• Journal of Electrical Engineering and Technology
• /
• v.4 no.2
• /
• pp.185-193
• /
• 2009

This paper presents a methodology for photovoltaic (PV) system allocation in distribution systems using a discrete particle swarm optimization (DPSO). The PV allocation problem is in the category of mixed integer nonlinear programming and its formulation may include multi-valued dis-crete variables. Thus, the PSO requires a scheme to deal with multi-valued discrete variables. This paper introduces a novel multi-level quantization scheme using a sigmoid function for discrete particle swarm optimization. The technique is employed to a standard PSO architecture; the same velocity update equation as in continuous versions of PSO is used but the particle's positions are updated in an alternative manner. The set of multi-level quantization is defined as integer multiples of powers-of-two terms to efficiently approximate the sigmoid function in transforming a particle's position into discrete values. A comparison with a genetic algorithm (GA) is performed to verify the quality of the solutions obtained.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.12 no.4
• /
• pp.114-122
• /
• 2013

This study introduces an optimization methodology for the determination of gate location that ensures the melt flow balance within a part cavity of injection mold. A new sequential direct-search scheme based on the recursive reduction of the designer-specified gate design area is developed, and it is integrated with a commercial flow simulation tool for optimization. To quantify the level of melt flow balance, we employ the maximum difference among the fill times for the melt fronts to reach the boundary elements of part cavity as objective function. The proposed methodology is successfully applied in the case study of melt flow balancing in molding of a bar code scanner model. The result shows that the melt flow balance at the optimized gate positions is significantly improved from that for the initial gate position.

• IEIE Transactions on Smart Processing and Computing
• /
• v.3 no.1
• /
• pp.1-9
• /
• 2014

This paper proposes a low-latency Sample Adaptive Offset filter (SAO) architecture and its Single Instruction Multiple Data (SIMD) optimization scheme to achieve fast High Efficiency Video Coding (HEVC) decoding in a multi-core environment. According to the HEVC standard and its Test Model (HM), SAO operation is performed only at the picture level. Most realtime decoders, however, execute their sub-modules on a Coding Tree Unit (CTU) basis to reduce the latency and memory bandwidth. The proposed low-latency SAO architecture has the following advantages over picture-based SAO: 1) significantly less memory requirements, and 2) low-latency property enabling efficient pipelined multi-core decoding. In addition, SIMD optimization of SAO filtering can reduce the SAO filtering time significantly. The simulation results showed that the proposed low-latency SAO architecture with significantly less memory usage, produces a similar decoding time as a picture-based SAO in single-core decoding. Furthermore, the SIMD optimization scheme reduces the SAO filtering time by approximately 509% and increases the total decoding speed by approximately 7% compared to the existing look-up table approach of HM.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.1
• /
• pp.61-80
• /
• 2018

Location information of individual nodes is important in the implementation of necessary network functions. While extensive studies focus on localization techniques in 2D space, few approaches have been proposed for 3D positioning, which brings the location closer to the reality with more complex calculation consumptions for high accuracy. In this paper, an effective range-free localization scheme is proposed for 3D space localization, and the sensitivity of parameters is evaluated. Firstly, we present an improved algorithm (MDV-Hop), that the average distance per hop of the anchor nodes is calculated by root-mean-square error (RMSE), and is dynamically corrected in groups with the weighted RMSE based on group hops. For more improvement in accuracy, we expand particle swarm optimization (PSO) of intelligent optimization algorithms to MDV-Hop localization algorithm, called PMDV-hop, in which the parameters (inertia weight and trust coefficient) in PSO are calculated dynamically. Secondly, the effect of various localization parameters affecting the PMDV-hop performance is also present. The simulation results show that PMDV-hop performs better in positioning accuracy with limited energy.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.39 no.6
• /
• pp.59-70
• /
• 2002

This paper proposes a position and speed sensorless vector control for Synchronous Reluctance Motor(SynRM) operating at optimum efficiency and high response, in which core loss is taken into account, and discusses the performance of system. The proposed control scheme is based on the flux estimation combined stator voltage and current. In this paper, current angle condition of efficiency optimization which minimizes the copper and iron losses is derived based on the equivalent circuit model of the SynRM. The research result of closed loop position and speed control with efficiency optimization control is given to verify the proposed scheme.

• Nuclear Engineering and Technology
• /
• v.49 no.1
• /
• pp.43-53
• /
• 2017

Increasing the operation cycle length can be an important goal in the fuel reload design of a nuclear reactor core. In this research paper, a new optimization approach, electromagnetism mechanism (EM), is applied to the fuel arrangement design of the Bushehr WWER-1000 core. For this purpose, a neutronic solver has been developed for calculating the required parameters during the reload cycle of the reactor. In this package, two modules have been linked, including PARCS v2.7 and WIMS-5B codes, integrated in a solver for using in the fuel arrangement optimization operation. The first results of the prepared package, along with the cycle for the original pattern of Bushehr WWER-1000, are compared and verified according to the Final Safety Analysis Report and then the results of exploited EM linked with Purdue Advanced Reactor Core Simulator (PARCS) and Winfrith Improved Multigroup Scheme (WIMS) codes are reported for the loading pattern optimization. Totally, the numerical results of our loading pattern optimization indicate the power of the EM for this problem and also show the effective improvement of desired parameters for the gained semi-optimized core pattern in comparison to the designer scheme.

• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• v.11 no.4
• /
• pp.9-17
• /
• 2007

Network Mobility basic support protocol (NEMO Basic) extends the operation of Mobile IPv6 to provide uninterrupted Internet connectivity to the communicating nodes of mobile networks. The protocol uses a mobile router (MR) in the mobile network to perform prefix scope binding updates with its home agent (HA) to establish a bi-directional tunnel between the HA and MR. This solution reduces location-update signaling by making network movements transparent to the mobile nodes (MNs) behind the MR. However, delays in data delivery and higher overheads are likely to occur because of sub-optimal routing and multiple encapsulation of data packets. To manage the mobility of the mobile network, it is important to minimize packet overhead, to optimize routing, and to reduce the volume of handoff signals over the nested mobile network. This paper proposes en aggregate router-assisted route optimization (ARARO) scheme for nested mobile networks support which introduces a local anchor router in order to localize handoff and to optimize routing. With ARARO, a mobile network node (MNN) behind a MR performs route optimization with a correspondent node (CN) as the MR sends a binding update message (BU) to aggregate router (AGR) via root-MR on behalf of all active MNNs when the mobile network moves. This paper describes the new architecture and mechanisms and provides simulation results which indicate that our proposal reduces transmission delay, handoff latency and signaling overhead. To evaluate the scheme, we present the results of simulation.

• Steel and Composite Structures
• /
• v.39 no.5
• /
• pp.511-528
• /
• 2021

There are recently some advances in solving numerically topology optimization problems for large-scaled trusses based on ground structure approach. A disadvantage of this approach is that the final design usually includes many bars, which is difficult to be produced in practice. One of efficient tools is a so-called filter scheme for the ground structure to reduce this difficulty and determine several distinct bars. In detail, this technique is valuable for practical uses because unnecessary bars are filtered out from the ground structure to obtain a well-defined structure during the topology optimization process, while it still guarantees the global equilibrium condition. This process, however, leads to a singular system of equilibrium equations. In this case, the minimization of least squares with Tikhonov regularization is adopted. In this paper, a proposed algorithm in controlling optimal Tikhonov parameter is considered in combination with the filter scheme due to its crucial role in obtaining solution to remove numerical singularity and saving computational time by using sparse matrix, which means that the discrete optimal topology solutions depend on choosing the Tikhonov parameter efficiently. Several numerical examples are investigated to demonstrate the efficiency of the filter parameter control algorithm in terms of the large-scaled optimal topology designs.

• Advances in nano research
• /
• v.13 no.6
• /
• pp.587-597
• /
• 2022

In the present study, a novel optimization method in formation control of multi -system vehicles based on the trajectory of the nearest neighbor trajectory is presented. In this regard, the state equations of each vehicle and multisystem is derived and the optimization scheme based on minimizing the differences between actual positions and desired positions of the vehicles are conducted. This formation control is a position-based decentralized model. The trajectory of the nearest neighbor are optimized based on the current position and state of the vehicle. This approach aids the whole multi-agent system to be optimized on their trajectory. Furthermore, to overcome the cumulative errors and maintain stability in the network a semi-centralized scheme is designed for the purpose of checking vehicle position to its predefined trajectory. The model is implemented in Matlab software and the results for different initial state and different trajectory definition are presented. In addition, to avoid collision avoidance and maintain the distances between vehicles agents at a predefined desired distances. In this regard, a neural fuzzy network is defined to be utilized in conjunction with the control system to avoid collision between vehicles. The outcome reveals that the model has acceptable stability and accuracy.

• Proceedings of the IEEK Conference
• /
• 2008.06a
• /
• pp.283-284
• /
• 2008

A cross-layer optimization is becoming a popular design methodology for the IP based next generation wireless network. We begin by investigating a cross-layer optimization scheme to enhance the system performance in wireless networks. By applying cross-layer optimization methodology to WiBro distributed network, the WiBro systems are expected to gain significant performance improvement and resource utilization enhanced. For further study we highlight some open challenges and new opportunities for cross-layer design.