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Knowledge based system is a computer system that is programmed to imitate human problem‐ solving by means of artificial intelligence and reference to a database of knowledge on a par‐ ticular subject .Jung et al [8] proposed an artifi‐ cial intelligent based reconfiguration methodol‐ ogy for load balancing in a distribution system. An expert system was applied to the heuristic search in order to reduce the search space and reduce the computational time for the recon‐ figuration. Wu et al [9] proposed a Petri net based recon‐ figuration methodology for restoration of the power system. A token passing and a backward search processes are used to identify the se‐ quence of restoration actions and their time. This method can help to estimate the time re‐ quired to restore a subsystem and obtain a sys‐ tematical method for identification of the se‐ quence of actions. Y.L.Ke [10] proposed a Petri net base approach for reconfiguring a distribu‐ tion system to enhance the performance of the power system by considering the daily load char‐ acteristics and the variations among customers due to the temperature increase in the power system. Jiang and Baldick [11] proposed a comprehen‐ sive reconfiguration algorithm for distribution system reconfiguration. They employed simu‐ lated annealing to optimize the switch configura‐ tion of a distribution system. The objective of the reconfiguration is to decrease the power loss in the distribution system. Matos and Melo [12] put forward a simulated annealing based multi objective reconfiguration for power system for loss reduction and service restoration. A recon‐ figuration for enhancing the reliability of the power system was proposed by Brown [13]. A predictive reliability model is used to compute reliability indices for the distribution system and a simulated annealing algorithm is used to find a MIMET Technical Bulletin Volume 1 (2) 2010 reconfiguration solution. Shu and Sun [14] proposed a reconfiguration methodology to maintain the load and genera‐ tion balance during the restoration of a power system. An ant colony optimization algorithm was used to search the proper reconfiguration sequence based on the Petri net model. Daniel et al [16] proposed an ant colony based recon‐ figuration for a distribution system. The objec‐ tive of the reconfiguration was to reduce the power loss in the power system. Salazar et al [16] proposed a feeder reconfigura‐ tion methodology for distribution system to minimize the power loss. A reconfiguration algo‐ rithm was proposed based on the artificial neu‐ ral network theory. Clustering techniques to de‐ termine the best training set for a single neural network with generalization ability are also pre‐ sented in that work. Hsu and Huang [17] put for‐ ward another artificial neural network based reconfiguration for a distribution system. The reconfiguration can achieve service restoration by using artificial neural network and pattern recognition method. Wang and Zhang [18] proposed a particle swarm optimization algorithm based reconfiguration methodology for distribution system. A modified particle swarm algorithm has been presented to solve the complex optimization problem. The objective of the methodology was to minimize the power loss in the power system. Jin et al [19] introduced a binary particle swam optimization based reconfiguration methodology for distribu‐ tion system. The objective of the reconfiguration was load balancing. The reconfiguration method‐ ology proposed in that work can only be applied in the power system with radial configuration. Heo and Lee [20] proposed MAS based intelli‐ gent identification system for power plant con‐ trol and fault diagnosis. The proposed methodol‐ | MARINE FRONTIER @ UniKL 89
ogy can achieve the online adaptive identifica‐ tion for control in real time power plant opera‐ tion and offline identification for fault diagnosis. Enacheanu et al [21] proposed a distribution sys‐ tem architecture that can make the reconfigura‐ tion in the power easy to achieve. The reconfigu‐ ration in that work is to locate and isolate the faults in the power system. A remote agent is used in that work as a central controller for the reconfiguration of power systems. Nagata et al [22] proposed MAS based restora‐ tion methodology for power systems. The MAS proposed was composed of bus agents and a single facilitator agent. The bus agent decides a suboptimal target configuration after faults oc‐ cur. A facilitator agent was developed to act as a manager for the decision process. The existence of the facilitator agents make the methodology centralized. Liu et al [37] put forward another restoration method for the power system. How‐ ever, this method is also centralized because the restoration decision is made with the help of coordinating agents that have global information in the MAS. Nagata et al [50] improved the method proposed in [22]. In the MAS proposed in [50], the coordi‐ nation functions were distributed to several fa‐ cilitator agents instead of one facilitator agent. The facilitator agents coordinate with each other autonomously. However, each facilitator agent works as centralized coordinator in the local area. So the MAS proposed in that work is not completely decentralized. The proposed restora‐ tion can only be applied to a radial power sys‐ tem. Also, the reconfiguration method was tested on a small power system simulated on a PC. The agents’ performance in the restoration for a large power system was not provided. Wang et al [24] proposed a fuzzy logic and evolu‐ tionary programming based reconfiguration methodology for distribution systems. In this MIMET Technical Bulletin Volume 1 (2) 2010 research, a fuzzy mutation controller is imple‐ mented to adaptively update the mutation rate during the evolutionary process. The objective of the reconfiguration is to reduce the power loss in the distribution system. Zhou et al [25] put forward another fuzzy logic based reconfigura‐ tion methodology for distribution system. A fuzzy logic based reconfiguration was developed for the purpose of restoration and load balanc‐ ing in a real‐time operation environment. Kuo and Hsu [26] proposed a service restoration methodology using fuzzy logic approach. In this research, the fuzzy logic based approach was estimated the loads in a distribution system and devised a proper service restoration plan follow‐ ing a fault. Various methods have been applied to the re‐ configuration process of the terrestrial power system. However, most of the reconfiguration methodologies are centralized. A central control‐ ler is a requirement to gather data from the power system, make reconfiguration decisions after calculation and analysis. Shipboard Power System Reconfiguration Compared to the terrestrial power systems, the SPS has its unique characteristics. Based on the unique characteristics of the SPS, some recon‐ figuration methods have been proposed. Some of the significant literature of the SPS reconfigu‐ ration process of the SPS is reviewed below. Butler and Sarma [27] propose a heuristics based general reconfiguration methodology for AC radial SPSs. The reconfiguration process is applied to the SPS for service restoration. The reconfiguration process is based on the initial configuration and desired configuration details of the system, such as the list of load con‐ nected /disconnected to the SPS, list of available component (cables, circuit breakers, etc) in the SPS, etc. | MARINE FRONTIER @ UniKL 90
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EDITORIAL CHIEF EDITOR Prof. Dato
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DIRECTIONAL STABILITY ANALYSIS IN S
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The determinant from equations (3)
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Table 1 ‐ Results of Stability Cr
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DO 20 J=1,7 REFF(J)=1+((J‐1)*1.5/
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Skeg Effectiveness Skeg Effectivene
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Skeg Effectiveness Skeg Effectivene
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A WATER FUELLED ENGINE FOR FUTURE M
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Stainless steel pipes are used as e
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Current development Hydrogen Oceanj
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Car industry A small group of local
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Big oil companies. For business sur
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Websites: 1. http://www.schatzlab.o
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Table 1: Global maritime Fleet Rank
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No. of Ships No. of Ships 300 250 2
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time players is 30. The demand for
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References [1] UNCTAD, Review Marit
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Table 3 : Tug boat Registered in Ma
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NO SHIP NAME OWNER/OPERATOR GRT DWT
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Page 54 and 55: Table 8 : Tankers Registered in Mal
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Page 64 and 65: age and appropriate steps must be t
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Page 76 and 77: Objective of the Research The resea
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Page 112 and 113: PDE equations (1) and (2). Comparis
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