• Journal of the Korean Society for Precision Engineering
• /
• v.22 no.3 s.168
• /
• pp.146-153
• /
• 2005

In this paper, a multi-step optimization using genetic algorithm with variable penalty function is introduced to the structural design optimization of a grinding machine. The design problem, in this study, is to find out the optimum configuration and dimensions of structural members which minimize the static compliance, the dynamic compliance, and the weight of the machine structure simultaneously under several design constraints such as dimensional constraints, maximum deflection limit, safety criterion, and maximum vibration amplitude limit. The first step is shape optimization, in which the best structural configuration is found by getting rid of structural members that have no contributions to the design objectives from the given initial design configuration. The second and third steps are sizing optimization. The second design step gives a set of good design solutions having higher fitness for lightweight and minimum static compliance. Finally the best solution, which has minimum dynamic compliance and weight, is extracted from the good solution set. The proposed design optimization method was successfully applied to the structural design optimization of a grinding machine. After optimization, both static and dynamic compliances are reduced more than 58.4% compared with the initial design, which was designed empirically by experienced engineers. Moreover the weight of the optimized structure are also slightly reduced than before.

• Journal of the Korean Institute of Telematics and Electronics
• /
• v.27 no.10
• /
• pp.130-137
• /
• 1990

The multipath multistage interconnection network is proposed which has the capability of dynamic rerouting. Construction of the network gets simpler, and the number of stages is reduced to $LOG_{2}$(N), so hardware complexity is reduced. This proposed algorithm makes it possible that destination adresses are used as routing tags, and it is easy to set up the path. Because this proposed network has a dynamic rerouting, backtracking is not necessary to set up another path when conflicts of switch faults are occurred. To estimate a performance, analytic methods are used and it is proved that probability of acceptance is improved in this Multipath MIN.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
• /
• v.20 no.3
• /
• pp.205-212
• /
• 2008

This paper describes the application of an inverse building model to a calibrated forward building model using EnergyPlus program. Typically, inverse models are trained using measured data. However, in this study, an inverse building model was trained using data generated by an EnergyPlus model for an actual office building. The EnergyPlus model was calibrated using field data for the building. A training data set for a month of July was generated from the EnergyPlus model to train the inverse model. Cooling load prediction of the trained inverse model was tested using another data set from the EnergyPlus model for a month of August. Predicted cooling loads showed good agreement with cooling loads from the EnergyPlus model with root-mean square errors of 4.11%. In addition, different control strategies with dynamic cooling setpoint variation were simulated using the inverse model. Peak cooling loads and daily cooling loads were compared for the dynamic simulation.

• Polymer(Korea)
• /
• v.25 no.2
• /
• pp.233-239
• /
• 2001

The ethylene propylene diene terpolymer (EPDM) blends with acrylonitrile butadiene rubber (NBR) were prepared by mechanical mixing method. Mooney viscosity, cure behaviors, compression set and dynamic mechanical properties were subsequently examined. Dynamic characteristics of the entire blends determined from a Rheovibron generally showed two glass transitions (T'$_{g}$s), -43$^{\circ}C$ and -4$^{\circ}C$ for NBR and EPDM, respectively. The tan $\delta$ peak monotonically shifted toward the higher temperature with increasing NBR content. It was also found that the optimum cure time was significantly decreased with loading of NBR.

• KIISE Transactions on Computing Practices
• /
• v.23 no.11
• /
• pp.632-640
• /
• 2017

Although solar insolation is the weather factor with the greatest influence on power generation in photovoltaic systems, the Meterological Agency does not provide solar insolation data for future dates. Therefore, it is essential to research prediction methods for solar insolation to efficiently manage photovoltaic systems. In this study, we propose a Dynamic Piecewise Prediction Model that can be used to predict solar insolation values for future dates based on information from the weather forecast. To improve the predictive accuracy, we dynamically divide the entire data set based on the sun altitude and cloudiness at the time of prediction. The Dynamic Piecewise Prediction Model is developed by applying a polynomial linear regression algorithm on the divided data set. To verify the performance of our proposed model, we compared our model to previous approaches. The result of the comparison shows that the proposed model is superior to previous approaches in that it produces a lower prediction error.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2007.05a
• /
• pp.157-160
• /
• 2007

The dynamic behavior of a steel bridge crossed by the Korean High Speed Train(KHST) has been investigated experimentally and numerically. The bridge is a 2-girder simply supported steel bridge which has 40m of span length. A set of experimental tests were performed during operation of KHST, and 13 accelerometers and 6 LVDTs were utilized for measurement of dynamic responses. Numerical analyses considering bridge-structure interaction were performed for validation of experimental results. Since structural type and dynamic characteristics of the bridge differ from those of the representative concrete box bridge, dynamic behavior of the concerning steel bridge shows differences, but dynamic performances are all satisfying specification requirements.

• Wind and Structures
• /
• v.21 no.6
• /
• pp.609-623
• /
• 2015

Seismic reliability analysis of a jacket-type support structure for an offshore wind turbine was performed. When defining the limit state function by using the dynamic response of the support structure, a number of dynamic calculations must be performed in a First-Order Reliability Method (FORM). That means analysis costs become too high. In this paper, a new reliability analysis approach using a static response is used. The dynamic effect of the response is considered by introducing a new parameter called the Peak Response Factor (PRF). The probability distribution of PRF can be estimated by using the peak value in the dynamic response. The probability distribution of the PRF was obtained by analyzing dynamic responses during a set of ground motions. A numerical example is presented to compare the proposed approach with the conventional static response-based approach.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2002.05a
• /
• pp.147-152
• /
• 2002

It has tried to predict dynamic response and establish answering about global or local dynamic problem in structures using experimental and analysis method. One of such a try, it be proposed Semi-Empirical Method that reduce error element of input information about dynamic analysis using dynamic experimental study and measurement data in the basis of real-structure. In this paper, the dynamic response problem about piping system that be set-up THERMOWELL produce flow fluid dynamic force using Semi-Empirical Method

• Journal of Electrical Engineering and Technology
• /
• v.2 no.4
• /
• pp.543-548
• /
• 2007

We present a dynamic Bayesian network (DBN) model of a generalized class of nonstationary birth-death processes. The model includes birth and death rate parameters that are randomly selected from a known discrete set of values. We present an on-line algorithm to obtain optimal estimates of the parameters. We provide a simulation of real-time characterization of load traffic estimation using our DBN approach.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.10 no.2
• /
• pp.128-133
• /
• 2010

This paper presents a method for estimating the parameters of dynamic models for induction motor dominating loads. Using particle swarm optimization, the method finds the adequate set of parameters that best fit the sampling data from the measurement for a period of time, minimizing the error of the outputs, active and reactive power demands and satisfying the steady-state error criterion.