cost. For this reason, their construction cost prediction is mainly dependent upon historic data and experience. In this paper, a cost-prediction model based on Case-Based Reasoning (CBR) in the design phase of public multi-family housing construction projects was developed. The developed model can determine the total construction cost by estimating the different Building, Civil, Mechanical, Electronic and Telecommunication, and Landscaping work costs. Model validation showed an accuracy of 97.56%, confirming the model's excellent viability. The developed model can thus be used to predict the construction cost to be shouldered by public owners before the design is completed. Moreover, any change orders during the design phase can be immediately applied to the model, and various construction costs by design alternative can be verified using this model. Therefore, it is expected that public owners can exercise effective design management by using the developed cost prediction model. The use of such an effective cost prediction model can enable the owners to accurately determine in advance the construction cost and prevent increase or decrease in cost arising from the design changes in the design phase, such as change order. The model can also prevent the untoward increase in the duration of the design phase as it can effectively control unnecessary change orders.

• Journal of Communications and Networks
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• v.1 no.3
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• pp.153-157
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• 1999

In this paper, we derive finite-dimensional non-linear fil-ters for optimally reconstructing speech signals in Switched Predic-tion vocoders, Code Excited Linear Prediction(CELP) and Differ-ential Pulse Code Modulation (DPCM). Our filter is an extension of the Hidden Markov filter.

• Journal of Energy Engineering
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• v.4 no.3
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• pp.338-343
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• 1995

The objective of this study is to predict the reliability of the electronic control module at ROD control system in nuclear power plant. Maintaining of the reliability is important issue in the complext system like nuclear plower plant, military equipment, satelite system, etc., because the failure of reliability brings etravagant economic loss and deteriorates public acceptance. In addition to the prediction of reliability, the fators affect the reliability including operating condition, environment, temperature and quality factors were analyzed and simulated. The result shows that the quality factors are more critical for the higher reliability than other two factors.

• Journal of information and communication convergence engineering
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• v.1 no.4
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• pp.189-193
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• 2003

A new algorithm is presented for adaptive notch filtering of narrow band or sine signals for embedded among broad band noise. The notch filter is implemented as a constrained infinite impulse response filter with a minimal number of parameters, Based on the recursive prediction error (RPE) method, the algorithm has the advantages of the fast convergence, accurate results and initial estimate of filter coefficient and its covariance is revealed. A convergence criterion is also developed. By using the information of the noise-to-signal power, the algorithm can self-adjust its initial filter coefficient estimate and its covariance to ensure convergence.

• Geotechnical Engineering
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• v.3 no.1
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• pp.25-38
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• 1987

The prediction of driving stresses in piles is necessary for optimum selection of driving hammers, better design of precast piles, enact assessment of drivabilities and complete description of piling specifications. However, the existing pile-driving formulas based on the theory of Newtonian impact have some defects and shortcomings; the numerical method by the wave equation analysis using electronic computer usually Involves various uncertainties and limitations which can yield erroneous outcomes because it employs soil constants of which the nature is unknown as essential parameters and ignores the effect of residual stresses set up in the pile .after each hammer blow; and the electronic measuring technique needs extra time and expense. The method developed herein is presented for the purpose of giving field engineers a reliable and convenient analytical procedure for the prediction of driving stresses along the full length of pile using the most effetive parameters without resort to electronic computer. This method is based on the fundamental mechanics of stress waves in elastic rods and takes into account the effect of residual stresses induced by reversed friction in piles.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.2 s.302
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• pp.9-18
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• 2005

Flicker is the 'variation in brightness or he perceived won stimulation by intermittent or temporally non uniform light'. This phenomenon is blown as the cause of eye strain and headaches. Many researchers are dedicated to reducing this phenomenon. The flicker phenomenon also exists in PDP as some other display types, and is a critical problem in 50 Hz PDP. However, it is difficult to define flicker by more than one subjective judgment. So, an objective measurement of flicker is necessary and convenient for research on displays. In this paper, a computational prediction model is proposed which is used to predict luminance flicker (not chromatic flicker) by giving a quantitative output that describes the probability of occurrence of flicker. Through this work, we expected to provide a practical tool for flicker-free design in PDP.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.5
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• pp.393-401
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• 2018

Reliability prediction of an electronic arm fire device(EAFD) was studied which is applied to prevent accidental ignition in a solid rocket motor. For predicting the reliability, the main components of the EAFD were first defined(Control unit, LEEFI, TBI) and the operating principle of each component was analyzed. Performance modeling of each part is established using selected input variables through system analysis. When complex analysis is required, we approximated it with polynomial equation using response surface method. Monte-Carlo simulation is applied to performance modeling to estimate the design reliability.

• Korean Journal of Materials Research
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• v.17 no.5
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• pp.250-255
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• 2007

The alloying effects on the electronic structures of Zinc are investigated using the relativistic $DV-X{\alpha}molecular$ orbital method in order to obtain useful information for alloy design. A new parameter which is the d obital energy level(Md) and the bonder order(Bo) of alloying elements in Zinc was introduced and used for prediction of the mechanical properties. The Md correlated with the atomic radius and the electronegativity of elements. The Bo is a measure of the strength of the covalent bond between M and X atoms. First-principles calculations of electronic structures were performed with a series of models composed of a MZn18 cluster and the electronic states were calculated by the discrete variational- $X{\alpha}method$ by using the program code SCAT. The central Zinc atom(M) in the cluster was replaced by various alloying elements. In this study energy level structures of pure Zinc and alloyed Zinc were calculated. From calculated results of energy level structures in MZn18 cluster, We found Md and Bo values for various elements of Zn. In this work, Md and Bo values correlated to the tensile strength for the Zn. These results will give some guide to design of zinc based alloys for high temperature applications and it is possible the excellent alloys design.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.3
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• pp.483-488
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• 2019

The propagation prediction model of the earth-space communication link used as an international standard was used to calculate and analyze the total losses on the communication path. The standard definition and scope of ITU-R Rec. were analyzed for each parameter(rain, scintillation, atmospheric gas, clouds) used to calculate the total loss. The total losses were calculated using the standard model for each parameter and the statistical data provided by ITU-R, and the results were analyzed using the validation examples data. The rain losses were calculated using long-term local rainfall attenuation statistics data measured in the region, and compared with the calculation results using a rainfall map in the ITU-R Recommendation. The data of Cheollian satellites for the L-Band and Ka-Band were used to calculate the rainfall attenuation. In the range of 0.01% to 0.1%, it was found to have a greater attenuation slope when using local data than attenuation by the model of ITU-R.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.7
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• pp.1986-2009
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• 2024

The integration of blockchain technology with the rapid growth of Internet of Things (IoT) devices has enabled secure and decentralised data exchange. However, security vulnerabilities and performance limitations remain significant challenges in IoT blockchain networks. This work proposes a novel approach that combines transaction representation and machine learning techniques to address these challenges. Various clustering techniques, including k-means, DBSCAN, Gaussian Mixture Models (GMM), and Hierarchical clustering, were employed to effectively group unlabelled transaction data based on their intrinsic characteristics. Anomaly transaction prediction models based on classifiers were then developed using the labelled data. Performance metrics such as accuracy, precision, recall, and F1-measure were used to identify the minority class representing specious transactions or security threats. The classifiers were also evaluated on their performance using balanced and unbalanced data. Compared to unbalanced data, balanced data resulted in an overall average improvement of approximately 15.85% in accuracy, 88.76% in precision, 60% in recall, and 74.36% in F1-score. This demonstrates the effectiveness of each classifier as a robust classifier with consistently better predictive performance across various evaluation metrics. Moreover, the k-means and GMM clustering techniques outperformed other techniques in identifying security threats, underscoring the importance of appropriate feature selection and clustering methods. The findings have practical implications for reinforcing security and efficiency in real-world IoT blockchain networks, paving the way for future investigations and advancements.