• Proceedings of the KIEE Conference
• /
• 1995.07b
• /
• pp.500-502
• /
• 1995

Whenever faults(a fault) occur(s) in a particular section of a distribution system, some of loads get disconnected and are left unsupplied. Service should be restored to these affected load points as quickly as possible through network reconfiguration. An efficient technique is presented in this paper for this purpose. Network reduction and strategy testing the voltage and current constraints are the main contributions of this paper.

• International Journal of Quality Innovation
• /
• v.10 no.1
• /
• pp.115-126
• /
• 2009

Video transmission effectiveness in the Ad Hoc network is becoming important recently, if different routing protocols are applied. Some researchers conclude that the reactive protocols are better for file transfer protocol (FTP) and constant bit rate (CBR) or hypertext transfer protocol (HTTP) transmission in an Ad Hoc wireless network but the performance report of video transmission is not much. This study adopts Qualnet (Network Simulator) as a simulation tool for environmental designing and performance testing, and employs an experimental design with eight groups. Our experiment shows that: (1) The performance of AODV (reactive) protocol is better than DSDV, ZRP and DSR when the transmission load has only one video stream; (2) Proactive (DSDV) and Hybrid protocols (ZRP) are better for a smaller Ad Hoc network when it transmits a video stream with some applications (VoIP, FTP and CBR). We conclude that packet loss rate is sensitive to the quality of video transmission and it has negative relationship with Peak Signal-to-Noise Ratio (PSNR) value. In addition, our experiment also shows that PSNR is a simple Metric for the performance evaluation of video transmission.

• Computers and Concrete
• /
• v.19 no.3
• /
• pp.293-303
• /
• 2017

Maximum deflection in a beam is a serviceability design criterion and occurs generally at or close to the mid-span. This paper presents a methodology using neural networks for rapid prediction of mid-span deflections in reinforced concrete beams subjected to service load. The closed form expressions are further obtained from the trained neural networks. The closed form expressions take into account cracking in concrete at in-span and at near the interior supports and tension stiffening effect. The expressions predict the inelastic deflections (incorporating the concrete cracking) from the elastic moments and the elastic deflections (neglecting the concrete cracking). Five separate neural networks are trained since these have been postulated to represent all beams having any number of spans. The training, validating, and testing data sets for the neural networks are generated using an analytical-numerical procedure of analysis. The proposed expressions have been verified by comparison with the experimental results reported elsewhere and also by comparison with the finite element method (FEM). The proposed expressions, at minimal input data and minimal computation effort, yield results that are close to FEM results. The expressions can be used in every day design since the errors are found to be small.

• Structural Engineering and Mechanics
• /
• v.44 no.3
• /
• pp.325-337
• /
• 2012

In this article, the multi-objective optimization of cylindrical aluminum tubes under axial impact load is presented. The specific absorbed energy and the maximum crushing force are considered as objective functions. The geometric dimensions of tubes including diameter, length and thickness are chosen as design variables. D/t and L/D ratios are constricted in the range of which collapsing of tubes occurs in concertina or diamond mode. The Non-dominated Sorting Genetic Algorithm-II is applied to obtain the Pareto optimal solutions. A back-propagation neural network is constructed as the surrogate model to formulate the mapping between the design variables and the objective functions. The finite element software ABAQUS/Explicit is used to generate the training and test sets for the artificial neural networks. To validate the results of finite element model, several impact tests are carried out using drop hammer testing machine.

• Journal of information and communication convergence engineering
• /
• v.3 no.2
• /
• pp.90-92
• /
• 2005

As the result of the study shows, Round Robin Algorithm allows servers to ensure definite traffic distribution, unless incoming data loads differ much. Although error levels are high in some cases, they were eventually alleviated by repeated tests for a long period of time. The study uses two software packages (Packet Capture and Round Robin Test Package) to check packet volume from Virtual Network Structure (data generator, virtual server, Server 1, 2, 3), and finds out traffic distribution toward Server 1, 2, and 3. The functions of implemented Round Robin Load Balancing Monitoring System include round robin testing, system monitoring, and graphical indication of data transmission and packet volume.

• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.38 no.7
• /
• pp.1-5
• /
• 2001

This study accomplish comparison and analysis multimedia access network speed configuration which is effected by different environmental variation. For this testing. Not only LAN configuration which is symmetric network, But also the ADSL which is asymmetric network is tested by network speed checking software which is designed by our lab in a 10BaseT environment. In the case of TCP receive speed, we find out the network speed is changed according to bands of network to 90% of CPU load. Also, If the TCP receive windows's size became 2 times rate more than MTU, it reduced the relation of speed rate.

• Computers and Concrete
• /
• v.18 no.6
• /
• pp.1113-1134
• /
• 2016

A methodology using neural networks has been proposed for rapid prediction of inelastic bending moments in reinforced concrete continuous beams subjected to service load. The closed form expressions obtained from the trained neural networks take into account cracking in concrete at in-span and at near the internal supports and tension stiffening effect. The expressions predict the inelastic moments (considering the concrete cracking) from the elastic moments (neglecting the concrete cracking) at supports. Three separate neural networks are trained since these have been postulated to represent all the beams having any number of spans. The training, validating, and testing data sets for the neural networks are generated using an analytical-numerical procedure of analysis. The proposed expressions are verified for example beams of different number of spans and cross-section properties and the errors are found to be small. The proposed expressions, at minimal input data and computation effort, yield results that are close to FEM results. The expressions can be used in preliminary every day design as they enable a rapid prediction of inelastic moments and require a computational effort that is a fraction of that required for the available methods in literature.

• Structural Engineering and Mechanics
• /
• v.71 no.6
• /
• pp.739-749
• /
• 2019

This article deals with the application of reliability analysis for determining the safety of simply supported beam under the uniformly distributed load. The uncertainties of the existing methods were taken into account and hence reliability analysis has been adopted. To accomplish this aim, Generalized Regression Neural Network (GRNN), Extreme Learning Machine (ELM) and Gaussian Process Regression (GPR) models are developed. Reliability analysis is the probabilistic style to determine the possibility of failure free operation of a structure. The application of probabilistic mathematics into the quantitative aspects of a structure and improve the qualitative aspects of a structure. In order to construct the GRNN, ELM and GPR models, the dataset contains Modulus of Elasticity (E), Load intensity (w) and performance function (${\delta}$) in which E and w are inputs and ${\delta}$ is the output. The achievement of the developed models was weighed by various statistical parameters; one among the most primitive parameter is Coefficient of Determination ($R^2$) which has 0.998 for training and 0.989 for testing. The GRNN outperforms the other ELM and GPR models. Other different statistical computations have been carried out, which speaks out the errors and prediction performance in order to justify the capability of the developed models.

• The Journal of Advanced Prosthodontics
• /
• v.9 no.6
• /
• pp.423-431
• /
• 2017

PURPOSE. The goal of this study was to evaluate the fracture resistances of various monolithic crowns fabricated by computer-aided design and computer-aided manufacturing (CAD/CAM) with different thickness. MATERIALS AND METHODS. Test dies were fabricated as mandibular molar forms with occlusal reductions using CAD/CAM. With different occlusal thickness (1.0 or 1.5 mm), a polymer-infiltrated ceramic network (Enamic, EN), and zirconia-reinforced lithium silicate (Suprinity, SU and Celtra-Duo, CD) were used to fabricate molar crowns. Lithium disilicate (e.max CAD, EM) crowns (occlusal: 1.5 mm) were fabricated as control. Seventy crowns (n=10 per group) were bonded to abutments and stored in water for 24 hours. A universal testing machine was used to apply load to crown until fracture. The fractured specimens were examined with a scanning electron microscopy. RESULTS. The type of ceramics and the occlusal thickness showed a significant interaction. With a recommended thickness (1.5 mm), the SU revealed the mean load similar to the EM, higher compared with those of the EN and CD. The fracture loads in a reduced thickness (1.0 mm) were similar among the SU, CD, and EN. The mean fracture load of the SU and CD enhanced significantly when the occlusal thickness increased, whereas that of the EN did not. CONCLUSION. The fracture loads of monolithic crowns were differently influenced by the changes in occlusal thickness, depending on the type of ceramics. Within the limitations of this study, all the tested crowns withstood the physiological masticatory loads both at the recommended and reduced occlusal thickness.

• Journal of Electrical Engineering and Technology
• /
• v.9 no.5
• /
• pp.1482-1488
• /
• 2014

The reliability evaluation of the electrical distribution networks (EDN) requires sufficient consideration of the effects of planned outages. The planned outages of the EDN can be divided, by outage models and their effects on the reliability into two major categories: by equipment and by feeder. After studying the characteristics of different categories of planned outages, this paper expands the classification of load points by outage time from 4 types to 7 types and defines corresponding reliability parameters for the different types. By using the section algorithm, this paper proposes a reliability evaluation technique of EDN considering equipment random failures and two categories of planned outages. The proposed technique has been applied to the RBTS-BUS6 test system and some practical EDNs in China. The study results demonstrate that the proposed technique is of higher practical value and can be used for evaluating the reliability performance of EDN more efficiently considering the planned outages.