• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.6
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• pp.1522-1545
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• 2012

Call admission control (CAC) plays an important role in mobile cellular network to guarantee the quality of service (QoS). In this paper, a dynamic hybrid CAC scheme with integrated cutoff priority and handoff queue for mobile cellular network is proposed and some performance metrics are derived. The unique characteristic of the proposed CAC scheme is that it can support any number of service types and that the cutoff thresholds for handoff calls are dynamically adjusted according to the number of service types and service priority index. Moreover, timeouts of handoff calls in queues are also considered in our scheme. By modeling the proposed CAC scheme with a one-dimensional Markov chain (1DMC), some performance metrics are derived, which include new call blocking probability ($P_{nb}$), forced termination probability (PF), average queue length, average waiting time in queue, offered traffic utilization, wireless channel utilization and system performance which is defined as the ratio of channel utilization to Grade of Service (GoS) cost function. In order to validate the correctness of the derived analytical performance metrics, simulation is performed. It is shown that simulation results match closely with the derived analytic results in terms of $P_{nb}$ and PF. And then, to show the advantage of 1DMC modeling for the performance analysis of our proposed CAC scheme, the computing complexity of multi-dimensional Markov chain (MDMC) modeling in performance analysis is analyzed in detail. It is indicated that state-space cardinality, which reflects the computing complexity of MDMC, increases exponentially with the number of service types and total channels in a cell. However, the state-space cardinality of our 1DMC model for performance analysis is unrelated to the number of service types and is determined by total number of channels and queue capacity of the highest priority service in a cell. At last, the performance comparison between our CAC scheme and Mahmoud ASH's scheme is carried out. The results show that our CAC scheme performs well to some extend.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2140-2156
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• 2023

The Wireless Sensor Network (WSN), is constructed out of teeny-tiny sensor nodes that are very low-cost, have a low impact on the environment in terms of the amount of power they consume, and are able to successfully transmit data to the base station. The primary challenges that are presented by WSN are those that are posed by the distance between nodes, the amount of energy that is consumed, and the delay in time. The sensor node's source of power supply is a battery, and this particular battery is not capable of being recharged. In this scenario, the amount of energy that is consumed rises in direct proportion to the distance that separates the nodes. Here, we present a Hybrid Firefly Glow-Worm Swarm Optimization (HF-GSO) guided routing strategy for preserving WSNs' low power footprint. An efficient fitness function based on firefly optimization is used to select the Cluster Head (CH) in this procedure. It aids in minimising power consumption and the occurrence of dead sensor nodes. After a cluster head (CH) has been chosen, the Glow-Worm Swarm Optimization (GSO) algorithm is used to figure out the best path for sending data to the sink node. Power consumption, throughput, packet delivery ratio, and network lifetime are just some of the metrics measured and compared between the proposed method and methods that are conceptually similar to those already in use. Simulation results showed that the proposed method significantly reduced energy consumption compared to the state-of-the-art methods, while simultaneously increasing the number of functioning sensor nodes by 2.4%. Proposed method produces superior outcomes compared to alternative optimization-based methods.

• Journal of Power Electronics
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• v.11 no.4
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• pp.408-417
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• 2011

The plug-in hybrid electric vehicles (PHEVs) are specialized hybrid electric vehicles that have the potential to obtain enough energy for average daily commuting from batteries. The PHEV battery would be recharged from the power grid at home or at work and would thus allow for a reduction in the overall fuel consumption. This paper proposes an integrated power electronics interface for PHEVs, which consists of a novel Eight-Switch Inverter (ESI) and an interleaved DC/DC converter, in order to reduce the cost, the mass and the size of the power electronics unit (PEU) with high performance at any operating mode. In the proposed configuration, a novel Eight-Switch Inverter (ESI) is able to function as a bidirectional single-phase AC/DC battery charger/ vehicle to grid (V2G) and to transfer electrical energy between the DC-link (connected to the battery) and the electric traction system as DC/AC inverter. In addition, a bidirectional-interleaved DC/DC converter with dual-loop controller is proposed for interfacing the ESI to a low-voltage battery pack in order to minimize the ripple of the battery current and to improve the efficiency of the DC system with lower inductor size. To validate the performance of the proposed configuration, the indirect field-oriented control (IFOC) based on particle swarm optimization (PSO) is proposed to optimize the efficiency of the AC drive system in PHEVs. The maximum efficiency of the motor is obtained by the evaluation of optimal rotor flux at any operating point, where the PSO is applied to evaluate the optimal flux. Moreover, an improved AC/DC controller based Proportional-Resonant Control (PRC) is proposed in order to reduce the THD of the input current in charger/V2G modes. The proposed configuration is analyzed and its performance is validated using simulated results obtained in MATLAB/ SIMULINK. Furthermore, it is experimentally validated with results obtained from the prototypes that have been developed and built in the laboratory based on TMS320F2808 DSP.

• The Korean Journal of Financial Management
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• v.14 no.3
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• pp.113-135
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• 1997

본 논문의 목적은 미국 손해보험회사간의 합병을 대상으로 하여 합병전과 후 기업의 X-효율성을 실증분석과 모의분석 방법을 이용해서 검증하고 합병의 잠재적인 X-효율성 효과를 분석하고자 하였다. X-효율성의 증진정도를 파악하기 위해서 횡단면적인 비용함수를 먼저 추정하였는데 비용함수 추정시 생산물이 제로(0)인 경우를 포함하기 위하여 혼용초월 로그비용함수(hybrid translog cost function)를 사용하였다. 그리고 Berger(1992)의 비분포방법(distribution free approach)를 사용하여 기업의 합병전,후 X-효율성을 추정하였다. 1986년부터 1990년 사이에 수평합병한 미국 손보사를 대상으로 피합병기업(merged firms)과 합병기업(merging firms)간의 X-효율성 차이를 검증하였으나 합병전 합병기업이 더 효율적이라는 통계적 증거는 없었다. 두 번째로 합병기업은 합병후 효율성이 증진하였느냐는 가설도 검증하였으나 이 가설을 지지할 만한 통계적 증거는 미약하였다. 가상합병을 통한 모의분석에서는 합병후 상당한 X-효율성 증진이 있을 것이라는 통계적으로 유의한 결과를 얻었으나 합병후 규모효율성의 중진에 대해서는 통계적 유의성이 없었다. 이는 합병의 최대 효익이 규모효율성 증대가 아닌 X-효율성 증대라는 Shaffer(1993)나 Berger and Humphrey(1993)의 연구결과와 일치한다. 실증분석 결과와 모의분석 결과를 비교하면 전자에서는 합병후 X-효율성의 증가효과가 거의 없었으나 후자에서는 합병후 상당한 X-효율성 증가가 있을 것으로 나타났다. 이는 실제 미국 손보사의 합병에서 과도한 합병프리미엄 지급, 규제에 의한 중복비용 둥으로 단기적으로 볼 때 합병사가 부담하는 합병비용이 합병에 의한 효익보다 컸을 것이라는 것을 시사한다.

• The KIPS Transactions:PartC
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• v.14C no.4
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• pp.341-348
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• 2007

Even though the secret information stored in home device in home network must be handled very safely and carefully, we have no measure for protecting the secret information without additional hardware support. Since already many home devices without consideration of the security have been used, the security protection method for those devices have to be required. In this paper, we suggest two schemes that protect the security information using networking function without additional hardware support, and those hybrid method to supplement the defects of each scheme. We also consider the our proposals in the aspects of security and cost.

• Journal of Electrical Engineering and Technology
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• v.12 no.5
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• pp.1729-1742
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• 2017

In designing System Protection Schemes (SPSs) in power systems, protecting transmission network against extreme undesired conditions becomes a significant challenge in mitigating the transmission line overloading. This paper presents an intelligent Special Protection and Control Scheme (SPCS) using of Differential Evolution with Adaptive Mutation (DEAM) approach to obtain the optimum generation rescheduling to solve the transmission line overloading problem in system contingency conditions. DEAM algorithm employs the attraction-repulsion idea that is applied in the electromagnetism-like algorithm to support the mutation process of the conventional Differential Evolution (DE) algorithm. Different N-1 contingency conditions under base and increase load demand are considered in this paper. Simulation results have been compared with those acquired from Genetic Algorithm (GA) application. Minimum severity index has been considered as the objective function. The final results show that the presented DEAM method offers better performance than GA in terms of faster convergence and less generation fuel cost. IEEE 30-bus test system has been used to prove the effectiveness and robustness of the proposed algorithm.

• Journal of Electrical Engineering and Technology
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• v.5 no.2
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• pp.179-190
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• 2010

This paper describes a new stochastic heuristic algorithm in engineering problem optimization especially in power system applications. An improved particle swarm optimization (PSO) called adaptive particle swarm optimization (APSO), mixed with simulated annealing (SA), is introduced and referred to as APSO-SA. This algorithm uses a novel PSO algorithm (APSO) to increase the convergence rate and incorporate the ability of SA to avoid being trapped in a local optimum. The APSO-SA algorithm efficiency is verified using some benchmark functions. This paper presents the application of APSO-SA to find the optimal location, type and size of flexible AC transmission system devices. Two types of FACTS devices, the thyristor controlled series capacitor (TCSC) and the static VAR compensator (SVC), are considered. The main objectives of the presented method are increasing the voltage stability index and over load factor, decreasing the cost of investment and total real power losses in the power system. In this regard, two cases are considered: single-type devices (same type of FACTS devices) and multi-type devices (combination of TCSC, SVC). Using the proposed method, the locations, type and sizes of FACTS devices are obtained to reach the optimal objective function. The APSO-SA is used to solve the above non.linear programming optimization problem for better accuracy and fast convergence and its results are compared with results of conventional PSO. The presented method expands the search space, improves performance and accelerates to the speed convergence, in comparison with the conventional PSO algorithm. The optimization results are compared with the standard PSO method. This comparison confirms the efficiency and validity of the proposed method. The proposed approach is examined and tested on IEEE 14 bus systems by MATLAB software. Numerical results demonstrate that the APSO-SA is fast and has a much lower computational cost.

• Journal of Construction Engineering and Project Management
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• v.5 no.1
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• pp.11-19
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• 2015

This paper presents optimized artificial neural networks (ANNs) claims prediction and decision awareness framework that guides owner organizations in their pre-bid construction project decisions to minimize claims. The framework is composed of two genetic optimization ANNs models: a Claims Impact Prediction Model (CIPM), and a Decision Awareness Model (DAM). The CIPM is composed of three separate ANNs that predict the cost and time impacts of the possible claims that may arise in a project. The models also predict the expected types of relationship between the owner and the contractor based on their behavioral and technical decisions during the bidding phase of the project. The framework is implemented using actual data from international projects in the Middle East and Egypt (projects owned by either public or private local organizations who hired international prime contractors to deliver the projects). Literature review, interviews with pertinent experts in the Middle East, and lessons learned from several international construction projects in Egypt determined the input decision variables of the CIPM. The ANNs training, which has been implemented in a spreadsheet environment, was optimized using genetic algorithm (GA). Different weights were assigned as variables to the different layers of each ANN and the total square error was used as the objective function to be minimized. Data was collected from thirty-two international construction projects in order to train and test the ANNs of the CIPM, which predicted cost overruns, schedule delays, and relationships between contracting parties. A genetic optimization backward analysis technique was then applied to develop the Decision Awareness Model (DAM). The DAM combined the three artificial neural networks of the CIPM to assist project owners in setting optimum values for their behavioral and technical decision variables. It implements an intelligent user-friendly input interface which helps project owners in visualizing the impact of their decisions on the project's total cost, original duration, and expected owner-contractor relationship. The framework presents a unique and transparent hybrid genetic algorithm-ANNs training and testing method. It has been implemented in a spreadsheet environment using MS Excel$^{(R)}$ and EVOLVERTM V.5.5. It provides projects' owners of a decision-support tool that raises their awareness regarding their pre-bid decisions for a construction project.

• Journal of the Society of Naval Architects of Korea
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• v.49 no.1
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• pp.68-78
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• 2012

A layout of an LNG FPSO should be elaborately determined as compared with that of an onshore plant because many topside process systems are installed on the limited area; the deck of the LNG FPSO. Especially, the layout should be made as multi-deck, not single-deck and have a minimum area. In this study, a multi-floor layout for the liquefaction process, the dual mixed refrigerant(DMR) cycle, of LNG FPSO was determined by using the optimization technique. For this, an optimization problem for the multi-floor layout was mathematically formulated. The problem consists of 589 design variables representing the positions of topside process systems, 125 equality constraints and 2,315 inequality constraints representing limitations on the layout of them, and an objective function representing the total layout cost. To solve the problem, a hybrid optimization method that consists of the genetic algorithm(GA) and sequential quadratic programming(SQP) was used in this study. As a result, we can obtain a multi-floor layout for the liquefaction process of the LNG FPSO which satisfies all constraints related to limitations on the layout.

• Smart Structures and Systems
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• v.14 no.5
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• pp.847-867
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• 2014

There are continuous efforts to mitigate structural losses from earthquakes and manage risk through seismic risk assessment; seismic fragility curves are widely accepted as an essential tool of such efforts. Seismic fragility curves can be classified into four groups based on how they are derived: empirical, judgmental, analytical, and hybrid. Analytical fragility curves are the most widely used and can be further categorized into two subgroups, depending on whether an analytical function or simulation method is used. Although both methods have shown decent performances for many seismic fragility problems, they often oversimplify the given problems in reliability or structural analyses owing to their built-in assumptions. In this paper, a new method is proposed for the development of seismic fragility curves. Integration with sophisticated software packages for reliability analysis (FERUM) and structural analysis (ZEUS-NL) allows the new method to obtain more accurate seismic fragility curves for less computational cost. Because the proposed method performs reliability analysis using the first-order reliability method, it provides component probabilities as well as useful byproducts and allows further fragility analysis at the system level. The new method was applied to a numerical example of a 2D frame structure, and the results were compared with those by Monte Carlo simulation. The method was found to generate seismic fragility curves more accurately and efficiently. Also, the effect of system reliability analysis on the development of seismic fragility curves was investigated using the given numerical example and its necessity was discussed.