• The KIPS Transactions:PartA
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• v.10A no.1
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• pp.15-24
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• 2003

In this paper, an optimization model for the clustered node allocation systems in the distributed computing environment is presented. In the presented model with a distributed file system framework, the dynamics of system behavior over times is carefully thought over the nodes and hence the functionality of the cluster monitor node to check the feasibility of the current set of clustered node allocation is given. The cluster monitor node of the node allocation system capable of distributing the parallel modules to clustered nodes provides a good allocation solution using Genetic Algorithms (GA). As a part of the experimental studies, the solution quality and computation time effects of varying GA experimental parameters, such as the encoding scheme, the genetic operators (crossover, mutations), the population size, and the number of node modules, and the comparative findings are presented.

• Journal of the Korea Society of Computer and Information
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• v.3 no.4
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• pp.110-119
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• 1998

This paper describes a C-API (Cryptographic-Application program Interface) mechanism that can serve cryptographic service to one or more application programmers in an open distributed computing system. Generic cryptographic service, provides application Programmers with cryptographic algorithms and interfaces which can be shared so that the programmers can program distributed applications containing security services even though they have no detailed knowledge of cryptographic algorithms. Therefore, in this paper, a generic C-API mechanism is designed that can be used independently from various application environments and basic system structures so that programmers can use it commonly. This mechanism has the advantage that allows application programmers be able to use some cryptographic services and key management services not considering of the application program and operating system.

• Journal of KIISE:Information Networking
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• v.29 no.4
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• pp.397-404
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• 2002

We propose a heuristic distributed unicast routing algorithm for minimizing the total cost of the path in a point to point network with do]ay constraint. The algorithm maintains a delay vector and a cost vector about the network states and finds the path using this information. In this paper, we show that our algorithm always finds a delay-constrained path if such a path exists and has O(│E│) message complexity(│E│is the number of links in the network). Also, simulation results show that the proposed algorithm has better cost performance than other delay-constrained routing algorithms.

• The KIPS Transactions:PartA
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• v.8A no.4
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• pp.439-446
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• 2001

The main stream of parallel programming today is using synchronous algorithms, where processor synchronization for correct computation and workload balance are essential. Overall performance of the whole system is dependent upon the performance of the slowest processor, if workload is not well-balanced or heterogeneous clusters are used. Asynchronous iteration is a way to mitigate such problems, but most of the works done so far are for shared memory systems. In this paper, we suggest and implement a parallel large sparse linear system solver that improves performance on distributed memory systems like clusters by reducing processor idle times as much as possible by asynchronous iterations.

• Smart Structures and Systems
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• v.15 no.1
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• pp.191-207
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• 2015

Proper placement of sensors plays a key role in construction and implementation of an effective structural health monitoring (SHM) system. This paper proposes a novel methodology called the distributed monkey algorithm (DMA) for the optimum design of SHM system sensor arrays. Different from the existing algorithms, the dual-structure coding method is adopted for the representation of design variables and the single large population is partitioned into subsets and each subpopulation searches the space in different directions separately, leading to quicker convergence and higher searching capability. After the personal areas of all subpopulations have been finished, the initial optimal solutions in every subpopulation are extracted and reordered into a new subpopulation, and the harmony search algorithm (HSA) is incorporated to find the final optimal solution. A computational case of a high-rise building has been implemented to demonstrate the effectiveness of the proposed method. Investigations have clearly suggested that the proposed DMA is simple in concept, few in parameters, easy in implementation, and could generate sensor configurations superior to other conventional algorithms both in terms of generating optimal solutions as well as faster convergence.

• Advances in aircraft and spacecraft science
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• v.4 no.3
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• pp.297-316
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• 2017

With rapid growth in the complexity of large scale engineering systems, the application of multidisciplinary analysis and design optimization (MDO) in the engineering design process has garnered much attention. MDO addresses the challenge of integrating several different disciplines into the design process. Primary challenges of MDO include computational expense and poor scalability. The introduction of a distributed, collaborative computational environment results in better utilization of available computational resources, reducing the time to solution, and enhancing scalability. SORCER, a Java-based network-centric computing platform, enables analyses and design studies in a distributed collaborative computing environment. Two different optimization algorithms widely used in multidisciplinary engineering design-VTDIRECT95 and QNSTOP-are implemented on a SORCER grid. VTDIRECT95, a Fortran 95 implementation of D. R. Jones' algorithm DIRECT, is a highly parallelizable derivative-free deterministic global optimization algorithm. QNSTOP is a parallel quasi-Newton algorithm for stochastic optimization problems. The purpose of integrating VTDIRECT95 and QNSTOP into the SORCER framework is to provide load balancing among computational resources, resulting in a dynamically scalable process. Further, the federated computing paradigm implemented by SORCER manages distributed services in real time, thereby significantly speeding up the design process. Part 1 covers SORCER and the algorithms, Part 2 presents results for aircraft panel design with curvilinear stiffeners.

• Advances in aircraft and spacecraft science
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• v.4 no.3
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• pp.317-334
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• 2017

With rapid growth in the complexity of large scale engineering systems, the application of multidisciplinary analysis and design optimization (MDO) in the engineering design process has garnered much attention. MDO addresses the challenge of integrating several different disciplines into the design process. Primary challenges of MDO include computational expense and poor scalability. The introduction of a distributed, collaborative computational environment results in better utilization of available computational resources, reducing the time to solution, and enhancing scalability. SORCER, a Java-based network-centric computing platform, enables analyses and design studies in a distributed collaborative computing environment. Two different optimization algorithms widely used in multidisciplinary engineering design-VTDIRECT95 and QNSTOP-are implemented on a SORCER grid. VTDIRECT95, a Fortran 95 implementation of D. R. Jones' algorithm DIRECT, is a highly parallelizable derivative-free deterministic global optimization algorithm. QNSTOP is a parallel quasi-Newton algorithm for stochastic optimization problems. The purpose of integrating VTDIRECT95 and QNSTOP into the SORCER framework is to provide load balancing among computational resources, resulting in a dynamically scalable process. Further, the federated computing paradigm implemented by SORCER manages distributed services in real time, thereby significantly speeding up the design process. Part 1 covers SORCER and the algorithms, Part 2 presents results for aircraft panel design with curvilinear stiffeners.

• Journal of IKEEE
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• v.27 no.4
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• pp.379-385
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• 2023

Distributed congestion control (DCC) is a technology that mitigates channel congestion and improves communication performance in high-density vehicular networks. Traditional DCC techniques operate to reduce channel congestion without considering quality of service (QoS) requirements. Such design of DCC algorithms can lead to excessive DCC actions, potentially degrading other aspects of QoS. To address this issue, we propose a deep reinforcement learning-based QoS-adaptive DCC algorithm. The simulation was conducted using a quasi-real environment simulator, generating dynamic vehicular densities for evaluation. The simulation results indicate that our proposed DCC algorithm achieves results closer to the targeted QoS compared to existing DCC algorithms.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.1-11
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• 2023

Federated learning (FL) is a ground breaking machine learning paradigm that allow smultiple participants to collaboratively train models in a cloud environment, all while maintaining the privacy of their raw data. This approach is in valuable in applications involving sensitive or geographically distributed data. However, one of the challenges in FL is dealing with heterogeneous and non-independent and identically distributed (non-IID) data across participants, which can result in suboptimal model performance compared to traditionalmachine learning methods. To tackle this, we introduce FedGCD, a novel FL algorithm that employs Graph Neural Network (GNN)-based community detection to enhance model convergence in federated settings. In our experiments, FedGCD consistently outperformed existing FL algorithms in various scenarios: for instance, in a non-IID environment, it achieved an accuracy of 0.9113, a precision of 0.8798,and an F1-Score of 0.8972. In a semi-IID setting, it demonstrated the highest accuracy at 0.9315 and an impressive F1-Score of 0.9312. We also introduce a new metric, nonIIDness, to quantitatively measure the degree of data heterogeneity. Our results indicate that FedGCD not only addresses the challenges of data heterogeneity and non-IIDness but also sets new benchmarks for FL algorithms. The community detection approach adopted in FedGCD has broader implications, suggesting that it could be adapted for other distributed machine learning scenarios, thereby improving model performance and convergence across a range of applications.

• Journal of Electrical Engineering and Technology
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• v.7 no.4
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• pp.486-492
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• 2012

This paper presents a novel objective function for distribution system reconfiguration for reliability enhancement. When islanding operations of distributed generators is prohibited, faults in the feeder interrupt the operation of distributed generators. For this reason, we include the customer interruption cost as well as the distributed generator interruption cost in the objective function in the network reconfiguration algorithm. The network reconfiguration in which genetic algorithms are used is implemented by MATLAB. The effect of the proposed objective function in the network reconfiguration is analyzed and compared with existing objective functions through case studies. The network reconfiguration considering the proposed objective function is suitable for a distribution system that has a high penetration of distributed generators.