• Journal of Digital Convergence
• /
• v.12 no.3
• /
• pp.211-218
• /
• 2014

Recently, wind energy is expanding to combination of computing to forecast of wind power generation as well as intelligent of wind powerturbine. Wind power is rise and fall depending on weather conditions and difficult to predict the output for efficient power production. Wind power is need to reliably linked technology in order to efficient power generation. In this paper, distributed power generation forecasts to enhance the predicted and actual power generation in order to minimize the difference between the power of distributed power short-term prediction model is designed. The proposed model for prediction of short-term combining the physical models and statistical models were produced in a physical model of the predicted value predicted by the lattice points within the branch prediction to extract the value of a physical model by applying the estimated value of a statistical model for estimating power generation final gas phase produces a predicted value. Also, the proposed model in real-time National Weather Service forecast for medium-term and real-time observations used as input data to perform the short-term prediction models.

• Fibers and Polymers
• /
• v.7 no.4
• /
• pp.404-413
• /
• 2006

This study examines multiple quality optimization of the injection molding for Polyether Ether Ketone (PEEK). It also looks into the dimensional deviation and strength of screws that are reduced and improved for the molding quality, respectively. This study applies the Taguchi method to cut down on the number of experiments and combines grey relational analysis to determine the optimal processing parameters for multiple quality characteristics. The quality characteristics of this experiment are the screws' outer diameter, tensile strength and twisting strength. First, one should determine the processing parameters that may affect the injection molding with the $L_{18}(2^1{\times}3^7)$ orthogonal, including mold temperature, pre-plasticity amount, injection pressure, injection speed, screw speed, packing pressure, packing time and cooling time. Then, the grey relational analysis, whose response table and response graph indicate the optimum processing parameters for multiple quality characteristics, is applied to resolve this drawback. The Taguchi method only takes a single quality characteristic into consideration. Finally, a processing parameter prediction system is established by using the back-propagation neural network. The percentage errors all fall within 2%, between the predicted values and the target values. This reveals that the prediction system established in this study produces excellent results.

• Journal of Korean Society of Steel Construction
• /
• v.8 no.3 s.28
• /
• pp.127-137
• /
• 1996

Connections as basic elements and an integrated part of a steel frame has an effect on the frame's performance. Conventional analysis and design techniques are based on either idealized fixed or pinned conditions. In fact, the use of rigid or pinned connection model in steel frame analysis serves the purpose of simplifying the analysis and design processes, but all connections used in current pratice possess stiffness and transfer moment which fall between the extreme cases of fully rigid and ideally pinned. To predict the behavior of the semi-rigid steel frames, it is necessary to predict the moment-rotation behavior of the beam-to-column connections. In this research, prediction equation for moment-rotation behavior of the beam-to-column connection is suggested and the effect of design parameters has investigated. Prediction model, in a nondimensional form shows the moment-rotation characteristic for connections. It is composed of the curve fitting power function using standardization constant K and 4 parameter $KM_o$, ${\theta}_0$, b, n based on the pretest result about moment-rotation behavior of connection.

• Nuclear Engineering and Technology
• /
• v.27 no.2
• /
• pp.189-202
• /
• 1995

An improved method is presented for the prediction of heat transfer coefficients in turbulent fall-ing liquid films with or without interfacial shear for both heating or condensation. A modified Mudawwar and El-Masri's semi-empirical turbulence model, particularly to extend its use for the turbulent falling film with high interfacial shear, is used to replace the eddy viscosity model incorporated in the unified approach unposed by Yih and Liu. The liquid film thickness and asymptotic heat transfer coefficients against the film Reynolds number for wide range of interfacial shear predicted by both present and existing methods are compared with experimental data. The results show that in general, predictions of the modified model agee more closely with experimental data than that of existing models.

• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.1
• /
• pp.665-673
• /
• 2021

Default has become an extreme concern in the current world due to the financial crisis. The previous prediction of companies' bankruptcy exhibits evidence of decision assistance for financial and regulatory bodies. Notwithstanding numerous advanced approaches, this area of study is not outmoded and requires additional research. The purpose of this research is to find the best classifier to detect a company's default risk and bankruptcy. This study used secondary data from the Pakistan Stock Exchange (PSX) and it is time-series data to examine the impact on the determinants. This research examined several different classifiers as per their competence to properly categorize default and non-default Pakistani companies listed on the PSX. Additionally, PSX has remained consistent for some years in terms of growth and has provided benefits to its stockholders. This paper utilizes machine learning techniques to predict financial distress in companies listed on the PSX. Our results indicate that most multi-stage mixture of classifiers provided noteworthy developments over the individual classifiers. This means that firms will have to work on the financial variables such as liquidity and profitability to not fall into the category of liquidation. Moreover, Adaptive Boosting (Adaboost) provides a significant boost in the performance of each classifier.

• Journal of the Korean Data and Information Science Society
• /
• v.23 no.2
• /
• pp.271-283
• /
• 2012

In this paper, we predicted the rise or the fall in eleven major industrial stock price indices unlike existing studies dealing with the prediction of KOSPI that combines all industries. We used as input variables not only domestic economic indices but also foreign economic indices including the U.S.A, Japan, China and Europe that have affected korean stock market. Numerical analysis through SAS E-miner showed above or below about 60% accuracy using the logistic regression and neural network model.

• Proceedings of the KAIS Fall Conference
• /
• 2008.05a
• /
• pp.248-250
• /
• 2008

This paper proposes a prediction method to prevent traffic accident and reduce to vehicle waiting time using neural network. Computer simulation results proved reducing average vehicle waiting time which proposed coordinating green time better than electro-sensitive traffic light system dose not consider coordinating green time. Moreover, we present neural network approach for traffic accident prediction with unnormalized (actual or original collected) data. This approach is not consider the maximum value of data and possible use the network without normalizing but the predictive accuracy is better. Also, the unnormalized method shows better predictive accuracy than the normalized method given by maximum value. Therefore, we can make the best use of this model in software reliability prediction using unnormalized data. Computer simulation results proved reducing traffic accident waiting time which proposed neural network better than conventional system dosen't consider neural network.

• Smart Structures and Systems
• /
• v.31 no.1
• /
• pp.1-11
• /
• 2023

Assessment of the compressive strength of concrete plays a major role during formwork removal and in the prestressing process. In concrete, temperature changes occur due to hydration which is an influencing factor that decides the compressive strength of concrete. Many methods are available to find the compressive strength of concrete, but the maturity method has the advantage of prognosticating strength without destruction. The temperature-time factor is found using a LM35 temperature sensor through the IoT technique. An experimental investigation was carried out with 56 concrete cubes, where 35 cubes were for obtaining the compressive strength of concrete using a universal testing machine while 21 concrete cubes monitored concrete's temperature by embedding a temperature sensor in each grade of M25, M30, M35, and M40 concrete. The mathematical prediction model equation was developed based on the temperature-time factor during the early age compressive strength on the 1^st, 2^nd, 3^rd and 7^th days in the M25, M30, M35, and M40 grades of concrete with their temperature. The 14^th, 21^st and 28^th day's compressive strength was predicted with the mathematical predicted equation and compared with conventional results which fall within a 2% difference. The compressive strength of concrete at any desired age (day) before reaching 28 days results in the discovery of the prediction coefficient. Comparative analysis of the results found by the predicted mathematical model show that, it was very close to the results of the conventional method.

• Journal of the Korean earth science society
• /
• v.45 no.4
• /
• pp.327-337
• /
• 2024

The ocean is linked to long-term climate variability, but there are very few methods to assess the short-term performance of forecast models. This study analyzes the short-term prediction performance regarding ocean temperature and salinity of the Global Seasonal prediction system version 5 (GloSea5). GloSea5 is a historical climate re-creation (2001-2010) performed on the 1st, 9th, 17th, and 25th of each month. It comprises three ensembles. High-resolution hindcasts from the three ensembles were compared with the Array for Real-Time Geostrophic Oceanography (ARGO) float data for the period 2001-2010. The horizontal position was preprocessed to match the ARGO float data and the vertical layer to the GloSea5 data. The root mean square error (RMSE), Brier Score (BS), and Brier Skill Score (BSS) were calculated for short-term forecast periods with a lead-time of 10 days. The results show that sea surface temperature (SST) has a large RMSE in the western boundary current region in Pacific and Atlantic Oceans and Antarctic Circumpolar Current region, and sea surface salinity (SSS) has significant errors in the tropics with high precipitation, with both variables having the largest errors in the Atlantic. SST and SSS had larger errors during the fall for the NINO3.4 region and during the summer for the East Sea. Computing the BS and BSS for ocean temperature and salinity in the NINO3.4 region revealed that forecast skill decreases with increasing lead-time for SST, but not for SSS. The preprocessing of GloSea5 forecasts to match the ARGO float data applied in this study, and the evaluation methods for forecast models using the BS and BSS, could be applied to evaluate other forecast models and/or variables.

• Tribology and Lubricants
• /
• v.20 no.5
• /
• pp.252-258
• /
• 2004

In this study, using high cycle fatigue (HCF) criteria, the simulation of rolling contact fatigue is conducted under elliptical contact. The HCF criteria fall into three categories: the critical plane approach, the stress invariant approach and the approach based on the mesoscopic scale. The accurate calculation of contact stresses and subsurface stresses is essential to the prediction of crack initiation life. Contact stresses are obtained by contact analysis of a semi-infinite solid based on the use of influence functions and the subsurface stress field is obtained using rectangular patch solutions. The simulation results show that the critical load is decreasing rapidly and the site of crack initiation also moves rapidly to the surface from the subsurface when the friction coefficient exceeds a specific value for all of three fatigue criteria.